Dynamic Expansion Research Articles

Expansion of a one-dimensional Bose gas: the role of interactions and kinetic-energy driving

We study the expansion of a one-dimensional boson gas by initialising it in a small region of a chain, and then suddenly allowing it to expand into the remainder of the chain. We consider three initial ground-state configurations: the Mott insulator, the conventional superfluid, whose momentum density is sharply peaked at zero momentum, and the cat-like state with momentum peaks at ±π/2, produced by kinetic driving, the latter being a particular case of a flat-band system. In turn, we consider three types of expansion: spectroscopic (with interactions tuned to zero), dynamic (with standard short-range repulsive interactions), and under kinetic driving. The numerical calculations are exact. We compute the momentum and real-space one-particle densities, as well as the two-particle momentum correlations. We find that the spectroscopic time-of-flight experiment reflects the initial momentum distribution except for the larger number of momentum states and at high momenta. For the dynamic expansion starting from an insulator, we recover the non-equilibrium quasi-condensation into momenta ±π/2, provide a physical explanation in terms of interacting bosons that is confirmed by the numerical simulation, and note the existence of nontrivial correlations in the momentum distribution. Under kinetic driving the expansion is comparatively slow, but we conjecture that at high densities it will be much faster. We compare various measures of the two-particle momentum correlations, noting that some of them tend to conceal the possible cat-like structure of a many-body state.

Design of Cloud Computing Platform for Large-Scale Multimedia Communication

In order to enlarge the scale of multimedia communication, based on the design of cloud computing platform, this paper makes an in-depth study on the design of multimedia communication platform. Firstly, we study from three methods: extracting high-quality content, broadening multimedia profit channels, and enhancing copyright awareness. Among them, the big data diversion system under the cloud computing platform is designed to optimize data clustering and pattern recognition. The traditional big data shunting system under the cloud computing platform adopts the open-source embedded system design method. The shunting system is connected to the switch of the cloud computing platform through the coupling coil, resulting in missing points and wrong points in the data in the shunting system. Using this method, it is concluded that the cloud computing platform cannot achieve the optimal allocation in terms of SLA violation rate, resource consumption, and power consumption. After experiments and research, the current development trend of media shows a deviation from this phenomenon. The development of media itself, that is, the self-appreciation of media, is gradually becoming the main purpose of media development. A power simulation cloud computing platform architecture with dynamic expansion, efficient computing, and convenient resource scheduling should be given to face large-scale multimedia communication.

Household Food Waste Research: The Current State of the Art and a Guided Tour for Further Development

Decreasing food waste is an important contribution to the practical achievement of Sustainable Development Goals of the United Nations. The last decades witnessed a dynamic expansion of food waste-related publications, parallel with this studies, systematic reviews and bibliometric analyses had been published on this topic. The novelty of the current publication is threefold: 1) it summarizes recent publications, and puts their results into development context; 2) applies the triangulation method by analyzing the food waste-based literature from the aspect of epistemological development, structural composition and scientometric mapping, 3) based on in-depth research of the literature and the determination of the most important ways of its development, the key steps of a modern waste research project as a function of research goals as well as available financial resources are outlined. The bibliometric research based on nearly three thousand resources has shown a considerable geographic disparity in food waste research: these topics are investigated mainly in developed and emerging countries. Bibliometric mapping highlights the importance of the application of qualitative methods for exploring motivational drivers and actual behaviour of households. A general workflow for food waste research is suggested by the authors based on a study carried out in developed countries. This method can be considered as a general, flexible framework, which could serve as a common platform for experts. The framework can be used independently from the of economic development level of the countries but it is especially useful for researchers in the global South because experiences gained by developed countries opens a favourable possibility to conceptualise, plan, realise and publish their food-waste related research.

Flexible 3D Nanonetworked Silica Film as a Polymer-Free Drug-Eluting Stent Platform to Effectively Suppress Tissue Hyperplasia in Rat Esophagus.

Loading and eluting drugs on self-expandable metallic stents (SEMSs) can be challenging in terms of fabrication, mechanical stability, and therapeutic effects. In this study, a flexible 3D nanonetworked silica film (NSF) capable of withstanding mechanical stress during dynamic expansion is constructed to function as a drug delivery platform on an entire SEMS surface. Despite covering a broad curved area, the synthesized NSF is defect-free and thin enough to increase the stent strut diameter (110µm) by only 0.4 percent (110.45µm). The hydrophobic modification of the surface enables loading of 4.7 times the sirolimus (SRL) concentration in NSF than Cypher, polymer-coated commercial stent, which is based on the same thickness of coating layer. Furthermore, SRL-loaded NSF exhibits a twofold delay in release compared to the control group without NSF. The SRL-loaded NSF SEMS significantly suppresses stent-induced tissue hyperplasia than the control SEMS in the rat esophagus (all variables, p <0.05). Thus, the developed NSF is a promising polymer-free drug delivery platform to efficiently treat esophageal stricture.

Cavity expansion in nonlinear viscoelastic solids: A nonlinear dynamic study

Dynamic cavity expansion has been studied for hyperelastic materials since the work of Knowles and Jakub in 1965. It has recently drawn new attention for potential applications in measuring the mechanical properties of soft polymer or biological tissues. These soft materials, however, are intrinsically rate-dependent, and the study of dynamic cavitation in nonlinear viscoelastic solid is relatively scarce. In this article, we utilize the established two-potential nonlinear viscoelastic framework to study the cavity behavior in infinite solid. Differently, we further introduced the surface energy into account and pay extra attention to the oscillation of the cavity surface based on nonlinear dynamics. The cavity behavior and stress change under a monotonically increasing pressure, creep experiment, and relaxation experiment are investigated. Considering surface effects, we notice the cavity surface has an oscillation process of expansion and shrink before infinitely large expansion before rupture. The oscillation amplitude gradually diminishes with time due to the energy consumption of viscosity. Regarding the viscoelastic material as a system with diminishing shear modulus, we analyze the stability of the cavity surface and find it can be either a stable center point or an unstable saddle point. The results of this study are helpful for understanding the cavitation phenomenon in viscoelastic solids caused by dynamic loading.

Imaging of Love Waves and Their Interaction with Magnetic Domain Walls in Magnetoelectric Magnetic Field Sensors

AbstractThe complex behavior of horizontally polarized surface shear waves in magnetoelectric surface acoustic wave based magnetic field sensor devices is revealed by time‐resolved magnetooptical microscopy with picosecond temporal and submicron spatial resolution. The imaging of the propagating waves in the magnetoelectric composites is realized through the functional soft‐magnetic layer by coupled magnetoelastic interactions. Partial surface wave reflections, wave front dephasing, and secondary wave generation occur, which originate from structures and magnetic domain walls. Closure domain structures bend and reflect the magnetic surface waves. Strain stimulated magnetic domain walls display dynamic periodic expansions, which propagate along the domain walls and change the magnetomechanical response also in the surrounding regions. The revealed spatial and temporally varying nondeterministic response restricts the noise performance of the surface acoustic wave based magnetic field sensors and thus confines the sensor's limit of detection. Magnetic time‐resolved optical imaging is shown to be a powerful method for the operando characterization of magnetoelectric devices and in‐plane displacement surface acoustic wave fields that are not accessible by other methods.

Nonlinear Dynamic Soft Sensor Development with a Supervised Hybrid CNN-LSTM Network for Industrial Processes.

A soft sensor is a key component when a real-time measurement is unavailable for industrial processes. Recently, soft sensor models based on deep-learning techniques have been successfully applied to complex industrial processes with nonlinear and dynamic characteristics. However, the conventional deep-learning-based methods cannot guarantee that the quality-relevant features are included in the hidden states when the modeling samples are limited. To address this issue, a supervised hybrid network based on a dynamic convolutional neural network (CNN) and a long short-term memory (LSTM) network is designed by constructing multilayer dynamic CNN-LSTM with improved structures. In each time instant, data augmentation is implemented by dynamic expansion of the original samples. Moreover, multiple supervised hidden units are trained by adding quality variables as part of the layer input to acquire a better quality-related feature learning performance. The effectiveness of the proposed soft senor development is validated through two industrial applications, including a penicillin fermentation process and a debutanizer column.

Cumulants of the chiral order parameter at lower RHIC energies

We study cumulants of the chiral order parameter as function of beam energy as a possible signal for the presence of a critical end point and first-order phase transition in the QCD phase diagram. We model the expansion of a heavy-ion collision by a fluid dynamic expansion coupled to the explicit propagation of the chiral order parameter sigma via a Langevin equation. We evolve the medium until a parametrized freeze-out condition is met where we calculate event-by-event fluctuations and cumulants of sigma which are expected to follow the trend of net-proton number cumulants. We emphasize the role of a nonequilibrium first-order phase transition: The presence of an unstable phase causes the well-known bending of the trajectories in the space of temperature and baryochemical potential. For these cases at lower beam energies, the system crosses the freeze-out line more than once, allowing us to calculate a range of cumulants for each initial condition which are overall enhanced for the second hit of the freeze-out line. We thus find not only the critical end point but also the phase transition of the underlying model clearly reflected in the cumulants. The impact of volume fluctuations is demonstrated to play a measurable role for fluid dynamical evolutions that last significantly long.

Longterm multisource satellite data fusion reveals dynamic expansion of lake water area and storage in a hyperarid basin of China

Lakes play an important role in global hydrologically and provide substantial water resources in hyperarid regions, and they are becoming even more significant as climate change impacts hydrological processes, especially in these sensitive regions. This study proposes a lake-monitoring framework that uses multisource satellite data to automatically estimate the spatiotemporal variations from 1987 to 2020 of lake area, lake water level, and lake water storage (LWS) in the Qaidam Basin (QB), which is a typical hyperarid basin in northwest China. The global surface water dataset provides long-time-series data for lake areas. Multisource satellite images (Landsat 7, Landsat 8, Sentinel 2) and altimetry data (CryoSat 2, ICESat, ICESat 2, Sentinel 3) are used to build hypsometric curves (the fit to the curve of lake area vs lake water level) to reconstruct lake water level before the 21st century. In contrast with traditional methods, we propose to verify the construction of a hypsometric curve to select a reasonable curve and thereby improve the accuracy of the extrapolation of lake water level. On this basis, we estimated the variation of LWS from 1987 to 2020 and to use products of the gravity recovery and climate experiment and of land surface model simulations to estimate the variation in total LWS in the QB for comparative analysis. The results show that the total LWS in the QB has increased by 82.63% over the past 33 years. Compared with the mean value from 2004 to 2009, the total LWS in 2020 increased by 4.41 km3, which is equivalent to 12.53%–19.49% of the increase in terrestrial water storage. This ratio cannot be ignored. The analysis of spatiotemporal variations in lakes shows that large shallow lakes are disappearing, leading to ecological deterioration in some areas. Conversely, there is increased water storage in small deep lakes, which may be beneficial for water conservation in hyperarid basins. In conclusion, the proposed automated processing program should facilitate the rapid monitoring of lake dynamics around the world. We emphasize that the variation of lake water storage in hyperarid basins cannot be ignored, which should stimulate more relevant research.

Quantum Cooperative Multicast in a Quantum Hybrid Topology Network

Quantum multicast is a significant transmission mode in a multiparty communication scenario. Multisource collaboration can further enhance the efficient multicast. However, it remains a challenge to realize quantum multicast with a cooperative way in a complex topology network. In this article, we propose a scheme of quantum cooperative multicast in a hybrid topology network. It provides information aggregation and simultaneous multipoint transmission services. First, collaborative information aggregation allows central network data to be integrated into the aggregation node. By exploiting the quantum multicast mode, the aggregation node can simultaneously deliver integrated quantum states to multiple targets. Second, our scheme is feasible for dynamic network expansion. It is capable of extending the network architecture iteratively, while the peer network requests can be handled in parallel. Finally, the new scheme shows great application potential in the distributed quantum network. It is a promising candidate for the implementation of quantum data disaster backup in future.

Urban Sprawl and COVID-19 Impact Analysis by Integrating Deep Learning with Google Earth Engine

Timely information on land use, vegetation coverage, and air and water quality, are crucial for monitoring and managing territories, especially for areas in which there is dynamic urban expansion. However, getting accessible, accurate, and reliable information is not an easy task, since the significant increase in remote sensing data volume poses challenges for the timely processing and analysis of the resulting massive data volume. From this perspective, classical methods for urban monitoring present some limitations and more innovative technologies, such as artificial-intelligence-based algorithms, must be exploited, together with performing cloud platforms and ad hoc pre-processing steps. To this end, this paper presents an approach to the use of cloud-enabled deep-learning technology for urban sprawl detection and monitoring, through the fusion of optical and synthetic aperture radar data, by integrating the Google Earth Engine cloud platform with deep-learning techniques through the use of the open-source TensorFlow library. The model, based on a U-Net architecture, was applied to evaluate urban changes in Phoenix, the second fastest-growing metropolitan area in the United States. The available ancillary information on newly built areas showed good agreement with the produced change detection maps. Moreover, the results were temporally related to the appearance of the SARS-CoV-2 (commonly known as COVID-19) pandemic, showing a decrease in urban expansion during the event. The proposed solution may be employed for the efficient management of dynamic urban areas, providing a decision support system to help policy makers in the measurement of changes in territories and to monitor their impact on phenomena related to urbanization growth and density. The reference data were manually derived by the authors over an area of approximately 216 km2, referring to 2019, based on the visual interpretation of high resolution images, and are openly available.

The Arthroscopic “Bellows” Sign Identifies Hidden Rotator Cuff Tears

The intraoperative recognition of rotator cuff tears continues to be a challenge in some cases, despite clinical suspicion, a physical examination suggesting a rotator cuff tear, and detection on preoperative imaging studies. Intraoperative identification can be elusive in partial tears including intratendinous tears and obscured full-thickness tears. The purpose of this Technical Note, and the associated technique video, is to discuss a visual sign for the detection of various rotator cuff tear pathologies. Derived from the dynamic fluid expansion and contraction of the affected tissues, this indicator is termed the arthroscopic “bellows” sign.

A Method for Expanding Predicates and Rules in Automated Geometry Reasoning System

Predicates and rules are usually enclosed as built-in functions in automated geometry reasoning systems, meaning users cannot add any predicate or rule, thus resulting in a limited reasoning capability of the systems. A method for expanding predicates and rules in automated geometry reasoning systems is, thus, proposed. Specifically, predicate and rule descriptions are transformed to knowledge trees and forests based on formal representations of geometric knowledge, and executable codes are dynamically and automatically generated by using “code templates”. Thus, a transformation from controlled natural language descriptions to mechanization algorithms is completed, and finally, the dynamic expansion of predicates and rules in the reasoning system is achieved. Moreover, the method has been implemented in an automated geometry reasoning system for Chinese college entrance examination questions, and the practicality and effectiveness of the method were tested. In conclusion, the enclosed setting, which is a shortcoming of traditional reasoning systems, is avoided, the user-defined dynamic expansion of predicates and rules is realized, the application scope of the reasoning system is extended, and the reasoning capability is improved.

The Rapidly Evolving Asymptotic Giant Branch Star, V Hya: ALMA Finds a Multiring Circus with High-velocity Outflows

We have observed the mass-losing carbon star V Hya that is apparently transitioning from an asymptotic giant branch star to a bipolar planetary nebula, at an unprecedented angular resolution of ∼0.″4–0.″6 with the Atacama Large Millimeter/submillimeter Array. Our 13CO and 12CO (J = 3–2 and J = 2–1) images have led to the discovery of a remarkable set of six expanding rings within a flared, warped disk structure undergoing dynamical expansion (DUDE) that lies in the system’s equatorial plane. We also find, for the first time, several bipolar, high-velocity outflows, some of which have parabolic morphologies, implying wide-opening angles, while one (found previously) is clumpy and highly collimated. The latter is likely associated with the high-velocity bullet-like ejections of ionized gas from V Hya; a possible molecular counterpart to the oldest of the four bullets can be seen in the 12CO images. We find a bright, unresolved central source of continuum emission (FWHM size ≲165 au); about 40% of this emission can be produced in a standard radio photosphere, while the remaining 60% is likely due to thermal emission from very large (millimeter-sized) grains, having mass ≳10−5 M ⊙. We have used a radiative transfer model to fit the salient characteristics of the DUDE’s 13CO and 12CO emission out to a radius of 8″ (3200 au) with a flared disk of mass 1.7 × 10−3 M ⊙, whose expansion velocity increases very rapidly with the radius inside a central region of size ∼200 au, and then more slowly outside it, from 9.5 to 11.5 km s−1. The DUDE’s underlying density decreases radially, interspersed with local increases that represent the observationally well-characterized innermost three rings.

Olive Cellulosic Fibre Based Epoxy Composites: Thermal and Dynamic Mechanical Properties

ABSTRACT This study deals with the evaluation of the impact of three different olive tree residues: olive tree small branch (OTS), olive tree big brunch (OTB) and olive tree leaves (OTL) as a filler on thermal properties of olive/epoxy biocomposites. Olive residue-based epoxy composites were processed at 40% filler loading to fabricate biocomposites by hand lay-up techniques. The thermal stability was investigated by thermal gravimetric analysis (TGA) while dynamic mechanical properties and thermal expansion of fiber composites were analyzed by the dynamic mechanical analyzer (DMA) and thermomechanical analyzer (TMA). The OTL/epoxy composite showed improvement in thermal and DMA (storage modulus, loss modulus, and damping factor) as compared to OTB and OTS/epoxy composites. On the other hand, OTS filled epoxy matrix exhibited the greatest thermal degradation temperature while CTE was the lowest and greatest dynamic mechanical properties over all composites. DMA results revealed that the OTS/epoxy composite possesses the highest storage modulus in view of the strong fiber/matrix interfacial. It is evident from obtained results that the incorporation of olive biomass enhanced thermal, dimensional, and dynamic mechanical characterizations of epoxy composites and appropriate use for automotive or materials applications of building that mandate high-dimensional stability and dynamic mechanical characterizations.

Optimized Convolutional Neural Network-Based Capacity Expansion Framework for Electric Vehicle Charging Station

The usage rate of electric vehicles (EVs) is gradually increasing. Recharging of EVs should be carried out repeatedly over time, and the energy needed for this is high and increasing. With the present infrastructure, we cannot supply the required energy, and therefore, we need to implement a model that expands the power grid to satisfy our energy requirements. This paper proposes a convolutional neural network-based dynamic capacity expansion (CNN-DCE) for EV charging. Flower pollination optimization algorithm (FPOA) was used to improve the hyperparameters of CNN during training. The main aim is to reduce the cost of installing additional capacity resources and to reduce the operational cost. To cope with the load growth, different capacity resources are installed at different years of the planning boundary. Five statistical indices, such as mean squared error, mean absolute error, correlation coefficient, and scatter index, are used to evaluate the performance of CNN. The capacity expansion plan in the microgrid is achieved by expanding the energy of battery energy storage systems, microturbines, and solar and wind energy systems. The queuing delay for the EVs waiting in a queue for recharging has been considered. The performance of the proposed CNN-DCE is studied and compared with three other state-of-the-art methods. The results show that the resources reduce the planning cost to 26% for the short-term planning horizon, the long-term plan has 150% of the expansion, and the wind energy system covers 48% of the expansion cost.

Single-cell RNA sequencing of mast cells in eosinophilic esophagitis reveals heterogeneity, local proliferation, and activation that persists in remission

Mast cells (MCs) are pleiotropic cells that accumulate in the esophagus of patients with eosinophilic esophagitis (EoE) and are thought to contribute to disease pathogenesis, yet their properties and functions in this organ are largely unknown. This study aimed to perform a comprehensive molecular and spatial characterization of esophageal MCs in EoE. Esophageal biopsies obtained from patients with active EoE, patients with EoE in histologic remission, and individuals with histologically normal esophageal biopsies and no history of esophageal disease (ie, control individuals) were subject to single-cell RNA sequencing, flow cytometry, and immunofluorescence analyses. This study probed 39,562 single esophageal cells by single-cell RNA sequencing; approximately 5% of these cells were MCs. Dynamic MC expansion was identified across disease states. During homeostasis, TPSAB1highAREGhigh resident MCs were mainly detected in the lamina propria and exhibited a quiescent phenotype. In patients with active EoE, resident MCs assumed an activated phenotype, and 2 additional proinflammatory MC populations emerged in the intraepithelial compartment, each linked to a proliferating MKI67high cluster. One proinflammatory activated MC population, marked as KIThighIL1RL1highFCER1Alow, was not detected in disease remission (termed "transient MC"), whereas the other population, marked as CMA1highCTSGhigh, was detected in disease remission where it maintained an activated state (termed "persistent MC"). MCs were prominent producers of esophageal IL-13 mRNA and protein, a key therapeutic target in EoE. Esophageal MCs comprise heterogeneous populations with transcriptional signatures associated with distinct spatial compartmentalization and EoE disease status. In active EoE, they assume a proinflammatory state and locally proliferate, and they remain activated and poised to reinitiate inflammation even during disease remission.

Dynamic long-term expansion planning of electric vehicle parking lots considering lost opportunity cost and energy saving

In this paper, a practical approach is presented to long-term expansion planning of electric vehicle (EV) parking lots which aims to maximize the parking lot operator’s profit. The optimal number, location, capacity, and time for construction or expansion of EV parking lots have also been determined. Moreover, a method based on drivers’ travel patterns is proposed to estimate the number of EVs referring to each area, in which the unscented transformation method (UTM) is used to calculate the annual growth rate of EVs. An innovative and real-time method for optimal charge and discharge scheduling of EVs is also provided, which can calculate the parking lots’ demand at different times. The most important advantages of this method are its independence from iterative-based optimization algorithms and from modeling some input parameters such as the initial state of charge (SOC) and the arrival time of EVs into the parking lot as uncertain parameters. Also, a Lagrange interpolation-based method is proposed to minimize the energy consumption required for EVs to reach a parking lot. In addition, in order to prevent the reduction of parking lot operator’s profit in case of non-fulfillment of the obligations of the EV owners, an approach based on paying the lost opportunity cost (LOC) is presented. Real-world data has been used for numerical studies. The results show that based on travel patterns and estimating the number of EVs available in each municipal district and using the proposed real-time scheduling method, parking lot’s demand can be calculated as the main prerequisite for parking lots expansion planning. Also, the proposed method for parking selection compared to two other methods will save energy by 26.6% and 8.33%, respectively. Moreover, the proposed approach increases the total parking lot operator’s profit by 4.23%.

Investigation on gas drainage effect under different borehole layout via 3D monitoring of gas pressure

Gas drainage using underground boreholes is an important method for the prevention of coal gas mine accidents. To study the influence of the increase in the number of boreholes on the gas drainage effect using a self-developed device for gas drainage, pressure sensors and branch boreholes were installed in a coal body. Four groups of physical simulation experiments of gas drainage were conducted. The results revealed that with an increase in the number of boreholes, the attenuation rate of the gas pressure is accelerated; the decrease in the gas pressure accelerates in the form of a natural logarithmic function. The effective drainage area, with characteristics of dynamic expansion and variations, has an approximately circular expansion with the borehole as the center. It gradually increases with the advancement of drainage. The relationship between the effective drainage area and the extraction time is in the form of a power function. With an increase in the number of boreholes, the effective drainage area increases more rapidly, and the regional outburst elimination occurs earlier. The effective stress and matrix shrinkage effect exhibit a coupled influence on the permeability, which causes the permeability evolution to exhibit periodic variations. At the early stage of drainage, the effective stress plays a major role, which reduces the permeability. At the middle and late stages of drainage, the matrix shrinkage effect plays a major role, which increases the permeability. With an increase in the number of boreholes, the attenuation rate and recovery amplitude of the permeability increase.

New Insights Into the Lineage-Specific Expansion and Functional Diversification of Lamprey AID/APOBEC Family.

The AID/APOBEC family which converts cytidine to uridine on RNA or DNA experienced dynamic expansion in primates in order to resist exogenous viruses and endogenous retrotransposons. Recently, expansion of AID/APOBEC-like homologs has also been observed in the extant jawless vertebrate lamprey. To reveal what causes such expansion and leads to the functional diversification of lamprey cytosine deaminases (CDAs), we reassessed the CDA genes in Lethenteron japonicum (Lj). We first confirmed the expansion of LjCDA1L1 (CDA1-like 1) genes and found the expression correlation of LjCDA2 and LjCDA1L2 with LjVLRs (variable lymphocyte receptors). Among up to 14 LjCDA1L1 proteins, LjCDA1L1_4a has an extremely high deamination activity on ssDNA and buDNA and, unexpectedly, on dsDNA. LjCDA1L1s can also restrict the infection of HSV-1 particles. Thus, the arms race between the host and pathogens along with the recruitment by VLR assembly may participate together to form a driving force in the expansion and diversification of the lamprey AID/APOBEC family.