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The deviations of cryogenic collector current–voltage characteristics of SiGe heterojunction bipolar transistors (HBTs) from ideal drift-diffusion theory have been a topic of investigation for many years. Recent work indicates that direct tunneling across the base contributes to the non-ideal current in highly scaled devices. However, cryogenic discrepancies have been observed even in older-generation devices for which direct tunneling is negligible, suggesting that another mechanism may also contribute. Although similar non-ideal current–voltage characteristics have been observed in Schottky junctions and were attributed to a spatially inhomogeneous junction potential, this explanation has not been considered for SiGe HBTs. Here, we experimentally investigate this hypothesis by characterizing the collector current ideality factor and built-in potential of a SiGe HBT vs temperature using a cryogenic probe station. The temperature dependence of the ideality factor and the relation between the built-in potential as measured by capacitance–voltage and current–voltage characteristics are in good qualitative agreement with the predictions of a theory of electrical transport across a spatially inhomogeneous junction. These observations suggest that inhomogeneities in the base–emitter junction potential may contribute to the cryogenic non-idealities. This work helps to identify the physical mechanisms limiting the cryogenic microwave noise performance of SiGe HBTs. [ABSTRACT FROM AUTHOR]

Comb-like polymers have shown potential as advanced materials for a diverse palette of applications due to the tunability of their polymer architecture. To date, however, it still remains a challenge to understand how the conformational properties of these polymers arise from the interplay of their architectural parameters. In this work, extensive simulations were performed using dissipative particle dynamics to investigate the effect of grafting density, backbone length, and sidechain length on the conformations of comb-like polymers immersed in a good solvent. To quantify the effect of these architectural parameters on polymer conformations, we computed the asphericity, radius of gyration, and backbone and sidechain end-to-end distances. Bond–bond correlation functions and effective Kuhn lengths were computed to quantify the topological stiffness induced by sidechain–sidechain interactions. Simulation results reveal that the effective Kuhn length increases as grafting density and sidechain length increase, in agreement with previous experimental and theoretical studies. This increase in stiffness results in comb-like polymers adopting extended conformations as grafting density and sidechain length increase. Simulation results regarding the radius of gyration of comb-like polymers as a function of grafting density are compared with scaling theory predictions based on a free energy proposed by Morozova and Lodge [ACS Macro Lett. 6, 1274–1279 (2017)] and scaling arguments by Tang et al. [Macromolecules 55, 8668–8675 (2022)]. [ABSTRACT FROM AUTHOR]

The instability of a cryogenic 4He jet exiting through a small nozzle into vacuum leads to the formation of 4He drops, which are considered ideal matrices for spectroscopic studies of embedded atoms and molecules. Here, we present a He-density functional theory (DFT) description of droplet formation resulting from jet breaking and contraction of superfluid 4He filaments. Whereas the fragmentation of long jets closely follows the predictions of linear theory for inviscid fluids, leading to droplet trains interspersed with smaller satellite droplets, the contraction of filaments with an aspect ratio larger than a threshold value leads to the nucleation of vortex rings, which hinder their breakup into droplets. [ABSTRACT FROM AUTHOR]

Concentration-polarization electroosmosis (CPEO) refers to steady-state electroosmotic flows around charged dielectric micro-particles induced by low-frequency AC electric fields. Recently, these flows were shown to cause repulsion of colloidal particles from the wall of a microfluidic channel when an electric field is applied along the length of the channel. In this work, we exploit this mechanism to demonstrate fractionation of micron-sized polystyrene particles and bacteria in a flow-focusing device. The results are in agreement with predictions of the CPEO theory. The ease of implementation of CPEO-based fractionation in microfluidics makes it an ideal candidate for combining with current techniques commonly used to generate particle lift, such as inertial or viscoelastic focusing, requiring no extra fabrication steps other than inserting two electrodes. [ABSTRACT FROM AUTHOR]

Video prediction theories have quickly progressed especially after a great revolution of deep learning methods. The prediction architectures based on pixel generation produced a blurry forecast, but it is preferred in many applications because this model is applied on frames only and does not need other support information like segmentation or flow mapping information making getting a suitable dataset very difficult. In this approach, we presented a novel end-to-end video forecasting framework to predict the dynamic relationship between pixels in time and space. The 3D CNN encoder is used for estimating the dynamic motion, while the decoder part is used to reconstruct the next frame based on adding 3DCNN CONVLSTM2D in skip connection. This novel representation of skip connection plays an important role in reducing the blur predicted and preserved the spatial and dynamic information. This leads to an increase in the accuracy of the whole model. The KITTI and Cityscapes are used in training and Caltech is applied in inference. The proposed framework has achieved a better quality in PSNR=33.14, MES=0.00101, SSIM=0.924, and a small number of parameters (2.3 M). [ABSTRACT FROM AUTHOR]

The quantitative prediction and control of assembly stress are crucial for the accuracy of precision optical instruments during their manufacturing process. As a typical optical component, the multipoint bonded flat lens is widely used in precision optical instruments, in which the main assembly stress (i.e., radial adhesive stress) and its induced nonuniform stress field in the lens vary with environmental parameters and time. These factors ultimately determine the accuracy and stability of such precision instruments. Effective prediction of the radial adhesive stress is important for bonding parameter optimization and accuracy enhancement but remains difficult to obtain due to the lack of direct measurement devices and effective prediction methods. Here, a novel prediction method is presented to estimate the radial adhesive stress applied on a multipoint bonded flat lenses. First, a quantitative characterization method (QCM) is developed to concisely describe the nonuniform stress field in a lens using quantitative characterization parameters (QCPs). Then, based on the QCM and the theoretical model established in our previous work (Xiong et al., 2022), a prediction method is developed for the radial adhesive stress applied on a lens by analyzing the stress field in the lens. In this method, a backpropagation neural network is constructed to obtain the mapping relationship between the key QCPs of the stress field and the radial adhesive stress applied on the lens. Finally, an experimental verification of the prediction method is carried out. This paper proposes a novel method to predict the radial adhesive stress for multipoint bonded optical lenses, which can be adopted to monitor the variation characteristics of assembly stress in optical instruments during the entire life cycle and then further employed to optimize the bonding parameters during the lens manufacturing process. This paper attempts to construct a direct mapping relationship between assembly quality and assembly parameters for precision instruments, supporting the quantitative optimization of assembly parameters and quantitative control and prediction of assembly quality during the manufacturing and working process. [Display omitted] • A quantitative characterization method to describe the stress field in a lens is proposed. • Quantitative features of the stress field in multipoint bonded lenses are revealed. • The radial adhesive stress is predicted by analyzing the stress field in the lens. • A mapping relationship between assembly quality and assembly parameter is constructed. [ABSTRACT FROM AUTHOR]

The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρdmax still suffer from uncertainties, inefficiencies, and lack of intelligence. These deficiencies can lead to insufficient assessments for the high-speed railway subgrade compaction quality, further impacting the operational safety of high-speed railways. In this paper, a novel method for full-section assessment of high-speed railway subgrade compaction quality based on ML-interval prediction theory is proposed. Firstly, based on indoor vibration compaction tests, a method for determining the ρdmax based on the dynamic stiffness Krb turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting ρdmax is determined based on three typical machine learning (ML) algorithms, which are back propagation neural network (BPNN), support vector regression (SVR), and random forest (RF). Thirdly, the interval prediction theory is introduced to quantify the uncertainty in ρdmax prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of ρdmax across the full-section can be determined, and a model for full-section assessment of compaction quality is developed based on the compaction standard (95%). Moreover, the proposed method is applied to determine the optimal compaction thicknesses (H0), within the station subgrade test section in the southwest region. The results indicate that: (1) The PSO-BPNN-AdaBoost model performs better in the accuracy and error metrics, which is selected as the POA model for predicting ρdmax. (2) The Bootstrap-POA-ANN interval prediction model for ρdmax can construct clear and reliable prediction intervals. (3) The model for full-section assessment of compaction quality can provide the full-section distribution interval for K. Comparing the H0 of 50~60 cm and 60~70 cm, the compaction quality is better with the H0 of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades. [ABSTRACT FROM AUTHOR]

This systematic literature review presents an interdisciplinary overview of theories tested in experiments on the effects of communicating uncertainty. Using a machine learning-aided pipeline, we selected and manually coded 413 experimental studies. We discuss core assumptions and predictions of the main theories of uncertainty communication. Most normative theorizing (e.g., Bayesianism, Expected Utility Theory) is rooted in Probability Theory, which is only suitable for addressing shallow and medium uncertainty. This explains the underrepresentation of experimental research into deep uncertainty communication. To foster a more comprehensive understanding of uncertainty communication effects, we identify research questions and theories deserving greater attention. [ABSTRACT FROM AUTHOR]

Accurately predicting the phase behavior and physical properties of carbon dioxide (CO2) in pure water/NaCl mixtures is crucial for the design and implementation of carbon capture, utilization, and storage (CCUS) technology. However, the prediction task is complicated by CO2 liquefaction, CO2 hydrate formation, multicomponent and multiphase coexistence, etc. In this study, an improved method that combines Benedict–Webb–Rubin–Starling equation of state (BWRS EOS) + hydrate thermodynamic theories was proposed to predict CO2 solubility and phase equilibrium conditions for a mixed system across various temperature and pressure conditions. By modifying the interaction coefficients in BWRS EOS and the Van der Waals–Platteeuw model, this new method is applicable to complex systems containing two liquid phases and a CO2 hydrate phase, and its high prediction accuracy was verified through a comparative evaluation with a large number of reported experimental data. Furthermore, based on the calculation results, the characteristics of CO2 solubility and the variation of phase equilibrium conditions of the mixture system were discussed. These findings highlight the influence of hydrates and NaCl on CO2 solubility characteristics and clearly demonstrate the hindrance of NaCl to the formation of CO2 hydrates. This study provides valuable insights and fundamental data for designing and implementing CCUS technology that contribute to addressing global climate change and environmental challenges. [ABSTRACT FROM AUTHOR]

The precision with which a theory predicts behavior speaks both to the quality of that theory and to its potential utility in real-world applications. Unfortunately, predictive precision is frequently overlooked when evaluating theories in social psychology. Here we call attention to the benefits of placing more emphasis on predictive precision. We define what we mean by predictive precision, consider its role in theory evaluation from a philosophy of science perspective, and suggest a method for quantifying it: by constructing prediction intervals. A prediction interval is the range of values within which an empirical observation (e.g., a sample mean) is expected to fall some specified percentage (e.g., 95%) of the time. The size of a prediction interval reflects the degree of fluctuation in the empirical observations expected by theory, and so the precision of its prediction. Prediction intervals are useful because they simplify theory testing and provide a metric by which theory improvement—and so scientific progress—can be gauged. Prediction intervals are easily created when theories are expressed formally as agent-based models. We illustrate the process of creating and using prediction intervals in three detailed examples, each involving a different agent-based model concerned with the behavior of small interacting groups. [ABSTRACT FROM AUTHOR]

Competition is a prominent mechanism driving population dynamics and structuring community assemblage, which can be investigated by linking shifts in species' ecological niche and the densities of sympatric species because the ecological release from competitive constraints is a density‐dependent process. In this work we determine how a steppe passerine community segregates their ecological niches and evaluate the role of competition in inducing changes in the ecological niche of species. We built multidimensional ecological niches (with Gaussian kernel density estimators) using data on the habitat features used by 10 bird species collected from seven sites in the natural steppes of Central Spain over 2 consecutive years. We computed distance and niche similarity metrics to explore the ecological niche partitioning of the bird community. Next, we ran multivariate linear regression models to evaluate the effects of conspecific and heterospecific density (as proxies of intraspecific and interspecific competition, respectively) on niche breadth and/or position of the three most abundant species. We found low niche overlap in the community assemblage but varying levels of niche similarity among pairs of species, which could increase the likelihood of current competition operating in the community. However, we found no effect of heterospecific density on niche breadth or position, although conspecific density was negatively related to niche breadth. Contrary to predictions of competition theory, increased density of conspecifics caused niche contraction. Our results from a multispecies system contribute to advanced knowledge of the biotic mechanisms structuring wildlife communities within the framework of ecological niche theory. [ABSTRACT FROM AUTHOR]

A biocompatible fluorescence sensor for cysteine detection receives wide appreciation recently, because of its importance in the medical field. Functionalized graphene quantum dots (GQDs) are recently emerging biocompatible quantum dots, which are considered as suitable candidates for biomolecule detection. Motivated by this concept, here we have developed a versatile fluorescent probe based on 3‐aminocoumarin (AMC) functionalized GQDs for the detection of cysteine (Cys). Modification on GQD with AMC resulted in a stable fluorescent probe with an enhancement in quantum yield of about 84% and 40 nm redshift in emission peak compared with bare GQD. The modified GQD is then used for the sensitive and selective detection of cysteine in aqueous media. The detection of Cys within the linear range of 50 nM to 1.5 μM was achieved with a detection limit (LOD) of 0.86 nM. Here, the AMC‐GQD exhibit a turn‐off fluorescence sensing behavior. The quenching mechanism was also explored. The sensing process follows dynamic quenching mechanism, which is attributed to the photoinduced charge transfer from AMC‐GQD to Cys. The Stern–Volmer plot, energy‐level alignment obtained from cyclic voltammetry measurements and density functional theory predictions give a valid proof for this. Furthermore, the sensor was applied efficiently to the determination of Cys in real water samples. [ABSTRACT FROM AUTHOR]

The efficient coding hypothesis posits that early sensory neurons transmit maximal information about sensory stimuli, given internal constraints. A central prediction of this theory is that neurons should preferentially encode stimuli that are most surprising. Previous studies suggest this may be the case in early visual areas, where many neurons respond strongly to rare or surprising stimuli. For example, previous research showed that when presented with a rhythmic sequence of full-field flashes, many retinal ganglion cells (RGCs) respond strongly at the instance the flash sequence stops, and when another flash would be expected. This phenomenon is called the 'omitted stimulus response'. However, it is not known whether the responses of these cells varies in a graded way depending on the level of stimulus surprise. To investigate this, we presented retinal neurons with extended sequences of stochastic flashes. With this stimulus, the surprise associated with a particular flash/silence, could be quantified analytically, and varied in a graded manner depending on the previous sequences of flashes and silences. Interestingly, we found that RGC responses could be well explained by a simple normative model, which described how they optimally combined their prior expectations and recent stimulus history, so as to encode surprise. Further, much of the diversity in RGC responses could be explained by the model, due to the different prior expectations that different neurons had about the stimulus statistics. These results suggest that even as early as the retina many cells encode surprise, relative to their own, internally generated expectations. Author summary: A long-standing theory suggests that sensory neurons work to transmit as much visual information as possible using little energy. According to this idea, sensory neurons should put most energy into encoding stimuli that are unexpected or surprising. Previous research has shown that this may be true for certain neurons in the retina, that respond strongly when they see something unusual, like a sudden change in a sequence of flashes. Here, to test this theory, we presented the retina with random sequences of light flashes, and measured whether retinal neuron responses correlated with how surprising each flash in the sequence was. Consistent with the theory, we found that retinal neuron responses in our experiment could be explained by a model that assumed that they encode surprise. Moreover, differences between each neuron's responses could be predicted due to their different expectations about which stimuli were most likely. [ABSTRACT FROM AUTHOR]

A central prediction of evolutionary theory is that energy invested into reproduction comes at the expense of somatic maintenance and repair, accelerating biological aging. Supporting this prediction are findings that high fertility among women predicts shorter lifespan and poorer health later in life. However, biological aging is thought to begin before age-related health declines, limiting the applicability of morbidity and mortality for studying the aging process earlier in life. Here, we examine the relationship between reproductive history and biological aging in a sample of young (20 to 22yo) men and women from the Cebu Longitudinal Health and Nutrition Survey, located in the Philippines (n = 1,735). We quantify biological aging using six measures, collectively known as epigenetic clocks, reflecting various facets of cellular aging, health, and mortality risk. In a subset of women, we test whether longitudinal changes in gravidity between young and early-middle adulthood (25 to 31yo) are associated with changes in epigenetic aging during that time. Cross-sectionally, gravidity was associated with all six measures of accelerated epigenetic aging in women (n = 825). Furthermore, longitudinal increases in gravidity were linked to accelerated epigenetic aging in two epigenetic clocks (n = 331). In contrast, the number of pregnancies a man reported fathering was not associated with epigenetic aging among same-aged cohort men (n = 910). These effects were robust to socioecological, environmental, and immunological factors, consistent with the hypothesis that pregnancy accelerates biological aging and that these effects can be detected in young women in a high-fertility context. [ABSTRACT FROM AUTHOR]

Time-dependent density functional theory (TDDFT) offers a tractable means to predict electronic excitation spectra of ices if sufficient care is taken in selecting the DFT functional, basis sets, and the number of excited states. In this work 48 functionals in conjunction with various correlation consistent basis sets were benchmarked for predicting electronic spectra. A training set of important astromolecules was chosen: carbon monoxide, water, hydrogen cyanide, ammonia, methanol, and the hydroxyl radical. Vertical excitation energies between the ground and first excited state and some higher excited states were calculated using multireference configuration interaction calculations to serve as benchmarks for gas phase TDDFT calculations. When used in conjunction with aug-cc-pVDZ quality correlation consistent basis sets, two functionals, MPW1K and ωB97, emerged as very good choices to model electronic excitations for most of the molecules in the training set. Cluster calculation predictions for the UV spectra of amorphous ices of water and ammonia were performed at the TDDFT-MPW1K/AVDZ and TDDFT-ωB97/AVDZ levels and then compared against experimental data. The calculations qualitatively reproduce the shapes of the spectra for the most part and quantitatively predict the position of the critical first absorption peak. [ABSTRACT FROM AUTHOR]

The reinforcement learning (RL) theory of the reward positivity (RewP), an event‐related potential (ERP) component that measures reward responsivity, suggests that the RewP should be largest when positive outcomes are unexpected and has been supported by work using appetitive outcomes (e.g., money). However, the RewP can also be elicited by the absence of aversive outcomes (e.g., shock). The limited work to‐date that has manipulated expectancy while using aversive outcomes has not supported the predictions of RL theory. Nonetheless, this work has been difficult to reconcile with the appetitive literature because the RewP was not observed as a reward signal in these studies, which used passive tasks that did not involve participant choice. Here, we tested the predictions of the RL theory by manipulating expectancy in an active/choice‐based threat‐of‐shock doors task that was previously found to elicit the RewP as a reward signal. Moreover, we used principal components analysis to isolate the RewP from overlapping ERP components. Eighty participants viewed pairs of doors surrounded by a red or green border; shock delivery was expected (80%) following red‐bordered doors and unexpected (20%) following green‐bordered doors. The RewP was observed as a reward signal (i.e., no shock > shock) that was not potentiated for unexpected feedback. In addition, the RewP was larger overall for unexpected (vs expected) feedback. Therefore, the RewP appears to reflect the additive (not interactive) effects of reward and expectancy, challenging the RL theory of the RewP, at least when reward is defined as the absence of an aversive outcome. Reinforcement learning (RL) theory suggests that the reward positivity (RewP) should be largest when positive outcomes are unexpected, though this has only been shown using appetitive outcomes (e.g., money). Here, we defined reward as the absence of aversive outcomes (i.e., shock), while varying feedback expectancy. Results showed that the RewP was potentiated by the additive (not interactive) effects of absent aversive outcomes and unexpected feedback, challenging RL theory. [ABSTRACT FROM AUTHOR]

Perception is suggested to occur in discrete temporal windows, clocked by cycles of neural oscillations. An important testable prediction of this theory is that individuals' peak frequencies of oscillations should correlate with their ability to segregate the appearance of two successive stimuli. An influential study tested this prediction and showed that individual peak frequency of spontaneously occurring alpha (8–12 Hz) correlated with the temporal segregation threshold between two successive flashes of light [Samaha, J., & Postle, B. R. The speed of alpha-band oscillations predicts the temporal resolution of visual perception. Current Biology, 25, 2985–2990, 2015]. However, these findings were recently challenged [Buergers, S., & Noppeney, U. The role of alpha oscillations in temporal binding within and across the senses. Nature Human Behaviour, 6, 732–742, 2022]. To advance our understanding of the link between oscillations and temporal segregation, we devised a novel experimental approach. Rather than relying entirely on spontaneous brain dynamics, we presented a visual grating before the flash stimuli that is known to induce continuous oscillations in the gamma band (45–65 Hz). By manipulating the contrast of the grating, we found that high contrast induces a stronger gamma response and a shorter temporal segregation threshold, compared to low-contrast trials. In addition, we used a novel tool to characterize sustained oscillations and found that, for half of the participants, both the low- and high-contrast gratings were accompanied by a sustained and phase-locked alpha oscillation. These participants tended to have longer temporal segregation thresholds. Our results suggest that visual stimulus drive, reflected by oscillations in specific bands, is related to the temporal resolution of visual perception. [ABSTRACT FROM AUTHOR]

The tungsten-copper alloy is commonly used for the electrical contact in high current vacuum switch, where precise temperature monitoring of the contact is crucial for ensuring stable operation of the switch by radiation thermometry. However, accurate emissivity data of this alloy is necessary when using radiation thermometry for temperature measurement. Thus, the directional spectral emissivity of tungsten-copper alloy is investigated within the temperature range of 400–700 ℃ under vacuum in this work. The hemispherical total emissivity is calculated by numerically integrating the directional spectral emissivity. Experimental results found this tungsten-copper alloy agrees with the theoretical prediction of the electromagnetic theory, that is, the emissivity increases with increasing temperature, and decreases with increasing wavelength. A convergence phenomenon of spectral emissivity occurs when the polar angle exceeds approximately 50°. This means that the normal wavelength dependence undergoes a shift at high polar angles, which is typical behavior of metallic emissivity. Additionally, the effects of thermal cycle, surface roughness, and chemical composition on emissivity are analyzed in detail. Surface stress relaxation process results in a significant decrease in emissivity. Emissivity increases as the surface roughness and tungsten composition of alloy increases. However, the effects of surface roughness and chemical composition on emissivity gradually disappear at long wavelengths and high polar angles. [ABSTRACT FROM AUTHOR]

The structure and scaling properties of inwardly curved polymer brushes, tethered under good solvent conditions to the inner surface of spherical shells such as membranes and vesicles, are studied by extensive molecular dynamics simulations and compared with earlier scaling and self-consistent field theory predictions for different molecular weights of the polymer chains N and grafting densities σg in the case of strong surface curvature, R−1. We examine the variation of the critical radius R*(σg), separating the regimes of weak concave brushes and compressed brushes, predicted earlier by Manghi et al. [Eur. Phys. J. E 5, 519–530 (2001)], as well as various structural properties such as the radial monomer- and chain-end density profiles, orientation of bonds, and brush thickness. The impact of chain stiffness, κ, on concave brush conformations is briefly considered as well. Eventually, we present the radial profiles of the local pressure normal, PN, and tangential, PT, to the grafting surface, and the surface tension γ(σg), for soft and rigid brushes, and find a new scaling relationship P N (R) ∝ σ g 4 , independent of the degree of chain stiffness. [ABSTRACT FROM AUTHOR]

[Display omitted] Nanobubbles (NBs) are classified in two distinct categories: surface and bulk. Surface NBs are readily observed using atomic force microscopy (AFM), while the existence of bulk NBs has been a subject of debate, conflicting with the diffusion theory's predictions. Current methodologies for identifying bulk NBs yield inconclusive results. In this study, Langmuir Blodgett (LB) technique and AFM, are utilized to visualize NB imprints on anionic, cationic and zwitterionic lipid films deposited on glass-slide substrates. Our analysis of Langmuir monolayers compression isotherms reveals the impact of bulk NBs on lipid monolayer development. AFM scans of the deposited lipid films consistently show NB imprints. Notably, cationic and zwitterionic film depositions exhibit NB formations from the 1st layer, whereas in anionic films, these formations are observed only after the 3rd layer. These results suggest that the origin of these imprinted formations may be attributed to bulk NBs. [ABSTRACT FROM AUTHOR]

The elastic moduli and thermal expansion of MoAlB, Fe2AlB2, and Mn2AlB2 are investigated by first‐principles calculations, in particular to improve the density functional theory (DFT) prediction accuracy after clarifying the critical role of their lattice constants. By calculating the lattice constants and elastic moduli using the typical exchange‐correlation functionals, the observed positive correlation between the calculation errors of elastic moduli and those of cell volumes suggests a method to significantly improve the calculation accuracy for elastic moduli using the experimental cell volume to correct the input model (volume correction method), resulting in satisfactory results. Furthermore, a two‐step strategy is proposed for the precise high‐temperature elastic moduli with the help of quasi‐harmonic approximation for the most accurate high‐temperature cell volume using an exchange‐correlation functional in the first step, and another one used in the volume correction method in the second step. Similarly, the calculation accuracy of thermal expansion also heavily depends on that of cell volumes and configurations, where their thermal expansion is estimated with great accuracy by using the exchange‐correlation functional that well reproduces the lattice constants. [ABSTRACT FROM AUTHOR]

Mass timber has gained increasing focus as a sustainable alternative to concrete structures, and recent innovations have allowed timber buildings as high as 25 stories. However, although critical factors such as fire safety have been studied thoroughly, other facets, such as long-term material performance, have been neglected. Thorough assessments of sustainability must consider the full service life of a building, including its time-dependent behavior. Deformations in timber elements undergoing these time-dependent effects, namely creep, will have notable implications for the serviceability of a building. This study proposes a comprehensive model for predicting moisture-dependent creep in timber, based on the theory of microprestress in partially saturated porous materials. The model is implemented numerically using a Kelvin-chain approximation for basic creep and midpoint integration for mechanosorptive effects, resulting in a computationally efficient approach. Results show the model is able to fully capture the effects of moisture cycling, including effects of hygrothermal gradients. Using both calibrated and estimated parameters the model also predicts with reasonable accuracy the long-term moisture-dependent creep in structural-scale mass timber elements. [ABSTRACT FROM AUTHOR]

A model of symmetry breaking (SB) charge separation in symmetric excited dyads and dimers is presented. The minimal model should include at least four basis electronic states due to a small energy gap between the locally excited and charge separated (zwitterionic) states of the chromophores. There are electronic couplings between all these states. The model includes the following interactions: (i) the Coulomb interaction between charges on the chromophores of the dyad, (ii) the interaction of the dipole moment of the asymmetric dyad with the solvent polarization, and (iii) the electronic–vibrational interaction. SB becomes possible only if the intensity of these interactions exceeds a threshold value. The threshold vanishes if there is a degeneration of the levels. Unusual resonant dependencies of the dissymmetry degree on the model parameters are revealed. Resonances arise due to the degeneration of energy levels. The ranges of the parameters in which energy level crossings occur are established. The oddity lies in the dependence of the resonance shape on the parameters of the model. A variation in the electronic couplings and the energy gap between the locally excited and ionic states, which leads to a broadening of the resonance, simultaneously leads to an increase in the resonant height. This opens up wide possibilities for controlling the charge separation degree. The predictions of the theory agree with the available experimental data. The charge separation SB is predicted to accompany by SB in the excitation distribution on the branches of dyads. [ABSTRACT FROM AUTHOR]

The remaining useful life (RUL) is one of the critical factors for proton exchange membrane fuel cells (PEMFCs), as it is hindering the commercialization of PEMFCs in various fields. In this research, a novel deep learning (DL) algorithm, i.e. the Non-stationary Transformer is newly applied to the RUL prediction of a PEMFC stack. For better prediction accuracy, the discrete wavelet transform (DWT) is utilized to denoise the original data before the normalization process. Prediction results of the proposed Non-stationary Transformer-based PEMFC RUL prediction method are analyzed under different lengths of training datasets, under different time steps ahead prediction, and for different PEMFC End of Life (EoL) threshold values and compared to those of other DL-based prediction methods including the long short-term memory (LSTM)-based and echo state network (ESN)-based methods. Results show that the proposed method outperforms the LSTM-based and ESN-based methods for all different cases studied in this research. • The Non-stationary Transformer is newly applied to PEMFC RUL prediction method. • The DWT method is utilized to denoise original data to accurate the prediction. • The proposed RUL prediction method is compared to LSTM and ESN-based methods. • RUL prediction methods are analyzed under various dataset lengths and time steps. • The proposed RUL prediction method outperforms LSTM and ESN-based in accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] The smallest nanodrop tractable with macroscopic notions such as the interfacial energy could be determined by comparing heterogeneous nucleation observations and capillary theory predictions at decreasing drop diameters d p. This is done here for the condensation of n -butanol vapors on polyethylene glycol nano-globules (3 nm ≤ d p ≤ 9 nm). We use published activation probability measurements P(w,d p), where w is the accurately controlled saturation ratio of n -butanol vapor in a gas stream exiting a saturator. The maximal saturation ratio achieved in the nucleation region by cooling this gas-vapor stream in the apparatus of Gallar et al. satisfies S max = Cw. The key unknown constant C and the preexponential term K governing the nucleation rate are determined by assuming that classical theory applies to the largest particles used. This yields P(S max ,d p) data, directly comparable with capillary theory with perfect wetting. Excellent agreement is found above 5 nm for the critical dependence S max (d p) resulting from the constraint P(S max ,d p) = 0.5. The entire P(S max ,d p) curves also agree closely between 5 and 7 nm. Smaller particles depart only slightly from theory, even at d p = 3 nm. Capillary theory hence describes accurately the heterogeneous nucleation process above 3–5 nm, provides a reliable method to determine S max , and yields experimentally the nucleation rate constant K. [ABSTRACT FROM AUTHOR]

Convection in stars and planets must be maintained against viscous and Ohmic dissipation. Here, we present the first systematic investigation of viscous dissipation in simulations of rotating, density-stratified plane layers of convection. Our simulations consider an anelastic ideal gas, and employ the open-source code Dedalus. We demonstrate that when the convection is sufficiently vigorous, the integrated dissipative heating tends towards a value that is independent of viscosity or thermal diffusivity, but depends on the imposed luminosity and the stratification. We show that knowledge of the dissipation provides a bound on the magnitude of the kinetic energy flux in the convection zone. In our non-rotating cases with simple flow fields, much of the dissipation occurs near the highest possible temperatures, and the kinetic energy flux approaches this bound. In the rotating cases, although the total integrated dissipation is similar, it is much more uniformly distributed (and locally balanced by work against the stratification), with a consequently smaller kinetic energy flux. The heat transport in our rotating simulations is in good agreement with results previously obtained for 3D Boussinesq convection, and approaches the predictions of diffusion-free theory. [ABSTRACT FROM AUTHOR]

Future state prediction for nonlinear dynamical systems is a challenging task. Classical prediction theory is based on a, typically long, sequence of prior observations and is rooted in assumptions on statistical stationarity of the underlying stochastic process. These algorithms have trouble predicting chaotic dynamics, "Black Swans" (events which have never previously been seen in the observed data), or systems where the underlying driving process fundamentally changes. In this paper we develop (1) a global and local prediction algorithm that can handle these types of systems, (2) a method of switching between local and global prediction, and (3) a retouching method that tracks what predictions would have been if the underlying dynamics had not changed and uses these predictions when the underlying process reverts back to the original dynamics. The methodology is rooted in Koopman operator theory from dynamical systems. An advantage is that it is model-free, purely data-driven and adapts organically to changes in the system. While we showcase the algorithms on predicting the number of infected cases for COVID-19 and influenza cases, we emphasize that this is a general prediction methodology that has applications far outside of epidemiology. [ABSTRACT FROM AUTHOR]

This paper is the fourth in a series dedicated to the mathematically rigorous asymptotic analysis of gravitational radiation under astrophysically realistic set-ups. It provides an overview of the physical ideas involved in setting up the mathematical problem, the mathematical challenges that need to be overcome once the problem is posed, as well as the main new results we will obtain in upcoming work. From the physical perspective, this includes a discussion of how post-Newtonian theory provides a prediction on the gravitational radiation emitted by N infalling masses from the infinite past in the intermediate zone, i.e. up to some finite advanced time. From the mathematical perspective, we then take this prediction, together with the condition that there be no incoming radiation from I− , as a starting point to set up a scattering problem for the linearized Einstein vacuum equations around Schwarzschild and near spacelike infinity, and we outline how to solve this scattering problem and obtain the asymptotic properties of the scattering solution near i0 and I+. The full mathematical details will be presented in the sequel to this paper. This article is part of a discussion meeting issue 'At the interface of asymptotics, conformal methods and analysis in general relativity'. [ABSTRACT FROM AUTHOR]

We analyze how curiosity drives news consumption. We test predictions of the information‐gap theory of curiosity using over 100,000 WeChat news articles, applying NLP methods to construct measures of salience, importance, and surprisingness associated with news headlines, experimentally validating these measures, and using them to predict clicks. Our findings confirm that people tend to consume news when: the headline sparks a salient question; the content appears more important (e.g., emphasized by the headline's position on the webpage or an exclamation mark); the headline refers to more surprising topics (measured as the KL‐divergence from a baseline topic distribution); and the headline has lower valence. Information‐gap theory helps predict aggregate news consumption. Yet our data also reveal a small negative correlation between the number of clicks and the ratio of likes to clicks, suggesting that while inducing curiosity can drive short‐term news consumption, it doesn't necessarily enhance long‐term reader engagement. [ABSTRACT FROM AUTHOR]

Many‐body expansion (MBE) fragment approaches have been applied to accurately compute nuclear magnetic resonance (NMR) parameters in crystalline systems. Recent examples demonstrate that electric field gradient (EFG) tensor parameters can be accurately calculated for 14N and 17O. A key additional development is the simple molecular correction (SMC) approach, which uses two one‐body fragment (i.e., isolated molecule) calculations to adjust NMR parameter values established using 'benchmark' projector augmented‐wave (PAW) density functional theory (DFT) values. Here, we apply a SMC using the hybrid PBE0 exchange‐correlation (XC) functional to see if this can improve the accuracy of calculated 35Cl EFG tensor parameters. We selected eight organic and two inorganic crystal structures and considered 15 chlorine sites. We find that this SMC improves the accuracy of computed values for both the 35Cl quadrupolar coupling constant (CQ) and the asymmetry parameter (ηQ) by approximately 30% compared with benchmark PAW DFT values. We also assessed a SMC that offers local improvements not only in terms of the quality of the XC functional but simultaneously in the quality of the description of relativistic effects via the inclusion of spin–orbit effects. As the inorganic systems considered contain heavy atoms bonded to the chlorine atoms, we find further improvements in the accuracy of calculated 35Cl EFG tensor parameters when both a hybrid functional and spin–orbit effects are included in the SMC. On the contrary, for chlorine‐containing organics, the inclusion of spin–orbit relativistic effects using a SMC does not improve the accuracy of computed 35Cl EFG tensor parameters. [ABSTRACT FROM AUTHOR]

Positive affect is often considered the "hallmark of well-being," associated with better health, longevity, and success. Self-determination theory (SDT) proposes that satisfying three basic psychological needs for autonomy, competence and relatedness (BNS) fosters optimal functioning, thriving, and positive affect. Meanwhile, broaden-and-build theory suggests that positive emotions predict future psychosocial resources such as need satisfaction. Previous research on the BNS–positive affect link has not sufficiently established to what extent changes in BNS precede changes in positive affect or vice versa. We tested this in two 3-wave longitudinal studies, conducted over 2 years in the UK (Study 1: N = 958) and over 2 months in Latin America (Study 2: N = 1200). Bivariate latent trait-state-occasion models revealed that within-person fluctuations in BNS significantly predicted subsequent fluctuations in positive affect in both studies, but fluctuations in positive affect predicted subsequent fluctuations in BNS only in Study 2. These findings consistently support SDT predictions, whereas they only partially support broaden-and-build theory predictions, helping to clarify the likely causal relations between BNS and positive affect. [ABSTRACT FROM AUTHOR]

We characterize the full classes of M -estimators for semiparametric models of general functionals by formally connecting the theory of consistent loss functions from forecast evaluation with the theory of M -estimation. This novel characterization result allows us to leverage existing results on loss functions known from the literature on forecast evaluation in estimation theory. We exemplify advantageous implications for the fields of robust, efficient, equivariant and Pareto-optimal M -estimation. [ABSTRACT FROM AUTHOR]

Shear stresses for a two-dimensional (2D) beam are calculated modifying the classical method developed by Jouravski, for the case the linearized strain tensor is assumed to be a nonlinear function of the Cauchy stresses. Two problems are studied, namely, the case of a cantilever beam with a point load on its free edge (considering a rectangular and a circular cross-section) and the three-point flexural test for a beam of rectangular cross-section. Numerical results are obtained for the particular case of a bimodular constitutive model for rock, and the results for the shear stresses are compared with the predictions of the classical theory of strength of materials for such problems, assuming a linearized elastic body. [ABSTRACT FROM AUTHOR]

The NA48/2 experiment at CERN reports the first observation of the K± → π0π0μ±ν decay based on a sample of 2437 candidates with 15% background contamination collected in 2003–2004. The decay branching ratio in the kinematic region of the squared dilepton mass above 0.03 GeV2/c4 is measured to be (0.65 ± 0.03) × 10−6. The extrapolation to the full kinematic space, using a specific model, is found to be (3.45 ± 0.16) × 10−6, in agreement with chiral perturbation theory predictions. [ABSTRACT FROM AUTHOR]

Higher-derivative corrections in the AdS/CFT correspondence allow us to capture finer details of the dual CFT and to explore the holographic dictionary beyond the infinite N and strong coupling limits. Following an effective field theory approach, we investigate extremal AdS black hole solutions in five-dimensional supergravity with higher-derivative corrections. We provide a general analysis of near-horizon geometries of rotating extremal black holes and show how to obtain their corresponding charges and chemical potentials. We discuss the near-horizon solutions of the two-derivative theory, which we write using a novel parametrization that eases our computation of the higher-derivative corrections. The charges and thermodynamic properties of the black hole are computed while clarifying the ambiguities in their definitions. The charges and potentials turn out to satisfy a near-horizon version of the first law of thermodynamics whose interpretation we make clear. In the supersymmetric case, the results are shown to match the field theory prediction as well as previous results obtained from the on-shell action. [ABSTRACT FROM AUTHOR]

Virus mitigation behavior has been and still is a powerful means to fight the COVID‐19 pandemic irrespective of the availability of pharmaceutical means (e.g., vaccines). We drew on health behavior theories to predict health‐protective (coping‐specific) responses and hope (coping non‐specific response) from health‐related cognitions (vulnerability, severity, self‐assessed knowledge, efficacy). In an extension of this model, we proposed orientation to internal (problem‐focused coping) and external (country capability) coping resources as antecedents of health protection and hope; health‐related cognitions were assumed as mediators of this link. We tested these predictions in a large multi‐national multi‐wave study with a cross‐sectional panel at T1 (Baseline, March‐April 2020; N = 57,631 in 113 countries) and a panel subsample at two later time points, T2 (November 2020; N = 3097) and T3 (April 2021; N = 2628). Multilevel models showed that health‐related cognitions predicted health‐protective responses and hope. Problem‐focused coping was mainly linked to health‐protective behaviors (T1‐T3), whereas country capability was mainly linked to hope (T1‐T3). These relationships were partially mediated by health‐related cognitions. We conceptually replicated predictions of health behavior theories within a real health threat, further suggesting how different coping resources are associated with qualitatively distinct outcomes. Both patterns were consistent across countries and time. [ABSTRACT FROM AUTHOR]

The development of an accurate electricity demand forecasting model is of paramount importance for promoting global energy efficiency and sustainability. Nonetheless, the presence of outliers and inappropriate model training can result in suboptimal performance. To tackle these challenges, this study explores the potential of Convolutional Neural Network (CNN) and active learning theory as forecasting solutions, offering high efficiency and advantages for long time series. In this study, a hybrid model that combines Isolation Forest (IF), Outlier Reconstruction (OR), CNN and Random Forest (RF) is conducted to mitigate computational complexity and enhance the accuracy of electricity demand forecasting in the presence of outliers. IF is employed to detect outliers in electricity demand time series, while OR is used to reconstruct subsequences based on calendrical heterogeneity for training. CNN is applied for both training and forecasting, and the final output is combined using RF. The effectiveness of the proposed IF-OR-CNN-RF model is validated using electricity data collected from recent sources in Australia at different sampling frequency. The experimental results demonstrate that, in comparison with other popular CNN-based electricity demand forecasting models, IF-OR-CNN-RF model outperforms with significantly improved performance metrics. Specifically, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R-squared values are 77.92, 179.18 and 0.9769 in 5-minute frequency; 162.67, 353.96 and 0.9775 in 10-minute frequency; 841.27, 1374.79 and 0.9622 in 30-minute frequency; 2746.01, 3824.00 and 0.9262 in 60-minute frequency; 9106.08, 12269.04 and 0.8044 in 120-minute frequency. IF-OR-CNN-RF model represents a valuable framework for future electricity demand forecasting, particularly in scenarios involving outliers. [ABSTRACT FROM AUTHOR]

We dendrogram the Leike et al. 3D dust map, leveraging its ∼1 pc spatial resolution to produce a uniform catalog of molecular clouds in the solar neighborhood. Using accurate distances, we measure the properties of 65 clouds in true 3D space, eliminating much of the uncertainty in mass, size, and density. Clouds in the catalog contain a total of 1.1 × 105 M ☉, span distances of 116−440 pc, and include a dozen well-studied clouds in the literature. In addition to deriving cloud properties in 3D volume density space, we create 2D dust extinction maps from the 3D data by projecting the 3D clouds onto a 2D "Sky" view. We measure the properties of the 2D clouds separately from the 3D clouds. We compare the scaling relation between the masses and sizes of clouds following Larson. We find that our 2D projected mass–size relation, M ∝ r 2.1, agrees with Larson's Third Relation, but our 3D derived properties lead to a scaling relation of about one order larger: M ∝ r 2.9. Validating predictions from theory and numerical simulations, our results indicate that the mass–size relation is sensitive to whether column or volume density is used to define clouds, since mass scales with area in 2D (M ∝ r 2) and with volume in 3D (M ∝ r 3). Our results imply a roughly constant column and volume density in 2D and 3D, respectively, for molecular clouds, as would be expected for clouds where the lower density, larger volume-filling gas dominates the cloud mass budget. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) roughness of discontinuity affects the quality of the rock mass, but 3D roughness is hard to be measured due to that the discontinuity is invisible in the engineering. Two-dimensional (2D) roughness can be calculated from the visible traces, but it is difficult to obtain enough quantity of the traces to directly derive 3D roughness during the tunnel excavation. In this study, a new method using Bayesian theory is proposed to derive 3D roughness from the low quantity of 2D roughness samples. For more accurately calculating 3D roughness, a new regression formula of 2D roughness is established firstly based on wavelet analysis. The new JRC3D prediction model based on Bayesian theory is then developed, and Markov chain Monte Carlo (MCMC) sampling is adopted to process JRC3D prediction model. The discontinuity sample collected from the literature is used to verify the proposed method. Twenty groups with the sampling size of 2, 3, 4, and 5 of each group are randomly sampled from JRC2D values of 170 profiles of the discontinuity, respectively. The research results indicate that 100%, 90%, 85%, and 60% predicting JRC3D of the sample groups corresponding to the sampling size of 5, 4, 3, and 2 fall into the tolerance interval [JRCtrue-1, JRCtrue + 1]. It is validated that the sampling size of 5 is enough for predicting JRC3D. The sensitivities of sampling results are then analyzed on the influencing factors, which are the correlation function, the prior distribution, and the prior information. The discontinuity across the excavation face at ZK78 + 67.5 of Daxiagu tunnel is taken as the tunnel engineering application, and the results further verify that the predicting JRC3D with the sampling size of 5 is generally in good agreement with JRC3D true values. [ABSTRACT FROM AUTHOR]

The surcharge load at the ground surface inevitably breaks the original equilibrium state between the underneath tunnel and the surrounding soil, which will impact the service performance of a subway tunnel. This paper presents a novel semi-analytical approach for assessing the time-dependent, longitudinal responses of a subway tunnel in soft soil strata induced by the surcharge load. The solution is developed based on the framework of the classical "two-stage method" but innovatively incorporates the effects of ground stratification, the consolidation process, and the longitudinal stiffness reduction of the lining. Biot's poroelastic theory in conjunction with the Laplace-Fourier transform technique is selected to model the deformation of the stratified ground, while the Timoshenko beam on a Pasternak foundation is employed to model the mechanical responses of the tunnel. The proposed semi-analytical solution is validated not only by comparison with benchmark solutions and a finite element model, but also by predicting a well-documented field measurement. Parametric analyses are conducted to investigate the effects of the elastic modulus and the permeability coefficient of the stratified ground on the longitudinal responses of the tunnel. It is expected that the proposed solution can serve as a useful tool for evaluating the effects of the surcharge load on the longitudinal responses of a subway tunnel. [ABSTRACT FROM AUTHOR]

The technical challenges associated with deep underground space activities have become increasingly significant. Among these challenges, one major concern is the assessment of rockburst risks and the instability of rock masses. Extensive research has been conducted by numerous scholars to mitigate the risks and prevent occurrences of rockburst through various assessment methods. Rockburst incidents commonly occur during the excavation of hard rock in underground environments, posing severe threats to personnel safety, equipment integrity, and operational continuity. Thus, it is crucial to systematically document real cases of rockburst, allowing for a comprehensive understanding of the underlying mechanisms and triggering conditions. This understanding will contribute to the advancement of rockburst prediction and prevention methods. Proper selection of an appropriate rockburst assessment method is a fundamental aspect in underground operations. However, there is a limited number of studies that summarize and compare different prediction and prevention methods of rockburst. This paper aims to address this gap by analyzing global trends using CiteSpace software since 1990. It discusses rockburst classification and characteristics, comprehensively reviews research findings related to rockburst prediction, including empirical, simulation, mathematical modeling, and microseismic monitoring methods. Additionally, the paper presents a compilation of current rockburst prevention measures. Notably, the paper emphasizes the significance of control strategies, which provide key insights into the effective utilization of stored energy within rock. Finally, the paper concludes by suggesting six directions for implementing intelligent management techniques to mitigate hazards during underground operations and reduce the probability of rockburst incidents. [ABSTRACT FROM AUTHOR]

This work is a review of a group of results on the stochastic Burgers equation with small viscosity, obtained during the last two decades. These results jointly show that the equation makes a good model of hydrodynamical turbulence. The model provides natural and rigorously justified analogies of a number of key predictions of the theory of turbulence, including the main assertions of the Kolmogorov approach to turbulence, known as the K41 theory. The presentation is based on the recent book by Boritchev and Kuksin, One-Dimensional Turbulence and the Stochastic Burgers Equation (AMS, 2021) and on two papers that develop the book's results. Part of the assertions that we review here together with their rigorous proofs were earlier heuristically proved in works of physicists. [ABSTRACT FROM AUTHOR]

Despite numerous studies examining the fitness consequences of animal personalities, predictions concerning the relationship between personality and survival are not consistent with empirical observations. Theory predicts that individuals who are risky (i.e. bold, active and aggressive) should have higher rates of mortality; however, empirical evidence shows high levels of variation in behaviour–survival relationships in wild populations.We suggest that this mismatch between predictions under theory and empirical observations results from environmental contingencies that drive heterogeneity in selection. This uncertainty may constrain any universal directional relationships between personality traits and survival. Specifically, we hypothesize that spatiotemporal fluctuations in perceived risk that arise from variability in refuge abundance and competitor density alter the relationship between personality traits and survival.In a large‐scale manipulative experiment, we trapped four small mammal species in five subsequent years across six forest stands treated with different management practices in Maine, United States. Stands all occur within the same experimental forest but contain varying amounts of refuge and small mammal densities fluctuate over time and space. We quantified the effects of habitat structure and competitor density on the relationship between personality traits and survival to assess whether directional relationships differed depending on environmental contingencies.In the two most abundant species, deer mice and southern red‐backed voles, risky behaviours (i.e. higher aggression and boldness) predicted apparent monthly survival probability. Mice that were more aggressive (less docile) had higher survival. Voles that were bolder (less timid) had higher survival, but in the risky forest stands only. Additionally, traits associated with stress coping and de‐arousal increased survival probability in both species at high small mammal density but decreased survival at low density. In the two less abundant study species, there was no evidence for an effect of personality traits on survival.Our field experiment provides partial support for our hypothesis: that spatiotemporal fluctuations in refuge abundance and competitor density alter the relationship between personality traits and survival. Our findings also suggest that behaviours associated with stress coping and de‐arousal may be subject to density‐dependent selection and should be further assessed and incorporated into theory. [ABSTRACT FROM AUTHOR]

Geometries of eroding landscapes contain important information about geologic, climatic, biotic and geomorphic processes. They are also characterized by variability, which makes disentangling their origins challenging. Observations and physical models of fluvial processes, which set the pace of erosion on most continents, emphasize complexity and variability. By contrast, the spectral content of longitudinal river profiles and similarity of geometries at scales greater than approximately 100km highlight relatively simple emergent properties. A general challenge then, addressed in this manuscript, is development of a theory of landscape evolution that embraces such scale-dependent insights. We do so by incorporating randomness and probability into a theory of fluvial erosion. First, we explore the use of stochastic differential equations of the Langevin type, and the Fokker--Planck equation, for predicting migration of erosional fronts. Second, analytical approaches incorporating distributions of driving forces, critical thresholds and associated proxies are developed. Finally, a linear programming approach is introduced, that, at its core, treats evolution of longitudinal profiles as a Markovian stochastic problem. The theory is developed essentially from first principles and incorporates physics governing fluvial erosion. We explore predictions of this theory, including the natural growth of discontinuities and scale-dependent evolution, including local complexity and emergent simplicity. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous development of the capital market and intelligent internet of things (IIoTs) technologies, investors have focused more on IIoTs enterprises' stocks. Since the stocks of IIoTs enterprises have the characteristics of heavy capital flows and high stock price volatility, the effective prediction of IIoTs stock price changes plays an extremely important role in improving investment returns and controlling investment risks. According to the above characteristics, our model takes stock trend fluctuations and time series indicator changes into consideration and comprehensively captures IIoTs stock information from both the temporal domain and the spatial domain. Specifically, the proposed model is a dual-view model that incorporates selected trading indicators to predict the closing prices of stocks. In the first view, an RNN model is designed to enlarge the receptive field of the model. In the second view, we introduce an attention mechanism to extract the influences of individual stock trends on the forecasting target. To verify the validity of this prediction method, we compare it with six other stock prediction methods. The results show that on the Ping An (601318) and IFLYTEK (002230) datasets, our method achieves the best results, that is, the lowest RMSE values. [ABSTRACT FROM AUTHOR]

THE PAPER PRESENTS A LINEAR ELASTIC ONE-DIMENSIONAL Discrete Layer--Wise (DLW) analytical model of a composite girder consisting of two beams bonded together with a layer of a flexible adhesive. The model takes into account both longitudinal and transverse deformation of component beams, the First Order Shear Deformation Theory (FSDT) for these adherends as well as extensibility of the adhesive layer. A system of governing equations is derived and a general solution is found with the use of the method of generalized eigenvectors. Two examples are analyzed both with the use of the considered 1D analytical model and a 3D Finite Element Analysis (FEA) in order to validate predictions of the introduced theory. Satisfactory agreement is found between theoretical and numerical results. [ABSTRACT FROM AUTHOR]

In the non-relativistic limit, two types of dark matter axion interactions with fermions are thought to dominate: one is induced by the spatial gradient of the axion field and called the axion wind, and the other by the time-derivative of the axion field, generating axioelectric effects. By generalizing Schiff theorem, it is demonstrated that this latter operator is actually strongly screened. For a neutral fermion, it can be entirely rotated away and is unobservable. For charged fermions, the only effect that can peek through the screening is an axion-induced electric dipole moment (EDM). These EDMs are not related to the axion coupling to gluons, represent a prediction of the Dirac theory analogous to the g = 2 magnetic moments, are not further screened by the original Schiff theorem, and are ultimately responsible for inducing the usual axioelectric ionization. The two main phenomenological consequences are then that first the axion-induced nucleon EDM could be significantly larger than expected from the axion gluonic coupling, and second, that the electron EDM could also become available, and could actually be highly sensitive to relic axions. [ABSTRACT FROM AUTHOR]

Cooperative behavior is a vital social interaction which plays a vital role in improving human survival and reproduction. However, few empirical studies have examined the differences between cooperative behaviors and the underlying neural substrates. In the present study, the brain activity of familiar dyads of the same sex was measured using functional near-infrared spectroscopy during three cooperative tasks (cooperative button-press, tangram, and Jenga tasks). We also measured the dyads' empathic abilities and personality traits to investigate the relationships between individual characteristics and neural markers. The results showed that first, there were significant differences in intra-brain activation and inter-brain synchronization among different cooperative tasks in three dimensions: social cognition, behavioral response, and cognitive processing. Second, male participants require stronger intra-brain activation to achieve the same inter-brain synchronization level as women in cooperative tasks. Third, when performing cooperative tasks involving high cognitive demands, Big Five Neuroticism may be an important predictor of neural activation in female participants. Inter-brain synchronization plays an important role in the frontal and temporoparietal junctions during interpersonal cooperation. Furthermore, this study demonstrates that mutual prediction theory is crucial for understanding the neural mechanisms of cooperative behavior. [ABSTRACT FROM AUTHOR]

In this work, we review and extend a version of the old attempt made by Louis de Broglie for interpreting quantum mechanics in realistic terms, namely, the double solution. In this theory, quantum particles are localized waves, i.e., solitons, that are solutions of relativistic nonlinear field equations. The theory that we present here is the natural extension of this old work and relies on a strong time-symmetry requiring the presence of advanced and retarded waves converging on particles. Using this method, we are able to justify wave–particle duality and to explain the violations of Bell's inequalities. Moreover, the theory recovers the predictions of the pilot-wave theory of de Broglie and Bohm, often known as Bohmian mechanics. As a direct consequence, we reinterpret the nonlocal action-at-a-distance in the pilot-wave theory. In the double solution developed here, there is fundamentally no action-at-a-distance but the theory requires a form of superdeterminism driven by time-symmetry. [ABSTRACT FROM AUTHOR]

The initial version of the article by Clithero, Karmarkar, Nave, and Plassmann (Journal of Consumer Psychology, 2024) was critiqued by open comments from a small group of scholars. Their suggestions encouraged the authors to clarify challenging relationships between brain processes and emotions, beliefs, and actions. The revision expanded fMRI and EEG to include measures of vision, facial expression, breathing, heart rhythms, and blood chemistry. The paper provides multiple avenues of joint work between neurological and psychological scholars. The comments below reflect different reactions to the final article. Wes Hutchinson acknowledges that neuroscience insights complement cognitive measures that generate explicit measures of thought, emotion, or preferences, but he warns that repeated measures over time are problematic for both types of measurement, and the inherent complexity of brain–behavior relationships is often underestimated. With both orientations, understanding the functioning of human behavior is akin to making sense of an orchestra, where the interactive blending of different instruments and musicians reflects a complex activity that generates sounds, emotions, and stories. Both consumer neuroscientists and psychologists need to broaden their paradigmatic approaches with bodily measures and advanced psychological procedures to overcome challenges to joint progress. Martin Reiman asserts that despite difficulties with measures that have different levels of abstraction or velocity, research has provided remarkable associations between brain activity and consumer behavior. Effective studies merging brain and behavior can effectively proceed with studies that differ in two dimensions: first, by altering the number of variables, and second, by shifting whether the scientific paradigm is inductive or deductive. In its simple form, the Excavation path explores brain activity when a person is exposed to specific statements or emotions. In its most challenging form, Integrative Studies generate predictions from theories that test the convergent validity of divergent measures and leverage skills from different researchers. Studies reflecting high levels on one dimension but low levels on the other can also provide fruitful research opportunities. Brian Knutson, like Reimann, counters the idea that consumer psychology has not lived up to its promises. He references studies showing that activity from very specific areas of the brain reliably predicts choices better than explicit ratings or choices. Such research generates deductions from increasingly precise neural maps that enable confirmation of theory. That said, he acknowledges that consumer neuroscience is not able to identify a brain button that would alter choice through manipulated neurostimulation. However, since human brains are similar across people, the depth of neural insights that are consistent across a small sample of 40 respondents may generate greater insights than conventional marketing research with 2000 respondents. The cost of neuroscience will further decrease with gains in reliability, validity, and generalizability, particularly if augmented with bodily measures. He acknowledges that the theoretical side has developed more slowly than applications, particularly applications that are supported by sponsoring organizations more satisfied with local insights than general models. [ABSTRACT FROM AUTHOR]