Variable Sampling Research Articles

Sexual harassment among higher education students: a worldwide review

Abstract Introduction Sexual harassment is an uprising problem in higher education systems worldwide with major impacts on students. The aim of this study was to describe and meta-analyze quantitative data on sexual harassment prevalence among higher education students. Methods We searched in seven databases and included studies published in English and French from 2000 until 2023 if they measured sexual harassment prevalence on students attending higher education, and they were quantitative cross-sectional studies. Prevalence estimates disaggregated by gender identity, and world region were meta-analyzed using a random-effects model and reported following PRISMA guidance. Results We identified 53 articles, from 6 different regions of the world, with highly variable sample sizes, ranging from 164 to 779,170. 45% of the samples were from the United States. 32% of studies used convenience or purposive sampling and 39% random sampling. The meta-analyzed prevalence of sexual harassment was globally 36.9% (95% CI [30.6, 43.6], 43.7% for women, 16.8% for men, 33.4% for non-binary. In additional, variations in prevalence were observed across the different world regions. In particular, the Eastern Mediterranean region presented the highest prevalence estimates for sexual harassment among women, reaching 67.5%, while the Americas region presented the highest prevalence estimates for sexual harassment among men, at 18.8%. Discussions Higher education institutions, especially in low- and middle-income countries, should commit to the conduct of surveys to monitor the prevalence of sexual harassment, and policymakers should allocate specific funds and resources to support the implementation of sexual harassment prevention and intervention initiatives in higher education, prioritizing the safety and well-being of students. Key messages • High prevalence of sexual harassment in higher education underscores urgent need for proactive measures to ensure student safety and well-being. • Policymakers should allocate specific funds and resources to support the implementation of SH prevention and intervention initiatives in higher education.

Impact of the Extent of Population Aging on Economic Development and Countermeasure

With the comprehensive prosperity and development of human society, human life expectancy shows a rising trend, fertility rate shows a declining trend, the world is facing a severe problem of population aging, the ensuing economic impact is the focus we need to pay attention to. Taking various countries and regions in the world from 1960 to 2019 as samples, this paper conducted an empirical test on transnational panel data and found that old age dependency would significantly hinder economic growth. The conclusion remained valid after the robustness test of replacing explanatory variables, instrumental variable method and changing sample interval. Further study found that old-age dependency ratio will also significantly reduce consumption expenditure. Based on this, this paper gets inspiration and puts forward corresponding policy suggestions to mitigate the adverse impact of population aging on China's social and economic development.

Variable Parameters t Chart: An Application in Automobile Engine Piston Rings Manufacturing Process

The [Formula: see text] type charts possess a drawback as they may falsely indicate out-of-control (OOC) situations when the process standard deviation is unstable. They are not robust against estimation errors or variations in the process standard deviation. To overcome this setback, the [Formula: see text] type charts were developed. This research introduces the variable parameters (VP) [Formula: see text] chart for effectively monitoring the process mean. This study encompasses the implementation, optimal designs and performance evaluation of the proposed VP [Formula: see text] chart. The average time to signal (ATS), standard deviation of the time to signal (SDTS) and expected average time to signal (EATS) criteria are used to assess and compare the performance of the VP [Formula: see text] chart with existing charts, such as Shewhart (SH) [Formula: see text], synthetic (Syn) [Formula: see text], side-sensitive group runs (SSGR) [Formula: see text], exponentially weighted moving average (EWMA) [Formula: see text] and variable sampling interval (VSI) EWMA [Formula: see text] charts. From extensive analysis, we observe that the VP [Formula: see text] chart outperforms the SH [Formula: see text], Syn [Formula: see text] and SSGR [Formula: see text] charts for all sizes of mean shifts. However, the EWMA [Formula: see text] and VSI EWMA [Formula: see text] charts exhibit better performance for small shifts. Additionally, to demonstrate the implementation and usage of the proposed VP [Formula: see text] chart, we present a real-world manufacturing example.

Bayesian control chart using variable sample size with engineering applications

In this study, we suggested an innovative approach by introducing an Adaptive Exponential Weighted Moving Average (AEWMA) control chart utilizing Variable Sample Size (VSS) under Bayesian methodology. The proposed methodology utilized an integer linear function to dynamically adjust sample sizes according to the AEWMA statistic. Another appealing feature of our adaptive framework is the integration of the smoothing constant of an EWMA chart, which enhances monitoring responsiveness. We reveal the superiority of our recommended control chart by extensive simulations to existing Bayesian EWMA and Bayesian AEWMA control charts using Fixed sample size (FSS). The offered Bayesian VAEWMA control chart is more sensitive to detection improvement, a decrease in the false alarm rate, and overall more effective than the existing methods. These findings provide additional justification for the basic notion that process control statistical tools needed to be dynamic, as the manufacturing process itself was dynamic. The results suggest the importance of introducing adaptive SPC methods in dynamic manufacturing environments. A real data application is performed to evaluate the validity and optimal performance of our recommended chart."Please check article if captured correctly."="Dear Editor we have checked and found corrrect."As per standard instruction, city is required for affiliations; however, this information is missing in affiliations [1, 5]. Please check if the provided city is correct and amend if necessary."Dear Editor we have checked and found correct. thanks youPlease check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary."Dear Editor we have checked and found correct. thank you"

Improving bird abundance estimates in harvested forests with retention by limiting detection radius through sound truncation

Abstract An inherent challenge with acoustically surveying birds is that the distance at which they can be detected depends on how far their song can be heard. We developed a distance-based sound detection space truncation method to correct for variable sampling radii due to surveying in forested or open conditions. The method was pivotal in evaluating bird responses to retention patches; without this methodological advancement, the impact of retention patches on songbird abundance was vastly underestimated. In the boreal forest, these patches of live trees are retained in regenerating harvested forests to provide ecological services for species adapted to natural disturbances. Although we did not verify our a priori assumption with ground observations, our findings suggest that limited-distance sampling better captures the effects of retention patches on bird use of harvested forests. When evaluated using unlimited distance surveys, retained trees had a negligible effect on bird abundance, whereas applying detection distance truncation highlighted the importance of retention on forest birds. We found that early to mid-seral forest songbirds benefited from retention patches, with notable increases in abundance after 10 years of regeneration. The size of retention patches, ranging from 0.1 to 1.2 hectares, did not have a linear relationship with bird abundance. Instead, edge effects stemming from the configuration of these patches emerged as key determinants of abundance for the majority of the species studied. Retention patches that were nearest to unharvested forests were used the most, compared to further into harvest areas. Our research not only highlights the underestimated impact of small-scale live tree retention on forest songbirds but also introduces a significant methodological innovation in the field of acoustic monitoring.

Evaluation of the effectiveness and safety of Korean red ginseng extract as immune-enhancer in Vietnamese adults: A randomized placebo-controlled trial

Purpose: To investigate the safety and immune-enhancing effect of Korean red ginseng (KRG) extract in Vietnamese adults. Methods: Participants in this randomized, placebo-controlled double-blinded study were administered either 960 mg of KRG extract (n = 51) or placebo capsules (n = 50) for 12 weeks at Hanoi Medical University, Vietnam. The KRG extract was standardized to contain 5.27 mg of 3 ginsenosides (Rg1, Rb1, and Rg3) per gram. Blood samples for assessment of treatment effectiveness were collected from the subjects on days 21 and 84. Immune cytokines were quantified using enzyme-linked immunosorbent assay (ELISA). Blood samples for safety assessment, hematological and biochemical variables, and urinalysis were performed at the beginning and end of the intervention (days 21 and 84, respectively). Results: The KRG group showed a significant increase in lymph T cell % at the end of the 12-week intervention (p < 0.001). There were significant decreases in numbers of white blood cells (WBC) and natural killer (NK) cells in both groups. However, there were no significant differences in populations of WBC and NK cells between the 2 groups. Serum concentrations of cytokines, i.e., TNF-α, β, γ; IFN- α, γ; IL-1β and 4β were decreased in the KRG group when compared to baseline values. There were no probable or definite adverse events (AEs) related to KRG. A total of 9 participants (4 in KRG group and 5 in placebo group) tested positive for COVID-19 during the trial. Conclusion: The KRG extract-induced increase in population of T-cells and reductions in cytokine levels following a 12-week exposure may be beneficial for COVID-19 patients.

Soil sampling design matters - Enhancing the efficiency of digital soil mapping at the field scale

Optimisation of sampling design (methods chosen to select the samples) and sample size (number of samples) remains a key challenge in digital soil mapping, especially in the area of precision farming with the expected economic benefits from the introduction of new technologies. As the existing information is available in the form of relevant environmental covariates, its combination with non-parametric machine learning techniques requires careful planning from the initial field sampling to the final production of digital soil maps. The aim of this study is to compare widely used covariate-wise sampling designs combined with variable sample sizes for supervised prediction of common soil drivers of agricultural productivity (pH, soil organic carbon, soil macronutrients) in a real case study of a field (35 ha) with heterogeneous soil properties. From a total of 200 samples, we evaluated different sample sets where 10, 30 and 60 field samples were selected by conditioned Latin Hypercube Sampling (cLHS) and Feature Space Coverage Sampling (FSCS) to calibrate random forest (RF) models. The evaluation was performed on independently in-situ sampled test points. In addition to these datasets, we also compared the investigated methods with Simple Random Sampling (SRS) in a numerical benchmark experiment with increasing sample size, comparing the global accuracies of the predicted maps on the test points, but using interpolated maps as the artificial true population for each soil characteristic. The results of the study in both the field experiment and the numerical experiment showed slightly better results for the FSCS method, especially when the number of samples was small. At smaller training sample sizes, the risk of insufficiently accurate prediction models was slightly lower for FSCS and the difference decreased as the sample size increased. Nevertheless, sample size proved to be the most important factor in the accuracy of RF models, regardless of the sampling technique. The results suggest that a sample size between 18 and 30 training samples (0.6 to 1 sample ha−1) seems plausible for covariate-wise predictions using RF at field scale in our case study. The relative importance of each auxiliary variable for each RF calibration was also assessed for the field experiment. The results showed that the additional introduction of spatial proxies overshadowed the importance of other covariates, but only significantly improved the model calibration at larger sample sizes. The calibrated models without spatial proxies showed the strongest effect of remotely sensed surface characteristics.

12468 Loss Of Enterococcus And Faecalibacterium Associate With Polycystic Ovary Syndrome And Hirsutism In A Sample Of Hispanic Patients

Abstract Disclosure: L.A. Gonzalez-Rodriguez: Research Investigator; Self; Eli Lilly & Company. C.M. Casas Loyola: None. C.I. Martinez-Hernandez: None. L. El Musa Penna: None. J. Romaguera: None. F. Godoy-Vitorino: None. Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age. It is characterized by symptoms such as irregular menstrual periods, excess androgen levels and polycystic ovaries. Women with PCOS often display insulin resistance independent of fat and have been associated with an increased risk of diabetes mellitus, cardiovascular disease, and mood disorders. The gut microbiota, the community of microbes living in the human gastrointestinal tract, plays a crucial role in human health and has been implicated in various diseases, including metabolic and inflammatory conditions. We aimed to characterize the gut microbiota in a sample of Hispanic women living in Puerto Rico (n=50). Of these 38 had confirmed PCOS as per Rotterdam criteria and 12 were controls. A stool sample was collected for genomic DNA extractions followed by amplification and sequencing of 16S rRNA genes (V4 region) with Illumina MiSEQ. Data analyses were performed with standard pipelines with variables sample group (PCOS vs Controls), insulin resistance assessed using HOMA-IR and hirsutism status according to modified-Ferriman Gallwey (mFG) score and controlled for age and other clinical variables. Despite there were no changes in alpha or beta diversity among the microbiota of these groups, composition was significantly different. Women with PCOS had significantly lower levels of Enterococcus species as well as Hungatella, taxa that are part of a physiologically normal gut flora. Enterococcus species contribute to the balance of the microbial community and play roles in the metabolism and immune function and are very resilient. Hungatella species are a known source of catabolic enzymes that degrade glycosaminoglycans which are important in tissue repair. These dominant taxa in controls was substituted by other taxa in PCOS. Women with prominent hirsutism had significantly lower levels of Faecalibacterium while those with insulin resistance had increased levels of Clostridium. We found that higher levels Clostridium, and lower levels of protective anti-inflammatory taxa such as Faecalibacterium or Enterococci, which are important for gut health and metabolic regulation, are linked to this metabolic disturbance. Even though this is a preliminary report, it opens potential therapeutic strategies, such as the use probiotics, and dietary modifications to modulate the gut microbiome and improve PCOS outcomes. Presentation: 6/1/2024

12468 Loss Of Enterococcus And Faecalibacterium Associate With Polycystic Ovary Syndrome And Hirsutism In A Sample Of Hispanic Patients

Abstract Disclosure: L.A. Gonzalez-Rodriguez: Research Investigator; Self; Eli Lilly & Company. C.M. Casas Loyola: None. C.I. Martinez-Hernandez: None. L. El Musa Penna: None. J. Romaguera: None. F. Godoy-Vitorino: None. Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age. It is characterized by symptoms such as irregular menstrual periods, excess androgen levels and polycystic ovaries. Women with PCOS often display insulin resistance independent of fat and have been associated with an increased risk of diabetes mellitus, cardiovascular disease, and mood disorders. The gut microbiota, the community of microbes living in the human gastrointestinal tract, plays a crucial role in human health and has been implicated in various diseases, including metabolic and inflammatory conditions. We aimed to characterize the gut microbiota in a sample of Hispanic women living in Puerto Rico (n=50). Of these 38 had confirmed PCOS as per Rotterdam criteria and 12 were controls. A stool sample was collected for genomic DNA extractions followed by amplification and sequencing of 16S rRNA genes (V4 region) with Illumina MiSEQ. Data analyses were performed with standard pipelines with variables sample group (PCOS vs Controls), insulin resistance assessed using HOMA-IR and hirsutism status according to modified-Ferriman Gallwey (mFG) score and controlled for age and other clinical variables. Despite there were no changes in alpha or beta diversity among the microbiota of these groups, composition was significantly different. Women with PCOS had significantly lower levels of Enterococcus species as well as Hungatella, taxa that are part of a physiologically normal gut flora. Enterococcus species contribute to the balance of the microbial community and play roles in the metabolism and immune function and are very resilient. Hungatella species are a known source of catabolic enzymes that degrade glycosaminoglycans which are important in tissue repair. These dominant taxa in controls was substituted by other taxa in PCOS. Women with prominent hirsutism had significantly lower levels of Faecalibacterium while those with insulin resistance had increased levels of Clostridium. We found that higher levels Clostridium, and lower levels of protective anti-inflammatory taxa such as Faecalibacterium or Enterococci, which are important for gut health and metabolic regulation, are linked to this metabolic disturbance. Even though this is a preliminary report, it opens potential therapeutic strategies, such as the use probiotics, and dietary modifications to modulate the gut microbiome and improve PCOS outcomes. Presentation: 6/1/2024

Spatiotemporal coupling deep neural network for time-resolved flow field reconstruction around a circular cylinder

We propose a spatiotemporal coupling deep neural network approach for time-resolved reconstruction of the velocity field around a circular cylinder. The neural network leverages two distinct data types: (1) non-time-resolved velocity field around the cylinder, consisting of fixed frequency sampling and variable frequency sampling velocity field, and (2) the time-resolved surface pressure sequence around the cylinder. The deep neural network comprises two sub-networks: a convolutional autoencoder (CAE) for nonlinear mode extraction and a Transformer for sequence-to-sequence learning. We refer to this architecture as CTNet (CAE-Transformer Network). The encoder in the CAE maps non-time-resolved velocity field to a latent vector, enabling the extraction of nonlinear modal coefficients. An appropriate time window length for the surface pressure sequence is then selected to establish a Transformer sequence learning model, using the chosen sequence as input to predict the corresponding nonlinear modal coefficients. Once the Transformer is well trained, the time-resolved nonlinear modal coefficients of velocity field can be achieved. Along with the well-trained decoder in the CAE, the time-resolved velocity field can be reconstructed from the output of the Transformer. We verify the performance of CTNet by a simulated dataset at a representative Reynolds number of 3900. The results show a relative reconstruction error of just 6.3% for the time-resolved velocity field, demonstrating high reliability in the reconstruction. We further compare the reconstructed velocity field obtained with and without the utilization of variable frequency sampling velocity field. Notably, the inclusion of variable frequency sampling velocity field significantly improves the reconstruction quality.

Dynamic uncertainty evaluation of cylindricity error based on Bayesian deep neural network propagation method

Most of the existing methods for measuring and evaluating product geometric tolerances are for static processes, which use lower dimensions, are more complex to calculate, and have poor traceability and dynamic manageability and control of the measurement process. In this paper, a dynamic evaluation model of cylindricity error uncertainty based on the Bayesian deep neural network propagation method is proposed. Firstly, the input random variable sampling sample containing error uncertainty is generated by proposing the improved Monte Carlo sampling method (IMCS), which has the characteristics of homogeneity and validity; then the principle and construction process of a Bayesian neural network model are introduced to generate the propagation model of multidimensional random error variables; further, the Gaussian mixture model is trained by the Monte Carlo method, and the probability density function of the target response is generated by using the Gaussian mixture model. Through numerical experiments, test analysis of bearing outer rings, and comparison with ISO standard methods, the results show that the model based on the dynamic Bayesian deep neural network propagation method can correctly and effectively assess the uncertainty dynamically, and the whole estimation process is stable.

Machine learning-enabled techno-economic uncertainty analysis of sustainable aviation fuel production pathways

Stochastic techno-economic analysis (TEA) is pivotal in assessing the financial viability and risks inherent in biofuel production processes. In this method, the Monte Carlo approach entails the random sampling of input variables and multiple runs of the TEA model to create probability distributions of economic metrics. However, traditional Monte Carlo TEA, reliant on iterative calls to process simulation, is resource-intensive and time-consuming, hindering widespread adoption. To address these challenges, we present an accessible framework that harnesses machine learning methods to estimate techno-economic uncertainty in biofuel production pathways. Our approach streamlines the conventional simulation process by automating dataset generation and machine learning model training. These trained models enable rapid predictions of minimum fuel selling prices at any scale, accommodating randomized input variables based on their defined distributions. We illustrate the efficacy of our framework through examples from sustainable aviation fuel production pathways. Our research entails identifying the primary factors influencing uncertainties in minimum selling prices, exploring the synergistic effects of pathway inputs, and assessing how price variability is impacted by financial, technical, and supply chain factors. These examples underscore the framework's effectiveness in addressing breakeven price uncertainties in biofuel production across diverse input scenarios.

Jump around: Selecting Markov Chain Monte Carlo parameters and diagnostics for improved food web model quality and ecosystem representation

Capturing ecological data variability in food web models is an important step for improving model representation of empirical systems. One approach is to use linear inverse modelling and Markov Chain Monte Carlo (LIM-MCMC) techniques to set up an inverse LIM problem using empirical data constraints, and then sample multiple plausible food webs from the inverse problem using an MCMC algorithm. We describe the set of plausible food webs as an ‘ensemble’ of solutions to the inverse problem sampled with the LIM-MCMC algorithm. The extent of data variability eventually integrated into an ensemble depends on how well the LIM-MCMC algorithm samples the solution space. Algorithm quality can be adjusted via user-defined parameters describing starting points, jump sizes, and number of iterations or food webs produced. However, little information exists on how each LIM-MCMC algorithm parameter affects the degree of empirical data variability introduced into the ensemble. Further, post hoc algorithm quality diagnostics with commonly used trace plots and the coefficient of variation (CoV) rarely address critical aspects of algorithm quality, such as (1) if the returned ensemble successfully targeted the solution space distribution (stationarity), (2) correlation between ensemble solutions (mixing), and (3) if the ensemble contains enough solutions to adequately capture input data variability (sampling efficiency). Therefore, we used several established MCMC convergence diagnostics to (1) quantify how algorithm parameters affect ensemble flow values and if these differences propagate to ecological indicators and (2) evaluate algorithm quality and compare to current evaluation and ecosystem modelling methods. We applied 30 LIM-MCMC algorithm combinations of varying starting points, jump sizes, and number of iterations to solve food web ensembles from a single food web model. We analysed ensembles with Ecological Network Analysis (ENA) to calculate indicators describing system function. Results show that LIM-MCMC algorithm parameters, in particular the jump size, affect ensemble flow values, which propagate to ecological indicators describing different ecosystem function of the same model. Thereafter, comparisons of post hoc diagnostics show that MCMC convergence diagnostics provided more robust estimates of algorithm quality than trace plots and CoV. Together, these findings underpin several novel recommendations to enhance LIM-MCMC algorithm parameter selection and quality assessments applicable to any ecological ensemble network study.

Effects of aerobic and anaerobic training on freedivers' performance.

Freediving is a sport that could be defined as both aerobic and anaerobic. Therefore, this study aimed to evaluate the effect of aerobic-anaerobic training on the performance of moderate and elite freedivers. The sample of participants included 26 freedivers (9 females) (average age of 26.62±3.34 years, body height of 178.95±9.19 cm, and body mass of 74.64±11.97 kg). The sample of variables included: anthropometric indices, relative maximal oxygen consumption (rVO<inf>2max</inf>), Diving anaerobic sprint test (DAST), Swimming anaerobic sprint test (SAST), 100-meter crawl sprint test (100 m), maximal dynamic apnea with monofin (DYN), maximal swimming length for 2 minutes (2 min). The study procedure included a 5-month aerobic-anaerobic training intervention. This intervention was conducted during a 4-phase (5-week each) training period. The results showed that moderate group showed a significant decrease in 100 m (final 83.94±15.68; initial 88.29±16.73; P<0.00), DAST<inf>max</inf> (final 10.91±1.46; initial 12.01±1.38; P<0.00), DAST (final 70.29±8.95; initial 79.40±10.25; P<0.00), SAST<inf>max</inf> (final 16.81±2.24; initial 18.01±2.69; P<0.00), SAST(final 112.87±19.19; initial 122.65±21.55; P<0.00), and increase in 2 min (final 140.56±21.53; initial 128.68±19.33; P<0.00), and DYN (final 130.48±26.89; initial 91.65; P<0.00). Similarly, the elite group experienced a decrease in 100 m (final 72.18±9.77; initial 75.00±11.36; P=0.02), DAST<inf>max</inf> (final 10.14±0.95; initial 10.88±0.99; P=0.03), DAST (final 65.55±6.50; initial 71.24±7.32; P=0.02), SAST<inf>max</inf> (final 14.82±1.84; initial 15.76±1.80; P=0.03), and increase in DYN (final 194.94±27.70; initial 161.11±27.70; P<0.00). The results of this study demonstrate that dynamic apnea, as a main performance factor, had increase in all phases of procedure, with highest increase during anaerobic phases.

Searching for new cataclysmic variables in the Chandra Source Catalog

Aims. Cataclysmic variables (CVs) are compact binary systems in which a white dwarf accretes matter from a Roche-lobe-filling companion star. For this study we searched for new CVs in the Milky Way in the Chandra Source Catalog v2.0, cross-matched with Gaia Data Release 3 (DR3). Methods. We identified new CV candidates by combining X-ray and optical data in a color-color diagram called the X-ray main sequence. We used two different cuts in this diagram to compile pure and optically variable samples of CV candidates. We undertook optical spectroscopic follow-up observations with the Keck and Palomar Observatories to confirm the nature of these sources. Results. We assembled a sample of 25 887 Galactic X-ray sources and found 14 new CV candidates. Seven objects show X-ray and/or optical variability. All sources show X-ray luminosity in the 1029 − 1032 erg s−1 range, and their X-ray spectra can be approximated by a power-law model with photon indices in the Γ ∼ 1 − 3 range or an optically thin thermal emission model in the kT ∼ 1 − 70 keV range. We spectroscopically confirmed four CVs, discovering two new polars, one low accretion rate polar and a WZ Sge-like low accretion rate CV. X-ray and optical properties of the other nine objects suggest that they are also CVs (likely magnetic or dwarf novae), and one other object could be an eclipsing binary, but revealing their true nature requires further observations. Conclusions. These results show that a joint X-ray and optical analysis can be a powerful tool for finding new CVs in large X-ray and optical catalogs. X-ray observations such as those by Chandra are particularly efficient at discovering magnetic and low accretion rate CVs, which could be missed by purely optical surveys.

Optimal and economic design of variables resampling scheme based on double specification limits

In this manuscript, designing methodologies of a variables sampling plan (VSP) for resubmitted lots are proposed for the application of measurable quality characteristics having double specification limits. The optimal plan parameters of the proposed variables resampling scheme (VRS) are obtained for both known and unknown sigma (standard deviation) cases and satisfy both the producer’s risk and consumer’s risk simultaneously at their respective quality levels. Two types of fraction nonconforming levels namely symmetric fraction nonconforming and asymmetric fraction nonconforming based on the double specification limits are also considered for determining the optimal plan parameters. A nonlinear optimization problem is formulated to design the optimal plan parameters. Merits of the proposed VRS are also investigated. A real-life application of the proposed sampling scheme is explained. In addition, an economic aspect of design of the VRS based on double specification limits is investigated.

Variational Autoencoders and Generative Adversarial Networks for Multivariate Scenario Generation

When making decisions with lasting implications over a medium to long timeframe, it is essential to consider not only the most probable scenario, possibly obtained through a forecasting model, but also a range of potential outcomes. This approach allows for effective risk mitigation across a spectrum of scenarios, including less probable ones, and enhances the resilience of planning strategies. In this paper, we demonstrate the development of a generative model capable of learning the multivariate joint probability distribution of link speeds on a road network, using real sensor data. To further enhance the performance of our Generative Adversarial Network model, we employed a Variational AutoEncoder for pre-training the generator network. Experimental results, conducted on three distinct benchmark datasets, highlight the potential of the proposed model in generating new scenario samples of multivariate variables. The Wasserstein distance between the generated distribution and the real data, confirms the good performance of our model compared to state-of-the-art models, based on copulae. The proposed model has shown its ability to generate scenarios that preserve correlations among variables, while producing samples that faithfully represent the empirical marginal distributions.

From gut to brain: unveiling probiotic effects through a neuroimaging perspective-A systematic review of randomized controlled trials.

The gut-brain axis, a bidirectional communication network between the gastrointestinal system and the brain, significantly influences mental health and behavior. Probiotics, live microorganisms conferring health benefits, have garnered attention for their potential to modulate this axis. However, their effects on brain function through gut microbiota modulation remain controversial. This systematic review examines the effects of probiotics on brain activity and functioning, focusing on randomized controlled trials using both resting-state and task-based functional magnetic resonance imaging (fMRI) methodologies. Studies investigating probiotic effects on brain activity in healthy individuals and clinical populations (i.e., major depressive disorder and irritable bowel syndrome) were identified. In healthy individuals, task-based fMRI studies indicated that probiotics modulate brain activity related to emotional regulation and cognitive processing, particularly in high-order areas such as the amygdala, precuneus, and orbitofrontal cortex. Resting-state fMRI studies revealed changes in connectivity patterns, such as increased activation in the Salience Network and reduced activity in the Default Mode Network. In clinical populations, task-based fMRI studies showed that probiotics could normalize brain function in patients with major depressive disorder and irritable bowel syndrome. Resting-state fMRI studies further suggested improved connectivity in mood-regulating networks, specifically in the subcallosal cortex, amygdala and hippocampus. Despite promising findings, methodological variability and limited sample sizes emphasize the need for rigorous, longitudinal research to clarify the beneficial effects of probiotics on the gut-brain axis and mental health.

Sensitivity Analysis of Fatigue Life for Cracked Carbon-Fiber Structures Based on Surrogate Sampling and Kriging Model under Distribution Parameter Uncertainty

The quality and reliability of wind turbine blades, as core components of wind turbines, are crucial for the operational safety of the entire system. Carbon fiber is the primary material for wind turbine blades. However, during the manufacturing process, manual intervention inevitably introduces minor defects, which can lead to crack propagation under complex working conditions. Due to limited understanding and measurement capabilities of the input variables of structural systems, the distribution parameters of these variables often exhibit uncertainty. Therefore, it is essential to assess the impact of distribution parameter uncertainty on the fatigue performance of carbon-fiber structures with initial cracks and quickly identify the key distribution parameters affecting their reliability through global sensitivity analysis. This paper proposes a sensitivity analysis method based on surrogate sampling and the Kriging model to address the computational challenges and engineering application difficulties in distribution parameter sensitivity analysis. First, fatigue tests were conducted on carbon-fiber structures with initial cracks to study the dispersion of their fatigue life under different initial crack lengths. Next, based on the Hashin fatigue failure criterion, a simulation analysis method for the fatigue cumulative damage life of cracked carbon-fiber structures was proposed. By introducing uncertainty parameters into the simulation model, a training sample set was obtained, and a Kriging model describing the relationship between distribution parameters and fatigue life was established. Finally, an efficient input variable sampling method using the surrogate sampling probability density function was introduced, and a Sobol sensitivity analysis method based on surrogate sampling and the Kriging model was proposed. The results show that this method significantly reduces the computational burden of distribution parameter sensitivity analysis while ensuring computational accuracy.

Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise

ABSTRACTIn addressing the quantization noise challenge in high impedance fault (HIF) localization within resonant distribution networks, we propose a cutting‐edge, explainable deep learning approach that significantly advances existing methods. This approach utilizes differential zero‐sequence voltage (DZSV) and zero‐sequence current (ZSC) and introduces a novel “Vague” classification to improve localization accuracy by effectively managing quantization noise‐distorted signals. This approach extends beyond the conventional binary classification of “Fault” and “Sound,” incorporating a multi‐scale feature attention (MFA) mechanism for enriched internal explainability and applying gradient‐weighted class activation mapping (Grad‐CAM) to visualize critical input areas precisely. Our model, validated in an industrial prototype, exhibits unparalleled adaptability across various environmental conditions, including environmental noise, variable sampling rates, and triggering deviations. Comparative analysis reveals that our approach outperforms existing methods in managing diverse fault scenarios.