Regular Time Steps Research Articles

Dynamic effects on the nonlinear optical properties of donor acceptor stenhouse adducts: insights from combined MD + QM simulations.

The second-order nonlinear optical (NLO) responses of a donor-acceptor stenhouse adduct (DASA) are investigated by using a computational approach combining molecular dynamics simulations and density functional theory (DFT) calculations. Specific force fields for the open and closed photoswitching forms are first parameterized and validated according to the Joyce protocol, in order to finely reproduce the geometrical features and potential energy surfaces of both isomers in chloroform solution. Then, DFT calculations are performed on structural snapshots extracted at regular time steps of the MD trajectories to address the influence of the thermalized conformational dynamics on the NLO responses related to hyper-Rayleigh scattering (HRS) experiments. We show that accounting for the structural dynamics largely enhances the HRS hyperpolarizability (βHRS) compared to DFT calculations considering solely equilibrium geometries, and greatly improves the agreement with experimental measurements. Furthermore, we show that the NLO responses of the NLO-active open form are correlated with the bond order alternation along the triene bridge connecting the donor and acceptor moieties, which is rationalized using simple essential state models.

Deep Learning‐Based Bluetooth Low‐Energy 5.1 Multianchor Indoor Positioning with Attentional Data Filtering

Indoor positioning system (IPS) technologies have widespread applications in logistics, intelligent manufacturing, healthcare monitoring, etc. The recently released Bluetooth low‐energy (BLE) 5.1 specification enables in‐phase and quadrature‐phase (I/Q) data measurements. It allows angle of arrival estimation and becomes a natural choice for IPS implementation. Conventional BLE 5.1 IPSs use multiple anchors to provide massive redundancy to improve system robustness. It however demands effective approaches to leverage redundancy. Besides, interference due to various environmental factors can introduce severe errors to I/Q data and affect positioning accuracy. Facing these challenges, herein, a novel deep learning‐based multianchor BLE 5.1 IPS is proposed. The system aggregates measurements from multiple anchors and makes them available at regular time steps. Then, a novel attentional filtering network tailored to infer high‐quality I/Q sample data is developed and a spatial regularization loss incorporating spatial location relationships to strengthen the feature embedding discrimination is proposed. Two multianchor BLE 5.1 I/Q sample datasets are developed and released for public download. Numerical experiments are carried out to compare the proposed method with previous BLE 5.1 IPS methods and methods utilizing other radio frequency data. Results indicate that the proposed method consistently achieves submeter accuracy and significantly outperforms the state‐of‐the‐art approaches.

Understanding of the properties of neural network approaches for transient light curve approximations

Context. Modern-day time-domain photometric surveys collect a lot of observations of various astronomical objects and the coming era of large-scale surveys will provide even more information on their properties. Spectroscopic follow-ups are especially crucial for transients such as supernovae and most of these objects have not been subject to such studies. Aims. Flux time series are actively used as an affordable alternative for photometric classification and characterization, for instance, peak identifications and luminosity decline estimations. However, the collected time series are multidimensional and irregularly sampled, while also containing outliers and without any well-defined systematic uncertainties. This paper presents a search for the best-performing methods to approximate the observed light curves over time and wavelength for the purpose of generating time series with regular time steps in each passband. Methods. We examined several light curve approximation methods based on neural networks such as multilayer perceptrons, Bayesian neural networks, and normalizing flows to approximate observations of a single light curve. Test datasets include simulated PLAsTiCC and real Zwicky Transient Facility Bright Transient Survey light curves of transients. Results. The tests demonstrate that even just a few observations are enough to fit the networks and improve the quality of approximation, compared to state-of-the-art models. The methods described in this work have a low computational complexity and are significantly faster than Gaussian processes. Additionally, we analyzed the performance of the approximation techniques from the perspective of further peak identification and transients classification. The study results have been released in an open and user-friendly Fulu Python library available on GitHub for the scientific community.

A dataset of hourly sea surface temperature from drifting buoys

A dataset of sea surface temperature (SST) estimates is generated from the temperature observations of surface drifting buoys of NOAA’s Global Drifter Program. Estimates of SST at regular hourly time steps along drifter trajectories are obtained by fitting to observations a mathematical model representing simultaneously SST diurnal variability with three harmonics of the daily frequency, and SST low-frequency variability with a first degree polynomial. Subsequent estimates of non-diurnal SST, diurnal SST anomalies, and total SST as their sum, are provided with their respective standard uncertainties. This Lagrangian SST dataset has been developed to match the existing and on-going hourly dataset of position and velocity from the Global Drifter Program.

Temporal interpolation of groundwater level hydrographs for regional drought analysis using mixed models

Large-scale studies of the spatial and temporal variation of groundwater drought status require complete inventories of groundwater levels on regular time steps from many sites so that a standardised drought index can be calculated for each site. However, groundwater levels are often measured sporadically, and inventories include missing or erroneous data. A flexible and efficient modelling framework is developed to fill gaps and regularise data in such inventories. It uses linear mixed models to account for seasonal variation, long-term trends and responses to precipitation and temperature over different temporal scales. The only data required to estimate the models are the groundwater level measurements and freely available gridded weather products. The contribution of each of the four types of trends at a site can be determined and thus the causes of temporal variation of groundwater levels can be interpreted. Validation reveals that the models explain a substantial proportion of groundwater level variation and that the uncertainty of the predictions is accurately quantified. The computation for each site takes less than 130 s and requires little supervision. Hence, the approach is suitable to be upscaled to represent the variation of groundwater levels in large datasets consisting of thousands of boreholes.

Galaxy flybys: evolution of the bulge, disc, and spiral arms

ABSTRACT Galaxy flybys are as common as mergers in low-redshift Universe and are important for galaxy evolution as they involve the exchange of significant amounts of mass and energy. In this study, we investigate the effect of minor flybys on the bulges, discs, and spiral arms of Milky Way mass galaxies for two types of bulges – classical bulges and boxy/peanut pseudo-bulges. Our N-body simulations comprise of two disc galaxies of mass ratios 10:1 and 5:1, where the discs of the galaxies lie in their orbital plane and the pericentre distance is varied. We performed photometric and kinematic bulge–disc decomposition at regular time-steps and traced the evolution of the disc size, spiral structure, bulge sersic index, bulge mass, and bulge angular momentum. Our results show that the main effect on the discs is disc thickening, which is seen as the increase in the ratio of disc scale height to scale radius. The strength of the spiral structure A2/A0 shows small oscillations about the mean time-varying amplitude in the pseudo-bulge host galaxies. The flyby has no significant effect on non-rotating classical bulge, which shows that these bulges are extremely stable in galaxy interactions. However, the pseudo-bulges become dynamically hotter in flybys indicating that flybys may play an important role in accelerating the rate of secular evolution in disc galaxies. This effect on pseudo-bulges is a result of their rotating nature as part of the bar. Also, flybys do not affect the time and strength of bar buckling.

Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel

Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements, serving as an important planning tool for decision makers. In the Sahel area, such information is valuable for risk management and mitigation in challenging sectors like food security, flood control, and urban planning. Due to its uniform quality across large areas in regular time steps, remote sensing imageries are essential input for producing land use maps. However, spatially and temporally heterogeneous landscapes in Sahel make classification of landscape features difficult. Our overall goal is to create an accurate, high resolution land use map covering Niamey, the capital of Niger and its surroundings which represents the unique landscape features in the Sahel using Sentinel-1 and Sentinel-2 archives. We assessed the performance of three commonly used classifiers (i.e. Maximum Likelihood (ML), Support Vector Machine (SVM) and Random Forest (RF)) for land use classification. To understand the utility of different features from Sentinel-1 and Sentinel-2 imagery for classification, we performed feature selection and compared mapping accuracies with and without feature selection. To leverage the strength of each classifier, we developed a classifier ensemble (CE) map based on the mapping accuracy of each land use class and each classifier. The results of this study showed that the performance of individual classifiers depends on feature selection method and accuracies can be improved by combining different classifiers. The ensemble map had an overall accuracy of 72 ± 3.9% and it was found superior in terms of accuracy particularly with respect to built-up areas compared to the existing global land cover products in the study area. Our classification scheme also better characterized the regional environment in the Sahel. For example, we mapped rice and bare rocks that have important regional implication, which are not included in the existing products. Overall, our approach highlights the potentiality of combining multi-modal imageries and multiple classifiers for mapping a heterogenous environment such as the Sahel with high spatial resolution.

Bacterial skin recolonization in the operating room: comparison between various antisepsis protocols

Surgical site infection (SSI) largely implicates the patient's endogenous skin microbiota. Perioperative disinfection protocols do not follow a general agreement. To compare antisepsis and skin protection protocols on quantitative analysis of recolonization in the operating room at regular time-steps. The study hypothesis was that one protocol would be more effective than others. A single-centre prospective interventional study was conducted between January and June 2019. Healthy volunteers were randomized between protocols and served as their own controls. The protocols began ahead of scheduled orthopaedic surgery with a preoperative shower, mechanical cleansing, application of major antiseptics (alcoholic Bétadine™ 5% or alcoholic chlorhexidine 0.5%), sterile draping, then adhesive draping (3M™ Steri-Drape™ or iodine-impregnated 3M™ Ioban2™). Sampling was by swabbing in the operating room at 30 min intervals up to 90 min after draping. Cultures were performed under aerobic and anaerobic conditions. Qualitative and quantitative (cfu/mL) bacteriology was performed in the laboratory by direct reading on the blood agar plates. Thirty subjects were included; none was lost to follow-up or excluded from analysis. Bacterial load before manipulation (T0) was significantly higher in males (P < 0.0001) despite a significantly shorter shower-to-sampling interval (P= 0.03). Smoking (P= 0.85), body mass index (P= 0.38), and depilation (P= 0.50) did not significantly affect preoperative load. Mean load increased significantly under all protocols up to T90 min, without significant superiority for any one protocol. Associated Bétadine™/Ioban™ showed the lowest T90 load, and chlorhexidine alone the highest, but without significant difference. Isolates at T0 were predominantly healthy skin commensals: coagulase-negative staphylococci, micrococci, and coryneforms. No one protocol demonstrated superiority, whether in immediate bactericidal action or in preventing skin recolonization in the operating room. Further studies are needed to define generally agreed protocols for SSI risk management.

"New insights for setting up contractual options for demand side flexibility "

"This paper exploits the Duration-of-Use of the demand patterns as a key concept for dealing with demand side flexibility. Starting from the consideration that fine-grained energy metering is not used at the point of supply of the electricity consumers, i.e., the granularity of the energy measured (at time steps of 15 minutes, 30 minutes or one hour) is not sufficient to represent the variability of the demand patterns, event-based energy metering (EDM) is indicated as a viable option to provide a very detailed reconstruction of the demand patterns. In particular, the use of EDM enables high-quality tracking of the demand peaks with a reduced number of data with respect to the ones needed to measure energy at regular time steps for reaching a similar peak tracking capability. From the EDM outcomes, a new class of options for setting up tariffs or contracts for flexibility, based on the demand duration curve, is envisioned. "

Generalizing the first-difference correlated random walk for marine animal movement data

Animal telemetry data are often analysed with discrete time movement models. These models are defined with regular time steps. However, telemetry data from marine animals are observed irregularly. To account for irregular data, a time-irregularised first-difference correlated random walk model with drift is introduced. The model generalizes the commonly used first-difference correlated random walk with regular time steps by allowing irregular time steps, including a drift term, and by allowing different autocorrelation in the two coordinates. The model is applied to data from a ringed seal collected through the Argos satellite system, and is compared to related movement models through simulations. Accounting for irregular data in the movement model results in accurate parameter estimates and reconstruction of movement paths. Further, the introduced model can provide more accurate movement paths than the regular time counterpart. Extracting accurate movement paths from uncertain telemetry data is important for evaluating space use patterns for marine animals, which in turn is crucial for management. Further, handling irregular data directly in the movement model allows efficient simultaneous analyses of several animals.

Kinetic Nearest Neighbor Search in Black-Box Model

Proximity problems is a class of important problems which involve estimation of distances between geometric objects. The nearest neighbor search which is a subset of proximity problems, arises in numerous fields of applications, including Pattern Recognition, Statistical classification, Computer vision and etc. In this study, a nearest neighbor search is presented to move points in the plane, while query point is static. The proposed method works in the black-box KDS model, in which the points location received at regular time steps while at the same time, an upper bound dmax is known on the maximum displacement of any point at each time step. In this paper, a new algorithm is presented for kinetic nearest neighbor search problem in the black-box model, under assumptions on the distribution of the moving point set P. It has been shown how the kinetic nearest neighbor will be updated at each time step in O(k∆k log n) amortized time, where ∆k is the k-spread of a point set P . Key words: Computational Geometry, Black Box Model, Kinetic, Nearest Neighbor.

Symbolic pattern recognition for sequential data

ABSTRACTSources of sequential data surround and pervade our lives. Our bodies continuously generate sequential data such as heart rate and blood pressure. In global finance, stock indices and currency exchange rates change every second. The movement of clouds, the coordinates of the planets, the score of a soccer game, etc., are all examples of sequential data transitioning from one state to another in regular time steps. There is a mature body of literature related to modeling time-dependent sequential data, or time series, in which every model relies upon specific assumptions for applicability to data. However, there other kinds of sequential data that are not collected with respect to time; for example, DNA sequences (if we ignore the gene mutations). Modeling of this type of sequential data does not have a body of literature that is as mature. This is somewhat perplexing, since sequential data modeling should cover both time-dependent and non-time-dependent data.In this study, we introduce a framework called Symbolic Pattern Recognition for modeling pattern transition behavior of sequential data that is expressed by a finite alphabet of symbols. Our framework can be used to characterize, predict, simulate, cluster, and classify multiple series based on their observed pattern transition behaviors.We document our proposed framework and apply it to perform unsupervised clustering of 13 different species of mollusks based on their DNA sequences. Our model correctly clusters the mollusks into their respective genera, matching results obtained via more traditional, supervised genetic analysis.

Incorporating Telemetry Error into Hidden Markov Models of Animal Movement Using Multiple Imputation

When data streams are observed without error and at regular time intervals, discrete-time hidden Markov models (HMMs) have become immensely popular for the analysis of animal location and auxiliary biotelemetry data. However, measurement error and temporally irregular data are often pervasive in telemetry studies, particularly in marine systems. While relatively small amounts of missing data that are missing-completely-at-random are not typically problematic in HMMs, temporal irregularity can result in few (if any) observations aligning with the regular time steps required by HMMs. Fitting HMMs that explicitly account for uncertainty attributable to location measurement error, temporally irregular observations, or other forms of missing data typically requires computationally demanding techniques, such as Markov chain Monte Carlo (MCMC). Using simulation and a real-world bearded seal (Erignathus barbatus) example, I investigate a practical alternative to incorporating measurement error and temporally irregular observations into HMMs based on multiple imputation of the position process drawn from a single-state continuous-time movement model. This two-stage approach is relatively simple, performed with existing software using efficient maximum likelihood methods, and completely parallelizable. I generally found the approach to perform well across a broad range of simulated measurement error and irregular sampling rates, with latent states and locations reliably recovered in nearly all simulated scenarios. However, high measurement error coupled with low sampling rates often induced bias in both the estimated probability distributions of data streams derived from the imputed position process and the estimated effects of spatial covariates on state transition probabilities. Results from the two-stage analysis of the bearded seal data were similar to a more computationally intensive single-stage MCMC analysis, but the two-stage analysis required much less computation time and no custom model-fitting algorithms. I thus found the two-stage multiple-imputation approach to be promising in terms of its ease of implementation, computation time, and performance. Code for implementing the approach using the R package “momentuHMM” is provided.Supplementary materials accompanying this paper appear online.

Development of an operational low-flow index for hydrological drought monitoring over Europe

ABSTRACTNear real-time monitoring of hydrological drought requires the implementation of an index capable of capturing the dynamic nature of the phenomenon. Starting from a dataset of modelled daily streamflow data, a low-flow index was developed based on the total water deficit of the discharge values below a certain threshold. In order to account for a range of hydrological regimes, a daily 95th percentile threshold was adopted, which was computed by means of a 31-day moving window. The observed historical total water deficits were statistically fitted by means of the exponential distribution and the corresponding probability values were used as a measure of hydrological drought severity. This approach has the advantage that it directly exploits daily streamflow values, as well as allowing a near real-time update of the index at regular time steps (i.e. 10 days, or dekad). The proposed approach was implemented on discharge data simulated by the LISFLOOD model over Europe during the period 1995–2015; its reliability was tested on four case studies found within the European drought reference database, as well as against the most recent summer drought observed in Central Europe in 2015. These validations, even if only qualitative, highlighted the ability of the index to capture the timing (starting date and duration) of the main historical hydrological drought events, and its good performance in comparison with the commonly used standardized runoff index (SRI). Additionally, the spatial evolution of the most recent event was captured well in a simulated near real-time test case, suggesting the suitability of the index for operational implementation within the European Drought Observatory.

Asymptotic properties of the Dirac quantum cellular automaton

We show that the Dirac quantum cellular automaton [Ann. Phys. 354 (2015) 244] shares many properties in common with the discrete-time quantum walk. These similarities can be exploited to study the automaton as a unitary process that takes place at regular time steps on a one-dimensional lattice, in the spirit of general quantum cellular automata. In this way, it becomes an alternative to the quantum walk, with a dispersion relation that can be controlled by a parameter, which plays a similar role to the coin angle in the quantum walk. The Dirac Hamiltonian is recovered under a suitable limit. We provide two independent analytical approximations to the long term probability distribution. It is shown that, starting from localized conditions, the asymptotic value of the entropy of entanglement between internal and motional degrees of freedom overcomes the known limit that is approached by the quantum walk for the same initial conditions, and are similar to the ones achieved by highly localized states of the Dirac equation.

Multiple Extended Target Tracking for Through-Wall Radars

Tracking moving targets hidden behind visually opaque structures as building walls is a crucial issue in many surveillance, rescue, and security applications. The electromagnetic waves at the low microwave frequency range penetrate into common building materials and thereby enable the radar to expose behind the wall scene. However, due to complexity of the scattering scenario, the radar signal undergoes multipath propagation phenomena. These typically manifest themselves as environmental clutter which may impair detection and tracking of true targets. In this paper, a signal processing strategy is proposed to track multiple extended targets in a scene by means of a wide-band monostatic through-wall radar. The system collects data sets at regular time steps which are first processed by a microwave tomographic technique. Then, a detection/tracking stage is implemented in order to track the position and dynamics of targets in real time. An extended target-tracking approach is applied to properly exploit at the tracking stage the information related to extended nature of targets. The effectiveness of the proposed signal processing chain is assessed by numerical tests based on full-wave data pertaining to an indoor scenario.

Response of concrete gravity dam by damage model under seismic excitation

The aim of this study is to investigate the earthquake damage response of the concrete gravity dam. It is well known that simulation of concrete structures up to rupture requires nonlinear behavior of the models. The material behavior whose principle is based on the damage mechanics has been implemented in a finite element program. This model takes into account the dissymmetry of the behavior of concrete and considers cracking in tension and rupture in compression. The dynamic equilibrium equations of motion are solved by using the explicit central difference method which is based on approximation of velocities and accelerations with quotients of finite difference of known values of displacements with regular time step. For numerical application, two dimensional seismic analysis of Koyna gravity dam is performed by using the 1967 Koyna earthquake records. The results obtained are shown in the time history graphs of the horizontal and vertical displacements of the nodal point located at the crest of the dam. These are the stresses, the strains and the cumulated damage at the integration points respectively.

Linking Spatialized Indicators of Desertification Risks with Observed Land Use/Land Cover Change: An Operational Monitoring System of Desertification

The need of useful information for decision-makers to fight against desertification in Tunisian dry zones leads to conceive assessing and monitoring systems that can supply synthetic indicators. These latter should integrate socioeconomic and environmental dimensions with their spatial and temporal diversity at the local scale. This paper proposes an example of information system entitled SIELO (information system for operational desertification monitoring at the local scale). This system attempts to create the link between i) spatialized indicators of desertification risks, built in connection with the systematic complexity of desertification and ii) observed Land Use/ Land Cover (LU/LC) Change. The first type of data arises from pre-existing environmental model: LEIS model (Local Environmental Information System). The second type of data is extracted from satellite images acquired according to regular time steps. The proposed approach is based also on the spatializing of knowledge, via the ‘‘landscape’’ tool in particular. We illustrate the feasibility and the operational effectiveness of a developed software prototype (SIELO v1.0) with an initial application in a Tunisian dry zone. This system showed that it has the capacity to feed an operational monitoring of desertification according to the state of LU/LC directly observed or measured in several dates. Then, it can be useful for the decision-makers in their programs of fighting against desertification, but also to manage uncertainties in southeastern Tunisia, especially climate variability and climate change.

Applying POT methods to the Revised Joint Probability Method for determining extreme sea levels

Newly exposed concepts of POT declustering (Bernardara et al., 2014) within the GPD-Poisson model are applied to the joint probability of tide and surge for determining extreme sea levels, as a variation of the Revised Joint Probability Method (RJPM, Tawn and Vassie, 1989). A mixture model is proposed for the meteorological residual (surge) component with a non-parametric (empirical) density for the bulk values and parametric models for both the lower and upper tails. In particular, a distinction is made between values observed at regular time steps, called sequential values, and the clusters of extreme values, or events, on which the statistical extrapolations are performed. The sea level distribution is obtained by convolution of the tide and surge density functions. Confidence intervals are also proposed. This model is applied to the case study of Brest, France using both hourly and high water values. Two methods for handling tide–surge interaction are presented and discussed and a comparison with a direct approach is made.

Evidence for Proteomic and Metabolic Adaptations Associated with Alterations of Seed Yield and Quality in Sulfur-limited Brassica napus L

In Brassica napus, seed yield and quality are related to sulfate availability, but the seed metabolic changes in response to sulfate limitation remain largely unknown. To address this question, proteomics and biochemical studies were carried out on mature seeds obtained from plants grown under low sulfate applied at the bolting (LS32), early flowering (LS53), or start of pod filling (LS70) stage. The protein quality of all low-sulfate seeds was reduced and associated with a reduction of S-rich seed storage protein accumulation (as Cruciferin Cru4) and an increase of S-poor seed storage protein (as Cruciferin BnC1). This compensation allowed the protein content to be maintained in LS70 and LS53 seeds but was not sufficient to maintain the protein content in LS32 seeds. The lipid content and quality of LS53 and LS32 seeds were also affected, and these effects were primarily associated with a reduction of C18-derivative accumulation. Proteomics changes related to lipid storage, carbohydrate metabolism, and energy (reduction of caleosins, phosphoglycerate kinase, malate synthase, ATP-synthase β-subunit, and thiazole biosynthetic enzyme THI1 and accumulation of β-glucosidase and citrate synthase) provide insights into processes that may contribute to decreased oil content and altered lipid composition (in favor of long-chain fatty acids in LS53 and LS32 seeds). These data indicate that metabolic changes associated with S limitation responses affect seed storage protein composition and lipid quality. Proteins involved in plant stress response, such as dehydroascorbate reductase and Cu/Zn-superoxide dismutase, were also accumulated in LS53 and LS32 seeds, and this might be a consequence of reduced glutathione content under low S availability. LS32 treatment also resulted in (i) reduced germination vigor, as evidenced by lower germination indexes, (ii) reduced seed germination capacity, related to a lower seed viability, and (iii) a strong decrease of glyoxysomal malate synthase, which is essential for the use of fatty acids during seedling establishment.