Window Duration Research Articles

Temporal associations between nightly sleep with daytime eating and activity levels in free-living young adults.

This study aimed to quantify the temporal associations between nightly sleep quantity and timing with daytime eating behavior and activity levels in free-living (i.e. non-experimental) settings. Generally healthy young adults (N = 63; 28.9 ± 7.1 years) completed concurrent sleep (wrist actigraphy), eating (photo-assisted diet records), and activity (waist actigraphy) assessments over 14 days. Multilevel models quantified the associations between nightly sleep (total sleep time, timing of sleep and wake onset) with next-day eating behavior (diet quality, caloric intake, timing of eating onset/offset, eating window duration) and activity levels (total physical activity, sedentary time). Associations in the reverse direction (i.e. eating and activity predicting sleep) were explored. Models adjusted for demographic and behavioral confounders and accounted for multiple testing. At within- and between-subject levels, nights with greater-than-average total sleep time predicted a shorter eating window the next day (all p ≤ 0.002). Later-than-average sleep and wake timing predicted within- and between-subject delays in next-day eating onset and offset, and between-subject reductions in diet quality and caloric intake (all p ≤ 0.008). At within- and between-subject levels, total sleep time was bidirectionally, inversely associated with sedentary time (all p < 0.001), while later-than-average sleep and wake timing predicted lower next-day physical activity (all p ≤ 0.008). These data underscore the complex interrelatedness between sleep, eating behavior, and activity levels in free-living settings. Findings also suggest that sleep exerts a greater influence on next-day behavior, rather than vice versa. While testing in more diverse samples is needed, these data have potential to enhance health behavior interventions and maximize health outcomes.

Effect of intermittent fasting on circulating inflammatory markers in obesity: A review of human trials.

Obesity is associated with low-grade inflammation. Weight loss, by means of dietary restriction, has been shown to reduce systemic inflammation. Intermittent fasting has recently gained popularity as a weight loss diet, but its effects on inflammatory markers in individuals with obesity have yet to be summarized. Accordingly, this review examined how the two main forms of intermittent fasting, i.e., time restricted eating (TRE) and alternate day fasting (ADF), impact body weight and key circulating inflammatory markers (i.e., C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-alpha), and interleukin-6 (IL-6)), in adults with obesity. Results from this review reveal that TRE with various eating window durations (4-10 h per day) has no effect on circulating levels of CRP, TNF-alpha or IL-6, with 1-5% weight loss. As for ADF, reductions in CRP concentrations were noted when >6% weight loss was achieved. However, ADF had no effect on TNF-alpha or IL-6 concentrations, with this degree of weight loss. Thus, intermittent fasting has little or no effect on key inflammatory markers, but more research is warranted to confirm these preliminary findings.

Comparative efficacy and optimal duration of first-line antibiotic regimens for acute otitis media in children and adolescents: a systematic review and network meta-analysis of 89 randomized clinical trials.

Antibiotic use for acute otitis media (AOM) is one of the major sources of antimicrobial resistance. However, the effective minimal antibiotic duration for AOM remains unclear. Moreover, guidelines often recommend broad ranges (5-10days) of antibiotic use, yet the clinical impact of such a wide window has not been assessed. We systematically searched PubMed/MEDLINE, Embase, Scopus, Web of Science, and Cochrane Library from database inception to 6 October 2021. Network meta-analysis was conducted on randomized controlled trials that assessed antibiotic treatment for AOM in children (PROSPERO CRD42020196107). For amoxicillin and amoxicillin-clavulanate, 7-day regimens were noninferior to 10-day regimens in clinical responses [amoxicillin: risk ratio (RR) 0.919 (95% CI 0.820-1.031), amoxicillin-clavulanate: RR 1.108 (0.957-1.282)], except for ≤ 2years. For the third-generation cephalosporins, 7-day and 10-day regimens had similar clinical responses compared to placebo [7-day: RR 1.420 (1.190-1.694), 10-day: RR 1.238 (1.125-1.362) compared to placebo]. However, 5-day regimens of amoxicillin-clavulanate and third-generation cephalosporins were inferior to 10-day regimens. Compared to amoxicillin, a shorter treatment duration was tolerable with amoxicillin-clavulanate. Our findings indicated that 10days of antibiotic use may be unnecessarily long, while the treatment duration should be longer than 5days. Otherwise, 5-day regimens would be sufficient for a modest treatment goal. Our findings revealed that the current wide range of recommended antibiotic durations may have influenced the clinical outcome of AOM, and a narrower antibiotic duration window should be re-established.

Improving Long-Term Guided Wave Damage Detection With Measurement Resampling

This paper studies strategies for improving long-term guided wave damage detection in an outdoor, uncontrolled environment. We leverage the reconstruction difference between a short-term and long-term principal component analysis to distinguish damage from other variations. While principal component analysis damage detection methods are not new, few are capable of detecting long-term damage in a dynamic environment and in a computationally feasible manner. In this paper, we study the factors that reduce damage detection performance, including the time window duration, the damage duration, and the number of principal components. We then propose a monotonic decreasing sampling strategy that reduces these issues and improves damage detection. Results show that the best receiver operating curve area under the curve score, using an 80-day window of guided wave data and 20-day damage duration, increases from 0.88 to 0.92 while the running time and required memory reduces to less than 1/4 of the original cost.

A New ECG Data Processing Approach to Developing an Accurate Driving Fatigue Detection Framework with Heart Rate Variability Analysis and Ensemble Learning

More than 1.3 million people are killed in traffic accidents annually. Road traffic accidents are mostly caused by human error. Therefore, an accurate driving fatigue detection system is required for drivers. Most driving fatigue detection studies concentrated on improving feature engineering and classification methods. We propose a novel driving fatigue detection framework concentrating on the development of the preprocessing, feature extraction, and classification stages to improve the classification accuracy of fatigue states. The proposed driving fatigue detection framework measures fatigue using a two-electrode ECG. The resampling method and heart rate variability analysis were used to extract features from the ECG data, and an ensemble learning model was utilized to classify fatigue states. To achieve the best model performance, 40 possible scenarios were applied: a combination of 5 resampling scenarios, 2 feature extraction scenarios, and 4 classification model scenarios. It was discovered that the combination of a resampling method with a window duration of 300 s and an overlap of 270 s, 54 extracted features, and AdaBoost yielded an optimum accuracy of 98.82% for the training dataset and 81.82% for the testing dataset. Furthermore, the preprocessing resampling method had the greatest impact on the model’s performance; it is a new approach presented in this study.

Influence of winter natural ventilation on thermal environment of university dormitories under central heating mode in severe cold regions of China

The indoor thermal environment impacts people’s health, while it can be adjusted by natural ventilation. However, studies on the effects of winter natural ventilation on the thermal environment in severe cold regions are minimal. Through field measurements in university dormitories in winter in Shenyang, China, this study explored, under the central heating mode, the thermal environment differences in different orientations of the dormitory and its different indoor positions under natural ventilation and the influence of window opening area and duration on the thermal environment. The results show that under winter natural ventilation, the air temperature, mean radiation temperature, and adaptive predicted mean vote (APMV) of dormitories facing different orientations were mainly affected by the direct solar radiation reception. When the ventilation duration reached 15mins, with the ratio of window opening area to a ground area of 0.05, 0.11, and 0.16, the APMV decreased by about 0.35, 0.5, and 1, respectively.

A Method For Autoregression Modeling of a Speech Signal

The problem of autoregressive modeling of a speech signal based on the data of the discrete Fourier transform in the mode of a sliding observation window of small duration (milliseconds) is considered. The problem of stability of the formed autoregressive model is investigated. To overcome it, it is proposed to use the envelope of the Schuster periodogram as a reference spectral sample. A new method of autoregressive modeling has been developed, in which the detection of the spectral envelope is carried out using a recirculator of a sequence of samples in the frequency domain. An example of its practical implementation is considered, a full-scale experiment is set up and carried out. Based on the results of the experiment, conclusions were drawn about achieving a significant gain in terms of not only stability, but also the accuracy of the autoregressive model of the speech signal.

A nanoadjuvant that dynamically coordinates innate immune stimuli activation enhances cancer immunotherapy and reduces immune cell exhaustion.

Although conventional innate immune stimuli contribute to immune activation, they induce exhausted immune cells, resulting in suboptimal cancer immunotherapy. Here we suggest a kinetically activating nanoadjuvant (K-nanoadjuvant) that can dynamically integrate two waves of innate immune stimuli, resulting in effective antitumour immunity without immune cell exhaustion. The combinatorial code of K-nanoadjuvant is optimized in terms of the order, duration and time window between spatiotemporally activating Toll-like receptor 7/8 agonist and other Toll-like receptor agonists. K-nanoadjuvant induces effector/non-exhausted dendritic cells that programme the magnitude and persistence of interleukin-12 secretion, generate effector/non-exhausted CD8+ T cells, and activate natural killer cells. Treatment with K-nanoadjuvant as a monotherapy or in combination therapy with anti-PD-L1 or liposomes (doxorubicin) results in strong antitumour immunity in murine models, with minimal systemic toxicity, providing a strategy for synchronous and dynamic tailoring of innate immunity for enhanced cancer immunotherapy.

Time restricted feeding is associated with poor performance in specific cognitive domains of Suburb-Dwelling older Chinese

The purpose of this study was to investigate the association between time restricted feeding (TRF) and different areas of cognitive function in the elderly in Chinese communities. This study consisted of 1353 community-dwelling Chinese older adults aged 60 years and older in Chongming area, Shanghai (563 males; the mean age, 73.38 ± 6.16 years). Mild cognitive impairment (MCI) and six different cognitive domains was assessed by the Chinese-version of Mini Mental State Examination (MMSE). Recording the eating time of each meal through oral inquiry to calculate the time window between the first meal and the last meal of the average day. Participants with an eating time window duration of more than 10 h were then identified, as well as those with eating time restricted to less than 10 h (TRF). Our study found that TRF may be associated with a higher incidence rate of cognitive impairment. TRF only limited the eating time window and did not change the frequency of participants' dietary intake. We used a linear regression model to study the association of TRF with cognitive function. After adjusting for confounding variables, the results showed that TRF was related to MMSE score (P < 0.001), "Orientation to place" (P < 0.001) and "Attention/calculation" (P < 0.001) functions. Among Chinese older community-dwellers, TRF was associated with a higher prevalence of CI and negatively correlated with the "Orientation to place" and "attention/calculation" functions.

Pharmacological inhibition of SK-channels with AP14145 prevents atrial arrhythmogenic changes in a porcine model for obstructive respiratory events.

Obstructive sleep apnea (OSA) creates a complex substrate for atrial fibrillation (AF), which is refractory to many clinically available pharmacological interventions. We investigated atrial antiarrhythmogenic properties and ventricular electrophysiological safety of small-conductance Ca2+ -activated K+ (SK)-channel inhibition in a porcine model for obstructive respiratory events. In spontaneously breathing pigs, obstructive respiratory events were simulated by intermittent negative upper airway pressure (INAP) applied via a pressure device connected to the intubation tube. INAP was applied for 75 s, every 10 min, three times before and three times during infusion of the SK-channel inhibitor AP14145. Atrial effective refractory periods (AERP) were acquired before (pre-INAP), during (INAP) and after (post-) INAP. AF-inducibility was determined by a S1S2 atrial pacing protocol. Ventricular arrhythmicity was evaluated by heart rate adjusted QT-interval duration (QT-paced) and electromechanical window (EMW) shortening. During vehicle infusion, INAP transiently shortened AERP (pre-INAP: 135 ± 10 ms vs. post-INAP 101 ± 11 ms; p = .008) and increased AF-inducibility. QT-paced prolonged during INAP (pre-INAP 270 ± 7 ms vs. INAP 275 ± 7 ms; p = .04) and EMW shortened progressively throughout INAP and post-INAP (pre-INAP 80 ± 4 ms; INAP 59 ± 6 ms, post-INAP 46 ± 10 ms). AP14145 prolonged baseline AERP, partially prevented INAP-induced AERP-shortening and reduced AF-susceptibility. AP14145 did not alter QT-paced at baseline (pre-AP14145 270 ± 7 ms vs. AP14145 268 ± 6 ms, p = .83) or QT-paced and EMW-shortening during INAP. In a pig model for obstructive respiratory events, the SK-channel-inhibitor AP14145 prevented INAP-associated AERP-shortening and AF-susceptibility without impairing ventricular electrophysiology. Whether SK-channels represent a target for OSA-related AF in humans warrants further study.

336 TEMPORAL CONNECTION BETWEEN HOME MONITORING TRENDS OF THORACIC IMPEDANCE AND SUSTAINED VENTRICULAR ARRHYTHMIAS

Abstract Introduction Implantable cardioverter defibrillators (ICDs) provide daily values of thoracic impedance (TI) that are inversely correlated with fluid accumulation in the lungs. Since sustained ventricular arrhythmias (SVA) are known to have a short-term relationship with heart failure exacerbations, our objective was to assess TI trends temporally related to SVA episodes. Methods This study analyzed data daily transmitted from patients with ICD or cardiac resynchronization therapy defibrillator (CRT-D) of the nationwide Home Monitoring Expert Alliance network. Device-detected SVAs were adjudicated for appropriateness. Patients were randomly split into a derivation and validation cohort. To identify the most significant TI trend (TI-index), several numerical TI transformations were tested in a cross-sectional analysis of the derivation cohort modelling the odds of first SVA with univariate logistic regressions. In the same cohort, the threshold of the selected transformation was identified to maximize the projected specificity. The relative risk of SVA for TI-index above threshold was estimated in the validation cohort by applying Cox proportional hazard models stratified by individual patients to 60-day duration windows. Analyses were performed separately in the ICD and CRT-D groups. Results The study cohort included 2,384 patients with 69 years of age (interquartile range: 60, 77); 19% were women, 42% had coronary artery disease, and 43% had a CRT-D. After a median follow-up of 2.0 (1.0, 3.4) years, there were 3,298 appropriate SVA episodes in 727 patients (30%). The derived IT-index consisted of the percentage of 6-day intervals of the rolling average of TI values showing monotone decrease in the last 82 days. The increase in the risk of SVA was 3% (p&amp;lt;0.0001) per unit of percentage. The threshold of 60% of intervals with monotone decrease was associated with 99.9% projected specificity and 70.3% accuracy. In the validation cohort, the TI-Index was associated with a 42% increased risk of SVA episodes in the ICD group (hazard ratio [HR] 1.42, 95% confidence interval [CI] 1.05-1.92, p=0.023). The TI-index exceeded the 60% threshold before the episode in 38% of the detected episodes. The association was not significant in the CRT-D group (HR 0.96, 95% CI 0.62-1.47, p=0.84). Conclusions In our analysis of remote monitoring data, a specific monotonic decreasing trend of TI was temporally associated with SVA in patients with ICD. Careful monitoring of TI can identify a period of susceptibility to ventricular arrhythmias that deserves more intensive attention.

Forecasting Hospital Visits Due to Influenza Based on Emergency Department Visits for Fever: A Feasibility Study on Emergency Department-Based Syndromic Surveillance.

This study evaluated the use of chief complaint data from emergency departments (EDs) to detect the increment of influenza cases identified from the nationwide medical service usage and developed a forecast model to predict the number of patients with influenza using the daily number of ED visits due to fever. The National Health Insurance Service (NHIS) and the National Emergency Department Information System (NEDIS) databases from 2015 to 2019 were used. The definition of fever included having an initial body temperature ≥ 38.0 °C at an ED department or having a report of fever as a patient’s chief complaint. The moving average number of visits to the ED due to fever for the previous seven days was used. Patients in the NHIS with the International Classification of Diseases-10 codes of J09, J10, or J11 were classified as influenza cases, with a window duration of 100 days, assuming the claims were from the same season. We developed a forecast model according to an autoregressive integrated moving average (ARIMA) method using the data from 2015 to 2017 and validated it using the data from 2018 to 2019. Of the 29,142,229 ED visits from 2015 to 2019, 39.9% reported either a fever as a chief complaint or a ≥38.0 °C initial body temperature at the ED. ARIMA (1,1,1) (0,0,1)7 was the most appropriate model for predicting ED visits due to fever. The mean absolute percentage error (MAPE) value showed the prediction accuracy of the model. The correlation coefficient between the number of ED visits and the number of patients with influenza in the NHIS up to 14 days before the forecast, with the exceptions of the eighth, ninth, and twelfth days, was higher than 0.70 (p-value = 0.001). ED-based syndromic surveillances of fever were feasible for the early detection of hospital visits due to influenza.

Broadband transient absorption spectroscopy using an incoherent white-light source as probe.

Time-resolved spectroscopy and, in particular, transient absorption methods have been widely employed to study the dynamics of materials, usually achieving time resolution down to femtoseconds with measurement windows up to a few nanoseconds. Various techniques have been developed to extend the measurement duration up to milliseconds and beyond to permit probing slower dynamics. However, most of these either demand complicated and expensive equipment or do not provide broadband spectral coverage. This paper proposes a transient absorption technique in which an ultra-short pulse laser and a broadband incoherent continuous-wave light source are employed as pump and probe, respectively. Detection of the transient probe transmission is performed in a time-resolved fashion with a fast photodiode after a monochromator and the data is recorded with an oscilloscope. The time resolution is determined by the electronic bandwidth of the detection and acquisition devices and is ∼1 ns, with a measurement duration window of up to milliseconds and a spectral resolution of <2 nm covering from 0.4 to 2 µm. In addition, the setup can be employed to measure time- and spectrally-resolved photoluminescence.

Using random matrix theory to quantify pulmonary fibrosis: Investigating the effect of time window duration

Random matrix theory (RMT) exploits the distribution of singular values of the inter-element response matrix (IRM). If multiple scattering dominates the propagation, the singular value distribution follows the quarter circle law. However, dominance of single scattering results in Henkel function behavior of the singular value distribution. In our previous work, we have shown that this can be exploited to estimate severity of bleomycin-induced fibrosis (measured by histology) in rodent lungs. We showed that E(x), the expected value of the singular value distribution, as well as the singular value with the highest probability, correlated significantly with histology scores. Here, we investigate the sensitivity of these metrics to the time window duration used for the Singular Value Decomposition of the IRM, which is performed in the frequency domain, using overlapping time windows. A linear transducer with a central frequency of 7.8 MHz and a Verasonics scanner were used to obtain IRMs in 24 rat lungs. Different degrees of pulmonary fibrosis were induced using bleomycin in 18 rats while 6 rats were left as controls. The IRMs were time-windowed with different duration of 2T, 4T, and 6T where T is the transmitted pulse period. E(x) and were evaluated for each time window duration for all rat lungs. For all time window durations significant correlations were observed between and E(x), and histology scores. Wilcoxon ranksum tests show that the distributions obtained for and E(x) are not affected by the time window duration.

One-year mortality and varying ascertainment windows for a Medicare claims-based proxy for frailty in women with stage I-III breast cancer.

396 Background: Frail cancer patients are more likely to die or experience treatment toxicity than non-frail patients. The Faurot frailty index (FFI) is a validated Medicare claims-based proxy that predicts frailty in individuals. Frailty is dynamic and can change over time. We assessed the distribution of the FFI and associations with 1-year mortality using varying durations of frailty ascertainment windows in breast cancer patients. Methods: We identified women (66+ yrs) with stage I-III breast cancer in the SEER-Medicare database who had ≥1 year of continuous enrollment in Medicare Parts A and B prior to diagnosis. The FFI-predicted probability of frailty was assessed using demographic and diagnostic and procedural billing information during varying pre-diagnosis ascertainment windows: 3, 6, 8, or 12 months or using all available pre-diagnosis claims. We described the distributions of the claims-based frailty measures using each of the five ascertainment windows and reported 1-year mortality for individuals with high (≥0.20), medium (0.05 - &lt; 0.20), or low (&lt; 0.05) claims-based frailty using Kaplan-Meier analysis. Follow-up began on the date of cancer diagnosis. Results: We identified 235,145 women with breast cancer. Using a 3-month ascertainment window, 76%, 20%, and 4% were classified as having low, medium, and high claims-based frailty. Although the distributions of claims-based frailty were similar using 3, 6, 8, and 12-month frailty ascertainment windows, the use of all available lookback led a wider interquartile range (IQR) for claims-based frailty (Table). Overall, 4% of women died within 1 year of diagnosis. Higher claims-based frailty was strongly associated with increased 1-year mortality risk, using each ascertainment window. Mortality associations were strongest with a 3-month ascertainment window: risk difference (RD) high vs. low frailty 20% (95% CI, 19-20%). Associations were weakest with an all available lookback window: RD high vs. low frailty 11% (95% CI, 11-12%). Conclusions: Varying frailty ascertainment window durations did not lead to substantial differences in the distribution of the FFI in women with stage I-III breast cancer. Associations between the FFI and 1-year mortality monotonically decreased with increasing ascertainment window durations. This may be due to claims in the distant past having less etiological relevance for predicting mortality during the year following cancer diagnosis.[Table: see text]

3D time-dependent scattering about complex shapes using high order difference potentials

We compute the scattering of unsteady acoustic waves about complex three-dimensional bodies with high order accuracy. The geometry of a scattering body is defined with the help of CAD. Its surface is represented as a collection of non-overlapping patches, each parameterized independently by means of high order splines (NURBS). As a specific example, we consider a submarine-like scatterer constructed using five different patches.The acoustic wave equation on the region exterior to the scatterer is solved by first reducing it to a system of Calderon's boundary operator equations. The latter are obtained using the method of difference potentials coupled with a compact fourth order accurate finite difference scheme. When solving the boundary operator equations, we employ Huygens' principle. It allows us to work on a sliding time window of non-increasing duration rather than keep the entire temporal history of the solution at the boundary.The proposed methodology demonstrates grid-independent computational complexity at the boundary and sub-linear complexity with respect to the grid dimension. It efficiently handles complex non-conforming geometries on Cartesian grids with no penalty for either accuracy or stability due to the cut cells. Its performance does not deteriorate over arbitrarily long simulation times. The exact treatment of artificial outer boundaries is inherently built in. Finally, multiple similar problems can be solved efficiently at a low individual cost per problem. This is important when, for example, the boundary condition on the surface changes but the scattering body stays the same.

A pre-impact fall detection data segmentation method based on multi-channel convolutional neural network and class activation mapping

Objective. A segmentation method for pre-impact fall detection data is investigated. Specifically, it studies how to partition data segments that are important for classification from continuous inertial sensor data for pre-impact fall detection. Approach. In this study, a trigger-based algorithm combining multi-channel convolutional neural network (CNN) and class activation mapping was proposed to solve the problem of data segmentation. First, a pre-impact fall detection training dataset was established and divided into two parts. For falls, the 1 s data was divided from the peak value of the acceleration signal magnitude vector to the starting direction. For activities of daily living, the cycle segmentation was performed for a 1 s window size. Second, a heat map of the class activation regions of the sensor data was formed using a multi-channel CNN and a class activation mapping algorithm. Finally, the data segmentation strategy was established based on the heat map, the basic law of falls and the real-time requirements. Main results. This method was verified by the SisFall dataset. The obtained segmentation strategy (i.e. to start segmenting a small data segment with a window duration of 325 ms when the acceleration signal magnitude vector is less than 9.217 m s−2) met the real-time requirements for pre-impact fall detection. Moreover, it was suitable for various machine learning algorithms, and the accuracy of the machine learning algorithms used exceeded 94.8%, with the machine learning algorithms verifying the data segmentation strategy. Significance. The proposed method can automatically identify the class activation area, save the computing resources of wearable devices, shorten the duration of segmentation window, and ensure the real-time performance of pre-impact fall detection.

Persistent white matter vulnerability in a mouse model of mild traumatic brain injury

BackgroundFollowing one mild traumatic brain injury (mTBI), there is a window of vulnerability during which subsequent mTBIs can cause substantially exacerbated impairments. Currently, there are no known methods to monitor, shorten or mitigate this window.MethodsTo characterize a preclinical model of this window of vulnerability, we first gave male and female mice one or two high-depth or low-depth mTBIs separated by 1, 7, or 14 days. We assessed brain white matter integrity using silver staining within the corpus callosum and optic tracts, as well as behavioural performance on the Y-maze test and visual cliff test.ResultsThe injuries resulted in windows of white matter vulnerability longer than 2 weeks but produced no behavioural impairments. Notably, this window duration is substantially longer than those reported in any previous preclinical vulnerability study, despite our injury model likely being milder than the ones used in those studies. We also found that sex and impact depth differentially influenced white matter integrity in different white matter regions.ConclusionsThese results suggest that the experimental window of vulnerability following mTBI may be longer than previously reported. Additionally, this work highlights the value of including white matter damage, sex, and replicable injury models for the study of post-mTBI vulnerability and establishes important groundwork for the investigation of potential vulnerability mechanisms, biomarkers, and therapies.

A data multiverse analysis investigating non-model based SCR quantification approaches.

Electrodermal signals are commonly used outcome measures in research on arousal, emotion, and habituation. Recently, we reported on heterogeneity in skin conductance response quantification approaches and its impact on replicability. Here we provide complementary work focusing on within-approach heterogeneity of specifications for skin conductance response quantification. We focus on heterogeneity within the baseline-correction approach (BLC) which appeared as particularly heterogeneous-for instance with respect to the pre-CS baseline window duration, the start, and end of the peak detection window. We systematically scrutinize the robustness of results when applying different BLC approach specifications to one representative pre-existing data set (N=118) in a (partly) pre-registered study. We report high agreement between different BLC approaches for US and CS+ trials, but moderate to poor agreement for CS- trials. Furthermore, a specification curve of the main effect of CS discrimination during fear acquisition training from all potential and reasonable combinations of specifications (N=150) and a prototypical trough-to-peak (TTP) approach indicates that resulting effect sizes are largely comparable. A second specification curve (N=605 specific combinations) highlights a strong impact of different transformation types. Crucially, however, we show that BLC approaches often misclassify the peak value-particularly for CS- trials, leading to stimulus-specific biases and challenges for post-processing and replicability of CS discrimination across studies applying different approaches. Lastly, we investigate how negative skin conductance values in BLC, appearing most frequently for CS- (CS- &gt; CS+ &gt; US), correspond to values in TTP quantification. We discuss the results considering prospects and challenges of the multiverse approach and future directions.

METHOD FOR EXTRACTING DIAGNOSTIC FEATURES OF THE FACILITIES TECHNICAL CONDITION IN THE SYSTEM FOR MONITORING

Technical diagnostics of facilities is an urgent problem during its operation. An integral part of the implementation of diagnostic monitoring systems is the development of a decision support system (DSS) based on the analysis of acoustic emission (AE) diagnostic data and machine learning methods. A necessary condition for the application of machine learning methods in the development of DSS is the process of extracting diagnostic features from the AE signal. In the present work, an improved method is proposed for extracting diagnostic features from time series of AE signals. This includes two successive steps. At the first step, the frequency and frequency-time characteristics are calculated in a sliding window of short duration, which describe local changes in the shape and structure of single pulses. At the second step, the resulting matrix of informative features is aggregated by calculating statistical moments of various orders, which makes it possible to effectively detect long-term trends in the AE signal changes emitted by the defect. Verification of the proposed method was carried out on a full-scale control object of the oil tank RVS No. 3 ("NTEK LLC"). Based on the results obtained, a conclusion was made about the effectiveness of the proposed method in the development of diagnostic monitoring systems based on acoustic emission data.