Set Of Intervals Research Articles

The interval number of a planar graph is at most three

The interval number of a graph G is the minimum k such that one can assign to each vertex of G a union of k intervals on the real line, such that G is the intersection graph of these sets, i.e., two vertices are adjacent in G if and only if the corresponding sets of intervals have non-empty intersection.Scheinerman and West (1983) [14] proved that the interval number of any planar graph is at most 3. However the original proof has a flaw. We give a different and shorter proof of this result.

Modulation of Individual Alpha Frequency with tACS shifts Time Perception.

Previous studies have linked brain oscillation and timing, with evidence suggesting that alpha oscillations (10 Hz) may serve as a “sample rate” for the visual system. However, direct manipulation of alpha oscillations and time perception has not yet been demonstrated. To test this, we had 18 human subjects perform a time generalization task with visual stimuli. Additionally, we had previously recorded resting-state EEG from each subject and calculated their individual alpha frequency (IAF), estimated as the peak frequency from the mean spectrum over posterior electrodes between 8 and 13 Hz. Participants first learned a standard interval (600 ms) and were then required to judge if a new set of temporal intervals were equal or different compared with that standard. After learning the standard, participants performed this task while receiving occipital transcranial Alternating Current Stimulation (tACS). Crucially, for each subject, tACS was administered at their IAF or at off-peak alpha frequencies (IAF ± 2 Hz). Results demonstrated a linear shift in the psychometric function indicating a modification of perceived duration, such that progressively “faster” alpha stimulation led to longer perceived intervals. These results provide the first evidence that direct manipulations of alpha oscillations can shift perceived time in a manner consistent with a clock speed effect.

Length-Matching-Constrained Region Routing in Rapid Single-Flux-Quantum Circuits

It is known that the pipelined architecture in a rapid single-flux-quantum (RSFQ) circuit can be constructed by inserting a set of path-balancing D-type flip-flops (DFFs) into some gate columns. Based on the assignment of the gates involving the splitters (SPLs) inside each gate column in the placement stage, the passive transmission line (PTL) region between two adjacent gate columns can be formed for a set of 2-pin connections in the routing stage. In this article, given a set of 2-pin connections with length-matching constraints inside one PTL region, based on the efficient utilization of available space in two routing layers, a new grid-based Manhattan routing model can be defined to use available space inside two routing layers for the insertion of the extension lengths on the given connections. To minimize the routing width inside one PTL region, a two-way track-assignment-based routing algorithm using the defined routing model can be first proposed to assign two partitioned sets of vertical intervals onto the used tracks inside two routing layers and connect the corresponding horizontal segments for the given connections with no extension length. Based on the definition of the available areas in the initial two-layer routing result, an iterative flow-based insertion algorithm can be further proposed to insert the feasible detouring paths onto the available areas for the extension lengths on the given connections. If there is no available area for the extension lengths on the unsatisfied connections, an efficient insertion algorithm can be proposed to insert the detouring paths onto one extra area for the extension lengths on the unsatisfied connections. Compared with Kito's routing algorithm and Cheng's routing algorithm in length-matching-constrained region routing, the experimental results show that our proposed routing algorithm can use reasonable CPU time to decrease 22.2% and 16.3% of the region width for 12 tested examples on the average, respectively.

Novel Stable Approach with Probability Distribution for Multi-Criteria Decision-Making Problems of Multi-Valued Neutrosophic Sets

In this paper, a novel approach and framework based on interval-dependent degree and probability distribution for multi-criteria decision-making problems with multi-valued neutrosophic sets (MVNSs) is proposed. First, a simplified dependent function and distribution function are given and integrated into a concise formula, which is called the interval-dependent function and contains interval computing and probability distribution information in an interval. Then a transformation operator is defined and it is shown how to convert MVNSs into an interval set. Subsequently, the interval-dependent function with the probability distribution of MVNSs is deduced. Finally, an example and comparative analysis are provided to verify the feasibility and effectiveness of the proposed method. In addition, uncertainty analysis, which reflects the dynamic change of the ranking result with decision-makers’ preferences, is performed by setting different distribution functions, which increases the reliability and accuracy of the proposed method.

Factorial Analysis of Maternal Mortality Audits: A Case of Migori County Referral Hospital, Kenya

Background: Maternal mortality is a global health concern especially in developing countries with 99% of all maternal deaths. Kenya reports a mortality rate of 342/100000 with majority of these occurring in northern and western parts of the country with Migoricounty recording annual maternal mortality rate of 673/100000. As part of Sustainable Development Goals, the target is to reduce the global maternal mortality ratio to below 70 per 100 000 live births. Maternal death reviews are globally accepted as a means of mitigating against avoidable maternal deaths. Kenya adopted and rolled this in 2004. The efficiency of this intervention in major referral facilities remain unmonitored and undocumented. Objectives: Major goal of this study was to examine the effect of maternal death review on maternal outcomes with specific aims being: To determine the prevalence and compare the trends of maternal mortality over two years, identify factors contributing to maternal mortality and to examine and compare the trends of factors leading to maternal mortality between the two years in the referral hospital..Methodology: Data on all audited maternal deaths that occurred in Migori County Referral hospital between 2018-19 were abstracted from District Health Information system and analyzed using excel version 2007. Both descriptive and inferential statistics were used. We used both chi-square test and test of difference for proportions with CI interval set at 95% p=0.05 to test our null hypotheses.Results: Total of 36 maternal deaths were recorded during the study period with 16 (329/100000) and 20 (469/100000) occurring in 2018 and 2019 respectively. We found a significant association between parity and maternal mortality, p-value=0.0027 and a significant association between gestation and maternal deaths, p-value =0.0006.Conclusion: Maternal death reviews has not led to reduction of maternal mortality in the referral hospital.

Acoustical damage detection of wind turbine yaw system using Bayesian network

Abstract Yaw system plays a significant role in increasing wind power production and protecting the wind turbine. However, the working yaw system suffers from complex alternating stresses and could result in failure and significant economic losses. This paper develops an acoustical damage detection method of the yaw system based on Bayesian network (BN). In the method, the sound pressure level (SPL) features are first extracted from the measuring acoustic signal to characterize the state of yaw system. Subsequently, a data discretization method based on self-organizing map and information gain rate is proposed to convert continuous SPL features into a finite set of intervals with respect to attribute values. Besides, a three-layer BN diagnostic model combined with the structure learning strategy based on Bayesian information criterion is designed for damage detection of the yaw system. Finally, experiments are conducted in practical wind farm to validate the feasibility and efficiency of the proposed method.

Selection of time instants and intervals with Support Vector Regression for multivariate functional data

When continuously monitoring processes over time, data is collected along a whole period, from which only certain time instants and certain time intervals may play a crucial role in the data analysis. We develop a method that addresses the problem of selecting a finite and small set of short intervals (or instants) able to capture the information needed to predict a response variable from multivariate functional data using Support Vector Regression (SVR).In addition to improving interpretability, storage requirements, and monitoring cost, feature selection can potentially reduce overfitting by mitigating data autocorrelation. We propose a continuous optimization algorithm to fit the SVR parameters and select intervals and instants. Our approach takes advantage of the functional nature of the data by formulating a new bilevel optimization problem that integrates selection of intervals and instants, tuning of some key SVR parameters and fitting the SVR. We illustrate the usefulness of our proposal in some benchmark data sets.

COVID-19 Airway Management Isolation Chamber.

ObjectiveDuring the coronavirus pandemic (COVID-19), health care workers are innovating patient care and safety measures. Unfortunately, many of these are not properly tested for efficacy. The objective of this study was to determine the efficacy of the novel COVID-19 Airway Management Isolation Chamber (CAMIC) to contain and evacuate particulate.Study DesignMulti-institutional proof-of-concept study.SettingTwo academic institutions: Walter Reed National Military Medical Center (WRNMMC) and Madigan Army Medical Center (MAMC).Subjects and MethodsSmoke, saline nebulizer, and simulated working port models were developed to assess the efficacy of the CAMIC to contain and remove ultrafine particles. Particulate counts were collected at set time intervals inside and outside the system.ResultsWith the CAMIC on, smoke particulate counts inside the chamber significantly decreased over time: r(18) = −0.88, P < .001, WRNMMC; r(18) = −0.91, P < .001, MAMC. Similarly, saline nebulizer particulate counts inside the chamber significantly decreased over time: r(23) = −0.82, P < .001, WRNMMC; r(23) = −0.70, P < .001, MAMC. In the working port model, particulate counts inside the chamber significantly decreased over time: r(23) = −0.95, P < .001, WRNMMC; r(23) = −0.85, P < .001, MAMC. No significant leak was detected in the smoke, saline nebulizer, or working port model when the CAMIC was turned on.ConclusionsThe CAMIC system appears to provide a barrier that actively removes particles from within the chamber and limits egress. Further studies are necessary to determine clinical applicability. The CAMIC may serve as an adjunct to improve health care worker safety and patient outcomes.

Cotangent similarity measure of single-valued neutrosophic interval sets with confidence level for risk-grade evaluation of prostate cancer

The indeterminacy/inconsistency information of physicians’ confident degrees regarding their judgments is not considered in existing risk-grade evaluation methods of prostate cancer (PC). To overcome the insufficiency, based on the single-valued neutrosophic interval sets (SvNISs) expressing the hybrid information of both the uncertain judgment given by an interval number and the confident degree regarding the uncertain judgment expressed by a single-valued neutrosophic number, this original study contributes a cotangent similarity measure of SvNISs with confidence level, and a novel risk-grade evaluation method of PC by using the confidence level-based cotangent similarity measure. Then, 16 PC actual clinical cases are used to demonstrate the applicability and effectiveness of the developed risk-grade evaluation method in SvNIS setting. Finally, the comparison analysis with other existing evaluation methods and the sensitivity analysis of confidence levels show that the proposed risk-grade evaluation method of PC is reasonable and effective.

Seqpare: a self-consistent metric of similarity between genomic interval sets.

Searching genomic interval sets produced by sequencing methods has been widely and routinely performed; however, existing metrics for quantifying similarities among interval sets are inconsistent. Here we introduce Seqpare, a self-consistent and effective metric of similarity and tool for comparing sequences based on their interval sets. With this metric, the similarity of two interval sets is quantified by a single index, the ratio of their effective overlap over the union: an index of zero indicates unrelated interval sets, and an index of one means that the interval sets are identical. Analysis and tests confirm the effectiveness and self-consistency of the Seqpare metric.

Periodicity Pitch Perception.

This study presents a computational model to reproduce the biological dynamics of “listening to music.” A biologically plausible model of periodicity pitch detection is proposed and simulated. Periodicity pitch is computed across a range of the auditory spectrum. Periodicity pitch is detected from subsets of activated auditory nerve fibers (ANFs). These activate connected model octopus cells, which trigger model neurons detecting onsets and offsets; thence model interval-tuned neurons are innervated at the right interval times; and finally, a set of common interval-detecting neurons indicate pitch. Octopus cells rhythmically spike with the pitch periodicity of the sound. Batteries of interval-tuned neurons stopwatch-like measure the inter-spike intervals of the octopus cells by coding interval durations as first spike latencies (FSLs). The FSL-triggered spikes synchronously coincide through a monolayer spiking neural network at the corresponding receiver pitch neurons.

New two-sided confidence intervals for binomial inference derived using Walley's imprecise posterior likelihood as a test statistic

Starting with the Sterne's confidence regions, there have been a string of related attempts to improve on the two-sided Clopper–Pearson bounds for binomial inference. That work is brought to fruition using Walley's imprecise posterior likelihood as the basis for a new test statistic. The results are expressed as a consonant confidence structure, from which a new set of exact confidence intervals for binomial inference can be efficiently computed.

Numerical Linear Programming under Non-Probabilistic Uncertainty Models — Interval and Fuzzy Sets

This paper considers a linear optimisation problem under uncertainty with at least one element modelled as a non-probabilistic uncertainty. The uncertainty is expressed in the coefficient matrices of constraints and/or coefficients of goal function. Previous work converts such problems to classical (linear) optimisation problems and eliminates uncertainty by converting the linear programming under uncertainty problem to a decision problem using imprecise probability and imprecise decision theory. Our aim here is to generalise this approach numerically and present three methods to calculate the solution. We investigate what numerical results can be obtained for interval and fuzzy types of uncertainty models and compare them to classical probabilistic cases — for two different optimality criteria: maximinity and maximality. We also provide an efficient method to calculate the maximal solutions in the fuzzy set model. A numerical example is considered for illustration of the results.

Managing and Measuring Emergency Department Care: Results of the Fourth Emergency Department Benchmarking Definitions Summit.

A shared language and vocabulary are essential for managing emergency department (ED) operations. This Fourth Emergency Department Benchmarking Alliance (EDBA) Summit brought together experts in the field to review, update, and add to key definitions and metrics of ED operations. Summit objectives were to review and revise existing definitions, define and characterize new practices related to ED operations, and introduce financial and regulatory definitions affecting ED reimbursement. Forty-six ED operations, data management, and benchmarking experts were invited to participate in the EDBA summit. Before arrival, experts were provided with documents from the three prior summits and assigned to update the terminology. Materials and publications related to standards of ED operations were considered and discussed. Each group submitted a revised set of definitions prior to the summit. Significantly revised, topical, or controversial recommendations were discussed among all summit participants. The goal of the in-person discussion was to reach consensus on definitions. Work group leaders made changes to reflect the discussion, which was revised with public and stakeholder feedback. The entire EDBA dictionary was updated and expanded. This article focuses on an update and discussion of definitions related to specific topics that changed since the last summit, specifically ED intake, boarding, diversion, and observation care. In addition, an extensive new glossary of financial and regulatory terminology germane to the practice of emergency medicine is included. A complete and precise set of operational definitions, time intervals, and utilization measures is necessary for timely and effective ED care. A common language of financial and regulatory definitions that affect ED operations is included for the first time. This article and its companion dictionary should serve as a resource to ED leadership, researchers, informatics and health policy leaders, and regulatory bodies.

Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks.

Neural responses of oddball tasks can be used as a physiological biomarker to evaluate the brain potential of information processing under the assumption that the differential contribution of deviant stimuli can be assessed accurately. Nevertheless, the non-stationarity of neural activity causes the brain networks to fluctuate hugely in time, deteriorating the estimation of pairwise synergies. To deal with the time variability of neural responses, we have developed a piecewise multi-subject analysis that is applied over a set of time intervals within the stationary assumption holds. To segment the whole stimulus-locked epoch into multiple temporal windows, we experimented with two approaches for piecewise segmentation of EEG recordings: a fixed time-window, at which the estimates of FC measures fulfill a given confidence level, and variable time-window, which is segmented at the change points of the time-varying classifier performance. Employing the weighted Phase Lock Index as a functional connectivity metric, we have presented the validation in a real-world EEG data, proving the effectiveness of variable time segmentation for connectivity extraction when combined with a supervised thresholding approach. Consequently, we performed a piecewise group-level analysis of electroencephalographic data that deals with non-stationary functional connectivity measures, evaluating more carefully the contribution of a link node-set in discriminating between the labeled oddball responses.

A Unified Model and Algorithms for Temporal Map Labeling

We consider map labeling for the case that a map undergoes a sequence of operations such as rotation, zoom and translation over a specified time span. We unify and generalize several previous models for dynamic map labeling into one versatile and flexible model. In contrast to previous research, we completely abstract from the particular operations and express the labeling problem as a set of time intervals representing the labels’ presences, activities and conflicts. One of the model’s strength is manifested in its simplicity and broad range of applications. In particular, it supports label selection both for map features with fixed position as well as for moving entities (e.g., for tracking vehicles in logistics or air traffic control). We study the active range maximization problem in this model. We prove that the problem is NP-complete and W[1]-hard, and present constant-factor approximation algorithms. In the restricted, yet practically relevant case that no more than k labels can be active at any time, we give polynomial-time algorithms as well as constant-factor approximation algorithms.

Multiple attribute decision making based on immediate probabilities aggregation operators for single-valued and interval neutrosophic sets

The objective of this paper is to present novel algorithms for solving the multiple attribute decision-making problems under the neutrosophic set environment. Single-valued and interval neutrosophic sets are the important mechanisms for directing the decision-making queries with unknown and indeterminant data by employing a degree of “acceptance”, “indeterminacy”, and “non-acceptance” in quantitative terms. Also, to describe the behavior of the decision-maker objectively (in terms of probability) and subjectively (in terms of weights), a concept of probabilistic information plays a dominant role in the investigation. Keeping these features in mind, this paper presents several probabilistic and immediate probability-based averaging and geometric aggregation operators for the collection of the single-valued and interval neutrosophic sets. The advantage of these proposed operators is that it simultaneously combines the objective and subjective behavior of the decision-maker during the process. The various salient features of the proposed operators are studied. Later, we develop two new algorithms based on the aggregation operators to solve multiple attribute decision-making problems with single-valued and interval neutrosophic sets features. A numerical example related to the demonetization is given to demonstrate the presented approaches, and the advantages, as well as comparative analysis, are given to shows its influence over existing approaches.

A new approach to precise interval estimation for the parameters of the hypergeometric distribution

We study interval estimation for both parameters of the hypergeometric distribution: (i) the number of successes in a finite population and (ii) the size of the population. In contrast to traditional methods that specify intervals via a formula, our approach is to first establish the coverage probability function of an ideal procedure. This in turn determines the set of confidence intervals. In the case when the population size is known and we wish to estimate the number of successes, our approach is superior to existing methods in terms of average interval length and in fact achieves the minimum possible average length. In the case of estimating population size, our procedure also tends to produce shorter intervals than existing methods. Both procedures also possess an attractive coverage property.

Effect of Ageing on the Structure and Properties of Model Liquid-Infused Surfaces.

Liquid-infused surfaces (LISs) exhibit unique properties that make them ideal candidates for a wide range of applications, from antifouling and anti-icing coatings to self-healing surfaces and controlled wetting. However, when exposed to realistic environmental conditions, LISs tend to age and progressively lose their desirable properties, potentially compromising their application. The associated ageing mechanisms are still poorly understood, and results reflecting real-life applications are scarce. Here, we track the ageing of a model LIS composed of glass surfaces functionalized with hydrophobic nanoparticles and infused with silicone oil. The LISs are fully submerged in aqueous solutions and exposed to acoustic pressure waves for set time intervals. The ageing is monitored by periodic measurements of the LIS’s wetting properties. We also track the changes to the LIS’s nanoscale structure. We find that the LISs rapidly lose their slippery properties because of a combination of oil loss, smoothing of the nanoporous functional layer, and substrate degradation when directly exposed to the solution. The oil loss is consistent with water microdroplets entering the oil layer and displacing oil away from the surface. These mechanisms are general and could play a role in the ageing of most LISs.

Some matheuristic algorithms for multistage stochastic optimization models with endogenous uncertainty and risk management

Two matheuristic decomposition algorithms are introduced. The first one is a Progressive Hedging type so-named Regularized scenario Cluster Progressive Algorithm. The second one is a Frank-Wolfe PH type so-named Regularized scenario Cluster Simplicial Decomposition Progressive Algorithm. An extension of endogenous Type III uncertainty is considered for representing the decision-dependent scenario probability and outlook. Its performance is tested in the time-consistent Expected Conditional Stochastic Dominance risk averse environment. As a result of the modeling, the typical risk neutral multistage mixed 0–1 linear stochastic problem under uncertainty is replaced with an enlarged model that is equivalent to the required mixed 0–1 bilinear model. Based on the special features of the problem, it is unrealistic to seek the optimal solution for large-scale instances. Feasible solutions and lower bounds on the solution value of the original model are provided. In total, 48 strategies are considered, each one consists of a combination of a regularization norm, a calibration type for the PH pseudo-gradient computation, and a set of value intervals of the influential variables on a representative endogenous uncertainty-based piecewise function in the scenarios. Computational results are reported for a large-scale extension of a well-known real-life pilot case for preparedness resource allocation planning aiming to natural disaster relief. The matheuristics outperform the plain use of a state-of-the-art solver.