Consecutive Observations Research Articles

Measurements of spin and orbital parameters in Cen X-3 by Insight-HXMT

We present a detailed temporal analysis for the eclipsing high-mass X-ray binary system Cen X-3 using the Insight-HXMT data in 2018 and 2020. Three consecutive and high statistic observations among data are used for the precise timing analysis. The pulse profiles are revealed to vary with energy and time. The pulse profiles for the 2018 observations showed a double peak in the low energy bands below 10 keV and evolved to a single peak in higher energies without the correlation between pulse fraction and flux, and profiles in low energies changed with time. But the pulse profile for the 2020 observation only showed a broad single-peaked pulse in all energy bands with a positive relation between pulse fraction and flux, which may indicate the transition of the emission patterns from a mixture of a pencil and a fan beam to a dominated pencil-like beam. With performing a binary orbital fitting of spin periods, we obtain an accurate value for the spin period and the orbital parameters. The intrinsic spin period of the neutron star is found to be 4.79920±0.00006 s at MJD 58852.697, with the orbital period determined at Porb=2.08695634±0.00000001 day, and its decay rate of -(1.7832 ± 0.0001) × 10−6 yr−1 for the binary.

A Distributed Approach for Time-Dependent Observation Scheduling Problem in the Agile Earth Observation Satellite Constellation

The increasing number of agile earth observation satellites (AEOSs) in orbit have advanced maneuverable capabilities, enabling the AEOS constellation to provide richer observation services. Therefore, observation scheduling in the AEOS constellation is crucial for improving the performance of satellite remote sensing systems. This paper focuses on the problem of distributed observation scheduling in the AEOS constellation, where a period of transition time is required between two consecutive observations, and this constraint depends on the start time of observations. We define a new fitness function that not only maximizes the profit sum but also considers system load balancing. Based on the fundamental idea of a distributed performance impact (PI) algorithm, we develop a PI-based distributed scheduling method (PIDSM) that runs concurrently on all AEOSs via local inter-satellite link (ISL)-based communications. The PIDSM iterates between two phases: target inclusion and consensus and target removal. The first phase aims to select the optimal task for each AEOS, while the second phase reaches a consensus over all AEOSs and removes targets that may decrease overall fitness. Experimental results demonstrate that the PIDSM can schedule more targets, reduce communication overhead, and achieve higher fitness values than existing algorithms. Sensitivity analyses further validate the effectiveness of the PIDSM.

Stabilization of hybrid stochastic systems with time-varying delay by discrete-time state feedback control

In this paper, we are concerned with the stabilization of hybrid stochastic systems with variable delay by discrete-time state feedback control. By using Lyapunov functionals, we obtain an upper bound tau ^{*} on the duration τ between two consecutive state observations. Meantime, we show that hybrid stochastic systems with variable delay can be stabilized by discrete-time state feedback control as long as tau <tau ^{*}. Finally, two examples are given to demonstrate the applicability of our work.

Evaluation of Teaching Reading Strategies Used by Early Grade Teachers

Importance of reading skills is admissible especially for early graders’future learning. However, there is interdependency between students’reading skills and their teachers’ ‘teaching reading strategies’. Hence thefocus of the current study was to evaluate ‘teaching reading’, i.e. phonemicawareness, word recognition, reading fluency and text comprehension, toearly grades by reading teachers. For this purpose, quantitative researchwith survey method was based on field observation was conducted. Selfdeveloped tool i.e. structured observation schedule was used for datacollection. The data was collected through performance checklist, hence amanageable sample, through convenient sampling in the sense ofwillingness to be observed, was selected of primary school Englishteachers (n = 10). Three consecutive observations were conducted of eachparticipant of the study. Descriptive statistics, i.e., frequencies, percentageand mean were used to analyze the collected data. The results of the studyshow that generally the teachers focus on oral expression and meanings ofdifficult words. While majority of them least use ‘teaching readingstrategies recommended for developing phonemic awareness, fluencythrough repeated reading practices among students and the strategieshelpful for text comprehension

Spherical autoregressive models, with application to distributional and compositional time series

We introduce a new class of autoregressive models for spherical time series. The dimension of the spheres on which the observations of the time series are situated may be finite-dimensional or infinite-dimensional, where in the latter case we consider the Hilbert sphere. Spherical time series arise in various settings. We focus here on distributional and compositional time series. Applying a square root transformation to the densities of the observations of a distributional time series maps the distributional observations to the Hilbert sphere, equipped with the Fisher–Rao metric. Likewise, applying a square root transformation to the components of the observations of a compositional time series maps the compositional observations to a finite-dimensional sphere, equipped with the geodesic metric on spheres. The challenge in modeling such time series lies in the intrinsic non-linearity of spheres and Hilbert spheres, where conventional arithmetic operations such as addition or scalar multiplication are no longer available. To address this difficulty, we consider rotation operators to map observations on the sphere. Specifically, we introduce a class of skew-symmetric operators such that the associated exponential operators are rotation operators that for each given pair of points on the sphere map the first point of the pair to the second point of the pair. We exploit the fact that the space of skew-symmetric operators is Hilbertian to develop autoregressive modeling of geometric differences that correspond to rotations of spherical and distributional time series. Differences expressed in terms of rotations can be taken between the Fréchet mean and the observations or between consecutive observations of the time series. We derive theoretical properties of the ensuing autoregressive models and showcase these approaches with several motivating data. These include a time series of yearly observations of bivariate distributions of the minimum/maximum temperatures for a period of 120 days during each summer for the years 1990-2018 at Los Angeles (LAX) and John F. Kennedy (JFK) international airports. A second data application concerns a compositional time series with annual observations of compositions of energy sources for power generation in the U.S..

Spaceborne Observations of Lightning NO2 in the Arctic.

The Arctic region is experiencing notable warming as well as more lightning. Lightning is the dominant source of upper tropospheric nitrogen oxides (NOx), which are precursors for ozone and hydroxyl radicals. In this study, we combine the nitrogen dioxide (NO2) observations from the TROPOspheric Monitoring Instrument (TROPOMI) with Vaisala Global Lightning Dataset 360 to evaluate lightning NO2 (LNO2) production in the Arctic. By analyzing consecutive TROPOMI NO2 observations, we determine the lifetime and production efficiency of LNO2 during the summers of 2019-2021. Our results show that the LNO2 production efficiency over the ocean is ∼6 times higher than over continental regions. Additionally, we find that a higher LNO2 production efficiency is often correlated with lower lightning rates. The summertime lightning NOx emission in the Arctic (north of 70° N) is estimated to be 219 ± 116 Mg of N, which is equal to 5% of anthropogenic NOx emissions. However, for the span of a few hours, the Arctic LNO2 density can even be comparable to anthropogenic NO2 emissions in the region. These new findings suggest that LNO2 can play an important role in the upper-troposphere/lower-stratosphere atmospheric chemical processes in the Arctic, particularly during the summer.

Fetal intestinal loop dilatation: Follow-up and outcome of a series of 133 consecutive cases (the DILDIG study).

To define the prognostic markers of fetal dilated bowel loops. National non-interventional study of 133 consecutive prenatal observations of dilated loops including ultrasound examinations, complementary laboratory tests, magnetic resonance imaging (MRI), outcomes, and postnatal diagnosis. One hundred twenty seven cases were classified according to outcome: Group 1, very severe (n=43), Group 2, children needing specific care (n=39), and Group 3, healthy children (n=45). Prenatal ultrasound scan suggested duodenal obstruction in 30 cases, small bowel obstruction in 81, colonic obstruction in 11, and diffuse dilatation in 5. Diameter of dilated loops did not significantly differ between the groups. A poor prognosis was significantly associated with duodenal obstruction, genetic anomalies (53% vs. 21.8%), including aneuploidies or CFTR gene mutations and abnormal amniotic fluid biochemistry (86.4% vs. 38.7%). A good prognosis was associated with regression of dilatation and normal MRI. In this study, postnatal outcomes for fetuses with intestinal dilatation were best predicted by assessing the level of obstruction with prenatal ultrasound and MRI, determining the presence of associated malformations, amniotic fluid biochemical and genetic testing, and monitoring for regression of bowel dilatation. These results should help inform future guidelines on the prenatal and neonatal management of congenital intestinal obstruction.

K-corruption Intermittent Attacks for Violating the Diagnosability

This work addresses the problem of diagnosability analysis of a networked discrete event system under external malicious attacks, in which the considered system is modeled by labeled Petri net, and is attacked by a given replacement structure (i.e., the original transition label is replaced by a different label or empty string). In addition, we assume that the attack is stealthy and intermittent, which requires that the corrupted observations should belong to the normal behavior of the system, and the replaced transition labels must be recovered within a bounded of consecutive corrupted observations (called as K-corruption intermittent attack), respectively. The aim of this work is, from an attacker viewpoint, to design a K-corruption intermittent attacks for violating the diagnosability of systems. To this end, an attack automaton and complete unfolded verifier, i.e., a special automaton, are established to analyze all the potential attacked paths. Then an algorithm is proposed to obtain the K-corruption intermittent attacks, and examples are used to illustrate the proposed attack method.

Divide-and-conquer Bayesian inference in hidden Markov models

Divide-and-conquer Bayesian methods consist of three steps: dividing the data into smaller computationally manageable subsets, running a sampling algorithm in parallel on all the subsets, and combining parameter draws from all the subsets. These methods use the combined parameter draws for efficient posterior inference in massive data settings. Existing divide-and-conquer methods have a major limitation in that their first two steps assume that the observations are independent. We address this problem by developing a divide-and-conquer method for Bayesian inference in parametric hidden Markov models, where the state space is known and finite. Our main contributions are two-fold. First, we partition the data into smaller blocks of consecutive observations and modify the likelihood on every time block. For any time block, the posterior distribution computed using the modified likelihood is such that its variance has the same asymptotic order as that of the true posterior. Second, suppose the number of subsets is chosen appropriately depending on the mixing properties of the hidden Markov chain. In that case, we show that the subset posterior distributions defined using the modified likelihood are asymptotically normal as the subset sample size tends to infinity. This result facilitates using any existing combination algorithm in the third step. We show that the combined posterior distribution obtained using one such algorithm is close to the true posterior distribution in 1-Wasserstein distance under widely used regularity assumptions. Our numerical results show that the proposed method provides an accurate approximation of the true posterior distribution than its competitors in simulation studies and a real data analysis.

Inverse linear-quadratic discrete-time finite-horizon optimal control for indistinguishable homogeneous agents: A convex optimization approach

The inverse linear-quadratic optimal control problem is a system identification problem whose aim is to recover the quadratic cost function and hence the closed-loop system matrices based on observations of optimal trajectories. In this paper, the discrete-time, finite-horizon case is considered, where the agents are also assumed to be homogeneous and indistinguishable. The latter means that the agents all have the same dynamics and objective functions and the observations are in terms of “snap shots” of all agents at different time instants, but what is not known is “which agent moved where” for consecutive observations. This absence of linked optimal trajectories makes the problem challenging. We first show that this problem is globally identifiable. Then, for the case of noiseless observations, we show that the true cost matrix, and hence the closed-loop system matrices, can be recovered as the unique global optimal solution to a convex optimization problem. Next, for the case of noisy observations, we formulate an estimator as the unique global optimal solution to a modified convex optimization problem. Moreover, the statistical consistency of this estimator is shown. Finally, the performance of the proposed method is demonstrated by a number of numerical examples.

Enhanced secret-key generation from atmospheric optical channels with the use of random modulation.

Secret-key extraction from atmospheric optical channels can generate common keys used by two communicating parties to encrypt their transmitted confidential information. The correlation time of turbulence-induced optical fluctuations imposes a restriction on the number of extracted uncorrelated key bits per second. To break this restriction, we propose a novel randomness sharing scheme between two communicating parties using an atmospheric optical channel equipped with random modulation and develop mathematical models for the common randomness source created by such a randomness-sharing scheme. Our randomness-sharing scheme provides the legitimate parties with the ability to decrease the temporal autocorrelation of the said common randomness source, which is called controllable common randomness source with memory (CCRSM), thereby enabling an enhanced secret-key extraction that can break the aforementioned restriction. Both the autocorrelation of the legitimate parties' observations of the CCRSM and the cross-correlation of the two legitimate parties' observations of the CCRSM are formulated and examined theoretically. It is found that the performing random modulation can decorrelate consecutive observations of the CCRSM obtained by the legitimate parties using a sampling interval smaller than the correlation time of turbulence-induced optical fluctuations. The cross-correlation coefficient of the eavesdropper's and legitimate parties' observations of the CCRSM is dealt with theoretically and the eavesdropping risk in the CCRSM-based key extraction is inspected for the fully-disclosed-single-modulation worst eavesdropping (FDSM-WE) scenario, partially-disclosed-single-modulation worst eavesdropping (PDSM-WE) scenario and double-modulation worst eavesdropping (DM-WE) scenario. It is shown that the FDSM-WE scenario has the highest degree of eavesdropping risk. Finally, the lowest limit of the secret-key capacity in consideration of using the CCRSM is theoretically formulated. The effects of random modulation on such the lowest limit are quantitatively analyzed from an information-theoretic perspective. It is manifested that random modulation does not harm the potential of extracting secret keys from the CCRSM's randomness component stemming from turbulence-induced optical fluctuations.

The Shewhart-type RZ control chart for monitoring the ratio of autocorrelated variables

In many industrial manufacturing processes, the quality of products can depend on the relative amount between two quality characteristics X and Y. Often, this calls for the on-line monitoring of the ratio Z = X/Y as a quality characteristic itself by means of a control chart. A large number of control charts monitoring the ratio have been investigated in the literature under the assumption of independent normal observations of the two quality characteristics. In practice, due to the high frequency in sensor data collection, both autocorrelation and cross-correlation between consecutive observations can exist for X and Y and should be modelled to protect against the false alarm rate inflation when implementing a control chart for monitoring the ratio Z = X/Y. In this paper, we tackle this problem by investigating the performance of the Phase II Shewhart-type RZ control chart monitoring the ratio of two normal variables whose relationship is captured by a bivariate time series autoregressive model VAR(1), which can also account for the cross-correlation between the two quality characteristics. With the numerical study, we discuss how the design and the statistical performance of the Shewhart-type RZ control chart change with the VAR(1) model's parameters. We also provide an example to illustrate the use of the Shewhart-type RZ control chart with bivariate time series of observations in a furnace process.

The impacts of the COVID-19 pandemic on multimodal human mobility in London: A perspective of decarbonizing transport

ABSTRACT Decarbonizing transport is one of the core tasks for achieving Net Zero targets, but the COVID-19 pandemic disrupts human mobility and the established transport development strategies. Although existing research has explored the relationship between virus transmission, human mobility, and restrictions policies, few have studied the responses of multimodal human mobility to the pandemic and their impacts on the achievement of decarbonizing transport. This paper employs 32 consecutive biweekly observations of mobile phone application data to understand the influences of the pandemics on multimodal human mobility from February 2020 to April 2021 in London. We here illustrate that multimodal travel behavior and traffic flows significant changed after the pandemic and related lockdowns, but the decline or recovery varies across different travel modes and lockdowns. The car mode has shown the most resilience throughout the pandemic, but the travel modes in the public transit sector were hit hard. Cycle and walk modes remained high at the beginning of the pandemic, but the trend did not continue as the pandemic developed and the season changed. Our findings suggest that the COVID-19 pandemic brought more challenges to travel mode shifting and the achievement of decarbonizing transport rather than opportunities. This analysis will assist transport authorities to optimize the established transport policies and to redistribute limited resources for accelerating the achievement of decarbonizing transport.

Tests of serial dependence for multivariate time series with arbitrary distributions

In this paper, one studies tests of serial independence using a fixed number p of consecutive observations from a stationary time series, first in the univariate case, and then in the multivariate case, where even high-dimensional vectors can be used. The common distribution function is not assumed to be continuous, and the test statistics are based on the multilinear copula process. A bootstrap procedure based on multipliers is also proposed and shown to be valid. Tests based on Spearman’s rho and Kendall’s tau statistics are also considered, extending the results known for the case of continuous distributions. Contiguity results are obtained for some specific models and sufficient conditions for consistency of test statistics are stated, as well as a data-driven procedure to select p. Also, numerical experiments are performed to assess the finite sample level and power of the proposed tests. A case study using a time series of Arctic sea ice extent images is used to illustrate the usefulness of the proposed methodologies. The R package MixedIndTests (Nasri et al., 2022) includes all the methodologies proposed in this article.

Instructional Strategies and Integration of Critical Thinking Skills of Preservice English Teachers

Retaining quality teachers and declining numbers of language programs that will help students reach the goal of high proficiency levels for their success in today’s world has been a challenge in recent years. Language teachers have a great role to play in addressing the 21st-century learning needs of the students, e.g., critical thinking skills, which greatly rely on their innovative teaching pedagogies. This current study aimed to investigate the preparedness and effectiveness of the preservice English teachers with their instructional strategies in integrating critical thinking skills in language instruction. The population of the study was comprised of preservice English teachers assigned in secondary schools during their practice teaching stage. A modified researcher-made questionnaire-checklist was developed and validated with items on a four-point Likert Scale used to evaluate the strategies employed in integrating critical thinking skills in English language teaching through consecutive actual classroom teaching observations by the two (2) groups of mentors and by the preservice teachers themselves. The data revealed that based on the three (3) raters, the preservice English teachers have extensively integrated critical thinking skills in language instruction through the use of the higher-order- level of questioning along with analyzing, evaluating, and creating. The reported highest mean was in creating, and the lowest was in evaluating. The PSETs' extent of integration of critical thinking skills was secondly manifested through the provision of communicative tasks. The PSETs have signified three (3) communicative tasks that obtained the highest mean scores: [24] role plays (mean=3.53), [25] paired-shared activities (mean=3.52), and [19] small group work (mean=3.51). Likewise, they have identified the three (3) communicative activities with the lowest means, namely: [23] keeping a diary (mean=2.54), [6] interviews (mean=2.55), and jigsaw activities (mean=2.62). It was concluded that the preservice English teachers have extensively integrated critical thinking skills in English language teaching though there are instructional strategies along with questioning strategy through the use of higher-order-level of questions and the provision of communicative tasks that garnered low mean scores which recommend preservice English teachers to enhance maximum utilization of these teaching skills for effective integration of critical thinking skills in language instruction and eventually cater the learning needs of the 21st-century language learners. Hence, a capability enhancement program or training on the integration of critical thinking skills to preservice English teachers was also recommended based on the findings of the study.

Stability and spread: A novel method for quantifying transitions within multivariate binary time series data.

We present a novel method for quantifying transitions within multivariate binary time series data, using a sliding series of transition matrices, to derive metrics of stability and spread. We define stability as the trace of a transition matrix divided by the sum of all observed elements within that matrix. We define spread as the number of all non-zero cells in a transition matrix divided by the number of all possible cells in that matrix. We developed this method to allow investigation into high-dimensional, sparse data matrices for which existing binary time series methods are not designed. Results from 1728 simulations varying six parameters suggest that unique information is captured by both metrics, and that stability and spread values have a moderate inverse association. Further, simulations suggest that this method can be reliably applied to time series with as few as nine observations per person, where at least five consecutive observations construct each overlapping transition matrix, and at least four time series variables compose each transition matrix. A pre-registered application of this method using 4 weeks of ecological momentary assessment data (N = 110) showed that stability and spread in the use of 20 emotion regulation strategies predict next timepoint affect after accounting for affect and anxiety's auto-regressive and cross-lagged effects. Stability, but not spread, also predicted next timepoint anxiety. This method shows promise for meaningfully quantifying two unique aspects of switching behavior in multivariate binary time series data.

Multi-View Integrative Attention-Based Deep Representation Learning for Irregular Clinical Time-Series Data.

Electronic health record (EHR) data are sparse and irregular as they are recorded at irregular time intervals, and different clinical variables are measured at each observation point. In this work, to handle irregular multivariate time-series data, we consider the human knowledge of the aspects to be measured and time to measure them in different situations, known as multi-view features, which are indirectly represented in the data. We propose a scheme to realize multi-view features integration learning via a self-attention mechanism. Specifically, we devise a novel multi-integration attention module (MIAM) to extract complex information that is inherent in irregular time-series data. We explicitly learn the relationships among the observed values, missing indicators, and time interval between the consecutive observations in a simultaneous manner. In addition, we build an attention-based decoder as a missing value imputer that helps empower the representation learning of the interrelations among multi-view observations for the prediction task this decoder operates only in the training phase so that the final model is implemented in an imputation-free manner. We validated the effectiveness of our method over the public MIMIC-III and PhysioNet challenge 2012 datasets by comparing with and outperforming the state-of-the-art methods in three downstream tasks i.e., prediction of the in-hospital mortality, prediction of the length of stay, and phenotyping. Moreover, we conduct a layer-wise relevance propagation (LRP) analysis based on case studies to highlight the explainability of the trained model.

Seasonal changes in questing efficiency of wild Amblyomma americanum (Acari: Ixodidae) nymphs

Understanding the factors which influence host-seeking behavior of ticks is essential to determine the risk they pose as a vector of pathogens. While many studies have evaluated the impact of environmental variables on tick behavior, few have examined how seasonal changes in physiological status may further modify patterns of activity. In this study, we measured differences in questing behavior of mid spring- and early summer-caught Amblyomma americanum nymphs held under standardized laboratory conditions. As both groups represent the same cohort of overwintered nymphs, we hypothesized that age-related changes in the older summer ticks may influence questing behavior. In each season, we collected nymphs from field and forest habitats in northeast Missouri, after which we placed each nymph individually in a desiccating vertical questing apparatus with a hydrating microenvironment at the base. On the day following collection, we recorded the height of each nymph in the apparatus bi-hourly from 04:00 to 22:00 and calculated the vertical displacement between consecutive observations. Despite displaying no differences in mean questing height throughout the experiment, active ticks collected in the summer group (n = 89) travelled greater cumulative distances between desiccating and hydrating microenvironments than those collected in the spring (n = 119). This suggests that questing efficiency decreases in summer nymphs to accommodate increased time allocation towards rehydration. While we observed no direct association between body size and distance travelled, body size of the nymphal population also decreased significantly from spring to summer. Overall, our results demonstrate that there are seasonal changes in how A. americanum responds to environmental conditions. To more accurately predict host-seeking behavior of ticks across seasons, models should incorporate physiological parameters of the active ticks in a given population.

Population Dynamics and Determinants of Annual Fluctuations of the Endangered Smith's Blue Butterfly, Euphilotes enoptes smithi (Lycaenidae)

A mark-release-recapture study of the federally-listed Smith's Blue butterfly, Euphilotes enoptes smithi, was conducted in 1986 at Marina State Beach (Monterey County, CA) in coastal sand dune habitat. Demographic parameters estimated included daily and generation population numbers, residency, vagility, and emigration rates. Manly-Parr and Jolly-Seber analyses indicated an estimated 2,657 to 5,875 individuals comprised the 1986 generation, with average residence from 6.7 to 10.1 days (maximum 20 days). Average movements between consecutive observations on different days were 95.1 m for males and 91.0 m for females, while the longest lifetime dispersal observed was 1,230 m by a female. Emigration rates from areas of low quality habitat were as much as 8x greater than those from high quality habitat. Between 1997 and 2017 this butterfly was monitored yearly at three neighboring sand dune remnants in Sand City, CA to determine annual generation sizes. Transect counts of adults conducted during the 21 flight seasons, in conjunction with the frequencies of observed residencies from the 1986 study, indicated estimated generation numbers from 360 to 2,151 for the three sites combined. The observed year-to-year fluctuations in generation sizes were strongly correlated with annual numbers of flowerheads of this butterfly's Eriogonum foodplants, which in turn was correlated with annual precipitation.

Weighted maximum likelihood estimation for individual growth models

We apply a class of stochastic differential equations to model individual growth in a randomly fluctuating environment using cattle weight data. We have used maximum likelihood theory to estimate the parameters. However, for cattle data, it is often not feasible to obtain animal's observations at equally spaced ages nor even at the same ages for different animals and there is typically a small number of observations at older ages. For these reasons, maximum likelihood estimates can be quite inaccurate, being interesting to consider in the likelihood function a weight function associated to the elapsed times between two consecutive observations of each animal, which results in the weighted maximum likelihood method. We compare the results obtained from both methods in several data structures and conclude that the weighted maximum likelihood improves the estimation when observations at older ages are scarce and the observation instants are unequally spaced, whereas the maximum likelihood estimates are recommended when animals are weighted at equally spaced ages. For unequally spaced observations, a bootstrap estimation method was also applied to correct the bias of the maximum likelihood estimates; it revealed to be a more precise alternative, except when the available data only has young animals.