Determination Of Optimal Threshold Research Articles

Changes in the ratio of neutrophils to albumin depending on the severity of abdominal sepsis in patients with benign and malignant non-traumatic perforation of the left colon

Background. According to the Third International Consensus (Sepsis-3), sepsis is defined as a life-threatening organ dysfunction caused by an uncontrolled response of the affected organism to a new infection, and septic shock as a sepsis-related condition characterized by the development of circulatory failure accompanied by arterial hypotension, with lactatemia and requiring maintenance of blood pressure with vasopressors. Despite progress in diagnosing and treating sepsis in patients with intra-abdominal infections, it continues to be the leading cause of mortality among patients in emergency departments worldwide. Purpose – the objective of this study was to ascertain the capacity of the neutrophilto-albumin ratio (NAR) before surgery to predict the severity of abdominal sepsis and 30-day mortality in patients with benign and malignant non-traumatic perforation left side of the colon. Methods. This was a retrospective, cross-sectional study that analyzed 117 patients of different age groups with sepsis caused by colon perforation who were hospitalized and operated on from January 2014 to March 2024. To evaluate the diagnostic value of INA in predicting sepsis severity and 30-day mortality, we analyzed the biomarker’s ROC curves, including the determination of optimal cut-off thresholds and associated specificity and sensitivity. The association of INA, sepsis severity, and perforation type with 30-day mortality was investigated using survival analysis, including the comparison of Kaplan–Meier curves and the development and analysis of Cox proportional hazards regression models. Comparisons between groups of study patients concerning quantitative characteristics were conducted, with the presence of septic shock and treatment outcome serving as the independent variables. This was done using nonparametric criteria for independent measurements, specifically the Kruskal–Wallis and Mann–Whitney tests. The statistical significance of the relationships between the qualitative indicators of the study was assessed based on the χ2 criterion and the odds ratio. Results. The results demonstrated that the INR has a high prognostic value in determining the severity of sepsis. This was evidenced by a high value of the area under the ROC curve (AUC = 0.948, p = 2, 309×10–41 < 0.05, 95% CI = 0.882–1.013). It has been demonstrated that the development of septic shock can be predicted by a preoperative INA value not exceeding 22.1, with the highest possible specificity (93.4%) and sensitivity (92.3%). The INA was also found to be an effective predictor of 30-day mortality in our data. The optimal INA threshold for mortality prediction was 23.8, with a specificity of 77.1 and sensitivity of 78.7% (AUC = 0.862, p = 6,072 ×10–28 < 0.05, 95% CI = 0.786-0.919). Furthermore, no statistically significant association was observed between the type of perforation and patient outcomes (χ2 = 0.00493, p = 0.944, p > 0.05). However, our study did confirm higher 30-day mortality in patients with colonic perforation in the presence of septic shock compared to patients with sepsis. Conclusions. The neutrophil-to-albumin ratio has been identified as an accurate and reliable biomarker for predicting the severity of abdominal sepsis. The biomarker demonstrated a specificity of 93.4%, a sensitivity of 92.3%, and a cut-off value of 22.1 for stratifying patients with septic shock. Furthermore, this biomarker was identified as an important predictor of 30-day mortality in both types of perforation, with an area under the ROC curve (AUC) of 0.862. Nevertheless, the value of this indicator for predicting mortality was somewhat lower than for determining septic shock. Furthermore, the results of the Cox regression analysis indicated that the impact of IA on survival was more pronounced in patients with malignant colon perforation. In patients with benign colon perforation, the model demonstrated a satisfactory fit to the input data. The presence or absence of septic shock did not influence the survival of patients in this category. The findings of the study indicated a significant correlation between INA and sepsis severity, suggesting that this indicator may serve as a straightforward prognostic tool for determining the severity of sepsis before surgery. Ultimately, the stratification of patients according to the severity of sepsis before surgery may lead to improved clinical outcomes for these patients.

Denoising of partial discharges in switchgear insulation material using hybrid wavelet denoising-optimization-machine learning

Partial discharge (PD) diagnosis is essential for assessing the insulation status of power equipment, but onsite interferences often contaminate PD signals with noise, impacting diagnostic accuracy. This work proposes an adaptive wavelet threshold denoising technique, where the PD signal is first decomposed into wavelet coefficients using discrete wavelet transform (DWT). Traditional threshold selection methods rely on experience and statistical factors, challenging optimal threshold determination. To address this issue, Particle Swarm Optimization (PSO), Energy Valley Optimization (EVO) and Subtraction Average Based Optimization (SABO) are applied to achieve the best adaptive threshold. The proposed method is evaluated against traditional sqtwolog-based threshold methods using root mean square error (RMSE) and the recognition accuracy of classifiers, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT) and K-Nearest Neighbours (KNN). The results show that the proposed technique can find the best threshold and increase the recognition accuracy by 19% compared to the traditional method, demonstrating its superior performance.

Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques.

Addressing the profound impact of Tapping Panel Dryness (TPD) on yield and quality in the global rubber industry, this study introduces a cutting-edge Otsu threshold segmentation technique, enhanced by Dung Beetle Optimization (DBO-Otsu). This innovative approach optimizes the segmentation threshold combination by accelerating convergence and diversifying search methodologies. Following initial segmentation, TPD severity levels are meticulously assessed using morphological characteristics, enabling precise determination of optimal thresholds for final segmentation. The efficacy of DBO-Otsu is rigorously evaluated against mainstream benchmarks like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM), and compared with six contemporary swarm intelligence algorithms. The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm's practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.

How to optimize container withholding decisions for reuse in the hinterland?

This study investigates how a hinterland consignee (importer) makes decisions regarding the storage of empty containers for reuse by a shipper (exporter). The system is modeled as a double-ended queue with non-zero matching times, limited truck resources, and both consignee and shipper having fixed withholding capacities. The consignee’s withholding threshold is strategically set to minimize overall transport and detention costs. We derive closed-form expressions for performance measures in the case of a single storage facility at the shipper, utilizing a matrix-based approach. We extend this methodology numerically to the general case. Additionally, we present an accurate fixed-point approximation facilitating the determination of performance measures and optimal threshold. Our findings show the importance of withholding decisions in import and balanced areas for cost reduction. In export areas, a policy of full reuse proves nearly optimal. Analyzing dynamic state-dependent consignee decisions via a Markov decision process, we establish that the optimal policy involves withholding thresholds increasing with stored quantity at the shipper. While optimal, state-dependent thresholds yield limited cost savings compared to a fixed threshold, suggesting minimal impact from consignee-shipper information sharing. Additionally, we examine the influence of variability in matching and production times, observing decreased costs with reduced variability, particularly in export areas, but with minor impacts on withholding decisions.

Research on Optimal Group-Purchase Threshold and Pricing Strategy of Community Group Purchase

This study delves into the rapidly evolving community group-buying model, specifically focusing on the determination of optimal group-buying thresholds and pricing strategies for merchants. Aiming to bridge the gap in the existing literature, the methodology employs optimization models, integrating a numerical analysis to construct and evaluate a single merchant model. The findings reveal a nuanced relationship: within a specific threshold interval, a unique group-purchase threshold exists where merchants can maximize profits by balancing group and ordinary sales. The study shows that factors like ordinary selling price, group-buying publicity, and associated costs significantly influence these thresholds and pricing strategies. A critical insight is the threshold’s variability in response to market conditions, highlighting a strategic balance for maximizing profitability. The research underscores the need for merchants to adapt their strategies in response to evolving market dynamics and consumer behaviors. However, the study acknowledges its limitations due to its theoretical nature and focus on the Chinese market, suggesting the potential for future empirical studies in diverse cultural and economic contexts. Overall, this research contributes both theoretically and practically by providing a foundational framework for merchants to optimize group-purchase thresholds and pricing strategies in the dynamic realm of community group buying.

Determination of Optimal Eosinophil Thresholds for Diagnosis of Eosinophilic Gastritis and Duodenitis: A Pooled Analysis of 4 Prospective Studies.

Consensus is lacking regarding the number of eosinophils (eos) required for the diagnosis of eosinophilic gastritis (EoG) and eosinophilic duodenitis (EoD). In addition, thresholds that require multiple high-power fields (HPFs) may not be practical for clinical use, resulting in delayed or missed diagnoses. This pooled analysis of 4 prospective studies assessed thresholds for multiple and single HPFs used to diagnose EoG and EoD. Studies included the phase 2 ENIGMA1, the phase 3 ENIGMA2, an EoG/EoD prevalence study and a healthy volunteer study. Eos were quantified in the epithelium and lamina propria for controls and symptomatic participants. Symptomatic participants were further divided by histologic diagnosis of EoG/EoD. Peak eos counts were assessed, and the area under the receiver operating characteristic curve was analyzed to identify eos cutoffs for detection of EoG/EoD using the Youden index and sensitivity and specificity equality approaches. Based on the highest specificity analysis in 740 patients, the optimal eos threshold was determined to be 20 eos/HPF in 5 gastric HPFs for EoG (71% sensitivity and 94% specificity) and 33 eos/HPF in 3 duodenal HPFs for EoD (49% sensitivity and 100% specificity). For single-field analysis, the optimal eos thresholds were 33 eos/HPF (EoG) and 37 eos/HPF (EoD), both corresponding to 93% sensitivity and 93% specificity. Highly specific single gastric and duodenal HPF thresholds may have more clinical applicability than thresholds requiring multiple HPFs and could better facilitate development of practical histopathologic guidelines to aid pathologists and clinicians in the detection and diagnosis of EoG and/or EoD.

Multi-axial load spectrum extrapolation method for fatigue durability of special vehicles based on extreme value theory

A method for the multi-axial load spectrum extrapolation based on extreme value theory has been proposed herein, for the first time to the best of our knowledge. The relationship between the internal damage of the structure with the external multi-axial load spectrum is first established prior to extrapolation. The multi-axial load spectrum is then equivalently synthesized into a uni-axial load spectrum based on the interaction between the external forces and the internal stress state of the structure. The basic theory of the time-domain multi-axial load spectrum extrapolation method is developed, including (1) the multi-axial load spectrum extrapolation model establishment; (2) the optimal threshold determination based on grey relational analysis (GRA); (3) the fitting of the distribution function parameters based on the Bayesian parameter estimation method; and (4) the judgment of phase relationship between each channel of the multi-axial load spectrum. The rationality of the method proposed in this paper is verified through an application example. In the example, the multi-axial load spectrum measurement of a special vehicle is introduced, and the proposed multi-axial extrapolation method is applied. Finally, the validation of the multi-axial load spectrum extrapolation method – in terms of the characterized comparison and the durability simulation – is performed.

Feature extraction-reduction and machine learning for fault diagnosis in PV panels

With the rapid expansion and installation of Photovoltaic (PV) power plants, developing a proper Fault Detection and Diagnosis (FDD) strategy has become a significant issue within the objective to organize and plan for maintenance and reduce repair time, which in turn ensures the availability of operated systems. In this context, supervised Machine Learning (ML) algorithms are undergoing extensive development and their performances are analyzed in terms of different metrics including computation time detection accuracy, precision, recall, and F1-score in addition to False Alarm and missed detection rates. However, such algorithms generally require the extraction of significant features to handle irrelevant and redundant features while reducing the computational burden. Considering that different dimensionality reduction methods directly impact the FDD performances, this paper presents a novel Data Dimensionality Reduction Strategy (DDRS) based on a proposed algorithm for Information Gain (IG) score optimal threshold determination. This strategy also relies on Principal Component Analysis (PCA) to obtain the optimal subset which represents the lowest dimensional feature in the training and testing stages of different supervised ML algorithms. FDD performances are then evaluated by using faulty and non-faulty datasets acquired experimentally from a Grid-connected PV system (GPVS) emulator. The experimental results show the efficiency of the proposed strategy and its ability to improve the FDD based on supervised ML algorithms.

Methods for detecting heat stress in hutch-housed dairy calves in a continental climate

Dairy calves exposed to solar radiation, elevated ambient temperature, and humidity are at risk of impaired welfare and productivity. Initial detection of thermal discomfort requires determination of optimal heat stress indicators and thresholds. Such values have recently been established in calves in chronic, subtropical, and acute continental environments but not in continuous, temperate conditions. Herein, the objectives were to determine associations between animal-based and environmental heat stress indicators and establish environmental breakpoints for hutch-raised dairy calves during a continental summer. From June to August, dairy calves (n = 63; 14 to 42 d of age) were individually hutch-housed and managed according to the dairy standard operating procedures in Arlington, Wisconsin. Calf respiration rates (RR), rectal temperatures (RT), shaved or unshaved skin temperatures (ST), and hutch internal and external air speed were measured thrice weekly at 0700 and 1400 h after a 15 min hutch restriction. Environmental indices including dry bulb temperature (Tdb), black globe temperature, and relative humidity were measured every 15 min, averaged hourly, and used to calculate temperature-humidity index (THI) using 8 different equations (THI1-8). Correlation and linear regression models were used to determine relationships within and between animal-based and environmental indicators. Environmental breakpoints were established using segmented regression models to estimate THI and Tdb thresholds for abrupt changes in animal responses. There were strong, positive correlations between animal-based indicators and Tdb or THI1-8, with the strongest association observed between unshaved ST and Tdb (r = 0.80). The linear regression of animal-based indicators with the best fit included Tdb or Tdb plus relative humidity and air speed. The threshold at which RR and RT began to rise was at a THI of 69 for both or at a Tdb of 21.0 or 21.5°C, respectively. No threshold was established for ST. Together, these outcomes indicate that Tdb is an appropriate measurement to detect thermal discomfort for calves in a temperate summer climate and individual hutch housing. Monitoring of calves is warranted before ambient temperature reaches 21.0°C, corresponding to RR of 40 breaths per minute and RT of 38.5°C, to promote calf comfort and reduce the risk of hyperthermia-related welfare and productivity consequences.

Decision-Theoretic Rough Sets based automated scheme for object and background classification in unevenly illuminated images

This paper addresses the problem of classification of object and background in unevenly illuminated images using Decision-Theoretic Rough Set (DTRS) framework. The proposed scheme employs adaptive windowing technique to partition the image into different windows. Thereafter, the proposed DTRS based method is applied on each window to find out the optimal threshold that is used for classification of the window. Determination of optimal threshold of a given window is dependent on the optimal granule size used for the window. The problem of determination of optimal granule size and optimal threshold is cast in optimization framework. The optimal threshold obtained for each window is used to classify the window and the classification of the entire image is the union of classifications over all the windows. Manual tuning of parameters is not required to determine the optimal threshold. The proposed scheme is tested on different images considered from Berkeley image database. The performance of the proposed scheme is compared with other granular and non-granular computing based schemes. Evaluation of different quantitative measures demonstrates the improved performance of the proposed schemes over others.

A novel approach for optimal replacement threshold and inspection scheme for condition based maintenance

PurposeThe purpose of this paper is the simultaneous determination of optimal replacement threshold and inspection scheme for a system within condition-based maintenance (CBM) framework.Design/methodology/approachA proportional hazards model (PHM) is used for risk of failure and a Markovian process to model the system covariates. Total expected long-run cost (including replacement, inspection and downtime costs) is formulated in terms of replacement threshold and inspection scheme. Through an iterative procedure, for all different values of replacement thresholds, their associated optimal inspection scheme is determined using an effective search algorithm. By evaluating the corresponding costs, the optimal replacement threshold and its associated optimal inspection scheme are, then, identified.FindingsThe mathematical formulation, that takes into account all different costs, required for the simultaneous determination of optimal replacement threshold and optimal inspection scheme for an item subjected to CBM using PHM is provided. The proposed approach is compared against classical age policy and one state-of-the-art policy through a numerical example. The results show that the proposed approach outperforms other comparing policies.Practical implicationsIn practical situations where CBM is implemented, inspections and downtime often incur cost. Under such circumstances, findings of this paper can be utilized for the determination of optimal replacement threshold and optimal inspection scheme so that the CBM cost is minimized.Originality/valueIn most of the reported researches, it is often assumed that inspections have no cost and/or that the time for replacements (either preventive or at failure) is negligible. In the contrary, in this paper the author takes all cost factors including inspection costs, replacement time(s) and their associated downtime costs into account in the simultaneous determination of optimal replacement threshold and optimal inspection scheme.

FRI015 - Identification of significant fibrosis in patients with non-alcoholic steatohepatitis using non-invasive tests: determination of optimal thresholds based on cross-sectional analyses of patients screened for a phase 3 randomised controlled trial

FRI015 - Identification of significant fibrosis in patients with non-alcoholic steatohepatitis using non-invasive tests: determination of optimal thresholds based on cross-sectional analyses of patients screened for a phase 3 randomised controlled trial

Non-contact tests for identifying people at risk of primary angle closure glaucoma.

Primary angle closure glaucoma (PACG) accounts for 50% of glaucoma blindness worldwide. More than three-quarters of individuals with PACG reside in Asia. In these populations, PACG often develops insidiously leading to chronically raised intraocular pressure and optic nerve damage, which is often asymptomatic. Non-contact tests to identify people at risk of angle closure are relatively quick and can be carried out by appropriately trained healthcare professionals or technicians as a triage test. If the test is positive, the person will be referred for further specialist assessment. To determine the diagnostic accuracy of non-contact tests (limbal anterior chamber depth (LACD) (van Herick test); oblique flashlight test; scanning peripheral anterior chamber depth analyser (SPAC), Scheimpflug photography; anterior segment optical coherence tomography (AS-OCT), for identifying people with an occludable angle. We searched the following bibliographic databases 3 October 2019: CENTRAL; MEDLINE; Embase; BIOSIS; OpenGrey; ARIF and clinical trials registries. The searches were limited to remove case reports. There were no date or language restrictions in the searches. We included prospective and retrospective cross-sectional, cohort and case-control studies conducted in any setting that evaluated the accuracy of one or more index tests for identifying people with an occludable angle compared to a gonioscopic reference standard. Two review authors independently performed data extraction and quality assessment using QUADAS2 for each study. For each test, 2 x 2 tables were constructed and sensitivity and specificity were calculated. When four or more studies provided data at fixed thresholds for each test, we fitted a bivariate model using the METADAS macro in SAS to calculate pooled point estimates for sensitivity and specificity. For comparisons between index tests and subgroups, we performed a likelihood ratio test comparing the model with and without the covariate. We included 47 studies involving 26,151 participants and analysing data from 23,440. Most studies were conducted in Asia (36, 76.6%). Twenty-seven studies assessed AS-OCT (analysing 15,580 participants), 17 studies LACD (7385 participants), nine studies Scheimpflug photography (1616 participants), six studies SPAC (5239 participants) and five studies evaluated the oblique flashlight test (998 participants). Regarding study quality, 36 of the included studies (76.6%) were judged to have a high risk of bias in at least one domain.The use of a case-control design (13 studies) or inappropriate exclusions (6 studies) raised patient selection concerns in 40.4% of studies and concerns in the index test domain in 59.6% of studies were due to lack of masking or post-hoc determination of optimal thresholds. Among studies that did not use a case-control design, 16 studies (20,599 participants) were conducted in a primary care/community setting and 18 studies (2590 participants) in secondary care settings, of which 15 investigated LACD. Summary estimates were calculated for commonly reported parameters and thresholds for each test; LACD ≤ 25% (16 studies, 7540 eyes): sensitivity 0.83 (95% confidence interval (CI) 0.74, 0.90), specificity 0.88 (95% CI 0.84, 0.92) (moderate-certainty); flashlight (grade1) (5 studies, 1188 eyes): sensitivity 0.51 (95% CI 0.25, 0.76), specificity 0.92 (95% CI 0.70, 0.98) (low-certainty); SPAC (≤ 5 and/or S or P) (4 studies, 4677 eyes): sensitivity 0.83 (95% CI 0.70, 0.91), specificity 0.78 (95% CI 0.70, 0.83) (moderate-certainty); Scheimpflug photography (central ACD) (9 studies, 1698 eyes): sensitivity 0.92 (95% CI 0.84, 0.96), specificity 0.86 (95% CI 0.76, 0.93) (moderate-certainty); AS-OCT (subjective opinion of occludability) (13 studies, 9242 eyes): sensitivity 0.85 (95% CI 0.76, 0.91); specificity 0.71 (95% CI 0.62, 0.78) (moderate-certainty). For comparisons of sensitivity and specificity between index tests we used LACD (≤ 25%) as the reference category. The flashlight test (grade 1 threshold) showed a statistically significant lower sensitivity than LACD (≤ 25%), whereas AS-OCT (subjective judgement) had a statistically significant lower specificity. There were no statistically significant differences for the other index test comparisons. A subgroup analysis was conducted for LACD (≤ 25%), comparing community (7 studies, 14.4% prevalence) vs secondary care (7 studies, 42% prevalence) settings. We found no evidence of a statistically significant difference in test performance according to setting. Performing LACD on 1000 people at risk of angle closure with a prevalence of occludable angles of 10%, LACD would miss about 17 cases out of the 100 with occludable angles and incorrectly classify 108 out of 900 without angle closure. The finding that LACD performed as well as index tests that use sophisticated imaging technologies, confirms the potential for this test for case-detection of occludable angles in high-risk populations. However, methodological issues across studies may have led to our estimates of test accuracy being higher than would be expected in standard clinical practice. There is still a need for high-quality studies to evaluate the performance of non-invasive tests for angle assessment in both community-based and secondary care settings.

Deep learning-based rapid recognition of oasis-desert ecotone plant communities using UAV low-altitude remote-sensing data

The oasis-desert ecotone plant community is a protective barrier for an oasis. With the continuous expansion of oasis ecosystems and gradual increases in the intensity of human activities, the degradation of plant communities in oasis-desert ecotones has become increasingly prominent. Timely and accurate detection of such degradation is a prerequisite for vegetation restoration. Currently, vegetation information extraction has been primarily based on an analysis of spectral features; however, the vegetation coverage area and the soil background are easily confused. In addition, conventional supervised classification methods have a strong dependence on the training samples, and this technique can fail due to the complicated image processing procedure, relatively lower recognition ability, and optimal threshold determination for a multi-temporal image. In this study, the aim was to accurately extract the plant community features, distribution area, and the image background using two automatic recognition algorithm models known as the convolution neural network (CNN)-based VGG16 and VGG19 models. These models were used to investigate an oasis-desert ecotone in an arid area using an unmanned aerial vehicle (UAV) remote-sensing image. Additionally, the impacts of a change in the training sample size on the automatic classification accuracy of the models were evaluated. The results showed that the size of the training samples has a significant impact on the classification accuracy, and with an increase in the sample sizes, the generalization ability of the models gradually improved. The modeling accuracy of the VGG16 and VGG19 increased from 88.25% and 95.25% to 88.50% and 96.73%, respectively. The classification accuracy of the VGG16 model varied from 79.6 to 93.8%, and the classification accuracy of VGG19 model varied from 82.3 to 95.6%. The size of the training samples was 300, so both models presented the best classification results. Compared with the conventional supervised classification methods, the deep learning algorithm-based models yielded significantly higher classification accuracies. These models can provide technical support for the realization of the unsupervised automatic classification of oasis-desert ecotone plant communities in arid areas.

A Fraud Detection Method for Low-Frequency Transaction

The effectiveness of transaction fraud detection methods directly affects the loss of users in online transactions. However, for low-frequency users with small transaction volume, the existing methods cannot accurately describe their transaction behaviors for each user, or lead to a high misjudgment rate. So we propose a new method for individual behavior construction, which can make the behavior of low-frequency users more accurate by migrating the current transaction group behavior and transaction status. Firstly, we consider the user's only historical transactions, combined with the optimal risk threshold determination algorithm, to form the user's own transaction behavior benchmark. Secondly, through the DBSCAN clustering algorithm, the behavior characteristics of all current normal samples and fraud samples are extracted to form the common behavior of the current transaction group. Finally, based on historical transaction records, the current transaction status is extracted using a sliding window mechanism. The combination of the three constitutes a new transaction behavior of the user. On this basis, a multi-behavior detection model based on new transaction behavior is proposed. According to the result of each behavior, Naive Bayes model is used to calculate the probability that current transaction belongs to fraud, and finally determine whether current transaction is fraud. Experiments prove that the method proposed in this paper can have a good effect on low-frequency users, which can accurately identify fraud transactions and has a low misjudgment rate for normal transactions.

Multiresolution simultaneous upscaling of two features of the reservoir using the bandwidth of the kernel function and wavelet transformation

In this paper, we employ a new multiresolution upscaling method for the data in porous media. The method employs an adaptive bandwidth of the kernel function as the multiresolution upscaling operator. We have implemented a hierarchical multiresolution upscaling strategy, which is based on maintaining the important fine scale model structures of the reservoir. In this method, bandwidth is a function of the variability of cell, which controls the upscaling process. In areas with a high variability of the considered feature, small bandwidth leads to maintaining the fine scale structures; and in homogeneous reservoir areas, large bandwidths will lead to coarse-sized cells. In this paper, we have used this proposed method for simultaneous upscaling of two features: density and neutron logs. Determination of optimal bandwidth or threshold for each feature is an important challenge in the bandwidth method. Due to different variabilities of two properties or features, simultaneous upscaling should be performed to capture the variability of both features in the model. The concept of simultaneous upscaling provides a common coarse model for two properties. To prepare the SUTF model, two minimum and maximum bandwidth approaches are used. The upscaling algorithm has been applied for density and neutron logs. Although the fine scale model contains 2290 cells, the number of coarsened cells in SUTF model based on the minimum bandwidth and maximum bandwidth approaches are 402 and 110, respectively. In addition to this new proposed method, the method of wavelet transform has also been used as a well-known method for upscaling, and finally, the results of the two methods are compared. By comparing the results of the two methods, we observe that the upscaling error in the kernel bandwidth method is less than that in the wavelet transform method. The advantage of the kernel bandwidth method over the wavelet transform is the reduction of the loss of the main information as well as the reduction of the numerical dispersion in the new proposed method compared to the wavelet transform method. These benefits have appeared at the upscaling error that is less in the proposed method.

Optimization of on-condition thresholds for a system of degrading components with competing dependent failure processes

An optimization model has been formulated and solved to determine on-condition failure thresholds and inspection intervals for multi-component systems with each component experiencing multiple failure processes due to simultaneous exposure to degradation and shock loads. In this new model, we consider on-condition maintenance optimization for systems of degrading components, which offers cost benefits over time-based preventive maintenance or replace-on-failure policies. For systems of degrading components, this can be a particularly difficult problem because of the dependent degradation and dependent failure times. In previous research, preventive maintenance and periodic inspection models have been considered; however, for systems whose costs due to failure are high, it is prudent to avoid the event of failure, i.e., the components or system should be repaired or replaced the before the failure happens. The determination of optimal on-condition thresholds for all components is effective to avoid failure and to minimize cost. Low on-condition thresholds can be inefficient because they waste component's life, and high on-condition thresholds are risky because the components are prone to costly failure. In this paper, we formulated and solved a new optimization model to determine optimal on-condition thresholds and inspection intervals. In our model, when the system is inspected, all components are inspected at that time. An inspection interval may be optimal for one component, but might be undesirable for another component, so the optimization requires a compromise. The on-condition maintenance optimization model is demonstrated on several examples.

Optimal threshold determination based on the mean excess plot

Choosing a suitable threshold has been an issue in practice. Based on the mean excess plot (MEP), the eyeball inspection approach (EIA) is mainly used to determine the threshold. This involves fitting the threshold at the point the plot becomes approximately linear solely using one’s sense of judgement in such a way that Generalized Pareto model is valid. This is a rather subjective choice. In this paper, we propose an alternative way of selecting the threshold where, instead of choosing individual thresholds in isolation and testing their fit, we make use of the bootstrap aggregate of these individual thresholds which are formulated in terms of quantiles.The method incorporates the visual technique and is aimed at reducing the subjectivity associated with solely using the EIA. The new approach is implemented using simulated datasets drawn from three different distributions. An application to the NSE All share Nigerian stock index is presented. The performance of the proposed model and the EIA are judged based on standard error, Negative log likelihood, the Akaike Information Criteria and the Bayesian Information Criteria. The results show that the new technique gives similar estimates as the EIA and in some cases it performs better. In comparison to other existing methods, the proposed model performs well.

Оптико-електронна cистема оцінювання динамічних змін біомедичних зображень мікросудин очного дна

The paper presents an opto-electronic system for estimating dynamic changes in biomedical images by calculating informative diagnostic features of biomedical images, which allows to evaluate the condition of vessels and determine the conjunctival index. Automatic gradient filter selection method based on using data supplied by gradient methods and providing optimal threshold determination that improves the prediction speed of dynamic changes in biomedical images and determines the optimal threshold level under dynamic disturbance factors.

Optimal Threshold Determination for the Maximum Product of Spacing Methodology with Ties for Extreme Events

Extreme events are defined as values of the event below or above a certain value called threshold. A well chosen threshold helps to identify the extreme levels. Several methods have been used to determine threshold so as to analyze and model extreme events. One of the most successful methods is the maximum product of spacing (MPS). However, there is a problem encountered while modeling data through this method in that the method breaks down when there is a tie in the exceedances. This study offers a solution to model data even if it contains ties. To do so, an optimal threshold that gives more optimal parameters for extreme events, was determined. The study achieved its main objective by deriving a method that improved MPS method for determining an optimal threshold for extreme values in a data set containing ties, estimated the Generalized Pareto Distribution (GPD) parameters for the optimal threshold derived and compared these GPD parameters with GPD parameters determined through the standard MPS model. The study improved maximum product of spacing method and used Generalized Pareto Distribution (GPD) and Peak over threshold (POT) methods as the basis of identifying extreme values. This study will help the statisticians in different sectors of our economy to model extreme events involving ties. To statisticians, the structure of the extreme levels which exist in the tails of the ordinary distributions is very important in analyzing, predicting and forecasting the likelihood of an occurrence of the extreme event.