Predictions Of Theory Research Articles

Modeling and control of electrical discharge wire sawing of single-crystal silicon

Electrical discharge wire sawing (EDWS) is a new composite processing method that combines wire electrical discharge machining with fixed abrasive wire sawing. During processing, relying on the grinding effect of the abrasive grains, craters and recast layers generated by discharge are removed. The surface roughness of the workpiece is reduced while the cutting efficiency is improved. In this study, the relationship between discharge voltage and workpiece surface roughness in composite machining is investigated. A single-input single-output control system model with applied resistance as the input and discharge voltage as the output is established. Based on the theory of system identification and parameter estimation, the order of the model is identified by calculating the variance of the model residuals and using the F-test method. The model parameters of the control system are estimated online using a recursive least squares algorithm with a forgetting factor. A generalized predictive controller is designed on the basis of the predictive control theory and the identified discharge voltage control system model. Using LabVIEW software, a discharge voltage prediction control system is constructed for the processing of single-crystal silicon by EDWS, and its prediction control performance is experimentally verified. The results show that the discharge voltage control system designed in this study can successfully control the composite machining of single-crystal silicon in real time. Under the same machining conditions, the surface roughness of the workpiece machined with predictive control was 23.5 % lower than that without predictive control.

Explainable machine learning accelerated density functional theory prediction for diffusive transport behaviour of elements in aluminium matrix and graphene/aluminium interface

In this paper, the diffusive migration behaviour of alloy atoms in aluminium matrix and different types of graphene/aluminium interfaces is systematically investigated by using a machine learning accelerated density functional theory. A small sample dataset is established by first principles calculation, the types of input and output eigenvalues are determined by feature engineering, and the number of input features for perfect interfaces, defective interfaces, and aluminium matrix are finally determined to be 6, 5, and 4 by taking into account the effects of model complexity and prediction accuracy. With a five-fold crossover and by comparing more than a dozen machine learning models, the CatBoost algorithm possesses the lowest error as well as a better coefficient of determination. We further optimized the CatBoost algorithm with further parameters and adjusted the regularization term coefficients to avoid the risk of overfitting. The impact of each feature on the model prediction results was quantitatively described by constructing a matrix of SHAP values. The best performing Catboost model was used to predict the full periodic table data, which in turn was used to screen out the elemental species that are easy to move towards the graphene/aluminium composite interface. Those alloying elements are beneficial for modifying the defective graphene in the composite by comparing the results of elemental diffusive migration in the aluminium matrix as well as at different graphene/aluminium interfaces. The results of machine learning accelerated first principles calculations can provide a theoretical basis for further development of new aluminium alloy composite.

A Koopman operator-based prediction algorithm and its application to COVID-19 pandemic and influenza cases

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.

A temporal approach to online discussion during disasters: Applying SIR infectious disease model to predict topic growth and examining effects of temporal distance

Discussions on social media during major disasters are robust and often have multiple frames of reference. Temporal perspectives, however, are still lacking in current understandings of social-mediated discussions during disasters and crises, but incorporating temporal perspectives can significantly enhance environmental scanning efforts as prescribed in the issues management framework. The purpose of the current research is twofold: to apply and validate the SIR (Susceptible-Infectious-Recovered) model to examine topics’ growth over time on social media and to understand how future orientation of social media users (an indicator of temporal distance) affects their construal of a disaster through supervised machine learning. We based our analysis on Twitter discussions during the Texas winter storm in 2021. Results of the study show great fit of the SIR model for topic growth, and that temporal distance affects users’ construal of the event in line with core predictions of construal level theory. Theoretical, methodological, and practical implications on social-mediated discussions related to climate change-induced and -intensified disasters and issues management are discussed.

Analyzing and identifying predictable time range for stress prediction based on chaos theory and deep learning

ProposeStress is a common problem globally. Prediction of stress in advance could help people take effective measures to manage stress before bad consequences occur. Considering the chaotic features of human psychological states, in this study, we integrate deep learning and chaos theory to address the stress prediction problem.MethodsBased on chaos theory, we embed one’s seemingly disordered stress sequence into a high dimensional phase space so as to reveal the underlying dynamics and patterns of the stress system, and meanwhile are able to identify the stress predictable time range. We then conduct deep learning with a two-layer (dimension and temporal) attention mechanism to simulate the nonlinear state of the embedded stress sequence for stress prediction.ResultsWe validate the effectiveness of the proposed method on the public available Tesserae dataset. The experimental results show that the proposed method outperforms the pure deep learning method and Chaos method in both 2-label and 3-label stress prediction.ConclusionIntegrating deep learning and chaos theory for stress prediction is effective, and can improve the prediction accuracy over 2% and 8% more than those of the deep learning and the Chaos method respectively. Implications and further possible improvements are also discussed at the end of the paper.

Production of two, three, and four Higgs bosons: where SMEFT and HEFT depart

In this article we study the phenomenological implications of multiple Higgs boson production from longitudinal vector boson scattering in the context of effective field theories. We find compact representations for effective tree-level amplitudes with up to four final state Higgs bosons. Total cross sections are then computed for scenarios relevant at the LHC in which we find the general Higgs Effective Theory (HEFT) prediction avoids the heavy suppression observed in Standard Model Effective Field Theory (SMEFT).

Near-horizon geometries and black hole thermodynamics in higher-derivative AdS5 supergravity

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.

Predicting performance of elite kickboxers using the multi‐states theory framework

AbstractUsing the multi‐states (MuSt) theory framework, this study examined the interplay between self‐confidence, emotional arousal control, worry, concentration disruption, challenge and threat appraisals, psychobiosocial experiences, and self‐evaluated performance of medalist kickboxers involved in the WAKO World Kickboxing Championship 2021. Participants were 103 gold, silver, or bronze medalists (58 women and 45 men), aged 18–39 (M = 25.16 ± 4.54 years), who were contacted via email and social media and asked to fill an online survey 3 months after the event. According to the MuSt theory predictions, self‐confidence and emotional arousal control were positively related to challenge appraisal, functional psychobiosocial experiences, and self‐evaluated performance. Worry and concentration disruption were positively associated with threat appraisal, and negatively related to functional psychobiosocial experiences; concentration disruption was also negatively related to self‐evaluated performance. Results from path analysis revealed a positive indirect link from self‐confidence to self‐evaluated performance via challenge appraisal and psychobiosocial experiences. Negative indirect links from worry and concentration disruption to self‐evaluated performance through threat appraisal and psychobiosocial experiences were significant. A positive indirect effect from emotional arousal control to self‐evaluated performance via psychobiosocial experiences was also shown. The findings are discussed in light of the MuSt theory.

Forecasting for Police Officer Safety: A Demonstration of Concept

PurposePolice officers in the USA are often put in harm’s way when responding to calls for service. This paper provides a demonstration of concept for how machine learning procedures combined with conformal prediction inference can be properly used to forecast the amount of risk associated with each dispatch. Accurate forecasts of risk can help improve officer safety.MethodsThe unit of analysis is each of 1928 911 calls involving weapons offenses. Using data from the calls and other information, we develop a machine learning algorithm to forecast the risk that responding officers will face. Uncertainty in those forecasts is captured by nested conformal prediction sets.ResultsFor approximately a quarter of a holdout sample of 100 calls, a forecast of high risk was correct with the odds of at least 3 to 1. For approximately another quarter of the holdout sample, a forecast of low risk was correct with an odds of at least 3 to 1. For remaining cases, insufficiently reliable forecasts were identified. A result of “can’t tell” is an appropriate assessment when the data are deficient.ConclusionsCompared to current practice at the study site, we are able to forecast with a useful level of accuracy the risk for police officers responding to calls for service. With better data, such forecasts could be substantially improved. We provide examples.

Sustainable cycling and mechanism of Co(II)/Co(IV)/Co(III) with S(IV) highly triggered by Co(IV) in E/Co(II)/S(IV) for enhanced removal of reactive brilliant red X-3B

The massive discharge and inadequate treatment of dye wastewater have led to the extensive accumulation of azo dyes in natural water bodies, which is a severe threat to human health. In this study, a novel homogeneous electrochemical system, denoted E/Co(II)/S(IV), was developed by the incorporation of an electric field and cobalt/sulfite. Notably, the decontamination efficiency of the E/Co(II)/S(IV) system for reactive brilliant red X-3B (X-3B) was 95.14 % within 10 min, which was accomplished by utilizing only a trace amount of Co(II). The entire process exhibited an exceptionally low energy consumption of only 0.331 kWh/m3. Single-factor experiments were conducted to explore the influence of various operational parameters and water matrix components on the system decontamination efficiency. By combining the chelator EDTA and chemical probe PMSO, the presence of Co(III) and Co(IV) was detected. The results showed that Co(IV) mainly triggered the chain reaction. The activation products of S(IV), including SO3•−, SO4•−, SO5•− and HO∙, played a dominant role in the degradation of X-3B, with SO4•− having the most substantial contribution. Under anodic oxidation, Co(II) underwent double-electron transfer, leading to the conversion of Co(II) into Co(IV). Subsequently, Co(IV) activated S(IV) via a single-electron transfer mechanism, initiating a free radical chain reaction. During the reaction, Co(IV) transformed into Co(III) and Co(II), thus achieving the Co(II)/Co(IV)/Co(III) cycle. The generation of oxygen as a result of the electrochemical process offers a viable solution for addressing the issue of hypoxia stemming from the rapid activation of S(IV). The active sites in the X-3B molecule were elucidated through density functional theory predictions, and three degradation pathways for X-3B were identified via mass spectrometry analysis. The toxicological characteristics of both X-3B and its degradation products were comprehensively assessed using toxicity estimation software tool and algal culture experiments. This research offers valuable insights into the refinement of cobalt/sulfite systems and introduces innovative concepts for electrochemical applications, which have broad-reaching implications for dye wastewater purification.

Robust Constrained Model Predictive Voltage Control Scheme and its Classification

In this study, a prospective power system is suggested (PPS), predictive current control (PCC), enhanced voltage control (EVC), model predictive control (MPC), induction motor (IM) circuit. Instead of monitoring the torque and speed as in conventional MP DTC, the stator voltage is directly regulated in the suggested PVC model thanks to the predictive control theory. A voltage source inverter's use of a model predictive control approach. For all potential voltage vectors produced by the inverter, this method forecasting value of the load using a discrete model of the system. "Voltage-source converter based high voltage direct current (VSC-HVDC)" connected offshore wind farms using a "model predictive control (MPC)" based Enhanced Voltage Control Strategy (EVCS) (OWFs). All wind farm generator (WTGs) and wind farm side VSCs are effectively synchronized in the proposed Midi controller EVCS to maintain voltages within a practical range and reduce system power loss. Review of induction motor defects, including mechanical and insulating issues. The summary of static current signature of machine failures. Numerous useful feature extraction techniques have been introduced for induction motor problem detection.

Gravitational p → ∆+ transition form factors in chiral perturbation theory

The gravitational form factors of the transition from the proton to the ∆+ resonance are calculated to leading one-loop order using a manifestly Lorentz-invariant formulation of chiral perturbation theory. We take into account the leading electromagnetic and strong isospin-violating effects. The loop contributions to the transition form factors are found to be free of power-counting violating pieces, which is consistent with the absence of tree-level diagrams at the considered order. In this sense, our results can be regarded as predictions of chiral perturbation theory.

A molecular dynamics study on the boundary between homogeneous and heterogeneous nucleation.

The large discrepancy among the nucleation kinetics extracted from experimental measurements and computer simulations and the prediction of the classical nucleation theory (CNT) has stimulated intense arguments about its origin in the past decades, which is crucially relevant to the validity of the CNT. In this paper, we investigate the atomistic mechanism of the nucleation in liquid Al in contact with amorphous substrates with atomic-level smooth/rough surfaces, using molecular dynamics (MD) simulations. This study reveals that the slightly distorted local fcc/hcp structures in amorphous substrates with smooth surfaces can promote heterogeneous nucleation through a structural templating mechanism, and on the other hand, homogeneous nucleation will occur at a larger undercooling through a fluctuation mechanism if the surface is rough. Thus, some impurities, previously thought to be impotent, could be activated in the homogeneous nucleation experiments. We further find that the initial growth of the nucleus on smooth surfaces of amorphous substrates is one order of magnitude faster than that in homogeneous nucleation. Both these factors could significantly contribute to the discrepancy in the nucleation kinetics. This study is also supported by a recent study of the synthesis of high-entropy alloy nanoparticles assisted with the liquid metal Ga [Cao et al., Nature 619, 73 (2023)]. In this study, we established that the boundary existed between homogeneous and heterogeneous nucleation, i.e., the structural templating is a general mechanism for heterogeneous nucleation, and in its absence, homogeneous nucleation will occur through the fluctuation mechanism. This study provides an in-depth understanding of the nucleation theory and experiments.

Intelligent Electrocardiogram Acquisition Via Ubiquitous Photoplethysmography Monitoring.

Recent advances in machine learning, particularly deep neural network architectures, have shown substantial promise in classifying and predicting cardiac abnormalities from electrocardiogram (ECG) data. Such data are rich in information content, typically in morphology and timing, due to the close correlation between cardiac function and the ECG. However, the ECG is usually not measured ubiquitously in a passive manner from consumer devices, and generally requires 'active' sampling whereby the user prompts a device to take an ECG measurement. Conversely, photoplethysmography (PPG) data are typically measured passively by consumer devices, and therefore available for long-period monitoring and suitable in duration for identifying transient cardiac events. However, classifying or predicting cardiac abnormalities from the PPG is very difficult, because it is a peripherally-measured signal. Hence, the use of the PPG for predictive inference is often limited to deriving physiological parameters (heart rate, breathing rate, etc.) or for obvious abnormalities in cardiac timing, such as atrial fibrillation/flutter ("palpitations"). This work aims to combine the best of both worlds: using continuously-monitored, near-ubiquitous PPG to identify periods of sufficient abnormality in the PPG such that prompting the user to take an ECG would be informative of cardiac risk. We propose a dual-convolutional-attention network (DCA-Net) to achieve this ECG-based PPG classification. With DCA-Net, we prove the plausibility of this concept on MIMIC Waveform Database with high performance level (AUROC 0.9 and AUPRC 0.7) and receive satisfactory result when testing the model on an independent dataset (AUROC 0.7 and AUPRC 0.6) which it is not perfectly-matched to the MIMIC dataset.

Paying Managers of Complex Portfolios: Evidence on Compensation and Performance from Endowments

Abstract We examine compensation for endowment Chief Investment Officers (CIOs) overseeing portfolios with significant allocations to alternatives. We find widespread use of bonuses and that large endowments with high alternative allocations hire CIOs with stronger backgrounds, pay them more, and have higher pay-for-performance sensitivity. We find weak evidence of a relationship between compensation and future performance. Our results align with contract theory predictions but differ from empirical findings on pension funds. Endowments pay CIOs more, rely more on bonuses, attract more experienced professionals, and have lower turnover than pensions. This suggests more effective talent management compared to politically influenced public pensions.

Closed and open superconducting microwave waveguide networks as a model for quantum graphs.

We report on high-precision measurements that were performed with superconducting waveguide networks with the geometry of a tetrahedral and a honeycomb graph. They consist of junctions of valency three that connect straight rectangular waveguides of equal width but incommensurable lengths. The experiments were performed in the frequency range of a single transversal mode, where the associated Helmholtz equationis effectively one-dimensional and waveguide networks may serve as models of quantum graphs with the joints and waveguides corresponding to the vertices and bonds. The tetrahedral network comprises T junctions, while the honeycomb network exclusively consists of Y junctions, that join waveguides with relative angles 90^{∘} and 120^{∘}, respectively. We demonstrate that the vertex scattering matrix, which describes the propagation of the modes through the junctions, strongly depends on frequency and is nonsymmetric at a T junction and thus differs from that of a quantum graph with Neumann boundary conditions at the vertices. On the other hand, at a Y junction, similarity can be achieved in a certain frequency range. We investigate the spectral properties of closed waveguide networks and fluctuation properties of the scattering matrix of open ones and find good agreement with random matrix theory predictions for the honeycomb waveguide graph.

Recolonization of secondary forests by a locally extinct Caribbean anole through the lens of range expansion theory.

Disturbance and recovery dynamics are characteristic features of many ecosystems. Disturbance dynamics are widely studied in ecology and conservation biology. Still, we know less about the ecological processes that drive ecosystem recovery. The ecological processes that mediate ecosystem recovery stand at the intersection of many theoretical frameworks. Range expansion theory is one of these complementary frameworks that can provide unique insights into the population-level processes that mediate ecosystem recovery, particularly fauna recolonization. Although the biodiversity patterns that follow the fauna recolonization of recovering forests have been well described in the literature, the ecological processes at the population level that drive these patterns remain conspicuously unknown. In this study, we tested three fundamental predictions of range expansion theory during the recolonization of recovering forests in Puerto Rico by a shade specialist anole, Anolis gundlachi. Range expansion theory predicts that individuals at the early stages of recolonization (i.e., younger forests) would have a high prevalence of dispersive traits, experience less density dependence, and suffer less parasitism. To test these predictions, we conducted a chronosequence study applying space-for-time substitution where we compared phenotypic traits (i.e., body size, body condition, and relative limb size), population density, population growth rates, and Plasmodium parasitism rates among lizard populations living in young (<30 years), mid (~40-70 years), and old-growth forests (>75 years). Lizard populations in younger forests had lower densities, higher population growth rates, and lower rates of Plasmodium parasitism compared with old-growth forests. Still, while we found that individuals had larger body sizes, and longer forelimbs in young forests in one site, this result was not consistent among sites. This suggests a potential trade-off between the traits that provide a dispersal advantage during the initial stages of recolonization and those that are advantageous to establish in novel environmental conditions. Overall, our study emphasizes the suitability of range expansion theory to describe fauna recolonization but also highlights that the ecological processes that drive recolonization are time-dependent, complex, and nuanced.

A mechanism for deviance detection and contextual routing in the thalamus: a review and theoretical proposal.

Predictive processing theories conceptualize neocortical feedback as conveying expectations and contextual attention signals derived from internal cortical models, playing an essential role in the perception and interpretation of sensory information. However, few predictive processing frameworks outline concrete mechanistic roles for the corticothalamic (CT) feedback from layer 6 (L6), despite the fact that the number of CT axons is an order of magnitude greater than that of feedforward thalamocortical (TC) axons. Here we review the functional architecture of CT circuits and propose a mechanism through which L6 could regulate thalamic firing modes (burst, tonic) to detect unexpected inputs. Using simulations in a model of a TC cell, we show how the CT feedback could support prediction-based input discrimination in TC cells by promoting burst firing. This type of CT control can enable the thalamic circuit to implement spatial and context selective attention mechanisms. The proposed mechanism generates specific experimentally testable hypotheses. We suggest that the L6 CT feedback allows the thalamus to detect deviance from predictions of internal cortical models, thereby supporting contextual attention and routing operations, a far more powerful role than traditionally assumed.

Theory of nonlocal modal hydrodynamic functions for beam and plate vibrations in viscous fluids

In this paper, we introduce a new nonlocal modal hydrodynamic theory for fluid–structure interactions (FSI) of light, flexible cantilever beams and plates undergoing small amplitude vibrations in Newtonian, incompressible, viscous, heavy fluids otherwise at rest. For low aspect ratio flexible structures and high mode numbers, three dimensional (3D) and nonlocal fluid effects become prominent drivers of the coupled dynamics, to the point that existing local hydrodynamic theories based on two dimensional (2D) fluid approximations become inadequate to predict the system response. On the other hand, our approach is based on a rigorous, yet efficient, 3D treatment of the hydrodynamic loading on cantilevered thin structures. The off-line solution of the FSI problem results in the so-called nonlocal modal hydrodynamic function matrix, that is, the representation of the nonlocal hydrodynamic load operator on a basis formed by the structural modes. Our theory then integrates the nonlocal hydrodynamics within a fully coupled structural modal model in the frequency domain. We compare and discuss our theory predictions in terms of frequency response functions, mode shapes, hydrodynamic loads, quality factors, added mass ratios with the predictions of the classical local approaches, for different actuation scenarios, identifying the limitations of the hypotheses underlying existing treatments. Importantly, we also validate our new model with experiments conducted on flexible square plates. While computationally efficient, our fully coupled theory is exact up to numerical truncation and can bridge knowledge gaps in the design and analysis of FSI systems based on low aspect ratio flexible beams and plates.

Investigation on Disaster Mechanism of Diversion Tunnel Induced by Gripper TBM in Hydrokarst Erosion Stratum and Engineering Measures

In gripper tunnel boring machine (TBM) tunneling through complex geological formations, the safe and efficient recovery from large-scale collapses remains a formidable challenge. In this study, we investigate the causes of a 1246 m3 collapse that occurred during the gripper TBM tunneling in the diversion tunnel in Xinjiang, China. Various techniques including TSP seismic waves, CFC advanced water exploration, laboratory point load tests and packer permeability tests were employed for thorough research. The examination discloses that the water softening in biotite-quartz schist in fractured zones contributes significantly to the loosening and dislocation of rock layers along joints. The gripper TBM’s cutterhead exacerbates this process through cutting action and vibrations, causing large-scale instability and eventual rock mass collapse. To tackle this engineering problem, we propose a three-step treatment scheme comprising “Reinforcement-Backfill-Re-excavation”. Furthermore, we propose a technique to handle TBM collapses by creating a “protective shell” within the cavity. The safety and feasibility of these proposed solutions were thoroughly validated through numerical simulations. Also, we utilized the Hoek-Brown theory and Rostami prediction formula to establish recommended values for the total thrust and total torque of the TBM during the collapsed section. The proposed treatment scheme and estimated parameters were successfully applied, resulting in a comprehensive solution from collapse handling to tunneling. This study offers valuable details on effectively managing large-scale collapses in gripper TBM tunneling, which can be useful for similar tunnel engineering and improve safety and efficiency.