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The periodic nonuniform sampling series, involving periodic samples of both the function and its first r derivatives, was initially introduced by Nathan (Inform Control 22: 172–182, 1973). Since then, various authors have extended this sampling series in different contexts over the past decades. However, the application of the periodic nonuniform derivative sampling series in approximation theory has been limited due to its slow convergence. In this article, we introduce a modification to the periodic nonuniform sampling involving derivatives by incorporating a Gaussian multiplier. This modification results in a significantly improved convergence rate, which now follows an exponential order. This is a significant improvement compared to the original series, which had a convergence rate of O (N - 1 / p) where p > 1 . The introduced modification relies on a complex-analytic technique and is applicable to a wide range of functions. Specifically, it is suitable for the class of entire functions of exponential type that satisfy a decay condition, as well as for the class of analytic functions defined on a horizontal strip. To validate the presented theoretical analysis, the paper includes rigorous numerical experiments. [ABSTRACT FROM AUTHOR]

Let X, Y be continuous random variables with unknown distributions. The aim of this paper is to study the problem of estimating the probability θ : = P (X < Y) based on independent random samples from the distributions of X ′ , Y ′ , ζ and η , where X ′ = X + ζ , Y ′ = Y + η and X, Y, ζ , η are mutually independent random variables. In this context, ζ , η are referred to as measurement errors. We apply the ridge-parameter regularization method to derive a nonparametric estimator for θ depending on two parameters. Our estimator is shown to be consistent with respect to mean squared error if the characteristic functions of ζ , η only vanish on Lebesgue measure zero sets. Under some further assumptions on the densities of X, Y, ζ and η , we obtain some upper and lower bounds on the convergence rate of the estimator. A numerical example is also given to illustrate the efficiency of our method. [ABSTRACT FROM AUTHOR]

An online correction method for phase current sampling gain errors utilizing the phase current zero-crossing principle is proposed in this paper to solve the problems of torque/speed pulsation and inaccurate position estimation in permanent magnet synchronous motor (PMSM) drives. First, a mathematical model considering three-phase current sampling errors was built and a variable-gain sliding mode observer (SMO) was designed. Second, the impacts of current gain errors on electromagnetic torque and position estimation were analyzed. When any one of the three-phase currents crosses zero, the amplitudes of the other two-phase currents are the same but the signs are opposite. Based on this principle, the current gain ratios of these two-phase currents can be calculated. Finally, comparing three sets of current gain ratios, the faulty phase can be determined, and online correction of the current gain error is executed. Experimental results show that the proposed method can determine faults regardless of whether there is a large or small gain error. In addition, the proposed method can balance the three-phase currents, improve the accuracy of position estimation, reduce torque pulsation, and enhance the speed control performance. [ABSTRACT FROM AUTHOR]

This study demonstrates the advantages of scale‐ and variable‐dependent localization (SDL and VDL) on three‐dimensional ensemble variational data assimilation of the hourly‐updated high‐resolution regional forecast system, the Rapid Refresh Forecast System (RRFS). SDL and VDL apply different localization radii for each spatial scale and variable, respectively, by extended control vectors. Single‐observation assimilation tests and cycling experiments with RRFS indicated that SDL can enlarge the localization radius without increasing the sampling error caused by the small ensemble size and decreased associated imbalance of the analysis field, which was effective at decreasing the bias of temperature and humidity forecasts. Moreover, simultaneous assimilation of conventional and radar reflectivity data with VDL, where a smaller localization radius was applied only for hydrometeors and vertical wind, improved precipitation forecasts without introducing noisy analysis increments. Statistical verification showed that these impacts contributed to forecast error reduction, especially for low‐level temperature and heavy precipitation. Plain Language Summary: In atmospheric data assimilation based on ensemble forecasts, the analysis increment is limited to the vicinity of each observation by spatial localization to prevent spurious analysis increments due to sampling error caused by the small ensemble size. Scale‐ and variable‐dependent localization (SDL and VDL) make it possible to set optimal localization radii separately for each spatial scale and variable. Sensitivity experiments in this study with a high‐resolution forecast system showed that SDL could decrease the bias of temperature and humidity forecasts and that VDL could improve precipitation forecasts without introducing noisy analysis increments. Key Points: This study implements scale‐ and variable‐dependent localization (SDL and VDL) for data assimilation of the Rapid Refresh Forecast SystemSDL decreases the imbalance of the analysis field and the bias of temperature and humidity forecasts by the larger localization radiusVDL enables simultaneous assimilation of conventional and radar reflectivity data without introducing noisy analysis increments [ABSTRACT FROM AUTHOR]

This paper investigates safety-critical event-triggered control (ETC) for nonlinear systems. It proposes a new ETC strategy that ensures event-triggered safety by using a tunable input-to-state safe barrier function. Then, a method is devised to effectively save communication resources and reduce computational load through the construction of a suitable dynamic event-triggered mechanism. Furthermore, the exclusion of Zeno behavior is guaranteed for the closed-loop system. Finally, this paper culminates by presenting two illustrative examples that showcase the effectiveness of proposed control strategies. • Address the issue of sampling errors and provide a less conservative approach. • Ensure event-triggered safety of nonlinear systems. • The definite exclusion of Zeno behavior in event-triggered control. [ABSTRACT FROM AUTHOR]

In this paper, an extension of ratio and product type estimators be studied to estimate population mean in the context of post-stratification design while the data is affected by measurement errors. The expressions of extended estimators to calculate bias and MSE up to first order of approximation has been derived. By using some real data sets, an empirical study has been done to check the performance of estimators under measurement errors. [ABSTRACT FROM AUTHOR]

Stochastic root-finding problems are fundamental in the fields of operations research and data science. However, when the root-finding problem involves rare events, crude Monte Carlo can be prohibitively inefficient. Importance sampling (IS) is a commonly used approach, but selecting a good IS parameter requires knowledge of the problem's solution, which creates a circular challenge. In "Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation," He, Jiang, Lam, and Fu propose an adaptive IS approach to untie this circularity. The adaptive IS simultaneously estimates the root and the IS parameters, and can be embedded in sample average approximation–type algorithms and stochastic approximation–type algorithms. They provide theoretical analysis on strong consistency and asymptotic normality of the resulting estimators, and show the benefit of adaptivity from a worst-case perspective. They also provide specialized analyses on extreme quantile estimation under milder conditions. In solving simulation-based stochastic root-finding or optimization problems that involve rare events, such as in extreme quantile estimation, running crude Monte Carlo can be prohibitively inefficient. To address this issue, importance sampling can be employed to drive down the sampling error to a desirable level. However, selecting a good importance sampler requires knowledge of the solution to the problem at hand, which is the goal to begin with and thus forms a circular challenge. We investigate the use of adaptive importance sampling to untie this circularity. Our procedure sequentially updates the importance sampler to reach the optimal sampler and the optimal solution simultaneously, and can be embedded in both sample-average-approximation-type algorithms and stochastic-approximation-type algorithms. Our theoretical analysis establishes strong consistency and asymptotic normality of the resulting estimators. We also demonstrate, via a minimax perspective, the key role of using adaptivity in controlling asymptotic errors. Finally, we illustrate the effectiveness of our approach via numerical experiments. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72293562, 72121001, and 72171060], the National Science Foundation [Grants CAREER CMMI-1834710 and IIS-1849280], and the Air Force Office of Scientific Research [Grant FA95502010211]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2023.2484. [ABSTRACT FROM AUTHOR]

Riverine sampling of pollutants is commonly used to understand pollutants' transport pathways, relationships with hydrology, and overall presence in a waterbody. However, temporal gaps between sample collection introduce errors to these efforts, and guidance prescribing sampling frequency remains sparse. The magnitude of error often depends on a contaminant's transport mechanisms and local hydrologic conditions, making the creation of comprehensive sampling guidance difficult. This study analyzed a unique dataset that measured 18 analytes, including pesticides, nutrients, and pathogens, in three Iowa rivers for 90 consecutive days (May 4–August 1, 2000). This dataset provided a novel opportunity to relate pollutants to local hydrology and quantify errors associated with recurring sampling. Pesticide concentrations followed the spring flush phenomenon, where values were greatest during high streamflow in May and June but often depleted by July. Fecal coliform and total phosphorus (TP) also coincided with high flow, but unlike pesticides, their concentrations never diminished. Nitrate exhibited more complex behavior; concentrations were diluted during high flows and then increased as streamflow receded. Autocorrelations were significant for nitrate and atrazine in larger rivers but negligible for fecal coliform and TP. Loads were calculated for four pollutants with minimal non-detects (atrazine, fecal coliform, nitrate, and TP). We simulated intermittent sampling by selecting evenly spaced subsets of measured values to estimate loads, which were compared to the loads calculated using every daily sample to quantify error. This method typically overestimated nitrate loads but underestimated other pollutants, and errors often decreased in larger watersheds. Nitrate generally had the lowest error, while fecal coliform had the highest. We used these results to approximate the sampling frequency needed to bind errors within a certain threshold. [ABSTRACT FROM AUTHOR]

Purpose: To acquire accurate volumetric multi‐channel B1+$$ {\mathrm{B}}_1^{+} $$ maps in under 14 s whole‐brain or 23 heartbeats whole‐heart for parallel transmit (pTx) applications at 7 T. Theory and Methods: We evaluate the combination of three recently proposed techniques. The acquisition of multi‐channel transmit array B1+$$ {\mathrm{B}}_1^{+} $$ maps is accelerated using transmit low rank (TxLR) with absolute B1+$$ {\mathrm{B}}_1^{+} $$ mapping (Sandwich) acquired in a B1+$$ {\mathrm{B}}_1^{+} $$ time‐interleaved acquisition of modes (B1TIAMO) fashion. Simulations using synthetic body images derived from Sim4Life were used to test the achievable acceleration for small scan matrices of 24 × 24. Next, we evaluated the method by retrospectively undersampling a fully sampled B1+$$ {\mathrm{B}}_1^{+} $$ library of nine subjects in the brain. Finally, Cartesian undersampled phantom and in vivo images were acquired in both the brain of three subjects (8Tx/32 receive [Rx]) and the heart of another three subjects (8Tx/8Rx) at 7 T. Results: Simulation and in vivo results show that volumetric multi‐channel B1+$$ {\mathrm{B}}_1^{+} $$ maps can be acquired using acceleration factors of 4 in the body, reducing the acquisition time to within 23 heartbeats, which was previously not possible. In silico heart simulations demonstrated a RMS error to the fully sampled native resolution ground truth of 4.2° when combined in first‐order circularly polarized mode (mean flip angle 66°) at an acceleration factor of 4. The 14 s 3D B1+$$ {\mathrm{B}}_1^{+} $$ maps acquired in the brain have a RMS error of 1.9° to the fully sampled (mean flip angle 86°). Conclusion: The proposed method is demonstrated as a fast pTx calibration technique in the brain and a promising method for pTx calibration in the body. [ABSTRACT FROM AUTHOR]

Poverty is the oldest social problem that ever existed and is difficult to reverse. It is multidimensional and unmeasurable. Thus, measuring by decomposing rural multidimensional poverty is critical. Most poverty studies are usually generic, exposed to large sampling errors, and intended for macroeconomic decisions. Thus, measuring poverty for a specific locality with various configurations (15) is critical for economic development. The paper combines predictive analytics and advanced econometrics to decompose poverty at the micro-level by utilizing the Community-Based Monitoring system at complete enumeration (L = 34, S = 4). Logistic Regression (78) Models with 19 Independent Variables and 12 Intervening Variables were fitted. Headcount Analysis (0.2138–0.9845), Poverty Gap (0.2228–0.0502), Severity statistics (0.0723–0.0168) and Watts Index (0.2724–0.0618) are scrutinized. Poverty levels vary by location; a significant fraction of the population (P0i = 68.50%, P0f = 55.80%) and households (P0i = 63.70%, P0f = 50.70%) live below the poverty line and food threshold. It has been revealed that poverty is extreme in Isarog (i = 0.7793), moderate in Poblacion (p = 0.4019), intense in Ranggas (r = 0.6542), and severe in Salog (s = 0.6353). Multidimensional variables (13VAR) significantly predict poverty outcomes (p-value = 0.0000, PseudoR2 = 0.75). Moreover, intervening variables have been impacting poverty across all locals. All models tested are significant across all sectors and correctly predicted by the model classifications (Estat = 73.29–74.12%). Poverty is multifaceted; thus, it requires different interventions. Finally, policy proposals (54) were outlined to alleviate poverty and promote local economic development. [ABSTRACT FROM AUTHOR]

The Argo array of profiling floats has considerably increased the observing capability of the three-dimensional global ocean and the knowledge of the ocean response to climate change. In particular, the Argo sampling has allowed observing relevant ocean indicators over the whole Mediterranean Sea especially during the last decade. In this study, the Mediterranean Argo network is comprehensively described from its spatio-temporal coverage to its capability to observe ocean monitoring indicators at sub-regional scale. For this purpose, the Argo array, as a non-interpolated product of profiles, is used to estimate the ocean heat and salt contents integrated within the upper, intermediate and deep layers over the period 2013-2022 in the different sub-regions of the basin. The same computational method is also applied to a model reanalysis product to estimate the impact of sampling of the sole Argo array. The sampling error is defined at sub-regional scale by comparing estimations from the whole model grid (full-sampled model) and from the Argo-like sampled model grid (sub-sampled model). Warming and salinification trends are well captured by the Argo array over the period of study, warming trends being the highest in the sub-regions of the western Mediterranean Sea from surface to depth and salinification trends being higher in the eastern sub-basin for the upper layer and in the western sub-basin for the deeper layers. This study also demonstrates the capability of the Argo array to capture local ocean structures and dynamics (e.g. anticyclonic and cyclonic gyres, intermediate and deep convection events and Atlantic Water inflows) and to account for their impact in the sub-regional variability of ocean heat and salt contents in the upper, intermediate and deep layers from seasonal to interannual scales. Considering these structures is fundamental for the understanding of the thermohaline circulation and changes observed in the Mediterranean Sea, and thus for future climate studies. [ABSTRACT FROM AUTHOR]

The spatial–temporal sampling errors arising from the differences in geographical locations and measurement times between co-located Global Navigation Satellite System (GNSS) radio occultation (RO) and radiosonde (RS) data represent systematic errors in the three-cornered hat (3CH) method. In this study, we propose a novel spatial–temporal sampling correction method to mitigate the sampling errors associated with both RO–RS and RS–model pairs. We analyze the 3CH processing chain with this new correction method in comparison to traditional approaches, utilizing Fengyun-3E (FY-3E) GNSS Occultation Sounder II (GNOS II) RO data, atmospheric models, and RS datasets from the Hailar and Xisha stations. Overall, the results demonstrate that the improved 3CH method performs better in terms of spatial–temporal sampling errors and the variances of atmospheric parameters, including refractivity, temperature, and specific humidity. Subsequently, we assess the error variances of the FY-3E GNOS II RO, RS and model atmospheric parameters in China, in particular the northern China and southern China regions, based on large ensemble datasets using the improved 3CH data processing chain. The results indicate that the FY-3E GNOS II BeiDou navigation satellite system (BDS) RO and Global Positioning System (GPS) RO show good consistency, with the average error variances of refractivity, temperature, and specific humidity being less than 1.12%2, 0.13%2, and 700%2, respectively. A comparison of the datasets from northern and southern China reveals that the error variances for refractivity are smaller in northern China, while temperature and specific humidity exhibit smaller error variances in southern China, which is attributable to the differing climatic conditions. [ABSTRACT FROM AUTHOR]

The flow dependence of forecast distributions is studied using a 120‐member ICON‐D2 ensemble for two representative case studies of weak and strong synoptic forcing of convection. The bootstrapping technique is used to investigate the convergence of sampling error for a range of surface and midtroposphere variables. Convergence is generally observed for the mean and the standard deviation, but not for the 95th percentile, especially in strong forcing. Additionally, maps of uncertainty are introduced, which allow for a more detailed analysis of the spatial pattern of uncertainty and facilitate the interpretation of the sources and evolution of forecast uncertainty in different synoptic forcing conditions. It is found that convection significantly increases uncertainty in model variables and shapes the spatial uncertainty pattern, with weak forcing leading to patchy uncertainty and strong forcing resulting in a more coherent pattern due to large convective systems. Overall, the main result of this work is the strong connection between uncertainty of forecast variables and convection, with synoptic forcing being crucial in determining the spatial distribution and uncertainty evolution within 24 h. [ABSTRACT FROM AUTHOR]

Weather radar is a crucial tool for rainfall observation and forecasting, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources – including attenuation, ground clutter, beam blockage and drop-size distribution – with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods and imposing path-integrated attenuation effects, a stochastic drop-size-distribution field, and sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field and the corrected "best-guess" rainfall field which would be obtained if they were observed in a real-world case. The structure of these errors is then investigated, with a focus on the frequency and behaviour of "rainfall shadows". Half of the simulated weather radar images have at least 3 % of their significant rainfall rates shadowed, and 25 % have at least 45 km2 containing rainfall shadows, resulting in underestimation of the potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well in generating realistic weather radar images visually for a large range of event types. [ABSTRACT FROM AUTHOR]

The Kingdom of Saudi Arabia is facing challenges related to water scarcity, however, the Cambrian-Ordovician Saq Aquifer System, in Al Qassim Province, provides vital water resources. This article assesses the petrophysical properties and hydrochemical characteristics of the aquifer system utilizing downhole cam recording and geostatistical analysis. The evaluation aims to assign the hydrogeological attributes, groundwater potential, and associated risks using an open-petrophysical aquifer system approach. The petrophysical evaluation appraises the prevailing lithology, zonation, hydrogeological properties, and salinity patterns. Sandstones with low shale content and dispersed distribution possess effective porosity that is fully saturated with groundwater containing calcium and bicarbonate ions. The majority of groundwater samples exhibit simple dissolution or lack prevailing ionic concentrations, indicating an ancient marine water genesis and a fossilized water type. The resistivity-depth profiles reveals three potential water-bearing aquifers including a disconnected compartment of an unconfined aquifer, a continuous compartment of a confined aquifer, and a continuous compartment of an unconfined aquifer. Petrophysical and hydrochemical parameters have been analyzed using geostatistical methods to assess their spatial variability and reduce potential sampling errors. Three distinct risk segments (RSs) with varying levels of risk characterize the aquifer system. RS-A represents a potential aquifer with low risk, RS-B poses moderate risks, and RS-C carries high risks. A fairway map of the aquifer system assigns geologic-hydro related factors that influence aquifer assessment and risk mapping. Segment-A is deemed an attractive long-term investment opportunity with low risk, while Segment-B offers a good investment opportunity with moderate risk. Segment-C provides a fair investment opportunity but entails high risks related to petrophysical qualities, hydrochemical characterizations, and irrigation utilities. The extraction and utilization of groundwater present promising investment opportunities, while employing a petrophysical approach can effectively evaluate and manage groundwater resources for sustainable utilization. [ABSTRACT FROM AUTHOR]

Background and Objectives Materials and Methods Results Conclusion Previously published studies have consistently identified significant variation in red blood cell (RBC) transfusions during cardiac surgery. Clinical guidelines can be effective at improving the average quality of care; however, their impact on variation in practice is rarely studied. Herein, we estimated how variation in RBC use across cardiac surgeons changed after the publication of national patient blood management guidelines.We performed a pre–post study estimating change in variation in RBC transfusions across 80 cardiac surgeons in 29 hospitals using a national cardiac surgery registry. Variation across surgeons was estimated using fixed‐effects regressions controlling for surgery and patient characteristics and an empirical Bayes shrinkage to adjust for sampling error. RBC use was measured by three metrics—the total number of units transfused, the proportion of patients transfused and the number of units transfused, conditional on receiving RBC.The primary analysis utilized 35,761 elective cardiac surgeries performed between March 2009 and February 2015 and identified a 24.5% reduction (p < 0.0001) in mean total units transfused accompanied by a 37.2% reduction (p = 0.040) in the variation across surgeons. The reduction in mean total units was driven by both the proportion of patients transfused and the number of units transfused, conditional on receiving RBC, while the reduction in variation was only driven by the latter.In our study of RBC transfusions across cardiac surgeons, the surgeons who used more RBC in the pre‐guideline period experienced larger reductions in RBC use after the guidelines were published. [ABSTRACT FROM AUTHOR]

This study proposes a novel 6-transistor bootstrapped switch with minimized sampling and holding error obtained through sampling window error analysis for SAR ADC design. The proposed switch design strategically mitigates channel charge injection and minimizes the input signal dependency of on-resistance by optimizing its sizing parameters. To counteract channel charge effects, dummy NMOS and PMOS components are judiciously employed, culminating in a substantial improvement in the effective number of bits (ENOB). The complete analysis of the proposed circuit is done using the Cadence Virtuoso SCL 0.18 μ m CMOS process. For a 51.514 kHz sinusoidal 1 V peak-to-peak differential input signal with a 1 MSPs clock speed, the proposed circuit achieves 2.0141 mV maximum sampling window error, 0.131 μ W power consumption, 84.67 dB signal-to-noise ratio (SNR), 84.67 signal-to-noise and distortion (SINAD) ratio and 86.02 dB spurious-free dynamic range (SFDR), which produces 13.77 bits ENOB. For the impacts of process variations and mismatch on switch performance, a comprehensive 500-point Monte Carlo (MC) simulation of the proposed bootstrap switch is conducted in this study. Post-layout results show that the proposed circuit is suitable for IoT applications. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive law that uses a quasi-convex function and a novel sliding variable in an adaptive sliding mode control (ASMC) scheme for robot manipulators. Since the dynamic equations of robot manipulators inevitably include model uncertainties and disturbances, time-delay estimation (TDE) errors occur when using the time-delay control (TDC) approach. Further, the ASMC method used to compensate for TDE errors naturally causes a chattering phenomenon. To improve tracking performance while reducing or maintaining chattering, this paper proposes an adaptive law based on a quasi-convex function that is convex at the origin and concave at the gain switching point, respectively. We also adopt a novel sliding variable that uses previously sampled tracking errors and their time derivatives. Further, this paper proves that the sliding variable of the robot manipulator controlled by the proposed ASMC satisfies uniformly ultimately bounded stability. The simulation and experimental results illustrate the effectiveness of the proposed methods in terms of tracking performance. [ABSTRACT FROM AUTHOR]

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Net cage aquaculture has a significant impact on the regional water environment, and studying the changes in water quality parameters in net cage aquaculture areas is of great practical significance for understanding the impact of regional aquaculture on the water environment. This article uses GF-1WFV data to construct an inversion model for the total suspended solids concentration in the net cage aquaculture area, which has grown exponentially in the Manwan Reservoir area in recent years. The results indicate that the model has high accuracy, with an average relative error of 9.65% between the inverted values and the measured values, and a root mean square error of 0.33mg/L. Based on the constructed inversion model and satellite imagery, the total suspended solids concentration in the Manwan net cage aquaculture area was inverted, and the variation patterns of total suspended solids concentration in the reservoir area and different net box positions were analyzed. Research has found that the variation pattern of suspended solids concentration in the Manwan reservoir area and different positions of net cages is basically consistent. The total suspended solids concentration in net cages has not shown any abnormalities due to the phenomenon of local "delineation" in aquaculture, but is lower than the overall suspended solids concentration in the reservoir area. This is mainly because the variation of total suspended solids concentration is mainly affected by precipitation, surface runoff, water flow velocity, etc. Net cage aquaculture can reduce its impact on the total suspended solids concentration in local "delineation" areas. This study has certain reference significance for understanding the changes in total suspended solids concentration in the Manwan reservoir area and net cage aquaculture area. In the future, a multi-source remote sensing data remote sensing estimation model for water quality parameters will be developed for understanding the changing patterns in water environment in the Manwan net cage aquaculture area. [ABSTRACT FROM AUTHOR]

The article published in the British Journal of Dermatology explores the development of melanoma from pre-existing naevi in individuals with a germline CDKN2A pathogenic variant. The study found that 78% of melanomas in carriers of this variant originated from pre-existing naevi, with total-body photography being a useful tool for surveillance. The findings suggest the importance of routine monitoring and early detection in individuals with a CDKN2A pathogenic variant to prevent melanoma development. The study acknowledges limitations in sample size and patient demographics, emphasizing the need for further research in this area. [Extracted from the article]

Many sampling strategies commonly used in molecular dynamics, such as umbrella sampling and alchemical free energy methods, involve sampling from multiple states. The Multistate Bennett Acceptance Ratio (MBAR) formalism is a widely used way of recombining the resulting data. However, the error of the MBAR estimator is not well-understood: previous error analyses of MBAR assumed independent samples. In this work, we derive a central limit theorem for MBAR estimates in the presence of correlated data, further justifying the use of MBAR in practical applications. Moreover, our central limit theorem yields an estimate of the error that can be decomposed into contributions from the individual Markov chains used to sample the states. This gives additional insight into how sampling in each state affects the overall error. We demonstrate our error estimator on an umbrella sampling calculation of the free energy of isomerization of the alanine dipeptide and an alchemical calculation of the hydration free energy of methane. Our numerical results demonstrate that the time required for the Markov chain to decorrelate in individual states can contribute considerably to the total MBAR error, highlighting the importance of accurately addressing the effect of sample correlation. [ABSTRACT FROM AUTHOR]

Maintaining high levels of process quality is crucial to the competitiveness of manufacturing firms in today's increasingly global marketplace. To ensure the quality of manufactured products meets customer needs, process capability indices (PCIs) are widely used to analyze the process performance of various processing characteristics. Products characterise by processing characteristics of both unilateral and bilateral specifications are common in the current sales market. Manufacturing firms must often adopt multiple PCIs to analyze the process performance of a single product, which is inefficient in practical applications and management. Yield-based index C p k is not subject to this limitation. For this reason, we employed C p k to evaluate process performance and the effectiveness of improvement measures. In practice, C p k is estimated from samples, which means that misjudgment may occur in the assessment of process performance and improvement effectiveness due to sampling errors. We therefore derived the 100 (1 − α) % confidence interval of C p k and, based on the producer's perspective, used the upper confidence limit to evaluate improvement effectiveness. To lower the risk of misjudgment and increase the reliability of improvement effectiveness in the case of data uncertainty, this paper further proposes fuzzy estimation using the right-sided confidence interval of C p k and develops the fuzzy judgement model. [ABSTRACT FROM AUTHOR]

Meteorological observations of surface air temperature have provided fundamental data for climate change detection and attribution. However, the weather stations are unevenly distributed, and are still very sparse in remote regions. The possible sampling error is well known, but not well quantified because we are lack of the adequate and regularly distributed measurements. The high resolution of satellite land surface temperature retrieval during night time provide a nice proxy for near surface temperature as both temperatures controlled by surface longwave radiative cooling and the nocturnal temperature inversion depress land‐atmosphere turbulent exchange. The sampling error of mean value and trend were assessed by comparing station point measurements (pixel of ∼0.01°) with grid (1°) mean and national mean from 2001 to 2021. This method permits us to make the first assessment of under‐sampling error and spatial representative error on both national mean and trend of air temperature during nighttime collected at ∼2,400 weather stations over China. The sampling error in national mean temperature is more than 3°C. The under‐sampling error due to lack of observation explains two thirds and the spatial representative error due to the difference between station and grid/regional mean elevation contribute the other one third. The sampling error in trend account for one third of the national mean trend. The urban heat island effect associated with urbanization around the weather stations (spatial representative error) can explain four fifths of the sampling error in trend, which is consistent with existing studies based on air temperature collected at paired weather station. Plain Language Summary: Meteorological observations provide fundamental measurements of surface air temperature. These station‐based temperature records are usually processed to grid data sets considering the uneven distribution of meteorological stations. The gridding procedure represents a non‐uniformly sampling. In this paper, we quantified the sampling error over China using remote sensed temperature record as proxy of near surface temperature during nighttime. The causes of sampling error both in mean value and trend were both quantified. Key Points: Sampling error of mean and trend of temperature collected at weather stations at nighttime over China were investigatedThe sampling error in national mean temperature were found to be more than 3°C due to uneven distribution of weather stationsThe sampling error in temperature trend account for one thirds of the national mean temperature trend due to local urbanization [ABSTRACT FROM AUTHOR]

In this article, we present an event‐triggered control law for systems with an unknown control direction, where the adaptation dynamics of the Nussbaum gain are sampled. Specifically, we employ the emulation method and introduce a specific set of sampling errors. This approach enables the design of an event‐triggered law without encountering Zeno behavior. Additionally, we introduce a lemma to address the non‐integrability issue of the Nussbaum gain dynamics during the event‐triggered law design. We apply this design philosophy to co‐design both the continuous‐time controller and the event‐triggered law, aiming to achieve the global stabilization of systems with an unknown control direction. Finally, we validate the theoretical results through a numerical example. [ABSTRACT FROM AUTHOR]

Background: Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for rare cell types, subtleties of cell states, and details of gene regulatory networks—there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually). Results: We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization—a step that skews distributions, particularly for sparse data—and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene–gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships. Conclusions: New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene–gene correlations. [ABSTRACT FROM AUTHOR]

Stereotactic needle biopsy stands as a crucial method for diagnosing intracranial lesions unsuitable for surgical intervention. Nonetheless, the potential for sampling errors lead to innovative approaches to enhance diagnostic precision. This study contrasts the outcomes of patients undergoing fluorescein-assisted frameless stereotactic needle biopsy with those receiving traditional biopsies to evaluate the impact on diagnostic accuracy and safety. This study included patients with contrast-enhancing intracranial lesions, comprising a prospective group undergoing fluorescein-assisted biopsies and a retrospective group undergoing conventional biopsies at the same institution. We've collected data on demographics, procedural specifics, diagnostic outcomes, and postoperative events. A comparative analysis involved 43 patients who received fluorescein-assisted biopsies against 77 patients who underwent conventional biopsies. The average age was 60.5 years. The fluorescein group exhibited a 93% success rate in diagnosis, markedly higher than the 70.1% in the non-fluorescein group (OR = 5.67; 95%IC: 1.59–20.24; p < 0.01). The rate of complications was statistically similar across both cohorts. Despite its established value, stereotactic needle biopsy is susceptible to inaccuracies and complications. The application of fluorescence-based adjuncts like 5-ALA and fluorescein has been investigated to improve diagnostic fidelity and reduce risks. These technologies potentially minimize the necessity for multiple biopsies, decrease surgical duration, and provide immediate verification of tumor presence. Fluorescein-assisted stereotactic biopsy emerges as an effective, secure alternative to conventional methods. [ABSTRACT FROM AUTHOR]

The incidence of wildlife roadkill significantly threatens the persistence of wildlife populations and disrupts the ecological functionality of ecosystems. This study investigates the impact of roadkills on wildlife in the Paraguayan Chaco, focusing on a 250‐km segment of Route 9 'Dr. Carlos Antonio López' between Villa Hayes and Pozo Colorado. We conducted a road survey for 15 months and recorded 2338 carcasses, identifying 87 species, with mammals (41.3%), reptiles (32.3%) and birds (19.8%) being the most observed groups. The species most frequently killed included Cerdocyon thous, Caracara plancus, Thamnodynastes hypoconia and Procyon cancrivorus. We also recorded species with conservation concern. Additionally, we estimated mortality rates by accounting for sampling errors such as carcass removal and searcher efficiency, revealing annual roadkill rates of 5183 mammals, 19 402 birds and 5020 reptiles on the 250 km per year. Spatial analysis using Ripley's K statistic and HotSpot Identification highlighted significant variation in roadkill distribution across different taxonomic groups and seasons, with 51 km of road identified as hotspots when analysing all groups together. Notably, there was minimal overlap in hotspot locations between seasons and taxonomic groups, emphasizing the need for targeted mitigation strategies. Our findings challenge previous macroecological assessments suggesting low roadkill rates in Paraguay, underscoring the importance of local studies in accurately assessing ecological impacts. This study provides critical baseline data for conservation efforts and calls for further research to develop and implement effective roadkill mitigation strategies in Latin America, especially in Chaco region. [ABSTRACT FROM AUTHOR]

County level estimates of mean sheet and rill erosion from the Conservation Effects Assessment Project (CEAP) are useful for program development and evaluation. Since county sample sizes in the CEAP survey are insufficient to support reliable direct estimators, small area estimation procedures are needed. The quantity of water runoff is a useful covariate but is unavailable for the full population. We use an estimate of mean runoff from the CEAP survey as a covariate in a small area model with sheet and rill erosion as the response. As the runoff and sheet and rill erosion are estimators from the same survey, the measurement error in the covariate is important as is the correlation between the measurement error and the sampling error. We conduct a detailed investigation of small area estimation in the presence of a correlation between the measurement error in the covariate and the sampling error in the response. In simulations, the proposed predictor is superior to small area predictors that assume the response and covariate are uncorrelated or that ignore the measurement error entirely. [ABSTRACT FROM AUTHOR]

This systematic review and meta‐analysis assesses independently the acute effects of anodal and cathodal motor cortex transcranial direct current stimulation (tDCS) on athletic performance in healthy adults. Besides, it evaluates the unique and conjoint effects of potential moderators (i.e., stimulation parameters, exercise type, subjects' training status and risk of bias). Online database search was performed from inception until March 18th 2024 (PROSPERO: CRD42023355461). Forty‐three controlled trials were included in the systematic review, 40 in the anodal tDCS meta‐analysis (68 effects), and 9 (11 effects) in the cathodal tDCS meta‐analysis. Performance enhancement between pre‐ and post‐stimulation was the main outcome measure considered. The anodal tDCS effects on physical performance were small to moderate (g =.29, 95%CI [.18,.40], PI = −.64 to 1.23, I2 = 64.0%). Exercise type, training status and use of commercial tDCS were significant moderators of the results. The cathodal tDCS effects were null (g =.04, 95%CI [−.05,.12], PI = −.14 to.23, I2 = 0%), with a small to moderate heterogeneity entirely due to sampling error, thus impairing further moderator analysis. These findings hold significant implications for the field of brain stimulation and physical performance, as they not only demonstrate a small to moderate effect of acute tDCS but also identify specific categories of individuals, devices and activities that are more susceptible to improvements. By addressing the multidimensional factors influencing the mechanisms of tDCS, we also provide suggestions for future research. Results indicate a small favouring effect of anodal tDCS on athletic performance with large heterogeneity. Exercise type, training status and use of commercial tDCS significantly moderate anodal tDCS effects. Cathodal tDCS shows null effects, with small to moderate heterogeneity entirely due to sampling error. [ABSTRACT FROM AUTHOR]

In the process of landslide susceptibility prediction (LSP) modelling, there are some problems in the model dataset relating to landslide and non‐landslide samples, such as landslide sample errors, subjective randomness and low accuracy of non‐landslide sample selection. In order to solve the above problems, a semi‐supervised machine learning model for LSP is innovatively proposed. Firstly, Yanchang County of Shanxi Province, China, is taken as the study area. Secondly, the frequency ratio values of 12 environmental factors (elevation, slope, aspect, etc.) and the randomly selected twice non‐landslides are used to form the initial model datasets. Thirdly, an extreme gradient boosting (XGBoost) model is adopted for training and testing the initial datasets, so as to produce initial landslide susceptibility maps (LSMs) which are divided into very low, low, moderate, high and very high susceptibility levels. Next, the landslide samples in initial LSMs with very low and low susceptibility levels are excluded to improve the accuracy of landslide samples, and the unlabelled twice non‐landslide samples in initial LSMs with low and very low susceptibility levels are randomly selected to ensure the accuracy of non‐landslide samples. These new obtained landslide and non‐landslide samples are reimported into XGBoost model to construct the semi‐supervised XGBoost (SSXGBoost) model. Finally, accuracy, kappa coefficient and statistical indexes of susceptibility indexes are adopted to assess the LSP performance of XGBoost and SSXGBoost models. Results show that SSXGBoost model has remarkably better LSP performance than that of XGBoost model. Conclusively, the proposed SSXGBoost model effectively overcomes the problems that the accuracy of landslide samples needs to be further improved and that non‐landslide samples are difficult to select accurately. [ABSTRACT FROM AUTHOR]

Researchers and practitioners are typically familiar with descriptive statistics and statistical inference. However, outside of regression techniques, little attention may be given to questions around prediction. In the current paper, we introduce prediction intervals using fundamental concepts that are learned in descriptive and inferential statistical training (i.e., sampling error, standard deviation). We walk through an example using simple hand calculations and reference a simple R package that can be used to calculate prediction intervals. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the variability in urinary stone composition analysis due to sampling and suggest potential solutions. Materials and Methods: We collected 1,135 stone fragments from 149 instances that had undergone a stone removal at Hanoi Medical University Hospital from January 2022 to August 2022. Each fragment was ground into fine powder and divided into separate specimens if the amount was abundant. For composition analyzing every specimen, Fourier transform infrared spectroscopy was performed. The composition of a given fragment was the average of its belonging specimens. The variability in composition was assessed on the fragment level (i.e., between fragments of an instance). We defined an instance as "significantly variable" if the maximum difference in any composition across its belonging fragments was equal to or greater than a given threshold. Results: On average, there were 7.6±3.3 stone fragments per instance and 2.3±0.5 specimens per fragment. We found that the variability could be substantial on the fragment level. Eighty-nine (69.5%) and 70 (54.7%) out of 128 multiple-component instances were significantly variable if the threshold was set at 20% and 30%, respectively. The variability of an instance on the fragment level was correlated with the size of fragment and the number of components. Conclusions: Our study demonstrated the significant variability in urinary stone composition and showed that it correlated with the size and the impurity of samples. Mapping denotation while sampling and analyzing as well as reporting the composition of individual fragments could be valuable to reduce potential variability. [ABSTRACT FROM AUTHOR]

Aim: This article explores the changes observed in meaning in life in contemporary Croatian society based on the data collected in a population survey of VAL-DE-END project. Methods: The survey was conducted in 2019 on a three-stage random sample, stratified by regions, counties, and locations within those counties (N = 1203) with a maximum sample error of ± 2,8 %. The response rate was 30 %. By including weights, the sample became nationally representative in terms of sex, age, education, and regional representation. The instrument used consisted of 90 items. Results: 87,5 % of respondent think that family and friends give life meaning. More than 67% of respondents find it in contributing to the community. 64,1 % find meaning in life in self-realisation of one' own possibilities and 56,7 % in living as comfortably as possible. 47,1 % agree with the statement that death can have its meaning only when a person believes in God. Conclusion: If we compare our data to previously done surveys in Croatian population, we can observe process of secularisation and individuation taking place which can have implications for the medical practice. [ABSTRACT FROM AUTHOR]

Evaluating the performance of city construction not only helps optimize city functions and improve city quality, but it also contributes to the development of sustainable cities. However, most of the scoring rules for evaluating the performance of city construction are overly cumbersome and demand very high data integrity. Moreover, the properties, change scale, and scope of different evaluation indicators of city construction often lead to uncertain and ambiguous results. In this study, a hybrid fuzzy method is proposed to conduct the performance evaluation of city construction in two phases. Firstly, a city performance index (CPI) was developed by combining the means and standard deviations of indicators of city construction to address the volatility of historical statistical data as well as different types of data. Considering the sampling errors in data analysis, the parameter estimation method was used to derive the 100% × (1 − α) confidence interval of the CPI. Buckley's fuzzy approach was then adopted to extend the statistical estimators from the CPI into fuzzy estimators, after which a fuzzy CPI was proposed. To identify the specific improvement directions for city construction, the fuzzy axiom design (fuzzy AD) method was applied to explore the relationship between the targets set by city managers and actual performance. Finally, an example of six cities in China is provided to illustrate the effectiveness and practicality of the proposed method. The results show that the performance of Chongqing on several evaluation indicators is lower than that of other cities. The proposed method takes into account the issues of uniformity and diversity in the performance evaluation of city construction. It can enable a quantitative assessment of the city construction level in all cities and provide theoretical support and a decision-making basis for relevant government departments to optimize city construction planning and scientifically formulate city construction policies. [ABSTRACT FROM AUTHOR]

This research was carried out to determine the relationship and the direction of the relationship between burnout levels, social support perceptions and psychological well-being of university students and their involuntary procrastination tendencies. For this purpose, the scales in the relevant literature were used. In addition, a descriptive-relationship-seeking type of research was preferred in this study. The study was carried out by reaching 328 students out of 1376 students studying at the health services vocational school of a university. In the research, proportional stratified sampling method was used to reduce sampling errors and to create samples with higher representativeness. In line with the research data, a moderate positive relationship was found between students' burnout levels and involuntary procrastination tendencies, a weak negative relationship between their perceived social support perceptions and involuntary procrastination tendencies, and a moderate negative relationship between their psychological well-being and involuntary procrastination tendencies. According to the results of the regression analysis between the variables, it was concluded that the burnout level, perceived social support and psychological well-being of the students had a significant effect on the involuntary procrastination tendency. As a result, it is thought that the research will contribute to a better understanding of the underlying causes of involuntary procrastination behaviour, which is an important factor affecting students' academic achievement in the field of educational psychology, and will provide an important tool for the development of programs to increase students' motivation. [ABSTRACT FROM AUTHOR]

This paper is devoted to the event-triggered consensus control for a class of uncertain multiple Euler-Lagrange (EL) systems with actuator faults. Different from the related works where strict conditions are imposed on system uncertainties and the measurements of the leader's output, more serious uncertainties are involved since all the system dynamic matrices are unknown while both actuator faults and external disturbance are considered; and moreover, fewer measurements of the leader's output are required since its time derivatives are not necessarily available for feedback. Mainly because of these, the consensus problem is hard to solve by straightforwardly extending the existing results. To solve the control problem, a dynamic gain with a smart choice of its updating law is introduced to overcome the serious uncertainties and the sampling error of the control signal. By incorporating the dynamic gain into the vectorial backstepping procedure, an adaptive consensus controller joined with an event-triggered mechanism is designed for each follower to ensure the consensus of the multi-agent system in the sense that all the states of the closed-loop system are bounded while the output of each follower tracks the leader's output. Finally, the effectiveness of the proposed method is verified by a simulation example. [ABSTRACT FROM AUTHOR]

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This paper investigates the containment control problem of linear multiagent systems (MASs) with multiple non‐autonomous leaders on a directed graph. First, an event‐triggered compensator is designed for each follower to estimate the convex combination of non‐autonomous leaders' states. The compensator is designed with the following three features: (1) It can estimate the convex combination of non‐autonomous leaders' states over directed graphs; (2) it is fully distributed; and (3) the designed event‐triggered mechanisms (ETMs) are composite dynamic ETMs, and each follower does not have to monitor the sampling errors continuously. Then, a state observer is developed for each follower based on the output feedback. Based on the designed compensator and state observer, an event‐triggered controller is developed, ensuring that each follower's state converges exponentially to an arbitrarily small neighborhood centered on the convex combination of leaders' states. Additionally, the proposed DETMs are free of Zeno behavior. Finally, the effectiveness of the developed control protocol is verified by two simulation examples. [ABSTRACT FROM AUTHOR]

Predicting the rate of penetration (ROP) is important for optimizing drilling parameters, improving drilling efficiency, and optimizing economic benefits throughout the drilling process. The current prediction model of ROP based on machine learning algorithms does not consider the interference of outliers. Therefore, in this study, we propose a method to predict ROP based on fuzzy support vector regression (FSVR). First, appropriate input parameters were selected from the controllable parameters. Second, based on the local outlier factor, a fuzzy membership degree was assigned to each sample. Finally, the sample with the fuzzy membership value was input into the model for ROP prediction. The results demonstrated that the goodness of fit (R2) of the improved FSVR model is 0.9634, and the mean absolute error is 0.1974. Compared with standard SVR and other models, the improved FSVR model has a stronger anti-interference ability, smaller prediction error for normal samples, and higher accuracy. [ABSTRACT FROM AUTHOR]

A phenomenon exists among high school students who lack understanding of their cultural awareness and that of others, failing to appreciate ethnic and cultural differences. Consequently, these disparities often become subjects of ridicule or amusement due to each group's varied modes of speech, styles, and customs. To address this issue, cultural awareness is essential, making it pertinent to investigate the impact of knowledge on cultural awareness mediated by attitudes. This study employs a quantitative methodology described as explanatory research. The study used Simple Random sample methodology, whereas the analysis utilized Structural Equation Modelling (SEM). The sample size was determined using the Raosoft sample size calculator with a 5% sampling error rate, resulting in a sample size of 213 students. The study's results indicated that information affects attitudes and cultural awareness, with attitudes potentially mediating the relationship between knowledge and cultural awareness. This study's conclusions pertain to Resource-Based Theory, suggesting that student behavior concerning cultural awareness can be influenced by cognitive expertise, namely in the areas of knowledge, comprehension, application, analysis, synthesis, and assessment. An elevated degree of knowledge can enhance students' attitudes, as seen by behaviors reflecting tolerance, empathy, openness, and respect within the framework of multicultural education. The recommendations for Twelfth-grade high schools to enhance their educational effectiveness with an emphasis on character development. [ABSTRACT FROM AUTHOR]

The two-parameter logistic (2PL) item response theory model is a statistical model for analyzing multivariate binary data. In this article, two groups are brought onto a common metric using the 2PL model using linking methods. The linking methods of mean–mean linking, mean–geometric–mean linking, and Haebara linking are investigated in nonrobust and robust specifications in the presence of differential item functioning (DIF). M-estimation theory is applied to derive linking errors for the studied linking methods. However, estimated linking errors are prone to sampling error in estimated item parameters, thus resulting in artificially increased the linking error estimates in finite samples. For this reason, a bias-corrected linking error estimate is proposed. The usefulness of the modified linking error estimate is demonstrated in a simulation study. It is shown that a simultaneous assessment of the standard error and linking error in a total error must be conducted to obtain valid statistical inference. In the computation of the total error, using the bias-corrected linking error estimate instead of the usually employed linking error provides more accurate coverage rates. [ABSTRACT FROM AUTHOR]

Accurately determining if the sample parameters from a natural gas pipeline's sampling system reflect the fluid characteristics of the main pipe has been a significant industry concern for many years. In this paper, samples of natural gas in a horizontal pipeline are investigated. CFD is used in this work and the turbulence is considered in the simulation. Firstly, the critical diameter for particles affected by gravity within such pipeline is determined. And then, the effects of the operation pressure and velocity of sampling branches on sample parameters, and the influence of particle density on these sample parameters, are analyzed. Finally, four different structures of sample branches for natural gas in a horizontal pipeline are compared. It is found that 100 μm is the critical diameter at which particles are affected by gravity; the operating pressure of the sampling branch has a significant impact on the particle mass concentration. The particle density has little impact on the sampling system. Overall, the design of the sampling branches does not cause significant sampling errors. This study provides guidance for optimal sampling in existing natural gas pipelines and enables effective monitoring of particle impurity content and properties in natural gas. [ABSTRACT FROM AUTHOR]

Composite DNA letters, by merging all four DNA nucleotides in specified ratios, offer a pathway to substantially increase the logical density of DNA digital storage (DDS) systems. However, these letters are susceptible to nucleotide errors and sampling bias, leading to a high letter error rate, which complicates precise data retrieval and augments reading expenses. To address this, Derrick‐cp is introduced as an innovative soft‐decision decoding algorithm tailored for DDS utilizing composite letters. Derrick‐cp capitalizes on the distinctive error sensitivities among letters to accurately predict and rectify letter errors, thus enhancing the error‐correcting performance of Reed‐Solomon codes beyond traditional hard‐decision decoding limits. Through comparative analyses in the existing dataset and simulated experiments, Derrick‐cp's superiority is validated, notably halving the sequencing depth requirement and slashing costs by up to 22% against conventional hard‐decision strategies. This advancement signals Derrick‐cp's significant role in elevating both the precision and cost‐efficiency of composite letter‐based DDS. [ABSTRACT FROM AUTHOR]

The assumption space of ID3 algorithm contains all decision trees, and the search space is also a complete assumption space. The current assumptions based on the information gain standard, and uses the information gain to reduce the sensitivity to individual training sample errors. Based on ID3 decision tree algorithm, this paper provides new ideas and methods for the quality optimization of tourist attractions. Firstly, ID3 decision tree algorithm model is constructed based on information entropy theory; Then, based on the literature research and system research methods, the quality evaluation indicator system of tourist attractions is constructed; Finally, based on the mathematical model and evaluation indicator system, the empirical research was carried out, and the data of 17 tourist attractions were collected. The expected research results were obtained according to the data preprocessing, data calculation process, decision tree construction and rule generation process. The research finds that the quality evaluation process of tourist attractions based on ID3 decision tree model, through data preprocessing, data calculation, decision tree construction and decision rules generation, etc. it can be concluded that the "Tourism environment quality" attribute is the most important in this evaluation system, and "Tourism landscape quality" is relatively important. Tourist attractions can improve the work and quality of tourist attractions according to the research results. [ABSTRACT FROM AUTHOR]

Objective: To assess the cost-effectiveness of using cheaper-but-noisier outcome measures, such as a short questionnaire, for large simple clinical trials. Background: To detect associations reliably, trials must avoid bias and random error. To reduce random error, we can increase the size of the trial and increase the accuracy of the outcome measurement process. However, with fixed resources, there is a trade-off between the number of participants a trial can enrol and the amount of information that can be collected on each participant during data collection. Methods: To consider the effect on measurement error of using outcome scales with varying numbers of categories, we define and calculate the variance from categorisation that would be expected from using a category midpoint; define the analytic conditions under which such a measure is cost-effective; use meta-regression to estimate the impact of participant burden, defined as questionnaire length, on response rates; and develop an interactive web-app to allow researchers to explore the cost-effectiveness of using such a measure under plausible assumptions. Results: An outcome scale with only a few categories greatly reduced the variance of non-measurement. For example, a scale with five categories reduced the variance of non-measurement by 96% for a uniform distribution. We show that a simple measure will be more cost-effective than a gold-standard measure if the relative increase in variance due to using it is less than the relative increase in cost from the gold standard, assuming it does not introduce bias in the measurement. We found an inverse power law relationship between participant burden and response rates such that a doubling the burden on participants reduces the response rate by around one third. Finally, we created an interactive web-app (https://benjiwoolf.shinyapps.io/cheapbutnoisymeasures/) to allow exploration of when using a cheap-but-noisy measure will be more cost-effective using realistic parameters. Conclusion: Cheaper-but-noisier questionnaires containing just a few questions can be a cost-effective way of maximising power. However, their use requires a judgement on the trade-off between the potential increase in risk of information bias and the reduction in the potential of selection bias due to the expected higher response rates. Key messages: A cheaper-but-noisier outcome measure, like a short form questionnaire, is a more cost-effective method of maximising power in large simple clinical trials than an error free gold standard measure when the percentage increase in noise from using the cheaper-but-noisier measure is less than the relative difference in the cost of administering the two measures. We have created an R-shiny app to facilitate the exploration of when this condition is met at https://benjiwoolf.shinyapps.io/cheapbutnoisymeasures/ Cheaper-but-noisier outcome measures are more likely to introduce information bias than a gold standard but may reduce selection bias because they reduce loss-to-follow-up. Researchers therefore need to form a judgement about the relative increase or decrease in bias before using a cheap-but-noisy measure. We encourage the development and validation of short form questionnaires to enable the use of high quality cheaper-but-noisier outcome measures in randomised controlled trials. [ABSTRACT FROM AUTHOR]

AbstractIn genomic research, identifying the exon regions in eukaryotes is the most cumbersome task. This article introduces a new promising model-independent method based on short-time discrete Fourier transform (ST-DFT) and fine-tuned variational mode decomposition (FTVMD) for identifying exon regions. The proposed method uses the N/3 periodicity property of the eukaryotic genes to detect the exon regions using the ST-DFT. However, background noise is present in the spectrum of ST-DFT since the sliding rectangular window produces spectral leakage. To overcome this, FTVMD is proposed in this work. VMD is more resilient to noise and sampling errors than other decomposition techniques because it utilizes the generalization of the Wiener filter into several adaptive bands. The performance of VMD is affected due to the improper selection of the penalty factor (α), and the number of modes (K). Therefore, in fine-tuned VMD, the parameters of VMD (K and α) are optimized by maximum kurtosis value. The main objective of this article is to enhance the accuracy in the identification of exon regions in a DNA sequence. At last, a comparative study demonstrates that the proposed technique is superior to its counterparts. [ABSTRACT FROM AUTHOR]

When sampling data of specific classes (i.e., known classes) for a scientific task, collectors may encounter unknown classes (i.e., novel classes). Since these novel classes might be valuable for future research, collectors will also sample them and assign them to several clusters with the help of known-class data. This assigning process is known as novel class discovery (NCD). However, category confusion is common in the sampling process and may make the NCD unreliable. To tackle this problem, this paper introduces a new and more realistic setting, where collectors may misidentify known classes and even confuse known classes with novel classes—we name it NCD under unreliable sampling (NUSA). We find that NUSA will empirically degrade existing NCD methods if taking no care of sampling errors. To handle NUSA, we propose an effective solution, named hidden-prototype-based discovery network (HPDN): (1) we try to obtain relatively clean data representations even with the confusedly sampled data; (2) we propose a mini-batch K-means variant for robust clustering, alleviating the negative impact of residual errors embedded in the representations by detaching the noisy supervision timely. Experiments demonstrate that, under NUSA, HPDN significantly outperforms competitive baselines (e.g., 6 % more than the best baseline on CIFAR-10) and remains robust when encountering serious sampling errors. [ABSTRACT FROM AUTHOR]

Medical datasets may be imbalanced and contain errors due to subjective test results and clinical variability. The poor quality of original data affects classification accuracy and reliability. Hence, detecting abnormal samples in the dataset can help clinicians make better decisions. In this study, we propose an unsupervised error detection method using patterns discovered by the Pattern Discovery and Disentanglement (PDD) model, developed in our earlier work. Applied to the large data, the eICU Collaborative Research Database for sepsis risk assessment, the proposed algorithm can effectively discover statistically significant association patterns, generate an interpretable knowledge base for interpretability, cluster samples in an unsupervised learning manner, and detect abnormal samples from the dataset. As shown in the experimental result, our method outperformed K-Means by 38 % on the full dataset and 47 % on the reduced dataset for unsupervised clustering. Multiple supervised classifiers improve accuracy by an average of 4 % after removing abnormal samples by the proposed error detection approach. Therefore, the proposed algorithm provides a robust and practical solution for unsupervised clustering and error detection in healthcare data. [ABSTRACT FROM AUTHOR]