Formulation Of Model Research Articles

A risk assessment framework utilizing bivariate copula for contaminate monitoring in groundwater.

Regular groundwater quality monitoring in resource-constrained regions present formidable challenges in terms of funding, testing facilities and manpower; necessitating the development of easily implementable monitoring techniques. This study proposes a copula-based risk assessment model utilizing easily measurable indicators (e.g., turbidity, alkalinity, pH, total dissolved solids (TDS), conductivity), to monitor the contaminates in groundwater which are otherwise difficult to measure (i.e., iron, nitrate, sulfate, fluoride, etc.). Preliminary correlation between the indicators and the target contaminates were identified using Pearson coefficient. Best representative univariate distributions for these pairs were selected using the Akaike Information Criterion (AIC), which were used in the formulation of the copula model. Validation against observed data showcased the model's high accuracy, supported by consistent Kendall Tau correlation coefficients. Through this model, conditional probabilities of the contaminants not exceeding the permissible limits set by the Bureau of Indian Standards (BIS) were calculated using indicator concentration. Notably, an inverse correlation between iron concentration and conductivity was noted, with the likelihood of iron exceeding BIS limits decreasing from 90 to 50% as conductivity rose from 500 to 2000 micromhos/cm. TDS emerged as a pivotal indicator for nitrate and sulfate concentrations, with the probability of sulfate surpassing 10mg/l decreasing from 75 to 25% as TDS increased from 250 to 750mg/l. Likewise, the probability of nitrate exceeding 1mg/l decreased from 90 to 60% with TDS levels reaching 1500mg/l. Furthermore, a 63% probability of fluoride concentrations remaining below 1mg/l was observed at turbidity levels of 0-10 NTU. These findings hold significant implications for policymakers and researchers since the model can provide crucial insights into the risks associated with the contaminates exceeding the permissible limit, facilitating the development of an efficient monitoring and management strategies to ensure safe drinking water access for vulnerable populations.

Evaluation of the adjuvant effect of imiquimod and CpG ODN 1826 in chimeric DNA vaccine against Japanese encephalitis

Vaccines represent a significant milestone in the history of human medical science and serve as the primary means for controlling infectious diseases. In recent years, the geographical distribution of Japanese encephalitis viruses (JEV) of various genotypes has become increasingly complex, which provides a rationale for the development of safer and more effective vaccines. The advent of subunit and nucleic acid vaccines, especially propelled by advancements in genetic engineering since the 1980s, has accelerated the application of novel adjuvants. These novel vaccine adjuvants have diversified into toll-like receptor (TLR) agonists, complex adjuvants, nanoparticles and so on. However, the efficacy of adjuvant combinations can vary depending on the host system, disease model, or vaccine formulation, sometimes resulting in competitive or counteractive effects. In our previous study, we constructed a pJME-LC3 chimeric DNA vaccine aimed at inducing an immune response through autophagy induction. Building on this, we investigated the impact of the TLR7/8 agonist imiquimod (IMQ) and the TLR9 agonist CpG ODN 1826 as adjuvants on the immunogenicity of the Japanese encephalitis chimeric DNA vaccine. Our findings indicate that the combination of the pJME-LC3 vaccine with IMQ and CpG ODN 1826 adjuvants enhanced the innate immune response, promoting the maturation and activation of antigen-presenting cells in the early immune response. Furthermore, it played a regulatory and optimizing role in subsequent antigen-specific immune responses, resulting in effective cellular and humoral immunity and providing prolonged immune protection. The synergistic effect of IMQ and CpG ODN 1826 as adjuvants offers a novel approach for the development of Japanese encephalitis nucleic acid vaccines.

Navigating the Startup Innovation Ecosystem: Strategies for Effective External Resource Management

This research examines how innovation ecosystems facilitate startup innovation across the creation, development, and market phases. We analyze the roles of diverse ecosystem actors, emphasizing their contributions to resource dynamics using the case method approach. Semi-structured interview-based data was analyzed using content analysis. The study revealed the significance of the strategy of using external resources for the success of startups. During the creation phase, non-market-oriented entities like universities and incubators support startups with crucial early-stage R&D funding and business model formulation. In the development phase, startups refine their products with backing from accelerators, investors, and customers, utilizing innovation resources for prototyping and validation, and transitioning into the market phase, startups scale operations with market-oriented actors, focusing on organizational readiness and leveraging established business models for growth. Throughout these phases, startups balance exploration and exploitation strategies, enabling innovation ambidexterity. Our findings highlight how innovation ecosystem actors provide essential resources—physical, social, financial, and human—that foster startup resilience and maturity. Grounded in the Resource-Based View framework, this study offers empirical insights into strategic adaptations within innovation ecosystems, illustrating their transformative impact on startup success.<

Efficiency evaluation of 28 health systems by MCDA and DEA

BackgroundPolicymakers, who are constantly discussing growing health expenditures, should know whether the health system is efficient. We can provide them with such information through international health system efficiency evaluations. The main objectives of this study are: (a) to evaluate the efficiency of health systems in 28 developed countries by multiple-criteria decision analysis (MCDA) and data envelopment analysis (DEA) and (b) to identify reasonable benchmark countries for the Czech Republic, for which we collect information on the relative importance of health system inputs and outputs.MethodsWe used MCDA and DEA to evaluate the efficiency of the health systems of 28 developed countries. The models included four health system inputs (health expenditure as a relative share of GDP, the number of physicians, nurses, and hospital beds) and three health system outputs (life expectancy at birth, healthy life expectancy, and infant mortality rate). The sample covers 27 OECD countries and Russia, which is also included in the OECD database. To determine the input and output weights, we used a questionnaire sent to health policy experts in the Czech Republic.ResultsWe obtained subjective information on the relative importance of the health system inputs and outputs from 27 Czech health policy experts. We evaluated health system efficiency using four MCDA and two DEA models. According to the MCDA models, Turkey, Poland, and Israel were found to have efficient health systems. The Czech Republic ranked 16th, 19th, 15th, and 17th. The benchmark countries for the Czech Republic’s health system were Israel, Estonia, Luxembourg, Italy, the UK, Spain, Slovenia, and Canada. The DEA model with the constant returns to scale identified four technically efficient health systems: Turkey, the UK, Canada, and Sweden. The Czech Republic was found to be one of the worst-performing health systems. The DEA model with the variable returns to scale identified 15 technically efficient health systems. We found that efficiency results are quite robust. With two exceptions, the Spearman rank correlations between each pair of models were statistically significant at the 0.05 level.ConclusionsDuring the model formulation, we investigated the pitfalls of efficiency measurement in health care and used several practical solutions. We consider MCDA and DEA, above all, as exploratory methods, not methods providing definitive answers.

Code smells analysis for android applications and a solution for less battery consumption

In the digitization era, the battery consumption factor plays a vital role for the devices that operate Android software, expecting them to deliver high performance and good maintainability.The study aims to analyze the Android-specific code smells, their impact on battery consumption, and the formulation of a mathematical model concerning static code metrics hampered by the code smells. We studied the impact on battery consumption by three Android-specific code smells, namely: No Low Memory Resolver (NLMR), Slow Loop (SL) and Unclosed Closable, considering 4,165 classes of 16 Android applications. We used a rule-based classification method that aids the refactoring ideology. Subsequently, multi-linear regression (MLR) modeling is used to evaluate battery usage against the software metrics of smelly code instances. Moreover, it was possible to devise a correlation for the software metric influenced by battery consumption and rule-based classifiers. The outcome confirms that the refactoring of the considered code smells minimizes the battery consumption levels. The refactoring method accounts for an accuracy of 87.47% cumulatively. The applied MLR model has an R-square value of 0.76 for NLMR and 0.668 for SL, respectively. This study can guide the developers towards a complete package for the focused development life cycle of Android code, helping them minimize smartphone battery consumption and use the saved battery lives for other operations, contributing to the green energy revolution in mobile devices.

Immunogenicity of an Inactivated Senecavirus A Vaccine with a Contemporary Brazilian Strain in Mice.

Senecavirus A (SVA) is a picornavirus that is endemic in swine, causing a vesicular disease clinically indistinguishable from other vesicular diseases, like foot-and-mouth disease. The widespread viral circulation, constant evolution, and economic losses caused to the swine industry emphasize the need for measures to control the agent. In this study, we evaluated the immunogenicity of a whole-virus-inactivated vaccine using a representative contemporary Brazilian SVA strain in Balb/ByJ mice. The animals were vaccinated with two doses by an intramuscular route. The humoral response induced by the vaccination was evaluated by an in-house ELISA assay for IgG detection. The cellular response was assessed by flow cytometry after in vitro SVA stimulation in splenocyte cultures from vaccinated and non-vaccinated groups. Protection against SVA was assessed in the experimental groups following an oral challenge with the homologous virus. The vaccination induced high levels of IgG antibodies and the proliferation of CD45R/B220+sIgM+, CD3e+CD69+, and CD3e+CD4+CD44+CD62L- cells. These results indicate the immunogenicity and safety of the vaccine formulation in a murine model and the induction of humoral and cellular response against SVA.

Adaptive machine learning for forecasting in wind energy: A dynamic, multi-algorithmic approach for short and long-term predictions

This study elucidates the formulation and validation of a dynamic hybrid model for wind energy forecasting, with a particular emphasis on its capability for both short-term and long-term predictive accuracy. The model is predicated on the assimilation of time-series data from past wind energy generation and employs a triad of machine learning algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN). Empirical data, harvested from a 2 MW grid-connected wind turbine, served as the basis for the training and validation phases. A comparative evaluation methodology was devised to scrutinize the performance of each constituent algorithm across a diverse array of metrics. This evaluation framework facilitated the identification of individual algorithmic limitations, which were subsequently mitigated through the implementation of a dynamic switching mechanism within the hybrid model. This innovative feature enables the model to adaptively select the most efficacious forecasting technique based on historical performance data. The hybrid model demonstrated superior forecasting accuracy in both, short-term energy forecasts at 15-min intervals over a day, and in broad, long-term. It recorded a Normalized Mean Absolute Error (NMAE) of 5.54 %, which is notably lower than the NMAE range of 5.65 %–9.22 % observed in other tested models, and significantly better than the average NMAE found in the literature, which spans from 6.73 % to 10.07 %. Such versatility renders it invaluable for grid operators and wind farm management, aiding in both operational and strategic planning. The study's findings not only contribute to the existing body of knowledge in renewable energy forecasting but also suggest the hybrid model's broader applicability in various other predictive analytics domains.

Benchmarking the Performance of the Actuator-Disk Method for Low-Pressure Axial Flow Fan Simulation

Abstract The actuator-disk method is a cost-effective simulation tool that implicitly represents the rotor of a turbomachine using a blade-element approach combined with two-dimensional (2D) airfoil coefficient input data. Actuator-disk models further rely upon empirical coefficient corrections to modify the 2D input data to better mimic physical blade aerodynamic characteristics. However, the fabrication of high-fidelity, general-purpose corrections remains a formidable challenge, so the limits of actuator-disk model accuracy have never been rigorously tested and remain uncertain. This is especially the case for low-pressure axial flow fan models, given the relative lack of empirical corrections conceived specifically for fan rotor simulation. In this study, benchmark performance limits of the actuator-disk method for axial flow fan analysis are explored. The limits of the modeling approach are interrogated by simulating actuator-disk fan models embedded with accurate physical blade data derived from explicit three-dimensional (3D) fan model computations. It is subsequently shown that even with a near-precise coefficient description, the method remains unreliable. However, using an unconventional actuator-disk model formulation that is based directly on 3D blade force inputs, it is demonstrated that the accuracy of conventional models can be noticeably enhanced if the input coefficient data is artificially manipulated. This nonphysical tuning of the input coefficient data is required to compensate for the simplified flow fields produced by the reduced-order modeling approach. The needed coefficient adjustments, referred to as modeling corrections, are subsequently defined and explained alongside the presentation of new realistic performance targets for future actuator-disk fan model variants.

Discretisations of mixed-dimensional Thermo-Hydro-Mechanical models preserving energy estimates

In this study, we explore mixed-dimensional Thermo-Hydro-Mechanical (THM) models in fractured porous media accounting for Coulomb frictional contact at matrix fracture interfaces. The simulation of such models plays an important role in many applications such as hydraulic stimulation in deep geothermal systems and assessing induced seismic risks in CO2 storage. We first extend to the mixed-dimensional framework the thermodynamically consistent THM models derived in [19] based on first and second principles of thermodynamics. Two formulations of the energy equation will be considered based either on energy conservation or on the entropy balance, assuming a vanishing thermo-poro-elastic dissipation. Our focus is on space time discretisations preserving energy estimates for both types of formulations and for a general single phase fluid thermodynamical model. This is achieved by a Finite Volume discretisation of the non-isothermal flow based on coercive fluxes and a tailored discretisation of the non-conservative convective terms. It is combined with a mixed Finite Element formulation of the contact-mechanical model with face-wise constant Lagrange multipliers accounting for the surface tractions, which preserves the dissipative properties of the contact terms. The discretisations of both THM formulations are investigated and compared in terms of convergence, accuracy and robustness on 2D test cases. It includes a Discrete Fracture Matrix model with a convection dominated thermal regime, and either a weakly compressible liquid or a highly compressible gas thermodynamical model.

Kinetic modeling of the photocatalytic degradation of acetaminophen and its main transformation products

In this study, a kinetic model of the heterogeneous photocatalytic degradation of acetaminophen and its main transformation products is presented. Kinetic photocatalytic modeling and photon absorption rate modeling were included. Monte Carlo method was used to model the photon absorption process. Experiments were carried out in a reactor operated in batch mode and TiO2 nanotubes were used as photocatalyst irradiated with 254 nm UVC. Kinetic parameters were estimated from the experiments data by applying a non-linear regression procedure. Intrinsic expressions to the kinetics of acetaminophen degradation and its main transformation products were derived. Model, kinetics and photon absorption formulations and parameters proved to be affordable for describing the photocatalytic degradation of acetaminophen, but improvements should be done for better description of formation and oxidation kinetics of main transformation products. The model should be tested with other pharmaceuticals and emergent pollutants to calibrate it and evaluate its applicability in a wide range of compounds.

Development and validation of a novel predictive model for postpancreatectomy hemorrhage using lasso-logistic regression: an international multicenter observational study of 9,631 pancreatectomy patients.

Hemorrhage following pancreatectomy represents a grave complication, exerting a significant impact on patient prognosis. The formulation of a precise predictive model for postpancreatectomy hemorrhage risk holds substantial importance in enhancing surgical safety and improving patient outcomes. This study utilized the patient cohort from the American College of Surgeons National Surgical Quality Improvement Program database, who underwent pancreatectomy between 2014 and 2017 (n=5779), as the training set to establish the Lasso-logistic model. For external validation, a patient cohort (n=3852) from the Chinese National Multicenter Database of Pancreatectomy Patients, who underwent the procedure between 2014 and 2020, was employed. A predictive nomogram for postpancreatectomy hemorrhage was developed, and polynomial equations were extracted. The performance of the predictive model was assessed through the receiver operating characteristic curve, calibration curve, and decision curve analysis. In the training and validation cohorts, 9.0% (520/5779) and 8.5% (328/3852) of patients, respectively, experienced postpancreatectomy hemorrhage. Following selection via lasso and logistic regression, only nine predictive factors were identified as independent risk factors associated with postpancreatectomy hemorrhage. These included five preoperative indicators (BMI, ASA ≥3, preoperative obstructive jaundice, chemotherapy within 90 days before surgery, and radiotherapy within 90 days before surgery), two intraoperative indicators (total operation time, vascular resection), and two postoperative indicators (postoperative septic shock, pancreatic fistula). The new model demonstrated high predictive accuracy, with an area under the receiver operating characteristic curve of 0.87 in the external validation cohort. Its predictive performance significantly surpassed that of the previous five postpancreatectomy hemorrhage risk prediction models (P<0.001, likelihood ratio test). The Lasso-logistic predictive model we developed, constructed from nine rigorously selected variables, accurately predicts the risk of PPH. It has the potential to significantly enhance the safety of pancreatectomy surgeries and improve patient outcomes.

The quantum Gaussian-Schell model: a link between classical and quantum optics.

The quantum theory of the electromagnetic field uncovered that classical forms of light were indeed produced by distinct superpositions of nonclassical multiphoton wave packets. This situation prevails for partially coherent light, the most common kind of classical light. Here, for the first time, to our knowledge, we demonstrate the extraction of the constituent multiphoton quantum systems of a partially coherent light field. We shift from the realm of classical optics to the domain of quantum optics via a quantum representation of partially coherent light using its complex-Gaussian statistical properties. Our formulation of the quantum Gaussian-Schell model (GSM) unveils the possibility of performing photon-number-resolving (PNR) detection to isolate the constituent quantum multiphoton wave packets of a classical light field. We experimentally verified the coherence properties of isolated vacuum systems and wave packets with up to 16 photons. Our findings not only demonstrate the possibility of observing quantum properties of classical macroscopic objects but also establish a fundamental bridge between the classical and quantum worlds.

A Comparative Investigation of the Pulmonary Vasodilating Effects of Inhaled NO Gas Therapy and Inhalation of a New Drug Formulation Containing a NO Donor Metabolite (SIN-1A).

Numerous research projects focused on the management of acute pulmonary hypertension as Coronavirus Disease 2019 (COVID-19) might lead to hypoxia-induced pulmonary vasoconstriction related to acute respiratory distress syndrome. For that reason, inhalative therapeutic options have been the subject of several clinical trials. In this experimental study, we aimed to examine the hemodynamic impact of the inhalation of the SIN-1A formulation (N-nitroso-N-morpholino-amino-acetonitrile, the unstable active metabolite of molsidomine, stabilized by a cyclodextrin derivative) in a porcine model of acute pulmonary hypertension. Landrace pigs were divided into the following experimental groups: iNO (inhaled nitric oxide, n = 3), SIN-1A-5 (5 mg, n = 3), and SIN-1A-10 (10 mg, n = 3). Parallel insertion of a PiCCO system and a pulmonary artery catheter (Swan-Ganz) was performed for continuous hemodynamic monitoring. The impact of iNO (15 min) and SIN-1A inhalation (30 min) was investigated under physiologic conditions and U46619-induced acute pulmonary hypertension. Mean pulmonary arterial pressure (PAP) was reduced transiently by both substances. SIN-1A-10 had a comparable impact compared to iNO after U46619-induced pulmonary hypertension. PAP and PVR decreased significantly (changes in PAP: -30.1% iNO, -22.1% SIN-1A-5, -31.2% SIN-1A-10). While iNO therapy did not alter the mean arterial pressure (MAP) and systemic vascular resistance (SVR), SIN-1A administration resulted in decreased MAP and SVR values. Consequently, the PVR/SVR ratio was markedly reduced in the iNO group, while SIN-1A did not alter this parameter. The pulmonary vasodilatory impact of inhaled SIN-1A was shown to be dose-dependent. A larger dose of SIN-1A (10 mg) resulted in decreased PAP and PVR in a similar manner to the gold standard iNO therapy. Inhalation of the nebulized solution of the new SIN-1A formulation (stabilized by a cyclodextrin derivative) might be a valuable, effective option where iNO therapy is not available due to dosing difficulties or availability.

Strengthening the Capacity of BUMDes Astaguna Trihanggo Village in Facing the Industrial Revolution 4.0

The implementation of community service regarding strengthening the capacity of the establishment of Village-Owned Enterprises (BUMDes) Astaguna in facing the Industrial Revolution 4.0 aims to realize information and communication technology in managing BUMDes operations; it is intended to provide BUMDes with a survival strategy for competitors. The renewal effort is motivated by the social behaviour of society, which is dependent on technological developments, marked by the Industrial Revolution, which disrupted multi-fields and had a global impact. The implementation time was held in the Even Semester of 2023 for 30 days from January 1, 2023, to January 30, 2023. Implementing the service begins with planning, activities, and evaluation involving the partner group as the subject and object of service. The conclusion of the service implementation is the impact of increased understanding, which is realized in BUMDes operational activities, which include BUMDes digitalization, which focuses on providing awareness of digital marketing strategies. Then, there is a business plan preparation using the canvas model and SWOT formulation to find BUMDes strategies, then compiling BUMDes governance to be implemented according to the principles of transparency and accountability.

Developing GA-FuL: A Generic Wide-Purpose Library for Computing with Geometric Algebra

The Geometric Algebra Fulcrum Library (GA-FuL) version 1.0 is introduced in this paper as a comprehensive computational library for geometric algebra (GA) and Clifford algebra (CA), in addition to other classical algebras. As a sophisticated software system, GA-FuL is useful for practical applications requiring numerical or symbolic prototyping, optimized code generation, and geometric visualization. A comprehensive overview of the GA-FuL design is provided, including its core design intentions, data-driven programming characteristics, and extensible layered design. The library is capable of representing and manipulating sparse multivectors of any dimension, scalar kind, or metric signature, including conformal and projective geometric algebras. Several practical and illustrative use cases of the library are provided to highlight its potential for mathematical, scientific, and engineering applications. The metaprogramming code optimization capabilities of GA-FuL are found to be unique among other software systems. This allows for the automated production of highly efficient code, based on powerful geometric modeling formulations provided by geometric algebra.

On a fair and risk‐averse urban air mobility resource allocation problem under demand and capacity uncertainties

AbstractUrban air mobility (UAM) is an emerging air transportation mode to alleviate the ground traffic burden and achieve zero direct aviation emissions. Due to the potential economic scaling effects, the UAM traffic flow is expected to increase dramatically once implemented, and its market can be substantially large. To be prepared for the era of UAM, we study the fair and risk‐averse urban air mobility resource allocation model (FairUAM) under passenger demand and airspace capacity uncertainties for fair, safe, and efficient aircraft operations. FairUAM is a two‐stage model, where the first stage is the aircraft resource allocation, and the second stage is to fairly and efficiently assign the ground and airspace delays to each aircraft provided the realization of random airspace capacities and passenger demand. We show that FairUAM is NP‐hard even when there is no delay assignment decision or no aircraft allocation decision. Thus, we recast FairUAM as a mixed‐integer linear program (MILP) and explore model properties and strengthen the model formulation by developing multiple families of valid inequalities. The stronger formulation allows us to develop a customized exact decomposition algorithm with both benders and L‐shaped cuts, which significantly outperforms the off‐the‐shelf solvers. Finally, we numerically demonstrate the effectiveness of the proposed method and draw managerial insights when applying FairUAM to a real‐world network.

Smoothed point interpolation methods for phase-field modelling of pressurised fracture

The problem of hydraulic fracturing is of great relevance to various areas and is characterised by the occurrence of complex crack patterns with bifurcations and branches. For this reason, an interesting approach is the modelling of hydraulic fracture using a phase-field model. In addition to the discretisation using the Finite Element Method (FEM), some works have already explored the discretisation of the phase-field model with meshfree methods, including the Smoothed Point Interpolation Methods (SPIM) family. Seeking to take advantage of the good convergence results of SPIM for phase-field modelling of brittle fractures, this paper proposes the use of SPIM for phase-field modelling of pressurised fractures. In order to limit the computational cost, a prescribed SPIM-FEM coupling is employed, with the purpose of concentrating the meshless discretisation only in the regions of expected crack propagation. The model is characterised by a constant internal pressure load along the fracture that is applied indirectly from the formulation of the phase-field model. A series of numerical simulations is presented. The aim is to evaluate the proposed model, verify the results and point out characteristics of the phase-field model with internal pressure.

Red blood cell dynamics during malaria infection challenge the assumptions of mathematical models of infection dynamics.

For decades, mathematical models have been used to understand the course and outcome of malaria infections (i.e., infection dynamics) and the evolutionary dynamics of the parasites that cause them. A key conclusion of these models is that red blood cell (RBC) availability is a fundamental driver of infection dynamics and parasite trait evolution. The extent to which this conclusion holds will in part depend on model assumptions about the host-mediated processes that regulate RBC availability i.e., removal of uninfected RBCs and supply of RBCs. Diverse mathematical functions have been used to describe host-mediated RBC supply and clearance, but it remains unclear whether they adequately capture the dynamics of RBC supply and clearance during infection. Here, we use a unique dataset, comprising time-series measurements of erythrocyte (i.e., mature RBC) and reticulocyte (i.e., newly supplied RBC) densities during Plasmodium chabaudi malaria infection, and a quantitative data-transformation scheme to elucidate whether RBC dynamics conform to common model assumptions. We found that RBC clearance and supply are not well described by mathematical functions commonly used to model these processes. Furthermore, the temporal dynamics of both processes vary with parasite growth rate in a manner again not captured by existing models. Together, these finding suggest that new model formulations are required if we are to explain and ultimately predict the within-host population dynamics and evolution of malaria parasites.

Modeling soybean growth: A mixed model approach.

The evaluation of plant and animal growth, separately for genetic and environmental effects, is necessary for genetic understanding and genetic improvement of environmental responses of plants and animals. We propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth. These tools are widely used in many fields but very rarely in plant biology. During model formulation, a nonlinear function describes the shape of growth, and random effects describe genetic and environmental effects and their variability. Genetic relationships among the varieties were also integrated into the model using a genetic relationship matrix. The SAEM algorithm was chosen as an efficient alternative to MCMC methods, which are more commonly used in the domain. It was implemented to infer the expected growth patterns in the analyzed population and the expected curves for each variety through a maximum-likelihood and a maximum-a-posteriori approaches, respectively. The obtained estimates can be used to predict the growth curves for each variety. We illustrate the strengths of the proposed approach using simulated data and soybean plant growth data obtained from a soybean cultivation experiment conducted at the Arid Land Research Center, Tottori University. In this experiment, plant height was measured daily using drones, and the growth was monitored for approximately 200 soybean cultivars for which whole-genome sequence data were available. The NLMEM approach improved our understanding of the determinants of soybean growth and can be successfully used for the genomic prediction of growth pattern characteristics.

On the functional regression model and its finite-dimensional approximations

AbstractThe problem of linearly predicting a scalar response Y from a functional (random) explanatory variable $$X=X(t),\ t\in I$$ X = X ( t ) , t ∈ I is considered. It is argued that the term “linearly” can be interpreted in several meaningful ways. Thus, one could interpret that (up to a random noise) Y could be expressed as a linear combination of a finite family of marginals $$X(t_i)$$ X ( t i ) of the process X, or a limit of a sequence of such linear combinations. This simple point of view (which has some precedents in the literature) leads to a formulation of the linear model in terms of the RKHS space generated by the covariance function of the process X(t). It turns out that such RKHS-based formulation includes the standard functional linear model, based on the inner product in the space $$L^2[0,1]$$ L 2 [ 0 , 1 ] , as a particular case. It includes as well all models in which Y is assumed to be (up to an additive noise) a linear combination of a finite number of linear projections of X. Some consistency results are proved which, in particular, lead to an asymptotic approximation of the predictions derived from the general (functional) linear model in terms of finite-dimensional models based on a finite family of marginals $$X(t_i)$$ X ( t i ) , for an increasing grid of points $$t_j$$ t j in I. We also include a discussion on the crucial notion of coefficient of determination (aimed at assessing the fit of the model) in this setting. A few experimental results are given.