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The bound state solutions of the Schrödinger equation (SE) under a deformed hyperbolic potential are used in this paper to investigate the correlation between the information content, such as Shannon entropies and Fisher information, and the variance of a quantum system in both momentum and position spaces. The variance was obtained from the expectation moments in conjugated spaces and used to get the uncertainty products, Fisher information products and Shannon entropic sums for different potential parameters. These information measures were observed to vary with the potential parameters and obey their lowest bound inequalities. An increase in the Fisher information leads to a lower uncertainty and less information while decreases in the Fisher information result in higher uncertainties. We proposed a relationship between the Shannon entropies, Fisher information and the variance where an increase in the Fisher information leads to a lower Shannon entropy or a lower spread of the probability distribution and vice versa. The numerical results obtained with the proposed relations agree with the most utilized method in the literature. This current work simplifies the calculation of the Shannon entropies notably for complex potential functions. [ABSTRACT FROM AUTHOR]

AbstractAnalysis of bulk wheat consignments for naturally-occurring contaminants and residues from plant protection products is common, and helps manage potential health risks to consumers. The heterogeneous distribution of some mycotoxins in wheat has been described, however the distribution of other contaminants and residues has not yet been reported. This study characterized distributions of deoxynivalenol, ochratoxin A, ergot alkaloids, cadmium, and glyphosate in nine large consignments of wheat by analysing composite samples representing sub-lots prepared from increments obtained during the entire loading process. The widest span of concentrations within a consignment occurred for ochratoxin A (<0.5–22.9 µg/kg) and ergot alkaloids (0.009–0.486 mg/kg), followed by deoxynivalenol (<0.05–0.76 mg/kg) and glyphosate (<0.3–5.01 mg/kg), and then cadmium (0.022–0.102 mg/kg). Experimental semivariograms were plotted to model the spatio-temporal correlation of analytes within consignments during loading. Analyses demonstrated that distributions of contaminants and residues within a particular consignment differed, and that distributions of a particular contaminant or residue differed among consignments. The results indicate that sampling during only a portion of a loading or unloading process can result in a composite sample that is not representative of the consignment and thus increase the risk of misclassifying a consignment as compliant. [ABSTRACT FROM AUTHOR]

Process capability indices (PCIs) have been widely used for process performance assessment. It provides a quantitative measure of performance of an industrial production process. Several PCIs have been proposed in the literature. Cpk is one of the most useful capability index. Statistical properties of the estimator of Cpk have been discussed by many researches under the assumption of identically and independently normal distribution. Some statistical properties of the estimator of Cpk for autocorrelated data in presence of gauge measurement errors have studied by Scagliarini (2010). However, we found some critical error in the expression of expectation and variance of the estimator when process is autocorrelated as well as measurement error is present. Through this article, we present a corrected version of expectation and variance of the estimator. [ABSTRACT FROM AUTHOR]

In statistics, there exists several means such as the arithmetic, geometric, harmonic, or power means. Using axiomatic deduction, they have been unified into the generalized f-mean. Similarly, there are several standard deviations and they have been unified into the generalized f-standard deviation, even though without derivation. However, standard score, as known in its arithmetic and geometric versions, has never been f-generalized. The goal of this article is to expose a full derivation of generalized f-statistics, adopting a parameter estimation point of view. Generalized f-mean and generalized f-variance are derived thanks to a maximum likelihood estimation. The non trivial relation between generalized f-standard deviation and generalized f-variance is also studied. Furthermore, generalized f-standard score is defined and extended to multivariate case. Finally, these generalized f-statistics are applied on simulated data for pre-processing and outlier detection. [ABSTRACT FROM AUTHOR]

The Z-number deserves further exploration in uncertain environments to effectively address fuzziness and reliability in practical issues simultaneously. Based on the credibility distribution and conversion rules, we define its expected value, variance, and semi-variance, and demonstrate the feasibility of these calculations by deriving formulas. Moreover, we explore three characteristics inherent in symmetrical Z-numbers. The link between the variance and semi-variance of Z-numbers is discovered and proved. Furthermore, we apply the formulas for expected value and variance to Z-numbers in examples, the results of which validate our proposed formulas. The findings underscore the significance of our study in applying the expected value and variance of fuzzy sets across diverse fields. [ABSTRACT FROM AUTHOR]

Statistically based experimental designs have been available for over a century. However, many preclinical researchers are completely unaware of these methods, and the success of experiments is usually equated only with ' p < 0.05'. By contrast, a well-thought-out experimental design strategy provides data with evidentiary and scientific value. A value-based strategy requires implementation of statistical design principles coupled with basic project management techniques. This article outlines the three phases of a value-based design strategy: proper framing of the research question, statistically based operationalisation through careful selection and structuring of appropriate inputs, and incorporation of methods that minimise bias and process variation. Appropriate study design increases study validity and the evidentiary strength of the results, reduces animal numbers, and reduces waste from noninformative experiments. Statistically based experimental design is thus a key component of the 'Reduction' pillar of the 3R (Replacement, Reduction, Refinement) principles for ethical animal research. [ABSTRACT FROM AUTHOR]

The aims of this study were to: (1) assess the stability (mean and normative) of the lap performance, and a set of clean swim and turn variables of junior male swimmers in the 200 m freestyle, and; (2) verify the relationship between the start, clean swim, turn, and finish phases in the 200 m freestyle. Seventy-six individual races in the 200 m freestyle at the 2019 long-course LEN European Junior Championships were analysed. Start, clean swim, turn, and finish variables were assessed. The lap performance showed a significant variance. The highest variation was verified between the first and third lap (Coefficient of Variation = 7.37%). The clean swim and the total turn also presented a significant variance. Normative stability indicated a moderate to very-high stability for all variables. All phases of the race had significant correlations with the final race time (p < 0.001). The total turn (i.e., the total time spent to perform the turn), specifically turn #3, showed the largest correlation with the total race performance. The significant correlation between all phases of the race and the final race time indicates that coaches and swimmers should customise the swimmers' preparation and race strategy at major international competitions, based on the individual characteristics of each swimmer. [ABSTRACT FROM AUTHOR]

In health technology assessment, matching‐adjusted indirect comparison (MAIC) is the most common method for pairwise comparisons that control for imbalances in baseline characteristics across trials. One of the primary challenges in MAIC is the need to properly account for the additional uncertainty introduced by the matching process. Limited evidence and guidance are available on variance estimation in MAICs. Therefore, we conducted a comprehensive Monte Carlo simulation study to evaluate the performance of different statistical methods across 108 scenarios. Four general approaches for variance estimation were compared in both anchored and unanchored MAICs of binary and time‐to‐event outcomes: (1) conventional estimators (CE) using raw weights; (2) CE using weights rescaled to the effective sample size (ESS); (3) robust sandwich estimators; and (4) bootstrapping. Several variants of sandwich estimators and bootstrap methods were tested. Performance was quantified on the basis of empirical coverage probabilities for 95% confidence intervals and variability ratios. Variability was underestimated by CE + raw weights when population overlap was poor or moderate. Despite several theoretical limitations, CE + ESS weights accurately estimated uncertainty across most scenarios. Original implementations of sandwich estimators had a downward bias in MAICs with a small ESS, and finite sample adjustments led to marked improvements. Bootstrapping was unstable if population overlap was poor and the sample size was limited. All methods produced valid coverage probabilities and standard errors in cases of strong population overlap. Our findings indicate that the sample size, population overlap, and outcome type are important considerations for variance estimation in MAICs. [ABSTRACT FROM AUTHOR]

Acoustic emissions (AEs) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNRs (signal-to-noise ratios) generated in rocks. The basic idea is that the variances in the crack wave and noise have different characteristics, depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The proposed algorithm for source localization can be used for evaluating and monitoring damage in tunnels or other underground facilities in real time. [ABSTRACT FROM AUTHOR]

Interval multi-objective linear programming (IMOLP) ímodels are one of the methods to tackle uncertainties. In this paper, we propose two methods to determine the efficient solutions in the IMOLP models through the expected value, variance and entropy operators which have good properties. One of the most important properties of these methods is to obtain different efficient solutions set according to decision makers' preferences as available information. We first develop the concept of the expected value, variance and entropy operators on the set of intervals and study some properties of the expected value, variance and entropy operators. Then, we present an IMOLP model with uncertain parameters in the objective functions. In the first method, we use the expected value and variance operators in the IMOLP models and then we apply the weighted sum method to convert an IMOLP model into a multi-objective non-linear programming (MONLP) model. In the second method, the IMOLP model using the expected value, variance and entropy operators can be converted into a multi-objective linear programming (MOLP) model. The proposed methods are applicable for large scale models. Finally, to illustrate the efficiency of the proposed methods, numerical examples and two real-world models are solved. [ABSTRACT FROM AUTHOR]

The quality assurance of the earth-atmosphere system, which runs primarily to temperature variations and other disturbances of the earth's climate, compels the assessment of greenhouse gases (GHG) emissions due to various Organizations for Economic Co-operation and Development (OECD) countries' human activities that disturb the radioactive energy balance. The purpose of this paper is to thus determine the usefulness and acceptability of statistical process control for assessing the level of GHG emissions from OECD countries. We apply the exponentially weighted moving average (EWMA) control charts with one-time, two-time, and three-time smoothing processes for monitoring the process variance under a repetitive sampling scheme, named S 2 -EWMA RS , S 2 -DEWMA RS and S 2 -TEWMA RS charts, and also utilize a cumulative sum (CUSUM) control chart under a repetitive sampling scheme, named as S2-CUSUMRS chart, to assess these countries' emission levels of GHG. The control charts are a graphical representation of the trend product of sequential procedures. All the OECD countries can thus independently measure their emission level of GHG as a self-assessment tool. Consequently, we recommend use of the advanced control charts as a tool for OECD assessment at an individual country level. [ABSTRACT FROM AUTHOR]

The aim of this article is to verify whether a relationship exists between partly laws and party organizational change. While the prevailing contextualist perspective enhances the weight of social, economic and technological factors in shaping party change, we maintain that the intensity of party regulation plays a major role in this respect. Based on some basic assumption of Organizational Institutionalism, the article adopts the rationale of the Political Parties Database (PPDB): we thus conduct an in-depth empirical analysis of three core party organisational dimensions (Resources, Representative Strategies, Structures), over a total of nineteen parties in four Western European democracies (Germany, the Netherlands, Sweden and the UK), from 1970s to 2010s. Our empirical findings show that, while a growing number of party laws have been introduced in the countries under consideration, their impact on party organizational convergence is far from univocal: we thus provide different possible explanations of these results. [ABSTRACT FROM AUTHOR]

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In this paper, we present various inequalities regarding the linear combinations of orthogonal projections. These results aim to generalize and refine well-known inequalities, such as those due to Buzano and Ostrowski. Additionally, we investigate a specific case of these linear combinations and introduce new refinements of the Cauchy–Schwarz inequality. Furthermore, we establish some findings related to the covariance and variance of bounded linear operators. Moreover, as applications of some of our results, we establish several inequalities involving the product of three operators, one of which is a linear combination of an orthogonal projection and the identity operator. Finally, we introduce a new positive operator construction in terms of an orthogonal projection and the identity operator, and we derive some norms and numerical radius inequalities involving it. [ABSTRACT FROM AUTHOR]

Turning is one of the most important material removal processes in manufacturing, where the proper understanding of the process is crucial for the quality of the final product. In this study, the stochastic cutting force is utilized to enhance the existing 1-degree-of-freedom turning model. A stochastic model is adopted to address the stochastic resonance phenomenon occurring near stability boundaries. Additionally, a novel simplified stochastic model is introduced with additive noise only. The comparison of the two models reveals that, with the recommended noise intensity of 0.1 to 1%, there is no significant difference in the stability charts and mean square characteristics between the two models. As a result, the time-consuming numerical methods can be bypassed, as the simplified model offers a computationally more efficient analytical approach to compute variance based on power spectral density (PSD). By combining techniques such as D-separation to determine stability boundaries and the PSD-based variance calculation, it only takes a minute instead of hours to construct a heatmap using the introduced simplified stochastic turning model that clearly outlines dangerous stochastic resonance regions inside the stable domain. [ABSTRACT FROM AUTHOR]

Learning from changing data streams is one of the important tasks of data mining. The phenomenon of the underlying distribution of data streams changing over time is called concept drift. In classification decision-making, the occurrence of concept drift will greatly affect the classification efficiency of the original classifier, that is, the old decision-making model is not suitable for the new data environment. Therefore, dealing with concept drift from changing data streams is crucial to guarantee classifier performance. Currently, most concept drift detection methods apply the same detection strategy to different data streams, with little attention to the uniqueness of each data stream. This limits the adaptability of drift detectors to different environments. In our research, we designed a unique solution to address this issue. First, we proposed a variance estimation strategy and a variance feedback strategy to characterize the data stream's characteristics through variance. Based on this variance, we developed personalized drift detection schemes for different data streams, thereby enhancing the adaptability of drift detection in various environments. We conducted experiments on data streams with various types of drifts. The experimental results show that our algorithm achieves the best average ranking for accuracy on the synthetic dataset, with an overall ranking 1.12 to 1.5 higher than the next-best algorithm. In comparison with algorithms using the same tests, our method improves the ranking by 3 to 3.5 for the Hoeffding test and by 1.12 to 2.25 for the McDiarmid test. In addition, they achieve a good balance between detection delay and false positive rates. Finally, our algorithm ranks higher than existing drift detection methods across the four key metrics of accuracy, CPU time, false positives, and detection delay, meeting our expectations. [ABSTRACT FROM AUTHOR]

Natural selection on traits expressed repeatedly by individuals is usually investigated with a focus on mean values, although within‐individual trait distributions often differ also in other aspects, such as their spread and shape. In plants producing multiple flowers during a season, there might not be a single optimal flowering time, but rather an optimal distribution of flower opening dates. This optimal distribution might depend on both resource allocation patterns and interactions with the abiotic and biotic environment. In this study, we quantified mean, variance, skewness and kurtosis of 495 individual flowering schedules (5287 flowers) over 3 years, and assessed phenotypic selection on these aspects of the within‐individual distribution of opening dates in the perennial herb Lathyrus vernus. We also explored how selection on within‐individual variation in flowering schedules was related to effects on two fitness components: fruit set and the proportion of seeds escaping pre‐dispersal predation. Within‐individual variation in phenology was larger than, or at least similar to, among‐individual variation in all years. We found phenotypic selection on several aspects of individual flowering schedules. In 1 year, selection favoured plants with higher variance in opening dates, and this coincided with a higher fruit set in plants with an increased spread of the flowering schedule. In two of the study years, selection favoured a higher asymmetry of the flowering schedule, and plants with more right‐skewed distributions had higher fruit set and higher proportions of seeds escaping predation. Both fruit set and seed predation increased with an earlier mean flowering, resulting in no net selection on mean flowering date. Synthesis: Our results suggest that phenotypic selection on the spread and shape of flower opening date distributions might be at least as important as selection on the mean flowering date. In a broad sense, this implies that we should consider the entire trait distribution if we aim to understand the evolution of traits that are expressed multiple times within individuals. [ABSTRACT FROM AUTHOR]

Basic management approaches are proposed for the risks that business entities may encounter when investing in Russian industry in the digital era. Possible means of reducing exposure to risks and indeterminacies associated with the formation of a digital space and threats to digital security are outlined. Assessment of the risks affecting project efficiency is considered. [ABSTRACT FROM AUTHOR]

Light detection and ranging (lidar) scanning tools are available that can make rapid digital estimations of biomass. Voxelization and convex hull are two algorithms used to calculate the volume of the scanned plant canopy, which is correlated with biomass, often the primary trait of interest. Voxelization splits the scans into regular-sized cubes, or voxels, whereas the convex hull algorithm creates a polygon mesh around the outermost points of the point cloud and calculates the volume within that mesh. In this study, digital estimates of biomass were correlated against hand-harvested biomass for field-grown corn, broom corn, and energy sorghum. Voxelization (r = 0.92) and convex hull (r = 0.95) both correlated well with plant dry biomass. Lidar data were also collected in a large breeding trial with nearly 900 genotypes of energy sorghum. In contrast to the manual harvest studies, digital biomass estimations correlated poorly with yield collected from a forage harvester for both voxel count (r = 0.32) and convex hull volume (r = 0.39). However, further analysis showed that the coefficient of variation (CV, a measure of variability) for harvester-based estimates of biomass was greater than the CV of the voxel and convex-hull-based biomass estimates, indicating that poor correlation was due to harvester imprecision, not digital estimations. Overall, results indicate that the lidar-based digital biomass estimates presented here are comparable or more precise than current approaches. [ABSTRACT FROM AUTHOR]

Climate data derived from long-term, multisource altimeter significant wave height (SWH) measurements are more valuable than those obtained from a single altimeter source. Such data facilitate exploration of long-term air–sea momentum transfer and more comprehensive investigation of weather system dynamics processes over the ocean. Despite the deployment of the first satellite in the Chinese Haiyang-2 (HY-2) series more than 12 years ago, validation and integration of SWH data from China's offshore waters, derived using Chinese altimeters, have been limited. This study constructed a high-resolution, long-term, multisource gridded SWH climate dataset using along-track data from the HY-2 series, CFOSAT, Jason-2, Jason-3, and Cryosat-2 altimeters. Validation against observations from 31 buoys covering China's offshore waters indicated that the SWH variances from HY-2A, HY-2B, HY-2C, CFOSAT, and Jason-3 altimeters correlated well with observations, with a temporal correlation coefficient of approximately 0.95 (except HY-2A, correlation: 0.89). These SWH measurements generally showed a robust linear relationship with the buoy data. Additionally, cross-calibration between Jason-3 and the HY-2A, HY-2B, HY-2C, and CFOSAT altimeters also demonstrated a typically linear relationship for SWH > 6.0 m. Using this relationship, the SWH data were linearly corrected and integrated into a 10 d mean, long-term, multisource altimeter gridded SWH dataset. Compared with in situ observations, the merged 10 d mean SWHs are more accurate and closely match the observations, with temporal correlation coefficients improving from 0.87 to 0.90 and bias decreasing from 0.28 to 0.03 m. The merged gridded SWHs effectively represent the local spatial distribution of SWH. This study revealed the importance of observational data in the process of merging and recalibrating long-term multisource altimeter SWH datasets, particularly before their application in specific ocean regions. [ABSTRACT FROM AUTHOR]

In recent years, research in animal breeding has increasingly focused on the topic of resilience, which is expected to continue in the future due to the need for high-yielding, healthy, and robust animals. In this context, an established approach is the calculation of resilience indicator traits with time series analyses. Examples are the variance and autocorrelation of daily milk yield in dairy cows. We applied this methodology to the German dairy cow population. Data from the 3 breeds (German Holstein, German Fleckvieh, and German Brown Swiss) were obtained, which included 13,949 lactations from 36 farms from the state Baden-Württemberg in Germany working with automatic milking systems. Using the milk yield data, the daily absolute milk yields, deviations between observed and expected daily milk yields, and relative proportions of daily milk yields in relation to lactation performance were calculated. We used the variance and autocorrelation of these data as phenotypes in our statistical analyses. We estimated a heritability of 0.047 for autocorrelation and heritabilities between 0.026 and 0.183 for variance-based indicator traits. Furthermore, significant breed differences could be observed, with a tendency of better resilience in Brown Swiss. The breed differences can be due to both genetic and environmental factors. A high value of a variance-based indicator trait indicates a low resilience. Performance traits were positively correlated with variance-based indicator traits calculated from absolute daily milk yields, but they were negatively correlated with variance-based indicators calculated from relative daily milk yields. Thus, they can be considered as different traits. Although variance-based indicators based on absolute daily milk yields were affected by the performance level, variance-based indicators based on relative daily milk yields were corrected for the performance level and also showed higher heritabilities. Thus, they seem to be more suitable for practical use. Further studies need to be conducted to calculate the correlations between resilience indicators, functional traits, and health traits. [ABSTRACT FROM AUTHOR]

Every positive multilinear map between C ∗ -algebras is separately weak ∗ -continuous. We show that the joint weak ∗ -continuity is equivalent to the joint weak ∗ -continuity of the multiplications of the C ∗ -algebras under consideration. We study the behavior of general tracial positive maps on properly infinite von Neumann algebras and by applying the Aron–Berner extension of multilinear maps, we establish that under some mild conditions every tracial positive multilinear map between general C ∗ -algebras enjoys a decomposition Φ = φ 2 ∘ φ 1 , in which φ 1 is a tracial positive linear map with the commutative range and φ 2 is a tracial completely positive map with the commutative domain. As an immediate consequence, tracial positive multilinear maps are completely positive. Furthermore, we prove that if the domain of a general tracial completely positive map Φ between C ∗ -algebra is a von Neumann algebra, then Φ has a similar decomposition. As an application, we investigate the generalized variance and covariance in quantum mechanics for arbitrary positive maps. Among others, an uncertainty relation inequality for commuting observables in a composite physical system is presented. [ABSTRACT FROM AUTHOR]

High-quality services and products give organizations a competitive advantage. Good quality reduces correction costs, waste, complaints and returns on defective products, and most importantly, it achieves customer satisfaction. Quality is the most important factor that affects the performance of an organization's production operations compared to its competitors. The main objective of this study is to use some statistical tools such as (X-bar Chart), which is one of the process control charts. In addition to using the ANOVA technique, this is among the statistical instruments employed to measure and analyses variance and identify sources of discrepancies between group averages. These tools were used in this study to monitor and control the quality of the production process to ensure that the products meet the required quality standards, and to identify and correct any problems or deviations from those standards as early as possible in the production process. The data collected from South Mineral Water Company was processed using the manual method as well as the Excel program. The research concluded that there was a variation in the level of water volumes inside the bottles within the sample as well as between the study samples, and after using the Tukey HSD equation, the largest difference between the variations was identified, which was between the average of observations 3 and 4, which had a value of 12.74. [ABSTRACT FROM AUTHOR]

The Susceptible–Infected–Susceptible (SIS) model in complex networks is one of the critical models employed in the modeling of virus spread. The study of the heterogeneous SIS model with a non-homogeneous nodal infection rate in finite-size networks has attracted little attention. Investigating the statistical properties of heterogeneous SIS epidemic dynamics in finite networks is thus intriguing. In this paper, we focus on the measure of variability in the number of infected nodes for the heterogeneous SIS epidemic dynamics in finite-size bipartite graphs and star graphs. Specifically, we investigate the metastable-state variance of the number of infected nodes for the SIS epidemic process in finite-size bipartite graphs and star graphs with heterogeneous nodal infection rates. We employ an extended individual-based mean-field approximation to analyze the heterogeneous SIS epidemic process in finite-size bipartite networks and star graphs. We derive the approximation solutions of the variance of the infected number. We verify the proposed theory by simulations. The proposed theory has the potential to help us better understand the fluctuations of SIS models like epidemic dynamics with a non-homogeneous infection rate. [ABSTRACT FROM AUTHOR]

Control charts are tools of paramount importance in statistical process control. They are broadly applied in monitoring processes and improving quality, as they allow the detection of special causes of variation with a significant level of accuracy. Furthermore, there are several strategies able to be employed in different contexts, all of which offer their own advantages. Therefore, this study focuses on monitoring the variability in univariate processes through variance using the Binomial version of the ATTRIVAR Same Sample S2 (B-ATTRIVAR SS S2) control chart, given that it allows coupling attribute and variable inspections (ATTRIVAR means attribute + variable), i.e., taking advantage of the cost-effectiveness of the former and the wealth of information and greater performance of the latter. Its Binomial version was used for such a purpose, since inspections are made using two attributes, and the Same Sample was used due to being submitted to both the attribute and variable stages of inspection. A computational application was developed in the R language using the Shiny package so as to create an interface to facilitate its application and use in the quality control of the production processes. Its application enables users to input process parameters and generate the B-ATTRIVAR SS control chart for monitoring the process variability with variance. By comparing the data obtained from its application with a simpler code, its performance was validated, given that its results exhibited striking similarity. [ABSTRACT FROM AUTHOR]

Identifying precursor information to rock failure is key to rock dynamic disaster warning. To solve the problem that a single in-situ monitoring method cannot accurately identify the precursors to rock failure, this paper combined a digital image correlation method, thermal infrared radiation and acoustic emission technology to conduct a multi-physical field joint monitoring experiment of granite under three-point bending. The information about strain, thermal infrared and acoustic emission of the rock brittle failure process could be obtained, and the variations in multi-physical field indices of the rock brittle failure process were analysed. Based on the theory of critical slowing-down, the study of the multi-physical field effects of critical slowing-down characteristics in the rock failure process, and further exploration of the precursor characteristics of the brittle failure of rock were undertaken. The results indicate that the strain, infrared, and acoustic emission parameters show sudden changes during brittle failure of granite. The lag step size has little effect on the variance of changes in multi-physical field parameters and exerts a similar influence on the changes in the autocorrelation coefficient curve. The degree of influence of the window length on the variance and autocorrelation coefficient curves of multi-physical field parameters is as follows: infrared field > strain field > acoustic emission field. Compared with the autocorrelation coefficient, the variance parameter is more universal and is better when the critical slowing-down theory is used to investigate the characteristics of multi-physical fields. The four parameters of the strain field variance, infrared field variance, the variance and autocorrelation coefficient for acoustic emission field increase sharply before fracture, representing precursor information to the brittle failure of granite. The four parameters can be used as disaster warning indicators in rock. Multi-mode joint monitoring and a comprehensive analysis of multi-physical information are beneficial to accurately determine the precursor points of rock failure disaster. [ABSTRACT FROM AUTHOR]

Abstract The metalworking industry faces challenges in maintaining process performance due to variables affecting product quality, particularly in processes requiring precise control over machined part finishes. Duplex stainless steels, known for their high strength, work hardening, and low thermal conductivity, pose specific machining challenges that can hinder producing high-quality components and equipment. This study aimed to determine optimal parameters for end milling of duplex stainless steel UNS S32205. Formulated as a combined objective function derived from minimizing the mean square error (MSE) for parameters Ra and ßf, subject to a common constraint. The optimization was conducted using generalized reduced gradient (GRG). A Pareto frontier was constructed, offering efficient results with Ra = 0.4534 μιη and Rt = 3.2671 μιη for varying weights assigned to Ra and Rt. Adjusting weights in the objective function allowed prioritization based on specific needs. Optimal input parameters were identified as cutting speed (vc) = 60.79 m/min, feed per tooth (fz = 0.15 mm/tooth, axial depth of cut (ap) = 0.90 mm, and radial depth of cut (ae) = 16.31 mm, simultaneously optimizing both parameters. This approach reduced the mean square error (RMSE), determining roughness Ra and Rt mean and variance, thus improving the machining process. Confirmatory trials using an orthogonal Taguchi arrangement (L9) yielded results within the algorithm's confidence interval. This research offers a robust methodology for optimizing machining parameters, enhancing product quality in the metalworking industry.

Aims: Manual impaction, with a mallet and introducer, remains the standard method of installing cementless acetabular cups during total hip arthroplasty (THA). This study aims to quantify the accuracy and precision of manual impaction strikes during the seating of an acetabular component. This understanding aims to help improve impaction surgical techniques and inform the development of future technologies. Methods: Posterior approach THAs were carried out on three cadavers by an expert orthopaedic surgeon. An instrumented mallet and introducer were used to insert cementless acetabular cups. The motion of the mallet, relative to the introducer, was analyzed for a total of 110 strikes split into low-, medium-, and high-effort strikes. Three parameters were extracted from these data: strike vector, strike offset, and mallet face alignment. Results: The force vector of the mallet strike, relative to the introducer axis, was misaligned by an average of 18.1°, resulting in an average wasted strike energy of 6.1%. Furthermore, the mean strike offset was 19.8 mm from the centre of the introducer axis and the mallet face, relative to the introducer strike face, was misaligned by a mean angle of 15.2° from the introducer strike face. Conclusion: The direction of the impact vector in manual impaction lacks both accuracy and precision. There is an opportunity to improve this through more advanced impaction instruments or surgical training. Cite this article: Bone Joint Res 2024;13(4):193–200.

This study tested the effect of variance in service quality ratings on overall scores of users in reviews (7,210 sites, 576,449 reviews) on the well-known hotel accommodation reservation site “Rakuten Travel.” The results revealed that the overall score was lower for a hotel with higher variance in service quality ratings for most functions. This trend was clearer for leisure travelers than for business travelers. An additional survey of general managers of the hotels in the first analysis suggested that practicing total quality management (TQM) in a hotel may reduce the variance in service quality ratings of users.

Accurate prediction of energy requirement at charging station is essential for optimizing infrastructure usage, ensuring grid stability, and minimizing operational cost. Literatures suggest deployment of machine learning techniques to forecast the station demand. One major challenge associated with development of machine learning models is the inherent uncertainty in electric vehicle charging behaviour that includes variations in charging patterns, user preferences, and vehicle types. The conventional pre-processing techniques fail to dislodge nonlinearities and highly random patterns that include very low or zero-charging. Employing such techniques affects the model's forecast accuracy. This article performs data-slotting during pre-processing stage and then selects the best among 1-h, 2-h, 3-h and 4-h slots, to frame the feature vectors. The 4-h data with minimum variance is suggested to frame the dataset. Four distinct datasets, comprising different combination of average and total demands as predictor and response respectively are considered. The created dataset is deployed in Random Forest, Categorical Boosting, Extreme Gradient Boosting and Light Gradient Boosting models. The article recommends Categorical Boosting Regression model with least mean absolute error, mean square error and root mean square error of 0.0726, 0.0112, and 0.1059 respectively. Furthermore, the use of feature vector comprising of aggregated load for prescribed slots and the response representing the aggregated demand is observed to provide the least prediction error by the suggested model. The suggested model fed by the proposed feature vector offers significant advantage to charging station operator by enhancing the operational efficiency while performing resource and cost management with strategic planning.

Purpose: This study aimed to quantify multi-segmental coordination using Uncontrolled Manifold (UCM) analysis to examine the effect of speed reduction on the control of stair descent. Methods: Twenty healthy participants performed stair descent at a self-comfortable pace for normal speed conditions and at a slow speed set to a metronome rhythm of 60 beats/min. UCM analysis was separately conducted for the center of mass (COM) and swing foot, with anteroposterior and vertical movements designated as task variables, and segment angles defined as elemental variables. ΔV, the normalized difference between the variance in segment angle that does not affect task performance (VUCM ) and the variance that does affect task performance (VORT) was calculated separately for the COM and swing foot and compared between normal and slow speeds. Results: The VORT for the COM and the swing foot in the anteroposterior direction were significantly lower at slow speeds than at normal speeds. The VORT of task-relevant segment angles affecting COM control in the vertical direction was significantly higher at slow speed compared to normal speed. Additionally, the ΔV in segment angle variance impacting swing foot control in the anteroposterior direction was significantly greater at slow speed than at normal speed. Conclusions: The findings suggest that descending stairs at reduced speed promotes enhanced coordination of lower limb segments for controlling the swing foot in the anteroposterior direction, while concurrently increasing segmental variability that destabilizes the vertical COM. [ABSTRACT FROM AUTHOR]

The entire spectrum of acoustic-gravity waves (AGWs), which can exist in an infinite isothermal atmosphere, is analyzed. The main attention in the study has been paid to those regions of the spectrum that are bandgaps for freely propagating waves. However, other types of waves that differ from the freely propagating AGWs in the way of propagation and in properties still may exist in these regions. Different types of bandgaps in the acoustic-gravity wave spectrum, which are found from the analysis of the dispersion equation obtained in the model of the infinite isothermal atmosphere, are studied. Classification of the types of bandgap regions in the AGW spectrum is proposed. The structure and the localization of the bandgaps relative to the regions of freely propagating waves and special points in the bandgaps of the AGW spectrum are studied using the corresponding spectral diagrams. In the bandgap region of type I, which separates the acoustic and gravity bands of freely propagating AGWs, horizontal waves with a purely imaginary value of the vertical wavenumber can exist. In the AGW spectrum, the possibility of the existence of special acoustic-gravity modes for which one of the perturbed quantities is zero has been considered and it is shown that they can exist only in the spectral bandgap of type I. A spectral bandgap in which vertical acoustic-gravity waves with a purely imaginary value of the horizontal wavenumber can exist was also analyzed. A spectral region in which the existence of acoustic-gravity waves is impossible but atmospheric oscillations may occur is also taken into consideration in this study. The properties of wave solutions in various types of spectral bandgaps, including the peculiarities of polarization ratios, are also analyzed. The theoretical analysis of spectral bandgap regions of AGWs can be used for the experimental search of new types of wave solutions in the atmosphere. [ABSTRACT FROM AUTHOR]

Drawing upon recent developments in structural equation modeling, the current study presents an analytical framework for addressing research questions in which, rather than focusing on means, it is intraindividual (or intragroup) variability that is of direct research interest. Beyond merely serving as an alternative to existing multilevel modeling approaches, this framework allows for extensions to accommodate a variety of complex research scenarios by parameterizing variability as a latent variable that can in turn be embedded within a broader covariance and mean structure involving other observed and/or latent variables. The estimation procedures and parameter interpretation for the latent random variability models are discussed. The versatility of the proposed methods is demonstrated through four empirical examples. The Mplus, BUGS, and Stan model syntax for the illustrative examples are supplied to facilitate the application of the methods. In many research and applied settings across the social, behavioral, and health sciences, it is variability, rather than averages, that is of key interest. Examples include consistency/stability of an individual over multiple measurements (intraindividual variability), and cohesiveness among members within a group or team (intragroup variability). Drawing upon recent developments in structural equation modeling, the current study presents an analytical framework for addressing research questions that focus on intraindividual, or intragroup, variability. Beyond merely serving as an alternative to existing multilevel modeling approaches, this framework allows for extensions to accommodate a variety of complex research scenarios by parameterizing variability as a latent variable, which can be studied as the outcome, predictor, and/or mediators simultaneously in relation to other observed and/or latent variables. This study delineates the latent random variability models and offers a discussion of model estimation as well as parameter interpretation. To demonstrate the versatility of the proposed methods, the latent random variability models are fit to empirical data and parameter estimates are obtained via Bayesian estimation. The Mplus, BUGS, and Stan model syntax for the illustrative examples are supplied for applied researchers' reference. [ABSTRACT FROM AUTHOR]

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This paper proposes an improved attitude estimation algorithm based on the extended Kalman filter (EKF), and it is applied to suppress the accuracy reduction in attitude estimation caused by fusing magnetometer data under large angular motion. In the proposed attitude estimation structure, the approximate variance of the estimated horizontal northbound magnetic vector is used to dynamically adjust the participation of magnetometer data in attitude estimation, as the approximate variance increases significantly under large angular motion and fusing magnetometer data will reduce estimation accuracy. A three-axis position-velocity controlled turntable is used to conduct rocking experiments for validating the proposed attitude estimation algorithm. The results show a significant improvement in yaw angle estimation accuracy with the proposed attitude estimation algorithm and correspondingly enhance the distribution of pitch and roll angle errors. [ABSTRACT FROM AUTHOR]

The Jensen-variance (JV) distance measure is introduced and some properties are developed. The JV distance measure can be expressed using two interesting representations: the first one is based on mixture covariances, and the second one is in terms of the scaled variance of the absolute difference of two random variables. The connections between the JV distance measure and some well-known informationmeasures, such as Fisher information, Ginimean difference, cumulative residual entropy, Fano factor, varentropy, varextropy, and chi-square distance measures, are examined. Specifically, the JV distance measure possesses metric properties and unifies most of the information measures within a general framework. It also includes variance and conditional variance as special cases. Furthermore, an extension of the JV distance measure in terms of transformed variables is provided. Finally, to demonstrate the usefulness of proposed methods, JV distance is applied to a real-life dataset related to fish condition factor index and some numerical results assuming skew-normal-distributed samples are presented. [ABSTRACT FROM AUTHOR]

Background: Cross-platform normalization seeks to minimize technological bias between microarray and RNAseq whole-transcriptome data. Incorporating multiple gene expression platforms permits external validation of experimental findings, and augments training sets for machine learning models. Here, we compare the performance of Feature Specific Quantile Normalization (FSQN) to a previously used but unvalidated and uncharacterized method we label as Feature Specific Mean Variance Normalization (FSMVN). We evaluate the performance of these methods for bidirectional normalization in the context of nested feature selection. Results: FSQN and FSMVN provided clinically equivalent bidirectional model performance with and without feature selection for colon CMS and breast PAM50 classification. Using principal component analysis, we determine that these methods eliminate batch effects related to technological platforms. Without feature selection, no statistical difference was identified between the performance of FSQN and FSMVN of cross-platform data compared to within-platform distributions. Under optimal feature selection conditions, balanced accuracy was FSQN and FSMVN were statistically equivalent to the within-platform distribution performance in multivariable linear regression analysis. FSQN and FSMVN also provided similar performance to within-platform distributions as the number of selected genes used to create models decreases. Conclusions: In the context of generating supervised machine learning classifiers for molecular subtypes, FSQN and FSMVN are equally effective. Under optimal modeling conditions, FSQN and FSMVN provide equivalent model accuracy performance on cross-platform normalization data compared to within-platform data. Using cross-platform data should still be approached with caution as subtle performance differences may exist depending on the classification problem, training, and testing distributions. [ABSTRACT FROM AUTHOR]

This study examines the relationship between the variance of others' contributions, social norms (empirical and normative expectations), and cooperative behavior using a classic linear public goods game. The following results are observed. First, the variance of a participant's group members' contributions had a negative impact on their contributions, empirical expectations, and normative expectations. Second, deviations from the mean, whether negative or positive, were deemed less socially appropriate. Third, while there was a strong relationship between variance, social norms, and cooperative behavior, the mediating effect of social norms was found to be insignificant. Finally, there were some notable findings regarding behavior type. Although free riders and cooperators exhibited distinct behavioral patterns, their normative expectations were similar. Free riders expected others to cooperate, but their empirical expectations were significantly lower than cooperators' expectations, which were aligned with their actual contributions. These findings contribute to research on the relationship between distribution heterogeneity, social norms and cooperative behavior. Furthermore, these findings provide valuable insights into management practices. [ABSTRACT FROM AUTHOR]

In a recent paper, the author examined a correlation affinity measure for selecting the coarse degrees of freedom (CDOFs) or coarse nodes (C nodes) in systems of elliptic partial differential equations (PDEs). This measure was applied to a set of relaxed vectors, which exposed the near‐nullspace components of the PDE operator. Selecting the CDOFs using this affinity measure and constructing the interpolation operators using a least‐squares procedure, an algebraic multigrid (AMG) method was developed. However, there are several noted issues with this AMG solver. First, to capture strong anisotropies, a large number of test vectors may be needed; and second, the solver's performance can be sensitive to the initial set of random test vectors. Both issues reflect the sensitive statistical nature of the measure. In this article, we derive several other statistical measures that ameliorate these issues and lead to better AMG performance. These measures are related to a Markov process, which the PDE itself may model. Specifically, the measures are based on the diffusion distance/effective resistance for such process, and hence, these measures incorporate physics into the CDOF selection. Moreover, because the diffusion distance/effective resistance can be used to analyze graph networks, these measures also provide a very economical scheme for analyzing large‐scale networks. In this article, the derivations of these measures are given, and numerical experiments for analyzing networks and for AMG performance on weighted‐graph Laplacians and systems of elliptic boundary‐value problems are presented. [ABSTRACT FROM AUTHOR]