Observation Interval Research Articles

The Stability of Trend Management Strategies in Chaotic Market Conditions

This study investigates the stability of trend management strategies under stochastic chaos conditions, with a focus on speculative trading in the Forex market. The primary aim is to evaluate the feasibility and robustness of these strategies for asset management. The experimental setup involves sequential optimization and testing of trend strategies across three EURUSD observation intervals, where each subsequent interval alternates between training and testing roles. Methods include numerical data analysis, parametric optimization, and the use of both conventional and bidirectional exponential filters to isolate system components and improve trend detection. Observations reveal that while trend strategies optimized for specific intervals yield positive results, their effectiveness diminishes on unseen intervals due to inherent market instability. The results show significant limitations in using linear trend-based strategies in chaotic environments, with optimized strategies often leading to losses in subsequent periods. The discussion highlights the potential of integrating trend statistics into multi-expert decision systems, leveraging fuzzy solutions based on fundamental analysis to enhance decision-making reliability. In conclusion, while standalone trend strategies are unsuitable for stable asset management in chaotic markets, their integration into hybrid systems may provide a pathway for improved performance and resilience.

Behavioral Patterns and Honey Yield Between Native and Non-native Stingless Bees in Bali's Ecosystem

The coronavirus disease 2019 (COVID-19) pandemic has led to significant growth in honey bee cultivation in Bali, leading to the introduction of nonnative bee species alongside the indigenous Tetragonula laeviceps. However, the 75% failure rate of nonnative species cultivation between 2020 and 2023 necessitated a deeper understanding of species adaptability in the Bali ecosystem. This study specifically examined the behavioral adaptation and honey production of Heterotrigona itama compared with those of Tetragonula laeviceps in a shared cultivation environment. A seven-month comparative experimental study involving 30 bee colonies (15 H. itama introduced from Lampung and 15 local T. laeviceps) was conducted. Time series sampling was employed at three daily intervals (09:00, 13:00, and 16:00) for behavioral observations, while honey production was measured via nondestructive vacuum extraction. H. itama demonstrated superior honey production (516.3 ml average) to that of T. laeviceps (213.3 ml average). The species exhibited distinct foraging preferences, with T. laeviceps favoring Jatropha integerrima and H. itama preferring Antigonon leptopus and Xanthostemon chrynsanthus. Both species have developed different adaptive strategies for nectar–water content management, with H. itama utilizing sun exposure and T. laeviceps employing smaller storage pots. This study revealed successful coexistence between native and nonnative species through differentiated foraging behaviors and resource utilization strategies, with H. itama showing greater productive capacity attributed to morphological and behavioral differences.

The dynamics of thrombolysis over time in acute immunologic reactions

Allergic reactions and angioedema are important immunologic conditions in acute care settings. Prior data indicate an association between symptom severity and maximum lysis (ML) in thromboelastometry. We aimed to evaluate the dynamics of ML two hours after admission to the emergency department. We determined ML with thromboelastometry using extrinsic testing (EXTEM, ROTEM®, Werfen GmbH, Munich, Germany) at the presentation of patients with immunologic reactions and approximately two hours thereafter (after the observation interval). The median test was used to compare the extent of ML between individuals who were still symptomatic after the observation interval and those who were asymptomatic. We prospectively enrolled 16 individuals (10 (63%) female, mean age 50 ± 14 years). Of these, 6 (38%) were still symptomatic after the observation interval. These patients at that time had significantly higher ML than those who were asymptomatic (14% (IQR 12–17) vs. 8% (IQR 4–10), p = 0.002). To conclude, in emergency department patients with immunologic reactions, symptoms after 2 h of observation may be associated with alterations in ML.

An Event-Triggered Interval Observer Scheme for Fault Diagnosis of Cyber–Physical DC Microgrids

An Event-Triggered Interval Observer Scheme for Fault Diagnosis of Cyber–Physical DC Microgrids

Prediction of GNSS Velocity Accuracies Using Machine Learning Algorithms for Active Fault Slip Rate Determination and Earthquake Hazard Assessment

GNSS technology utilizes satellite signals to determine the position of a point on Earth. Using this location information, the GNSS velocities of the points can be calculated. GNSS velocity accuracies are crucial for studies requiring high precision, as fault slip rates typically range within a few millimeters per year. This study employs machine learning (ML) algorithms to predict GNSS velocity accuracies for fault slip rate estimation and earthquake hazard analysis. GNSS data from four CORS stations collected over 1-, 2-, and 3-year intervals with observation durations of 2, 4, 6, 8, and 12 h, were analyzed to generate velocity estimates. Position accuracies, observation intervals, and corresponding velocity accuracies formed two datasets for the East and North components. ML models, including Support Vector Machine, Random Forest, K-Nearest Neighbors, and Multiple Linear Regression, were used to model the relationship between position and velocity accuracies. The findings reveal that the Random Forest, which makes more accurate and reliable predictions by evaluating many decision trees together, achieved over 90% accuracy for both components. Velocity accuracies of ±1.3 mm/year were obtained for 1-year interval data, while accuracies of ±0.6 mm/year were achieved for the 2- and 3-year intervals. Three campaigns were deemed sufficient for Holocene faults with higher slip rates. However, for Quaternary faults with lower slip rates, longer observation periods or additional campaigns are necessary to ensure reliable velocity estimates. This highlights the need for GNSS observation planning based on fault activity.

Interval Observer-Based Model Predictive Control in the Propofol Delivery Process

Due to the anesthesia delivery-related consequences during general surgery, it is desirable to regulate hypnosis during the surgery through an anesthesia infusion closed-loop system. In this paper, a closed-loop scheme is based on the patient’s kinetic and dynamic drug model, where the Bispectral index is the available output. In order to generate the feedback and recover the necessary states, an interval observer is designed based on the system’s positivity and cooperativity properties. Then, a Model Predictive Control (MPC) is implemented to ensure the correct propofol delivery using the estimated states and output. Finally, the proposed interval observer and MPC performances are validated by simulations in three silico patients.

Quantifying phenotypic and genetic variation for cow fertility phenotypes in American Simmental using total herd reporting data.

Reproduction plays a major role in the production efficiency of livestock species. However, cow-centric reproductive traits tend to be lowly heritable and are not expressed until later in an animal's lifetime, making phenotypic selection alone inefficient at generating genetic gain. Genetic progress can be accelerated by focusing selection on the predicted genetic component of reproductive traits using Expected Progeny Differences (EPDs). We used the American Simmental Association's performance and Total Herd Enrollment data, made up of 533,155 calving records from 303,158 females (132,403 cows and 170,755 heifers), 33,732 of which are genotyped, to explore three continuous and two discrete phenotypes focused on quantifying early and sustained fertility in beef cows. We analyzed calving date (cow's calving date relative to the start of the calving season), calving interval (days between calves), first calving interval (calving interval observation between the first and second calving record for a female), heifer pregnancy (did the animal calve as 2-year-old), and discrete early calving (did animal calve in the first 30 days of the calving season) as distinct, but correlated measures of fertility. This dataset provides insight into population-wide trends related to cow attrition, calving season lengths, and phenotypic variation in fertility. We used pedigree and genomic REML to estimate these six phenotypes' genetic, permanent environment, and temporary environmental variance components. Pedigree estimated heritabilities were 0.06 (±0.000011) for calving date, (0.04 ±0.000005) for calving interval, 0.07 (±0.000016) for discrete early calving, 0.05 ±0.000041 for first calving interval, and 0.23 (±0.000099) for heifer pregnancy, consistent with other fertility traits across beef and dairy cattle. The incorporation of genomics increased the heritability estimate for heifer pregnancy (0.24 ±0.000098) and decreased the estimate for first calving interval (0.04 ±0.000029). Positive phenotypic and genetic correlations were found among these phenotypes (rG = 0.01-0.96). These results call for further work in optimizing genetic predictions and exploration of the genetic architecture through genome-wide studies. Whole herd reporting date frameworks represent opportunities for measuring new reproductive phenotypes, but their utility in genetic evaluations will rely on novel trait initiatives and consistent recording that captures more detailed data.

Finite‐Time Functional Interval Observer Design for Nonlinear Discrete‐Time Switched Descriptor Systems

ABSTRACTIn this article, the finite‐time functional interval estimation method for nonlinear discrete‐time switched descriptor systems with perturbations is investigated. To deal with the nonlinear terms in the switched system, the nonlinear error system is converted into a linear parameter varying form using the reformulated Lipschitz property. Firstly, a functional observer with finite‐time gain is constructed for the nonlinear switched descriptor system. Then, based on the proposed finite‐time functional observer, the reachability analysis is applied to design a finite‐time functional interval observer. Finally, the superiority and effectiveness of the interval estimation method presented in this paper are verified through a simulation of the circuit system.

Stability of Markovian Jump Bidirectional Associative Memory Neural Networks Under Lévy Noise and Discrete Observation

ABSTRACTThe focus of this paper is to discuss the asymptotic stability and exponential stability problems of Markovian jump bidirectional associative memory neural networks with Lévy noise based on the discrete observation. Compared with the existing literature, this paper gives a weaker assumption regarding Lévy noise intensity functions. Furthermore, for the sake of solving difficulties caused by the discrete observation and Lévy noise, this paper designs a new Lyapunov‐Krasovskii functional which not only depends on the discrete observation interval, but also intensity measures. In virtue of the M‐matrix theory, three theorems are obtained ensuring the mean‐square asymptotic stability and mean‐square exponential stability of the trivial solution. Our theorems can not only deal with the discrete observation, but also with constant delays and variable time delays. Ultimately, two numerical examples are presented to illustrate the correctness of our theory findings.

Distributed Interval Observer and Distributed Unknown Input Observer Designs

Distributed Interval Observer and Distributed Unknown Input Observer Designs

GNSS-A Trajectory Optimization and Its Rose-Curve Solution Regarding Acoustic Delay Parameter Identification

Global Navigation Satellite System (GNSS)-Acoustic (GNSS-A) trajectory design is vitally important for precise seafloor geodetic positioning. However, GNSS-A trajectory optimization based on an ordinary GNSS-A observation model suffers a major setback due to a lack of consideration for the spatio-temporal variations of sound speed. We propose a GNSS-A trajectory optimization criterion based on a GNSS-A observation model appended with acoustic delay parameters characterizing the sound speed variations. In addition, we put the problem into a continuous function space to obtain a concise curve solution that is free from the sampling interval of observations. Particularly, a family of rose trajectory solutions is derived for developing unmanned GNSS-A systems. It shows that the optimal size of the rose trajectory is 2.8 times the water depth for the ordinary observation model, but the optimal size becomes at least 8.3 times the water depth for the observation model with the acoustic delay parameters. The more the parameters used to characterize the time-varying nature of the acoustic delay are adopted, the larger the trajectory size is needed. The proposed trade-off strategy between the cost increase and the precision benefit is recommended to shrink the trajectory size to reduce the ship-time cost and avoid the limitation of signal propagation. The proposed results are verified through the simulation tests and actual measurements. It shows that we should adopt the derived optimal trajectory to ensure the geometric precision of the acoustic delay parameters. The size of the GNSS-A trajectory adopted conventionally is far from the optimal size for inverting the sound speed field structure.

On exact observability of a nonlinear evolutionary equation with a bounded right-hand side operator on a small interval

For the Cauchy problem associated with a nonlinear ordinary differential equation in a Hilbert space $X$, we obtain sufficient conditions for exact observability on a small interval. By means of the condition of boundedness from below by a positive constant on the unit sphere with respect to a linear observer (observation operator) and with the help of Minty–Browder's theorem, the observability problem is reformulated as an operator (integral) equation with the right-hand side involving (in addition to the Volterra-type term, which is “local” in time) a nonlocal term as well. The unique solvability of the obtained operator equation (the equation of state reconstruction from observation) is proved with the help of the contraction map principle and the hypothesis about smallness of the observation interval. Moreover, we prove two theorems on global reconstruction of a state: 1) from observation on a small interval and under the condition of global solvability of some majorant integral equation in the space $\mathbb{R}$; 2) from a series of observations on small intervals in the presence of a priori information on the belonging of the state values to a bounded ball in $X$. As an example (of an independent interest), a semilinear equation of the global electric circuit in the Earth's atmosphere is considered.

Interval observer-based consensus tracking for multi-agent systems under Markovian switching topologies with stealthy attack

This article investigates the bounded consensus tracking problem for uncertain linear multi-agent systems (MASs) under Markovian switching topologies in the presence of unknown but bounded disturbances and stealthy attacks. First, a class of interval observers (IOs) is designed to estimate confidence bounds of agents states and stealthy attack signals. The stealthy attack is considered as that the output information is modified by the malicious attacker. Second, distributed control protocols under Markovian switching topologies are proposed for the MASs based on the designed IOs to achieve bounded consensus. The mean-square error bounds depend on the error bounds of disturbances. After rigorous mathematical proofs, some sufficient conditions are acquired for achieving control goals. Simulations are provided to validate the effectiveness of the proposed IOs and control protocols.

Respiratory manifestations of pediatric granulomatosis with polyangiitis: A 12-year experience from a tertiary care facility.

Pediatric granulomatosis with polyangiitis (GPA) is associated with several pulmonary manifestations. This study aims to describe these manifestations at time of diagnosis and longitudinally at a tertiary-care pediatric hospital. We performed a retrospective chart review of patients with GPA treated at our facility between 1 January 2010 through 31 December 2021. We collected baseline demographics, reported symptoms, imaging findings, pulmonary function tests (PFTs), and laboratory data at time of diagnosis. Data were collected using 6-month observation intervals to follow recurrence of respiratory manifestations, testing during recurrence, and resultant treatment modifications. Of 13 patients treated for GPA during the study period, 12 developed respiratory tract involvement. A total of 87 6-month observation periods were analyzed. At time of diagnosis, 83% (10/12) of subjects reported respiratory symptoms, 92% (11/12) had abnormal chest computed tomography (CT) imaging, and 42% (5/12) had abnormal PFTs. Fewer than half of the patients were seen by pulmonology within 6 months of diagnosis. Eight subjects (75%) had respiratory manifestations during subsequent observation periods. Chest CT or PFTs were obtained in 23/44 (52%) of observations periods with respiratory symptoms, with pulmonary consultation in only 9/44 (20%). This is the first US study to describe respiratory manifestations in pediatric GPA patients longitudinally, finding they are common and frequently recurrent. Our cohort had almost universally abnormal imaging at diagnosis regardless of respiratory symptoms. Early collaboration with pediatric pulmonology in the care of GPA patients may allow rheumatology teams to efficiently evaluate recurrent symptoms and address concomitant lung disease.

Temperature-compensated acoustoelastic measurements of the stress in bolts

Temperature is an important factor influencing the results of non-destructive acoustoelastic measurements of the internal stress in objects like bolts owing to its impact on the elastic modulus of the material. However, conventional methods that seek to obtain the temperature field of the measurement object independently suffer from high complexity and low accuracy. The present work addresses this issue by developing a method that eliminates the influence of temperature on the acoustoelastic measurements of stress in bolts based on the time interval between the head and coda waves of ultrasonic signals. The origin of coda waves in rod-shaped objects is investigated theoretically, and this understanding is applied for analyzing the relationship between the temperature and internal stress of the object and the time interval between the head and coda waves of ultrasonic signals. The analysis demonstrates that the observed time interval is related to temperature and stress in accordance with a linear relationship with the velocity of the longitudinal wave and the rod diameter. Finally, the obtained relationship is applied within an acoustoelastic measurement model to eliminate the influence of temperature from the measurement results.

A Study on Typhoon Center Localization Based on an Improved Spatio-Temporally Consistent Scale-Invariant Feature Transform and Brightness Temperature Perturbations

Extreme weather events like typhoons have become more frequent due to global climate change. Current typhoon monitoring methods include manual monitoring, mathematical morphological methods, and artificial intelligence. Manual monitoring is accurate but labor-intensive, while AI offers convenience but requires accuracy improvements. Mathematical morphology methods, such as brightness temperature perturbation (BTP) and a spatio-temporally consistent (STC) Scale-Invariant Feature Transform (SIFT), remain mainstream for typhoon positioning. This paper enhances BTP and STC SIFT methods for application to Fengyun 4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) L1 data, incorporating parallax correction for more accurate surface longitude and latitude positioning. The applicability of these methods for different typhoon intensities and monitoring time resolutions is analyzed. Automated monitoring with one-hour observation intervals in the northwest Pacific region demonstrates high positioning accuracy, reaching 25 km or better when compared to best path data from the China Meteorological Administration (CMA). For 1 h remote sensing observations, BTP is more accurate for typhoons at or above typhoon intensity, while STC SIFT is more accurate for weaker typhoons. In the current era of a high temporal resolution of typhoon monitoring using geostationary satellites, the method presented in this paper can serve the national meteorological industry for typhoon monitoring, which is beneficial to national pre-disaster prevention work as well as global meteorological research.

Incidence of Concurrent Cerebral Desaturation and Electroencephalographic Burst Suppression in Cardiac Surgery Patients.

Increased intraoperative electroencephalographic (EEG) burst suppression is associated with postoperative delirium. Cerebral desaturation is considered as one of the factors associated with burst suppression. Our study investigates the association between cerebral desaturation and burst suppression by analyzing their concurrence. Additionally, we aim to examine their association with cardiac surgical phases to identify potential for targeted interventions. We retrospectively analyzed intraoperative 1-minute interval observations in 51 patients undergoing cardiac surgery. Processed EEG and cerebral oximetry were collected, with the anesthesiologists blinded to the information. The associations between cerebral desaturation (defined as a 10% decrease from baseline) and burst suppression, as well as with phase of cardiac surgery, were analyzed using the Generalized Logistic Mixed Effect Model. The results were presented as odds ratio and 95% confidence intervals (CIs). A value of P < .05 was considered statistically significant. The odds of burst suppression increased 1.5 times with cerebral desaturation (odds ratio [OR], 1.52, 95% CI, 1.11-2.07; P = .009). Compared to precardiopulmonary bypass (pre-CPB), the odds of cerebral desaturation were notably higher during CPB (OR, 22.1, 95% CI, 12.4-39.2; P < .001) and post-CPB (OR, 18.2, 95% CI, 12.2-27.3; P < .001). However, the odds of burst suppression were lower during post-CPB (OR, 0.69, 95% CI, 0.59-0.81; P < .001) compared to pre-CPB. Compared to pre-CPB, the odds of concurrent cerebral desaturation and burst suppression were notably higher during CPB (OR, 52.3, 95% CI, 19.5-140; P < .001) and post-CPB (OR, 12.7, 95% CI, 6.39-25.2; P < .001). During CPB, the odds of cerebral desaturation (OR, 6.59, 95% CI, 3.62-12; P < .001) and concurrent cerebral desaturation and burst suppression (OR, 10, 95% CI, 4.01-25.1; P < .001) were higher in the period between removal of aortic cross-clamp and end of CPB. During the entire surgery, the odds of burst suppression increased 8 times with higher inhalational anesthesia concentration (OR, 7.81, 95% CI, 6.26-9.74; P < .001 per 0.1% increase). Cerebral desaturation is associated with intraoperative burst suppression during cardiac surgery, most significantly during CPB, especially during the period between the removal of the aortic cross-clamp and end of CPB. Further exploration with simultaneous cerebral oximetry and EEG monitoring is required to determine the causes of burst suppression. Targeted interventions to address cerebral desaturation may assist in mitigating burst suppression and consequently enhance postoperative cognitive function.

Method for Detecting Fire Indoors Based on Differences in Sample Averages of an Arbitrary Gas Environment Dangerous Parameter at Adjacent Observation Intervals

The object of the research is the series average of the dangerous parameters of the gas environment during the ignition of materials. The practical importance consists of using the difference between the average dangerous parameters of the gas environment on the intervals of absence and the presence of ignition to detect the ignition of materials. The theoretical substantiation of the method of detecting fires in premises is carried out based on sample averages fixed samples of current measurements of an arbitrary, dangerous parameter of the gas environment, which correspond to the general population of reliable absence and presence of fire. At a given significance level, the method determines the unbiased, uniformly most powerful fire detection rule. This allows you to determine how significant differences in sample means with a given significance level are due to ignition or are random. Laboratory experiments were conducted to verify the method of detecting the ignition of test materials. It was established, for example, that the maximum size of the effect ignition on the carbon monoxide concentration is typical for alcohol (exceeds the threshold by 8.9 times) and textiles (exceeds the threshold by 9.3 times). The size of the effect of the smoke density upon ignition of all materials is approximately the same and is determined by exceeding the corresponding thresholds from 4.62 to 3.9 times. The size of the effect on the temperature of the gaseous environment during ignition of all test materials is approximately the same order. It is characterized by exceeding the corresponding thresholds from 5.95 to 3.58 times. It is shown that the method of detecting fires allows to establish the extent to which the detected differences in sample means for samples from the general population of hazardous parameters of the gas environment are reliable with a given level of significance and are due to the ignition of materials or the action of random factors.

Study on the neurotoxicity of the temephos impregnated kenaf cellulose nanofiber (KCNF+T) aerosols against the female Aedes aegypti mosquitoes.

The study has generated visible aerosols with the diameter of 11 to 35 µm from the kenaf cellulose nanofiber (KCNF) impregnated with the temephos (KCNF+T) in water suspension. The study aimed to determine whether the KCNF+T aerosols are capable to elicit neurotoxicity in the adult mosquitoes via the respiratory exposure route by observing their behavioural response and measuring its body acetylcholine esterase (AChE) activity. Adult Aedes aegypti mosquitoes were assigned to one negative control group and three treatment groups namely, distilled water (vehicle control), KCNF and KCNF+T. The study adopted the MS1911 whereby the aerosols generated are released into the insecticide bioassay glass chamber (IBGC) separately to achieve four different aerosols concentrations of 6.4 ml/m3, 12.8 ml/m3, 19.2 ml/m3, and 25.6 ml/m3. Then the 20 sugar-fed mosquitoes were released free-flying into each IBGC to observe its behavioural response (knockdown) at 30, 60, 120, 180, 240, 300 minutes intervals and 24th hour mortality. Results showed that only mosquitoes exposed to KCNF+T aerosols exhibited persistent knockdown. There was significant difference (p< 0.05) between observation intervals with the cumulative knockdown of 84.8%, 92.8%, 99.0%, 100.0%, 100.0%, 100.0% compared with the KCNF aerosols. Further, it was distinctive that only KCNF+T is capable to cause the female mosquitoes moribund/mortality at the 24th hour with 90% at the lowest aerosol concentration of 6.4 ml/m3. The neuroenzyme assay on the mosquito that died from the KCNF+T exposure verified to have reduced AChE enzyme activity. The behavioural response and reduction of the AChE activity strongly suggests the temephos from the KCNF+T aerosols has been released into the mosquito body causing the neurotoxicity but KCNF alone is not neurotoxic.

A longitudinal 5-year study for the prediction of HCC, death and liver decompensation by ARFI elastography.

Numerous studies have evaluated elastography for the staging of liver fibrosis. Fewer studies were performed investigating the prognostic relevance using transient elastography (TE), although with promising results. This study was designed to evaluate the prognostic relevance of ARFI elastography. Patients receiving ARFI elastography in our ultrasound department between 2010 and 2012 were initially screened for an ARFI examination with a clinical follow-up of at least 5 years. The following events were recorded: liver related death, liver unrelated death, HCC, liver decompensation/ variceal bleeding. A total of 335 patients were included in the final analysis with an ARFI examination of the liver and a follow-up of 60 months. Within the observation interval the number of events with corresponding AUROCs (shown with 95% confidence interval) were: overall death (n=49, 0.76 [0.69 - 0.83]), liver related death (n=25, 0.85 [0.77 - 0.93]), liver unrelated death (n=24, 0.66 [0.55 - 0.77]), HCC (n=15, 0.80 [0.72 - 0.87]), liver related complications/variceal bleeding (n=34, 0.87 [0.82 - 0.93]). In the group of patients with ARFI values suggestive of cirrhosis (equal to or above 1.80 m/s; n=110) higher values (<2.5 m/s vs. >= 2.5 m/s) where associated with a significant decline in liver related survival (p=0.007). ARFI elastography seems to have a good diagnostic accuracy for the prediction of liver related death and decompensation. Further it seems to allow a risk stratification in patients with cirrhosis suspicious elastography values.