Linear Response Model Research Articles

Optimization of Starch-Based Bioplastic from Sweet Potato using Box-Behnken Design with Plasticizer and Filler for Reduced Water Absorption

Plastic is a ubiquitous material used in a wide range of applications, but its disposal has become a significant environmental concern. Bioplastics, which offer several advantages over conventional plastics, including a smaller carbon footprint, greater biodegradability, and adaptability, are being explored as a potential alternative. Among the bioplastics, starch-based polymers have gained significant attention due to their natural abundance, biocompatibility, low cost, and renewability. In this study, a starch-based bioplastic was developed using glycerol and sorbitol as plasticizers and wood dust as a filler through the casting method. Starch was extracted from sweet potato (Ipomoea batatas). The optimal conditions for preparing the bioplastic with minimum water adsorption were determined using a three-level Box-Behnken design. The amount of starch, plasticizer, and filler were the key parameters for this study. The percentage of water absorption was determined by immersing the prepared bioplastic in distilled water for 24 hours and weighing it. The result of the Fourier Transform Infrared (FTIR) spectroscopy of extracted starch showed a similar observation peak pattern when compared to the FTIR spectrum of commercial starch. The linear response model produced a good coefficient of determination (R2 = 0.8237), indicating that the chosen parameters implicitly affect the percentage of water absorption. The prediction percentage of water absorption after optimization is 20.069 with a desirability value of 0.806. This study shows that the response surface approach helps in determining the optimum conditions for the minimum percentage of water adsorption from starch-based bioplastic.

Corrected generalized cross-validation for finite ensembles of penalized estimators

Abstract Generalized cross-validation (GCV) is a widely used method for estimating the squared out-of-sample prediction risk that employs scalar degrees of freedom adjustment (in a multiplicative sense) to the squared training error. In this paper, we examine the consistency of GCV for estimating the prediction risk of arbitrary ensembles of penalized least-squares estimators. We show that GCV is inconsistent for any finite ensemble of size greater than one. Towards repairing this shortcoming, we identify a correction that involves an additional scalar correction (in an additive sense) based on degrees of freedom adjusted training errors from each ensemble component. The proposed estimator (termed CGCV) maintains the computational advantages of GCV and requires neither sample splitting, model refitting, or out-of-bag risk estimation. The estimator stems from a finer inspection of the ensemble risk decomposition and two intermediate risk estimators for the components in this decomposition. We provide a non-asymptotic analysis of the CGCV and the two intermediate risk estimators for ensembles of convex penalized estimators under Gaussian features and a linear response model. Furthermore, in the special case of ridge regression, we extend the analysis to general feature and response distributions using random matrix theory, which establishes model-free uniform consistency of CGCV.

Neural activity shaping in visual prostheses with deep learning

Objective. The visual perception provided by retinal prostheses is limited by the overlapping current spread of adjacent electrodes. This reduces the spatial resolution attainable with unipolar stimulation. Conversely, simultaneous multipolar stimulation guided by the measured neural responses—neural activity shaping (NAS)—can attenuate excessive spread of excitation allowing for more precise control over the pattern of neural activation. However, defining effective multipolar stimulus patterns is a challenging task. Previous attempts focused on analytical solutions based on an assumed linear nonlinear model of retinal response; an analytical model inversion (AMI) approach. Here, we propose a model-free solution for NAS, using artificial neural networks (ANNs) that could be trained with data acquired from the implant. Approach. Our method consists of two ANNs trained sequentially. The measurement predictor network (MPN) is trained on data from the implant and is used to predict how the retina responds to multipolar stimulation. The stimulus generator network is trained on a large dataset of natural images and uses the trained MPN to determine efficient multipolar stimulus patterns by learning its inverse model. We validate our method in silico using a realistic model of retinal response to multipolar stimulation. Main results. We show that our ANN-based NAS approach produces sharper retinal activations than the conventional unipolar stimulation strategy. As a theoretical bench-mark of optimal NAS results, we implemented AMI stimulation by inverting the model used to simulate the retina. Our ANN strategy produced equivalent results to AMI, while not being restricted to any specific type of retina model and being three orders of magnitude more computationally efficient. Significance. Our novel protocol provides a method for efficient and personalized retinal stimulation, which may improve the visual experience and quality of life of retinal prosthesis users.

Linear and nonlinear flame response prediction of turbulent flames using neural network models

Modelling the flame response of turbulent flames via data-driven approaches is challenging due, among others, to the presence of combustion noise. Neural network methods have shown good potential to infer laminar flames’ linear and nonlinear flame response when externally forced with broadband signals. The present work extends those studies and analyses the ability of neural network models to evaluate the linear and nonlinear flame response of turbulent flames. In the first part of this work, the neural network is trained to evaluate and interpolate the linear flame response model when presented with data obtained at various thermal conditions. In the second part, the neural network is trained to infer the nonlinear flame response model when presented with time series exhibiting sufficient large amplitudes. In both cases, the data is obtained from a large eddy simulation of an academic combustor when acoustically forced by broadband signals.

Anti-Coulomb ion-ion interactions: A theoretical and computational study

The free energy of ion solvation can be decomposed into enthalpic and entropic contributions. This helps one to understand the connection between the dielectric properties and the underlying forces. We present a simple linear-response model of screened charge interactions that provides an alternative understanding of solvation barriers. Moreover, it explains the “anti-Coulomb” interactions (attraction between like-charged ions and repulsion between opposite-charged ions) observed in both simulations and experiments. We show that this is a universal behavior associated to the nonlocal response function of any dielectric or metallic systems. Published by the American Physical Society 2024

A Model of Linear Response with Progressive Decay Equally Suited for RiboGreen® Quantitation of RNA and Dissolution of Solid Oral Dosage forms

A new regression model is presented which offers flexibility, freedom from subjective determinations of linear range, and very wide applicability to measurement systems of industrial importance. This “progressive decay” model starts as a deceptively simple ordinary differential equation. We show here that its solution faithfully describes real but seemingly unconnected data from a plate-based assay for quantitation of RNA with RiboGreen® and dissolution data for a triple fixed-dose combination solid oral dosage form.

Time Series Data Generation by Linear Response Model

Time Series Data Generation by Linear Response Model

Linear systems characterization of the topographical spatial resolution of optical instruments

Lateral resolving power is a key performance attribute of Fizeau interferometers, confocal microscopes, interference microscopes, and other instruments measuring surface form and texture. Within a well-defined scope of applicability, limited by surface slope, texture, and continuity, a linear response model provides a starting point for characterizing spatial resolution under ideal conditions. Presently, the instrument transfer function (ITF) is a standardized way to quantify linear response to surface height variations as a function of spatial frequency. In this paper, we build on the ITF idea and introduce terms, mathematical definitions, and appropriate physical units for applying a linear systems model to surface topography measurement. These new terms include topographical equivalents of the point-, line-, and edge-spread functions, as well as a complex-valued transfer function that extends the ITF concept to systems with spatial-frequency-dependent topography distortions. As an example, we consider the experimental determination of lateral resolving power of a coherence scanning interference microscope using a step-height surface feature to measure the ITF directly. The experiment illustrates the proposed mathematical definitions and provides a direct comparison to theoretical calculations performed using a scalar diffraction model.

Structural Relaxation Time of a Polymer Glass during Deformation.

In order to determine the structural relaxation time of a polymer glass during deformation, a strain rate switching experiment is performed in the steady-state plastic flow regime. A lightly cross-linked poly(methylmethacrylate) glass was utilized and, simultaneously, the segmental motion in the glass was quantified using an optical probe reorientation method. After the strain rate switch, a nonmonotonic stress response is observed, consistent with previous work. The correlation time for segmental motion, in contrast, monotonically evolves toward a new steady state, providing an unambiguous measurement of the structural relaxation time during deformation, which is found to be approximately equal to the segmental correlation time. The Chen-Schweizer model qualitatively predicts the changes in the segmental correlation time and the observed nonmonotonic stress response. In addition, our experiments are reasonably consistent with the material time assumption used in polymer deformation modeling; in this approach, the response of a polymer glass to a large deformation is described by combining a linear-response model with a time-dependent segmental correlation time.

Validation of MARS-F modeling of plasma response to RMPs using internal measurements on DIII-D

The linear resistive plasma response model is validated against the plasma internal measurement data from DIII-D edge-localized mode (ELM) control experiments with applied resonant magnetic perturbation (RMP). Considered are DIII-D discharges where the n = 1, 2, and 3 (n is the toroidal mode number) RMP field was applied. Experimental data for the plasma boundary displacement, as well as the three-dimensional (3D) pressure perturbation in the edge pedestal region, are deduced from the vertical Thomson scattering (TS) system and the horizontal charge exchange recombination (CER) system on DIII-D. The linear response model produces results that are in reasonable quantitative agreement with the DIII-D internal measurements. The plasma boundary displacement of up to 15 mm is modeled, with the pedestal pressure perturbation reaching 3 kPa. As an important insight, the larger plasma displacement measured by the vertical TS system, as compared to that measured by the horizontal CER system, is due to the contribution from the tangential component of the plasma displacement to the former. This mixing of displacement components is also found to influence the sensitivity of the CER measurement comparisons. The results of this study provide further confidence in the linear resistive plasma response model for analyzing ELM control experiments.

Adverse Effects of Ozone Pollution on Net Primary Productivity in the North China Plain

AbstractTropospheric ozone significantly damages vegetation and reduces net primary productivity (NPP). We developed a stable linear NPP response model based on accumulated ozone exposure over a threshold of 40 parts per billion (ppb) (AOT40). We then estimated the effects of regional ozone damage on NPP for different vegetation types. The study suggests an average decrease in NPP of 24.7% due to ozone pollution in the North China Plain, similar to previous estimates ranging from 10.1% to 24.7%, with a maximum reduction exceeding 200 [g C m‐2 yr‐1] and more than 50%. Vegetation types such as broadleaf forests, needleleaf forest, crops, and grasses showed significant NPP decreases of 47.1%, 37.8%, 36.7%, and 44.6%, respectively. Declining NPP also had negative impacts on several Chinese crop species. Our work highlights the need for urgent and effective action to mitigate ozone pollution's substantial detrimental effects on ecosystem health and productivity.

Quantum Simulation of Molecular Response Properties in the NISQ Era.

Accurate modeling of the response of molecular systems to an external electromagnetic field is challenging on classical computers, especially in the regime of strong electronic correlation. In this article, we develop a quantum linear response (qLR) theory to calculate molecular response properties on near-term quantum computers. Inspired by the recently developed variants of the quantum counterpart of equation of motion (qEOM) theory, the qLR formalism employs "killer condition" satisfying excitation operator manifolds that offer a number of theoretical advantages along with reduced quantum resource requirements. We also used the qEOM framework in this work to calculate the state-specific response properties. Further, through noiseless quantum simulations, we show that response properties calculated using the qLR approach are more accurate than the ones obtained from the classical coupled-cluster-based linear response models due to the improved quality of the ground-state wave function obtained using the ADAPT-VQE algorithm.

Single Cell Profiling Unravels Plasma Cell Gene Expression Signatures Predictive of Response to Daratumumab Based Therapy, and Prognosis in Multiple Myeloma

Background: Multiple Myeloma (MM) is the second most common hematologic malignancy and remains incurable. Daratumumab (dara) is a potent anti-CD38 monoclonal antibody used for MM treatment, but responses are heterogeneous, and resistance is inevitable. There is hence an urgent need for biomarkers to predict the response to dara. We hypothesized that gene expression profiles (GEP) of tumor subpopulations could be used to predict the response to dara-based therapy. Methodology: We used the Enhanced Single-Cell Analysis with Protein Expression (ESCAPE) RNA-Seq platform (Singleron Biotechnologies) to simultaneously profile the gene and protein expression of plasma cells from the bone marrow of 15 MM patients prior to treatment with dara-based regimens. These data were combined with publicly available single-cell data from Cohen et al. 2021, resulting in a total of 32 samples. Response was defined based on the international myeloma working group (IMWG) criteria, considering patients achieving a very good partial response (VGPR) or better as “responders,” while those achieving a partial response (PR) or worse were deemed “non-responders”. The data were split into 23 training samples (15 responders and 8 non-responders) and 9 validation samples (3 responders and 6 non-responders), each set containing a combination of cases from the two sources of single-cell data. An interpretable machine-learning model was then built to predict the response of each individual cell. The median response score for each sample was used to predict the patient-level response. The Shapley Additive Explanations (SHAP) values from the dara response prediction model were then used as a starting point for creating a general prognostic model. The 200 most influential genes from the dara response prediction model were used to create a linear response model based on the TCGA (MMRF-Compass) bulk RNAseq dataset. The bulk RNAseq dataset was split 80:20 into training and test datasets. Results from the test dataset are presented here. Results: We analyzed single-cell data from a total of 13,094 plasma cells from 32 BM samples. These included 17 cases of relapsed MM described by Cohen et al., who were treated with dara, carfilzomib, lenalidomide, and dexamethasone. The patients treated at our centre comprised 7 cases of relapsed MM treated with dara, thalidomide, and dexamethasone, and 8 samples from patients with newly diagnosed MM treated with dara, bortezomib, and dexamethasone on clinical trials. All 9 patients in the test dataset had their responses correctly predicted (Fig 1). A separate cohort of non-dara-treated patient samples was also tested using the same model, and the response prediction was not accurate (AUC=0.67), suggesting that this model has specificity for predicting response to dara and is not a general response classifier (data not shown). Based on the SHAP values from the dara response prediction model, we developed a prognostic model from bulk RNAseq data consisting of four genes. A combination of this four-gene signature plus the international staging system (ISS) score resulted in a superior prediction of overall survival compared to the ISS alone with a hazard ratio of 13.5 compared to 2.5 for the ISS alone in the same dataset (Fig 2). Conclusions: Combining single-cell RNA sequencing with machine-learning methods may have value in predicting response to immunotherapy in MM. We propose that gene signatures derived from single-cell data may augment clinical decision-making for determining treatment allocation. We also show that these signatures may be combined with existing prognostic scores, resulting in improved delineation of risk groups. Furthermore, the applicability of our gene signature to a bulk gene expression dataset enhances its clinical relevance. Our study demonstrates the power of single-cell omics to identify novel predictors of response to therapy and prognosis in MM, which may be translated into clinical use.

A market-based framework for CO2 emissions reduction in China's civil aviation industry

This paper proposes a novel aviation CO2 emissions reduction method by introducing a market-based incentive mechanism. An aviation carbon emissions prediction model is proposed to characterize both the microscopic flight dynamics and the macroscopic air traffic demand trend. Based on emissions prediction, a linear climate response model is applied to evaluate the environmental impact of the aviation carbon emissions. Finally, a market-based framework for aviation CO2 emissions reduction is established based on a two-phase carbon trading model, which provides a piecewise charge method through emission taxation and cap-and-trade. A case study is carried out to demonstrate the effectiveness of the proposed framework using a real-world dataset from the Hefei Air Traffic Control Zone in China. Three scenarios are defined to describe the possible development speed of sustainable aviation fuel application in the future: not applied, medium-speed development, and high-speed development. The results show that the carbon peak will be reached in 2025 in the high-speed development scenario, when the emission amount and associated environmental impact will be reduced by 23.7% and 22.3%, respectively. Thus, the market-based framework proposed in this paper can effectively reduce aviation CO2 emissions and mitigate the effects of global warming.

Predictive TDDFT Methodology for Aromatic Molecules UV‐Vis properties: from Benchmark to Applications

AbstractThe absorption and emission properties of a series of 44 aromatic molecules have been investigated in Time Dependent Density Functional Theory (TDDFT) using different exchange‐correlation functionals. Solvent effects have been included within linear response (LR) and state specific (SS) polarisable continuum model (PCM). The comparison with experimental UV‐Vis data showed reasonable agreement for all the aromatic molecules when ωB97XD with SS‐PCM is used. In particular, we found an accurate linear correlation between experimental and theoretical results which is revealed in an equation for absorption and another equation for emission derived from a linear fitting between theoretical and experimental data. Through these linear equations we propose a simple, pragmatic and effective approach able to describe and predict the absorption and the emission of aromatic molecules with a reasonable computational cost.

Real-time machine-learning-driven control system of a deformable mirror for achieving aberration-free X-ray wavefronts.

A neural-network machine learning model is developed to control a bimorph adaptive mirror to achieve and preserve aberration-free coherent X-ray wavefronts at synchrotron radiation and free electron laser beamlines. The controller is trained on a mirror actuator response directly measured at a beamline with a real-time single-shot wavefront sensor, which uses a coded mask and wavelet-transform analysis. The system has been successfully tested on a bimorph deformable mirror at the 28-ID IDEA beamline of the Advanced Photon Source at Argonne National Laboratory. It achieved a response time of a few seconds and maintained desired wavefront shapes (e.g., a spherical wavefront) with sub-wavelength accuracy at 20 keV of X-ray energy. This result is significantly better than what can be obtained using a linear model of the mirror's response. The developed system has not been tailored to a specific mirror and can be applied, in principle, to different kinds of bending mechanisms and actuators.

Dose-dependent positive-to-negative shift of litter effects on seedling growth: a modelling study on 35 plant litter types

Modelling the inter-relationships between litter accumulation and plant–soil feedback is a major challenge to predict natural and agricultural ecosystem dynamics. At increasing levels of undecomposed plant litter, seedling growth tends to show a multi-faceted response trend, characterised by a peak of positive stimulation at lower dosage followed by inhibition at higher dosage. In this study, a new logistic model was developed to describe such trend and disentangle substrate-specific positive and negative effects of plant litter. The model was tested on 35 litter types applied to the common phytometer Lepidium sativum; all litter types were collected in Mediterranean shrublands of Campania region (southern Italy). Model fitting resulted to be generally higher relative to the widely used linear log response model, although in only half of the cases it also gave more parsimonious results in terms of minimising information loss. Positive and negative effects of plant litter resulted to be uncorrelated, showing that the overall trend is probably the result of the combined action of separate factors rather than the effect of a single factor behaving differently at the different doses. The results of this work provide new tools to finely tune the optimal doses in experiments on hormesis and litter phytotoxicity, through the identification of the most suited doses to centre the range of nearly linear response to litter concentration. A wide screening is also presented on the phytotoxicity profiles of a number of spontaneous plant species widely distributed in the Mediterranean area.

Modelling, design and simulation of plasma magnetic control for the Spherical Tokamak for Energy Production (STEP)

Magnetic controller performance requirements and design solutions for the Spherical Tokamak for Energy Production (STEP) are driven by the need to produce equilibrium and scenario trajectories, maintaining steady plasma vertical stabilization and shape control for a period of thousands of seconds or longer while avoiding contact with the plasma-facing components (PFCs). Axisymmetric magnetic control schemes including vertical stabilization, plasma current control, plasma shape control and poloidal field coil current control for the STEP Prototype Reactor (SPR) concept are being developed using a suite of control analysis tools (known collectively as TokSys) supporting the integrated plasma control design process. The vertical growth rate based on a linear rigid plasma response model in TokSys is used for assessing the controllability of the vertical instability in SPR. TokSys closed-loop simulations with the axisymmetric non-linear, free-boundary evolution code, GSevolve, are performed for the assessment, identification, and verification of algorithm implementation and controller performance for the various axisymmetric control systems. Dynamic control performance and controllability are shown to be consistent with noise-affected scenario requirements under reasonable power supply and sensor performance assumptions.

Manhattan Project genetic studies: Flawed research discredits LNT recommendations

This paper reexamines the technical report (∼ one page) of Uphoff and Stern (1949) in Science that was highly relied upon by the US National Academy of Sciences (NAS) Biological Effects of Atomic Radiation (BEAR) I Genetics Panel to support a linearity dose response for radiation risk assessment. The present paper demonstrates that research of Uphoff and Stern (1949) to evaluate whether total dose or dose rate best estimated radiation risks included two variables, thereby precluding the ability to accurately derive a reliable conclusion about this topic. Furthermore, the acute dose selected by Uphoff and Stern was given at a strikingly low dose rate that may have precluded the capacity to adequately test the total dose/dose rate hypothesis, even with a proper study design which also this research did not possess. The issue of total dose and dose rate was much later successfully addressed by Russell et al. (1958) using a murine model, yielding a dose-rate rather than a total dose conclusion. The failure to subject the experimental details of the Uphoff and Stern (1949) study to peer-review and publication in the open literature precluded a rigorous and necessary evaluation, profoundly and improperly impacting the adoption of the linear dose response model.

C-sections and hospital characteristics: a long term analysis on low-risk deliveries

BackgroundPolicymakers aim to reduce C-section (CS) rates, due to well documented overtreatment. However, little is known about how hospital characteristics relate to their c-section rates on low-risk deliveries (CSR-LRD).MethodsCSR-LRD were computed using inpatient data from all Portuguese National Health Service hospitals (2002-2011). Linear and Fractional Response Models were estimated to quantify the relationship between CSR-LRD and a set of hospital characteristics: hospital size, type (exclusively obstetrics or not), Neonatal Intensive Care Unit (NICU) availability, obstetrician-to-obstetric bed ratio, and teaching status.ResultsCSR-LRD increased from 11.7% (2002) to 14.1% (2008), declining to 12.5% in 2011. While larger hospitals and hospitals with NICU had higher CSR-LRD rates, teaching status and obstetrician-to-obstetric bed ratio had no significant effect. Adjusted estimates, controlling for those four characteristics, indicate 91% of the variation in the CSR-LRD is left unexplained.ConclusionHospital characteristics do not explain variation in CSR-LRD rates. Further studies considering medical practice, financial incentives to hospitals and/or physicians, and patient education are needed.