Comprehensive Theory Research Articles

Analysis of drilling response under ultra-high-speed diamond drilling: Theory and experiment

The ultra-high-speed diamond drilling (UHSDD) technology represents a groundbreaking and effective approach to deep hard rock drilling. This innovative method addresses the critical need for reduced weight on bit (WOB) while simultaneously achieving remarkable enhancements in drilling velocities. However, the absence of a comprehensive fundamental theory poses challenges, such as drill wear and inconsistent rock crushing efficiency. In this paper, the experimental data obtained from UHSDD was processed and analyzed by leveraging the drilling response model under standard rotational speeds (D-D model). The interface laws of diamond bit based on rate-relatedness were proposed. Notably, the depth of cut per revolution d of the bit is influenced by a complex interplay of rotational speed and weight on bit variables. A new variable Γ was introduced to measure the force required per revolution for d at ultra-high speeds. Within the proposed model framework, the quantitative information from drilling data was extracted, encompassing the wear state of the bit, drilling parameters, and rock properties. To validate the accuracy of the new model, a series of extensive laboratory experiments on the UHSDD test experimental platform was conducted. The reliability of the model was preliminary verified. The results of this study enhanced our understanding of the drilling response of UHSDD in practical drilling applications.

Local Electric Field Accelerates Zn2+ Diffusion Kinetics for Zn‐V Battery

AbstractVanadium‐based aqueous zinc‐ion batteries (AZIBs) exhibit significant potential for large‐scale energy storage applications, attributed to their inherent safety characteristics. Addressing the slow transport kinetics of divalent Zn2+ within the cathode lattice, thereby enhancing the rate capability and stability, is essential for the Zn‐V battery system. In this study, a local electric field (LEF) strategy is introduced to accelerate the Zn2+ diffusion by creating abundant oxygen vacancies (Ov) in V2O5. Comprehensive characterization and density functional theory (DFT) calculations reveal the formation of the Ov induced atomic‐level donor‐acceptor couple configuration, verify and visualize the LEF. The fabricated LEF‐enhanced vanadium oxide (LEF‐VO) exhibits exceptional rate capability, achieving 338.3 mA h g−1 at a current density of 10 A g−1, and maintaining 66.4% of its capacity over a range from 0.2 to 20 A g−1. Furthermore, the influence of the LEF on expediting Zn2+ diffusion kinetics is elucidated, correlating to the electrical force. This novel LEF approach offers valuable insights for advancing high‐rate cathode materials.

Phosphorus-Water Interaction Drives Active Center Evolution into the Water-Adaptive Structure in the High-Humidity NH3-SCR Reaction.

The impact of water on catalyst activity remains inconclusive due to its dependence on the specific reaction environment. To maximize the exploitation of water's promoting effect, we employed ammonia selective catalytic reduction (NH3-SCR) as a probe reaction and proposed a phosphorus modification strategy for Cu-ZSM-5 catalysts. The objective of this approach was to construct water-adaptive microstructures through directional arrangement. To investigate the effect of phosphorus on the transformation of framework copper sites in humid environments, we conducted comprehensive characterizations and density functional theory calculation. Results reveal that water molecules cleave the oxygen bridges between phosphorus oxide and copper, leading to the formation of active isolated [Cu(OH)]+ groups and phosphate. The phosphate species weaken the interaction between exchanged Cu2+ groups and the zeolite framework, leading to the generation of highly migratory hydrated Cu2+ species. This work will potentially guide the rational design of water-adaptive catalysts for gas pollution abatement in a humid environment.

Numerical parametrization of stationary axisymmetric black holes in a theory agnostic framework

The pursuit of a comprehensive theory of gravity has led to the exploration of various alternative models, necessitating a model-independent framework. The Konoplya-Rezzolla-Zhidenko (KRZ) parametrization offers a robust method for approximating stationary axisymmetric black hole spacetimes, characterized by a rapidly converging continued-fraction expansion. However, while analytical metrics benefit from this approach, numerical metrics derived from complex gravitational theories remain presenting computational challenges. Bridging this gap, we propose a method for a numerical KRZ parametrization, tested and demonstrated on pseudonumerical Kerr and Kerr-Sen spacetimes. Our approach involves constructing numerical grids to represent metric coefficients and using the grids for fitting the parameters up to an arbitrary order. We analyze the accuracy of our method across different orders of approximation, considering deviations in the metric functions and shadow images. In both Kerr and Kerr-Sen cases, we observe rapid convergence of errors with increasing orders of continued fractions, albeit with variations influenced by spin and charge. Our results underscore the potential of the proposed algorithm for parametrizing numerical metrics, offering a pathway for further investigations across diverse gravity theories. Published by the American Physical Society 2024

Unlocking narratives: Longitudinal associations between theory of mind and reading comprehension.

This study explores the longitudinal association between Theory of Mind (ToM) and reading comprehension (RC) in middle childhood, focusing on three advanced ToM (AToM) components: social reasoning, reasoning about ambiguity and recognition of social norm transgressions. Over the course of a year, 112 nine-year-olds (61 girls, 51 boys; Mage = 9; 0 years, ±4 months at wave 1) were followed from Grade 3 to Grade 4 and assessed for AToM predictors of Grade-4 RC. Findings show that only social reasoning predicts RC, independent of general intelligence and prior RC performance. In turn, RC did not predict any AToM component. These findings contribute to understanding cognitive development in educational contexts, emphasizing the significance of AToM, particularly social reasoning, in RC.

DFT Study on Retigerane-Type Sesterterpenoid Biosynthesis: Initial Conformation of GFPP Regulates Biosynthetic Pathway, Ring-Construction Order and Stereochemistry.

Retigerane-type sesterterpenoids, which feature a unique 5/6/5/5/5 fused pentacyclic structure with an angular-type triquinane moiety, are biosynthesized via successive carbocation-mediated reactions triggered by terpene cyclases. However, the precise biosynthetic pathways/mechanisms, wherein steric inversion of the carbon skeleton occurs at least once, remain elusive. Two plausible reaction pathways have been proposed, which differ in the order of ring cyclization: A → B/C → D/E-ring(s) (Route 1) and A → E → B → C/D-ring(s) (Route 2). Since the reaction intermediates of these complicated domino-type reaction sequences are experimentally inaccessible, we employed comprehensive density functional theory (DFT) calculations to evaluate these routes. The results indicate that retigeranin biosynthesis proceeds via Route 2 involving a multistep carbocation cascade, in which the order of ring cyclization (A → E → B → C/D) is the key to constructing the angular 5/5/5 triquinane structure with the correct stereochemistry at C3. The result also suggests that slight differences in the initial conformation have a significant effect on the order of cyclization and steric inversion. The computed pathway/mechanism also provides a rational basis for the formation of various related terpenes/terpenoids.

Efficient analysis of deep neural networks for vision via biologically-inspired receptive field angles: An in-depth survey

Efficient feature extraction is a pivotal requirement for Deep Neural Network (DNN) models, particularly in the realm of visual tasks where effective feature extraction relies on well-designed receptive fields. Nevertheless, the evaluation and fulfillment of the criteria for “effective receptive fields”, along with their essential conditions, continue to pose numerous unresolved mysteries. In this Analysis we amalgamated principles from the field of biologically inspired vision to reevaluate the role of receptive fields within neural networks, and have proposed a comprehensive design theory aimed at characterizing and guiding the research of efficient DNNs. Our design theory draws from both the principles of biological vision and the latest advances in DNN design theory. It encompasses five fundamental axes of receptive field design research: the Global Artificial Neuron Workspace (GANW), Layered Processing Theory (LPT), Multi-Receptive Field Integration Design (M-RFID), Heterogeneous Receptive Field (HRF), and Receptive Field Plasticity Theory (RFPT). We have applied these design theories to validate traditional and cutting-edge neural networks, conducting in-depth analyses of the outcomes to provide a summary of the current landscape of research on visual receptive fields. Furthermore, we have classified current visual receptive fields into three categories: sample-specific local receptive fields, global spatial receptive fields, and adaptive dynamic receptive fields. From the perspective of perceptual fields and network lightweight design, we have introduced some classic and state-of-the-art DNN models, with a particular emphasis on those employing innovative algorithms that have yielded the most recent achievements. We have concluded by highlighting the key areas of interest in the field of visual receptive fields for future research endeavors.

Theory and mitigation of motional eddy current in high-field eddy current shielding.

Eddy current shielding by a Faraday cage is an effective way to shield alternating-current magnetic fields in scientific instrumentation. In a strong static magnetic field, however, the eddy current in the conductive shield is subject to the Lorentz force, which causes the shield to vibrate. In addition to mechanical issues (e.g., acoustic noise), such vibration induces motional eddy current in the shield that can dominate the original, electromagnetic eddy current to undermine the conductor's shielding capability. In this work, we investigate a method to control motional eddy current by making cut-out patterns in the conductor that follow the electromagnetic eddy current image. This effectively limits the surface current of the plate to a single mode and prevents the proliferation of uncontrolled motion-induced surface currents that disrupts eddy current shielding. After developing a comprehensive theory of magneto-mechanical interaction in a conductive plate, the proposed method was tested on a flat-geometry testbed experiment inside a 3 T magnetic resonance imaging (MRI) magnet. It was found that the magnetic field generated by the motional eddy current was much more localized in space and frequency for a patterned-copper shield compared to a solid copper. The magnetic field of the patterned shield could be accurately predicted from the impedance measurement in the magnet. Implications of our results for improved shielding of gradient fields in high-field MRI are discussed.

Unlocking the potential of lattice oxygen evolution in stainless steel to achieve efficient OER catalytic performance

Excellent OER catalysts can enhance the efficiency of hydrogen production from electrolytic water. The development of low-cost, abundant, and highly efficient catalysts remains crucial for the electrolytic hydrogen production industry. In this work, we have developed a highly efficient and cost-effective stainless steel-based OER catalyst using a strategy combining anodic corrosion and structural reconstruction. This approach is not only simple and easy to scale up production, but more importantly, it can also achieve controllable switching of lattice oxygen evolution mechanism (LOM). Specifically, in a 1 M KOH electrolyte, this catalyst achieves current densities of 500 mA/cm² and 1000 mA/cm² with remarkably low overpotentials of only 347 mV and 382 mV, respectively. Moreover, the catalyst exhibits exceptional stability even under high industrial current densities. In-situ differential electrochemical mass spectrometry (DEMS) experiments provide direct evidence of the transition from the adsorbate evolution mechanism (AEM) to the LOM. Through comprehensive characterizations and density functional theory (DFT) calculations, we have elucidated the underlying mechanism responsible for the improved performance and identified key factors influencing the induction of the LOM. This work provides support and new insights for the design and preparation of high-performance steel-based electrolytic water catalysts.

Modelling of Static and Dynamic Elastomer Friction in Dry Conditions

Understanding the tribological behavior of elastomers in dry conditions is essential for sealing applications, as dry contact may occur even in lubricated conditions due to local dewetting. In recent decades, Persson and co-authors have developed a comprehensive theory for rubber contact mechanics and dry friction. In this work, their model is implemented and extended, particularly by including static friction based on the bond population model by Juvekar and coworkers. Validation experiments are performed using a tribometer over a wide range of materials, temperatures and speeds. It is shown that the friction model presented in this work can predict the static and dynamic dry friction of various commercial rubber materials with different base polymers (FKM, EPDM and NBR) with an average accuracy of 10%. The model is then used to study the relevance of different elastomer friction contributions under various operating conditions and for different roughness of the counter surface. The present model will help in the development of novel optimized sealing solutions and provide a foundation for future modeling of lubricated elastomer friction.

Unraveling the Solvent Regulation in the Heteroatom-Doped Endohedral Gold Clusters: A Theoretical Study on the Electronic Properties and O2 Activation.

Liquid-phase synthesis of atomically precise nanoclusters has experienced rapid development recently, where polar solvents are indispensable in such a process. However, the regulation effect of solvents on the structural and electronic properties of different metal clusters and cluster assembly materials is still not well understood. Herein, a comprehensive density functional theory calculation has been performed to explore the solvation effect on heteroatom-doped endohedral gold clusters that always have remarkable stabilities and tunable electronic structures. The solvation free energy of the M@Au12 clusters (M = Cr, Mo, W, Co, Rh, Ir, Cu, Ag, and Au) was found to be related to the charge distribution of the central doped-atom M and the outer Au12 cage. Moreover, the aqueous solvent was observed to be able to increase the adsorption capacity of M@Au12 to O2 following the activation of O2 through the charge transfer from M@Au12 to O2, in which the transferred electrons occupy the π antibonding orbital of O2. In addition, the water solvent can also improve the hydrogenation reaction of O2 to form OOH over M@Au12, where the activation energy barrier for this process is very low with the participation of the solvent. Considering the importance of solvents in the liquid-phase synthesis of atomically precise clusters, these findings highlighted here could provide valuable theoretical guidance in potential applications of functional gold nanoclusters, especially in the liquid-phase cluster catalysis.

Parallel MCMC algorithms: theoretical foundations, algorithm design, case studies

Abstract Parallel Markov Chain Monte Carlo (pMCMC) algorithms generate clouds of proposals at each step to efficiently resolve a target probability distribution $\mu $. We build a rigorous foundational framework for pMCMC algorithms that situates these methods within a unified ‘extended phase space’ measure-theoretic formalism. Drawing on our recent work that provides a comprehensive theory for reversible single-proposal methods, we herein derive general criteria for multiproposal acceptance mechanisms that yield ergodic chains on general state spaces. Our formulation encompasses a variety of methodologies, including proposal cloud resampling and Hamiltonian methods, while providing a basis for the derivation of novel algorithms. In particular, we obtain a top-down picture for a class of methods arising from ‘conditionally independent’ proposal structures. As an immediate application of this formalism, we identify several new algorithms including a multiproposal version of the popular preconditioned Crank–Nicolson (pCN) sampler suitable for high- and infinite-dimensional target measures that are absolutely continuous with respect to a Gaussian base measure. To supplement the aforementioned theoretical results, we carry out a selection of numerical case studies that evaluate the efficacy of these novel algorithms. First, noting that the true potential of pMCMC algorithms arises from their natural parallelizability and the ease with which they map to modern high-performance computing architectures, we provide a limited parallelization study using TensorFlow and a graphics processing unit to scale pMCMC algorithms that leverage as many as 100k proposals at each step. Second, we use our multiproposal pCN algorithm (mpCN) to resolve a selection of problems in Bayesian statistical inversion for partial differential equations motivated by fluid measurement. These examples provide preliminary evidence of the efficacy of mpCN for high-dimensional target distributions featuring complex geometries and multimodal structures.

Efficiency and Mechanism of Catalytic Siloxane Exchange in Vitrimer Polymers: Modeling and Density Functional Theory Investigations.

Of late, siloxane-containing vitrimers have gained significant interest due to their fast dynamic characteristics over a reasonable temperature range (180-220 °C), making them well-suited for diverse applications. The exchange reaction pathway in the siloxane vitrimers is accountable for the covalent adaptive network, with the reaction's effectiveness being regulated by either organic or organometallic catalysts. However, directly studying the exchange reaction pathway in the bulk phase using experimental approaches is challenging because of the intricate and interconnected structure of these vitrimers. Here, we perform comprehensive density functional theory (DFT) and experimental investigations to discover the detailed catalytic efficacy of siloxane exchange and provide direction for the reaction process using a 1,5,7-triazabicyclo[4.4.0]dec-5-ene (TBD) catalyst. The calculated transition barrier energy and catalytic efficiency of hexamethyldisiloxane and dihydroxy-dimethylsilane exchange derived from the nudged elastic band with transition-state calculations strongly agree with the experimental findings. In addition, Fukui indices, along with partial charges, are employed to evaluate the nucleophilic and electrophilic behaviors of silanol and siloxane molecules. Our analysis revealed that by utilizing the Fukui indices of both the acid and the base, we can make an approximate estimation of the respective kinetics of the SN2 process in the siloxane exchange reaction mechanism. These findings establish a foundation for comprehending a crucial aspect of the exchange mechanism in siloxane vitrimer systems and could aid in the development of novel catalysts.

Differences in perceptual representations in multilinguals' first, second, and third language.

Two experiments were conducted to investigate the differences in perceptual representations among multilingual individuals. In Experiment 1, the immediate sentence-picture verification paradigm was used to investigate perceptual representations in the working memory stage. The results suggest a match effect within the first language (Cantonese), but not within the second language (Mandarin) or the third language (English), showing perceptual representations only in first language comprehension. In Experiment 2, the delayed sentence-picture verification paradigm was used to investigate perceptual representations in long-term memory. Similarly, the results suggest a match effect within the first language (Mandarin), but not within the second language (English). The findings of both experiments suggest that the first language was perceptually represented, regardless of whether it was Cantonese or Mandarin, regardless of the processing in working memory or long-term memory. No evidence was found for perceptual representations in the later-learned languages, regardless of high or low proficiency. Our study has implications for theories of language comprehension and embodied cognition.

A Comprehensive Density Functional Theory Analysis on Structural, Electronic, and Optical Properties of BiOF

Based on density functional theory (DFT), the electronic, structural, and optical properties of bismuth oxyfluoride (BiOF) are investigated by using various exchange–correlation functionals. The local density approximation (LDA), Perdew-Burke-Ernzerhof generalized gradient approximation (PBE-GGA), Perdew-Burke-Ernzerhof generalized gradient approximation for solids (PBEsol), and Wu-Cohen (WC) functionals are implemented in Wien2k software. Modified Becke-Johnson (mBJ) and unmodified Becke-Johnson (umBJ) potentials are also applied to obtain an enhanced band gap. Spin–orbit coupling effect (SOC) is also taken into consideration due to the heavy Bi atom. When employing the PBE+umBJ potential, BiOF exhibits the most enhanced band gap energy, with a direct band gap energy of 3.72 eV. Moreover, optical properties such as dielectric functions, absorption coefficients, and optical conductivities are also calculated for photon energies up to ̴̴ 14 eV. In comparison to other available theoretical and experimental findings, the calculated results are in good agreement.

Measuring Active Ageing: A Scoping Review and the Applicability to the Situation in China.

Ageing has become one of the major global public issues and active ageing has become a global goal. Accurate and reproducible assessment tools are a prerequisite for robust and reliable measurement of active ageing and policy formulation. However, a broad scoping review describing the characteristics and heterogeneity of assessment tools for active ageing is lacking. This study aims to comprehensively portray current active ageing assessment tools and their features. We conducted a scoping review, focusing on the Active Ageing Assessment Tool, and searched seven databases: CNKI, WanFang, PubMed, Embase, Web of Science Core Collection, Medline, and Proquest. The research process adhered to the methodological framework of Arkey and O'Malley and the PRISMA-ScR specification. More so, we registered the research program with the Open Science Framework. Ultimately, we included twenty-two pieces of literature. The development of the active ageing assessment tool predominantly occurred between 2012 and 2023, with a focus on foreign countries (16 studies). All included literature presented multidimensional Active ageing assessment tools. Eighteen studies examined active ageing assessment tools at the macro level, while four studies focused on the individual level. Also, fourteen out of the twenty-two studies were based on the World Health Organization's Theoretical Framework for Active Ageing. The literature contained only two active ageing assessment tools designed for specific subgroups of older people. Future development of active ageing assessment tools should integrate more comprehensive concepts and social theories of active ageing. Additionally, there is a need to explore active ageing measurement tools tailored for diverse subgroups of the older adults at various levels.

The application of comprehensive incentive model theory in debridement, plastic surgery and suturing of preschool children.

The application of comprehensive incentive model theory in debridement, plastic surgery and suturing of preschool children.

Exploring the multi-dimensional human mind: Model-based and text-based approaches

In this study, we conceptualize two approaches, model-based and text-based, grounded on mental models and discourse comprehension theories, to computerized summary analysis. We juxtapose the model-based approach with the text-based approach to explore shared knowledge dimensions and associated measures from both approaches and use them to examine changes in students' summaries over time. We used 108 cases in which we computed model-based and text-based measures for two versions of students' summaries (i.e., initial and final revisions), resulting in a total of 216 observations. We used correlations, Principal Components Analysis (PCA), and Linear Mixed-Effects models. This exploratory investigation suggested a shortlist of text-based measures, and the findings of the PCA demonstrated that both model-based and text-based measures explained the three-dimensional model (i.e., surface, structure, and semantic). Overall, model-based measures were better for tracking changes in the surface dimension, while text-based measures were descriptive of the structure dimension. Both approaches worked well for the semantic dimension. The tested text-based measures can serve as a cross-reference to evaluate students' summaries along with the model-based measures. The current study shows the potential of using multidimensional measures to provide formative feedback on students' knowledge structure and writing styles along the three dimensions.

Resonancelike emergence of chaos in complex networks of damped-driven nonlinear systems.

Characterizing the emergence of chaotic dynamics of complex networks is an essential task in nonlinear science with potential important applications in many fields such as neural control engineering, microgrid technologies, and ecological networks. Here, we solve a critical outstanding problem in this multidisciplinary research field: the emergence and persistence of spatiotemporal chaos in complex networks of damped-driven nonlinear oscillators in the significant weak-coupling regime, while they exhibit regular behavior when uncoupled. By developing a comprehensive theory with the aid of standard analytical methods, a hierarchy of lower-dimensional effective models, and extensive numerical simulations, we uncover and characterize the basic physical mechanisms concerning both heterogeneity-induced and impulse-induced emergence, enhancement, and suppression of chaos in starlike and scale-free networks of periodically driven, dissipative nonlinear oscillators.

Atomically Dispersed Iron-Copper Dual-Metal Sites Synergistically Boost Carbonylation of Methane.

The direct liquid-phase oxidative carbonylation of methane, utilizing abundant natural gas, offers a mild and straightforward alternative. However, most catalysts proposed for this process suffer from low acetic acid yields due to few active sites and rapid C1 oxygenate generation, impeding their industrial feasibility. Herein, we report a highly efficient 0.1Cu/Fe-HZSM-5-TF (TF denotes template-free synthesis) catalyst featuring exclusively mononuclear Fe and Cu anchored in the ZSM-5 channels. Under optimized conditions, the catalyst achieved an unprecedented acetic acid yield of 40.5 mmol gcat -1 h-1 at 50 °C, tripling the previous records of 12.0 mmol gcat -1 h-1. Comprehensive characterization, isotope-labeled experiments and density functional theory (DFT) calculations reveal that the homogeneous mononuclear Fe sites are responsible for the activation and oxidation of methane, while the neighboring Cu sites play a key role in retarding the oxidation process, promoting C-C coupling for effective acetic acid synthesis. Furthermore, the methyl-group carbon in acetic acid originates solely from methane, while its carbonyl-group carbon is derived exclusively from CO, rather than the conversion of other C1 oxygenates. The proposed bimetallic catalyst design not only overcomes the limitations of current catalysts but also generalizes the oxidative carbonylation of other alkanes, demonstrating promising advancements in sustainable chemical synthesis.