Computational Theory Research Articles

Analog Programmable‐Photonic Computation

AbstractDigital electronics is a technological cornerstone in this modern society that has covered the increasing demand for computing power during the last decades thanks to a periodic doubling of transistor density in integrated circuits. Currently, such scaling law is reaching its fundamental limit, leading to the emergence of a large gamut of applications that cannot be supported by digital electronics, specifically, those that involve real‐time multi‐data processing, e.g., medical diagnostic imaging, robotic control, and autonomous driving, among others. In this scenario, an analog computing approach implemented in a real‐time reconfigurable nonelectronic hardware such as programmable integrated photonics (PIP) can be more efficient than digital electronics to perform these emerging applications. However, actual analog computing models such as quantum and neuromorphic computation were not conceived to extract the unique benefits of PIP (and integrated photonics in general). Here, the foundations of a new computation theory are presented, termed Analog Programmable‐Photonic Computation (APC), explicitly designed to unleash the full potential of PIP technology. Interestingly, APC enables overcoming basic theoretical and technological limitations of existing computational models and can be implemented in other technologies (e.g., in electronics, acoustics or using metamaterials), consequently exhibiting the potential to spark a ground‐breaking impact on the information society.

On the size of maximal intersecting families

AbstractWe show that an $n$ -uniform maximal intersecting family has size at most $e^{-n^{0.5+o(1)}}n^n$ . This improves a recent bound by Frankl ((2019) Comb. Probab. Comput.28(5) 733–739.). The Spread Lemma of Alweiss et al. ((2020) Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing.) plays an important role in the proof.

Study on hydrodynamic diffusion law of the swelling particle slurry in karst pipeline

The swelling particle grouting material has demonstrated remarkable plugging effectiveness in high-pressure and large-flow burst water within karst pipelines. Currently, current research on the rheological model, flow computation theory, and plugging mechanism of this material is lacking. The conventional grouting slurry diffusion process, using the liquid-liquid two-phase flow method, fails to accurately simulate high solubility slurry and particle swelling. To address these limitations, this study established a precise constitutive model to describe the swelling particle slurry diffusion process in dynamic water. Additionally, a coupling calculation method was proposed to analyze the spatiotemporal heterogeneity of viscosity during slurry diffusion by considering the migration of slurry and the changes in viscosity. To investigate the interaction between particle swelling and flow field changes, a Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) coupling model was developed for the diffusion of swelling particle slurry. It is demonstrated that slurry viscosity increases exponentially within the diffusion front as the particle swelling rate rises, and the drag force exhibits an intriguing behavior of initially increasing and then decreasing as the slurry flows through the pipeline. Furthermore, the CFD-DEM coupling model proved to be more accurate in describing viscosity distribution and diffusion distance compared to the finite element solution. The primary objective of this paper is to reveal the plugging mechanism and provide theoretical support for the engineering application of the swelling particle grouting material.

A granular sigmoid extreme learning machine and its application in a weather forecast

Extreme Learning Machine (ELM) is a class of machine learning systems or methods based on feedforward neural networks, which are suitable for supervised learning problems. The ELM is regarded as a special kind of FNN in the research, which is characterized by the fact that the weights of hidden layer nodes are randomly or artificially given. As a result, the ELM feature mapping is random. The generic approximation theorem states that any nonlinear piecewise continuous function may be used as the feature map. By introducing the theory of granular computing, this paper proposes a new granulation method of random sigmoid function. The Granular Sigmoid Extreme Learning Machine (GSELM) algorithm was proposed by combining the random sigmoid function with the extreme learning machine algorithm. After granulated data sets are processed by GSELM, the granulated multi-feature data will form granular vectors. The granular vector is processed in parallel, which makes the granular sigmoid extreme learning machine parallel computing. The GSELM is utilized to address the fundamental real-time issue of weather forecasting. The GSELM algorithm can predict whether the next day will be sunny or rainy more accurately and quickly, which is crucial to the field of meteorology. Several UCI data sets are used to test the feasibility of the GSELM algorithm. The verified GSELM algorithm is applied to the Australian weather forecast data set. According to the experimental findings, the GSELM algorithm can predict whether the next day will be sunny or rainy more accurately and quickly.

Understanding the prototype catalyst TiO2 surface with the help of density functional theory calculation

AbstractTitanium dioxide (TiO2) is one of the most technologically promising oxides with a broad range of catalytic and photocatalytic activities. Theoretical modeling, especially density functional theory calculations, has been extensively carried out to understand the geometric structure, electronic structure, reactivity, and reaction mechanisms of TiO2 systems, as well as to develop new catalysts with improved performances. This review summarizes the recent theoretical progress on the well‐defined surfaces of TiO2 crystalline, and focuses on the structures, adsorptions, and reactions on the surface and at the interface. The theoretical methods and models, surface defects, surface doping, water splitting and H2 evolution, methanol conversion, CO2 reduction and CO oxidation, SOx and NOx degradation, CH4 conversion, organic pollutant degradation, CH bond activation and CC bond formation, dye sensitization, as well as the applications of TiO2 in some other fields, have been discussed in detail.This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Structure and Mechanism > Computational Matmandatory approximaterials Science Electronic Structure Theory > Density Functional Theory

Tissue P Systems With States in Cells

Tissue-like P systems with channel states are a type of classical membrane systems in which objects transferred among regions are controlled by states placed in the channels between regions. However, an important biological fact is the existence of a “barrier” to the diffusion of signal molecules, which tend to remain confined to some particular micro-habitat. This feature allows quorum sensing to convey information about the physiological state of spatially separated sub-populations. Therefore, in this article, we design a novel class-variant of P systems named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tissue P systems with states in cells</i> (TSIC P systems). Here, each cell contains one and only one state at any moment (the environment has no state), and objects transferred among regions are controlled by states (or a state) that are placed in the corresponding cells (or a cell). We discuss the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">computability theory</i> of TSIC P systems by showing that Turing universality is acquired by TSIC P systems, which are worked both in a flat maximal parallelism and in a maximal parallelism. In addition, when cell division is considered in TSIC P systems, then tissue P systems with states in cells and cell division (TSICD P systems) are constructed. The (presumed) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">computational efficiency</i> of TSICD P systems is reached by offering a uniform solution to the satisfiability problem.

Quantum double structure in cold atom superfluids

The theory of topological quantum computation is underpinned by two important classes of models. One is based on non-abelian Chern–Simons theory, which yields the so-called SU(2)k anyon models that often appear in the context of electrically charged quantum fluids. The physics of the other is captured by symmetry broken Yang–Mills theory in the absence of a Chern–Simons term and results in the so-called quantum double models. Extensive resources have been invested into the search for SU(2)k anyon quasi-particles, in particular, the so-called Ising anyons (k = 2) of which Majorana zero modes are believed to be an incarnation. In contrast to the SU(2)k models, quantum doubles have attracted little attention in experiments despite their pivotal role in the theory of error correction. Beyond topological error correcting codes, the appearance of quantum doubles has been limited to contexts primarily within mathematical physics, and as such, they are of seemingly little relevance for the study of experimentally tangible systems. However, recent works suggest that quantum double anyons may be found in spinor Bose–Einstein condensates. In light of this, the core purpose of this article is to provide a self-contained exposition of the quantum double structure, framed in the context of spinor condensates, by constructing explicitly the quantum doubles for various ground state symmetry groups and discuss their experimental realisability. We also derive analytically an equation for the quantum double Clebsch–Gordan coefficients from which the relevant braid matrices can be worked out. Finally, the existence of a particle-vortex duality is exposed and illuminated upon in this context.

Integrating artificial intelligence-based methods into qualitative research in physics education research: A case for computational grounded theory

Integrating artificial intelligence-based methods into qualitative research in physics education research: A case for computational grounded theory

Enhanced production and control of liquid alkanes in the hydrogenolysis of polypropylene over shaped Ru/CeO2 catalysts

The hydrogenolysis of polypropylene waste to liquid hydrocarbons offers a promising pathway for the chemical recycling of waste polymers. This work describes the importance of reaction conditions and support morphology to produce high liquid yields with enhanced control of chain length over highly active shaped and non-shaped Ru/CeO2 catalysts. The shaped 2 wt% Ru/CeO2 exhibit high liquid alkane yields (58–81%) when compared to the non-shaped 2 wt% Ru/CeO2 (liquid yield: 34–58%) under optimized reaction conditions (220 °C, 16 h, 30 bar H2). In particular, the 2 wt% Ru/CeO2 nanocube catalyst exhibits the highest activity yielding lighter hydrocarbons. This was rationalized to be a combination of small Ru cluster formation and enhanced metal-support interactions. The influence of larger Ru particles (≥1.5 nm) was confirmed mechanistically using a computational density functional theory study on the hydrogenolysis of pentane (C5) to determine the favorable formation of methane in the non-shaped Ru/CeO2 catalyst.

An introduction to computational complexity and statistical learning theory applied to nuclear models

The fact that we can build models from data, and therefore refine our models with more data from experiments, is usually given for granted in scientific inquiry. However, how much information can we extract, and how precise can we expect our learned model to be, if we have only a finite amount of data at our disposal? Nuclear physics demands an high degree of precision from models that are inferred from the limited number of nuclei that can be possibly made in the laboratories.In manuscript I will introduce some concepts of computational science, such as statistical theory of learning and Hamiltonian complexity, and use them to contextualise the results concerning the amount of data necessary to extrapolate a mass model to a given precision.

The gap between theoretical computational theories and application models

This paper examined two key generalization assumptions in the theoretical and computational fields and discussed three dominant pre-training models from multiple computer science concentrations. Then, this paper illustrated the increasingly severe gap between theoretical research and model applications for current machine learning studies. This paper discussed four advanced concepts in academia regarding possible solutions for the gap. The researchers developed these concepts to redirect future machine learning work by helping the academia re-understand the concept of “learning” and its appropriate implementation in the machine learning field. Finally, one possible reason for such a gap to exist is explained, and based on the interpretation of advanced concepts, possible solutions the academia can try to implement are also discussed.

Crane: An Integrated Computational Design Platform for Functional, Foldable, and Fabricable Origami Products

Despite the recent trend of computational origami for human-computer interaction (HCI) and digital fabrication, it is still difficult for designers to complete a series of design, simulation, and fabrication of objects leveraging computational origami theory. In this paper, we propose Crane, an integrated origami design platform implemented with Grasshopper. With this platform, users can seamlessly (1) design the 2D and 3D crease pattern, (2) simulate 3D folding transformation from the given crease pattern, (3) inversely find a new pattern under design constraints, (4) thicken the 2D pattern into a 3D volume along with the appropriate hinge structures for different fabrication methods, and (5) optionally connect the resulting design to other Rhinoceros or Grasshopper plugins for post-processes. To help understand how to use our system and demonstrate its feasibility, we showed three examples of origami products designed using our system. We also reported user feedback from the workshop as an evaluation.

Structural Analysis and Reactivity Insights of (E)-Bromo-4-((4-((1-(4-chlorophenyl)ethylidene)amino)-5-phenyl-4H-1,2,4-triazol-3-yl)thio)-5-((2-isopropylcyclohexyl)oxy) Furan-2(5H)-one: A Combined Approach Using Single-Crystal X-ray Diffraction, Hirshfeld Surface Analysis, and Conceptual Density Functional Theory

This study presents a comprehensive exploration of the structure–reactivity relationship of (E)-3-bromo-4-((4-((1-(4-chlorophenyl)ethylidene)amino)-5-phenyl-4H-1,2,4-triazol-3-yl)thio)-5-((2-isopropylcyclohexyl)oxy)furan-2(5H)-one. The study embarked on an in-depth investigation into the solid-state crystal structure of this organic compound, employing computational Density Functional Theory (DFT) and related methodologies, which have not extensively been used in the examination of such compounds. A single-crystal X-ray diffraction (SCXRD) analysis was initially performed, supplemented by a Hirshfeld surfaces analysis. This latter approach was instrumental in visualizing and quantifying intermolecular interactions within the crystal structures, offering a detailed representation of the molecule’s shape and properties within its crystalline environment. The concept of energy framework calculations was utilized to understand the varied types of energies contributing to the supramolecular architecture of the molecules within the crystal. The Conceptual DFT (CDFT) was applied to predict global reactivity descriptors and local nucleophilic/electrophilic Parr functions, providing a deeper understanding of the compound’s chemical reactivity properties. The aromatic character and π–π stacking ability were also evaluated with the help of LOLIPOP and ring aromaticity measures. This comprehensive approach not only provides a detailed description of the structure and properties of the investigated compound but also offers valuable insights into the design and development of new materials involving 1,2,4-triazole systems.

Reinforcement effect and mechanism analysis of dredged sludge treated by alternating prefabricated radiant drain vacuum preloading method

The vacuum preloading method is widely applied in dredged sludge reinforcement, however, prefabricated vertical drain (PVD) clogging affects the effectiveness of ground reinforcement and leads to significant uneven settlement. To alleviate these deficiencies, this paper proposed an alternating prefabricated radiant drain vacuum preloading (APRD-VP) method considering the advantages of the alternating vacuum preloading (A-VP) method (delaying clogging) and prefabricated radiant drain vacuum preloading (PRD-VP) method (combining horizontal drainage and alleviating uneven settlement). The laboratory model experiments were conducted to explore the treatment effect of APRD-VP method, and the particle image velocimetry technology was employed to observe clogging range surrounding PVD. The experimental results demonstrate that APRD-VP method displayed a better reinforcement effect than the traditional method by analysis of water discharge, settlement, pore water pressure, water content, and vane shear strength. By investigating the clogging formation development for APRD-VP method, the maximum clogging range on both sides of the drainage board has been reduced by 3.15% and 14.02%. In addition, the empirical relationship between clogging range and settlement was established to can better judge and predict clogging range, which can lay the foundation for establishing the theory of computation of soft soil ground design suitable for APRD-VP method.

A Novel Back-Propagation Neural Network for Intelligent Cyber-Physical Systems for Wireless Communications

Wireless sensor networks, which play a significant role in monitoring complex environments that change rapidly over time, were used in the Artificial Intelligence method. External factors or the device designers themselves are both responsible for this complex behavior. Sensor networks often use machine learning techniques to adapt to such conditions, eliminating the need for excessive redesign. Cyber-physical systems (CPS) appeared as the promising option for improving physical-virtual interactions. The quality of the system containing processing information is primarily determined by the system function. There are many benefits obtained while combining Artificial Intelligence (AI) and Cyber-Physical Systems (CPSs) in buildings. In CPS-based indoor environment has various design schemes containing measurement and intelligent buildings in the control system consisting of detection, tracking, execution, and communication modules. The Multi-Agent System (MAS) is the smallest control unit that simulates among neurons and it flexibly provides the information. To mimic the interactions between human neurons, multi-agents are used. In this paper, the CPS’s information world is built on the fundamental principle of granular formal concepts and the theory of granular computing is investigated. The calculation module is used by Back-Propagation Neural Network (BPNN) for pattern recognition and classification by environmental information. Various parameters namely the normalized root mean square error, peak signal-to-noise ratio, mean square error, and the mean absolute error are chosen as the objective assessment criteria to assess the benefits of the proposed method and the effectiveness of the proposed system is proven.

Insights into the Reaction Kinetics of Hydrazine-Based Fuels: A Comprehensive Review of Theoretical and Experimental Methods

While researchers have extensively studied the initial decomposition mechanism of Monomethylhydrazine (MMH, CH3NHNH2) in the MMH/dinitrogen tetroxide (NTO) system, the investigation of Unsymmetrical Dimethylhydrazine (UDMH, (CH3)2NNH2) has been limited due to its high toxicity, corrosiveness, and deterioration rate. Hence, the effects of UDMH’s deterioration products on combustion performance and gas-phase combustion reaction mechanisms remain unclear. This comprehensive review examines the existing research on the reaction kinetics of the three widely used hydrazine-based self-ignition propellants: Hydrazine (HZ, N2H4): MMH: and UDMH, emphasizing the necessity for further investigation into the reaction kinetics and mechanisms of UDMH. It also discusses the implications of these findings for developing safer and more efficient rocket propulsion systems. Additionally, this review underscores the importance of utilizing computational chemistry theory to analyze hydrazine-based fuels’ combustion and decomposition properties, constructing detailed pyrolysis and combustion reaction mechanisms to optimize rocket engine fuel performance and environmental concerns.

Theoretical model and numerical simulation of high-gravity backwashing process for deep bed filter in oilfield

With the large-scale application of polymer flooding technology, the polymer concentration in produced water is increasing, which leads to incomplete regeneration of filter media by gravity field hydraulic backwashing technology only. In this study, the high-gravity backwashing technology is constructed by coupling gravity field and swirl field, which promotes the development of the oilfield. Based on the cascade theory, this paper establishes a mathematical expression of radial collision force in the high-gravity backwashing process in the vortex tube and solves a mathematical equation of high-gravity backwashing velocity gradient G based on the efficient zone of collision force action. Meanwhile, based on the discrete element method and computational fluid dynamics theory, the inter-particle collision regulation of the high-gravity backwashing process is simulated by the coupled FLUENT-EDEM method, and the high-efficiency zone of the backwashing process is verified. In this high-efficiency zone, the theoretical and simulated values of velocity gradient G for different high-gravity values match well, with differences ranging from –5.8 % to +7.1 %, indicating that the theoretical model of high-gravity backwashing is correct. When the high-gravity value is 210–240 g, the total impulse of the filter bed per unit time reaches a great value, and the backwashing velocity gradient G is 3047.1 s−1, which is much larger than the G value of combined air-water backwashing. The high-gravity backwashing technology breaks through the technical bottleneck of low efficiency in the action of the gravity field. It enriches and develops the gravity field hydraulic backwashing technology of the filter bed.

Synthesis, Characterization, and Toxicity Studies on Three Amino Acid Schiff-Base Ligands and Their Potential Binding Sites to Ni(II) ion: Comparison with Computational Density Functional Theory (DFT) Studies

Synthesis, Characterization, and Toxicity Studies on Three Amino Acid Schiff-Base Ligands and Their Potential Binding Sites to Ni(II) ion: Comparison with Computational Density Functional Theory (DFT) Studies

Visuo-vestibular heading perception: a model system to study multi-sensory decision making.

Integrating noisy signals across time as well as sensory modalities, a process named multi-sensory decision making (MSDM), is an essential strategy for making more accurate and sensitive decisions in complex environments. Although this field is just emerging, recent extraordinary works from different perspectives, including computational theory, psychophysical behaviour and neurophysiology, begin to shed new light onto MSDM. In the current review, we focus on MSDM by using a model system of visuo-vestibular heading. Combining well-controlled behavioural paradigms on virtual-reality systems, single-unit recordings, causal manipulations and computational theory based on spiking activity, recent progress reveals that vestibular signals contain complex temporal dynamics in many brain regions, including unisensory, multi-sensory and sensory-motor association areas. This challenges the brain for cue integration across time and sensory modality such as optic flow which mainly contains a motion velocity signal. In addition, new evidence from the higher-level decision-related areas, mostly in the posterior and frontal/prefrontal regions, helps revise our conventional thought on how signals from different sensory modalities may be processed, converged, and moment-by-moment accumulated through neural circuits for forming a unified, optimal perceptual decision. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

Energy loss assessment of a two-stage pump under natural flow in the marine cooling system based on entropy production theory

Natural flow conditions are commonly used in the cooling systems of modern high-speed vessels and nuclear submarines. When the vessel's speed satisfies the requirements, it relies on its kinetic energy to supply seawater to the condenser in the cooling system. Under natural flow conditions, the pump is not driven, and the rotor will be “stuck” or rotated passively by the inlet flow shock. The local energy loss caused by the blade tip leakage vortex, surface separation flow, and wake flow significantly impact the system’s performance. Entropy production is an irreversible dissipation effect caused by the energy transfer process. According to the second law of thermodynamics, the entropy production theory can effectively characterize the intensity of local energy loss under natural flow conditions. In this study, the variation law of hydraulic loss and passive rotational speed under different flow rates of a two-stage pump were obtained by natural flow experiments. Moreover, the generation mechanism of intense energy loss under different natural flow conditions was investigated based on computational fluid dynamics (CFD) and entropy production theory. The results show that the entropy production induced by turbulent kinetic energy (TEP) is dominant, averaging 71.8%, followed by the wall entropy production (WEP), about 26.1%. The contribution of viscous dissipation entropy production (VDEP) can be negligible. When the impeller is “stuck” at low flow rates, entropy production is mainly from the flow separation near the blade and the flow shock region close to the guide vane back. The total entropy production of the guide vane accounts for 78%, higher than the impeller. However, when the impeller rotates passively at high flow rates, the energy loss inside the impeller is dominant, about 62%. The energy loss occurs around the blade wake region and the high velocity-gradient region near the guide vane. The flow field patterns between the two stages are similar, but the energy loss in the impeller and guide vane of stage Ⅱ is much higher. The TEP and WEP of the two impeller stages differed by 14% and 10%, respectively, and the guide vane of the two stages differed by 8% and 3%, at a flow rate of 196.8 m3/h.