Elimination Procedure Research Articles

Evidence for modulation of EEG microstates by mental workload levels and task types.

Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large-scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi-attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within-task and 78% for cross-task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.

Order independence for rationalizability

This paper provides a sufficient monotonicity condition for the solution of a rationalizability procedure to be independent of the order of elimination. The analysis unfolds in an abstract environment that applies to any game and elimination procedure. Monotonicity is satisfied by rationalizability procedures based on monotone belief operators, such as “directed” rationalizability, interim correlated rationalizability, or, in sequential games, initial and backwards rationalizability.

Temporal effect of imatinib adherence on time to remission in chronic myeloid leukemia patients.

Adherence to imatinib in chronic myeloid leukemia (CML) patients is estimated to be as low as 70% despite its clinical benefit, and our understanding of the impact of nonadherence in this population is limited. This study presents a novel application of the Alternating Conditional Estimation (ACE) algorithm in newly diagnosed CML patients to map the full dose-response curve (DRC) and determine how the strength of this curve varies over time. We applied the ACE algorithm alongside a backward elimination procedure to detect the presence of time dependence and nonlinearity in the relationship between imatinib adherence and time-to-remission. An extended Cox model allowing for the flexible modeling of identified effects via unpenalized B-splines was subsequently fit and assessed. The substantial improvement in model fit associated with the extended Cox approach suggests that traditional Cox proportional hazards model assumptions do not hold in this setting. Results indicate that the DRC for imatinib is non-linearly increasing, with an attenuated effect above a 74% adherence rate. The strength of this effect on remission varied over time and was strongest in the initial months of treatment, reaching a peak around 90 days post-initiation (log hazard ratio: 2.12, 95% confidence interval: 1.47 to 2.66). Most patients that achieved remission did so by 4 months (120 days) with consistently high adherence, suggesting that this could be a critical time and duration for realizing treatment benefit and patient monitoring. Findings regarding the relationship between adherence and remission can additionally help guide the design of future studies.

Predictors for Uptake of Vaccines Offered during the Second Year of Life: Second Dose of Measles-Containing Vaccine and Meningococcal Serogroup A-Containing Vaccine, Ghana, 2020.

Understanding the drivers of coverage for vaccines offered in the second year of life (2YL) is a critical focus area for Ghana's life course approach to vaccination. This study characterizes the predictors of vaccine receipt for 2YL vaccines-meningococcal serogroup A conjugate vaccine (MACV) and the second dose of measles-containing vaccine (MCV2)-in Ghana. 1522 children aged 18-35 months were randomly sampled through household surveys in the Greater Accra Region (GAR), Northern Region (NR), and Volta Region (VR). The association between predictors and vaccination status was modeled using logistic regression with backwards elimination procedures. Predictors included child, caregiver, and household characteristics. Coverage was high for infant vaccines (>85%) but lower for 2YL vaccines (ranging from 60.2% for MACV in GAR to 82.8% for MCV2 in VR). Predictors of vaccination status varied by region. Generally, older, first-born children, those living in rural settlements and those who received their recommended infant vaccines by their first birthday were the most likely to have received 2YL vaccines. Uptake was higher among those with older mothers and children whose caregivers were aware of the vaccination schedule. Improving infant immunization uptake through increased community awareness and targeted strategies, such as parental reminders about vaccination visits, may improve 2YL vaccination coverage.

Modeling climate change impacts on vector-borne disease using machine learning models: Case study of Visceral leishmaniasis (Kala-azar) from Indian state of Bihar

Visceral leishmaniasis or Kala-azar (KA) is a Vector-Borne Disease (VBD) that remains the second-largest parasitic killer across the globe (mortality rate: 75–95%). More than 60% of KA cases originate in South Asia, wherein India accounts for 2/3rd of the cases, and Bihar, a state in India, alone accounts for more than 50% of the Indian cases. Past studies suspected climate change vulnerabilities as a driving cause of KA outbreaks. The VBDs-based epidemic prediction systems have been developed to mitigate recurrent outbreaks; however, Machine Learning (ML) based approaches still need to be explored for modeling changing climate impacts on KA cases. This study, for the first time, develops a Radial Basis Function (RBF) kernel-based Support Vector Regression (SVR), hereinafter RBF-kernel-based-SVR model for the most-affected endemic districts of Bihar (northern-India), using the data from 2016 and 2021. Forward selection, backward elimination, and stepwise regression procedures were adopted while selecting influential climatic variables, followed by the k-fold cross-validation technique and, then, the RBF-kernel-based-SVR algorithm for classification. Results suggested that temperature, wind speed, rainfall, and population density significantly contributed to the KA outbreaks. This study also developed Multiple Linear Regression (MLR) and Multilayer Perceptron (MLP) models to compare SVR with other classification models. Findings indicated that the proposed RBF-kernel-based-SVR model [Correlation Coefficient (CC) = 0.82, Root-Mean-Square Error (RMSE) = 12.20, and Nash–Sutcliffe Efficiency (NSE) = 0.66] outperformed MLR (0.81, 14.20, 0.48) and MLP (0.81, 12.95, 0.61). Study recommends using the RBF-kernel-based-SVR model as a quick and efficient model capable of detecting KA cases with high predictability even under limited data availability. Such models can assist public health authorities, given monitoring KA spread, learning the climate impacts of outbreaks, and ensuring timelier health services.

Marathi text summarization through NLP and deep learning mechanism

<p>Every day, an ever-increasing amount of people gain access to the internet platform. This has proven to be efficient in creating cost-effective internet platform deployments and applications. The growth in the amount of people using the platform has resulted in a rise in the quantity of information accessible on the internet in the form of news, media, and other forms of communication. This causes evaluating and comprehending a significant amount of textual information a very challenging task. For the objective of generating textual summaries for Marathi texts, an effective and trustworthy approach is required. Through the use of machine learning methods, a successful strategy for extracting summary for the Marathi text has been generated for this objective. To obtain the Marathi text summary, the proposed method uses feature extraction as well as deep belief networks and decision tree methodologies. The experimentation was carried out on the performance of the Term Frequency-Inverse Document Frequency (TF-IDF) in the stopword elimination procedure, along with the evaluation of the summarization outcome which achieves a Mean Absolute Error (MAE) of 2.8 for the stopword removal approach through TF-IDF technique and a precision of 95.49% with an accuracy of 92.76%.</p>

Preference disaggregation analysis for sorting problems in the context of group decision-making with uncertain and inconsistent preferences

Preference disaggregation analysis is effective for inferring a preference model of decision makers from decision examples. Although multiple criteria sorting problems are often handled within the context of group decision-making, it has not been well studied how to estimate a collective sorting model for a group with uncertain and inconsistent preferences through preference disaggregation. To this end, this paper proposes a threshold-based value-driven preference disaggregation method to solve multiple criteria sorting problems involving multiple decision-makers. We allow decision-makers to reduce the cognitive effort of preference expressions by providing possible classes of reference alternatives or pairwise comparisons between them through uncertain expressions. In addition, we develop a two-stage inconsistency identification and elimination procedure that includes dealing with contradictory preference information provided by a single decision maker and managing disagreement among a group of decision makers. A collective preference model consisting of an additive value function and value thresholds is estimated by linear programming. The proposed method is demonstrated by a practical example of financial investment decisions for government-funded projects. The results show that dealing with uncertain and inconsistent preferences can improve the accuracy of preference disaggregation.

Generalized equations and their solutions in the (1/2,0)+(0,1/2) representations of the Lorentz group

We present explicit examples of generalizations in relativistic quantum mechanics. First of all, we discuss the generalized spin-1/2 equations for neutrinos. They have been obtained by means of the Gersten-Sakurai method for derivations of arbitrary-spin relativistic equations. Possible physical consequences are discussed. Next, it is easy to check that both Dirac algebraic equations Det(ˆp − m) = 0 and Det(ˆp + m) = 0 for u− and v− 4-spinors have solutions with p0 = ±Ep = ± p p2 + m2. The same is true for higher-spin equations. Meanwhile, every book considers the equality p0 = Ep for both u− and v− spinors of the (1/2, 0) ⊕ (0, 1/2) representation, thus applying the DiracFeynman-Stueckelberg procedure for elimination of the negative-energy solutions. The recent Ziino works (and, independently, the articles of several others) show that the Fock space can be doubled. We re-consider this possibility on the quantum field level for both S = 1/2 and higher spin particles. The third example is: we postulate the non-commutativity of 4-momenta, and we derive the mass splitting in the Dirac equation. The applications are discussed.

Explicit Chebyshev Petrov–Galerkin scheme for time-fractional fourth-order uniform Euler–Bernoulli pinned–pinned beam equation

Abstract In this research, a compact combination of Chebyshev polynomials is created and used as a spatial basis for the time fractional fourth-order Euler–Bernoulli pinned–pinned beam. The method is based on applying the Petrov–Galerkin procedure to discretize the differential problem into a system of linear algebraic equations with unknown expansion coefficients. Using the efficient Gaussian elimination procedure, we solve the obtained system of equations with matrices of a particular pattern. The L ∞ {L}_{\infty } and L 2 {L}_{2} norms estimate the error bound. Three numerical examples were exhibited to verify the theoretical analysis and efficiency of the newly developed algorithm.

Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features.

T-cell receptor (TCR) recognition of foreign peptides presented by the major histocompatibility complex (MHC) initiates the adaptive immune response against pathogens. While a large number of TCR sequences specific to different antigenic peptides are known to date, the structural data describing the conformation and contacting residues for TCR-peptide-MHC complexes is relatively limited. In the present study we aim to extend and analyze the set of available structures by performing highly accurate template-based modeling of these complexes using TCR sequences with known specificity. Identification of CDR3 sequences and their further clustering, based on available spatial structures, V- and J-genes of corresponding T-cell receptors, and epitopes, was performed using the VDJdb database. Modeling of the selected CDR3 loops was conducted using a stepwise introduction of single amino acid substitutions to the template PDB structures, followed by optimization of the TCR-peptide-MHC contacting interface using the Rosetta package applications. Statistical analysis and recursive feature elimination procedures were carried out on computed energy values and properties of contacting amino acid residues between CDR3 loops and peptides, using R. Using the set of 29 complex templates (including a template with SARS-CoV-2 antigen) and 732 specificity records, we built a database of 1585 model structures carrying substitutions in either TCRα or TCRβ chains with some models representing the result of different mutation pathways for the same final structure. This database allowed us to analyze features of amino acid contacts in TCR - peptide interfaces that govern antigen recognition preferences and interpret these interactions in terms of physicochemical properties of interacting residues. Our results provide a methodology for creating high-quality TCR-peptide-MHC models for antigens of interest that can be utilized to predict TCR specificity.

Gender Roles, Sexual Cognitions, and History of Victimization: A Preliminary Model of Reporting Behavior among College Students

Sexual cognitions that flow from gender roles and previous history of victimization are strong predictors of sexual assault victimization. Because these cognitions reside within the individual themselves, we hypothesized that these same cognitions will also be predictors of subsequent reporting behavior to formal campus supports. An online self-report survey for assessing student awareness and perception of sexual safety was administered in a random stratified sample of college students (<em>N </em>= 98; 78% female). Participants were shown a hypothetical vignette involving a quid pro quo sexual harassment incident involving a student and were asked to rate their likelihood of making a formal report if this happened to them. Controlling for Type 1 error rates, a backward-chunk-wise elimination procedure was used to build a regression model involving nine predictors grouped into five categories: (1) conformity to traditional gender roles; (2) rape myth acceptance beliefs; (3) token resistance; (4) sexual assertiveness; and (5) prior history of victimization. Three of the five categories remained in the model after a sequential process of eliminating non-significant predictors. Specifically, lower agreement with token resistance beliefs, stronger sexual assertiveness, and not being a victim in the past increased the likelihood of respondents stating they would initiate a formal report. When predicting reporting behavior among the general college student sample, sexual cognitions related to token resistance beliefs and sexual assertiveness appear to be more robust predictors compared to conformity to traditional gender roles alone. Programming activities could consider targeting these attitudes as opposed to broad aspects of traditional gender roles. Future studies should test this model in a confirmatory manner to determine its reliability in subsequent samples, in addition to the potential role of toxic masculinity attitudes.

Impact of Storage Methods on Sweet Potato Spoilage in Plateau State Nigeria

Spoilage of sweet potato could be enormous when inadequate methods of storage are used. This research investigated impact of methods of storage on spoilage of sweet potato in three major markets of Plateau State Nigeria. Data for the study were obtained from sellers of sweet potato in Yan Doya market, New market and Farin Gada market of Plateau State through administration of questionnaires. Analysis of the data was carried out using descriptive statistics, Pearson Product Moment Correlation (PPMC), F-test statistics, t-test and Multiple Linear Regression (MLR) with backward elimination procedure. Results from descriptive statistics showed that majority of sweet potato sellers adopted use of sack for storage of their goods. The second method commonly used for storage of sweet potato in the study area was found to be root cellar method. Pair-wise application of PPMC indicated that spoilage of sweet potato was positively correlated with adopted methods of storage which suggested existence of relationship among the variables. Analysis using F-test statistics indicated that the relationship did not occur by chance between the variables hence t-test aided determination of which independent variables mainly influenced the dependent variable of the study. From the use of MLR with backward elimination procedure, spoilages from sacks and root cellars methods were the independent variables which mainly influenced the total spoilage of sweet potato in Plateau State Nigeria. Therefore, policy holders in Plateau State need to offer agricultural extension services that would provide modern storage facilities or educate sellers of sweet potato on best way to use both basket and root cellar methods which were their adopted methods commonly used for storage of sweet potato.

A new procedure for elimination of atrial fibrillation associated with mitral valve disease：a proof-of-concept study.

Left atrial enlargement and fibrosis have been linked to the pathogenesis of atrial fibrillation (AF). We aimed to introduce a novel concept and develop a new procedure for AF treatment based on these characteristics. The study included three stages. The first stage was a descriptive study to clarify the characteristics of the left atrial enlargement and fibrosis' distribution in patients with mitral valve disease and long-standing persistent AF. Based on these characteristics, we introduced a novel concept for AF treatment, and then translated it into a new procedure. The second stage was a proof-of-concept study with this new procedure. The third stage was a comparative effectiveness research to compare the clinical outcomes between patients with this new procedure and those who received Cox-Maze IV treatment. Based on non-uniform fashion of left atrial enlargement and fibrosis' distribution, we introduced a novel concept: reconstructing a left atrium with appropriate geometry and uniform fibrosis' distribution for proper cardiac conduction, and translated it into a new procedure: left atrial geometric volume reduction combined with left appendage base closure. As compared to Cox-Maze IV procedure, the new procedure spent significantly shorter total surgery time, cardiopulmonary bypass time and aortic cross-clamp time (P<0.001). Besides, the new procedure was related to a shorter ICU stay period (OR=0.45, 95%CI=0.26-0.78), lower costs (OR=0.15, 95%CI=0.08-0.29) and a higher rate of A wave of transmitral and transtricuspid flow reappearance (OR=1.76, 95%CI=1.02-3.04). The new procedure is safe and effective for eliminating AF associated with mitral valve disease.

Exploring Gaussian elimination based solution methods of electric systems using circuit principles

AbstractIn power system simulation, the mathematical solution methods are typically regarded as pure numerical operations, which are independent of the circuit principles. In this letter, it is demonstrated that the computational procedures in the commonly used numerical solution methods for circuit equations are essentially equivalent to the basic principles of electricity. The computational procedures of the representative Gaussian elimination based solution methods (including GE and pivoting method for sparsity of GE/LU) are comprehensively analyzed and their deep relationships with the circuit principles are revealed: the computation process of these mathematics based numerical solution methods can be completely explained using circuit principles. The interesting findings could help understand the computational process of solution methods from the circuit perspective, which may also guide the design of new circuit solution methods.

Modeling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion

Fires in buildings are significant public safety hazards and can result in fatalities and substantial financial losses. Studies have shown that the socioeconomic makeup of a region can impact the occurrence of building fires. However, existing models based on the classical stepwise regression procedure have limitations. This paper proposes a more accurate predictive model of building fire rates using a set of socioeconomic variables. To improve the model’s forecasting ability, a backward elimination by robust final predictor error (RFPE) criterion is introduced. The proposed approach is applied to census and fire incident data from the South East Queensland region of Australia. A cross-validation procedure is used to assess the model’s accuracy, and comparative analyses are conducted using other elimination criteria such as p-value, Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and predicted residual error sum of squares (PRESS). The results demonstrate that the RFPE criterion is a more accurate predictive model based on several goodness-of-fit measures. Overall, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modeling of building fires.

Longitudinal Clinical and MRI Outcomes in Relapsing Multiple Sclerosis (RMS) Patients After Short-term Ponesimod Treatment Interruption and Re-initiation (P6-3.007)

Longitudinal Clinical and MRI Outcomes in Relapsing Multiple Sclerosis (RMS) Patients After Short-term Ponesimod Treatment Interruption and Re-initiation (P6-3.007)

Operational characteristics of full random effects modelling (‘frem’) compared to stepwise covariate modelling (‘scm’)

An adequate covariate selection is a key step in population pharmacokinetic modelling. In this study, the automated stepwise covariate modelling technique (‘scm’) was compared to full random effects modelling (‘frem’). We evaluated the power to identify a ‘true’ covariate (covariate with highest correlation to the pharmacokinetic parameter), precision, and accuracy of the parameter-covariate estimates. Furthermore, the predictive performance of the final models was assessed. The scenarios varied in covariate effect sizes, number of individuals (n = 20–500) and covariate correlations (0–90% cov-corr). The PsN ‘frem’ routine provides a 90% confidence intervals around the covariate effects. This was used to evaluate its operational characteristics for a statistical backward elimination procedure, defined as ‘fremposthoc’ and to facilitate the comparison to ‘scm’. ‘Fremposthoc’ had a higher power to detect the true covariate with lower bias in small n studies compared to ‘scm’, applied with commonly used settings (forward p < 0.05, backward p < 0.01). This finding was vice versa in a statistically similar setting. For ‘fremposthoc’, power, precision and accuracy of the covariate coefficient increased with higher number of individuals and covariate effect magnitudes. Without a backward elimination step ‘frem’ models provided unbiased coefficients with highly imprecise coefficients in small n datasets. Yet, precision was superior to final ‘scm’ model precision obtained using common settings. We conclude that ‘fremposthoc’ is also a suitable method to guide covariate selection, although intended to serve as a full model approach. However, a deliberated selection of automated methods is essential for the modeller and using those methods in small datasets needs to be taken with caution.

Bonferroni-Free and Indifference-Zone-Flexible Sequential Elimination Procedures for Ranking and Selection

The curse of dimensionality has long been one of the biggest challenges in solving large-scale simulation ranking and selection (R&amp;S) problems. As the number of systems grows, existing approaches to R&amp;S relying on the Bonferroni correction become increasingly conservative, rendering them overachieving in error control and consuming more computational resources than necessary. In “Bonferroni-Free and Indifference-Zone-Flexible Sequential Elimination Procedures for Ranking and Selection,” Wang, Wan, and Chen develop Bonferroni-free and indifference-zone-optional ranking and selection procedures to deliver the prescribed probabilistic guarantee without overshooting. Their approach is to conduct always valid and fully sequential hypothesis tests that enable continuous monitoring of each candidate system and control the probability of correct selection. In addition, the indifference-zone parameter becomes dispensable in their procedures; however, when provided appropriately, it could improve the procedures’ computational and statistical efficiency.

Relationship between serum iPTH and peritonitis episodes in patients undergoing continuous ambulatory peritoneal dialysis.

Peritonitis is considered as one of the most serious complications that cause hospitalization in patients undergoing continuous ambulatory peritoneal dialysis (CAPD). There is limited evidence on the impact of the parathyroid hormone (PTH) on the first peritoneal dialysis (PD)-associated peritonitis episode. We aimed to investigate the influence of serum intact parathyroid hormone (iPTH) on peritonitis in patients undergoing PD. This was a retrospective cohort study. Patients undergoing initial CAPD from a single center in China were enrolled. The baseline characteristics and clinical information were recorded. The primary outcome of interest was the occurrence of the first PD-associated peritonitis episode. Five Cox proportional hazard models were constructed in each group set. In group set 1, all participants were divided into three subgroups by tertiles of the serum concentration of iPTH; in group set 2, all participants were divided into three subgroups based on the serum concentration of iPTH with 150pg/ml interval (<150, 150-300, and >300pg/ml). Hazard ratios and 95% confidence intervals (CIs) were calculated for each model. The multivariate linear regression analysis elimination procedure assessed the association between the clinical characteristics at baseline and the iPTH levels. Restricted cubic spline models were constructed, and stratified analyses were also conducted. A total of 582 patients undergoing initial PD (40% women; mean age, 45.1±11.5years) from a single center in China were recruited. The median follow-up duration was 25.3months. Multivariate Cox regression analysis showed that, in the fully adjusted model, a higher serum iPTH level (tertile 3, iPTH>300pg/ml) was significantly associated with a higher risk of PD-associated peritonitis at 3years [tertile 3: hazard ratio (HR)=1.53, 95%CI=1.03-2.55, p=0.03; iPTH>300pg/ml: HR=1.57, 95%CI=1.08-2.27, p=0.02]. The hazard ratio for every 100pg/ml increase in serum iPTH level was 1.12 (95%CI=1.05-1.20, p<0.01) in the total cohort when treating iPTH as a continuous variable. An elevated iPTH level was significantly associated with an increased risk of peritonitis in patients undergoing CAPD.

Power Curve Modeling of Wind Turbines through Clustering-Based Outlier Elimination

The estimation of power curve is the central task for efficient operation and prediction of wind power generation. It is often the case, however, that the actual data exhibit a great deal of variations in power output with respect to wind speed, and thus the power curve estimation necessitates the detection and proper treatment of outliers. This study proposes a novel procedure for outlier detection and elimination for estimating power curves of wind farms by employing clustering algorithms of vector quantization and density-based spatial clustering of applications with noise. Testing different parametric models of power output curve, the proposed methodology is demonstrated for obtaining power curves of individual wind turbines in a Korean wind farm. It is asserted that the outlier elimination procedure for power curve modeling outlined in this study can be highly efficient at the presence of noises.