Sequential Sampling Method Research Articles

Relationship between sleep quality during pregnancy and postpartum anxiety: a prospective birth cohort study

The present study aimed to determine the relationship between sleep quality during pregnancy and postpartum anxiety.A total of 237 pregnant women participated in this birth cohort study, using the sequential sampling method. The data collection tools included a demographic information form, Pittsburgh Sleep Quality Inventory, and Spielberger State-Trait Anxiety Inventory. The PSQI was completed by the participants at 28 and 40 weeks of gestation, and the STAI was completed in two stages, that is, once in the third trimester and once at eight weeks postpartum.The mean global PSQI score of the participants was 8.11 (SD=5.41) in this study. Overall, 138 (58.2%) women had poor sleep quality. The mean score for overt anxiety was 36.58 (SD=9.37), and the mean score for latent anxiety was 37.56 (SD=9.44). There was a significant positive correlation between the total score of sleep quality during pregnancy and the scores of overt anxieties (r=0.314, P<0.001) and latent anxiety (r=0.344, P<0.001) after delivery.The results of the present study indicated a significant relationship between sleep quality during pregnancy and postpartum anxiety. Therefore, it is recommended to evaluate the quality of sleep of pregnant women and design appropriate interventions during pregnancy to reduce postpartum anxiety. Keywords: Sleep; pregnant woman; postpartum period.

An improved sequential importance sampling method for structural reliability analysis of high dimensional problems

Reliability analysis for rare events has proven to be a significant challenge, particularly in non-linear and complex scenarios. Despite the effectiveness of Sequential Importance Sampling (SIS) and Subset Simulation (SUS) in addressing high-dimensional small failure probability problems, the computational burden associated with mechanical simulations for complex structures remains substantial. To address this issue, this paper introduces a novel approach referred to as SIS and Kriging Metamodel Integration (SISAK), designed for computing small failure probabilities in high dimensions. This method involves generating a small number of samples from the original distribution to construct an adaptive metamodel, which is then iteratively optimized using a series of intermediate distributions introduced by SIS. By substituting the function for intermediate distributions with a continuously optimized metamodel, the enhanced approach significantly reduces the computational load associated with mechanical model evaluations compared to the direct use of SIS. This approach does not depend on determining the most probable failure point and does not require consideration of the failure domain shape. Additionally, it inherits the advantages of SIS in addressing high-dimensional small failure probabilities, making it well-suited for structural reliability analysis of nonlinear systems, discontinuous failure domains, cascading functions, and high-dimensional problems.

Sequential and adaptive probabilistic integration for Bayesian model updating

The present study attempts to introduce a Bayesian perspective to marginal likelihood estimation. For this purpose, the Bayesian marginal likelihood inference (BMLI) framework is first developed, where the posterior statistics of the marginal likelihood are estimated using efficient random process sampling. The sequential and adaptive probabilistic integration (SAPI) method is then proposed within the BMLI framework to efficiently infer the posterior distribution of the model parameters as a result of the marginal likelihood estimation. In this method, an adaptive tempering of the likelihood function is presented to draw samples from a series of intermediate distributions to perform probabilistic integration sequentially, and the samples are finally converged to the target posterior domain. An active learning process is also designed to accelerate the sequential integration with a minimum number of likelihood function observations. Four numerical examples are investigated and the superiority of the SAPI method over other sequential sampling methods is demonstrated.

Effect of Gestational Hypertension on Neonatal Hemoglobin Level

This study aims to determine the effect of gestational hypertension on neonatal hemoglobin levels. This cohort study was performed on 150 pregnant women, 60 of whom had gestational hypertension and 90 were healthy. Participants were selected using sequential and quota non-probability sampling methods, respectively. The data were collected from interviews and examination forms. The newborns’ umbilical cords were clamped 30-60 sec after the delivery in both groups. Umbilical cord blood samples were taken to determine neonatal hemoglobin levels and sent to the laboratory immediately. Independent t-test was used to compare the two groups in terms of mean hemoglobin; general linear model with an identical link function was used to compare the two groups in terms of mean hemoglobin, considering the effect of confounding variables. SPSS software version 25 was used for the statistical analysis of the data. The statistical significance level in this study was considered to be 0.05. The mean neonatal hemoglobin level in women with gestational hypertension was significantly higher than that in the healthy group (16.73±1.81 gr/dl vs. 15.56±1.79, P<0.001). This difference remained significant after adjusting for demographic and background variables as well as medical records of the participants (P=0.008). The results revealed the hemoglobin level of newborns of mothers who had gestational hypertension, was higher than newborns of healthy mothers. Therefore, performing proper screening tests and knowledge of the hemoglobin level in these infants routinely helps the healthcare staff to prevent, decide and provide more and more useful services.

Comparison of probabilistic structural reliability methods for ultimate limit state assessment of wind turbines

The probabilistic design of offshore wind turbines aims to ensure structural safety in a cost-effective way. This involves conducting structural reliability assessments for different design options and considering different structural responses. There are several structural reliability methods, and this paper will apply and compare different approaches in some simplified case studies. In particular, the well known environmental contour method will be compared to a more novel approach based on sequential sampling and Gaussian processes regression for an ultimate limit state case study on the maximum flapwise blade root bending moment. For one of the case studies, results will also be compared to results from a brute force simulation approach. Interestingly, the comparison is very different from the two case studies. In one of the cases the environmental contours method agrees well with the sequential sampling method but in the other, results vary considerably. Probably, this can be explained by the violation of some of the assumptions associated with the environmental contour approach, i.e. that the short-term variability of the response is large compared to the long-term variability of the environmental conditions. Results from this simple comparison study suggests that the sequential sampling method can be a robust and computationally effective approach for structural reliability assessment.

نمط الحساسية للمضادات الحيوية من السلالات البكتيرية الممرضة الشائعة المعزولة من المرضى المصابين بعدوى الجهاز التنفسي السفلي (LRTI)

Lower respiratory tract infections LRTIs are quickly becoming the most prevalent infectious disorders affecting people. This investigation focused on characterizing the current shift in the bacterial strains causing the infections of respiratory tract among patients and their antimicrobial sensitivity pattern since antibiotic resistance has developed in all major pathogens. In the microbiology lab of the Department of Biology Sciences / University of Baghdad, a cross- sectional research using non-probability sequential sampling method was carried out. Respiratory sputum samples taken from individuals with lower respiratory tract infections and collected 149 bacterial isolates. M. catarrhalis, S. pneumonia and H. influenzae were the bacterial pathogens diagnosed by VITEK 2 system and then gathered for further investigation. Frequencies along with percentages were calculated for categorical parameters including microorganism, age and gender. Continuous data like age was presented as mean + standard deviation SD. We used chi-square test for data analysis the value of Chi Square was deemed significant when the p value was P<0.05 . The pathogen that was identified most often was M. catarrhalis 60.4 percent, or 90 patients, followed by H. influenza 28.1 %, or 42 patients and S. pneumonia 11.4 %, or 17 patients. Levofloxacin had a greater sensitivity to S. pneumoniae n=17, 11.4%. Meropenem 42 28.2% was more effective against H. influenza. The M. catarrhalis pattern showed a 57% sensitivity to Co-amoxiclav, a 49% sensitivity to Ceftriaxone, and a 34.9% sensitivity to Erythromycin susceptibility. M. catarrhalis, H. influenzae and S. pneumoniae were the most frequently isolated bacteria from LRTIs. We found that the M. catarrhalis strain was highly resistant to moxifloxacin 49.7% and co-trimaxazole 53.7%. Co-amoxiclav resistance in S. pneumoniae was 8.1%, while Moxifloxacin resistance in H. influenzae was 5.4%.

On the Complexity of the Sequential Sampling Method

On the Complexity of the Sequential Sampling Method

Sequential sampling for functional estimation via Sieve

AbstractSequential sampling methods are often used to estimate functions describing models subjected to time‐intensive simulations or expensive experiments. These methods provide guidelines for point selection in the domain to capture maximum information about the function. However, in most sequential sampling methods, determining a new point is a time‐consuming process. In this paper, we propose a new method, named Sieve, to sequentially select points of an initially unknown function based on the definition of proper intervals. In contrast with existing methods, Sieve does not involve function estimation at each iteration. Therefore, it presents a greater computational efficiency for achieving a given accuracy in estimation. Sieve brings in tools from computational geometry to subdivide regions of the domain efficiently. Further, we validate our proposed method through numerical simulations and two case studies on the calibration of internal combustion engines and the optimal exploration of an unknown environment by a mobile robot.

Meta-model based sequential importance sampling method for structural reliability analysis under high dimensional small failure probability

Reliability analysis poses a significant challenge for complex structures with stringent reliability requirements. While Sequential Importance Sampling (SIS) and Subset Simulation (SUS) have proven highly effective in addressing high-dimensional problems with small failure probabilities, the computational burden of mechanical simulations remains substantial due to the time-consuming nature of numerical simulation processes. Consequently, this paper introduces a novel approach, denoted as AK-SIS, which combines SIS with Kriging metamodeling specifically designed to address computational challenges associated with small failure probabilities. The fundamental principle of this approach involves utilizing AK-MCS technology (Echard et al., 2011) [1] as a precursor to the SIS approach to initially generate metamodels. These metamodels are then employed in lieu of performance functions in subsequent steps, significantly reducing the number of function calls required to simulate complex engineering problems when applying SIS techniques directly. By inheriting the advantages of SIS, AK-SIS has demonstrated its suitability for reliability analysis in scenarios involving high-dimensional spaces and small fault probabilities. Furthermore, AK-SIS is not limited by the shape of the failure domain, eliminates the need to solve the design point, and is particularly well-suited for analyzing reliability in cases of discontinuous failure domains, multiple failure domains, as well as complex failure domains and rare events. The efficacy of AK-SIS is substantiated through rigorous evaluation encompassing nonlinear, high-dimensional examples, and an engineering application. These empirical validations collectively contribute to a robust methodological framework for reliability analysis of intricate structures characterized by stringent reliability requirements.

Vitamin D Status in the Adult Population of Romania-Results of the European Health Examination Survey.

Vitamin D deficiency is recognized as a general health condition globally and is acknowledged as a public health concern in Europe. In Romania, a national program of examination of the status of vitamin D for high-risk groups has demonstrated a vitamin D deficiency prevalence of 39.83%. No national data on the status of vitamin D in the general adult population are available to date. We used the framework of the European Health Examination Survey to analyze vitamin D levels in a sample population of adults aged 25-64 years, from 120 family doctors' patients lists, by using a sequential sampling method. Data were weighted to the Romanian population. Vitamin D deficiency was defined as 25(OH)D < 20 ng/mL. In total, 5380 adults aged 25-64 years were included in this study. The overall prevalence of vitamin D deficiency is 24.8%. Predictors of vitamin D deficiency were found to be obesity, female sex, living in rural areas, lower education level, and lower socioeconomic status. Specific recommendations for vitamin D screening and supplementation should be issued for women by specialist boards. Further studies are needed to identify seasonal variation and to establish a correlation with nutritional surveys.

Diagnostic Accuracy of MRI in Detecting Orbital Masses Keeping Histopathology as Gold Standard

Background: Orbital masses, encompassing a spectrum of benign and malignant lesions, pose significant diagnostic and therapeutic challenges. Magnetic resonance imaging (MRI) has been pivotal in the non-invasive evaluation of these lesions, owing to its superior soft tissue contrast and detailed anatomical resolution. The correlation between MRI findings and histopathological diagnosis remains crucial for accurate clinical decision-making. Objective: The study aimed to evaluate the diagnostic accuracy of MRI in identifying orbital masses, with a focus on differentiating between benign and malignant lesions, using histopathology as the gold standard. Methods: A prospective cross-sectional study was conducted at Jinnah Postgraduate Medical Centre (JPMC), Karachi, from January to December 2023. A total of 145 patients scheduled for surgery or biopsy, presenting with clinical symptoms indicative of orbital masses, were enrolled using a non-probability sequential sampling method. MRI evaluations were performed using a 1.5 Tesla machine. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of MRI in detecting orbital masses were calculated, comparing MRI findings with histopathological results. Results: Of the 145 patients, 55.2% were female, and 44.8% were male, with an age range predominantly between 18-30 years (87.6%). MRI identified 77.2% of cases as malignant and 22.8% as benign, whereas histopathology diagnosed 83.4% as malignant and 16.6% as benign. The diagnostic accuracy of MRI for benign masses showed a sensitivity of 81.82%, specificity of 96.43%, PPV of 87.10%, NPV of 94.74%, and an overall diagnostic accuracy of 93.10%. For malignant masses, MRI demonstrated a sensitivity of 90.16%, specificity of 86.90%, PPV of 83.33%, NPV of 92.41%, and a diagnostic accuracy of 88.28%. Conclusion: MRI exhibits a high diagnostic accuracy in identifying orbital masses, with excellent sensitivity and specificity. It proves to be a reliable diagnostic tool in differentiating between benign and malignant orbital lesions, supporting its integral role in the preoperative assessment and clinical management of patients with suspected orbital masses. Keywords: Orbital Masses, Magnetic Resonance Imaging, Diagnostic Accuracy, Histopathology, Benign Lesions, Malignant Lesions, MRI Sensitivity, MRI Specificity

Exploring COVID-19 patient's dignity and satisfaction: A cross-sectional study.

With the outbreak of COVID-19 and associated challenges such as increased workload for health providers and shortage of equipment, it became more challenging to maintain patients' dignity and satisfaction. This study evaluated the patients' dignity and satisfaction with COVID-19 in Kashan, Iran, in 2021. A cross-sectional descriptive study. In total, 385 patients recovered from COVID-19 were selected through sequential sampling method. Data were collected using demographic, patient dignity inventory, and patient satisfaction questionnaires. Data were analysed using descriptive analysis, independent t-test, ANOVA, and Spearman-Brown coefficient. The mean age of patients was 50.57. The mean scores of patients' dignity and satisfaction were 1.622 ± 0.653 and 3.851 ± 0.548 (out of 5), respectively. The dignity rating was associated with age, gender, education, underlying disease, and length of hospital stay (p < 0.05); but patient satisfaction was only associated with education (p = 0.002). The results indicated that dignity and satisfaction have a significant direct correlation (r = -0.23, p < 0.001). This study was designed based on the research priorities and needs in the field of clinical research and patients were involved in conducting the study via participating in data collection.

Optimal estimation of reliability parameter for inverse Pareto distribution with application to insurance data

PurposeThe purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).Design/methodology/approachWe estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.FindingsWe state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.Originality/valueThis study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

Acetabular Fracture; its Management and Early Functional Outcome

Aim: To evaluate the functional results of open reduction and rigid internal fixation with Reconstruction plates and screws in acetabular fractures Methods: This prospective case series study was carried out on 47 consecutive patients, of either gender and age ranging from 18 to 60 years, who presented with an acetabular fracture within a month of injury at the Orthopedic and Trauma Department of the Medical Teaching Institute Lady Reading Hospital (LRH) in Peshawar, Pakistan, from January 2018 to September 2023. Excluded from the research were patients receiving traction therapy with a trochanteric pin for an open fracture. A non-probabilistic sequential sampling method was applied. Clinical grading followed D'aubigne and Postel's guidelines, with Matta's modifications. We measured range of motion, pain, and gait. Results: There were 47 patients in total, ages ranging from 20 to 60, with an average age of 37.52. there were 31 males (66.0%) and 16 females (33%). In 19(40.4%) patients, 5(10.6%) patients have physical violence and 23(48.9%) of the patients, the cause of injury was a road traffic collision. Our study shows that of the patients, 29(61.7%) were involved on the right side and 18(38.3%) on the left. In five (10.6%) of the instances, there was plate loosing. It is shown that 24(51.1%) cases, excellent outcomes were attained, in 15(31.9%) case good, 5(10.6%) cases fair, 3(6.4%) cases poor outcome were attained. Conclusion: According to Matta Grading, all acetabular fractures, including those involving osteoporotic bone, respond excellently to rather well to open reduction and internal fixation using a 3.5-millimeter reconstruction plate and screws. Keywords: Acetabulam, Fracture, Internal fixation, Open Reduction, Rigid Fixation.

Investigating the Development and Psychometric Testing of the Information and Communication Technology Use Scale in Iranian Patients With Chronic Conditions

Background: The high prevalence and associated costs of chronic diseases have challenged global community health. The use of information and communication technology offers patients and healthcare providers the ability to respond to healthcare needs. This study develops an information and communication technology use scale for patients with chronic diseases and evaluates its psychometric properties. Methods: This exploratory sequential mixed-method study was conducted in Kashan City, Iran from September 2020 to July 2021. In the qualitative phase, 18 patients with chronic conditions were recruited via the purposeful sampling method. The initial pool of 33 items was extracted in this phase. Psychometric evaluation of the scale was examined through face, content, and construct validity. The internal consistency of the scale was assessed with the Cronbach α and its stability was examined by the test re-test. To evaluate the scale’s construct validity, a cross-sectional study was performed with 300 patients referring to hospital wards and private centers that provide services to patients with chronic diseases. A sequential sampling method was used to select the subjects. The exploratory factor analysis was performed using principal component analysis and the varimax rotation method in the SPSS software, version 16. Results: The initial version of the scale consisted of 33 items. A total of 6 items were removed in the face and content validity evaluation phase. Factor analysis with the removal of 4 items resulted in the extraction of 4 factors that explained 67.45% of the variance in the total scale score. The Cronbach α coefficient and intra-class correlation coefficient were estimated at 0.93 and 0.88, respectively. Conclusion: The scale developed in this study is a valid and reliable tool for measuring formation and communication technology use (including its dimensions) in patients with chronic diseases.

A New Sequential Sampling Method for Surrogate Modeling Based on a Hybrid Metric

Abstract Sequential sampling methods have gained significant attention due to their ability to iteratively construct surrogate models by sequentially inserting new samples based on existing ones. However, efficiently and accurately creating surrogate models for high-dimensional, nonlinear, and multimodal problems is still a challenging task. This paper proposes a new sequential sampling method for surrogate modeling based on a hybrid metric, specifically making the following three contributions: (1) a hybrid metric is developed by integrating the leave-one-out cross-validation error, the local nonlinearity, and the relative size of Voronoi regions using the entropy weights, which well considers both the global exploration and local exploitation of existing samples; (2) a Pareto-TOPSIS strategy is proposed to first filter out unnecessary regions and then efficiently identify the sensitive region within the remaining regions, thereby improving the efficiency of sensitive region identification; and (3) a prediction-error-and-variance (PE&amp;V) learning function is proposed based on the prediction error and variance of the intermediate surrogate models to identify the new sample to be inserted in the sensitive region, ultimately improving the efficiency of the sequential sampling process and the accuracy of the final surrogate model. The proposed sequential sampling method is compared with four state-of-the-art sequential sampling methods for creating Kriging surrogate models in seven numerical cases and one real-world engineering case of a cutterhead of a tunnel boring machine. The results show that compared with the other four methods, the proposed sequential sampling method can more quickly and robustly create an accurate surrogate model using a smaller number of samples.

Global sensitivity analysis for multivariate outputs using generalized RBF-PCE metamodel enhanced by variance-based sequential sampling

The mathematical and computational models in engineering applications commonly have multiple outputs, so it is critical to develop global sensitivity analysis (GSA) for multivariate outputs, which can be used to explore the effect of input parameters on output responses. Amongst the existing sensitivity analysis, the covariance-based method is one of the most widely used methods due to its understandability and validity. However, traditional GSA is calculated by Monte Carlo Simulation (MCS), leading to huge computational cost and large sample demand. In this study, the generalized hybrid metamodel using radial basis function (RBF) and sparse polynomial chaos expansion (PCE) is applied for covariance-based GSA. To improve the efficiency and performance of RBF-PCE in mult-models, an efficient sequential sampling method is proposed based on the local variance density of sample points. Three analytical functions and two engineering problems are employed to demonstrate the accuracy and validity of the proposed method. The results indicate that the proposed method has a significant improvement in accuracy and efficiency compared with the existing GSA methods, and the sample demand is reduced by three orders of magnitude compared with MCS-based Sobol' indices.

Sequential Sampling Methods for Finding Classification Boundaries in Engineering Applications

Sequential Sampling Methods for Finding Classification Boundaries in Engineering Applications

Diagnostic Accuracy of Haematological Scoring System in Paired cord Blood and Peripheral Venous Blood for early Detection of Neonatal Sepsis – A Prospective Analytical Study

Introduction: Sepsis is one of the major causes of neonatal morbidity and mortality. Early recognition and diagnosis of early onset neonatal sepsis (EONS) is difficult. Hence, there is a need for early predictive screening method for EONS, for which Haematological Scoring System (HSS) is used. It comprises of total leucocyte count, immature / total neutrophil ratio, total PMN count, immature PMN count, degenerative changes in PMN and platelet count.&#x0D; Methods: 100 inborn neonates with two or more risk factors for EONS, chosen by sequential sampling method were included in this prospective analytical study. Blood samples were collected from umbilical cord and peripheral vein and analysed for haematological parameters. Blood cultures were performed as gold standard for diagnosing neonatal sepsis and sepsis screen was done to corroborate the diagnosis of EONS.&#x0D; Results: Out of 100 neonates, 21 had sepsis, 14 had probable sepsis and 65 had no sepsis. Among the variables of HSS it was observed that elevated I:T ratio, thrombocytopenia, elevated I:M ratio and elevated immature neutrophil count have shown significant correlation with EONS with statistically significant p values (p value &lt; 0.05), with raised I:T ratio being highly sensitive in identifying neonatal sepsis and degenerative changes in neutrophils being highly specific.&#x0D; Conclusions: The HSS was found to be satisfactory in identifying EONS. It can be used as a simple, quick, cost effective and readily available screening test with decent sensitivity and high specificity, for detection of EONS.

Active learning-based structural reliability evaluation Kriging model and sequential importance sampling

The efficient analysis of structural system reliability with multiple failure modes and small failure probabilities presents significant challenges for active Kriging methods. This paper proposes a novel method, PAK-SEIS, which combines the Parallel Active learning Kriging model and the Sequential Importance Sampling method to overcome these challenges. The proposed method includes a new sequential importance sampling method that integrates the sequential Monte Carlo simulation (MCS) and kernel density estimation to construct the optimal importance density. Additionally, the proposed parallel learning strategy allows for the selection of multiple new training samples, reducing performance function calls and refining iterations of Kriging models. The PAK-SEIS method gradually approximates the optimal importance density by updating multiple new training samples and failure modes in parallel at each iteration based on a small number of candidate samples. Candidate samples generated in the final sequence can cover all limit state surfaces, and all Kriging models are accurately constructed. The PAK-SEIS method can effectively address system reliability problems with small failure probability, multiple failure regions, and implicit functions. Two examples demonstrate the efficiency and accuracy of the proposed PAK-SEIS method.