Observation Interval Research Articles

Modelling intermittent time series and forecasting COVID-19 spread in the USA

Forecasting intermittent time series represents a challenging task whose importance increases together with the number of series sporadically observed. However, given the difficulties in modelling the presence of zeros, few methods are available. This article introduces a novel state-space approach defined as Intermittent Local Level (ILL). Our approach allows integrating the intermittent nature of time series and forecasting efficiently. Indeed, the proposed state-space model assumes a Bernoulli dynamics that allows switching between zeros and positive values. Moreover, we derive the unobserved dynamics of the time series and provide a simple method for estimating and forecasting. In addition, our approach allows deriving prediction intervals for intermittent observations. Finally, we compare our method’s performance with those of standard intermittent models as well as other benchmarks, using the daily number of new cases of COVID-19 observed in nearly 3000 American counties. Predicting the number of newly infected people is important, not only for hospitals but also for policy makers in general. Empirical results show that the suggested approach clearly outperforms the Croston model and its variants when forecasting the number of new Coronavirus cases over a two-week period. In addition, it compares well with non-intermittent benchmarks both in point forecast and prediction intervals.

Development of novel green methods for preparation of lead-free preserved pidan (duck egg).

Pidan, a pickled duck egg, is a traditional Chinese cuisine and generally produced by soaking in metal ion containing strong alkaline solution such as NaOH solution. However, nowadays consumers possess negative perception for using strong alkali in food processing. Therefore, the objective of the current study was to determine the potential of incinerated eggshell powder and alkaline electrolyzed oxidized (EO) water for pidan production rather than harmful NaOH use. This study aims to obtain the optimal physicochemical and sensory qualities of pidan. Various dosing (1-5%) of the incinerated eggshell powder solution or alkaline EO water was used as a basic pickling solution. Duck eggs were pickled at 25-27°C for 15-30days with 3days of an observation interval. Actual commercial process commonly undergoes for 14days of ripening, after 25days of picking process with incinerated eggshell powder or EO water. Results showed that physicochemical and sensory attributes of pidan obtained by incinerated eggshell powder solution and alkaline EO water were not significantly different (P < 0.05) from the commercial product. This study reports a cost-effective and green alternative method for pidan processing by replacing costly NaOH without compromising their physico-chemical and sensory attributes.

Volumetric growth rates of untreated cavernous sinus meningiomas.

Meningiomas that arise primarily within the cavernous sinus are often believed to be more indolent in their growth pattern. Despite this perceived growth pattern, disabling symptoms can arise even with small tumors. While research has been done on cavernous sinus meningiomas (CSMs) and their treatment, very little is known about their natural growth rates. With a better understanding of the growth rate of CSM, patient treatment and guidance can be can optimized and individualized. The goal of this study was to determine volumetric growth rates of untreated CSMs. Thirty-seven patients with 166 MR images obtained between May 2004 and September 2019 were reviewed, with a range of 2-13 MR images per patient (average of 4.5 MR images per patient). These scans were obtained over an average follow-up period of 45.9 months (median 33.8, range 2.8-136.9 months). All imaging prior to any intervention was included in this analysis. Volumetric measurements were performed and assessed over time. The estimated volumetric growth rate was 23.3% per year (95% CI 10.2%-38.0%, p < 0.001), which is equivalent to an estimated volume doubling time (VDT) of 3.3 years (95% CI 2.1-7.1 years). There was no significant relationship between growth rate and patient age (p = 0.09) or between growth rate and patient sex (p = 0.78). The median absolute growth rate was 41% with a range of -1% to 1793%. With a definition of "growth" as an increase of greater than 20% during the observed period, 65% of tumors demonstrated growth within their observation interval. Growth rates for each tumor were calculated and tumors were segmented based on growth rate. Of 37 patients, 22% (8) demonstrated no growth (< 5% annual growth, equivalent to a VDT > 13.9 years), 32% (12) were designated as slow growth (annual growth rate 5%-20%, VDT 3.5-13.9 years), 38% (14) were found to have medium growth (annual growth rate 20%-100%, VDT 0.7-3.5 years), and 8% were considered fast growing (annual growth rate > 100%, VDT < 0.7 years). This study evaluated CSM volumetric growth rates. A deeper understanding of the natural history of untreated CSMs allows for better counseling and management of patients.

Optimal Interval Observer for Discrete-Time Switched Systems

The note is devoted to the interval observer design problem for discrete-time switched systems. By designing a suitable interval observer, it is convenient to achieve the interval estimation bounds of system states and reduce the effect of the unknown external disturbance. The interval observer design method has two main steps. They are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> observer design and interval hull design respectively. Besides, the superiority of the proposed method is proved in the aspect of theory. One comparative example is carried out to verify the conclusion above.

Event-triggered control for linear time-varying systems using a positive systems approach

This paper presents a new design for the event-triggered output feedback control of uncertain linear time-varying systems. Departing from the traditional treatments of event-triggered linear time-invariant systems, it is shown that factoring of fundamental solutions as products of solutions of matrix differential equations, together with small-gain arguments and the notions of interval observers and positive systems, lead to a new class of robust event-triggered output-feedback controllers. A key innovation is our use of new event triggers, which use vectors of absolute values instead of the usual Euclidean 2-norms. An illustrative example of a curve tracking system from marine robotics validates the efficacy of the proposed design scheme.

The minipig intraoral dental implant model: A systematic review and meta-analysis.

ObjectivesThe objective of this report was to provide a review of the minipig intraoral dental implant model including a meta-analysis to estimate osseointegration and crestal bone remodeling.MethodsA systematic review including PubMed and EMBASE databases through June 2021 was conducted. Two independent examiners screened titles/abstracts and selected full-text articles. Studies evaluating titanium dental implant osseointegration in native alveolar bone were included. A quality assessment of reporting was performed. Random-effects meta-analyses and meta-regressions were produced for bone-implant contact (BIC), first BIC, and crestal bone level.Results125 out of 249 full-text articles were reviewed, 55 original studies were included. Quality of reporting was generally low, omissions included animal characteristics, examiner masking/calibration, and sample size calculation. The typical minipig model protocol included surgical extraction of the mandibular premolars and first molar, 12±4 wks post-extraction healing, placement of three narrow regular length dental implants per jaw quadrant, submerged implant healing and 8 wks of osseointegration. Approximately 90% of studies reported undecalcified incandescent light microscopy histometrics. Overall, mean BIC was 59.88% (95%CI: 57.43–62.33). BIC increased significantly over time (p<0.001): 40.93 (95%CI: 34.95–46.90) at 2 wks, 58.37% (95%CI: 54.38–62.36) at 4 wks, and 66.33% (95%CI: 63.45–69.21) beyond 4 wks. Variability among studies was mainly explained by differences in observation interval post-extraction and post-implant placement, and implant surface. Heterogeneity was high for all studies (I2 > 90%, p<0.001).ConclusionsThe minipig intraoral dental implant model appears to effectively demonstrate osseointegration and alveolar bone remodeling similar to that observed in humans and canine models.

A neural network adaptive interval observer design for nonlinear systems

A design method of neural network adaptive interval observer for the continuous-time unknown nonlinear system is proposed. The bounds of unknown nonlinear functions are described by the state and the input, so that the bounds of the nonlinear function vector are unmeasurable. Therefore, this method can be used to observe systems with higher degree of nonlinearity and any priori knowledge about systems. In this work, the hyper basis function neural network (HBFNN) is presented to estimate the boundary (including lower and upper bounds) of the unknown nonlinear function vector. Furthermore, the non-negativity and stability of the error dynamic system are given, and the convergence and non-negativity are also guaranteed. Compared with the traditional interval observer, the efficiency of the proposed approach is verified through a numerical simulation example.

Trajectories of Pain in Very Old Age: The Role of Eudaimonic Wellbeing and Personality.

Pain is common in very old age and in the last years prior to death. However, little is known regarding longitudinal trajectories of pain in very old age and at the end of life. Moreover, whereas medical and morbidity-related factors contributing to pain are established, the role of psychosocial factors, such as eudaimonic wellbeing or personality as potential determinants of late-life pain trajectories has so far not been sufficiently investigated. We used data from the LateLine project. The sample consisted of n = 118 very old adults (M = 90.5 years, SD = 2.8 years) who were living alone at baseline and who had died between 2009 and 2021. They took part in up to 16 measurement occasions (M = 5.2, SD = 4.7, range 1–16) within an observational interval of 7 years. Assessment of pain was based on the SF-36 bodily pain subscale. Key indicators of eudaimonic wellbeing (autonomy, environmental mastery, and purpose in life) as well two of the Big Five personality traits (neuroticism and extraversion) were included as predictors. We controlled in all analyses for gender, education, subjective health, and depressive symptoms. Contrasting pain trajectories over chronological age (time since birth) vs. time to death, a time-to-death-related model resulted in a better model fit and accounted for a larger amount of pain variability than the age-related model. Mean-level change in pain, both over age and time to death, was not significant, but there was substantial interindividual variability in intraindividual trajectories. Age-related change in pain was significantly predicted by autonomy and neuroticism, with increasing pain among those who had lower initial autonomy scores and higher initial neuroticism scores. With regard to time-to-death-related trajectories of pain, higher purpose in life as well as lower extraversion at baseline predicted less increase or even steeper decrease in pain with approaching death. Our findings suggest that, despite overall mean-level stability in pain both over age and time to death, there is a substantial proportion of individuals who reveal deterioration in pain over time. Regarding the role of psychosocial predictors, personality traits and eudaimonic wellbeing are related with late-life pain trajectories both over age and time-to-death.

A simulated comparison of behavioural observation sampling methods

Behavioural research requires the use of sampling methods to document the occurrence of responses observed. Sampling/recording methods include ad libitum, continuous, pinpoint (instantaneous), and one-zero (interval) sampling. Researchers have questioned the utility of each sampling method under different contexts. Our study compared computerized simulations of both pinpoint and one-zero sampling to continuous recordings. Two separate computer simulations were generated, one for response frequency and one for response duration, with three different response frequencies (high, medium, or low) and response durations (short, medium, and long) in each simulation, respectively. Similarly, three different observation intervals (5, 50, and 500 s) were used to record responses as both pinpoint and one-zero sampling methods in the simulations. Under both simulations, pinpoint sampling outperformed one-zero sampling, with pinpoint sampling producing less statistical bias in error rates under all frequencies, durations, and observation intervals. As observation intervals increased, both mean error rates and variability in error rates increased for one-zero sampling, while only variability in error rate increased for pinpoint sampling. The results suggest that pinpoint sampling techniques are effective for measuring both frequency (event) and duration (state) behaviours, and that pinpoint sampling is a less statistically biased behavioural observation method than one-zero sampling.

Exact prediction intervals for future exponential and Pareto lifetimes based on ordered ranked set sampling of non-random and random size

In the present paper, two pivotal statistics are suggested to construct prediction intervals of future observations from the exponential and Pareto distributions in the context of ordered ranked set sample. Our study encompasses two cases. The first case, when the sample size is assumed to be fixed and the second case when the sample size is assumed to be a positive integer-valued random variable. In addition to deriving explicit forms for the distribution functions of the two pivotal statistics, we consider some special cases for the random size of the sample. Moreover, a simulation study is carried out to assess the efficiency of the suggested methods. Finally, an example representing lifetime data is analyzed.

Bayesian Interval Estimation for the Two-Parameter Exponential Distribution Based on the Right Type II Censored Sample

The Bayesian interval estimation of the scale parameter for two-parameter exponential distribution is proposed based on the right type II censored sample. Under this type of censoring, two methods of Bayesian joint confidence region of the two parameters are also proposed. The simulation results show that the Bayesian method has a higher coverage probability than the existing method, so the Bayesian method is recommended for use. This research is related to the topic of asymmetrical probability distributions and applications across disciplines. The predictive interval of the future observation based on the right type II censored sample is also provided. One biometrical example is given to illustrate the proposed methods for the Bayesian interval estimations and prediction interval.

FENGYUN-4A Advanced Geosynchronous Radiation Imager Layered Precipitable Water Vapor Products’ Comprehensive Evaluation Based on Quality Control System

A physical retrieval algorithm has been developed for deriving the layered precipitable water vapor (LPWs) product from infrared radiances of the Advanced Geosynchronous Radiation Imager (AGRI) onboard FengYun-4A (FY-4A), the first of the new generation of Chinese geostationary weather satellites (FengYun-4, or FY-4 Series). The FY-4A AGRI LPWs are evaluated with different types of reference datasets based on Quality Control System (QCS), including those from Himawari-8 AHI (Advanced Himawari Imager), MODIS (Moderate Resolution Imaging Spectroradiometer), Radiosonde, ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5), NCEP (National Centers for Environmental Prediction) reanalysis and CMA (China Meteorological Administration) forecast product from global medium range numerical weather prediction (NWP) system. QCS is one of the important components of FY-4A ground segment, which mainly focuses on the satellite products’ evaluation and validation. It is found that the AGRI LPW product has a good agreement with different evaluating sources and the quality is favorable and stable. With the capability of frequent (5-min interval) observations over the East Asia and Western Pacific regions, the AGRI LPW products can be used to investigate the atmospheric temporal and spatial variations in the pre-landfall environment for typhoons. The QCS is a useful tool to monitor, evaluate, and validate the AGRI LPW products.

Dynamics of hot random hyperbolic graphs.

We derive the most basic dynamical properties of random hyperbolic graphs (the distributions of contact and intercontact durations) in the hot regime (network temperature T>1). We show that for sufficiently large networks the contact distribution decays as a power law with exponent 2+T>3 for durations t>T, while for t<T it exhibits exponential-like decays. This result holds irrespective of the expected degree distribution, as long as it has a finite Tth moment. Otherwise, the contact distribution depends on the expected degree distribution and we show that if the latter is a power law with exponent γ∈(2,T+1], then the former decays as a power law with exponent γ+1>3. However, the intercontact distribution exhibits power-law decays with exponent 2-T∈(0,1) for T∈(1,2), while for T>2 it displays linear decays with a slope that depends on the observation interval. This result holds irrespective of the expected degree distribution as long as it has a finite Tth moment if T∈(1,2), or a finite second moment if T>2. Otherwise, the intercontact distribution depends on the expected degree distribution and if the latter is a power law with exponent γ∈(2,3), then the former decays as a power law with exponent 3-γ∈(0,1). Thus, hot random hyperbolic graphs can give rise to contact and intercontact distributions that both decay as power laws. These power laws, however, are unrealistic for the case of the intercontact distribution, as their exponent is always less than one. These results mean that hot random hyperbolic graphs are not adequate for modeling real temporal networks, in stark contrast to cold random hyperbolic graphs (T<1). Since the configuration model emerges at T→∞, these results also suggest that this is not an adequate null temporal network model.

Particle-filter-based volcanic ash emission inversion applied to a hypothetical sub-Plinian Eyjafjallajökull eruption using the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-chem) version 1.0

Abstract. A particle-filter-based inversion system is presented, which enables us to derive time- and altitude-resolved volcanic ash emission fluxes along with its uncertainty. The system assimilates observations of volcanic ash column mass loading as retrieved from geostationary satellites. It aims to estimate the temporally varying emission profile endowed with error margins. In addition, we analyze the dependency of our estimate on wind field characteristics, notably vertical shear, within variable observation intervals. Thus, the proposed system addresses the special challenge of analyzing the vertical profile of volcanic ash clouds given only 2D high temporal-resolution column mass loading data as retrieved by geostationary satellites. The underlying method rests on a linear combination of height–time emission finite elements of arbitrary resolution, each of which is assigned to a model run subject to ensemble-based space–time source inversion. Employing a modular concept, this setup builds the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-chem). It comprises a particle smoother in combination with a discrete-grid ensemble extension of the Nelder–Mead minimization method. The ensemble version of the EURopean Air pollution Dispersion – Inverse Model (EURAD-IM) is integrated into ESIAS-chem but can be replaced by other models. As initial validation of ESIAS-chem, the system is applied to simulated artificial observations of both ash-contaminated and ash-free atmospheric columns using identical-twin experiments. Thus, in this idealized initial performance test the underlying meteorological uncertainty is neglected. The inversion system is applied to two notional sub-Plinian eruptions of the Eyjafjallajökull volcano, Iceland, with strong ash emission changes with time and injection heights. It demonstrates the ability of ESIAS-chem to retrieve the volcanic ash emission fluxes from the assimilation of column mass loading data only. However, the analyzed emission profiles strongly differ in their levels of accuracy depending of the strength of wind shear conditions. While the error is only 10 %–20 % for the estimated emission fluxes under strong wind conditions, it increases up to 60 % under weak wind shear conditions. In case of increasing wind shear, the performance of the analysis may benefit from extending the assimilation window, in which new observations potentially contribute valuable information to the analysis system. For our test cases using an artificial volcanic eruption, we found an assimilation window length of 18 h, i.e., 10 h after the eruption terminated, to be sufficient for analyzing the extent and location of the artificial ash cloud. In the performed test cases, the analysis ensemble predicts the location of high volcanic ash column mass loading in the atmosphere with a very high probability of &gt; 95 %. Additionally, the analysis ensemble is able to provide a vertically resolved probability map of high volcanic ash concentrations to a high accuracy for both high and weak wind shear conditions.

Dex® carbohydrate drinks in trauma patients fasting preoperatively – A patient satisfaction study

Introduction Dex® is an alkaline carbohydrate (CHO) drink used preoperatively in patients fasting for elective surgery. Its utilisation in patients awaiting emergency surgery is yet to be ascertained. We undertook a study assessing patient satisfaction relating to the introduction of Dex® in trauma patients in a Level 1 adult trauma centre. Methods Patients fasting for surgery, and able to receive clear fluids in compliance with local guidelines, were eligible for inclusion. Patient satisfaction scores for predetermined variables were recorded via an interval observer scale prior to and following the introduction of Dex® to the trauma unit. Results Prior to the introduction of Dex® 14 satisfaction evaluations were completed. A further 13 evaluations were returned after Dex® was made available. Post-traumatic amnesia accounted for some patients being unable to complete the evaluation. Patients who completed the satisfaction evaluations after Dex® was introduced reported higher satisfaction in all but two variables (‘Headache’ and ‘Staff Annoyance’). ‘Hunger’ median scores were 7 (95% CI, 5–8) prior to the introduction of Dex® and 3 (95% CI, 2–6) after its’ introduction ( p = .004). ‘Thirst’ median scores were 7.5 (95% CI, 6–10) and 4 (95% CI, 3–8) prior to and after Dex® introduction, respectively ( p = .018). Conclusion No adverse events or theatre timing related issues were associated with the use of Dex®. A larger randomised study of CHO drink supplementation in trauma patients fasting preoperatively is warranted to further evaluate these satisfaction endpoints as well as the other variables investigated in this study. Postoperative evaluation of these markers should also be considered.

Time‐dependent Luenberger‐type interval observer design for uncertain time‐varying systems

AbstractThis article investigates the state interval observation problem for a class of continuous and discrete‐time linear time‐varying (LTV) systems with bounded uncertainties separately. First, by utilizing both the LTV transformation and the solutions to a type of time‐varying fully‐actuated Sylvester equations, a time‐dependent standard interval observer (IO) is established in a parametric form with some design degrees of freedom. Second, a new time‐dependent structure of Luenberger‐type IOs is proposed to relax the limitation of the LTV transformation‐based design method. Further, its sufficient existence conditions are given in virtue of positive system theory. Meanwhile, a parametric design technique of such an IO for uncertain LTV systems is also raised under rank conditions. Third, the obtained results are extended to the discrete‐time systems. Finally, the correctness and benefits of the proposed methods are verified using some numerical examples.

Distributed interval state estimation with [formula omitted]-gain optimization for cyber–physical systems subject to bounded disturbance and random stealthy attacks

This paper studies the distributed interval state estimation problem for cyber–physical systems with bounded disturbance and random stealthy attacks. Since conventional interval observers cannot complete the task of real-time monitoring system under random attacks, an attack-resistant distributed interval observer is designed by using attack frequency and interval attack estimation. Using the designed observer, upper- and lower-bounding estimation error systems are modeled by positive interconnected systems with hybrid deterministic and random bounded inputs. To explicitly attenuate the effect of disturbance and attacks, the resulting deterministic positive error system between upper- and lower-bounding estimates is formulated. By linear programming, the results of interval observer design and l∞-gain optimization are proposed. The remote monitoring of vehicle lateral dynamic is given for numerical verification of the results.

Monitoring water transparency, total suspended matter and the beam attenuation coefficient in inland water using innovative ground-based proximal sensing technology

Long-term and high-frequency observations are vital to reveal water quality dynamics and responses to climate change and human activities. However, the datasets collected from traditional in situ and satellite observations may miss the rapid dynamics of water quality in the short term due to low temporal-spatial monitoring frequency and cloudy or rainy weather. To address this shortage, innovative ground-based proximal sensing (GBPS) technology was proposed to monitor water quality and identify emergencies with a wavelength range of 400–1000 nm, a spectral resolution of 1 nm and a minimal observation interval of 30 s. The GBPS was equipped with a hyperspectral imager placed 4–5 m above the water surface to minimize the impacts of the atmosphere and clouds. In this study, combined with 583 water samples obtained from four field samplings, GBPS datasets were first applied to estimate the total suspended matter (TSM), Secchi disk depth (SDD) and beam attenuation coefficient at 550 nm (C(550)) in Taihu Lake (TL), Liangxi River (LR) and Funchunjiang Reservoir (FR). The results demonstrated good performance with the TSM (R2 = 0.83, RMSE = 8.35 mg/L, MAPE = 24.0%), SDD (R2 = 0.88, RMSE = 0.09 m, MAPE = 14.7%), and C(550) (R2 = 0.79, RMSE = 3.55 m-1, MAPE = 35.8%). The time series of TSM and C(550) at the second-minute level showed consistent changes, but they were opposite to those of SDD. Taking TSM as an example, the datasets captured two mutations in TL with an 853.6% increase in 65 min and a rapid change from 40.3 mg/L to 256.9 mg/L and then to 51.0 mg/L in 224 min on November 1 and 3, respectively. Meanwhile, a significant decreasing trend (r = −0.83, p < 0.01) in LR from November 7 to 9 and a periodic diurnal increasing trend of TSM in FR during November 11 to 13 (0.46 ≤ R2 ≤ 0.70, p < 0.01) were observed. GBPS, with the advantages of high-frequency observations and the applicability of complex weather conditions, compensates for the in situ, aircraft and satellite observation deficiencies. Therefore, GBPS allows us to capture more detailed water quality information and episodic events, which is an important part of an integrated air-space-ground monitoring system in the future.

Interval observer-based fault-tolerant control for a class of positive Markov jump systems

The problem of fault-tolerant controller design for positive Markov jump systems subject to interval uncertainties and time-varying actuator faults is investigated in this paper. First, under the assumption of stochastic stability, an interval observer is developed to achieve simultaneously estimation of the upper and lower bounds of the system state and the actuator faults. Then, an interval observer and a state-estimate stabilizing feedback controller are jointly designed. By utilizing the obtained interval estimates, the controller is constructed to achieve a satisfactory performance and fault tolerance. Owing to positivity, the joint observer and controller design is formulated in terms of linear programming. The effectiveness of the proposed approach is demonstrated through simulation.

Synthesis of Interval Observers for Bounded Jacobian Nonlinear Discrete-Time Systems

A systematic procedure to synthesize interval observers for nonlinear discrete-time systems is proposed. The feedback gains and other matrices are found by solving semidefinite feasibility programs. Two cases are considered: (1) the interval observer is in the same coordinate frame as the given system, and (2) the interval observer uses a coordinate transformation. Numerical examples are provided to showcase the effectiveness of the interval observers and show an application to sampled-data systems.