Standard Squares Research Articles

Complementary mean square deviation and stability analyses of the widely linear recursive least squares algorithm

The widely linear recursive least squares (WL-RLS) algorithm is useful for dealing with noncircular signals. In this paper, we focus on the theoretical prediction and stability of the WL-RLS algorithm. First, the steady-state complementary mean square deviation (CMSD) behavior under a time-varying system is analyzed, which is different from the traditional standard mean square analysis that takes the form of real-valued evolution. Second, the Lyapunov stability and numerical stability are taken into account to provide an in-depth understanding of the stability issue. Specifically, by employing the Lyapunov stability theory (LST) approach, we perform the stability analysis of the WL-RLS algorithm for time-invariant system and noise-free output, illustrating that the WL-RLS algorithm is exponentially convergent in the sense of Lyapunov. Moreover, the numerical stability is analyzed by considering the finite word-length effects, and it is shown that the WL-RLS algorithm is numerically stable when the forgetting factor λ<1. Numerical simulations for system identification scenarios verify the accuracy of the steady-state theoretical prediction.

Blood Pressure Variability and Complexity in Nursing Home Residents Before Death

Blood pressure (BP) is a complex dynamic system in the human body and an important determinant of healthy aging. Exploring BP as a dynamic data system may provide important insights into how BP patterns can provide complementary information to the static, one-time BP measurements that are more commonly used for clinical decision making. Thus, we sought to describe BP as a dynamic data system in older adults nearing death. Using a prospective cohort study design, we assessed BP measures 6 months before death in Veterans Health Administrative nursing home residents between 10/1/2006 and 9/30/2017. Variability was characterized using standard deviation and mean square error after adjusting for diurnal variations. Complexity (i.e., amount of novel information vs. redundancy) was examined using Shannon’s entropy (bits). Generalized linear models were used to examine factors associated with overall BP variability. We identified 17,953 patients (98.0% male, 82.5% White, mean age 80.2 years, and mean BP 125.7/68.6 mmHg). In the last 6 months of life, systolic BP decreased slightly (□-7.2mmHg). Variability was stable until the last month of life, at which point variability increased by as much as 30%. In contrast, complexity did not change in the 6 months before death (□0.02 bits). Factors associated with BP variability before death include hospitalizations, hospice care, and medication changes. Systolic BP decreases in the last 6 months before death, and BP variability increases in the last month of life. Further, the increase in BP variability may be driven by increasingly complex care patterns as one approaches death.

Research on Micro/Nano Surface Flatness Evaluation Method Based on Improved Particle Swarm Optimization Algorithm.

Flatness error is an important factor for effective evaluation of surface quality. The existing flatness error evaluation methods mainly evaluate the flatness error of a small number of data points on the micro scale surface measured by CMM, which cannot complete the flatness error evaluation of three-dimensional point cloud data on the micro/nano surface. To meet the needs of nano scale micro/nano surface flatness error evaluation, a minimum zone method on the basis of improved particle swarm optimization (PSO) algorithm is proposed. This method combines the principle of minimum zone method and hierarchical clustering method, improves the standard PSO algorithm, and can evaluate the flatness error of nano scale micro/nano surface image data point cloud scanned by atomic force microscope. The influence of the area size of micro/nano surface topography data on the flatness error evaluation results is analyzed. The flatness evaluation results and measurement uncertainty of minimum region method, standard least squares method, and standard PSO algorithm on the basis of the improved PSO algorithm are compared. Experiments show that the algorithm can stably evaluate the flatness error of micro/nano surface topography point cloud data, and the evaluation result of flatness error is more reliable and accurate than standard least squares method and standard PSO algorithm.

A Highly Sensitive and Miniature Optical Fiber Sensor for Electromagnetic Pulse Fields.

The detection of an electromagnetic pulse (EMP) field is of great significance in determining the field environment of tested equipment in small spaces. Finger-shaped miniature optical fiber sensors for electromagnetic pulse field measurement were designed. The antenna of a weak field sensor was integrated with a shielding shell, and the wire welded at the direct electro-optic converting circuit connected to an optical fiber through special structure and circuit design was taken as the antenna of a strong field sensor. Measurements in the time domain and frequency domain had been carried out for the two sensors. Experiment results demonstrate that the weak field sensor and the strong field sensor have flat responses from 100 kHz to 1 GHz with a variation of 2.3 dB and 2.9 dB, respectively, and the EMP waveform detected by the sensors agrees well with the applied standard square wave. Moreover, the strong field sensor exhibits linear responses from 645 V/m to 83 kV/m. The resolution of the weak field sensor is as low as 13 V/m. The result indicated that the designed sensors had good performance.

Asymptotics of sums of regression residuals under multiple ordering of regressors

We prove theorems about the Gaussian asymptotics of an empirical bridge built from linear model regressors with multiple regressor ordering. We study the testing of the hypothesis of a linear model for the components of a random vector: one of the components is a linear combination of the others up to an error that does not depend on the other components of the random vector. The results of observations of independent copies of a random vector are sequentially ordered in ascending order of several of its components. The result is a sequence of vectors of higher dimension, consisting of induced order statistics (concomitants) corresponding to different orderings. For this sequence of vectors, without the assumption of a linear model for the components, we prove a lemma of weak convergence of the distributions of an appropriately centered and normalized process to a centered Gaussian process with almost surely continuous trajectories. Assuming a linear relationship of the components, standard least squares estimates are used to compute regression residuals -- the differences between response values and the predicted ones by the linear model. We prove a theorem of weak convergence of the process of regression residuals under the necessary normalization to a centered Gaussian process.

Comparison of Two Methods for Estimating Parameters of the Model Binary Logistic Regression

This paper we deal with one of the most important nonlinear regression models widely used in modeling statistical applications, which is the binary logistic regression model, and then estimating the parameters of this model using statistical estimation methods. However, while using this model we face a problem in estimating its parameters as the number of parameters is (p+1), and finding the estimation of parameters using numerical methods sometimes does not provide the best solution because it depends on primitive estimations. In this paper, some ordinary estimation methods are employed to fit the estimation of the parameters of this type of non-linear regression model, and then we compare these estimation methods. Further, the comparison includes some of the important estimation methods, which are the ordinary estimation methods that included the Weighted Least Squares Method (WLS), and the Bayes Method (BM). In order to choose the best method for estimating, by taking a number of models and different sample sizes and using the statistical standard mean error squares (MSE) for the logistic model estimations for the purpose of comparison. Among the preferred methods for estimating model parameters, and it was generally concluded that the WLS method provides the MSE of estimators compared to the other methods. On the practical side, this model was also used to model data for people with diabetes and to estimate parameters using the best methods, and it was reached by comparing patients with diabetes. A census of diabetes with those who did not have diabetes with the appropriateness of the model in modeling this type of data and extracting the main cause of diabetes incidence, which is insulin, as well as the accuracy of the methods in estimating the model parameters.

The effect of borrower country financial system and corporate governance system types on the spread of syndicated loans

PurposeThis study aims to analyse the effect of borrower’s countries on syndicated loan spreads, featuring countries according to institutional factors, namely, financial systems and corporate governance systems.Design/methodology/approachThis study is an empirical investigation based on a unique sample of more than 85,000 syndicated loans from 122 countries. The paper uses standard and two-stage least squares regression analysis to test whether the types of financial and corporate governance systems affect loan spreads.FindingsThe paper finds that borrowers from countries with financial systems oriented towards the banking-based paradigm pay lower interest rate spreads than those from countries with financial systems oriented towards the market-based paradigm. In addition, there is evidence that borrowers from countries with more developed financial systems pay lower spreads. The results also show that borrowers from countries with an Anglo-Saxon governance system pay higher spreads than borrowers from countries with a Continental governance system.Research limitations/implicationsThis study does not consider potential promiscuous relationships that can arise at the ownership structure and governance level between banks and borrowers and may affect loan spreads.Practical implicationsThis study suggests that financial and corporate governance systems are essential factors in the financial intermediation process. Furthermore, the evidence indicates that corporates with higher potential agency costs and higher potential information asymmetry are requested to pay higher spreads. Therefore, the opportunities to such corporates invest optimally tend to be scarcer.Originality/valueThe paper highlights the impact of institutional factors on the cost of financing, characterising the countries according to the type of financial system and the type of corporate governance system. The study finds that borrowers from countries with bank-based financial systems pay lower interest rate spreads than those from countries with market-based financial systems. The paper also highlights how the level of financial development affects the cost of financing. The paper focusses on non-financial firms, unlike financial firms, which have been the focus of several empirical studies on topics relating to the cost of funding and corporate governance.

Redefining Hyperviscosity in Acute Leukemia: Implications for Red Cell Transfusions

Hyperleukocytosis, most commonly defined as a white blood cell (WBC) count greater than 100,000/μL, is an emergency in acute leukemia, possibly resulting in life-threatening microvascular obstruction, or leukostasis, leading to neurologic (CNS hemorrhage, thrombosis) or pulmonary (respiratory distress, hypoxia) complications. The underlying mechanisms remain poorly understood and are canonically attributed to blood hyperviscosity secondary to high WBC count and abnormal biophysical properties of leukemia cells themselves (leukemia immunophenotype, increased cell size, adhesion, and stiffness). Leukapheresis is a commonly-used therapy for rapid cytoreduction in symptomatic patients, but the procedure carries risk and existing guidelines are supported by scant evidence. Interestingly, despite hematocrit(Hct)/hemoglobin(Hgb) levels being major drivers of blood viscosity due to the high prevalence of circulating red cells (RBCs), how Hct/Hgb mediates hyperviscosity in acute leukemia is unknown. This is clinically important as Hct/Hgb often decrease as leukemic cell counts rise, and acute leukemia patients with anemia are often transfused. While sickle cell disease guidelines advise using a target post transfusion Hct of 30% to minimize iatrogenic hyperviscosity and its morbid complications, no guidelines have been established for acute leukemia. As such, can RBC transfusion actually increase leukostasis risk in acute leukemia?To explore this question requires new biophysical tools as the complexity of blood viscosity increases substantially at the microvascular level as the physical properties of the cells themselves become the major determinants of resistance to blood flow. To that end, we developed “microvasculature-on-a-chip” devices that recapitulate microvascular biophysical and hemodynamic conditions to investigate how the differing presentations of acute leukemia and transfusion support affect the effective blood viscosity at the microvascular level to cause “in vitro leukostasis.”A multiple-vessel “multiplex” microfluidic device that operates at the appropriate size scale and mimics the microvascular geometry was designed to enable assessing accurate biophysical measurements of blood hyperviscosity. The devices were microfabricated using standard polydimethylsiloxane-based photolithography (Figure 1). Acute B-cell lymphoblastic (B-ALL, 697), acute T-cell lymphoblastic (T-ALL, Jurkat) and acute myelocytic (AML, HL60) leukemia cell lines were maintained via standard cell culture conditions. Patient samples were obtained through our institution's IRB. RBCs from healthy donors were isolated and mixed with leukemia cells to achieve target Hct/Hgb and WBC levels. Various physiologic leukemia “mixtures” were then perfused under physiologic microcirculatory flow conditions through the microvascular device and microchannels occlusion was tracked via videomicroscopy (Figure 2).Using a standard least squares multivariable linear regression with first and second order effects, microchannel size, Hct/Hgb, WBC count and leukemia cell type all showed statically significant effect on in vitro leukostasis, or microchannel occlusion over time, (all p values < 0.03) (Figure 3). Overall, severe anemia appears to be protective against in vitro leukostasis and there appears to be Hct/Hgb thresholds above which in vitro leukostasis becomes more prevalent, though this is different for B-ALL versus T-ALL. This is in contrast to AML, where severe anemia does not appear to offer protection against in vitro leukostasis as occlusion was appreciated at all Hct/Hgb levels.These data suggest when determining risk for leukostasis, WBC count and leukemia immunophenotype should not be the sole determinants. Here we show Hct/Hgb levels affect microvascular blood viscosity and risk for microvascular occlusion. These results may impact decisions regarding RBC transfusions and possibly initiation of leukapheresis in asymptomatic patients. Having a model to assess risk associated with RBC transfusions and informing clinicians when a patient might become at risk for leukostasis can have a significant impact on their clinical outcome, morbidity and mortality. Ongoing studies incorporating patient lymphoid and myeloid leukemia cells are needed to support this cell line data. [Display omitted] DisclosuresKemp: Parthenon Therapeutics: Membership on an entity's Board of Directors or advisory committees. Lam: Sanguina, Inc.: Current holder of individual stocks in a privately-held company.

On the Complex and Chaotic Dynamics of Standard Logistic Sine Square Map

Abstract In this article, we set up a new nonlinear dynamical system which is derived by combining logistic map and sine square map in Mann orbit (a two step feedback process) for ameliorating the stability performance of chaotic system and name it Standard Logistic Sine Square Map (SLSSM). The purpose of this paper is to study the whole dynamical behavior of the proposed map (SLSSM) through various introduced aspects consisting fixed point and stability analysis, time series representation, bifurcation diagram and Lyapunov exponent. Moreover, we show that our map is significantly superior than existing other one dimensional maps. We investigate that the chaotic and complex behavior of SLSSM can be controlled by selecting control parameters carefully. Also, the range of convergence and stability can be made to increase drastically. This new system (SLSSM) might be used to achieve better results in cryptography and to study chaos synchronization.

An ex-post analysis of trade effects of the ASEAN–Korea Free Trade Area (AKFTA) from Korea’s Perspective

This paper empirically analyzes the effectiveness of the ASEAN–Korea Free Trade Area (AKFTA) focusing on the ex-post trade creation and diversion effects with controlling related intra-bloc and extra-bloc regional trade agreements (RTAs) and economic characteristics of interconnected economies. The quantitative analysis applies a gravity model regression analysis by specifying standard pooled ordinary least squares and fixed effects regression models. We observe that the AKFTA turned out to be more favorable for Korea in terms of trade balance with ASEAN. We find that the AKFTA is a desirable trade creating RTA strongly driven by intra-bloc export activities between members and does not divert but generates more export to and import from non-members. Thus, we strongly support that the AKFTA facilitates trade between intra-bloc members and their trade with extra-bloc non-members as well. Besides which, we find that the trade creation effects of the AKFTA are industry-specific and sector-specific. For the manufacturing industry, the trade creation effects of the AKFTA are generated by both intra-bloc and extra-bloc import activity but not from extra-bloc export activity. However, for the services industry, all the intra-bloc and extra-bloc export and import activities contribute to the trade creation effects. Interestingly, we find that the trade creation effects of the manufacturing industry are smaller than those of the services industry. Considering restrictive service schedules of specific commitments in the AKFTA agreement on trade-in services compared to trade-in goods provisions, rearranging the trade-in services provisions is necessary to generate more trade gains in the future.

Entrepreneurial Potentials of COVID-19 Vaccination

The study investigated the entrepreneurial potentials of COVID-19 vaccination. The objective of the study is to identify entrepreneurial potentials in COVID-19 vaccination, to determine the business viability of COVID-19 vaccine production and to identify how an entrepreneur can contribute to covid-19 vaccination. A descriptive survey research design was adopted for the study. The sample of the study was achieved using random sampling technique. The sample for this study was 69 respondents. These respondents were selected sixty nine (69) employees of health Centres and hospitals dispensing COVID-19 vaccines in Nigeria. The instrument used to collect data was the questionnaire. The data collected were analyzed using simple percentage, mean, standard and chi square. The overall finding of the study are that there is entrepreneurial potential in covid-19 vaccination, sales of vaccine, production of vaccine and distribution of vaccine are the entrepreneurial potentials in COVID-19 vaccination; the study also revealed that there is significant and positive relationship between Covid-19 vaccine production and profitability and It was also discovered that entrepreneurial innovation made Covid-19 vaccines possible and a viable business model. High demand of COVID-19 vaccine, global acceptance of the COVID-19 vaccine, effectiveness of the vaccine has made COVID-19 vaccine production a viable business. Base on the findings of the study, the following recommendations were made among others that government officials should support a more productive interaction and collaboration between them and pharmaceutical firms with a focus on capacity building for entrepreneurship development.

Exploring microwave absorption by non-periodic metasurfaces

In recent years there has been a large body of work investigating periodic metasurface microwave absorbers. However, surprisingly few investigations have focused on the absorption performance of similar non-periodic designs. In this work, the electromagnetic response of a large area (310 mm x 310 mm) microwave absorber that lacks a global periodicity is experimentally studied. The top metallic layer of the ultra-thin (0.3 mm) absorber is structured with rectangular patches given by a procedurally generated non-periodic pattern, known as the toothpick sequence. The specular reflectivity of both p-polarised and s-polarised incident radiation shows coupling to an additional low frequency mode when compared to a standard square patch periodic absorber. To further explore the coupling efficiency of such non-periodic absorbers, finite element models were used to investigate the influence of increasing sample size.

Integrated and shape-adaptable multifunctional flexible triboelectric nanogenerators using coaxial direct ink writing 3D printing

Triboelectric nanogenerators (TENGs), particularly those with high flexibility and sophisticated geometry, have shown great application foreground in portable and wearable electronics. However, traditional fabrication approaches remain complicated and non-versatile, which are also impractical in the preparation of complex shapes and structures. Herein, a one-pot coaxial direct ink writing (DIW) 3D printing technique has been proposed to fabricate a fully flexible single-electrode TENG (FFTENG) with sophisticated shapes and 3D structures for the purpose of harvesting and utilizing biomechanical energy. This FFTENG is made up of a silicone elastomer shell as the triboelectric layer and an inner silicone/carbon black (CB) core as the flexible electrode. Various factors that affect the output electric performance, including the ratio of the inner and outer diameter of the printed fiber, CB content, loading frequency, applied contact force, specimen size, and external load resistance, are investigated in detail via the contact-separation test. The results show that a standard square FFTENG with a size of 30 × 30 mm2 can yield an open-circuit voltage (Voc), a short-circuit current (Isc) and a short-circuit transferred charge (Qsc) of as high as 60 V, 0.23 μA and 58 nC, respectively, and a maximum output peak power density of 15.59 mW m−2 at a matched resistance of 80 MΩ. More importantly, various shape-adaptable FFTENGs can be designed and tailored to meet the diverse needs of different applications, such as self-powered LED systems, self-charging power systems, LED control devices, self-powered tactile sensors, flexible self-monitoring grip exercisers, and flexible self-powered keyboards, and open up new avenues for use in multifunctional self-powered electronic systems, including biomechanical energy harvesting and utilization, self-powered sensing and human-machine interaction.

Exploiting Agronomic and Biochemical Traits to Develop Heat Resilient Cotton Cultivars under Climate Change Scenarios

The development of high-yielding heat-tolerant cotton cultivars harboring plastic phenotypes across warming climatic regions is prime objectives of today’s cotton breeding programs. We evaluated eight parents and 15 F1 hybrids under normal and heat stress conditions. Agronomic and biochemical characters were analyzed using standard least square, correlation, principal component analysis (PCA), and hierarchical clustering. The results explained a significant reduction in all traits except hydrogen peroxide contents, catalase, and peroxidase activities with a prominent increase under heat stress. A significant positive correlation was observed among all agronomic and biochemical traits. POD was found to have a maximum positive correlation with CAT (0.947) and minimum with boll weight (0.050). PCA showed first two components accounting for 78.64% of the total variation, with 55.83% and 22.80% of the total variation, respectively. Based on multivariate analyses methods 23 genotypes have been placed in 3 groups: tolerant (cluster-3), moderately tolerant (cluster-2), and susceptible (cluster-1). In a general perspective hybrids have better performance across normal and heat stress supports the idea of hybrid adaptability across stress environments. In specific FH-458 × FH-313 cross performed best across both conditions for yield and physiological traits. Hence, the generated information from the present study would support breeders in developing heat-resilient cultivars to endure the prevailing extreme environmental conditions.

Fabrication of optical vortex array by fixing standing wave mediated periodic defects in nematic liquid crystals via photopolymerization

ABSTRACT Optical vortex beams have attracted significant research interest owing to their potential for application in optical communication and imaging. In this study, we demonstrated devices with electrically switchable topological defects as well as those with permanent topological defects using reactive mesogen (RM) and vertically aligned LC cells. A defect array was created via standing wave-mediated periodic LC orientation control by applying pulse train waves. At a low RM concentration (0.6 wt%), polymerisation induced a pretilt angle, and the periodic LC alignment was memorised with a topological defect array. Thus, the memorised defect structure reappeared upon applying a direct current field or standard square electric fields. In contrast, at a high RM concentration (3 wt.%), a permanent defect array device was realised. Thus, depending on the RM concentration, various types of defect arrays were selectively created. The devices developed in this study can be used for various potential applications, such as in digital data transfer and imaging systems.

A statistical model of the spatial variability of weather for use in building simulation practice

Traditionally, statistically representative (e.g. TMY) weather data is used as boundary condition in building simulation practice and is deemed homogenous for a defined region, e.g., a city. However, this presumed homogeneity does not exist in most cases because due to the effect of variable urban morphology, local weather may be different in different parts of the region. This paper uses Multiple Tensor on Tensor Regression (MTOT) for creation of meso-scale weather for applications in building simulation practice, which is fast and efficient, provided that enough local weather station data and regional morphology data are available. The derived statistical weather model captures variation in local weather due to change in urban morphology with an ensemble mean of standardized mean square error 0.038, i.e, 96.2% of the variation in weather may be predicted by this method. Chicago is used as a demonstration region with a case study of single family dwelling to exemplify the impact of local weather on building performance indicators. A standard deviation of approximately 372 kWh for end use heating, 33.6 kWh for end use cooling and 4 h for hours of discomfort in winters is observed, highlighting the variation caused by the use of local weather in building performance simulation.

Spatial Analysis of the 2018 Logistics Performance Index Using Multivariate Kernel Function to Improve Geographically Weighted Regression Models

This paper analyzes the 2018 Logistics Performance Index (LPI) from the World Bank to determine the spatial effects of countries’ logistics performance. Although the standardized ordinary least square (OLS) models show good results, the spatial lags and Moran’s I of LPI suggest the OLS assumptions are violated. Consequently, an improved geographically weighted regression (IGWR) model using multivariate kernel functions (MKF) is implemented. Through the analysis of the Moran scatter plot, the authors identified the countries that have different logistics performance development trends in the four quadrants representing the relationship between the spatial lags and the LPI. Using trade activity (i.e., import/export) in the MKF, the authors compared different MKF types and bandwidths to ensure the model’s predictability and accuracy and found that the adaptive Gaussian MKF is suitable. Finally, the IGWR model indicates both positive and negative influencing factors on LPI overall score. Specifically, the improvements of LPI are more associated to economic variables in mid- and low-income countries around the world, and are more related to import of construction equipment in the Middle East. Also, business environment is more important in Latin America and the Pacific. European countries are more sensitive to customs efficiency, whereas Pacific-Asian countries are more sensitive to quality of infrastructure and have higher coefficients than African and American countries. This spatial heterogeneity is related to the specific factors that promote the development of their logistics performance.

Improvement of HHV prediction model of biomass based on the ultimate analysis

This paper presents an improved prediction model for the higher heating value (HHV) of biomass based on the ultimate analysis by using the standard least squares method. This study intends for us to predict the HHV of biomass within a wide range of elemental distributions rather than the range of literature in order to create an optimal prediction model. To this end, many experimental data, comprising a wide range of biomass elements, regression models, and neural networks, are used for its comparative validation. As a result, the proposed prediction model, HHV = 2.8799 + 0.2965 * C + 0.4826 * H – 0.0187 * O demonstrates a better HHV prediction performance for biomass in a comparative validation of 250 samples presented in the literature, and the fitness model using a neural network shows a high fitness in the training, validation, and testing for 430 samples.

Simultaneous Calibration of Grapevine Phenology and Yield with a Soil–Plant–Atmosphere System Model Using the Frequentist Method

Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety–training systems in the Douro Demarcated Region, northern Portugal. An objective function is defined to search for the best-fit parameters that result in the minimum value of the unweighted sum of the normalized Root Mean Squared Error (nRMSE) of the studied variables. Parameter uncertainties are estimated as how a given parameter value can determine the total prediction variability caused by variations in the other parameter combinations. The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of −2 to 4 days for phenology and −232 to 159 kg/ha for yield. The corresponding variance in the observed data was generally well reproduced, except for one occasion. These parameters are a good trade-off to achieve results close to the best possible fit of each response variable. No parameter combinations can achieve minimum errors simultaneously for phenology and yield, where the best fit to one variable can lead to a poor fit to another. The proposed parameter uncertainty analysis is particularly useful to select the best-fit parameter values when several choices with equal performance occur. A global sensitivity analysis is applied where the fruit-setting parameters are identified as key determinants for yield simulations. Overall, the approach (including uncertainty analysis) is relatively simple and straightforward without specific pre-conditions (e.g., model continuity), which can be easily applied for other models and crops. However, a challenge has been identified, which is associated with the appropriate assumption of the model errors, where a combination of various calibration approaches might be essential to have a more robust parameter estimation.

Sour Taste SNP KCNJ2-rs236514 and Differences in Nutrient Intakes and Metabolic Health Markers in the Elderly.

Single nucleotide polymorphisms (SNPs) in taste receptors influence dietary choices that contribute to health and quality of life. Individual differences in sour taste perception and preference have been linked to heritable genetics, yet the impact of sour taste receptor SNPs on sour taste is under-researched, and studies on sour taste SNP associations to diet and health are lacking. Therefore, this study explored the relationships between the sour taste SNP KCNJ2-rs236514 and estimated macronutrient, vitamin and mineral intakes, and markers of metabolic health. Associations were explored in 523 participants aged 65 years and older with data analysed using standard least squares and nominal logistic regression modelling with post hoc student's t-tests and Tukey's HSD. Associations were found between the presence of the KCNJ2-rs236514 variant allele (A) and lower intakes of energy, total fat, monounsaturated fat and saturated fat. The lower fat intakes were significant in female carriers of the variant allele (A), along with lower water intake. Lower retinol, riboflavin, folate, calcium and sodium intakes were found in the KCNJ2-A allele carriers. In females, the variant allele was associated with lower sodium intake before and after Bonferroni adjustment. Higher body mass index, waist and waist-to-hip ratio measures were found in males carrying the variant allele. Lower levels of liver function biomarkers were associated with the presence of the KCNJ2-A allele. Overall and in males, the variant's association to lower gamma-glutamyl transferase (GGT) levels remained significant after Bonferroni adjustments. These novel findings suggest the sour taste SNP, KCNJ2-rs236514, may be modifying macronutrient, vitamin and mineral intakes, and markers of metabolic health. Research on the extra-oral functions of this SNP may improve health outcomes for those with overweight, obesity and liver disease.