Large-scale Signal Research Articles

Multi-Model Multi-Physics Ensemble: A Futuristic Way to Extended Range Prediction System

In an endeavor to design better forecasting tools for real-time prediction, the present work highlights the strength of the multi-model multi-physics ensemble over its operational predecessor version. The exiting operational extended range prediction system (ERPv1) combines the coupled, and its bias-corrected sea-surface temperature forced atmospheric model running at two resolutions with perturbed initial condition ensemble. This system had accomplished important goals on the sub-seasonal scale skillful forecast; however, the skill of the system is limited only up to 2 weeks. The next version of this ERP system is seamless in resolution and based on a multi-physics multi-model ensemble (MPMME). Similar to the earlier version, this system includes coupled climate forecast system version 2 (CFSv2) and atmospheric global forecast system forced with real-time bias-corrected sea-surface temperature from CFSv2. In the newer version, model integrations are performed six times in a month for real-time prediction, selecting the combination of convective and microphysics parameterization schemes. Additionally, more than 15 years hindcast are also generated for these initial conditions. The preliminary results from this system demonstrate appreciable improvements over its predecessor in predicting the large-scale low variability signal and weekly mean rainfall up to 3 weeks lead. The subdivision-wise skill analysis shows that MPMME performs better, especially in the northwest and central parts of India.

Large-scale climate signals of a European oxygen isotope network from tree rings

Abstract. We investigate the climate signature of δ18O tree-ring records from sites distributed all over Europe covering the last 400 years. An empirical orthogonal function (EOF) analysis reveals two distinct modes of variability on the basis of the existing δ18O tree-ring records. The first mode is associated with anomaly patterns projecting onto the El Niño–Southern Oscillation (ENSO) and reflects a multi-seasonal climatic signal. The ENSO link is pronounced for the last 130 years, but it is found to be weak over the period from 1600 to 1850, suggesting that the relationship between ENSO and the European climate may not be stable over time. The second mode of δ18O variability, which captures a north–south dipole in the European δ18O tree-ring records, is related to a regional summer atmospheric circulation pattern, revealing a pronounced centre over the North Sea. Locally, the δ18O anomalies associated with this mode show the same (opposite) sign with temperature (precipitation). Based on the oxygen isotopic signature derived from tree rings, we argue that the prevailing large-scale atmospheric circulation patterns and the related teleconnections can be analysed beyond instrumental records.

Vehicular Crowdsourcing for Congestion Support in Smart Cities

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various transportation authorities to solve problems that otherwise would either take an inordinate amount of time to solve or cannot be solved for lack for adequate municipal resources. VACCS offers direct benefits to both the driving public and the Smart City. By developing timing plans that respond to current traffic conditions, overall traffic flow will improve, carbon emissions will be reduced, and economic impacts of congestion on citizens and businesses will be lessened. It is expected that drivers will be willing to donate under-utilized on-board computing resources in their vehicles to develop improved signal timing plans in return for the direct benefits of time savings and reduced fuel consumption costs. VACCS allows the Smart City to dynamically respond to traffic conditions while simultaneously reducing investments in the computational resources that would be required for traditional adaptive traffic signal control systems.

Single Cell Label-Free Probing of Chromatin Dynamics During B Lymphocyte Maturation.

Large-scale intracellular signaling during developmental growth or in response to environmental alterations are largely orchestrated by chromatin within the cell nuclei. Chemical and conformational modifications of the chromatin architecture are critical steps in the regulation of differential gene expression and ultimately cell fate determination. Therefore, establishing chemical properties of the nucleus could provide key markers for phenotypic characterization of cellular processes on a scale of individual cells. Raman microscopy is a sensitive technique that is capable of probing single cell chemical composition—and sub-cellular regions—in a label-free optical manner. As such, it has great potential in both clinical and basic research. However, perceived limitations of Raman spectroscopy such as low signal intensity and the difficulty in linking alterations in vibrational signals directly with ensuing biological effects have hampered advances in the field. Here we use immune B lymphocyte development as a model to assess chromatin and transcriptional changes using confocal Raman microscopy in combination with microfluidic devices and correlative transcriptomics, thereby linking changes in chemical and structural properties to biological outcomes. Live B lymphocytes were assessed before and after maturation. Multivariate analysis was applied to distinguish cellular components within each cell. The spectral differences between non-activated and activated B lymphocytes were then identified, and their correlation with known intracellular biological changes were assessed in comparison to conventional RNA-seq analysis. Our data shows that spectral analysis provides a powerful tool to study gene activation that can complement conventional molecular biology techniques and opens the way for mapping the dynamics in the biochemical makeup of individual cells.

Harvesting Wind Energy by a Triboelectric Nanogenerator for an Intelligent High-Speed Train System

The operation cost of an intelligent high-speed train system is greatly increased by the enormous energy demand of large-scale signal and sensor networks. However, the wind energy generated by high...

Adaptive support-driven Bayesian reweighted algorithm for sparse signal recovery

This paper presents an adaptive support-driven Bayesian reweighted algorithm based on shrinkage thresholding, for large-scale sparse signal recovery. The proposed algorithm is based on the widely used sparse Bayesian learning (SBL) methods, which relies on parameterized prior. SBL leads to a nonconvex optimization problem that encourages model with few nonzero coefficients. In SBL, sparse signal recovery problems can be solved by a reweighted $$\ell _1$$ -regularization algorithm. However, this algorithm is expensive in computation and memory, especially for large-scale problem. To alleviate this problem, we proposed a restart strategy leading to the adaptive support estimate, which reduces the computational burden and memory simultaneously. Numerical experiments demonstrate the effectiveness and computational advantages of the proposed algorithm.

Citywide road-network traffic monitoring using large-scale mobile signaling data

Road-network traffic monitoring on city-scale is critical for a wide range of applications, such as traffic forecasting, congestion identification, traffic safety, and urban planning, etc. Despite the fruitful research outcomes, however, most traffic monitoring models suffered from limited coverage, data sparsity, and data deviation, which leads to a biased and inaccurate result. With the widespread usage of mobile phones, mobile signaling data is of great value for various fields, especially for monitoring urban traffic. Thousands cell towers are distributed in the urban area, which can serve as ubiquitous sensors. Specifically, a mobile phone will passively generate a mobile signaling record that contains users’ spatiotemporal information. When mobile phone users move with their phones, their phones will interact with cell towers and these towers can obtain their mobile signaling records. And these signaling records contain sufficient information for traffic monitoring. However, there also exists excessive noise in signaling records, which makes most monitoring models abandon these data. In this paper, we present the Urban-STM scheme, which utilizes large-scale anonymous and coarse-grained mobile signaling data to infer road-network traffic conditions. We apply our scheme to a real-world signaling dataset in Changchun city and present an extensive validation study based on 2000 taxicabs’ GPS trajectories. Experiment results show that our scheme improves traffic monitoring performance in terms of coverage and accuracy.

Large-scale regulatory and signaling network assembly through linked open data.

Huge efforts are currently underway to address the organization of biological knowledge through linked open databases. These databases can be automatically queried to reconstruct regulatory and signaling networks. However, assembling networks implies manual operations due to source-specific identification of biological entities and relationships, multiple life-science databases with redundant information and the difficulty of recovering logical flows in biological pathways. We propose a framework based on Semantic Web technologies to automate the reconstruction of large-scale regulatory and signaling networks in the context of tumor cells modeling and drug screening. The proposed tool is pyBRAvo (python Biological netwoRk Assembly), and here we have applied it to a dataset of 910 gene expression measurements issued from liver cancer patients. The tool is publicly available at https://github.com/pyBRAvo/pyBRAvo.

Joint Spatial-Propagation Modeling of Cellular Networks Based on the Directional Radii of Poisson Voronoi Cells

In coverage-oriented networks, base stations (BSs) are deployed in a way such that users at the cell boundaries achieve sufficient signal strength. The shape and size of cells vary from BS to BS, since the large-scale signal propagation conditions differ in different geographical regions. This work proposes and studies a joint spatial-propagation (JSP) model, which considers the correlation between cell radii and the large-scale signal propagation (captured by shadowing). We first introduce the notion of the directional radius of Voronoi cells, which has applications in cellular networks and beyond. The directional radius of a cell is defined as the distance from the nucleus to the cell boundary at an angle relative to the direction of a uniformly random location in the cell. We study the distribution of the radii in two types of cells in the Poisson Voronoi tessellations: the zero-cell, which contains the origin, and the typical cell. The results are applied to analyze the JSP model. We show that, even though the Poisson point process (PPP) is often considered as a pessimistic spatial model for BS locations, the JSP model with the PPP achieves coverage performance close to the most optimistic one-the standard triangular lattice model. Further, we show that the network performance depends critically on the variance of the large-scale path loss along the cell boundary.

Relationships between Large-Scale Climate Signals and Winter Precipitation Amounts and Patterns over Iran

Precipitation is one of the most complex weather phenomena; its successful spatiotemporal modeling provides substantial information for designing water management systems. This research aimed to determine the relationship of sixteen large-scale climate signals with weather types (WTs) as well as precipitation amounts over Iran during the winter season (January–March). Daily precipitation data covering the period 1991–2010 were collected from 130 weather stations across the country. In addition, four atmospheric variables affecting the precipitation process over Iran were derived from National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis datasets as predictors. Several nonhomogeneous hidden Markov models (NHMMs) were employed to identify the prevailing WTs governing the spatial distributions of daily precipitation in winter over Iran. A hidden state (i.e., WTs) in NHMMs was dependent on an immediately previous state of WT in the hidden layer and the reanalyzed atmospheric variables at the current time in the predictor layer. Daily WTs over the recording period were decoded using Viterbi’s algorithm. Results showed that the NHMM including the atmospheric variable of mean sea level pressure (Mslp) in the predictor layer had the minimum value (= 0) of Bayesian information criterion difference (BICD) among the other variables and was treated as the best model. The chosen model distinguished the eight spatially coherent WTs for winter that were in accordance with the eight synoptic patterns influencing the study area. Based on the Spearman’s rank method, amounts of winter precipitation at many stations in Iran were significantly correlated with four climate signals, including the atlantic multidecadal oscillation (AMO), East Atlantic/West Russia (EA/WR) pattern, Southern Oscillation Index (SOI), and Trans Polar Index (TPI). However, these station-based relationships could not exhibit well the spatial dependencies of the teleconnections and regional precipitation. It was also found that the unconditional and conditional frequencies of WTs were under the influence of a greater number of large-scale climate signals than the precipitation amounts, were under the influence of a greater number of large-scale climate signals. Moreover, the inherent nature of WTs in keeping spatial dependencies allowed for a better understanding of regional teleconnective relationships. These findings confirmed the advantage of regional rather than local assessments of teleconnective relationships.

Mobility and Social Change: Understanding the European Neolithic Period after the Archaeogenetic Revolution

This paper discusses and synthesizes the consequences of the archaeogenetic revolution to our understanding of mobility and social change during the Neolithic period in Europe (6500–2000 BC). In spite of major obstacles to a productive integration of archaeological and anthropological knowledge with ancient DNA data, larger changes in the European gene pool are detected and taken as indications for large-scale migrations during two major periods: the Early Neolithic expansion into Europe (6500–4000 BC) and the third millennium BC “steppe migration.” Rather than massive migration events, I argue that both major genetic turnovers are better understood in terms of small-scale mobility and human movement in systems of population circulation, social fission and fusion of communities, and translocal interaction, which together add up to a large-scale signal. At the same time, I argue that both upticks in mobility are initiated by the two most consequential social transformations that took place in Eurasia, namely the emergence of farming, animal husbandry, and sedentary village life during the Neolithic revolution and the emergence of systems of centralized political organization during the process of urbanization and early state formation in southwest Asia.

Large-Scale Traffic Signal Control Using a Novel Multiagent Reinforcement Learning.

Finding the optimal signal timing strategy is a difficult task for the problem of large-scale traffic signal control (TSC). Multiagent reinforcement learning (MARL) is a promising method to solve this problem. However, there is still room for improvement in extending to large-scale problems and modeling the behaviors of other agents for each individual agent. In this article, a new MARL, called cooperative double Q -learning (Co-DQL), is proposed, which has several prominent features. It uses a highly scalable independent double Q -learning method based on double estimators and the upper confidence bound (UCB) policy, which can eliminate the over-estimation problem existing in traditional independent Q -learning while ensuring exploration. It uses mean-field approximation to model the interaction among agents, thereby making agents learn a better cooperative strategy. In order to improve the stability and robustness of the learning process, we introduce a new reward allocation mechanism and a local state sharing method. In addition, we analyze the convergence properties of the proposed algorithm. Co-DQL is applied to TSC and tested on various traffic flow scenarios of TSC simulators. The results show that Co-DQL outperforms the state-of-the-art decentralized MARL algorithms in terms of multiple traffic metrics.

Assessment of Tidal Range Changes in the North Sea From 1958 to 2014.

We document an exceptional large‐spatial scale case of changes in tidal range in the North Sea, featuring pronounced trends between −2.3 mm/yr at tide gauges in the United Kingdom and up to 7 mm/yr in the German Bight between 1958 and 2014. These changes are spatially heterogeneous and driven by a superposition of local and large‐scale processes within the basin. We use principal component analysis to separate large‐scale signals appearing coherently over multiple stations from rather localized changes. We identify two leading principal components (PCs) that explain about 69% of tidal range changes in the entire North Sea including the divergent trend pattern along United Kingdom and German coastlines that reflects movement of the region’s semidiurnal amphidromic areas. By applying numerical and statistical analyses, we can assign a baroclinic (PC1) and a barotropic large‐scale signal (PC2), explaining a large part of the overall variance. A comparison between PC2 and tide gauge records along the European Atlantic coast, Iceland, and Canada shows significant correlations on time scales of less than 2 years, which points to an external and basin‐wide forcing mechanism. By contrast, PC1 dominates in the southern North Sea and originates, at least in part, from stratification changes in nearby shallow waters. In particular, from an analysis of observed density profiles, we suggest that an increased strength and duration of the summer pycnocline has stabilized the water column against turbulent dissipation and allowed for higher tidal elevations at the coast.

Prediction error signaling explains neuronal mismatch responses in the medial prefrontal cortex.

The mismatch negativity (MMN) is a key biomarker of automatic deviance detection thought to emerge from 2 cortical sources. First, the auditory cortex (AC) encodes spectral regularities and reports frequency-specific deviances. Then, more abstract representations in the prefrontal cortex (PFC) allow to detect contextual changes of potential behavioral relevance. However, the precise location and time asynchronies between neuronal correlates underlying this frontotemporal network remain unclear and elusive. Our study presented auditory oddball paradigms along with "no-repetition" controls to record mismatch responses in neuronal spiking activity and local field potentials at the rat medial PFC. Whereas mismatch responses in the auditory system are mainly induced by stimulus-dependent effects, we found that auditory responsiveness in the PFC was driven by unpredictability, yielding context-dependent, comparatively delayed, more robust and longer-lasting mismatch responses mostly comprised of prediction error signaling activity. This characteristically different composition discarded that mismatch responses in the PFC could be simply inherited or amplified downstream from the auditory system. Conversely, it is more plausible for the PFC to exert top-down influences on the AC, since the PFC exhibited flexible and potent predictive processing, capable of suppressing redundant input more efficiently than the AC. Remarkably, the time course of the mismatch responses we observed in the spiking activity and local field potentials of the AC and the PFC combined coincided with the time course of the large-scale MMN-like signals reported in the rat brain, thereby linking the microscopic, mesoscopic, and macroscopic levels of automatic deviance detection.

Context-specific network modeling identifies new crosstalk in β-adrenergic cardiac hypertrophy.

Cardiac hypertrophy is a context-dependent phenomenon wherein a myriad of biochemical and biomechanical factors regulate myocardial growth through a complex large-scale signaling network. Although numerous studies have investigated hypertrophic signaling pathways, less is known about hypertrophy signaling as a whole network and how this network acts in a context-dependent manner. Here, we developed a systematic approach, CLASSED (Context-specific Logic-bASed Signaling nEtwork Development), to revise a large-scale signaling model based on context-specific data and identify main reactions and new crosstalks regulating context-specific response. CLASSED involves four sequential stages with an automated validation module as a core which builds a logic-based ODE model from the interaction graph and outputs the model validation percent. The context-specific model is developed by estimation of default parameters, classified qualitative validation, hybrid Morris-Sobol global sensitivity analysis, and discovery of missing context-dependent crosstalks. Applying this pipeline to our prior-knowledge hypertrophy network with context-specific data revealed key signaling reactions which distinctly regulate cell response to isoproterenol, phenylephrine, angiotensin II and stretch. Furthermore, with CLASSED we developed a context-specific model of β-adrenergic cardiac hypertrophy. The model predicted new crosstalks between calcium/calmodulin-dependent pathways and upstream signaling of Ras in the ISO-specific context. Experiments in cardiomyocytes validated the model’s predictions on the role of CaMKII-Gβγ and CaN-Gβγ interactions in mediating hypertrophic signals in ISO-specific context and revealed a difference in the phosphorylation magnitude and translocation of ERK1/2 between cardiac myocytes and fibroblasts. CLASSED is a systematic approach for developing context-specific large-scale signaling networks, yielding insights into new-found crosstalks in β-adrenergic cardiac hypertrophy.

Adaptive training of vibration-based anomaly detector for wind turbine condition monitoring

Adaptive training of a vibration-based anomaly detector for wind turbine condition monitoring system (CMS) is carried out to achieve high-performance detection from the early stages of monitoring. Machine learning-based wind turbine CMSs are required to collect large-scale data to yield reli-able predictions. Existing studies in this area have postulated that both data for training a monitoring system and those during the operation of the system are obtained from identical devices. In addition, constant monitoring of data is desirable, but in practice, the data can be observed periodically (e.g., several tens of seconds of data are observed every two hours). In this case, collecting sufficient data is time consuming, making it difficult to conduct accurate predictions at the early stage of the CMS operation. To address this problem, a small amount of vibration data observed at a target wind turbine is utilized to adapt the anomaly detector that is trained on relatively large-scale vibration signals obtained from other wind turbines. In the present study, maximum a posteriori (MAP) adaptation is applied to a Gaussian mixture model (GMM)-based anomaly detector. Experimental comparisons using vibration data from the gearbox in the ex- perimental environment and those used in the wind turbine demonstrated that MAP-based GMM adaptation yielded an improvement in anomaly detection accuracy even when only a small amount of data is observed at the target gearbox.

Denoising of Hydrogen Evolution Acoustic Emission Signal Based on Non-Decimated Stationary Wavelet Transform

Monitoring the evolution of hydrogen gas on carbon steel pipe using acoustic emission (AE) signal can be a part of a reliable technique in the modern structural health-monitoring (SHM) field. However, the extracted AE signal is always mixed up with random extraneous noise depending on the nature of the service structure and experimental environment. The noisy AE signals often mislead the obtaining of the desired features from the signals for SHM and degrade the performance of the monitoring system. Therefore, there is a need for the signal denoising method to improve the quality of the extracted AE signals without degrading the original properties of the signals before using them for any knowledge discovery. This article proposes a non-decimated stationary wavelet transform (ND-SWT) method based on the variable soft threshold function for denoising hydrogen evolution AE signals. The proposed method filters various types of noises from the acquired AE signal and removes them efficiently without degrading the original properties. The hydrogen evolution experiments on carbon steel pipelines are carried out for AE data acquisition. Simulations on experimentally acquired AE signals and randomly generated synthetic signals with different levels of noise are performed by the ND-SWT method for noise removal. Results show that our proposed method can effectively eliminate Gaussian white noise as well as noise from the vibration and frictional activity and provide efficient noise removal solutions for SHM applications with minimum reconstruction error, to extract meaningful AE signals from the large-scale noisy AE signals during monitoring and inspection.

Annual flood damage influenced by El Niño in the Kan River basin, Iran

Abstract. Although many studies have explored the effect of teleconnection patterns on floods, few investigations have focused on the assessment of expected flood damage under such large-scale atmospheric signals. This study aims to determine the effect of the most emblematic teleconnection, El Niño, on the expected damage due to floods with short return periods in the Kan River basin, Iran. To determine the flood damage costs, the median of annual precipitation changes (ΔP) during El Niño conditions was used, although ΔP cannot necessarily be transferred to extreme values. Then the flooded area was determined under the increased rainfall due to El Niño for 5-, 10-, and 50-year return periods. The results showed that El Niño has increased the annual precipitation by 12.2 %. Flood damage assessment using damage–depth curves showed that the relative increase in expected damage during El Niño conditions is much higher for short return period floods than that for long return period floods. In general, a 12.2 % increase in the annual precipitation would increase the damage by 1671 % and 176 %, respectively, for the return periods of 5 and 10 years. However, in the case of a 50-year flood, this increased percentile decreased to 52 %. These results indicate the importance of small flood events in flood management planning during El Niño.

Detection of the effects of thermal anomalies caused by large-scale atmospheric-oceanic signals on the bleaching events of the Persian Gulf coral reefs

Detection of the effects of thermal anomalies caused by large-scale atmospheric-oceanic signals on the bleaching events of the Persian Gulf coral reefs

An enhanced block-based Compressed Sensing technique using orthogonal matching pursuit

The theory of compressed sensing asserts that one can recover signals in $$\mathbb {R}^n$$ from far fewer samples or measurements, if the signal has a sparse representation in some orthonormal basis; from non-adaptive linear measurements by solving a $$\mathbb {L}_1$$ norm minimisation problem. The non-adaptive measurements have the character of random linear combinations of the basis or frame elements. However, for large-scale 2D image signals, the randomized sensing matrix consumes enormous computational resources that makes it impractical. The problem has been addressed in the paper as a block compressed sensing (BCS) with sparsity normalization in the transformed domain in the preprocessing stage. The blocks obtained are converted to non-adaptive measurements using identically independent weighted Gaussian random matrices. The feasibility of reconstruction is verified using orthogonal matching pursuit. Simulation results show that better reconstruction performance can be achieved by the proposed technique in comparison with the existing BCS approaches.