Precise Quantitative Predictions Research Articles

Modeling Solubility Induced Elemental Fractionation of Noble Gases in Oils

This study explores the estimation of solubility-induced elemental fractionation of noble gases in hydrocarbon-based oils through existing empirical and theoretical models, complemented by a novel molecular simulation-based approach. Quantifying such fractionation is essential for a deeper understanding of fluid processes and migration in subsurface geological resources, an area currently lacking in experimental data. To address this, the research introduces a predictive model that employs the Peng-Robinson equation of state and a fugacity-coefficient-based method to assess noble gas elemental fractionation in hydrocarbons, including normal alkanes, cycloalkanes, and aromatics. However, this model struggles with precise quantitative predictions, prompting the introduction of adjusted cross-interaction parameters to enhance its performance. Furthermore, molecular simulations, in conjunction with the refined equation of state, are shown to offer a novel method for calculating noble gas fractionation coefficients across different hydrocarbon solvents. A key finding is the identification of a universal master curve, demonstrating that noble gas solubility fractionation at low pressure in simple hydrocarbon solvents can be quantitatively determined by temperature and density alone, without the need for detailed compositional information. Consequently, a new correlation is proposed for deriving elemental fractionation coefficients based solely on oil temperature and density, offering significant improvements over existing empirical methods for a wide range of temperatures. Although limitations are noted when applying this approach to oils rich in complex heavy components like resins and asphaltenes, it allows to address inconsistencies observed in traditionally used experimental correlations at high temperatures.

Nuclear dipolar relaxation induced by interacting ground state electron spins

In samples used for dynamic nuclear polarization (DNP), spin–lattice relaxation times are usefully increased by going to high magnetic field and low temperature, typically several tesla and below 1 K. But the relaxation times for dipolar components of the nuclear spin energy remain stubbornly shorter than those for the Zeeman energy: dipolar order decays faster than the polarization itself by a huge factor—up to four orders of magnitude or more in the materials studied thus far. Such fast nuclear dipolar relaxation poses experimental challenges, for instance, when transferring polarization between different nuclear spin species via intermediate nuclear order: a proven technique to polarize rare nuclear spins. The origin of this fast nuclear dipolar relaxation remained a mystery for a long time—existing theories of nuclear spin–lattice relaxation could at best predict about one order of magnitude difference between the nuclear dipolar and nuclear Zeeman relaxation rates—until it was recently discovered to be due to conversion of nuclear dipolar energy into super-hyperfine energy induced by nuclear flip-flop transitions. A previous article showed that the inclusion of this relaxation path enables a quantitative explanation of nuclear dipolar relaxation induced by photo-excited triplet states. This article extends the theory to nuclear dipolar relaxation induced by ground state electron spins and demonstrates that this new mechanism enables a precise quantitative prediction from first principles of the nuclear dipolar relaxation rate for Ca(OH)2 doped with O2- centres—in which DNP is caused by the solid effect (SE)—and LiF doped with F-centres—in which DNP is caused by thermal mixing (TM)—both at 5.5 T and 0.4 K. It is noticed that the proposed mechanism extends to other spin systems which has implications for e.g. TM and spectral diffusion.

The Rescorla-Wagner Model: The culmination of Hume’s theory of causation.

The associative learning theory of Robert Rescorla and Allan Wagner has been duly celebrated for its 50-year reign as the predominant model in learning science. One special recognition is warranted: its close correspondence with David Hume's associative theory of causality judgment. Hume's rules by which causes come to suggest effects are not only embraced by the Rescorla-Wagner model, but their mechanistic account makes precise quantitative predictions that can be assessed by empirical evidence rather than by speculation and argumentation. Framed in this way, the Rescorla-Wagner model truly represents the scientific culmination of Hume's philosophical theory of causation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Precise Detection and Quantitative Prediction of Blood Glucose Level With an Electronic Nose System

Blood glucose level is an important health indicator. Non-invasive, easy-to-use glucose detection and monitoring methods and tools are desperately needed, especially for patients with diabetes. In this work, we developed a new method to quantitively identify and analyze the blood glucose level by measuring the biomarkers in breath with an electronic nose (E-Nose) system based on a metal oxide (MOX) gas sensor array. Advanced machine-learning models have been studied and developed to precisely predict the blood glucose level based on the measurement of 41 participants for 10 days. The testing result shows that the E-Nose system and proposed analysis models identify blood glucose levels at an accuracy of 90.4% and a small average error of 0.69 mmol/L in blood glucose concentration. This study indicates that the E-Nose system enabled with machine learning is an efficient and precise method to achieve low-cost and non-invasive disease diagnosis.

Performance of three handheld NIR spectrometers for predicting grass silage quality

Description of the subject. Feed is the main variable cost in dairy farming. More efficient use of forage resources is one way to reduce production costs. Improving forage resource efficiency can start with a better assessment of the dry matter content and nutritional value of forages. Currently, analytical process time is often long and analyses are not repeatable while the quality of the fodder changes over time. Being able to analyze forages directly on-farm would make it possible to adapt the animal diet according to forage variability, in order to improve the profitability of the farm. Objectives. To propose in situ rapid analysis solutions to better characterize dry matter content and the chemical composition of fodder for assessing its feeding value. Method. The performance of three recently developed spectroscopic handheld devices, namely the Viavi’s MicroNIR 1700, the Ocean Insight’s FlameNIR and the Malvern Panalytical’s ASD FieldSpec 4, are evaluated to predict dry matter content and the chemical composition of fresh and unground grass silage in the framework of precision feeding and compared to the reference benchtop Foss’s XDS instrument. The conventional global PLS and local PLS are used as multivariate calibration methods. Results. The assessed handheld devices allow the dairy farmer to obtain a relatively precise quantitative prediction of the dry matter and crude fiber content (2.5% and 1.8% respectively on average, in terms of ratios between the local PLS error on fresh forage and the reference method error) in order to adapt the livestock diet. Crude protein, even if the prediction accuracy is lower (6.4%), is still well predicted. Higher errors are obtained for ash (9.2%), crude neutral (6.8%) and acid detergent fiber (6.9%). Conclusions. The studied devices should allow the dairy farmer to obtain a relatively precise quantitative prediction of those quality parameters in order to directly adapt the quantity of forage distributed to the animals. Performances could probably be improved by including more samples/spectra into the databases.

Psychological Theory and the Illusion of Scientific Prediction.

The attempt of empiricist psychology to achieve scientific respectability through reliance on quantification is deeply flawed. Not only does it come at the expense of the phenomena, which, in the study of the mind, must reference subjectivity, but it is incommensurate with the basic scientific principles on which it claims to operate. Specifically, psychological theory typically cannot support prognostication beyond the binary opposition of "effect present/effect absent." Accordingly, the "numbers" assigned to experimental results often amount to affixing names (e.g., more than, less than) to the members of an ordered sequence of outcomes. This, I contend, is one reason why psychologists find it difficult to discriminate between competing theories: without a well-specified theory capable of enabling precise and detailed quantitative prediction, inferring underlying mental mechanisms from experimental outcomes becomes a difficult, if not impossible, task.

Unified treatment of nonlinear optical force in laser trapping of dielectric particles of varying sizes

Optical trapping using laser tweezer has revolutionized the field of force spectroscopy having enormous applications in biological manipulation. While a number of theories were developed for particles of different sizes to estimate trapping force under continuous-wave excitation, they were not under short pulsed excitation which leads to nonlinear optical force. Here, we present a comparative study of various theories and provide a unified description for laser trapping under femtosecond pulsed excitation. Numerical results show that exact Mie theory (EMT) can provide a precise qualitative and quantitative prediction of trapping force when optical Kerr effect is included. Moreover, we also show how Mie interference phenomena, leading to observation of Fano resonance, are naturally captured within EMT. Thus, our findings pave the way for potential far-reaching applications in the accurate numerical estimation of nonlinear optical force on arbitrary-sized spherical dielectric particles.

Cyclic scope and processing difficulty in a Minimalist parser

A common view in the theoretical literature is that quantifier raising (QR) is a clause-bounded operation. But in a paper published in Glossa, Wurmbrand (2018) argues that (i) QR is not clause-bounded, and the apparent clause-boundedness of QR is due to the human parser’s difficulty in processing extraclausal QR; and (ii) the relative difficulty of extraclausal QR depends on the size of the embedded clause from which QR takes place. She then proposes a theory of scope processing in which parsing Logical Form (LF) movement is costly for the human parser, which in conjunction with independently motivated assumptions about A′-movement generates the desired results. In this paper, we accept Wurmbrand’s descriptive observations and proposed syntax but offer an alternative, rigorously defined metric of scope processing difficulty that makes precise quantitative predictions. Our proposal is formalized with Minimalist Grammars (Stabler 1997) and expands recent work by Kobele et al. (2013), among others, that uses this formalism to account for numerous processing phenomena. Our metric correctly handles Wurmbrand’s observations as well as cases that are problematic for her account, and points the way toward an explanatory theory of scope processing.

Discussion on Management-and-Control Mode for Precise Disposal of Construction and Demolition Waste

Faced with the severe challenges brought by construction and demolition waste (CDW), it is urgent to explore a more reasonable and effective management-and-control mode to dispose CDW. By analyzing the current situation of CDW disposal in China, it is found that the extant measures are mainly for the purpose of during- and post-event supervision, and the key factor to improve the status of CDW disposal is the government’s pre-planning for recycling purpose. In view of this, a government-led management-and-control mode for precise disposal of CDW is proposed. The mode is implemented by the government in advance to determine the target value of CDW recycling, and optimize the allocation of CDW generation-transport-disposal chain on the basis of meeting the requirements of resource compliance, with three characteristics (precise classification recycling and quantitative prediction, precise control of transportation quantity, and precise planning for resource allocation) and two operating modes (“target limit of mandatory recycling + optimal distribution” and “target limit of mandatory recycling + free decision”). To achieve this mode, a series of countermeasures are proposed from the aspects of formulating policies, planning aims, setting up institutions, and building a supporting system.

Mechanical feedback effects on primordial black hole accretion

Context. Dark matter may consist, at least partially, of primordial black holes formed during the radiation-dominated era. The radiation produced by accretion onto primordial black holes leaves characteristic signatures on the properties of the medium at high redshift, before and after hydrogen recombination. Therefore, reliable modeling of accretion onto these objects is required to obtain robust constraints on their abundance. Aims. We investigate the effect of mechanical feedback, that is, the impact of outflows (winds and– or –jets) on the medium, on primordial black hole accretion, and thereby on the associated radiation. Methods. Using analytical and numerical calculations, we studied for the first time the possibility that outflows can reduce the accretion rate of primordial black holes with masses similar to those detected by the LIGO-Virgo collaboration. Results. Despite the complexity of the accretion rate evolution, mechanical feedback is useful in to significantly reducing the primordial black hole accretion rate, at least by one order of magnitude, when outflows are aligned with the motion of the compact object. If the outflow is perpendicular to the direction of motion, the effect is less important, but it is still non-negligible. Conclusions. Outflows from primordial black holes, even rather weak ones, can significantly decrease the accretion rate, effectively weakening abundance constraints on these objects. Our results motivate further numerical simulations with a more realistic setup, which would yield more precise quantitative predictions.

Comparison of MOSCED (NRTL) model results with regular correlative and predictive models based on vapor-liquid equilibrium calculations for azeotropic systems

The Modified Separation of Cohesive Energy Density (MOSCED) model is an attractive and accurate engineering tool for process design and modeling. MOSCED is able to make qualitative as well as precise quantitative predictions of principal molecular-level interactions. However, the capability of this model in predicting Vapor-Liquid Equilibrium (VLE) of different azeotropic systems has not yet been fully assessed, when compared with the conventional methods. In this study, the ability of MOSCED model to predict the binary interaction parameters of NRTL model has been evaluated by comparing the obtained isobaric phase diagrams of the experimental sets of data for 11 binary isobaric systems. In order to better evaluate the performance of MOSCED model, the same calculations were performed via correlative (Wilson and NRTL) and predictive (UNIFAC) models. The values of statistical parameters have demonstrated acceptable predictions for the studied systems. Overall, the MOSCED model has provided an Average Absolute Relative Deviation(s) (AARD %) of 0.1978% and 4.3239% for temperature and K-value, respectively; which illustrate excellent performance of this model in spite of no dependence on experimental data, which is a big advantage over other methods.

Reconstructing Denisovan Anatomy Using DNA Methylation Maps.

Denisovans are an extinct group of humans whose morphology remains unknown. Here, we present a method for reconstructing skeletal morphology using DNA methylation patterns. Our method is based on linking unidirectional methylation changes to loss-of-function phenotypes. We tested performance by reconstructing Neanderthal and chimpanzee skeletal morphologies and obtained >85% precision in identifying divergent traits. We then applied this method to the Denisovan and offer a putative morphological profile. We suggest that Denisovans likely shared with Neanderthals traits such as an elongated face and a wide pelvis. We also identify Denisovan-derived changes, such as an increased dental arch and lateral cranial expansion. Our predictions match the only morphologically informative Denisovan bone to date, as well as the Xuchang skull, which was suggested by some to be a Denisovan. We conclude that DNA methylation can be used to reconstruct anatomical features, including some that do not survive in the fossil record.

Biochemical profiling, prediction of total lipid content and fatty acid profile in oleaginous yeasts by FTIR spectroscopy

BackgroundOleaginous yeasts are considered as a potential lipid source for food, feed and biofuel production. In order to make the yeast-based lipid production environmentally and economically sustainable, there is a need for screening studies in order to find the best yeast lipid producers on different substrates, and to optimize cultivation conditions. Since the target parameter of such screening studies are lipid amounts and profiles, an analytical technique that is able to perform lipid analyses rapidly, reproducible and with high precision is highly desirable. The main objective of this study was to establish the non-invasive high-throughput Fourier transform infrared (FTIR) spectroscopy analysis for the prediction of lipid content and profile in oleaginous yeasts.ResultsHigh-throughput FTIR spectroscopy allowed characterizing the total biochemical profile of oleaginous yeasts and enabled us to identify strains and substrate(s) providing the highest total lipid content. Some of the yeast strains grown under nitrogen-limiting conditions with glucose/xylose/mixture of glucose and xylose as carbon sources were accumulating lipids with a high proportion of free fatty acids. FTIR spectra were used to predict gravimetric and gas chromatography data by establishing multivariate calibration models. Coefficients of determination (R2) for calibration models were obtained in a range between 0.62 and 0.92 for predicting lipid content. When using an independent test set, R2 values between 0.53 and 0.79 were achieved for predicting fatty acid profile. The best spectral region(s) for the prediction of total lipid content was 3100–2800 cm−1 combined with 1800–700 cm−1, and for prediction of summed saturated (SAT), monounsaturated (MUFA) and polyunsaturated (PUFA) fatty acids: 3100–2800 cm−1, 3100–2800 cm−1 combined with 1700–1715 cm−1 and 3100–2800 cm−1 combined with 1800–1715 cm−1, respectively. The highest lipid accumulation was observed for strains Rhodotorula babjevae DBVPG 8058 on glucose and mixture of glucose and xylose and Lipomyces starkeyi CBS 2512 on xylose.ConclusionsApplying FTIR spectroscopy combined with multivariate data analysis allows performing rapid, non-invasive, reproducible and precise quantitative predictions of total lipid content and lipid profile. It allows also detecting different lipid fractions as triacylglycerols (TAGs) and free fatty acids and evaluating the total biochemical profile of cells. Several yeast strains with high lipid accumulation were identified.

Critical resolution: A superior measure of crowding

Visual object recognition is essential for adaptive interactions with the environment. It is fundamentally limited by crowding, a breakdown of object recognition in clutter. The spatial extent over which crowding occurs is proportional to the eccentricity of the target object, but nevertheless varies substantially depending on various stimulus factors (e.g. viewing time, contrast). However, a lack of studies jointly manipulating such factors precludes predictions of crowding in more heterogeneous scenes, such as the majority of real life situations.To establish how such co-occurring variations affect crowding, we manipulated combinations of 1) flanker contrast and backward masking, 2) flanker contrast and presentation duration, and 3) flanker preview and pop-out while measuring participants’ ability to correctly report the orientation of a target stimulus. In all three experiments, combining two manipulations consistently modulated the spatial extent of crowding in a way that could not be predicted from an additive combination. However, a simple transformation of the measurement scale completely abolished these interactions and all effects became additive. Precise quantitative predictions of the magnitude of crowding when combining multiple manipulations are thus possible when it is expressed in terms of what we label the ‘critical resolution’. Critical resolution is proportional to the inverse of the smallest flanker free area surrounding the target object necessary for its unimpaired identification. It offers a more parsimonious description of crowding than the traditionally used critical spacing and may thus constitute a measure of fundamental importance for understanding object recognition.

The Research of Reservoir Earthquake Prediction Based on the Design of Horizontal Well of Thin Carbonate Reservoir of J2 Gas Reservoir in Lingyinsi Structure

Compared to clastic rocks, the exploration and development of thin carbonate reservoir are 1more complex. Especially, the thin reservoir demands high resolution of seismic data. So the conventional methods are difficult to good exploration at the present time. According to the complexity of thin carbonate reservoir, we comprehensively analyze reservoir geology, logging and seismic response, combine natural gamma inversion, velocity inversion with porosity inversion for prediction of reservoir and obtain precise quantitative earthquake prediction. The analysis demonstrates the reliability of seismic reservoir prediction applied to design for horizontal well. On the basis of understanding the basic geological characteristics of the reservoir, this thesis takes the design for horizontal well of J21 for example, completes two sets of designs for horizontal wells group by combining prediction technology for reservoir seismic with geologic steering technique and greatly improve the drilling rate for thin reservoir.

Review of strongly-coupled composite dark matter models and lattice simulations

We review models of new physics in which dark matter arises as a composite bound state from a confining strongly-coupled non-Abelian gauge theory. We discuss several qualitatively distinct classes of composite candidates, including dark mesons, dark baryons, and dark glueballs. We highlight some of the promising strategies for direct detection, especially through dark moments, using the symmetries and properties of the composite description to identify the operators that dominate the interactions of dark matter with matter, as well as dark matter self-interactions. We briefly discuss the implications of these theories at colliders, especially the (potentially novel) phenomenology of dark mesons in various regimes of the models. Throughout the review, we highlight the use of lattice calculations in the study of these strongly-coupled theories, to obtain precise quantitative predictions and new insights into the dynamics.

Development of a dynamic computational model of social cognitive theory.

Social cognitive theory (SCT) is among the most influential theories of behavior change and has been used as the conceptual basis of health behavior interventions for smoking cessation, weight management, and other health behaviors. SCT and other behavior theories were developed primarily to explain differences between individuals, but explanatory theories of within-person behavioral variability are increasingly needed as new technologies allow for intensive longitudinal measures and interventions adapted from these inputs. These within-person explanatory theoretical applications can be modeled as dynamical systems. SCT constructs, such as reciprocal determinism, are inherently dynamical in nature, but SCT has not been modeled as a dynamical system. This paper describes the development of a dynamical system model of SCT using fluid analogies and control systems principles drawn from engineering. Simulations of this model were performed to assess if the model performed as predicted based on theory and empirical studies of SCT. This initial model generates precise and testable quantitative predictions for future intensive longitudinal research. Dynamic modeling approaches provide a rigorous method for advancing health behavior theory development and refinement and for guiding the development of more potent and efficient interventions.

ECONOMICS. From the lab to the real world.

Laboratory experiments provide precise quantitative predictions of peer effects in the field [Also see Report by Herbst and Mas ]

Statistical questions in experimental evolution

Recent advances in the mathematical analysis of models describing evolutionary dynamicsare rapidly increasing our ability to make precise quantitative predictions. These advanceshave created a growing need for corresponding improvements in our ability to observeevolutionary dynamics in laboratory evolution experiments. High-throughput experimentalmethods are particularly crucial, in order to maintain many replicate populations andmeasure statistical differences in evolutionary outcomes at both phenotypic andgenomic levels. In this paper, I describe recent technical developments which havegreatly increased the throughput of laboratory evolution experiments, and outline afew promising directions for further improvements. I then highlight a few waysin which these new experimental methods can help to answer simple statisticalquestions about evolutionary dynamics, and potentially guide future theoreticalwork.

Author reply: Empathy and the Brain: How We Can Make Progress

Neuroscientific research on empathy has made much progress recently. How far can we get and how should we do it? Two different routes have been suggested by Dziobek and Jacobs in their commentaries. The first is becoming ecologically more valid by using real-life settings as stimuli. The second is becoming more quantitative by specifying a neurocognitive model, allowing more precise quantitative predictions. Although neither approaches are mutually exclusive, I suggest that these two routes are in a certain tension to each other. I suggest an additional third, more indirect way, namely studying modulating factors of empathy like emotion regulation which have until now been largely neglected in empathy research.