Meaningless Values Research Articles

THE PROBLEM OF NATIONAL IDENTITY IN MARTIN MCDONAGH’S THE BEAUTY QUEEN OF LEENANE

The end of the nineteenth century was a turning point for Irish theatre with the establishment of the Irish Literary Theatre as playwrights attempted to write national plays disengaging from the British theatre. This period is known as the Celtic Revival, a period of building a national identity both by political and cultural action, during which the playwrights contributed to the rejuvenation of the old Irish myths and traditions. However, at the end of the twentieth century when Ireland became a multicultural country with multiple identities, and opened to the outer world politically and economically, the Irish theatre, too, started to change its outlook by problematizing the nationalistic elements of Irishness instead of glorifying the national and cultural values. The Irish playwright, Martin McDonagh has also maintained a pluralist approach challenging the traditional and national elements in his plays. Instead of adhering to a national identity, McDonagh believes in cultural pluralism to adapt to the new century. McDonagh’s pluralist approach will be discussed with Deleuze and Guattari’s concept of ‘nomadic identity’ and ‘rhizome’. Thus, this paper aims to analyze how McDonagh problematizes the national identity in The Beauty Queen of Leenane by portraying characters, who are incarcerated by meaningless national values and norms.

Evaluation of appropriateness of alerts overrides and physicians' responses of the medication-related clinical decision support system in China, a hospital-based study.

This study was designed to investigate the state quo of the appropriateness of alerts overrides of the medication-related clinical decision support system (MRCDSS) in China. The medication-related alerts in one hospital from Jan 2022 to Dec 2022 were acquired and sampled. Rates of alert overrides, appropriateness of alert generation and physicians' responses were observed. Total 14,612 medication-related alerts (≤ level 3) were recorded, of those, 12,659 (86.6%) alerts were overridden. The top 3 alert types were: drug and diagnosis contraindications (23.8%), drug and test value contraindications (23.3%), and compatibility issues (17.7%). Of all sampled 1,501 alerts, 80.2% of them were appropriately overridden by the physicians. The appropriate rate of alert generation was 57.9% and the inappropriate rate was 42.1%. The inappropriate rate of physicians' responses was 17.8%, and 2.0% physicians' responses were undetermined. A few medications accounted for over 10% of overrides, 88.3% of "overridden reasons" inputted by the physicians were meaningless characters or values, indicating an obvious "alert fatigue" in these physicians. Our results indicated that the overridden rate of MRCDSS in China was still high, and appropriateness of generation of alert was quite low. These data indicated that the MRCDSS currently using in China still needs constantly optimization and timely maintenance. Proper sensitivity to reduce triggering of useless alerts and generation of alert fatigue might play a vital role. We believed that these findings are helpful for better understanding the state quo of MRCDSS in China and providing useful insights for future developing and improving MRCDSS.

Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis

This research addresses the problem of detecting acute respiratory, urinary tract, and other infectious diseases in elderly nursing home residents using machine learning algorithms. The study analyzes data extracted from multiple vital signs and other contextual information for diagnostic purposes. The daily data collection process encounters sampling constraints due to weekends, holidays, shift changes, staff turnover, and equipment breakdowns, resulting in numerous nulls, repeated readings, outliers, and meaningless values. The short time series generated also pose a challenge to analysis, preventing the extraction of seasonal information or consistent trends. Blind data collection results in most of the data coming from periods when residents are healthy, resulting in excessively imbalanced data. This study proposes a data cleaning process and then builds a mechanism that reproduces the basal activity of the residents to improve the classification of the disease. The results show that the proposed basal module-assisted machine learning techniques allow anticipating diagnostics 2, 3 or 4 days before doctors decide to start treatment with antibiotics, achieving a performance measured by the area-under-the-curve metric of 0.857. The contributions of this work are: (1) a new data cleaning process; (2) the analysis of contextual information to improve data quality; (3) the generation of a baseline measure for relative comparison; and (4) the use of either binary (disease/no disease) or multiclass classification, differentiating among types of infections and showing the advantages of multiclass versus binary classification. From a medical point of view, the anticipated detection of infectious diseases in institutionalized individuals is brand new.

Learning-based Relaxation of Completeness Requirements for Data Entry Forms

Data entry forms use completeness requirements to specify the fields that are required or optional to fill for collecting necessary information from different types of users. However, because of the evolving nature of software, some required fields may not be applicable for certain types of users anymore. Nevertheless, they may still be incorrectly marked as required in the form; we call such fields obsolete required fields. Since obsolete required fields usually have “not-null” validation checks before submitting the form, users have to enter meaningless values in such fields to complete the form submission. These meaningless values threaten the quality of the filled data and could negatively affect stakeholders or learning-based tools that use the data. To avoid users filling meaningless values, existing techniques usually rely on manually written rules to identify the obsolete required fields and relax their completeness requirements. However, these techniques are ineffective and costly. In this article, we propose LACQUER, a learning-based automated approach for relaxing the completeness requirements of data entry forms. LACQUER builds Bayesian Network models to automatically learn conditions under which users had to fill meaningless values. To improve its learning ability, LACQUER identifies the cases where a required field is only applicable for a small group of users and uses SMOTE, an oversampling technique, to generate more instances on such fields for effectively mining dependencies on them. During the data entry session, LACQUER predicts the completeness requirement of a target based on the already filled fields and their conditional dependencies in the trained model. Our experimental results show that LACQUER can accurately relax the completeness requirements of required fields in data entry forms with precision values ranging between 0.76 and 0.90 on different datasets. LACQUER can prevent users from filling 20% to 64% of meaningless values, with negative predictive values (i.e., the ability to correctly predict a field as “optional”) between 0.72 and 0.91. Furthermore, LACQUER is efficient; it takes at most 839 ms to predict the completeness requirement of an instance.

Improving McDiarmid Tree Performance for Predicting Heart Disease from Data Streams with Missing and Meaningless Values

Building machine learning algorithms for real-time clinical decision support systems has become a current research hotspot. The success of these algorithms depends on their ability to handle data stream characteristics. For example, we cite as characteristics the large amounts of data, the high speed and rate of incoming data, and the change in data nature and distribution over time. The Very Fast Decision Tree (VFDT) is a method for incrementally building decision trees. Since its proposition in the literature, it has become one of the most popular tools for data stream classification. This paper aims to optimize a new version of VFDT called McDiarmid Tree (MT) for the early prediction of heart diseases in real-time clinical decision support systems. The proposed method for improving MT performance consists of two main mechanisms: detecting the presence of missing and meaningless values in data attributes and handling the impact of this presence. The proposed MT has been compared with MT and VFDT. Simulation results show that the proposed MT attains significantly higher prediction accuracy with less time and model cost (RAM-Hours) than the other two algorithms.

Age and stage-specific life table parameters of Harmonia dimidiata (Coleoptera: Coccinellidae) fed on Rhopalosiphum padi (Hemiptera: Aphididae) at different temperatures

BackgroundThe life history and predation rate of the ladybird beetle, Harmonia dimidiata Fabricius (Coleoptera: Coccinellidae) were compared at four different temperatures (16, 20, 24, and 28 °C). The beetles were fed on the bird cherry-oat aphid, Rhopalosiphum padi Linnaeus (Hemiptera: Aphididae) and investigated at 70 ± 10% RH with a photoperiod of 14:10 h (L:D).ResultsThe reproductive rates (R0) were 20.07, 51.37, 66.95, and 14.54 beetle offspring at 16, 20, 24, and 28 °C, respectively. Results indicated that temperature had good impacts on the feeding potential, development, survivorship and fecundity, especially at 24 and 28 C compared with the other tested temperatures. In addition, the jackknife and bootstrap techniques were employed to estimate the population parameters' means. The obtained means of R0 and other population parameters, using the bootstrap technique fit a normal distribution. Meanwhile, the jackknife technique generated biologically meaningless zero values for R0. Both finite and predation rates were incorporated into limited predation rates for comparison of predation potential.ConclusionBoth of the growth and predation rates indicated that H. dimidiata is more effective biocontrol agent for R. padi at 24 and 28 °C than at 20, 16 °C.

Верифікація гідродинамічної моделі кавітуючих насосів РРД за теоретичними та експериментальними передавальними матрицями насосів

Cavities at the pump inlet may lead to inadmissible cavitation self-oscillations in the feed system of liquid-propellant rocket engines (LPREs) and to POGO instability if the oscillation frequency of the liquid is close to that of the rocket structure. Because of this, it is important to prevent both cavitation and POGO oscillations as early as at the engine and rocket design stage. This calls for a reliable mathematical model of the dynamics of LPRE cavitating pumps. In this paper, a hydrodynamic model of LPRE cavitating pumps is verified using theoretical and experimental transfer matrices of cavitating pumps. The experimental transfer matrix was borrowed from Brennen, Meissner, Lo, and Hoffman’s work because it features the least spread of values among the matrices reported in the literature. The theoretical matrix was borrowed from Pilpenko and Kvasha’s work where is was constructed for a cavitating pump as a distributed-parameter system. Four versions of the hydrodynamic model of LPRE cavitating pumps are verified, and six possible model coefficients are considered. Only one coefficient, namely, the liquid inertance at the cavity location, takes a physically meaningless negative value, which makes its use impossible. The verification results show that a four-coefficient model of cavitating pipe dynamics adequately describes cavitation effects in LPRE pumps over the frequency range up to 200 Hz. The four coefficients are the cavitation elasticity, the cavitation resistance, the cavity-caused disturbance transfer delay time, and the cavity time constant or the cavitation resistance distribution coefficient.

Accessing the discriminatory performance of FRAIL-NH in two-class and three-class frailty and examining its agreement with the frailty index among nursing home residents in mainland China

BackgroundFRAIL-NH has been commonly used to assess frailty in nursing home residents and validated in many ethnic populations; however, it has not been validated in mainland China, where such an assessment tool is lacking. This study aimed to (1) assess the discriminatory performance of FRAIL-NH in two-class frailty (non-frail+ pre-frail vs. frail) and three-class frailty (non-frail vs. pre-frail vs. frail), based on the Frailty Index (FI), (2) determine the appropriate cutoff points for FRAIL-NH that distinguish two-class and three-class frailty, and (3) examine the agreement in classification between FRAIL-NH and FI.MethodsA cross-sectional study of 302 residents aged 60 years or older from six nursing homes in Changsha was conducted. The FRAIL-NH scale and 34-item FI were used to measure frailty. Two-way and three-way receiver operating characteristic (ROC) curves were used to estimate the performance of FRAIL-NH. Cohen’s Kappa statistics were used to examine the agreement between these two measures.ResultsThe agreement between FRAIL-NH and FI ranged from 0.33 to 0.55. Regardless of what FI cutoff points were based on, the volume under the ROC surface (VUS) for FRAIL-NH from the three-way ROC were higher than the VUS of a useless test (1/6), and the area under the ROC curve (AUC) for FRAIL-NH from the two-way ROC were higher than the clinically meaningless value (0.5). When using FI cutoff points of 0.20 for pre-frail and 0.45 for frail, FRAIL-NH cutoff points of 1 and 9 in classifying three-class frailty had the highest VUS and the largest correct classification rates. Whichever FI was chosen, the performance of FRAIL-NH in distinguishing between pre-frailty and frailty, and between non-frailty and pre-frailty was equivalent. According to FRAIL-NH, the proportion of individuals with frailty misclassified as pre-frailty was higher than that of individuals with non-frailty misclassified as pre-frailty.ConclusionFRAIL-NH can be used as a preliminary frailty screening tool in nursing homes in mainland China. FI should be further used especially for those classified as pre-frailty by FRAIL-NH. It is not advisable to simply combine adjacent two classes of FRAIL-NH to create a new frailty variable in research settings.

Nonlinear Coda Wave Interferometry: Sensitivity to wave-induced material property changes analyzed via numerical simulations in 2D

The numerical studies conducted in this paper are based on our previous research (Chen et al., 2017); through use of the spectral element method, parametric sensitivity studies of Nonlinear Coda Wave Interferometry (NCWI) are established here and divided into two parts. In the first part, CWI observables are found to be proportional to the product of the changes in elastic modulus within the Effective Damaged Zone (EDZ) and the EDZ surface area. The modifications to intrinsic properties are quantified via an overall wave velocity variation, as probed by a reverberated coda wave. However, for high-level changes, CWI may fail due to meaningless decorrelation values. In this context, parametric studies are conducted to yield a maximum range for EDZ contrast and area. To further validate these observations using a more realistic numerical model, instead of introducing a homogeneous EDZ model, the second part of this paper adds random cracks with random orientations into the EDZ of a material sample. The influence of a strong pump wave on localized nonlinear damage is reestablished; results show that the cracks added into the EDZ reduce the property changes required to match the previous experimental dataset.

Local and nonlocal buckling of Mooney-Rivlin rods

Closed-form solutions for the local and nonlocal buckling stretches and loads of Mooney-Rivlin rods for various (i) support conditions, (ii) values of the nonlocal parameter corresponding to (a) the assumption of linearly elastic material, (b) the nonlinear constitutive law of the Mooney-Rivlin material with first order and third order shear deformation theories for rubber-like materials are presented. A perturbation method is used for the solution corresponding to the third order shear deformation theory. Some of the solutions obtained in the current study are compared with those available in the relevant literature. Although the assumption of the linearly elastic behaviour of the material yields perfect local buckling stretch values interestingly (if the limitation on the magnitudes of strains were disregarded), it yields meaningless values for the local buckling loads; basing on the three-dimensional elasticity solution for the Mooney-Rivlin rods. Appealing results are obtained via the nonlinear constitutive law of the Mooney-Rivlin material.

An Improved Background-Correction Algorithm for Raman Spectroscopy Based on the Wavelet Transform.

In the traditional background correction algorithm based on the wavelet transform, approximation coefficients considered as frequency responses of background signal are usually set to zero. However, there are many meaningless negative values generated in the background-corrected spectrum because of the calibration errors of this algorithm. Intensities of some weak peaks even become negative and these peaks will disappear after the calibration of negative values. To solve these problems for the background correction of Raman spectrum, an improved intelligent algorithm which utilizes a suppression coefficient to modify approximation coefficients is proposed in this paper. A series of simulation analyses, as well as experimental investigations, are made to test the performance of this algorithm. It is proved that the use of the suppression coefficient could increase the background correction accuracy and decrease the number of meaningless negative values in the reconstructed spectra, which will prevent the disappearance of weak Raman peaks after the calibration of negative values and increase the sensitivity of Raman spectral analysis.

Investigating Complex Isochron Data Using Mixture Models

AbstractThe isochron technique for dating geological events is widely used in many fields of isotope geochemistry. However, data sets can be complex, and many samples may not behave as closed systems, resulting in excess scatter around a regression line. In certain data sets, however, excess scatter may result when geological samples are partially reset or when samples are comprised of multiple components recording different geological processes separated in time and/or initial isotope composition. Here we introduce a new approach for investigating evidence of such multicomponent mixtures within isochron space. We provide a mathematical framework to resolve the number of mixture components, without requiring any prior knowledge of this number. Crucially, the model reports the proportion of each individual sample that belongs to each mixture component, allowing the user to return to the samples and independently test model outputs with other techniques. It is important to distinguish accurate and meaningful ages and initial daughter isotope compositions from the meaningless values that are caused by mismatch between geological processes and the models that we use to describe them. First, we demonstrate our new model on a synthetic data set to show that it can successfully separate distinct isochrons in a complex mixture. We further validate our approach with a previously published data set using the 187Re‐187Os system in bitumen and 147Sm/144Nd system in magmatic rocks. We identify discrete components in these data sets and using complementary geological observations discuss the implications of treating these scattered data as recording multiple geological processes.

Big data pre-processing methods with vehicle driving data using MapReduce techniques

A huge amount of sensing data are generated by a large number of pervasive IoT devices. In order to find meaningful information from the big data, it is essential to perform pre-processing, in which many outlier data points need to be removed, because they deteriorate as time passes. Although pre-processing is essential in the big data field, there has been a significant lack of research works with case studies. In this paper, big data pre-processing methods are investigated and proposed. To evaluate the pre-processing methods for accurate analysis, we used a collection of digital tachograph (DTG) data. We obtained DTG sensing data of 6198 driving vehicles over a year. We studied five kinds of pre-processing methods: filtering ranges, excluding meaningless values, comparing filters from variables, applying statistical techniques, and finding driving patterns. In addition, we developed a MapReduce program using a Hadoop ecosystem and deployed big data to perform the pre-processing analysis. Through the pre-processing steps, we confirmed that the proportion of DTG sensing data points including any errors was up to 27.09%. Compared to the traditional brute-force way to detect, ours had 71.1% additional detection effect. In addition, we confirmed that outlier data points, which are difficult to detect through simple range error pre-processing, could be well detected.

The Fiji Tide-Gauge Stations

We analyse the location, stability and continuity of the two tide-gauge stations in Fiji. Both stations are awkwardly placed on heavy harbour constructions resting on soft sediments susceptible to serious compaction problems. The nearby GPS stations resting on a bedrock hill offer no solution to the stability problems. The Suva tide-gauge has been moved three times, and must accordingly be analysed in segments. Even the last location covering years 1989 to 2917 provides a mixed picture of 16 years of stability, 10 years of rapid rise, and 4 years of rapid fall in relative sea level. This suggests the interaction of subsidence and cyclic changes in sea level. Any application of mean trends would produce meaningless values rather misguiding than assisting in the handling of estimation of on-going absolute sea level changes. We find this vital for the discussions of local sea level changes to be held at the UN conference on “Our Oceans, Our Future” in June in New York and at the main COP23 conference in November in Bonn.

Reformulation of parameters of the logistic function applied to power curves of wind turbines

The current procedure for obtaining the parameters of the logistic function, used as a model for the power curve of wind turbines, provides meaningless values. These values are different for each wind turbine and obtaining them requires an optimization process. This paper proposes a procedure to obtain the parameters of the 4-parameter logistic function based on the features of the power curve, providing a model that is a function of the power curve parameters supplied by the manufacturer. Furthermore, that model can be used to derive another 4-parameter model and a 3-parameter model is proposed for certain conditions. The three models consist of a continuous function which simplifies the implementation of the curve in a computer program compared to piecewise models. In addition, the probability density function of the output power of a wind turbine is derived by using each model.

Deceleration Capacity of heart rate: Two new methods of computation

Deceleration Capacity (DC) expresses the property of the neural control of the heart extrinsically to decelerate its rate. For the computation of DC a mathematical method has been proposed and used. Although this method was proved of significant prognostic value, it may produce meaningless negative values for DC, something in contradiction with the principle of inter beat deceleration. In this paper we propose two new methods of computation, DCsgn (DC sign) and BBDC (Beat to Beat Deceleration Capacity), which not only give positive values for DC but could also improve the original method. DCsgn modifies the filtering procedure by totally excluding from computation segments that include possible artifacts. It also uses information of four successive beats in order to detect deceleration (acceleration) segments and not only from the anchor points. BBDC bases all computations on two and not on four successive beats, detecting in this way shorter-term relationships. In order to evaluate the proposed methods, a dataset of 20 young and 20 elderly subjects, all healthy, has been used. Experimental results verify our theoretical claims and show that the proposed method can discriminate more efficiently healthy young and elderly subjects than the original method.

The necessary and sufficient condition for GM(1, 1) grey prediction model

The grey theory demonstrates the optimal and unique ability of performing fitting predictions using small data sets and limited information to allow fast, concise, accurate, and effective predictions and understand future trends. The GM(1,1) model within the grey theory is used frequently by scholars as a prediction tool. To enhance the accuracy of GM(1,1) prediction, numerous studies have provided satisfactory results on revised grey prediction models. However, because the sufficient and necessary condition of GM(1,1) model that the grey development coefficient should not equal zero is neglected, singular phenomena occur when computers are used to calculate matrix values to obtain the grey development coefficient a. Additionally, if it is neglected that a should not equal zero and the result is substituted into the prediction equation, a meaningless predictive value is obtained. In this paper, we will demonstrate scientific and effective procedures to solve this singular phenomenon of GM(1,1) prediction model with the practical cases and show its application in forecasting the moving path of the typhoon MORAKOT.

Comparison of the life tables and predation rates of Harmonia dimidiata (F.) (Coleoptera: Coccinellidae) fed on Aphis gossypii Glover (Hemiptera: Aphididae) at different temperatures

The life histories and predation rates of the ladybird beetle Harmonia dimidiata (F.) were compared among beetles kept at 15, 20, and 25°C. The beetles were fed on Aphis gossypii Glover and were maintained at 70±10% RH and a 14:10 (L:D)h photoperiod. According to the age-stage, two-sex life table, the net reproductive rates (R0) were 147.4, 98.7, and 62.5 offspring for beetles kept at 15, 20, and 25°C, respectively. Additionally, we employed both the jackknife and bootstrap techniques for estimating the means, variances, and standard errors of the population parameters. The sample means of R0 and the other population parameters obtained using the bootstrap technique fit a normal distribution, but the jackknife technique generated biologically meaningless zero values for R0. The net predation rates were 10963, 13050, and 7492 aphids for beetles kept at 15, 20, and 25°C, respectively. For a comprehensive comparison of predation potential, we incorporated both the finite rate and the predation rate into the finite predation rate. When both the growth rate and the predation rate were considered, our results showed that H. dimidiata is a more efficient biological control agent for A. gossypii at 20 and 25°C than at 15°C.

Automotive engine power performance tuning under numerical and nominal data

Modern automotive engines are controlled by an electronic control unit (ECU), and engine power performance is significantly affected by the selection of both ECU parameters and engine components. The engine performance tuning is usually done by a trial-and-error method. In the current literature, very little research has considered the selection of engine parts because engine parts are complicated objects that are usually represented as nominal data. These data are meaningless values in terms of computation. This paper presents a novel multiple-input/output least-squares support vector machine plus one-of-n remapping method for modelling engine power performance using both numerical (ECU parameters) and nominal data (candidate engine parts). The Quasi-Newton method, a genetic algorithm and particle swarm optimisation are then applied to the engine model to determine the optimal engine setup automatically. A simple binary code synthesis rule is also proposed to optimise the nominal variable. Both experimental and simulation results show that the proposed methodology can successfully yield an optimal engine setup.

A Bayesian method with empirically fitted priors for the evaluation of environmental radioactivity: application to low-level radioxenon measurements

The decision that a given detection level corresponds to the effective presence of a radionuclide is still widely made on the basis of a classic hypothesis test. However, the classic framework suffers several drawbacks, such as the conceptual and practical impossibility to provide a probability of zero radioactivity, and confidence intervals for the true activity level that are likely to contain negative and hence meaningless values. The Bayesian framework being potentially able to overcome these drawbacks, several attempts have recently been made to apply it to this decision problem. Here, we present a new Bayesian method that, unlike the previous ones, presents two major advantages together. First, it provides an estimate of the probability of no radioactivity, as well as physically meaningful point and interval estimates for the true radioactivity level. Second, whereas Bayesian approaches are often controversial because of the arbitrary choice of the priors they use, the proposed method permits to estimate the parameters of the prior density of radioactivity by fitting its marginal distribution to previously recorded activity data. The new scheme is first mathematically developed. Then, it is applied to the detection of radioxenon isotopes in noble gas measurement stations of the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty.