Variance In Rates Research Articles

Use of diagnostic subtraction angiography for ischemic stroke (US DUTCH study) Regional variation and time-trend among medicare beneficiaries

IntroductionThere are no guideline recommendations for DSA in the ischemic stroke work-up. We studied the rate of DSA in ischemic stroke, the recent time-trend, hospital variation and associated factors. MethodsThis is a retrospective cross-sectional study among Medicare fee-for-service beneficiaries with ischemic stroke admitted between 2016 and 2020 in the United States. ICD-10 codes were used to determine ischemic stroke diagnosis and procedure codes for thrombectomy and DSA. Hospital trends and factors associated with DSA performance were analyzed in hospitals with DSA capacity. Results7.373 (0.7 %) of the 1,085,644 ischemic stroke patients, had a DSA for diagnostic purposes. In the patients that were admitted to a hospital with DSA facility, the following factors showed the strongest association with DSA: younger age (aOR=0.81 [95 % confidence interval (CI):0.81-0.83]), thrombectomy rate in that hospital (aOR=2549 [95 %CI:610-10663]), transfer (aOR=1.41[95 %CI:1.34-1.50]) and carotid disease (aOR=5.8 [95 %CI:5.6-6.1]). There was large variation in the hospital DSA rate, varying from 0.07 % to 11.1 %. Of the variance of DSA rates, 15 % was attributed to the residual effect hospital propensity to perform DSA. The top decile of hospitals with the highest DSA rate, performed DSA's in >2.3 % of patients, compared to the 0.6 % median. There was no change in DSA rates over time. ConclusionDSA is used infrequently in acute ischemic stroke patients and did not change between 2016 to 2020. Hospital variation in DSA use was however large, and not solely explained by patient and facility factors.

Polygenic risk for psychotic disorders in relation to cardiac autonomic dysfunction in unmedicated patients with schizophrenia.

Cardiac autonomic dysfunction (CADF), mainly characterized by increased heart rate, decreased heart rate variability, and loss of vagal modulation, has been extensively described in patients with schizophrenia (SCZ) and their healthy first-degree relatives. As such, it represents an apparent physiological link that contributes to the increased cardiovascular mortality in these patients. Common genetic variation is a putative underlying mechanism, along with lifestyle factors and antipsychotic medications. However, the extent to which CADF is associated with genetic factors for SCZ is unknown. A sample of 83 drug-naive SCZ patients and 96 healthy controls, all of European origin, underwent a 30-minute autonomic assessment under resting conditions. We incorporated parameters from several domains into our model, including time and frequency domains (mean heart rate, low/high frequency ratio) and compression entropy, each of which provides different insights into the dynamics of cardiac autonomic function. These parameters were used as outcome variables in linear regression models with polygenic risk scores (PRS) for SCZ as predictors and age, sex, BMI, smoking status, principal components of ancestry and diagnosis as covariates. Of the three CADF parameters, SCZ PRS was significantly associated with mean heart rate in the combined case/control sample. However, this association was was no longer significant after including diagnosis as a covariate (p = 0.29). In contrast, diagnostic status is statistically significant for all three CADF parameters, accounting for a significantly greater proportion of the variance in mean heart rate compared to SCZ PRS (approximately 16% vs. 4%). Despite evidence for a common genetic basis of CADF and SCZ, we were unable to provide further support for an association between the polygenic burden of SCZ and cardiac autonomic function beyond the diagnostic state. This suggests that there are other important characteristics associated with SCZ that lead to CADF that are not captured by SCZ PRS.

Orderly charging of electric vehicles: A two-stage spatial-temporal scheduling method based on user-personalized navigation

As electric vehicles (EVs) are cross-domain entities with dual attributes related to mobile loads and transportation, their travel modes and charging behaviors are stochastic and uncertain in time and space. Large-scale uncoordinated EV charging may cause problems such as the congestion of charging stations (CSs) and overloading of distribution networks (DNs). To address these challenges and the current research gap, and to optimize the spatial-temporal distribution of EV charging loads, a vehicle-road-network collaborative operation framework is constructed in this paper, and a two-stage spatial-temporal scheduling method for EV charging is proposed. In the first stage, real-time traffic and CSs information are introduced to improve Dijkstra's algorithm, and the value preferences of different types of users are considered to further improve the objective function of the algorithm as well. Thus, a dynamic personalized charging navigation model based on improved Dijkstra's algorithm is proposed to provide real-time guidance allowing users to choose their travel paths and CSs. In the second stage, we consider the charging demands of EV users and the operation status of the DN, and an orderly charging model is established to minimize the peak/valley load difference of the DN. Through comprehensive comparisons of experimental results, the variance in the average utilization rates of CSs and the peak valley difference for the DN are reduced by 80.77 % and 16.91 %, respectively. In addition, the time and economic costs of users can be reduced by 9 min and 7.9 RMB, respectively, thereby reducing travel and toll costs and increasing their willingness to participate in scheduling. The simulation experiment proves that the proposed method achieves multi-agent collaborative optimization of EV users, CSs, and DN.

Investigation of aerodynamic performance and noise of tip shape clearance in a diagonal flow fan

Unlike centrifugal fans, diagonal flow fans have an air inflow direction at a certain angle to the axis, with tip clearance (TC) significantly affecting aerodynamic performance and noise. This study focuses on investigating the effects of four different TC shapes based on blade height (BH) ratios. Large Eddy Simulation (LES) was used to capture the structure and development of tip leakage flow (TLF) and tip leakage vortex (TLV) accurately. Diagonal flow fan casings with different TC shapes were 3D-printed for aerodynamic performance experiments. The results from both numerical simulations and experiments show that diagonal flow fans with Tapering or Parallel TC shapes achieve superior aerodynamic performance compared to other geometries, with a 24.1% variance in flow rate. The study further indicates that different TC shapes significantly influence the flow field, altering the mechanisms governing turbulence transition on the suction side. Compared to the Diverging shape, reducing the TC width decreases the amplitude of the TLV, which in turn reduces the turbulence-affected area while increasing dominant mode frequencies. Experimental results also confirm that the Tapering-Diverging TC shape yields the lowest noise levels, with a 3.6 dB reduction in Sound Pressure Level (SPL).

Return to Play After Shoulder Stabilization –What If This Isn't Going Well?

Traumatic shoulder instability is prevalent among athletes, often leading to surgical intervention and challenging rehabilitation outcomes. This chapter explores the complexities surrounding return to play (RTP) following shoulder stabilization procedures, highlighting the significant variance in RTP rates and the common lag in returning to pre-injury performance levels, particularly in throwing athletes. Factors influencing successful outcomes include the nature of the instability, surgical approach, and individual athlete characteristics. A consensus on RTP criteria remains elusive, with a predominant reliance on time-based recovery, yet evidence suggests that other physical and psychological variables must be considered. Emphasizing a shared decision-making process and ongoing assessment of range of motion, strength, and proprioception, this chapter aims to identify key deficits and interventions that can enhance rehabilitation success for athletes struggling to return to play. Effective communication among the sports medicine team and the athlete is essential to optimize adherence to rehabilitation protocols and improve overall outcomes.

Powering Toward Los Angeles: Comparing Power Output and Pacing Approach Between Maximal 2000- and 1500-m On-Water Racing in Elite Rowers.

To compare power output and pacing between maximal 1500- and 2000-m on-water rowing performances. Twenty-six (female n = 4, male n = 22) international rowers, across 6 boat classes, completed maximal 1500- and 2000-m on-water races, separated by 24 to 48hours. Crew combinations and seat orders remained consistent between races. Peach PowerLine instrumentation measured power output and stroke rate. Differences in completion time, mean power output (MPO), percentage prognostic velocity (PPV;percentage of world record velocity in each boat class), stroke rate, and pacing variance were assessed using linear mixed modeling. Compared with 2000-m, completion times were 90.4 (6.1) seconds shorter over 1500m (-24.7% [0.7%]). Both MPO (P = .255, ηp2=.06) and PPV (P = .340, ηp2=.18) were not different between distances. Broadly, crews adopted a reverse-J-shaped pacing across both distances, demonstrating a reduced variance over 1500m (P = .035, ηp2=.62). Percentage change in MPO from 2000 to 1500m demonstrated a strong negative association with pacing variance over 1500m (R2 = .74, P = .027). International rowing crews did not increase MPO or PPV when racing maximally over 1500m compared to 2000m. Comparable strategies were adopted over both distances, with less variance in pacing observed over 1500m. The crews that demonstrated greater increases in MPO over the shorter race employed a flatter pacing strategy. To improve 1500-m on-water performance, rowers may need to adopt a more even pacing approach.

Unified inference for an integer-valued AR(1) model

. Conditional least squares estimation is often employed to infer an integer-valued AR(1) model and its convergence rate and asymptotic variance differ for the stable and nearly unstable cases. This article adopts a random weighted bootstrap method to provide a unified interval estimation and hypothesis test regardless of the underlying process being either stable or nearly unstable. A simulation study confirms the good finite sample performance of the proposed inference. We also apply it to test for a unit root test in a COVID-19 dataset.

Regional Variance of Rates of Involuntary Admission in Switzerland

Identification of predictors that contribute to explaining regional variance of involuntary admission (IA) in Switzerland. Multiple regression analysis including potential predictors and regional rates of IA at the level of utilisation-based care regions. Authorisation to issue involuntary admission, assistance/guardianship, outpatient consultation rate in psychiatric practices, hospitalisation rate and urbanisation are significantly related to regional variation in IA rates. Restrictive regulation of the authority to issue IA and voluntary outpatient psychosocial and administrative support measures can contribute to a reduction in the rates of IA.

Effects of the Real Effective Exchange Rate Volatility on the Trade Balance, The Case of Vietnam for Sutainable Development Goals

Objective: The objective of this study is to analyze the impact of exchange rate volatility on the trade balance of Vietnam. Analyzing and evaluating the degree of exchange rate fluctuations of the VND from the first quarter of 2000 to the second quarter of 2019. Then, proposing the orientations and some solutions to regulate the exchange rate to ensure the trade balance surplus in the near future. Method: Exchange rate fluctuation is the conditional variance or unconditional variance or standard deviation of the base rate. Auto Regressive Conditional Heteroskedasticity (ARCH) model was used to calculate real effective exchange rate fluctuations in VND. The study also uses Ordinary Least Square regression (OLS) method to evaluate the impact of exchange rate volatility on Vietnam's trade balance. Results and Discussion: The results show that a stable long-term equilibrium relationship exists between exports. Quantitative results show that exchange rate volatility have a positive impact on exports but have not yet shown a clear impact on Vietnam's imports. The paper also points out that in addition to the exchange rate fluctuations, many other factors such as foreign income, domestic income, trade conditions ... could also affect the trade balance. Research Implications: To achieve the trade balance surplus, the proposed solutions need coordination among ministries and agencies. Finally, the flexible control of State Bank on exchange rate volatility will enhance Trade Balance of Vietnam.

Feasibility of preimplantation genetic testing for aneuploidy on frozen-thawed embryos following conventional IVF insemination.

Intracytoplasmic sperm injection (ICSI) is commonly employed in preimplantation genetic testing (PGT) to minimize the risk of foreign sperm DNA contamination. Cryopreserved embryos from patients with recurrent miscarriage or repeated implantation failure, who have undergone conventional in vitro fertilization (IVF), can be thawed and biopsied for PGT. Therefore, we aimed to assess the accuracy and effectiveness of preimplantation genetic testing for aneuploidy (PGT-A) on frozen embryos using conventional IVF (c-IVF) insemination methods. From January 2021 to November 2023, our center conducted 107 thawed cryopreserved embryo biopsy cycles to screen for PGT-A. Among them, 58 cycles used c-IVF insemination, and 49 used ICSI insemination. Basic patient clinical information, laboratory data, PGT test results, and clinical outcome data were collected. To minimize the confounding effects of patient characteristics and embryo quality on PGT-A outcomes, clinical outcomes, and contamination assessment, these variables were included in the analysis. We then evaluated the blastocyst euploidy rate, clinical outcomes, and accuracy of PGT-A results between the two groups and analyzed potential contamination in the c-IVF insemination group. A total of 320 blastocysts underwent PGT-A testing, with 179 blastocysts from c-IVF insemination and 141 from ICSI insemination. Considering participants' baseline characteristics and embryological outcomes, no significant differences were found between the two groups regarding infertility type, average age, body mass index, percentage of PGT-A indications, or quality of embryonic development. Regarding PGT-A results, all 320 biopsy samples were successfully analyzed, showing no statistical variance in chromosomal euploidy, abnormality, or mosaicism rates between the two insemination methods. No parental contamination was detected in the c-IVF insemination group. When assessing clinical outcomes, parameters such as biochemical pregnancy, clinical pregnancy, and miscarriage rates did not exhibit significant discrepancies between the two groups, and no misdiagnoses were reported during the study period. Embryo transfer and PGT-A results are not affected by potential parental contamination in frozen-thawed embryos conceived via c-IVF. PGT-A guided embryo transfer in thawed embryos conceived by c-IVF is a viable and clinically effective approach.

On the Cosmic Variance of the Merger Rate Density of Binary Neutron Stars

The cosmic variance on the star formation history may lead to bias in the merger rate density estimation of binary neutron star (BNS) mergers by compact binary population synthesis. In this paper, we take advantage of the large box size of the Millennium Simulation combined with the semianalytic galaxy formation model GABE and the parameterized population binary star evolution model to examine how much effect the cosmic variance will introduce on the estimation of the merger rate density of BNS mergers. We find that for subbox sizes of 100 and 200 Mpc, the variance of merger rate density σ R /R at different redshifts is about 23%–35% and 13%–20%, respectively. On the one hand, as for the variance of the detection rate on BNS mergers with the current LIGO–Virgo–KAGRA (LVK) detector network, this value is very small at ≲10%, which indicates ignoring the cosmic variance is reasonable for estimating the merger rate density from current LVK observations. On the other hand, with next-generation gravitational wave detectors, it is possible to localize BNS mergers within subboxes possessing a length of 40 Mpc for a source redshift z s < 0.2. In such a small box, the cosmic variance of the merger rate density is significant, i.e., the value of σ R /R is about ∼55%. This hints that estimating the merger rate density of BNS in different sky areas may provide useful information on the cosmic variance.

Efficacy of ultrasonography and color-Doppler for early prediction of hemodialysis arteriovenous fistula unassisted maturation.

Arteriovenous fistula (AVF) failure is a prevalent concern for patients with end-stage kidney disease on hemodialysis. Recognizing the efficacy of ultrasound Doppler in post-operative AVF evaluation, this study sought to discern the predictive capabilities of various ultrasonographic and color-Doppler metrics for early AVF outcomes. This single-center, prospective cohort study spanned 1 year and, post ethical clearance, included all patients who underwent native AVF creation surgery and were subsequently referred for standard post-operative ultrasound Doppler assessment. Parameters such as fistula size, cephalic vein area and diameter, and AVF flow velocity and rates were assessed on post-operative day 2, week 2, and week 6. These initial findings were juxtaposed with later outcomes to determine unassisted AVF results. Of the initial cohort of 40 patients, 75% encountered AVF failure, whereas 25% realized successful unassisted AVF maturation. A notable observation was the significant variance in AVF flow rates as early as post-operative day 2. A threshold of >246 ml/min was indicative of successful unassisted AVF maturation, leading to a sensitivity of 80% and a specificity of 70%. Although the cephalic vein diameter on post-operative day 2 lacked a robust association with AVF outcomes, a cut-off of >3.4 mm, when combined with flow rate testing, augmented the cumulative sensitivity to 92%. Ultrasound Doppler stands out as a valuable quantitative imaging modality, adept at prognosticating AVF outcomes from as early as post-operative day 2. In particular, a flow rate exceeding 246 ml/min and a cephalic vein diameter surpassing 3.4 mm are salient indicators for the early prediction of successful AVF outcomes.

Two‐Decadal Variability of Lacustrine Groundwater Discharge: Coupled Controls From Weather and Hydrologic Changes

AbstractLacustrine groundwater discharge (LGD) is a vital water and solute source for lakes. However, the understanding of the long‐term temporal variability of LGD remains limited owing to insufficient insights into driving mechanisms, such as climatic and hydrologic changes. In this study, we examined the oxbow lake group in the central Yangtze River (YR) and assessed the LGD rates from 2000 to 2022 using 222Rn combined with meteorological and hydrological data. The findings revealed that groundwater was recharged during the wet season and discharged to the lakes during the dry season. We established a mathematical model to link the LGD rates to the meteorological and hydrological factors of the lakes, which accounted for 98.70% of the LGD rate variance. Using a predictive model combined with meteorological and hydrological data to assess the LGD rate over the past two decades, it was found that in wet years with higher precipitation and higher average YR water levels, the LGD rate was higher. The gradual increase in precipitation during the rising water and wet seasons, along with a slow rise in the YR water levels, will cause the LGD rate to exhibit a slightly increasing trend with fluctuations in the future. This study proposed an innovative approach to investigate the long‐term temporal variation in LGD and identify the weather and hydrological influences on LGD.

Comparative modeling reveals the molecular determinants of aneuploidy fitness cost in a wild yeast model

Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will be maintained only if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74%-94% of the variance in aneuploid strains' growth rates is explained by the cumulative cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of small nucleolar RNAs (snoRNAs) and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution.

A singular, broadly-applicable model for estimating on- and off-path walking travel rates using airborne lidar data

Accurate prediction of walking travel rates is central to wide-ranging applications, including modeling historical travel networks, simulating evacuation from hazards, evaluating military ground troop movements, and assessing risk to wildland firefighters. Most of the existing functions for estimating travel rates have focused on slope as the sole landscape impediment, while some have gone a step further in applying a limited set of multiplicative factors to account for broadly defined surface types (e.g., “on-path” vs. “off-path”). In this study, we introduce the Simulating Travel Rates In Diverse Environments (STRIDE) model, which accurately predicts travel rates using a suite of airborne lidar-derived metrics (slope, vegetation density, and surface roughness) that encompass a continuous spectrum of landscape structure. STRIDE enables the accurate prediction of both on- and off-path travel rates using a single function that can be applied across wide-ranging environmental settings. The model explained more than 80% of the variance in the mean travel rates from three separate field experiments, with an average predictive error less than 16%. We demonstrate the use of STRIDE to map least-cost paths, highlighting its propensity for selecting logically consistent routes and producing more accurate yet considerably greater total travel time estimates than a slope-only model.

Multidimensional Analysis of Physiological Entropy during Self-Paced Marathon Running.

The pacing of a marathon is arguably the most challenging aspect for runners, particularly in avoiding a sudden decline in speed, or what is colloquially termed a "wall", occurring at approximately the 30 km mark. To gain further insight into the potential for optimizing self-paced marathon performance through the coding of comprehensive physiological data, this study investigates the complex physiological responses and pacing strategies during a marathon, with a focus on the application of Shannon entropy and principal component analysis (PCA) to quantify the variability and unpredictability of key cardiorespiratory measures. Nine recreational marathon runners were monitored throughout the marathon race, with continuous measurements of oxygen uptake (V˙O2), carbon dioxide output (V˙CO2), tidal volume (Vt), heart rate, respiratory frequency (Rf), and running speed. The PCA revealed that the entropy variance of V˙O2, V˙CO2, and Vt were captured along the F1 axis, while cadence and heart rate variances were primarily captured along the F2 axis. Notably, when distance and physiological responses were projected simultaneously on the PCA correlation circle, the first 26 km of the race were positioned on the same side of the F1 axis as the metabolic responses, whereas the final kilometers were distributed on the opposite side, indicating a shift in physiological state as fatigue set in. The separation of heart rate and cadence entropy variances from the metabolic parameters suggests that these responses are independent of distance, contrasting with the linear increase in heart rate and decrease in cadence typically observed. Additionally, Agglomerative Hierarchical Clustering further categorized runners' physiological responses, revealing distinct clusters of entropy profiles. The analysis identified two to four classes of responses, representing different phases of the marathon for individual runners, with some clusters clearly distinguishing the beginning, middle, and end of the race. This variability emphasizes the personalized nature of physiological responses and pacing strategies, reinforcing the need for individualized approaches. These findings offer practical applications for optimizing pacing strategies, suggesting that real-time monitoring of entropy could enhance marathon performance by providing insights into a runner's physiological state and helping to prevent the onset of hitting the wall.

B - 62 Cognitive Training Targeting Reward Responsiveness Promotes more Efficient Affective Processing in an Interference Go/NoGo Task

Abstract Objective Abnormalities in emotional processing are commonly seen in depression and anxiety, disorders also associated with decreased reward sensitivity i.e., anhedonia. We tested a noninvasive, cognitive training program aimed at boosting reward responsiveness and gaged if it may also improve affective processing efficiency. Methods Individuals (N = 50; 54% Female; 18–65 yo) with low hedonic tone (Positive and Negative Affect Scale/PANAS score &amp;lt; 25) and diagnostic levels of depression and/or anxiety (Patient Health Questionnaire-8/PHQ-8 or General Anxiety Disorder-7/GAD-7 score ≥ 10) who responded to community-posted flyers completed an affective go/no-go (AGN) task before and after completing an exploratory reward learning task, i.e., a multi-arm bandit (MAB). Participants were randomly assigned to a high-reward MAB training with either high variance (HV) or low variance (LV) in reward rates, HV being expected to boost reward maximization. In the AGN, participants saw negative and positive pictures and had to respond as fast as possible to pictures of a given target category (positive or negative). Results Following the HV vs LV training MAB, participants were more accurate (higher likelihood of hits), which was more evident in negative target blocks relative to positive target blocks (odds ratio/OR:0.61, 95% CI = [0.38,0.98]; z = −2.045, p = 0.041). Participants exhibited stronger decreases in hits response times following the HV vs LV training MAB, but across all target blocks (B:-37 ms, 95% CI = [−56,-18]; t = −3.806, p &amp;lt; 0.001). Conclusions A novel cognitive training program aimed at boosting reward sensitivity may help improve patients’ processing of emotionally taxing stimuli. Targeting reward sensitivity is a critical component of improving clinical outcomes in depressed and anxious patients..

Psychophysiological distinctions in emotional responding: sensitivity to perceiving loss of connection.

Emotion involves oneself in relation to a subject of attention; e.g., sadness is to be sad about something/someone. This study examined emotional responses to perceiving a loss of connection from oneself. Evidence suggests that Europeans tend to perceive salient objects in the foreground, while East Asians are more likely to perceive holistically, considering the interrelationships between the context and the object. We studied how this distinction affected European Americans' (EA) and Chinese Americans' (CA) sensitivity to perceiving the loss of connection. Both groups were exposed to loss by playing Cyberball, a ball-tossing video game, and then watched a film clip on grief. We hypothesized that EA would respond with increasing heart rate (HR) variance around the mean when perceiving loss. CA were predicted to show no difference from controls. We also hypothesized that EA would feel sadder, in terms of decreased HR and increased respiratory sinus arrhythmia (RSA), earlier during the film clip. In total, 53 subjects were recruited, of which 40 were EA (47.5% women, age 21.08 ± 1.94 years) and 13 were CA (61.5% women, age 21.05 ± 1.74 years); 25 subjects (19 EA, 6 CA) received 2 out of 48 balls tossed in Cyberball and the controls received 10. ECG, respiration, and facial electromyography (fEMG) data were acquired. The results during Cyberball showed that EA's HR variance relative to baseline (HR SDc/b) had an upward trend on perceiving loss. Contrary to prediction, CA also showed higher levels of HR variance relative to baseline. The ANOVA of HR SDc/b revealed that the interaction effect of two factors, time and condition, was statistically significant (p = 0.009). However, as predicted, EA in the experimental condition had decreased HR and increased RSA, a sign of withdrawal in sadness, 30 to 60 s into the sad clip. fEMG data at the corrugator muscle revealed that EA activated higher peak intensity 5.5 s earlier than CA (increased 1.571 vs. 0.844). This difference, however, was not statistically significant. The evidence suggests that increased exposure to loss automatically led to increased HR variance in both groups even when subjects were informed that players were computer-generated. However, the effect was stronger on EA to increase their arousal and sensitivity to grief thereafter.

Predictive Modeling of Sleep Slow Oscillation Emergence on the electrode manifold: Toward Personalized Closed-Loop Brain Stimulation.

Sleep slow oscillations (SOs), characteristic of NREM sleep, are causally tied to cognitive outcomes and the health-promoting homeostatic functions of sleep. Due to these known benefits, brain stimulation techniques aiming to enhance SOs are being developed, with great potential to contribute to clinical interventions, as they hold promise for improving sleep functions in populations with identified SO deficits (e.g., mild cognitive impairment). SO-targeting closed-loop stimulation protocols currently strive to identify SO occurrences in real time, a computationally intensive step that can lead to reduced precision (compared to post-hoc detection). These approaches are also often limited to focusing on only one electrode location, thus inherently precluding targeting of SOs that is informed by the overall organization of SOs in space-time. Prediction of SO emergence across the electrode manifold would establish an alternative to online detection, thus greatly advancing the development of personalized and flexible brain stimulation paradigms. This study presents a computational model that predicts SO occurrences at multiple locations across a night of sleep. In combination with our previous study on optimizing brain stimulation protocols using the spatiotemporal properties of SOs, this model contributes to increasing the accuracy of SO targeting in brain stimulation applications. SOs were detected in a dataset of nighttime sleep of 22 subjects (9 females), acquired with polysomnography including 64 EEG channels. Modeling of SO occurrence was achieved for SOs in stage N3, or in a combination of stages N2 and N3 (N2&N3). We study SO emergence at progressively more refined time scales. First, the cumulative SO occurrences in successive sleep cycles were successfully fit with exponentials. Secondly, the SO timing in each individual was modeled with a renewal point process. Using an inverse Gaussian model, we estimated the probability density function of SO timing and its parameters μ (mean) and λ (shape, representing skewness) in successive cycles. We observed a declining trend in the SO count across sleep cycles, which we modeled using a power law relationship. The decay rate per cycle was 1.473 for N3 and 1.139 for N2&N3, with variances of the decay rates across participants being 1 and 0.53, respectively. This pattern mirrors the declining trend of slow wave activity (SWA) across sleep cycles, likely due to the inherent relationship between SWA and SO. Additionally, the SO timing model for N3 showed an increasing trend in the model parameters (μ, λ) across cycles. The increase rate per cycle followed a power law relationship with a rate of 0.83 and an exponential relationship with a rate of 4.59, respectively. The variances of the increase rates were 0.02 for μ and 0.44 for λ across participants. This study establishes a predictive model for SO occurrence during NREM sleep, providing insights into its organization in successive cycles and at different EEG channels, which is relevant to development of personalized stimulation paradigms. These findings imply that personalized model parameters can be estimated by incorporating SO information in the first sleep cycle, and hence SO timing can be predicted before its occurrence with a probability distribution, enabling more precise targeting of SOs.

Optimization Study on Sequential Emptying and Dredging for Water Diversity Reservoir Group

Reservoir sediment severely impacts water supply in water-scarce regions, making reservoir dredging an urgent global issue. The investment required for deep-water dredging far exceeds that for dry land dredging. Therefore, against the backdrop of the national water network construction, this study focuses on a typical inter-basin water transfer project in Northern China. To increase the proportion of dry land dredging volume and save costs, this study uses compensation reservoirs to replace the emptied reservoir in undertaking water supply tasks as a constraint. Single-objective optimization models for single reservoirs and multi-objective optimization models for reservoir groups are established, using game theory comprehensive subjective and objective weighting methods to select the optimal solution. The following conclusions are drawn from comparing the water supply effects under various emptying sequences: the optimal sequence for emptying reservoirs should be determined through precise quantitative analysis; as the dredging is completed, the water supply tends to stabilize; the satisfaction with the water supply and the variance of the water shortage rate are primarily related to reservoirs with a large inflow and storage capacity; dredging occurs according to the descending order of the storage capacity of reservoirs; and the startup proportion of pump stations shows an increasing trend.