Interval Variability Research Articles

Sensitivity analysis of aircraft structural fatigue reliability under mixed uncertainty

Fatigue is the main failure mode of the structure. The aircraft structure is subject to multi-level cyclic loading, which is prone to fatigue damage, reduces reliability, and affects flight safety. Various uncertainties are inevitably associated with the factors related to the fatigue performance of the structure. In particular, for the variables with insufficient information, their uncertainties are hard to describe by the probability density function. In this paper, these variables are treated as interval variables. The sensitivity analysis of the aircraft structure fatigue reliability model under mixed uncertainties is studied. On the basis of the definition of the distance between interval numbers, the interval distance indexes are proposed as a global sensitivity measure for a mixed random-interval model system. To reduce the computational cost, the method combining the space partition with unscented transformation (SP-UT) is used to calculate the proposed indexes. Lastly, the proposed indexes and method are applied to solve the aircraft structure fatigue problem. Compared with the traditional MCS method, the proposed method has proved to be more conducive and efficient.

Abstract 2123: Evaluation Of Autonomic Neuropathy In Patients With T2DM By Measuring R-R Interval During Rest, Standing And Deep Breathing

Objective: In patients of diabetes mellitus (DM), heart rate variability (HRV), an index of autonomic nervous system activity, tends to be lower than in healthy individuals. This reduction in HRV is associated with an increased risk of cardiovascular diseases. We aimed to evaluate HRV under varying conditions with novel indicators in patients with DM. Methods: The patients were recruited from patients with type 2 DM (T2DM) aged 20 to 79 years old from two facilities from January 2020 to December 2023. The control comprised patients without DM. Patients taking oral corticosteroids, adrenergic agonists and antagonists, and those with malignant tumors were excluded. Heart rate (HR) was measured under varying conditions of rest, standing and deep breathing (inhalation and exhalation alternating every 5 seconds). The R-R interval variation was continuously recorded in each segment of 33 beats. Subsequently, the peak HRs for each load were obtained: minimum HR during rest (mHr), maximum HR during standing (MHs), and minimum HR during deep breathing (mHb). Additionally, the SD-RR for each load was calculated. Results: We analyzed a total of 117 subjects, including 75 with T2DM and 42 without DM. The participant demographics were as follows: mean age of 55.4±13.9 years, BMI of 25.9±5.6, eGFR of 71.5±26.1 mL/min/1.73m 2 , HbA1c of 7.31±2.26 %, with 46 % of male, and duration of T2DM in patient group at 8.2±8.3 years. The subjects were categorized into three groups: G1 - non DM with CV-RR≧2, G2 - T2DM with CV-RR≧2, and G3 - T2DM with CV-RR<2.mHr was G1: 64.5±7.4, G2: 69.4±7.7, G3: 75.2±13.1 (beats/minute, p<0.001). G3 is markedly higher than G1 and G2. Furthermore, the HR change ratio from mHr to MHs was: G1: 1.36±0.12, G2: 1.31±0.11, G3: 1.22±0.10 (p<0.001). G3 is markedly lower than G1 and G2. The HR change ratio from mHr to mHb was: G1: 0.99±0.04, G2: 0.96±0.05, G3: 0.96±0.05 (p=0.043), with G3 being markedly lower than G1. The SD-RR during rest was: G1: 26.4±18.6, G2: 28.0±12.5, G3: 10.1±4.4 (p<0.001), with G3 being markedly lower than G1 and G2. Conclusion: Assessing R-R intervals under varying conditions of rest, standing, and deep breathing can indicate autonomic nervous system dysfunction among patients with T2DM.

Deep learning-driven interval uncertainty propagation for aeronautical structures

Interval Uncertainty Propagation (IUP) holds significant importance in quantifying uncertainties in structural outputs when confronted with interval input parameters. In the aviation field, the precise determination of probability models for input parameters of aeronautical structures entails substantial costs in both time and finances. As an alternative, the use of interval variables to describe input parameter uncertainty becomes a pragmatic approach. The complex task of solving the IUP for aeronautical structures, particularly in scenarios marked by pronounced nonlinearity and multiple outputs, necessitates innovative methodologies. This study introduces an efficient deep learning-driven approach to address the challenges associated with IUP. The proposed approach combines the Deep Neural Network (DNN) with intelligent optimization algorithms for dealing with the IUP in aeronautical structures. An inventive extremal value-oriented weighting technique is presented, assigning varying weights to different training samples within the loss function, thereby enhancing the computational accuracy of the DNN in predicting extremal values of structural outputs. Moreover, an adaptive framework is established to strategically balance the global exploration and local exploitation capabilities of the DNN, resulting in a predictive model that is both robust and accurate. To illustrate the effectiveness of the developed approach, various applications are explored, including a high-dimensional numerical example and two aeronautical structures. The obtained results highlight the high computational accuracy and efficiency achieved by the proposed approach, showcasing its potential for addressing complex IUP challenges in aeronautical engineering.

Blocked versus interleaved: How range contexts modulate time perception and its EEG signatures.

Accurate time perception is a crucial element in a wide range of cognitive tasks, including decision-making, memory, and motor control. One commonly observed phenomenon is that when given a range of time intervals to consider, people's estimates often cluster around the midpoint of those intervals. Previous studies have suggested that the range of these intervals can also influence our judgments, but the neural mechanisms behind this "range effect" are not yet understood. We used both behavioral tests and electroencephalographic (EEG) measures to understand how the range of sample time intervals affects the accuracy of people's subsequent time estimates. Study participants were exposed to two different setups: In the "blocked-range" (BR) session, short and long intervals were presented in separate blocks, whereas in the "interleaved-range" (IR) session, intervals of various lengths were presented randomly. Our findings indicated that the BR context led to more accurate time estimates compared to the IR context. In terms of EEG data, the BR context resulted in quicker buildup of contingent negative variation (CNV), which also reached higher amplitude levels and dissolved more rapidly during the encoding stage. We also observed an enhanced amplitude in the offset P2 component of the EEG signal. Overall, our results suggest that the variability in time intervals, as defined by their range, influences the neural processes that underlie time estimation.

An artificial intelligence-enabled Holter algorithm to identify patients with ventricular tachycardia by analysing their electrocardiogram during sinus rhythm.

Ventricular tachycardia (VT) is a dangerous cardiac arrhythmia that can lead to sudden cardiac death. Early detection and management of VT is thus of high clinical importance. We hypothesize that it is possible to identify patients with VT during sinus rhythm by leveraging a continuous 24 h Holter electrocardiogram and artificial intelligence. We analysed a retrospective Holter data set from the Rambam Health Care Campus, Haifa, Israel, which included 1773 Holter recordings from 1570 non-VT patients and 52 recordings from 49 VT patients. Morphological and heart rate variability features were engineered from the raw electrocardiogram signal and fed, together with demographical features, to a data-driven model for the task of classifying a patient as either VT or non-VT. The model obtained an area under the receiving operative curve of 0.76 ± 0.07. Feature importance suggested that the proportion of premature ventricular beats and beat-to-beat interval variability was discriminative of VT, while demographic features were not. This original study demonstrates the feasibility of VT identification from sinus rhythm in Holter.

A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants

The reliability of a pressurized water reactor power plant’s control rod drive mechanism (CRDM) is affected by many factors, such as operation states, unit performance, and dynamic environments. Multiple sources of uncertainties, including random, interval, and fuzzy, exist when analyzing the reliability of CRDMs. Modeling reliability without considering the fusion of multisource uncertainties may result in model distortion and may not provide realistic assessment results. This paper proposes a multisource uncertainty fusion method powered by the margin-based variable transformation and the Bayesian network propagation. The interval and fuzzy variables are transformed into random variables to obtain the corresponding generalized probability density function of the CRDM’s feature parameters. After that, the CRDM’s reliability can be evaluated via probability quantization of the Bayesian network inference result through sampling algorithms. A magnetic-jack type CRDM case is presented to verify the proposed method, and the results show that this method can fuse three types of uncertainty variable in a unified way and effectively obtain reliability evaluation results.

An extended moment-based trajectory accuracy reliability analysis method of robot manipulators with random and interval uncertainties

This paper proposes a novel trajectory accuracy reliability analysis method with random and interval variables to evaluate the impact of mixed uncertainties on the motion performance of robot manipulators. To effectively and accurately solve the hybrid reliability model, the interval analysis is first conducted on the positional error model established by differential kinematics, and a maximum positional error searching algorithm is developed based on geometric transformation and cell enumeration. Then, the trajectory accuracy reliability model is reconstructed by the eigen-decomposition technique, which further incorporates the adaptive weight vector-based anisotropic sparse-grid quadrature approach to derive the statistical moments of maximum positional error. Afterward, by matching with the cumulants of the original reliability model, a novel approximation method is proposed based on the Weibull distribution and minimized matching error model to complete the trajectory accuracy reliability analysis. The practicality and advantages of the proposed method are demonstrated by two illustrative examples of 6-degrees-of-freedom robot manipulators. Comparative results affirm that the proposed method outperforms existing state-of-the-art algorithms in terms of accuracy and efficiency for trajectory accuracy reliability analysis encompassing random and interval uncertainties. Overall, the outcomes of this paper contribute significantly to the design and analysis of safety and reliability in moving machinery.

Global sensitivity analysis for degraded braided composite with interval process

Global sensitivity analysis has predominantly focused on examining the effect of time-invariant parameters on the mechanical properties of fiber-reinforced composites, while ignoring degradation. However, fiber-reinforced composites are subject to degradation, which means that the physical system is dynamic. It is important to consider this dynamic behavior in sensitivity analysis. This paper presents a set of sensitivity indices based on the interval method to measure the influence of the time-invariant and time-variant parameters on the synthetic uncertainty of output in global sensitivity analysis for 2D triaxially braided composites (2DTBCs) considering degradation. To achieve this purpose, we represent time-variant uncertain parameter using an interval process and describe them with multiple uncorrelated interval variables using the interval Karhunen-Loève explansion method. We then introduce a two-dimensional interval process and similarity degree to define a new sensitivity index for time-invariant uncertain parameters. For time-variant uncertain parameter, a novel sensitivity index combining multi-dimensional interval process and similarity degree is proposed which considers the synthetic influence of the group of multiple uncorrelated interval variables. Additionally, an efficient sampling-based method is presented to estimate the global sensitivity analysis indices using the Monte Carlo method and Newton-Cotes formula. Finally, the feasibility and potential benefits of the proposed sensitivity indices are demonstrated by applying them to a 2DTBC made of carbon fiber reinforced polyether ether ketone with thermal ageing consideration.

Variability in the serial interval of COVID-19 in South Korea: a comprehensive analysis of age and regional influences.

Quantifying the transmissibility over time, particularly by region and age, using parameters such as serial interval and time-varying reproduction number, helps in formulating targeted interventions. Moreover, considering the impact of geographical factors on transmission provides valuable insights into the effectiveness of control measures. Drawing on a comprehensive dataset of COVID-19 cases in South Korea, we analyzed transmission dynamics with a focus on age and regional variations. The dataset, compiled through the efforts of dedicated epidemiologists, includes information on symptom onset dates, enabling detailed investigations. The pandemic was divided into distinct phases, aligning with changes in policies, emergence of variants, and vaccination efforts. We analyzed various interventions such as social distancing, vaccination rates, school closures, and population density. Key parameters like serial interval, heatmaps, and time-varying reproduction numbers were used to quantify age and region-specific transmission trends. Analysis of transmission pairs within age groups highlighted the significant impact of school closure policies on the spread among individuals aged 0-19. This analysis also shed light on transmission dynamics within familial and educational settings. Changes in confirmed cases over time revealed a decrease in spread among individuals aged 65 and older, attributed to higher vaccination rates. Conversely, densely populated metropolitan areas experienced an increase in confirmed cases. Examination of time-varying reproduction numbers by region uncovered heterogeneity in transmission patterns, with regions implementing strict social distancing measures showing both increased confirmed cases and delayed spread, indicating the effectiveness of these policies. Our findings underscore the importance of evaluating and tailoring epidemic control policies based on key COVID-19 parameters. The analysis of social distancing measures, school closures, and vaccine impact provides valuable insights into controlling transmission. By quantifying the impact of these interventions on different age groups and regions, we contribute to the ongoing efforts to combat the COVID-19 pandemic effectively.

Abstract B040: Former caregivers of patients with glioblastoma multiforme report the Preparedness Assessment for the Transition Home (PATH©) instrument is helpful throughout the disease trajectory

Abstract Purpose: The purpose of the study was to determine if the Preparedness Assessment for the Transition Home (PATH©), a validated caregiver assessment instrument, would be helpful in identifying gaps in preparation of primary caregivers of patients with glioblastoma multiforme (GBM). The PATH© was designed to assess caregiver readiness to assist the healthcare team in developing tailored solutions to meet the needs of caregiver/patient dyads. Methods: Former primary caregivers (spouses or life-partners) of now deceased patients with GBM who were members of the private Facebook (FB) group “Widows and Widowers of GBM” reviewed the instrument and completed a web-based survey via Qualtrics. Following IRB approval (#H22-0103) and approval from the FB group administrator, a survey link was posted on the FB group page and left open for 3 weeks in Spring 2023. Participants were asked to review the 25 PATH© items and respond to 4 yes/no or Likert scale questions about whether the instrument would have helped to better prepare them for the caregiving role across the phases of GBM disease trajectory (acute, living with disease progression, end-of-life). A free text option was included so caregivers could provide suggestions for tool revision to better address the needs of caregivers of patients with GBM. Summary statistics were calculated for interval and ratio variables. ANOVA and t-test were conducted to determine whether there were differences across stages among the variables (stage of disease trajectory and helpfulness score). Chi-square and Fisher’s exact test were conducted to examine whether helpfulness and overall helpfulness were independent. A linear regression was conducted to assess whether the different phases of the disease trajectory predicted tool helpfulness. The GBM Caregiver Study team met to review, analyze, code free text feedback, and identify and define code themes. Results: Demographics were completed by 191 study participants; 124 reviewed the PATH© instrument and completed the survey. Ninety-two percent of participants reported that the PATH© instrument would be helpful across all stages of the disease trajectory, with the majority selecting “Very” or “Somewhat” helpful. Only 8% of participants did not think the instrument would be helpful at end-of-life, and 6% of participants did not think the instrument would be helpful at other stages of the disease trajectory. Fifty-four participants suggested changes to the existing instrument for GBM caregivers. From these open-ended responses, four themes were identified: education, access to care team and resources, care at the end-of-life/after death, and self-care. Conclusion: Study results indicated using the PATH© instrument in the GBM population would be helpful to assess caregiver preparation. Study collaborators are actively planning future research, building on the findings of this study, to explore the impact of how implementing a systematic caregiver assessment and tailored care plans and/or interventions can improve caregiver readiness and reduce caregiver burden. Citation Format: Elizabeth James, Marlee Wallace, Megan Chard, Barbara Lutz, Laurie Minns. Former caregivers of patients with glioblastoma multiforme report the Preparedness Assessment for the Transition Home (PATH©) instrument is helpful throughout the disease trajectory [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr B040.

Unveiling the age-related dynamics in Sjögren's syndrome: Insights from heart rate variability and autonomic function.

Sjögren's Syndrome (SS), mainly affecting women in their midlife, is characterized by persistent inflammation in glands producing tears and saliva, often leading to significant complications. This study investigates the differences in autonomic system functioning between individuals with SS and healthy controls. From April 2019 to December 2022, 329 diagnosed primary SS (pSS) patients and 30 healthy controls were enrolled at Taipei Veterans General Hospital, Taipei, Taiwan. The study assessed autonomic nervous system functioning using various HRV metrics. Participants were divided based on age and AECG criteria, including salivary gland biopsy and autoantibody status. Significant differences in Heart Rate Variability (HRV) were observed between pSS patients and healthy controls. The total power index was notably lower in pSS patients (4.98 ± 1.29) than in controls (5.54 ± 1.21, p = .022). Additionally, Vagal (VAG) activity was significantly reduced in the pSS group (4.95 ± 1.33) compared to the healthy control group (5.47 ± 1.19, p = .041). Age-stratified analysis highlighted that the ≤50 years pSS group had a higher heart rate (77.74 ± 10.42) compared to the >50 years group (73.86 ± 10.35, p = .005). This group also showed a higher total power index (5.78 ± 1.30) versus the >50 years group (4.68 ± 1.19, p < .001), and significantly lower VAG activity (4.70 ± 1.26, p = .007) compared to healthy controls. Furthermore, the Standard Deviation of Normal-to-Normal Intervals (SDNN) was greater in the ≤50 years SS group (44.45 ± 37.12) than in the >50 years group (33.51 ± 26.18, p = .007). In pSS patients, those positive for both salivary gland biopsy and autoantibodies demonstrated a lower Total Power (4.25 ± 1.32) and R-wave validity (93.50 ± 4.79, p < .05) than other groups, suggesting more severe autonomic imbalance. The R-R interval variation (RRIV) was also significantly higher in this dual-positive group (696.10 ± 975.41, p < .05). Additionally, the ESSPRI for dryness was markedly higher in the dual-positive group (8.10 ± 1.45, p < .05), indicating more severe symptoms. These findings reveal significant variations in autonomic function in SS patients, especially in those with dual-positive biopsy and autoantibody status. This study demonstrates significant autonomic dysfunction in pSS patients compared to healthy controls, particularly in those positive for both salivary gland biopsy and autoantibodies. The age-stratified analysis further emphasizes the impact of aging on autonomic system functioning in pSS, suggesting a need for age-specific management approaches in pSS patient care.

Multi-Modal Urban Traffic Transfer Schedule Timetable Bi-Objective Optimization: Model, Algorithm, Comparison, and Case Study

The optimization of the connection between urban rail transit and the bus is an essential issue that benefits passenger travel and the urban structure and has social benefits, which can be realized by reasonably adjusting the bus departure schedule. This study is necessary because the development status quo of China’s urban transportation network planning is unreasonable, travel efficiency is not high, and operating costs are high. This paper sets up the decision variables of bus departure time and departure interval at each station, establishes a dual-objective optimization model with the minimum schedule change and the minimum transfer time, and studies the application of the augmented Chebyshev algorithm in the dual-objective optimization model. Secondly, based on the Shenzhen metro and public transportation integrated circuit card data, the case analysis uses the generalized Chebyshev algorithm and the non-dominated sorting genetic algorithm, respectively. The optimization results show that using the improved augmented and generalized Chebyshev algorithm in the bus schedule alteration time within a reasonable range can maximize the total transfer time, which compared with the original scheme is shortened by 68.06%. In contrast, genetic algorithms will make the complete bus schedule alteration prominent, and the whole transfer time is substantially increased. The results show that the improved augmented generalized Chebyshev algorithm is more suitable for solving the dual-objective rail transit connection problem.

A Novel SV-PRI Strategy and Signal Processing Approach for High-Squint Spotlight SAR

High-resolution and high-squint spaceborne spotlight synthetic aperture radar (SAR) has significant potential for extensive application in remote sensing, but its swath width effectiveness is constrained by a critical factor: severe range cell migration (RCM). To address this, pulse repetition interval (PRI) variation offers a practical scheme for raw data reception. However, the current designs for continuously varying PRI (CV-PRI) exhibit high complexity in engineering. In response to the issue, this paper proposes a novel strategy of stepwise varying PRI (SV-PRI), which demonstrates higher reconstruction accuracy compared with CV-PRI. Furthermore, confronting the azimuth non-uniform sampling characteristics induced by the PRI variation, this paper introduces a complete uniform reconstruction processing based on the azimuth partitioning methodology, which effectively alleviates the inherent contradiction between resolution and swath width. The processing flow, utilizing the temporal point remapping (TPR) concept, ensures the uniformity and coherence of dataset partitioning and reassembly in the context of the interpolation on non-uniform grids. Finally, according to the simulation results, the point target data, processed through the processing flow proposed in this study, have demonstrated effective focusing results.

E3 ubiquitin ligase rififylin has yin and yang effects on rabbit cardiac transient outward potassium currents (Ito) and corresponding channel proteins

Genome-wide association studies (GWAS) have reported a correlation between a single nucleotide polymorphism of the RING finger E3 ubiquitin protein ligase rififylin (RFFL) and QT interval variability in humans (Newton-Cheh et al., 2009). Previously, we have shown that RFFL down-regulates expression and function of the human-like ether-a-go-go-related gene (hERG) potassium channel and corresponding rapidly activating delayed rectifier potassium current (IKr) in adult rabbit ventricular cardiomyocytes (ARbCMs). Here we report that RFFL also affects the transient outward current (Ito), but in a peculiar way. RFFL overexpression in ARbCMs significantly decreases the contribution of its fast component (Ito,f) from 35% to 21%, and increases the contribution of its slow component (Ito,s) from 65% to 79%. Since Ito,f in rabbits is mainly conducted by Kv4.3, we investigated the effect of RFFL on Kv4.3 expressed in HEK293A cells. We found that RFFL overexpression reduced Kv4.3 expression and corresponding Ito,f in a RING domain-dependent manner in the presence or absence of its accessory subunit KChIP2. On the other hand, RFFL overexpression in Kv1.4-expressing HEK cells leads to an increase in both Kv1.4 expression level and Ito,s, similarly in a RING domain-dependent manner. Our physiologically detailed rabbit ventricular myocyte computational model shows that these yin and yang effects of RFFL overexpression on Ito,f, and Ito,s affect phase 1 of the action potential waveform and slightly decrease its duration in addition to suppressing IKr. Thus, RFFL modifies cardiac repolarization reserve via ubiquitination of multiple proteins that differently affect various potassium channels and cardiac action potential duration (APD).

A Retrospective Comparison of Onabotulinum Toxin A and Incobotulinum Toxin A in Terms of Efficacy, Tolerability, Duration of Effect, and Pain on Injection Administration Site for the Treatment of Chronic Migraine.

Background Onabotulinum toxin A (OnA) is a well-tolerated and effective treatment for chronic migraine (CM). However, based on research indications that incobotulinum toxin A (InA) would be equally effective, a VeteransHealth Administration medical center mandated a two-year trial of InA as a more cost-effective alternative to OnA. Although InA is used for many similar indications as OnA, it is not Food and Drug Administration-approved for treating CM, and complications occurred in several patients with CM following this treatment change. We conducted this retrospective analysis to evaluate differences in the efficacy of OnA and InA and identify the reasons for the adverse effects of InA in some of these patients. Methods We performed a retrospective review of 42 patients who had been effectively treated with OnA and were then switched to InA. The differences between treatment responses to OnA and InA were assessed through the evaluation of pain on injection, number of headache days, and duration of action. Patients received injections at 10- to 13-week intervals. Those who reported severe pain on injection of InA were switched back to OnA. Results Severe burning pain on InA injection was reported by 38% of patients (nine males and seven females, i.e., a total of 16 patients out of 42 patients). One male patient reported the same degree of pain fromboth InA and OnA injections. A total of 66.7% of women with obesity and 83.3% of men with obesity or diabetes experienced severe pain on injection. Neither migraine suppression nor the duration of effect was significantly different between OnA and InA. Conclusions OnA isbetter toleratedthan InA in the treatment of CM. InA appears to effectively suppress migraines, but some patients complain of a severe localized burning sensation during the injections. Some of these patients, all of whom were previously treated with OnA, requested to switch back to OnA. This suggested that InA is not equivalent to OnA in terms of tolerability and effectiveness.The present studyfound 2.38% of patients experienced an insufficient duration of effect withInA, and none withOnA. However, these lower rates may, in part, be due to variability in injection intervals in this sample, which could be because of scheduling considerations at the Harry S. Truman Veterans Health Administration Medical Center. In cases where OnA fails because of the development of antibodies, it might be reasonable to switch to InA treatment. Reformulation of InA with a pH-buffered solution may eliminate the difference in pain on injection. InA would then be a good alternative to OnA for treating CM.

Fire regimes of the Southern Appalachians may radically shift under climate change

BackgroundIncreased drought due to climate change will alter fire regimes in mesic forested landscapes where fuel moisture typically limits fire spread and where fuel loads are consistently high. These landscapes are often extensively modified by human land use change and management. We forecast the influence of varying climate scenarios on potential shifts in the wildfire regime across the mesic forests of the Southern Appalachians. This area has a long history of fire exclusion, land use change, and an expanding wildland urban interface. We considered interactions among climate, vegetation, and anthropogenic influences to forecast future fire regimes and changes to the forest structure. We used climate scenarios representing divergent drought patterns (overall drought trend and interannual variability) within a process-based fire model that captures the influence of climate, fuels, and fire ignition on wildfire patterns and suppression.ResultsCompared to simulations using historical climate (1972–2018), future total burned area (2020–2100: 782,302.7 (716,655.0–847,950.3) ha) increased by 42.3% under high drought variability (1,134,888.4 (1,067,437.2–1,202,339.6) ha), 104.8% under a substantial increase in drought trend (1,602,085.7 (1,511,837.5–1,692,334.0) ha), and 484.7% when combined (4,573,925.0 (4,434,910.5–4,712,939.5) ha). Landscape patterns of fire exclusion and suppression drove the spatial variability of fire return intervals (FRI). Our projections indicate wide spatial variability in future fire regimes with some areas experiencing multiple fires per decade while others experience no fire. More frequent fires corresponded with increased oak prevalence and a reduction in the biomass of mesic hardwoods and maple; however, mesic hardwoods remained prevalent under all fire intervals because of their contemporary dominance.ConclusionsOur study illustrates how future drought–fire–management interactions and a history of fire exclusion could alter future fire regimes and tree species composition. We find that increasing trends in drought magnitude and variability may increase wildfire activity, particularly in areas with minimal fire suppression. In ecosystems where fuel moisture (and not load) is the standard limitation to fire spread, increased pulses of drought may provide the conditions for more fire activity, regardless of effects on fuel loading. We conclude the effects of climate and human management will determine the novel conditions for both fire regime and ecosystem structure.

Random/Cognitive Hybrid Uncertainty Analysis of Ship Multitasking Cabin Layout

Identifying the optimal cabin layout is an important way to improve the efficiency of ship systems and ensure the efficient circulation of personnel and materials. The ship task state refers to the state maintained by the joint actions of ship machinery and equipment, cargo, and personnel when facing different jobs and tasks during operation. A cabin layout that facilitates multitasking states can improve the efficiency of collaboration between systems and ensure the operation of the ship. The demand for human flow and logistics is different in multitasking states. To express the demand in mathematical form, there is a certain random uncertainty in the numerical quantification of the demand. Thus, to better meet the needs of different states, the coefficient values of each state can be integrated using special methods. Ensuring values’ initial preference to the greatest extent inevitably produces a degree of cognitive uncertainty. Therefore, uncertainty analysis is necessary for cabin layout design to be used for multitasking states. In this paper, a deterministic optimization platform of cabin layout in multitasking states is obtained. Adjacent and circulating strength coefficients are obtained through numerical quantization of the demand for human flow and logistics. The random uncertainty in the input values of two coefficients was represented by random variables, and the cognitive uncertainty was represented by interval variables. In order to solve the problem of two types of variables, a random-interval hybrid uncertainty model was established. Through random intervalization and interval randomization, three cases, of random variables, interval variables, and random variables and interval variables, were studied. The probability distribution of the model function was used to evaluate the influence of different compositions of uncertainty parameters on the robustness of the cabin layout scheme. The necessity and effectiveness of uncertainty analysis in multitasking cabin layout are discussed below.

Similar Patterns of Dysautonomia in Myalgic Encephalomyelitis/Chronic Fatigue and Post-COVID-19 Syndromes.

There is a considerable overlap between the clinical presentation of post-COVID-19 condition (PCC) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Many of their common symptoms can be linked to dysregulation of the autonomic nervous system (dysautonomia). This study aimed to objectively assess autonomic function in a general group of patients with PCC and in a group of patients with ME/CFS whose disease was not related to COVID-19. We hypothesize that the similarity in the chronic symptoms of patients with PCC and ME/CFS extends to objective autonomic nervous system abnormalities. Synchronous recordings of an electrocardiogram and continuous dynamics of blood pressure in the digital artery using the Penaz method were obtained using the spiroarteriocardiorhythmography method in 34 patients diagnosed with ME/CFS, in whom the onset of the disease was not associated with COVID-19, 29 patients meeting the PCC definition and 32 healthy controls. Heart rate variability (HRV) and systolic and diastolic blood pressure variability (BPV) were assessed at rest and in tests with fixed respiratory rates. Indicators of baroreflex regulation (baroreflex effectiveness index and baroreflex sensitivity) were additionally determined at rest. The total power and power of low-frequency and high-frequency of RR interval variability at rest as well as baroreflex sensitivity were significantly lower both in PCC and ME/CFS patients compared to healthy controls. Several diagnostic prediction models for ME/CFS were developed based on HRV parameters. During slow breathing, the HRV parameters returned to normal in PCC but not in ME/CFS patients. The correlation analysis revealed a close relationship of HRV, BPV parameters and baroreflex sensitivity with fatigue, but not with HADS depressive/anxiety symptoms in the ME/CFS and PCC patients. A similar pattern of HRV and baroreflex failure with signs of a pathological acceleration of age-dependent dysautonomia was identified in the ME/CFS and PCC patients. The clinical, diagnostic and therapeutic implications of these findings are discussed, in light of previously described relationships between inflammation, vascular pathology, atherosclerotic cardiovascular disease and autonomic dysfunction.

A novel active learning Gaussian process modeling-based method for time-dependent reliability analysis considering mixed variables

The purpose of time-dependent reliability analysis is evaluating the reliability of a system or component within a specified timeframe. Several approaches have been suggested for addressing time-dependent reliability challenges, including analytical methods and methods based on response surrogate models. As a representative of Machine Learning methods, the Active Learning Gaussian process model has gained more and more popularity in time-dependent reliability analysis. In practical applications, certain uncertainties may not be adequately represented by random variables following deterministic distributions. In contrast, these uncertainties can be easily described by interval variables. This article introduces an innovative method based on Active Learning Gaussian process modeling for time-dependent reliability analysis, encompassing both random and interval variables. The initial step involves transforming stochastic processes into random parameters through spectral decomposition. Moreover, a new sampling strategy to manage mixed uncertainties is proposed, where the candidate samples of mixed variables with positive maximum extreme responses and negative minimum extreme responses are identified. These samples are utilized to construct and renew an extreme Gaussian process model. Lastly, we present an improved confidence-based learning function to accelerate the learning process, which is designed to locate the optimal random input and interval input for updating the extreme Gaussian process model. One mathematical example and two engineering examples are conducted to verify the performance of the proposed method. Results illustrate that our method offers high accuracy while reducing the evaluations of true performance functions in addressing time-dependent reliability issues under mixed uncertainties.

Synchronization analysis of time series obtained from anesthetized rats during painful action

The purpose of this work is to determine the possibility of detecting changes in the relationships between such physiological rhythms as the activity of neurons in the reticular formation of the medulla oblongata, fluctuations in the blood pressure and respiration in anesthetized rats before and during the development of a pathological state associated with painful colorectal distension. This stretch mimics the pain localized in the lower abdomen in patients with irritable bowel syndrome and it is accompanied by responses of the brain neurons, fluctuations in the blood pressure and respiration. The analysis of changes in the relationships of these rhythms consisted in identifying phase synchronization between the time series of the variability of neuronal activity intervals and the variability of blood pressure intervals at the respiratory rate before and during pain exposure. Methods. To solve this problem, the synchrosqueezed wavelet transform method was applied, which makes it possible to effectively calculate the instantaneous frequencies and phases of non-stationary signals. As indicators of synchronization, we used the values of the index and the duration of phase synchronization as a time interval during which the value of the synchronization index is close to 1. Results. It has been established that the pain effect provides an adjustment of the frequency of the neuronal activity variability and the occurrence of synchronization between this activity and the blood pressure variability at the respiratory rate or causes an adjustment of the frequency of the blood pressure variability and the occurrence of synchronization between the blood pressure variability and the respiratory rhythm. It was found that the pain effect increases the duration of phase synchronization between the variability of the blood pressure and the respiratory rhythm or reduces the duration of phase synchronization between the variability of neuronal activity and the respiratory rhythm. Conclusion. The effect of painful colorectal distension on changes in the parameters of phase synchronization between physiological rhythms in anesthetized rats was studied in detail.