Time Space Complexity Research Articles

Propagation of Perturbations in the Lower and Upper Atmosphere over the Central Mediterranean, Driven by the 15 January 2022 Hunga Tonga-Hunga Ha’apai Volcano Explosion

The Hunga Tonga-Hunga Ha’apai volcano (Pacific Ocean) generated a cataclysmic explosion on 15 January 2022, triggering several atmospheric disturbances at a global scale, as a huge increase in the total electron content (TEC) in the ionosphere, and a pressure wave travelling in the troposphere. We collected and analysed data over the Mediterranean to study these disturbances, and in particular, (i) data from the barometric and infrasonic stations installed on Italian active volcanoes by the Istituto Nazionale di Geofisica e Vulcanologia (INGV) for investigating the tropospheric pressure waves; (ii) barometric data from the INGV-TROPOMAG and SIAS (Sicilian Agro-meteorological Information System) networks, for investigating the interaction between the orography and pressure waves; (iii) ionograms from the Advanced Ionospheric Sounder-INGV ionosonde at Gibilmanna (Sicily, Italy); (iv) data from the RING (Rete Italiana Integrata GNSS) network, to retrieve the ionospheric TEC; (v) soil CO2 flux data from the INGV surveillance network of Vulcano Island. The analysis of the ground-level barometric data highlights that pressure waves were reflected and diffracted by the topographic surface, creating a complex space–time dynamic of the atmospheric disturbances travelling over Sicily, driven by the interference among the different wavefronts. The ionograms show that a medium-scale travelling ionospheric disturbance (MSTID), with a horizontal wavelength of about 220 km and a period of about 35 min, propagated through the ionospheric plasma in the correspondence of the first barometric variations. Moreover, comparing detrended TEC and barometric data, we further confirmed the presence of the aforementioned MSTID together with its close relation to the tropospheric disturbance.

총설] 한국 근·현대사를 바라보는 세밀한 시선과 담론의 좌표

In this issue of Journal of History , modern and contemporary Korean history research trends published in 2020-2021 were reviewed. During this period, researches on Korean history covering the modern and contemporary period increased quantitatively by 11.5% of papers and 24.8% of books compared to the previous two years. The keywords of the research trends for each period focused on Complex space-time, colonial society, and resisting subjects of modern society. Among them, I pointed out the mass-production of personal biographies of the King Gojong period, criticism on the March 1 Revolution theory, the origins of modern social science, and the current state of oral research on the victims of the Korean War.BR Regarding historiographic discourses, criticism of the modern long-term continuity theory in Korea and new issues on the division between North and South Korea were reviewed. Here, I pointed out that the conflict between landlords and farmers in the late Joseon Dynasty and the task of modern land reform should be included. In addition, I argued that the provision of an alternative system for the division of North and South Korea and understanding of the separation of the Korean people should be regarded as “today’s problems.” In the conclusion, I emphasized the responsibility of historians to recognize the problems of digital history concerning the outlook of history and to set the coordinates of discourse in advance for the establishment of a significant discourse in human history.

Computing spacetime

Inspired by the universality of computation, we advocate for a principle of spacetime complexity, where gravity arises as a consequence of spacetime optimizing the computational cost of its own quantum dynamics. This principle is explicitly realized in the context of the Anti-de Sitter/Conformal Field Theory correspondence, where complexity is naturally understood in terms of state preparation via Euclidean path integrals, and Einstein’s equations emerge from the laws of quantum complexity. We visualize spacetime complexity using Lorentzian threads which, conceptually, represent the operations needed to prepare a quantum state in a tensor network discretizing spacetime. Thus, spacetime itself evolves via optimized computation.

Electroweak monopoles and their stability

We apply a generalized field ansatz to describe the spherically symmetric sector of classical solutions in the electroweak theory. This sector contains Abelian magnetic monopoles labeled by their magnetic charge n=±1,±2,…, the non-Abelian monopole for n=±2 found previously by Cho and Maison (CM), and also the electric oscillating solutions. All magnetic monopoles have infinite energy. We analyze their perturbative stability and use the method of complex spacetime tetrad to separate variables and reduce the perturbation equations to multi-channel Schroedinger-type eigenvalue problems. The spectra of perturbations around the CM monopole do not contain negative modes, hence this solution is stable. The n=±1 Abelian monopole is also stable, but all monopoles with |n|≥2 are unstable with respect to perturbations with angular momentum j=|n|/2−1. The Abelian |n|=2 monopole is unstable only within the j=0 sector whereas the CM monopole also has |n|=2 and belongs to the same sector, hence it may be viewed as a stable remnant of the decay of the Abelian monopole. One may similarly conjecture that stable remnants exist also for monopoles with |n|>2, hence the CM monopole is just the first member of a sequence of non-Abelian monopoles with higher magnetic charges. Only the CM monopole is spherically symmetric while all non-Abelian monopoles with |n|>2 are not rotationally invariant.

Improved LDTW Algorithm Based on the Alternating Matrix and the Evolutionary Chain Tree.

Dynamic time warping under limited warping path length (LDTW) is a state-of-the-art time series similarity evaluation method. However, it suffers from high space-time complexity, which makes some large-scale series evaluations impossible. In this paper, an alternating matrix with a concise structure is proposed to replace the complex three-dimensional matrix in LDTW and reduce the high complexity. Furthermore, an evolutionary chain tree is proposed to represent the warping paths and ensure an effective retrieval of the optimal one. Experiments using the benchmark platform offered by the University of California-Riverside show that our method uses 1.33% of the space, 82.7% of the time used by LDTW on average, which proves the efficiency of the proposed method.

Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF

Improving the accuracy of wind power forecasting can guarantee the stable dispatch and safe operation of the grid system. Here, we propose an EMD-PCA-RF-LSTM wind power forecasting model to solve problems in traditional wind power forecasting such as incomplete consideration of influencing factors, inaccurate feature identification, and complex space–time relationships between variables. The proposed model incorporates Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), Random Forest (RF), and Long Short-Term Memory (LSTM) neural networks, And environmental factors are filtered by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm when pre-processing the data. First, the environmental factors are extended by the EMD algorithm to reduce the non-stationarity of the series. Second, the key influence series are extracted by the PCA algorithm in order to remove noisy information, which can seriously interfere with the data regression analysis. The data are then subjected to further feature extraction by calculating feature importance through the RF algorithm. Finally, the LSTM algorithm is used to perform dynamic time modeling of multivariate feature series for wind power forecasting. The above combined model is beneficial for analyzing the effects of different environmental factors on wind power and for obtaining more accurate prediction results. In a case study, the proposed combined forecasting model was verified using actual measured data from a power station. The results indicate that the proposed model provides the most accurate results when compared to benchmark models: MSE 7.26711 MW, RMSE 2.69576 MW, MAE 1.73981 MW, and adj-R2 0.9699203s.

Closed Isometric Linear Transformations of Complex Spacetime endowed with Euclidean, or Lorentz, or generally Isotropic Metric

Closed Isometric Linear Transformations of Complex Spacetime endowed with Euclidean, or Lorentz, or generally Isotropic Metric

Research on Solving Postdisaster Material Distribution and Scheduling with Improved NSGA-II Algorithm.

After the occurrence of major sudden disasters, the dispatching and distribution of disaster relief materials are particularly important, but in the process of distribution, there may be excessive distribution of similar emergency materials, unbalanced distribution volume of relief materials in different disaster-affected points, high distribution cost, and low effective distribution rate. In order to solve the above problems, based on the application of big data, this paper proposes a three-level network postdisaster material scheduling and distribution model and an improved NSGA-II algorithm. The model takes the loss degree of the disaster area and the dynamic change rate of the demand for postdisaster relief materials as the constraints, takes the demand prediction of postdisaster relief materials, the optimization of distribution path, distribution nodes, and the satisfaction of victims as the objectives, and designs the sample average approximation method and the improved NSGA-II algorithm. In order to verify the effectiveness of the proposed model and strategy, through the comparative experiment of NSGA and PSO, it can be seen from the experimental results that the three-level network allocation model and the improved NSGA-II algorithm (nondominated sorting genetic algorithm II) proposed in this paper can not only solve the existing postdisaster relief material allocation and scheduling problem but also reduce the space-time complexity of the problem.

Interaction Behaviours between Soliton and Cnoidal Periodic Waves for Nonlocal Complex Modified Korteweg–de Vries Equation

The reverse space-time nonlocal complex modified Kortewewg–de Vries (mKdV) equation is investigated by using the consistent tanh expansion (CTE) method. According to the CTE method, a nonauto-Bäcklund transformation theorem of nonlocal complex mKdV is obtained. The interactions between one kink soliton and other different nonlinear excitations are constructed via the nonauto-Bäcklund transformation theorem. By selecting cnoidal periodic waves, the interaction between one kink soliton and the cnoidal periodic waves is derived. The specific Jacobi function-type solution and graphs of its analysis are provided in this paper.

BlockTrail: A Service for Secure and Transparent Blockchain-Driven Audit Trails

Audit trails are critical components in enterprise business applications, typically used for storing, tracking, and auditing data. Entities in the audit trail applications have weak trust boundaries, which expose them to various security risks and attacks. To harden the security and develop secure by design applications, blockchain technology has been recently introduced in the audit trails. Blockchains take a consensus-driven clean slate approach to equip audit trails with secure and transparent data processing, without a trusted intermediary. On a downside, blockchains significantly increase the space-time complexity of the audit trails, leading to high storage costs and low transaction throughput. In this article, we introduce BlockTrail, a novel blockchain architecture that fragments the legacy blockchain systems into layers of codependent hierarchies, thereby reducing the space-time complexity and increasing the throughput. BlockTrail is prototyped on the “practical Byzantine fault tolerance” protocol with a custom-built blockchain. Experiments with BlockTrail show that compared to the conventional schemes, BlockTrail is secure and efficient, with low storage footprint.

Finely Crafted Features for Traffic Sign Recognition

Traffic sign recognition (TSR) is the basic technology of the Advanced Driving Assistance System (ADAS) and intelligent automobile, whileas high-qualified feature vector plays a key role in TSR. Therefore, the feature extraction of TSR has become an active research in the fields of computer vision and intelligent automobiles. Although deep learning features have made a breakthrough in image classification, it is difficult to apply to TSR because of its large scale of training dataset and high space-time complexity of model training. Considering visual characteristics of traffic signs and external factors such as weather, light, and blur in real scenes, an efficient method to extract high-qualified image features is proposed. As a result, the lower-dimension feature can accurately depict the visual feature of TSR due to powerful descriptive and discriminative ability. In addition, benefiting from a simple feature extraction method and lower time cost, our method is suitable to recognize traffic signs online in real-world applications scenarios. Extensive quantitative experimental results demonstrate the effectiveness and efficiency of our method.

Application of Fast Perturbational Complexity Index to the Diagnosis and Prognosis for Disorders of Consciousness.

Diagnosis and prognosis of patients with disorders of consciousness (DOC) is a challenge for neuroscience and clinical practice. Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is an effective tool to measure the level of consciousness. However, a scientific and accurate method to quantify TMS-evoked activity is still lacking. This study applied fast perturbational complexity index (PCIst) to the diagnosis and prognosis of DOC patients. TMS-EEG data of 30 normal healthy participants (NOR) and 181 DOC patients were collected. The PCIst was used to assess the time-space complexity of TMS-evoked potentials (TEP). We selected parameters of PCIst in terms of data length, data delay, sampling rate and frequency band. In addition, we collected Coma Recovery Scale-Revised (CRS-R) values for 114 DOC patients after one year. Finally, we trained the classification and regression model. 1) PCIst shows the differences among NOR, minimally consciousness state (MCS) and unresponsive wakefulness syndrome (UWS) and has low computational cost. 2) Optimal parameters of data length and delay after TMS are 300 ms and 101-300 ms. Significant differences of PCIst at 5-8 Hz and 9-12 Hz bands are found among NOR, MCS and UWS groups. PCIst still works when TEP is down-sampled to 250 Hz. 3) PCIst at 9-12 Hz shows the highest performance in diagnosis and prognosis of DOC. This study confirms that PCIst can quantify the level of consciousness. PCIst is a potential measure for the diagnosis and prognosis of DOC patients.

Lorentzian Threads as Gatelines and Holographic Complexity.

The continuous min flow-max cut principle is used to reformulate the "complexity=volume" conjecture using Lorentzian flows-divergenceless norm-bounded timelike vector fields whose minimum flux through a boundary subregion is equal to the volume of the homologous maximal bulk Cauchy slice. The nesting property is used to show the rate of complexity is bounded below by "conditional complexity," describing a multistep optimization with intermediate and final target states. Conceptually, discretized Lorentzian flows are interpreted in terms of threads or gatelines such that complexity is equal to the minimum number of gatelines used to prepare a conformal field theory (CFT) state by an optimal tensor network (TN) discretizing the state. We propose a refined measure of complexity, capturing the role of suboptimal TNs, as an ensemble average. The bulk symplectic potential provides a "canonical" thread configuration characterizing perturbations around arbitrary CFT states. Its consistency requires the bulk to obey linearized Einstein's equations, which are shown to be equivalent to the holographic first law of complexity, thereby advocating a notion of "spacetime complexity."

Repulsive chemotaxis and predator evasion in predator–prey models with diffusion and prey-taxis

The role of predator evasion mediated by chemical signaling is studied in a diffusive prey–predator model when prey-taxis is taken into account (model A) or not (model B) with taxis strength coefficients [Formula: see text] and [Formula: see text], respectively. In the kinetic part of the models, it is assumed that the rate of prey consumption includes functional responses of Holling, Beddington–DeAngelis or Crowley–Martin. Existence of global-in-time classical solutions to model A is proved in space dimension [Formula: see text] while to model B for any [Formula: see text]. The Crowley–Martin response combined with bounded rate of signal production precludes blow-up of solution in model A for [Formula: see text]. Local and global stability of a constant coexistence steady state which is stable for the corresponding ordinary differential equation (ODE) and purely diffusive model are studied along with mechanism of Hopf bifurcation for model B when [Formula: see text] exceeds some critical value. In model A, it is shown that prey-taxis may destabilize the coexistence steady state provided [Formula: see text] and [Formula: see text] are big enough. Numerical simulation depicts emergence of complex space-time patterns for both models and indicates existence of solutions to model A which blow-up in finite time for [Formula: see text].

Pandemic management by a spatio–temporal mathematical model

Abstract Many researchers have tried to predict the impact of the COVID-19 outbreak on morbidity, in order to help policy-makers find optimal isolation policies. However, despite the development and use of many models and sophisticated tools, these forecasting attempts have largely failed. We present a model that considers the severity of the disease and the heterogeneity of contacts between the population in complex space–time dynamics. Using mathematical and computational methods, the applied tool was developed to analyze and manage the COVID-19 pandemic (from an epidemiological point of view), with a particular focus on population heterogeneity in terms of age, susceptibility, and symptom severity. We show improved strategies to prevent an epidemic outbreak. We evaluated the model in three countries, obtaining an average mean square error of 0.067 over a full month of the basic reproduction number (R 0). The goal of this study is to create a theoretical framework for crisis management that integrates accumulated epidemiological considerations. An applied result is an open-source program for predicting the outcome of an isolation strategy for future researchers and developers who can use and extend our model.

A Study on Encryption and Decryption System

The presented document aims to review the methodologies used for cryptography. Cryptography, a Greek word meaning 'protected data with an unreadable format' has become the need of the hour. In the world of digitalization, cyber attacks, data leaks have become prominent. In order to prevent the same, data cryptography is highly in demand. Various techniques have been applied for the same. One such algorithm, RSA algorithm is another most significant method. However, its computation time has remained an unresolved concern. This paper discusses one such method to modify the following algorithm to reduce its space-time complexity.

Leonardo da Vinci and the search for order in neuroscience

Finding order in disorder is a hallmark of science and art. In the time of Leonardo da Vinci, the schism between science and art had yet to arise. In fact, Leonardo freely used scientific methods for his art and vice versa; for example, when he used his observations of turbulent, whirling water to guide his artistic imagination. Half a millennium later, a cornerstone of modern biology is the continuing search for order in dynamic processes. In neuroscience, the search has focussed on understanding complex spacetime brain dynamics. Recently, turbulence has been shown to be a guiding principle underlying the necessary information processing, supporting Leonardo's search for order in disorder. Here, we argue that Leonardo's seminal insights have ongoing relevance for modern neuroscience.

VO2 Carbon Nanotube Composite Memristor-Based Cellular Neural Network Pattern Formation

A cellular neural network (CNN) based on a VO2 carbon nanotube memristor is proposed in this paper. The device is modeled by SPICE at first, and then the cell dynamic characteristics based on the device are analyzed. It is pointed out that only when the cell is at the sharp edge of chaos can the cell be successfully awakened after the CNN is formed. In this paper, we give the example of a 5 × 5 CNN, set specific initial conditions and observe the formed pattern. Because the generated patterns are affected by the initial conditions, the cell power supply can be pre-programmed to obtain specific patterns, which can be applied to the future information processing system based on complex space–time patterns, especially in the field of computer vision.

WKB electron wave functions in a tightly focused laser beam

Available laser technology is opening the possibility of testing QED experimentally in the so-called strong-field regime. This calls for developing theoretical tools to investigate strong-field QED processes in electromagnetic fields of complex spacetime structure. Here, we propose a scheme to compute electron wave functions in tightly focused laser beams by taking into account exactly the complex spacetime structure of the fields. The scheme is solely based on the validity of the Wentzel-Kramers-Brillouin (WKB) approximation and the resulting wave functions, unlike previously proposed ones [Phys. Rev. Lett. \textbf{113}, 040402 (2014)], do not rely on approximations on the classical electron trajectory. Moreover, a consistent procedure is indicated to take into account higher-order quantum effects within the WKB approach depending on higher-and-higher powers of the Planck constant. In the case of a plane-wave background field the found wave functions exactly reduce to the Volkov states, which are then written in a new and fully quasiclassical form. Finally, by using the leading-order WKB wave functions to compute the probabilities of nonlinear Compton scattering and nonlinear Breit-Wheeler pair production, it is explicitly shown that, if additionally the energies of the charges are sufficiently large that the latter are not significantly deflected by the field, the corresponding Baier's formulas are exactly reproduced for an otherwise arbitrary classical electron/positron trajectory.

Uncertainty Principle for Space–Time Algebra-Valued Functions

In this paper, we present a set of important properties of the special relativistic Fourier transformation (SFT) on the complex space–time algebra $${\mathcal {G}}{(3,1)}$$ , such as inversion property, the Plancherel theorem, and the Hausdorff–Young inequality. The main objective of this article is to introduce the concept of the vector derivative in geometric algebra and using it together with the notion of the space–time split to derive the Heisenberg–Pauli–Weyl inequality. Finally, we apply the SFT properties for proving the Donoho–Stark uncertainty principle for $${\mathcal {G}}{(3,1)}$$ multi-vector functions.