Dynamic Snapshot Research Articles

Ultra-high pulse pressure induced particle surface thermal field assisted preparation of W–Al composite

Particle surface thermal field (PSTF) is an inhomogeneous thermal field that concentrates the heat effect on the surface of particles, and it can be utilized to fabricate composite materials with large differences in physical properties. However, it is difficult to form PSTF with large temperature gradients to meet the preparation requirement of materials with extreme differences in physical properties. In our work, a PSTF is proposed and realized by an ultra-high pulse pressure (UHPP) to prepare W–Al composite, the PSTF has a heating effect on the powder interface with a low-temperature interior of the powders. The combination of ex-situ microstructural observation and in-situ molecular dynamic simulation microstructural snapshot is used to demonstrate the densification mechanism. The PSTF is characterized as the temperature higher than 1200 °C on the W–Al powders interface but lower than 200 °C interior of the powders. The densification mechanisms are explained as interfacial jet penetration, interfacial friction welding and interfacial melting. In addition, interfacial temperature distribution creating gradient pressure on the W–Al interface illustrated the generation of gradient Al grains on the W surface, thus creating a high mechanical micro-hardness of 249.8 Hv and compressive strength of 473.2 MPa for the W–Al composite.

Fine-Grained Benchmarking and Targeted Optimization: Enabling Green IoT-Oriented Blockchain in the 6G Era

For 6G-empowered IoT, blockchain is widely regarded as a fundamental technique for addressing the security and privacy issues. Nonetheless, the current blockchains suffer from several flaws, especially the low performance and scalability. They seriously hinder the blockchain deployments in 6G-empowered IoT with high concurrency and complexity. To address this problem, we need to better understand blockchains and try to optimize them. However, we still lack a standard tool for comprehensively benchmarking the IoT-oriented blockchains in 6G era. As to blockchain optimization, the current general-purpose strategies can hardly hold efficiency all the time due to the heterogeneity of 6G-empowered IoT. In this paper, we present a novel pipeline for facilitating the green IoT-oriented blockchains in 6G era, called “fine-grained benchmarking + targeted optimization". Specifically, we first design a benchmarking framework which realizes the whole workflow of data acquisition, log parsing, and metric visualization, offering quantitative blockchain performance reports. Moreover, our framework provides ten metrics divided into four dimensions, leading blockchain benchmarking to the fine-grained level. Based on the acquired insights, we further present the concept of targeted optimization, i.e., directly designing optimization strategies based on the metrics that perform poorly in benchmarking. To better illustrate this pipeline, we take IOTA, the most potential IoT-oriented blockchain in 6G era, as a case study. We observe two defects of IOTA, i.e., low power efficiency and slow ledger synchronization. Following our pipeline, we design two targeted optimization mechanisms, namely Proof-of-Resource Efficiency and Dynamic Local Snapshot Protocol. Extensive experiments demonstrate that our pipeline can maximize the optimization efficiency, successfully addressing the issues hindering IOTA. Last but not least, we emphasize that the proposed pipeline can also be applied by other IoT-oriented blockchains in the 6G era.

Unique Mechanisms of Ion Storage in Polyaniline Electrodes for Pseudocapacitive Energy Storage Devices Unraveled by EQCM-D Analysis.

The optimal performance of organic electrodes for aqueous batteries requires their full compatibility with selected electrolyte solutions. Electrode materials having 1-3-dimensional structures of variable rigidity possess a confined space in their structure filled with water and electrolyte solutions. Depending on the rigidity and confined space geometry, insertion and extraction of ions into electrode structures are often coupled with incorporation/withdrawal of water molecules. Aside from the scientific interest in understanding the charging mechanism of such systems, co-insertion of solvent molecules affects strongly the charge storage capability of the electrodes for energy storage devices. We present herein in situ electrochemical quartz crystal microbalance with dissipation monitoring (EQCM-D) investigations of polyaniline (PANI) electrodes operating in various aqueous Na+-containing electrolytes, namely, Na2SO4, NaClO4, NaBF4, and NaPF6. Careful analysis of the EQCM-D results provides a dynamic snapshot of the mixed anionic/protonic fluxes and the accompanying water molecules' insertion/extraction to/from the PANI electrodes. Based on our observations, it was found that the charging mechanism, as well as the capacity values, strictly depends on the electrolyte pH, the chaotropic/kosmotropic character of the anionic dopants, and the amount of the extracted water molecules. This study demonstrates the effectiveness of analysis by EQCM-D in selecting electrolytes for batteries comprising organic electrodes.

Accurate detection and quantification of SARS-CoV-2 genomic and subgenomic mRNAs by ddPCR and meta-transcriptomics analysis

SARS-CoV-2 replication requires the synthesis of a set of structural proteins expressed through discontinuous transcription of ten subgenomic mRNAs (sgmRNAs). Here, we have fine-tuned droplet digital PCR (ddPCR) assays to accurately detect and quantify SARS-CoV-2 genomic ORF1ab and sgmRNAs for the nucleocapsid (N) and spike (S) proteins. We analyzed 166 RNA samples from anonymized SARS-CoV-2 positive subjects and we observed a recurrent and characteristic pattern of sgmRNAs expression in relation to the total viral RNA content. Additionally, expression profiles of sgmRNAs, as determined by meta-transcriptomics sequencing of a subset of 110 RNA samples, were highly correlated with those obtained by ddPCR. By providing a comprehensive and dynamic snapshot of the levels of SARS-CoV-2 sgmRNAs in infected individuals, our results may contribute a better understanding of the dynamics of transcription and expression of the genome of SARS-CoV-2 and facilitate the development of more accurate molecular diagnostic tools for the stratification of COVID-19 patients.

Artificial night light helps account for observer bias in citizen science monitoring of an expanding large mammal population.

The integration of citizen scientists into ecological research is transforming how, where, and when data are collected, and expanding the potential scales of ecological studies. Citizen-science projects can provide numerous benefits for participants while educating and connecting professionals with lay audiences, potentially increasing the acceptance of conservation and management actions. However, for all the benefits, collection of citizen-science data is often biased towards areas that are easily accessible (e.g. developments and roadways), and thus data are usually affected by issues typical of opportunistic surveys (e.g. uneven sampling effort). These areas are usually illuminated by artificial light at night (ALAN), a dynamic sensory stimulus that alters the perceptual world for both humans and wildlife. Our goal was to test whether satellite-based measures of ALAN could improve our understanding of the detection process of citizen-scientist-reported sightings of a large mammal. We collected observations of American black bears Ursus americanus (n=1,315) outside their primary range in Minnesota, USA, as part of a study to gauge population expansion. Participants from the public provided sighting locations of bears on a website. We used an occupancy modelling framework to determine how well ALAN accounted for observer metrics compared to other commonly used metrics (e.g. housing density). Citizen scientists reported 17% of bear sightings were under artificially lit conditions and monthly ALAN estimates did the best job accounting for spatial bias in detection of all observations, based on AIC values and effect sizes ( =0.81, 0.71-0.90 95% CI). Bear detection increased with elevated illuminance; relative abundance was positively associated with natural cover, proximity to primary bear range and lower road density. Although the highest counts of bear sightings occurred in the highly illuminated suburbs of the Minneapolis-St. Paul metropolitan region, we estimated substantially higher bear abundance in another region with plentiful natural cover and low ALAN (up to ~375% increased predicted relative abundance) where observations were sparse. We demonstrate the importance of considering ALAN radiance when analysing citizen-scientist-collected data, and we highlight the ways that ALAN data provide a dynamic snapshot of human activity.

A Continuous-Time, Forward-Looking, Implied Ambiguity Index

This paper introduces a real-time, instantaneous, forward-looking, implied ambiguity index. Ambiguity — the uncertainty of probabilities — is measured by the expected volatility of probabilities. Using option prices, the dynamic and continuous snapshot of the expected (implied) ambiguity in the financial markets is extracted. The ambiguity of risk-neutral probabilities is extracted as well. Similarly to the VIX, the proposed ambiguity indices can be traded. The well-documented relation between ambiguity and rates of return suggests a diversification benefit from including an ambiguity instrument in an investment portfolio. Based upon the proposed ambiguity indices, futures and options can be designed to deliver and trade pure ambiguity exposure in a single efficient package.

Don\u2019t Go Chasing Waterfalls: Motion Aftereffects and the Dynamic Snapshot Theory of Temporal Experience

The philosophical investigation of perceptual illusions can generate fruitful insights in the study of subjective time consciousness. However, the way illusions are interpreted is often controversial. Recently, proponents of the so-called dynamic snapshot theory have appealed to the Waterfall Illusion, a kind of motion aftereffect, to support a particular view of temporal consciousness according to which experience is structured as a series of instantaneous snapshots with dynamic qualities. This dynamism is meant to account for familiar features of the phenomenology of time, such as succession, continuity, and change. Previous theories have typically appealed to a subjective present occupying an interval of time; that is, a “specious present.” I argue, through analysis of motion aftereffect illusions and the rare condition of akinetopsia, i.e. motion-blindness, that the Waterfall Illusion fails to support the dynamic snapshot theory as intended. Furthermore, I suggest that future theories of subjective time should see temporal phenomenology as the result of non-localised processes closely tied to the mechanism underlying consciousness generally.

Link prediction in dynamic networks using time-aware network embedding and time series forecasting

As most real-world networks evolve over time, link prediction over such dynamic networks has become a challenging issue. Recent researches focus towards network embedding to improve the performance of link prediction task. Most of the network embedding methods are only applicable to static networks and therefore cannot capture the temporal variations of dynamic networks. In this work, we propose a time-aware network embedding method which generates node embeddings by capturing the temporal dynamics of evolving networks. Unlike existing works which use deep architectures, we design an evolving skip-gram architecture to create dynamic node embeddings. We use the node embedding similarities between consecutive snapshots to construct a univariate time series of node similarities. Further, we use times series forecasting using auto regressive integrated moving average (ARIMA) model to predict the future links. We conduct experiments using dynamic network snapshot datasets from various domains and demonstrate the advantages of our system compared to other state-of-the-art methods. We show that, combining network embedding with time series forecasting methods can be an efficient solution to improve the quality of link prediction in dynamic networks.

Assessing captures of recreational spearfishing in Abrolhos reefs, Brazil, through social media

Recreational fishing is an important economic and social activity in many countries. However low- and middle-income nations often lack basic data, like catch recording, on this fishery. To gather data to better understand recreational spearfishing catch in Abrolhos Bank, we assessed images posted on Facebook between June 2014 and January 2015. We described species richness, individual fish body size, and trophic group, as well as conservation status and local retail value. A total of 217 images were analyzed, featuring the capture of 962 individuals from 25 species. Captures were composed mainly of the piscivores black grouper Mycteroperca bonaci (36%), dog snapper Lutjanus jocu (35%), and great barracuda Sphyraena barracuda (11%). Herbivores comprised 6% of catches, represented mainly by the endangered the greenback parrotfish Scarus trispinosus. Overall, half of the captured fish were below the first gonadal maturation size and 43% are currently on the national Red List of endangered species. The retail value of 97% of specimens captured was high or very high, composed mainly of species with individuals longer than 40 cm. Social media can provide a complementary data source for recreational fisheries research, offering a rich and dynamic snapshot of these fisheries.

Superspreaders and superblockers based community evolution tracking in dynamic social networks

Detecting and tracking communities in dynamic social networks has been a grand multidisciplinary challenge. There are two key steps in tracking the community evolution of dynamic social networks, including dynamic community detection and evolutionary events identification. For dynamic community detection, incremental clustering has been used as one of the most efficient methods; however, incrementally detecting network communities may result in partition errors such that continuous error accumulation will cause a discrepancy between the computed community structure and the underlying ground-truth. For evolutionary events identification, core-node-based methods have been widely employed; however, they do not distinguish between the heterogeneous contributions of core nodes to different evolutionary events, thereby resulting in a reduced accuracy of evolutionary event identification. This paper introduces a novel two-stage method that circumvents both of these problems simultaneously. Firstly, we propose an error accumulation sensitive (EAS) incremental community detection method for dynamic social networks. In our novel EAS method, rather than updating the community structure partially, a dynamic network snapshot is totally re-partitioned once the error accumulation degree of incremental clustering exceeds a pre-defined threshold. Secondly, to identify different critical evolution events, we introduce a superspreaders and superblockers (SAS) based community evolution tracking method for dynamic social networks which utilizes the properties of superspreader and superblocker nodes, the two types of core nodes related to spreading outbreaks in social networks. Experiments conducted on artificial and real-world social networks demonstrate that our proposed method can both efficiently detect dynamic network communities and accurately identify all critical evolutionary events, outperforming a total of eight competing methods. Our two-stage EAS-SAS approach could thus represent a potential method of choice for many real-world applications to community discovery and community evolution tracking in dynamic social networks.

Dynamic perceptual completion and the dynamic snapshot view to help solve the ‘two times’ problem

Perceptual completion fills the gap for discrete perception to become continuous. Similarly, dynamic perceptual completion (DPC) provides an experience of dynamic continuity. Our recent discovery of the ‘happening’ (H) element of DPC completes the total experience for dynamism in the flow of time (FOT). However, a phenomenological explanation for these experiences is essential. The Snapshot Hypotheses especially the Dynamic Snapshot View provides the most comprehensive explanation. From that understanding the ‘two times’ problem (TTP) can be addressed. The static time of spacetime cosmologies has been irreconcilable with the dynamic FOT. Dismissing the FOT as an illusion is unsatisfactory. Therefore, we provide four hypotheses for the TTP. 1) Since cosmological static time demands that all events (cerebral included) are discrete, DPC elements for dynamism should likewise be expected to be discrete and accounted for by a snapshot phenomenology such as the DSV. 2) If temporality can be demonstrated to be similar to apparent motion by being a snapshot phenomenon and not demanding temporal extension it would confirm the DSV and permit reconciliation with static time. 3) If the ‘present moment’ (of the FOT) is subjective as static time theories suggest, it should be possible experimentally for an observer to choose his own ‘present’ by moving (perceptually) to various points in the past with the aid of virtual reality. 4) If dynamism e.g. motion can be precluded without significant information loss or violating physics principles it is a cognitive add-on, thereby contradicting non-static time theories which suggest that time is ‘real.’ We confirm those hypotheses.

Minima sensibilia: Against the dynamic snapshot model of temporal experience

AbstractIn our wakeful conscious lives, the experience of time and dynamic temporal phenomena—such as continuous motion and change—appears to be ubiquitous. How is it that temporality is woven into our conscious experience? Is it through perceptual experience presenting a series of instantaneous states of the world, which combine together—in a sense which would need to be specified—to give us experience of dynamic temporal phenomena? In this paper, I argue that this is not the case. Several authors have recently proposed dynamic snapshot models of temporal experience—such as Prosser and Arstila, building upon Le Poidevin—according to which, perceptual experience has no temporal content of a non‐zero extent. I argue that there is an absence of motivation for such a view; I develop and defend the claim that perceptual experience minimally presents something of some non‐zero temporal extent as such.

High-throughput metabolomics for discovering metabolic biomarkers from intestinal tumorigenesis in APC min/+ mice based on liquid chromatography/mass spectrometry

As a major public health concern, colon cancer is one of the most common cancer types, which is also the second cause of cancer death in developed countries and the third most common cancer in other parts of the world. It was reported that patients diagnosed at early stage have a chance to obtain 5-year survival rates at least compared to patients with late stage. Facing the multistep process in intestinal tumorigenesis, there is an urgent need to develop more effective early detection strategies for ameliorating the patient clinical outcome. Metabolomics open up a novel avenue of seeking valuable potential biomarkers for assessing disease severity and prognosticating course by dynamic snapshot of small molecule metabolites. The study aims to provide deeper insights into the discovery, identification and functional pathways analysis of differentially expressed metabolites in intestinal tumorigenesis in APC min/+ mice used by the serum metabolomics, and bring about useful information for further effective prevention and treatment of the disease. 17 marker metabolites and related metabolism pathway were identified using non-targeted metabolomics based on liquid chromatography/mass spectrometry (LC/MS) associated with multivariate statistical analysis. The ingenuity pathway analysis platform involved multiple-pathways was applied to metabolic network analysis for further understanding the relationship between functional metabolic pathways and disease.

Taking a goal-centred dynamic snapshot as a possibility for local homing in initially naïve bumblebees.

It is essential for central place foragers, such as bumblebees, to return reliably to their nest. Bumblebees, leaving their inconspicuous nest hole for the first time need to gather and learn sufficient information about their surroundings to allow them to return to their nest at the end of their trip, instead of just flying away to forage. Therefore, we assume an intrinsic learning programme that manifests itself in the flight structure immediately after leaving the nest for the first time. In this study, we recorded and analysed the first outbound flight of individually marked naïve bumblebees in an indoor environment. We found characteristic loop-like features in the flight pattern that appear to be necessary for the bees to acquire environmental information and might be relevant for finding the nest hole after a foraging trip. Despite common features in their spatio-temporal organisation, first departure flights from the nest are characterised by a high level of variability in their loop-like flight structure across animals. Changes in turn direction of body orientation, for example, are distributed evenly across the entire area used for the flights without any systematic relationship to the nest location. By considering the common flight motifs and this variability, we came to the hypothesis that a kind of dynamic snapshot is taken during the early phase of departure flights centred at the nest location. The quality of this snapshot is hypothesised to be 'tested' during the later phases of the departure flights concerning its usefulness for local homing.

Dynamic studies of H-Ras•GTPγS interactions with nucleotide exchange factor Sos reveal a transient ternary complex formation in solution.

The cycling between GDP- and GTP- bound forms of the Ras protein is partly regulated by the binding of Sos. The structural/dynamic behavior of the complex formed between activated Sos and Ras at the point of the functional cycle where the nucleotide exchange is completed has not been described to date. Here we show that solution NMR spectra of H-Ras∙GTPγS mixed with a functional fragment of Sos (SosCat) at a 2:1 ratio are consistent with the formation of a rather dynamic assembly. H-Ras∙GTPγS binding was in fast exchange on the NMR timescale and retained a significant degree of molecular tumbling independent of SosCat, while SosCat also tumbled largely independently of H-Ras. Estimates of apparent molecular weight from both NMR data and SEC-MALS revealed that, at most, only one H-Ras∙GTPγS molecule appears stably bound to Sos. The weak transient interaction between Sos and the second H-Ras∙GTPγS may provide a necessary mechanism for complex dissociation upon the completion of the native GDP → GTP exchange reaction, but also explains measurable GTP → GTP exchange activity of Sos routinely observed in in vitro assays that use fluorescently-labelled analogs of GTP. Overall, the data presents the first dynamic snapshot of Ras functional cycle as controlled by Sos.

Stoichiometry and Change of the mRNA Closed-Loop Factors as Translating Ribosomes Transit from Initiation to Elongation.

Protein synthesis is a highly efficient process and is under exacting control. Yet, the actual abundance of translation factors present in translating complexes and how these abundances change during the transit of a ribosome across an mRNA remains unknown. Using analytical ultracentrifugation with fluorescent detection we have determined the stoichiometry of the closed-loop translation factors for translating ribosomes. A variety of pools of translating polysomes and monosomes were identified, each containing different abundances of the closed-loop factors eIF4E, eIF4G, and PAB1 and that of the translational repressor, SBP1. We establish that closed-loop factors eIF4E/eIF4G dissociated both as ribosomes transited polyadenylated mRNA from initiation to elongation and as translation changed from the polysomal to monosomal state prior to cessation of translation. eIF4G was found to particularly dissociate from polyadenylated mRNA as polysomes moved to the monosomal state, suggesting an active role for translational repressors in this process. Consistent with this suggestion, translating complexes generally did not simultaneously contain eIF4E/eIF4G and SBP1, implying mutual exclusivity in such complexes. For substantially deadenylated mRNA, however, a second type of closed-loop structure was identified that contained just eIF4E and eIF4G. More than one eIF4G molecule per polysome appeared to be present in these complexes, supporting the importance of eIF4G interactions with the mRNA independent of PAB1. These latter closed-loop structures, which were particularly stable in polysomes, may be playing specific roles in both normal and disease states for specific mRNA that are deadenylated and/or lacking PAB1. These analyses establish a dynamic snapshot of molecular abundance changes during ribosomal transit across an mRNA in what are likely to be critical targets of regulation.

Static and dynamic snapshots for goal localization in insects?

Bees, wasps and ants navigate successfully between feeding sites and their nest, despite the small size of their brains which contain less than a million neurons. A long history of studies examining the role of visual memories in homing behavior show that insects can localize a goal by finding a close match between a memorized view at the goal location and their current view ("snapshot matching"). However, the concept of static snapshot matching might not explain all aspects of homing behavior, as honeybees are able to use landmarks that are statically camouflaged. In this case the landmarks are only detectable by relative motion cues between the landmark and the background, which the bees generate when they perform characteristic flight maneuvers close to the landmarks. The bees' navigation performance can be explained by a matching scheme based on optic flow amplitudes ("dynamic snapshot matching"). In this article, I will discuss the concept of dynamic snapshot matching in the light of previous literature.

Scheduling Temporal Data with Dynamic Snapshot Consistency Requirement in Vehicular Cyber-Physical Systems

Timely and efficient data dissemination is one of the fundamental requirements to enable innovative applications in vehicular cyber-physical systems (VCPS). In this work, we intensively analyze the characteristics of temporal data dissemination in VCPS. On this basis, we formulate the static and dynamic snapshot consistency requirements on serving real-time requests for temporal data items. Two online algorithms are proposed to enhance the system performance with different requirements. In particular, a reschedule mechanism is developed to make the scheduling adaptable to the dynamic snapshot consistency requirement. A comprehensive performance evaluation demonstrates the superiority of the proposed algorithms.

A novel approach to decision making of Mined Data using Dynamic Snapshot Pattern Recognition Algorithm (DS-PRA)

A new approach to pattern recognition and decision machines profiling is proposed, proved and tested. The technique adopts the Snapshot method dynamically as a function of both organization policy and the organization departments policies. Such policies are associated with individual products and services provided by the organization with departments policies derived from general organization profile with the organization policy being a function of the various departments profiles. It is proved through real data the ability of such algorithm to classify, detect and predict policy changes and identify differences between different organizations. Also, such algorithm combines the concepts of general Artificial intelligence through the use of knowledge bases and Neural Networks by utilizing a similar weights matrix.

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing (MTT)

Our goal is to obtain a comprehensive description of molecular processes occurring at cellular membranes in different biological functions. We aim at characterizing the complex organization and dynamics of the plasma membrane at single-molecule level, by developing analytic tools dedicated to Single-Particle Tracking (SPT) at high density: Multiple-Target Tracing (MTT). Single-molecule videomicroscopy, offering millisecond and nanometric resolution, allows a detailed representation of membrane organization by accurately mapping descriptors such as cell receptors localization, mobility, confinement or interactions. We revisited SPT, both experimentally and algorithmically. Experimental aspects included optimizing setup and cell labeling, with a particular emphasis on reaching the highest possible labeling density, in order to provide a dynamic snapshot of molecular dynamics as it occurs within the membrane. Algorithmic issues concerned each step used for rebuilding trajectories: peaks detection, estimation and reconnection, addressed by specific tools from image analysis. Implementing deflation after detection allows rescuing peaks initially hidden by neighboring, stronger peaks. Of note, improving detection directly impacts reconnection, by reducing gaps within trajectories. Performances have been evaluated using Monte-Carlo simulations for various labeling density and noise values, which typically represent the two major limitations for parallel measurements at high spatiotemporal resolution. The nanometric accuracy obtained for single molecules, using either successive on/off photoswitching or non-linear optics, can deliver exhaustive observations. This is the basis of nanoscopy methods such as STORM, PALM, RESOLFT or STED, which may often require imaging fixed samples. The central task is the detection and estimation of diffraction-limited peaks emanating from single-molecules. Hence, providing adequate assumptions such as handling a constant positional accuracy instead of Brownian motion, MTT is straightforwardly suited for nanoscopic analyses. Furthermore, MTT can fundamentally be used at any scale: not only for molecules, but also for cells or animals, for instance. Hence, MTT is a powerful tracking algorithm that finds applications at molecular and cellular scales.