Real Evolution Research Articles

Apollo: A comprehensive GPU-powered within-host simulator for viral evolution and infection dynamics across population, tissue, and cell.

Modern sequencing instruments bring unprecedented opportunity to study within-host viral evolution in conjunction with viral transmissions between hosts. However, no computational simulators are available to assist the characterization of within-host dynamics. This limits our ability to interpret epidemiological predictions incorporating within-host evolution and to validate computational inference tools. To fill this need we developed Apollo, a GPU-accelerated, out-of-core tool for within-host simulation of viral evolution and infection dynamics across population, tissue, and cellular levels. Apollo is scalable to hundreds of millions of viral genomes and can handle complex demographic and population genetic models. Apollo can replicate real within-host viral evolution; accurately recapturing observed viral sequences from an HIV cohort derived from initial population-genetic configurations. For practical applications, using Apollo-simulated viral genomes and transmission networks, we validated and uncovered the limitations of a widely used viral transmission inference tool.

Measurement-induced phase transitions by matrix product states scaling

We study the time evolution of long quantum spin chains subjected to continuous monitoring projected on matrix product states (MPS) with fixed bond dimension, by means of the Time-Dependent Variational Principle (TDVP) algorithm. The latter gives an effective unitary evolution which approximates the real quantum evolution up to the projection error. We show that such error displays, at large times, a phase transition in the monitoring strength, which can be well detected by scaling analysis with relatively low values of bond dimensions. Moreover, in the presence of U(1) global spin charge, we show the existence of a charge-sharpening transition well separated from the entanglement transition which we detect by studying the charge fluctuations of a local subpart of the system at large times. Our work shows that quantum monitored dynamics projected on MPS manifolds contains relevant information on measurement-induced phase transitions and provides a new method to identify measured-induced phase transitions in systems of arbitrary dimensions and sizes. Published by the American Physical Society 2024

Physical description and model validation of welded joint fatigue damage evolution

AbstractFor welded structures, the concept of damage is highly abstract and difficult to describe, leading to a lack of physical foundation and universality for existing fatigue damage accumulation models. A new fatigue damage parameter definition based on remaining bearing capacity is proposed in this paper, which is constructed by the equivalent stress on the remaining bearing surface and the equivalent stress intensity factors along the crack front curve. Tension and three‐point bending fatigue tests under two‐level loading block sequences were conducted, and the real fatigue damage evolution processes were obtained by combining measured crack front information and FEA simulation. Five damage accumulation models were utilized to estimate the fatigue damage evolution processes of the two specimens. Results show that there are differences in describing the damage evolution processes and predicting the early fatigue lives. The Rege model is most consistent with the measured data, demonstrating its applicability and effectiveness.

How to promote cooperative innovation in environmentally friendly technology: a case of agricultural biotechnology in China

This study explored the factors influencing cooperative innovation in environmentally friendly agricultural biotechnology in China. First, we constructed an evolutionary game model comprising the factors of net income of cooperative innovation, net income of independent innovation, market constraints, and government subsidies. Using MATLAB simulation, we assigned different values to the aforementioned variables to explore the evolutionary trend of innovators’ willingness to cooperate. Results showed that when the values of net income of cooperative innovation, net income of independent innovation, market constraints, and government subsidies exceeded the threshold, innovators’ willingness to cooperate was significantly enhanced. Furthermore, the proportion of innovators who cooperated with others gradually increased to 100%; otherwise, it gradually decreased to 0%. Comparing the simulation curve with the real evolution curve of cooperative innovation in agricultural biotechnology in China, we found that the gradual decline in the willingness to cooperate could be due to insufficient subsidies for cooperative innovation, low income from cooperative innovation, weak profitability of innovators, and weak market constraints.

Conditional heavy hitter monitoring and application of heterogeneous graph streams based on sketches

Graph streams offer valuable insights into real network evolution in the era of big data. Heavy hitters and conditional heavy hitters focus on identifying entities with significant network traffic, revealing network data characteristics. In this paper, we present two methods, BLSketch and MHLSketch, for identifying heavy hitters and conditional heavy hitters in heterogeneous graph streams. BLSketch uses the bidirectional mapper, Bimap, for efficient query response times while retaining candidate information. MHLSketch uses min heap to preserve a minimal set of candidates to minimize storage space, excelling in space efficiency. The two methods can be incorporated with any sketch based graph summarization method, showing excellent adaptability. Experimental results on four real-world heterogeneous datasets of varying scales with the largest dataset containing millions of edges demonstrate our methods significantly improve the quality of query answering, outperforming state-of-the-art methods, with precision close to 1 and average relative error close to 0. We further combine our methods with density clustering to introduce the SDC model, which efficiently identifies hot regions and key users in Location-Based Social Networks (LBSN). In experiments, SDC outperforms traditional clustering by reducing up to 93.1% of cold Points of Interest (POIs) and shows strong potential for practical applications.

Announcement of an approaching death: Glaciar Norte, Citlaltépetl volcano, Mexico

This study shows the dramatic but probable future evolution of Mexico's largest glacier. Mass and energy balances of this glacier have been previously studied, as well as reconstructions of its evolution up to the present time. In this work, for the first time, the real evolution of the glacier is compared with the first years of models of its future evolution.Two dynamic models of glacier thickness evolution have been developed based on a network of stakes and ground penetrating radar complemented by geometrical characterizations of the glacier bed shape. Calculated mean annual mass balance leads the equilibrium line altitude to be increasingly higher and even higher than that of the mountain summit.Two models that complement each other to describe the how and when of the different states and stages in which the glacier front retracts, the thickness decreases causing the separation of the glacier to finally disappear in the middle of the 21st century. This study provides the first view of a glacier that is about to disappear in Mexico, where the cryosphere represents the best climate proxy for future scenarios of related ecosystems, water availability.

Quantum Davidson algorithm for excited states

Excited state properties play a pivotal role in various chemical and physical phenomena, such as charge separation and light emission. However, the primary focus of most existing quantum algorithms has been the ground state, as seen in quantum phase estimation and the variational quantum eigensolver (VQE). Although VQE-type methods have been extended to explore excited states, these methods grapple with optimization challenges. In contrast, the quantum Krylov subspace (QKS) method has been introduced to address both ground and excited states, positioning itself as a cost-effective alternative to quantum phase estimation. However, conventional QKS methodologies depend on a pre-generated subspace through real or imaginary-time evolutions. This subspace is inherently expansive and can be plagued with issues like slow convergence or numerical instabilities, often leading to relatively deep circuits. Our research presents an economic QKS algorithm, which we term the quantum Davidson (QDavidson) algorithm. This innovation hinges on the iterative expansion of the Krylov subspace and the incorporation of a pre-conditioner within the Davidson framework. By using the residues of eigenstates to expand the Krylov subspace, we manage to formulate a compact subspace that aligns closely with the exact solutions. This iterative subspace expansion paves the way for a more rapid convergence in comparison to other QKS techniques, such as the quantum Lanczos. Using quantum simulators, we employ the novel QDavidson algorithm to delve into the excited state properties of various systems, spanning from the Heisenberg spin model to real molecules. Compared to the existing QKS methods, the QDavidson algorithm not only converges swiftly but also demands a significantly shallower circuit. This efficiency establishes the QDavidson method as a pragmatic tool for elucidating both ground and excited state properties on quantum computing platforms.

GAN-argcPredNet v2.0: a radar echo extrapolation model based on spatiotemporal process enhancement

Abstract. Precipitation nowcasting has important implications for urban operation and flood prevention. Radar echo extrapolation is a common method in precipitation nowcasting. Using deep learning models to extrapolate radar echo data has great potential. The increase of lead time leads to a weaker correlation between the real rainfall evolution and the generated images. The evolution information is easily lost during extrapolation, which is reflected as echo attenuation. Existing models, including generative adversarial network (GAN)-based models, have difficulty curbing attenuation, resulting in insufficient accuracy in rainfall prediction. To solve this issue, a spatiotemporal process enhancement network (GAN-argcPredNet v2.0) based on GAN-argcPredNet v1.0 has been designed. GAN-argcPredNet v2.0 curbs attenuation by avoiding blurring or maintaining the intensity. A spatiotemporal image correlation (STIC) prediction network is designed as the generator. By suppressing the blurring effect of rain distribution and reducing the negative bias by STIC attention, the generator generates more accurate images. Furthermore, the discriminator is a channel–spatial (CS) convolution network. The discriminator enhances the discrimination of echo information and provides better guidance to the generator in image generation by CS attention. The experiments are based on the radar dataset of southern China. The results show that GAN-argcPredNet v2.0 performs better than other models. In heavy rainfall prediction, compared with the baseline, the probability of detection (POD), the critical success index (CSI), the Heidke skill score (HSS) and bias score increase by 18.8 %, 17.0 %, 17.2 % and 26.3 %, respectively. The false alarm ratio (FAR) decreases by 3.0 %.

The return of comparative economic systems: An extension of János Kornai's contribution

AbstractKornai profoundly contributed to place the system to the centre of economic analysis. His multidisciplinary system paradigm is fundamental for understanding and analysing the variety of economic solutions and its consequences. The strong scientific foundation of Kornai's approach provides a fruitful opportunity to further develop and apply it to the most fundamental economic issues, such as explaining the different performance and strength of different economies and societies. The inclusion of a multidisciplinary perspective has the significant advantage of keeping under control non-economic factors that influence economic activities and performance and allows to account for the real behaviour and evolution of economies and societies. It can also contribute to explain the resilience of economic and social systems and economies. The paper takes stock of the importance of Kornai's analysis and method and applies them to better understanding the nature and working of the European Union and its difficulties. Based on Kornai's approach, the paper considers how the European Union should evolve to make itself workable and resilient in front of a threatening international environment.

Quantitative calculation of gases generation during low-temperature oxidation of coal.

The gases evolution during the low-temperature oxidation of coal is an essential parameter used to assess the state of coal oxidation and to estimate the gaseous pollutants. However, the current semi-quantitative method, which employs gas concentration as the measurement standard, is flawed. This paper presents a quantitative calculation method for gas products during coal oxidation. N2 is used as the tracer gas in the experiment, because nitrogen is an inert gas that will not participate in the reaction, and the amount of matter will not change in the reaction. According to the formula [Formula: see text], the corresponding mass flow rates of each gases component were calculated, and the gas yields during the reaction period were determined by comprehensive calculation. To this end, experiments were conducted on the low-temperature oxidation of coal using a flow reactor. After undergoing quantitative calculations, the main gas products' mass flow rates, yields, and energies, including CO, CO2, CH4, C2H4, C2H6, C2H2, and C3H8 between 30 and 180 °C were obtained. The findings showed that CO2 > CO > CH was generated in all the coal samples. The amount of gases produced in the low-temperature oxidation of coal is proportional to the level of oxygen concentration. When the oxygen concentration ranges from 0 to 21%, the gaseous production of MTH coal ranges from 381.44g/ton to 8562.80g/ton. The results of gaseous energy calculations showed that the energy loss for low temperature oxidation of the four coal samples ranged from 4334.14~26,772.73 kJ/ton under air atmosphere. Energy loss is also significantly affected by the oxygen concentration, and the energy loss of MTH coal increases significantly from 520.52 kJ/ton at 0% oxygen concentration to 26,772.73 kJ/ton at 21% oxygen concentration, an increase of about 50 times. Significantly, this method not only reflects the real gas evolution during low-temperature oxidation of coal but also computes the gas emission and energy loss, which is crucial for studying the mechanism of coal spontaneous combustion and assessing gases pollutants.

Reconstructing networks via discrete state dynamical data: A mini-review

The inference of network structure from dynamic data is one of the most challenging scientific problems in network science. To address this issue, researchers have proposed various approaches regarding different types of dynamical data. Since many real evolution processes or social phenomena can be described by discrete state dynamical systems, such as the spreading of epidemic, the evolution of opinions, and the cooperation behaviors, network reconstruction methods driven by discrete state dynamical data were also widely studied. In this letter, we provide a mini-review of recent progresses for reconstructing networks based on discrete state dynamical data. These studies encompass network reconstruction problems where the dynamical processes are known, as well as those where the dynamics are unknown, and extend to the reconstruction of higher-order networks. Finally, we discuss the remaining challenges in this field.

An Extended Gamma-Lindley Model and Inference for the Prediction of Covid-19 in Tunisia

ABSTRACT In this research paper, we introduce an extension of the Gamma-Lindley distribution using a particular exponentiation of its cumulative distribution function, which offers a more flexible model for lifetime data. Another attractive feature of this extension is that it has several particular cases: Weibull, generalized Pareto, Lindley, exponential and Gamma-Lindley distributions. Different statistical properties of this distribution are explored, such as the density, failure rate and the r th moments. We attempt to prove that the extended Gamma-Lindley distribution is characterized by its truncated moment of order statistics. One of the main merits of this work lies in the fact that it provides the ability of this characterization to simulate the new distribution compared with the inverse transform sampling method. Estimation of the parameters using the tailed regression method is investigated. The COVID-19 real data evolution in Tunisia illustrates the performance of the EGL distribution compared with its special cases through some criteria such as the Kolmogorov-Smirnov test, Mean Squared error and Kullback-Leibler divergence. The predictive ability of the extended Gamma-Lindley distribution proved to provide a better fit to expect the number of deaths by Corona-virus in Tunisia in the future period.

Constructing Microstructural Evolution System for Cement Hydration From Observed Data Using Deep Learning

Cement has been widely used in civil engineering directly and plays a critical role in cement-based materials, e.g., concrete. As the microstructural evolution of cement hydration predominates the final physical properties, an accurate simulation of hydration is highly required to enable scientists to evaluate the performance and help design new cementitious materials. However, despite significant effort and progress, a satisfactory model to realistically and accurately simulate the evolution of three-dimensional (3-D) microstructure has not yet to be constructed, mainly because cement hydration is one of the most complex phenomena in material science. In this work, a novel near-realistic microstructural model is proposed to simulate the cement hydration system using deep learning and cellular automata. It is designed to break through the bottleneck of fidelity to real microstructural evolution. The dynamical system is constructed based on a 3-D cellular automaton, in which behavior is controlled by deep neural networks distilled from microstructural images. In addition, a dynamic stratified sampling method with variable capacity is proposed to ensure the representativeness of samples for reducing the computation cost of training. Experiments manifest that the simulated hydration is in accordance with the actual development in different aspects, such as near-realistic microstructure and approximate process. Furthermore, the constructed system also demonstrates promising generalization capability even under various conditions.

Comparing Two Strategies for Locating Hydrogen Refueling Stations under High Demand Uncertainty

This research aims to model and compare two strategies for locating new hydrogen refueling stations (HRS) in a context of high uncertainty on H<sub>2</sub> demand and on the spatial distribution of demand points. The first strategy S1 represented by an agent-based model integrating a particle swarm optimization metaheuristic consists of finding the best HRS locations by adapting to the real evolution of the demand. A second strategy S2 consists in solving a classical capacitated <em>p</em>-median problem based on H<sub>2</sub> consumption forecasts over a given deterministic horizon in order to define in advance <em>p</em> optimal future HRS locations. Assuming that the same distributor gradually implements future HRSs in a given area between 2023 and 2030, both models minimize the sum of travel distances between each demand point and its assigned SRH. The results show that during the growth phase of the fuel cell electric vehicle (FCEV) market, with two different compound annual growth rates (medium and strong), the conservative S1 strategy performs better than S2 as these rates increase. However, while S2 remains suboptimal throughout the sales growth period, it becomes more effective once demand stabilizes. Another finding is that different uniform distributions of H<sub>2</sub> demand points in the same space have only a small long-term influence on the performance of these two models. This research advises investors to study the influence of different location strategies and models on the performance of a final HRS network in a given region. Models can be easily configured and adapted to a particular spatial distribution of demand points in a specific environment, more flexible H<sub>2</sub> production capabilities, or different behaviors of FCEV drivers that could be geo-located.

Thermal modeling of existing buildings in high-fidelity simulators: A novel, practical methodology

Optimizing efficiency in the operation of the HVAC system of existing buildings requires the construction of a thermal dynamic model of the building, which may be challenging because architectural metadata may be missing or obsolete. Based on a suitable set of measured data, this paper presents a novel practical methodology to create and automatically derive thermal models of existing buildings in high-fidelity simulators for energy management. To this end, the philosophy of grey-box strategies is followed to simplify the modeling and avoid the requirement of architectural metadata, facilitating and expediting the process. First, a building model with a highly reduced number of parameters is constructed by exploiting the existing similarities in the materials of the buildings and simplifying their elements to a simple one-layer parameterization. Then, the parameters of the derived model are iteratively updated while minimizing the error between the real temperature evolution and that generated by the model being identified. For this purpose, data of the room air temperature, estimated occupancy, weather conditions, and variables of the HVAC system are assumed to be available in suitable zones of the building to apply the creation and identification processes of the model, allowing that a whole digital twin of the building is constructed. The methodology is presented by its application to a real case study: the Nimbus Research Centre building at Munster Technological University, located in Cork (Ireland). The high-fidelity simulator software TRNSYS is used for the modeling task, together with the GenOpt optimization program. The results demonstrate that the proposed methodology yields a highly accurate model of the building, capable of representing reality with RMSE values consistently below 0.6C∘ during open-loop validation periods of up to four days. The findings suggest that this methodology may outperform other modeling techniques reported in the literature. Importantly, the proposed technique is less complex and time-consuming to implement than many of the alternatives.

Understanding digital platform evolution using compartmental models

Due to the growing impact of digital platforms, it is increasingly important to understand their evolution through mathematical models. As their value is dependent on their user base, we present an improved perspective on modeling the number of users. Modeling digital platforms is frequently constrained by the scarcity of available data. Thus, researchers resort to open access data like Google Trends. We provide a new interpretation of such data, using it as a proxy for the demand, in contrast with the previous method of considering it as the active user-base. This is implemented using compartmental methods, in which the expression of the demand is fitted to the number of Google Searches for a specific keyword. Two cases, the MMO World of Warcraft and Facebook are analyzed in this fashion, using two different compartmental models. The solutions given by both models replicate key features of the real evolution of the services at study, and the parameters of the fit are in accordance with the expected relative values.

Coevolution modeling of group behavior and opinion based on public opinion perception

It is important to study the evolution mechanism of group behavior for the prevention and control of harmful group behavior on social networks. Most researchers focus on the study of group behavior evolution in a deterministic environment, ignoring the influence of the uncertain environment formed by the randomness of behavioral decision-making, the incompleteness of public opinion information, and the instability of behavioral states. Therefore, this paper starts with the behavior decision-making process of individuals in an uncertain environment, defines the perception of public opinion, and builds a coevolution model of group behavior and opinion. First, considering the impact of public opinion information on group behavior evolution, this paper defines emotional resonance and topic popularity to quantify public opinion perception and designs an uncertain behavior decision-making model based on public opinion perception. Then, for the influence of the instability of the behavioral state on the opinion evolution process, this paper designs an opinion evolution model for perception based on the unstable behavioral state. Finally, we design the behavior state evolution mechanism based on the instability of the behavior state and fuse it with the behavior decision-making model and opinion evolution model to construct a coevolution model of group behavior and opinion. Compared with the experimental results of other models and real group behavior evolution, our model is effective.

Evolution of the real contact area of self-affine non-Gaussian surfaces

The impact of non-Gaussian height distribution on the real contact area evolution of elastic, frictionless and non-adhesive contact is studied. The Weibull probability function is used to model the height distribution, as it can capture surfaces of practical relevance. The set of variables needed to parametrise the problem is discussed, including the shape parameter of the height distribution, the topography’s wavelength ratio and the Hurst exponent. As the topographies become more non-Gaussian, a significant deviation from the Gaussian case is observed. Moreover, the spectral properties show a distinct effect on different non-Gaussian surfaces and the dependency on Nayak’s parameter is not inherited. A power-law evolution of the real contact area is found to fit the numerical results obtained with the boundary element method precisely. Finally, two semi-analytical asperity-based models are compared with numerical results, using the statistics of artificially generated topographies. The qualitative behaviour predicted with the numerical simulations is captured.

Long- and short-term dynamic stability of submarine slopes undergoing hydrate dissociation

Natural gas hydrates (NGHs) have recently been recognized as a promising source of relatively clean alternative energy and a significant factor in triggering marine geohazards. This paper presents a numerical method for calculating the transient excess pore pressure associated with hydrate dissociation in submarine sediments with THC (Thermo-Hydro-Chemical) coupling. Then, the dynamic stability of submarine slopes experiencing gas hydrate dissociation is evaluated based on limit equilibrium analysis considering the real evolution of excess pore pressure. Finally, this work is applied to investigate the dynamic responses of typical hydrate slopes in the Shenhu Sea area, South China Sea (SCS), under two different timescales: 1) Case I: gradual temperature increases at the seafloor due to climate warming and 2) Case II: sharp temperature increases in the interior of the hydrate deposit due to hydrate extraction. In Case Ⅰ, the timescale of hydrate dissociation is millennial. Due to the long-term temperature rise, the hydrate will dissociate slowly, which allows the generated free gas to migrate upwards and gradually accumulate at the transition zone between a porous layer and an overlying low-permeability layer. Eventually, the slow accumulation of free gas may lead to disc-shaped failure of the hydrate-bearing slope. In contrast, in Case Ⅱ, the temperature rises sharply over a short period of time, which leads to the drastic dissociation of the hydrate. The timescale of hydrate dissociation is decadal. As a result, the excess pore pressure accumulates rapidly. Under the influence of excess pore pressure, the sediment will deform dramatically, which may cause a penetration failure of the hydrate-bearing slope. These findings are relevant to the long-term (millennial) safety of human beings and short-term (decadal) utilization of energy resources.

Investigation on real slag characterization and slag evolution in cohesive zone based on dissected blast furnace

The real composition and evolution process of the slag phase in a dissected cohesive zone is systematically investigated in this paper. The slag phases within the individual charge are accurately identified. FeO-CaO-SiO2-Al2O3-MgO and FeO-CaO-SiO2-Al2O3-MgO-K2O-Na2O are the two main slag phases in cohesive zone. Local circulation of alkali metals in slag phases in the upper part of the cohesive zone promotes the bonding of the charge particles under low liquid phase precipitation. The slag in the middle and lower part of the cohesive zone is dominated by FeO-CaO-SiO2-Al2O3-MgO. As the temperature increases and the wustite content decreases, CaO-SiO2-Al3O2-MgO slag with high Al3O2 and low MgO becomes the dominant slag in the lower part of the cohesive zone. The hysteresis between the formation of liquid phase and the change in viscosity directly affects the cohesive zone. The essence of cohesive zone formation is the difference in properties of the coexisting multiple slag systems at the same temperature.