Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks.

Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage.

Non-linear connection between the triglyceride-glucose index and prediabetes risk among Chinese adults: a secondary retrospective cohort study.

BACKGROUND: The triglyceride-glucose (TyG) index has garnered recognition as a surrogate marker for insulin resistance, a pivotal factor in the pathogenesis of various metabolic disorders. Despite its emerging role, the empirical evidence delineating its association with prediabetes mellitus (Pre-DM) remains scant. This research aims to clarify the link between the TyG index and the likelihood of Pre-DM development within a Chinese demographic. METHODS: This investigation was structured as a retrospective cohort analysis, encompassing a sample of 179,177 Chinese adults. These individuals underwent medical examinations at the Rich Healthcare Group over a period spanning from 2010 to 2016. To ascertain the relationship between the TyG index and the incidence of Pre-DM, this study employed Cox regression analysis complemented by sensitivity and subgroup assessments. Furthermore, Cox proportional hazards regression with cubic spline functions and smooth curve fitting was incorporated to explore the existence of any non-linear connection within this association. RESULTS: Upon adjusting for a comprehensive array of confounding variables, a statistically significant positive correlation between the TyG index and the risk of Pre-DM was identified (HR: 1.60, 95%CI 1.56-1.65, P < 0.001). The analysis illuminated a non-linear relationship, with an inflection point at a TyG index value of 8.78. For TyG index values below and above this inflection point, the HR was calculated to be 1.94 (95%CI 1.86-2.03) and 1.26 (95%CI 1.20-1.33), respectively. Sensitivity analyses further fortified the reliability of these findings. CONCLUSIONS: This comprehensive examination delineated a significantly positive, non-linear correlation between the TyG index and the risk of Pre-DM within a Chinese population. Individuals with TyG index values below 8.78 have a significantly increased risk of developing prediabetes. These findings underscore the TyG index's potential efficacy as a predictive tool for assessing Pre-DM risk in clinical practice.

Impact of ICT diffusion and opportunity entrepreneurship on environmental sustainability in Saudi Arabia.

The need to prioritize sustainable development is expanding in light of the world's environmental concerns. To address these concerns, entrepreneurship is essential as a catalyst for inventions, economic expansion, and social change. Entrepreneurship activities have direct and indirect effects in the face of environmental risks and uncertainties. This study addresses gaps in understanding entrepreneurship's non-linear and complementarity effects, particularly opportunity-driven entrepreneurship and information and communication technologies (ICT), on the quality of the environment in Saudi Arabia. The dynamic ordinary least squares (DOLS) method is used to estimate long-run relationships. Saudi Arabia will be the perfect context for sustainability because the country has prioritized sustainability through its Vision 2030, and the Saudi government has substantially supported entrepreneurship. The main contribution of this paper to the existing literature is evident in its examination of the quadratic relationships between both opportunity-driven entrepreneurship and ICT diffusion, including ICT access, use, and skills, on environmental quality. In addition, the study delves into the ICT diffusion's modulating effects on the nexus of opportunity entrepreneurship with environmental quality, providing insights into how these factors can effectively improve environmental quality. The findings show that opportunity entrepreneurship and ICT diffusion initially deteriorate environmental quality before leading to improvements as their levels mature in the economy. Moreover, interactions between ICT proxies and opportunity entrepreneurship yield mixed effects, with negative net effects on CO2 emissions and ecological footprint countered by positive net effects on the environmental performance index. These findings highlight the dual role of ICT diffusion as a contributor to environmental challenges and a potential solution, depending on the level of its diffusion and interaction with entrepreneurial activities. Therefore, policymakers should create plans that encourage and direct business activity toward more environmentally friendly methods. They should also consider the short- and long-term effects of growing digital technologies on environmental sustainability and how they might revolutionize how entrepreneurship and sustainability goals are aligned.

Digital brains, green gains: Artificial intelligence's path to sustainable transformation.

The environmental impacts of artificial intelligence on a global scale remain underexplored. This study utilizes a balanced panel dataset to examine artificial intelligence's complex role in enhancing global green productivity between 2008 and 2019. The findings indicate that artificial intelligence robustly boosts green productivity, even after correcting for potential endogeneity using the legal system's origin as an instrument. A detailed mediation analysis underscores that artificial intelligence indirectly promotes green productivity by increasing renewable energy use, attracting skilled labor, and dampening stock market performance. Additional analysis confirms that financial development generally amplifies artificial intelligence's favorable effects on green productivity. However, the combined impact of financial institution access and artificial intelligence on green productivity initially appears hostile, an effect that can be reversed when financial access exceeds a certain threshold. These results offer valuable insights into the interconnection between artificial intelligence and the global shift towards greener practices.

Nonlinear relationship between triglyceride-glucose index and the risk of prediabetes and diabetes: a secondary retrospective cohort study.

Background: The triglyceride-glucose (TyG) index, recognized for its cost-efficiency and simplicity, serves as an accessible indicator of insulin resistance. Yet, its correlation with the risk of prediabetes and diabetes (Pre-DM/DM) in the Chinese demographic remains uncertain. Consequently, our study explored the association between the TyG index and the development of Pre-DM/DM within the Chinese population. Methods: The retrospective cohort study was carried out utilizing data from a health screening initiative. The study included 179541 adults over 20 who underwent medical examinations at the Rich Healthcare Group over a period spanning from 2010 to 2016. The correlation between the TyG index and Pre-DM/DM risk was investigated using Cox regression analysis. Furthermore, Cox proportional hazards regression with cubic spline functions and smooth curve fitting was incorporated to explore their non-linear connection. Results: The mean age of study participants was 41.18 ± 12.20 years old, and 95255 (53.05%) were male. During a median follow-up of 3.01 years, 21281 (11.85%) participants were diagnosed with Pre-DM/DM. After adjusting the potential confounding factors, the results showed that the TyG index was positively correlated with incident Pre-DM/DM (HR: 1.67, 95%CI: 1.62-1.71, P< 0.001). Additionally, a non-linear association was observed between the TyG index and the onset of Pre-DM/DM, with an inflection point identified at 8.73. Hazard ratios (HR) to the left and right of this inflection point were 1.95 (95%CI: 1.86-2.04) and 1.34 (95%CI: 1.27-1.42), respectively. Furthermore, sensitivity analyses confirmed the stability of these findings. Conclusion: The TyG index exhibited a non-linear positive relationship with the risk of Pre-DM/DM. These findings imply that maintaining the TyG index at a lower, specified threshold may be beneficial in mitigating the onset of Pre-DM/DM.

Task-guided attention increases non-linearity of steady-state visually evoked potentials.

Objective.Attention is a multifaceted cognitive process, with nonlinear dynamics playing a crucial role. We investigated the involvement of nonlinear processes in top-down visual attention.Approach.The research paradigm employed a contrast-modulated sequence of letters and numerals, encircled by a consistently flickering white square on a black background-a setup that generated steady-state visually evoked potentials. Nonlinear processes are recognized for eliciting and modulating the harmonics of constant frequencies. Using the rhythmic entrainment source separation technique, we examined the fundamental and harmonic frequencies of each stimulus to evaluate the underlying nonlinear dynamics during stimulus processing.Main results.In line with prior research, our findings indicate that the power spectrum density of electroencephalogram responses is influenced by both task presence and stimulus contrast. We discovered that actively searching for a target within a letter stream heightened the amplitude of the fundamental frequency and harmonics related to the background flickering stimulus. While the fundamental frequency amplitude remained unaffected by the stimulus contrast, a lower contrast led to an increase in the second harmonic's amplitude. We assessed the relationship between the contrast response function and the nonlinear-based harmonic responses.Significance.Our findings contribute to a more nuanced understanding of the nonlinear processes impacting top-down visual attention.

Numerical Investigation of Fracture Behaviour of Polyurethane Adhesives under the Influence of Moisture.

The mechanical behaviour of polymer adhesives is influenced by the environmental conditions leading to ageing and affecting the integrity of the material. The polymer adhesives have hygroscopic behaviour and tend to absorb moisture from the environment, causing the material to swell without applying external load. The focus of the work is to investigate the viscoelastic material behaviour under ageing conditions. The constitutive equations and the governing equations to numerically investigate the fracture in swollen viscoelastic material are discussed to describe the numerical implementation. Phase-field damage modelling has been used in numerical studies of ductile and brittle materials for a long time. The finite-strain phase-field damage model is used to investigate the fracture behaviour in aged viscoelastic polymer adhesives. The finite-strain viscoelastic model is formulated based on the continuum rheological model by combining spring and Maxwell elements in parallel. Commercially available post-cured crosslinked polyurethane adhesives are used in the current investigation. Post-cured samples of crosslinked polyurethane adhesives are prepared for different humidity conditions under isothermal conditions. These aged samples are used to perform tensile and tear tests and the test data are used to identify the material parameters from the curve fitting process. The experiment and simulation are compared to relate the findings and are the first step forward to improve the method to model crosslinked polymers.

Do bitcoin electricity consumption and carbon footprint exhibit random walk and bubbles? Analysis with policy implications.

One of the main current focuses of global economies and decision-makers is the efficiency of energy utilization in cryptocurrency mining and trading, along with the reduction of associated carbon emissions. Understanding the pattern of Bitcoin's energy consumption and its bubble frequency can greatly enhance policy analysis and decision-making for energy efficiency and carbon emission reduction. This research aims to assess the validity of the random walk hypothesis for Bitcoin's electricity consumption and carbon footprint. We employed both traditional methods (ADF and KPSS) and recently proposed unit root techniques that account for structural breaks and non-linearity in the data series. Our analysis covers daily data from July 2010 to December 2021. The empirical results revealed that traditional unit root techniques did not confirm the stationarity of both bitcoin's electricity consumption and carbon footprint. However, novel structural break (SB) and linearity tests conducted enabled us to discover five SB episodes between 2012 and 2020 and non-linearity of the variables, which informed our application of the newly developed non-linear unit root tests with structural breaks. With the new methods, the results indicated stationarity after accommodating the SB and non-linearity. Furthermore, based on Phillips and Shi (2019)'s test, we identified certain bubble episodes in the bitcoin energy and carbon variables between 2013 and 2021. The major drivers of the bubbles in bitcoin energy consumption and carbon footprint are variables relating to the bitcoin and financial markets activities and risks, including the global economic and political risks. The study's conclusion based on the above findings informs several policy implications drawn for energy and environmental management including the encouragement of green investments in cryptocurrency mining and trading.

Modelling insertion behaviour of PVP (Polyvinylpyrrolidone) and PVA (Polyvinyl Alcohol) microneedles.

A comprehensive investigation into the effects of nonlinear material behaviour of polymeric (MN) and skin on the dynamics of the MN insertion in skin was undertaken in this study using experiments and numerical simulations. The nonlinearity of the material behaviour was incorporated by employing the Ramberg-Osgood and neo-Hookean equations for stress-strain relationships for the MN materials and skin, respectively. For this purpose, a characteristic type of dissolving MN array was selected. This type of MN is made by a combination of poly(vinyl alcohol) and poly(vinyl pyrrolidone). The numerical simulations were validated using experimental investigations where the MNs were fabricated using laser-engineered silicone micromould templates technology. Young's modulus, Poisson's ratio, and compression breaking force for the MN polymers were determined using a texture analyser. The alignment between experimental findings and simulation data underscores the accuracy of the parameters determined through mechanical testing and mathematical calculations for both MN materials (PVP/PVA) and skin behaviour during the MN insertion. This study has demonstrated a strong alignment between the experimental findings and computational simulations, confirming the accuracy of the established parameters for MNs and skin interactions for modelling MN insertion behaviour in skin, providing a solid foundation for future research in this area.

Examination of nonlinear associations between pulse pressure index and incident prediabetes susceptibility: a 5-year retrospective cohort investigation.

Prediabetes and related complications constitute significant public health burdens globally. As an indicator closely associated with abnormal glucose metabolism and atherosclerosis, the utility of Pulse Pressure Index (PPI) as a prediabetes risk marker has not been explored. We performed a retrospective cohort analysis to investigate this putative association between PPI and prediabetes hazard. Our analysis encompassed 183,517 Chinese adults ≥ 20 years registered within the Rich Healthcare Group 2010-2016. PPI was defined as (systolic blood pressure - diastolic blood pressure)/systolic blood pressure. The relationship between PPI and prediabetes risk was assessed via Cox proportional hazards regression modeling. Non-linearity evaluations applied cubic spline fitting approaches alongside smooth curve analysis. Inflection points of PPI concerning prediabetes hazard were determined using two-piecewise Cox models. During a median follow-up of 3 years (2.17-3.96 years), new-onset prediabetes was documented in 20,607 patients (11.23%). Multivariate regression analysis showed that PPI was an independent risk factor for prediabetes, and the risk of prediabetes increased by 0.6% for every 1% increase in PPI (Hazard Ratio [HR]: 1.006, 95% Confidence Interval [CI] 1.004-1.008, P < 0.001). This association was non-significant for PPI ≤ 37.41% yet exhibited a sharp upsurge when PPI surpassed 37.41% (HR: 1.013, 95% CI 1.005-1.021, P = 0.0029). Our analysis unveils a positive, non-linear association between PPI and future prediabetes risk. Within defined PPI ranges, this relationship is negligible but dramatically elevates beyond identified thresholds.

IoT-Based Heartbeat Rate-Monitoring Device Powered by Harvested Kinetic Energy.

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method.

Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19.

Nowadays, epidemiological modeling is applied to a wide range of diseases, communicable and non-communicable, namely AIDS, Ebola, influenza, Dengue, Malaria, Zika. More recently, in the context of the last pandemic declared by the World Health Organization (WHO), several studies applied these models to SARS-CoV-2. Despite the increasing number of researches using spatial analysis, some constraints persist that prevent more complex modeling such as capturing local epidemiological dynamics or capturing the real patterns and dynamics. For example, the unavailability of: (i) epidemiological information such as the frequency with which it is made available; (ii) sociodemographic and environmental factors (e.g., population density and population mobility) at a finer scale which influence the evolution patterns of infectious diseases; or (iii) the number of cases information that is also very dependent on the degree of testing performed, often with severe territorial disparities and influenced by context factors. Moreover, the delay in case reporting and the lack of quality control in epidemiological information is responsible for biases in the data that lead to many results obtained being subject to the ecological fallacy, making it difficult to identify causal relationships. Other important methodological limitations are the control of spatiotemporal dependence, management of non-linearity, ergodicy, among others, which can impute inconsistencies to the results. In addition to these issues, social contact, is still difficult to quantify in order to be incorporated into modeling processes. This study aims to explore a modeling framework that can overcome some of these modeling methodological limitations to allow more accurate modeling of epidemiological diseases. Based on Geographic Information Systems (GIS) and spatial analysis, our model is developed to identify group of municipalities where population density (vulnerability) has a stronger relationship with incidence (hazard) and commuting movements (exposure). Specifically, our framework shows how to operate a model over data with no clear trend or seasonal pattern which is suitable for a short-term predicting (i.e., forecasting) of cases based on few determinants. Our tested models provide a good alternative for when explanatory data is few and the time component is not available, once they have shown a good fit and good short-term forecast ability.

Identifying time-varying dynamics of heart rate and oxygen uptake from single ramp incremental running tests.

Objective.The fact that ramp incremental exercise yields quasi-linear responses for pulmonary oxygen uptake (V˙O2) and heart rate (HR) seems contradictory to the well-known non-linear behavior of underlying physiological processes. Prior research highlights this issue and demonstrates how a balancing of system gain and response time parameters causes linearV˙O2responses during ramp tests. This study builds upon this knowledge and extracts the time-varying dynamics directly from HR andV˙O2data of single ramp incremental running tests.Approach.A large-scale open access dataset of 735 ramp incremental running tests is analyzed. The dynamics are obtained by means of 1st order autoregressive and exogenous models with time-variant parameters. This allows for the estimates of time constant (τ) and steady state gain (SSG) to vary with work rate.Main results.As the work rate increases,τ-values increase on average from 38 to 132 s for HR, and from 27 to 35 s forV˙O2. Both increases are statistically significant (p< 0.01). Further, SSG-values decrease on average from 14 to 9 bpm (km·h-1)-1for HR, and from 218 to 144 ml·min-1forV˙O2(p< 0.01 for decrease parameters of HR andV˙O2). The results of this modeling approach are line with literature reporting on cardiorespiratory dynamics obtained using standard procedures.Significance.We show that time-variant modeling is able to determine the time-varying dynamics HR andV˙O2responses to ramp incremental running directly from individual tests. The proposed method allows for gaining insights into the cardiorespiratory response characteristics when no repeated measurements are available.

The complex link between socioeconomic deprivation and COVID-19. Evidence from small areas of Catalonia.

This ecological study assesses the association between the incidence rate of COVID-19 confirmed cases and socioeconomic deprivation in the Catalan small areas for the first six waves of the pandemic. The association is estimated using Poisson regressions and, in contrast to previous studies, considering that the relationship is not linear but rather depends on the degree of deprivation. The results show that the association between deprivation and incidence varied between waves, not only in intensity but also in its sign. Although it was insignificant in the first, third and fourth waves, the association was positive and significant in the second, becoming significantly negative in the fifth and sixth waves. Interestingly, the evidence suggests that the link between both magnitudes was not homogeneous throughout the distribution of deprivation, the pattern also varying between waves. The results are discussed in view of the role of non-pharmacological interventions and vaccination, as well as potential biases (for example that associated with differences between population groups in the propensity to be tested in each wave).

From a false sense of safety to resilience under uncertainty.

Understanding and acting upon risk is notably challenging, and navigating complexity with understandings developed for stable environments may inadvertently build a false sense of safety. Neglecting the potential for non-linear change or "black swan" events - highly impactful but uncommon occurrences - may lead to naive optimisation under assumed stability, exposing systems to extreme risks. For instance, loss aversion is seen as a cognitive bias in stable environments, but it can be an evolutionarily advantageous heuristic when complete destruction is possible. This paper advocates for better accounting of non-linear change in decision-making by leveraging insights from complex systems and psychological sciences, which help to identify blindspots in conventional decision-making and to develop risk mitigation plans that are interpreted contextually. In particular, we propose a framework using attractor landscapes to visualize and interpret complex system dynamics. In this context, attractors are states toward which systems naturally evolve, while tipping points - critical thresholds between attractors - can lead to profound, unexpected changes impacting a system's resilience and well-being. We present four generic attractor landscape types that provide a novel lens for viewing risks and opportunities, and serve as decision-making contexts. The main practical contribution is clarifying when to emphasize particular strategies - optimisation, risk mitigation, exploration, or stabilization - within this framework. Context-appropriate decision making should enhance system resilience and mitigate extreme risks.

Optimization of a deep mutational scanning workflow to improve quantification of mutation effects on protein-protein interactions.

Deep Mutational Scanning (DMS) assays are powerful tools to study sequence-function relationships by measuring the effects of thousands of sequence variants on protein function. During a DMS experiment, several technical artefacts might distort non-linearly the functional score obtained, potentially biasing the interpretation of the results. We therefore tested several technical parameters in the deepPCA workflow, a DMS assay for protein-protein interactions, in order to identify technical sources of non-linearities. We found that parameters common to many DMS assays such as amount of transformed DNA, timepoint of harvest and library composition can cause non-linearities in the data. Designing experiments in a way to minimize these non-linear effects will improve the quantification and interpretation of mutation effects.

Modeling of fruit growth of tangor murcott and incidence of environmental factors on the estimated parameters / Modelación de crecimiento de frutos de tangor murcott e incidencia de factores medioambientales sobre los parámetros estimados

ABSTRACT Fruit production forecasts are a tool to plan the harvest and improve market strategies. To carry it out, it is essential to have information about the behavior of fruit development over time. The objective of this work was to find the mathematical-statistical model that best describes the growth pattern of tangor murcott fruit (Citrus reticulata x C. sinensis 'Murcott') and analyze how it is affected by environmental conditions. For this, in nine orchards, located in four locations in the province of Corrientes, Argentina, the equatorial diameter of 2,053 fruit from 82 days after full flowering to harvest were periodically registered during five seasons. The nonlinear models were compared: Logistic, Gompertz, Brody, Von Bertalanffy, Weibull, Morgan Mercer Flodin (MMF), Richards, and their respective re-parameterizations. The magnitudes of nonlinearity measures, coefficient of determination and estimates of residual deviation were considered as the main goodness-of-fit criteria. The selected model-parameterization combination was the fifth parameterization of the Logistic model with random effects on its three parameters. An Analysis of Variance model on the estimates of these parameters for each fruit showed that orchard and season factors were an important source of variability, mainly in those related to the initial size of the fruit and their growth rate. These results will allow the construction of growth tables, which in addition to making yield predictions, can be used to estimate fruit size distribution at harvest and improve the cultural practice of manual fruit thinning.

RESUMEN Los pronósticos de producción de fruta son una herramienta para planificar la cosecha y mejorar estrategias de mercado. Para su realización es imprescindible contar con información acerca del desarrollo de los frutos a lo largo del tiempo. El objetivo del presente trabajo fue encontrar el modelo matemático-estadístico que mejor describa el patrón de crecimiento de frutos tangor murcott (Citrus reticulata x C. sinensis 'Murcott') y analizar cómo es afectado por condiciones medioambientales. En nueve huertos, ubicados en cuatro localidades en la provincia de Corrientes, Argentina, se registró durante cinco temporadas el diámetro ecuatorial de 2053 frutos desde los 82 días después de plena floración hasta el momento de cosecha. Se compararon los modelos no lineales: Logístico, Gompertz, Brody, Von Bertalanffy, Weibull, Morgan Mercer Flodin (MMF), Richards, y sus respectivas re-parameterizaciones. Como principales criterios de bondad de ajuste se consideraron las magnitudes de medidas de no linealidad, coeficiente de determinación y estimaciones del desvío residual. La combinación modelo-parametrización seleccionada fue la quinta parametrización del modelo Logístico con efectos aleatorios en sus tres parámetros. Un modelo de análisis de la variancia sobre las estimaciones de estos parámetros para cada fruto mostró que los factores huerto y temporada eran una importante fuente de variabilidad, principalmente en los relacionados con el tamaño inicial de los frutos y su tasa de crecimiento. Estos resultados permitirán construir tablas de crecimiento, que además de realizar predicciones de rendimientos, podrán ser utilizadas para estimar distribución de tamaños de fruto a cosecha y mejorar la práctica cultural de raleo.

Biomechanical Analysis of Age-Dependent Changes in Fontan Power Loss.

The hemodynamics in Fontan patients with single ventricles rely on favorable flow and energetics, especially in the absence of a subpulmonary ventricle. Age-related changes in energetics for extracardiac and lateral tunnel Fontan procedures are not well understood. Vorticity (VOR) and viscous dissipation rate (VDR) are two descriptors that can provide insights into flow dynamics and dissipative areas in Fontan pathways, potentially contributing to power loss. This study examined power loss and its correlation with spatio-temporal flow descriptors (vorticity and VDR). Data from 414 Fontan patients were used to establish a relationship between the superior vena cava (SVC) to inferior vena cava (IVC) flow ratio and age. Computational flow modeling was conducted for both extracardiac conduits (ECC, n = 16) and lateral tunnels (LT, n = 25) at different caval inflow ratios of 2, 1, and 0.5 that corresponded with ages 3, 8, and 15+. In both cohorts, vorticity and VDR correlated well with PL, but ECC cohort exhibited a slightly stronger correlation for PL-VOR (>0.83) and PL-VDR (>0.89) than that for LT cohort (>0.76 and > 0.77, respectively) at all ages. Our data also suggested that absolute and indexed PL increase (p < 0.02) non-linearly as caval inflow changes with age and are highly patient-specific. Comparison of indexed power loss between our ECC and LT cohort showed that while ECC had a slightly higher median PL for all 3 caval inflow ratio examined (3.3, 8.3, 15.3) as opposed to (2.7, 7.6, 14.8), these differences were statistically non-significant. Lastly, there was a consistent rise in pressure gradient across the TCPC with age-related increase in IVC flows for both ECC and LT Fontan patient cohort. Our study provided hemodynamic insights into Fontan energetics and how they are impacted by age-dependent change in caval inflow. This workflow may help assess the long-term sustainability of the Fontan circulation and inform the design of more efficient Fontan conduits.

Unraveling the link: environmental tobacco smoke exposure and its impact on infertility among American women (18-50 years).

Purpose: The detrimental effects of environmental tobacco smoke (ETS) on women's reproductive health have been widely recognized. However, the detailed association between exposure to environmental tobacco smoke and the incidence of infertility remains under-explored. This investigation focuses on exploring this potential connection. Methods: For this analysis, we extracted data from the US National Health and Nutrition Examination Survey (NHANES) database, covering the years 2013 to 2018, focusing on individuals with recorded serum cotinine levels and infertility information. ETS exposure and fertility status were analyzed as independent and dependent variables, respectively. We applied weighted multivariate logistic regression method to evaluate the impact of ETS on infertility, including subgroup analyses for more detailed insights. Results: The study encompassed 3,343 participants. Logistic regression analysis revealed a notable positive correlation between ETS exposure and infertility, with an odds ratio (OR) of 1.64 (95% Confidence Interval [CI]: 1.14-2.36). We observed a non-linear relationship between ETS exposure and infertility risk. Notably, infertility risk increased by 64% in serum cotinine levels above 0.136 compared to that in serum cotinine levels below 0.011. Further, subgroup analysis and interaction tests showed consistent results across different segments, underscoring the robustness of the ETS-infertility link. Conclusion: Our findings suggest that environmental tobacco smoke exposure may be a contributing factor to infertility. These results reinforce the recommendation for women in their reproductive years to avoid ETS exposure, especially when planning for pregnancy.

Metrology of Short-Length Measurers-Development of a Comparator for the Calibration of Measurers Based on Image Processing and Interferometric Measurements.

For the calibration of linear scales, comparators are generally used. Comparators are devices that enable the movement of an evaluation apparatus over a calibrated scale along a linear base with high precision. The construction of a comparator includes a movable carriage that carries the device for the evaluation of the position of the given edge of the line scale relative to the beginning of the scale. In principle, it involves a camera capturing the scale of the measurer, where the position of the camera's projection center is measured using an interferometer. This article addresses the development of a comparator assembled from low-cost components, as well as the description of systematic influences related to the movement of individual parts of the system, such as the inclination and rotation of the camera and directional and height deviations during the carriage's movement. This article also includes an evaluation of the edge of the given scale with subpixel accuracy, addressing distortion elimination and excluding the influences of impurities or imperfections on the scale. The proposed solution was applied to linear-scale measurers, such as leveling rods with coded and conventional scales and measuring tapes. The entire process of measurement and evaluation was automated.