Method For Small Samples Research Articles

The Characteristics and Variation of the Golden Eagle Aquila chrysaetos Home Range

Satellite tracking allows for novel investigations into golden eagle home range characteristics. Understanding home range characteristics is important for conservation and for assessing the potential impact of landscape changes from forest planting, wind farms, etc. Small sample sizes, inconsistent definitions and methods restricted several previous studies. Our study involved 69 resident tagged eagles with over one year of data across five Scottish regions. Home range size was estimated from 95% isopleth contours extracted from Utilisation Distributions. Above a small threshold, estimated range size was not affected by the number of records but at least one year of data is required, largely because of the breeding and non-breeding seasonal differences. There were no significant range size differences between birds tagged as range holders and those previously tagged as nestlings. Across four regions, with considerable intra-regional variation, planar 95% isopleths did not differ (medians, km2): Argyll 58.9, Northwest Highlands 61.7, Northeast Highlands 89.3, South of Scotland 91.9. Ranges in the isolated Outer Hebrides region were exceptionally small, at 24.0 km2. Estimated range area was usually reduced to 70–80% of the planar area when restricted to usable habitat, as estimated by the Golden Eagle Topography (GET) model. Applying measures of known unsuitable habitat (closed-canopy commercial forest and wind turbines) further reduced usable open land. Loss of otherwise suitable habitat was substantially due to commercial forest. Larger ranges had larger extents of suitable habitat (according to GET), with no apparent optimum of preferred GET habitat. Range size was not different across a year between the sexes. Breeding ranges were smaller, and females’ breeding ranges were much smaller than those of males, but larger than males’ ranges in the non-breeding season. Breeding attempt duration was probably also influential. Our study provides novel insights into golden eagle home range characteristics and can guide further research and practical applications.

Multiobjective optimization of carbon fiber–reinforced epoxy composite properties using nonparametric modeling

Carbon fiber–reinforced epoxy composites are widely used in the marine and aviation sectors due to their superior mechanical properties. As research into these materials progresses, the demand for materials with even more complex and superior properties has increased. Traditional trial-and-error methods are limited to optimizing single factors and are plagued by heavy workloads and prohibitive costs when applied to multifactor composite designs. Addressing these problems, this study introduces a nonparametric multiobjective optimization process for composite materials. This approach employs a Gaussian process (GP) to establish a multiobjective regression model and uses nondominated sorting genetic algorithm II (NSGA II) to determine the Pareto front. Pareto solutions were ranked in accordance with their similarity to ideal solutions and on the basis of predetermined preference weights. The results demonstrate variations in optimal outcomes contingent on the weight values assigned. The differences between the optimized results and experimental validations reached 12.9%, with the minimum deviation being 0.6%. The effectiveness of the proposed method was demonstrated. The strategy combines GP and NSGA II to extend design methods for small samples and materials with multiple attributes. This method enables the simultaneous optimization of multiple conflicting objectives, offering greater efficiency than addressing them individually.

The single and double inverse Pareto–Burr XII distribution: Properties, estimation methods, and real-life data applications

Researchers consistently improve statistical tools to provide a flexible method to fit or predict. The solution needs to be more suitable since the approach is sometimes complex and complicated. Generally, statistical tools are generated from ordinary least squares and wavelet methods. However, the reliability tool provides a good fit comparable to state-of-the-art methods, including the complex posterior setting of hyperparameters. In this article, a flexible extension of the Burr XII model, which is the odd inverse Pareto–Burr XII (OIPBXII) distribution, is studied and investigated using statistical tools such as Markov chain Monte Carlo methods. The mathematical properties of the probability density distribution of OIPBXII are derived, and it shows a behavior shape, decreasing, increasing, J-shaped, reversed-J-shaped, bathtub, upside-down bathtub, decreasing–increasing–decreasing hazard rates, and right-skewed, symmetrical, and concave down densities. The OIPBXII parameters are investigated by using seven classical approaches to estimation. Extensive simulation results are presented to explore the performance of these methods for small and large samples. An application to two real-life sets of data from engineering and medicine is analyzed, showing the flexibility of the OIPBXII distribution as compared to existing Burr XII distributions.

Intelligent fault diagnosis of rolling bearings under small samples based on lightweight UNet with attention-fused residual block

In rotating machinery, rolling bearings are crucial, and their failure can lead to severe accidents and economic losses. Therefore, fault diagnosis of bearings is necessary to maintain the safe and stable operation of modern machinery and equipment. Currently, data-driven intelligent diagnosis methods are mainstream; however, in practical applications, problems such as insufficient fault samples and strong interference signals often exist. At the same time, a large number of edge-end and mobile devices put higher requirements for the size of the diagnostic model. This study addresses these issues by proposing a lightweight UNet (LWUNet) model, which integrates wavelet packet decomposition (WPD) and attention-fused residual block (AFRB) for fault diagnosis in rolling bearings under varying operating conditions, particularly for small sample sizes. Firstly, WPD is used to decompose and reconstruct the fault signal to achieve effective denoising. Secondly, a LW-UNet is constructed for pixel-level feature learning to reduce the number of parameters and improve the accuracy rate. Thereafter, to further enhance the model feature extraction capability, the AFRB is proposed and embedded into the LWUNet to develop the AFRB-LWUNet model. Finally, the reconstructed signals are input to the proposed model for training, and the model performance is examined using a test set. The proposed method is compared with other fault diagnosis models using small sample data of rolling bearings from the Case Western Reserve University, USA and the University of Paderborn, Germany. The results confirm the higher recognition accuracy, stronger generalization ability, and robustness of the proposed method for small samples under various working conditions and intense noise.

Does teacher judgment accuracy matter? How judgment accuracy, teaching quality, and student achievement development are related

Teacher judgment accuracy is assumed to be positively related to student achievement; however, the empirical evidence for this assumption is inconclusive. Using two accuracy indicators measured at different levels (class/teacher- and student-level), we examined the theoretically hypothesized effects of judgment accuracy on German language achievement over a three-year period, and tested whether teaching quality mediates these effects. We applied multilevel mediation models and small sample methods to data from 35 language teachers and 646 students from 42 classes. While no mediating effects were found, the student-level indicator positively predicted achievement, suggesting that student-level accuracy measures should receive more attention.

Small Sample Methods in Multilevel Analysis

Bayesian methods have been said to solve small sample problems in frequentist methods by reflecting prior knowledge in the prior distribution. However, there are dangers in strongly reflecting prior knowledge or situations where much prior knowledge cannot be used. In order to address the issue, in this article, we considered to apply two Bayesian methods, one with weakly informative prior for random effect parameters (BayesW) and another with non-informative prior (BayesN) and compared their performance of parameter recovery with restricted maximum likelihood (REML) method, a frequentist-based estimation method commonly used in multilevel analysis. Specifically, we conducted two simulation studies using a two-level linear multilevel model and compared the performance of the BayesW, the BayesN, and the REML in various conditions that include scenarios of small sample data with a combination of small sample size both at level-1 and level-2. The results showed that the REML performed better than the BayesW, which showed overall better performance than the BayesN, in terms of convergence, bias, and coverage in some small sample scenarios. Therefore, based on the results of the present study, it is recommended to use the REML when adapting the two-level linear multilevel model and analyzing real-world data with small samples.

Optofluidic-based deformability cytometry: Characterizing erythrocyte elasticity and assessing storage quality

Erythrocyte elasticity is an inherent, label-free marker for both physiological and pathological changes in erythrocytes, serving as a criterion for assessing the quality and suitability of stored blood. Although varieties of cell elasticity measurement techniques have been proposed, a small sample, non-contact, and continuous method is still in need. This article introduces an optofluidic-based deformability cytometry (O-DC) for characterizing erythrocyte elasticity, combining non-contact optical force with microfluidic technology. A tightly focused laser beam optically traps an erythrocyte, and the flow force induces cell deformation, allowing for elasticity characterization through force-dependent elongation. The O-DC technique offers high accuracy and desirable throughput for cell elasticity characterization, facilitated by quasi-static stretching and continuous cell delivery. The characterization throughput of O-DC is ∼3 cells per second at a flow rate of 30 μL/h and a laser power of 200 mW. Fresh erythrocytes exhibit measured elastic strains of 15.4–27.8% across flow rates of 10–30 μL/h, with an estimated elastic modulus of 2.6±0.3 μN/m. Using O-DC, the elasticity trend of erythrocytes stored for 0–6 weeks was analyzed, with their average elastic modulus increasing from 2.6±0.3 μN/m to 6.1±0.8 μN/m as the storage time increased. Our study highlights the potential of O-DC in clinical diagnostics and biomechanical research applications.

P–S–N curves fitting method of small samples obeying Weibull distribution

AbstractA three‐parameter Weibull small sample aggregation method is proposed to address the problem of small sample fatigue data in practical engineering. First, based on the empirical assumption that the shape parameter is a constant and data equivalent conversion and aggregation, the small sample fatigue life data are converted into “equivalent large samples.” Second, through the linear relationship between logarithmic scale parameter and stress, an iterative procedure is established to estimate the scale parameters of small sample data. At the same time, the moment and rank estimation methods are used to solve the location parameters quickly. Finally, P–S–N curves with different survival rates are given, and a 7‐series aluminum alloy is selected to verify the correctness and reliability of the method proposed in this study. The results show that the predicted life obtained by the method still has high accuracy under a high survival rate, which can provide a theoretical basis for solving practical engineering problems.

Battling with the low-resource condition for snore sound recognition: introducing a meta-learning strategy

Snoring affects 57 % of men, 40 % of women, and 27 % of children in the USA. Besides, snoring is highly correlated with obstructive sleep apnoea (OSA), which is characterised by loud and frequent snoring. OSA is also closely associated with various life-threatening diseases such as sudden cardiac arrest and is regarded as a grave medical ailment. Preliminary studies have shown that in the USA, OSA affects over 34 % of men and 14 % of women. In recent years, polysomnography has increasingly been used to diagnose OSA. However, due to its drawbacks such as being time-consuming and costly, intelligent audio analysis of snoring has emerged as an alternative method. Considering the higher demand for identifying the excitation location of snoring in clinical practice, we utilised the Munich-Passau Snore Sound Corpus (MPSSC) snoring database which classifies the snoring excitation location into four categories. Nonetheless, the problem of small samples remains in the MPSSC database due to factors such as privacy concerns and difficulties in accurate labelling. In fact, accurately labelled medical data that can be used for machine learning is often scarce, especially for rare diseases. In view of this, Model-Agnostic Meta-Learning (MAML), a small sample method based on meta-learning, is used to classify snore signals with less resources in this work. The experimental results indicate that even when using only the ESC-50 dataset (non-snoring sound signals) as the data for meta-training, we are able to achieve an unweighted average recall of 60.2 % on the test dataset after fine-tuning on just 36 instances of snoring from the development part of the MPSSC dataset. While our results only exceed the baseline by 4.4 %, they still demonstrate that even with fine-tuning on a few instances of snoring, our model can outperform the baseline. This implies that the MAML algorithm can effectively tackle the low-resource problem even with limited data resources.

On interval estimation methods for the location parameter of the Weibull distribution: An application to alloy material fatigue failure data

–The Weibull distribution is the most applied model in reliability field for lifetime analysis. The Weibull location parameter, characterizing the minimum possible life, plays a significant role in engineering applications. In this paper, we consider the interval estimation on the location parameter when the product’s lifetime follows the three-parameter Weibull distribution with a known shape parameter. A novel approach based on the relationship between the minimum order statistics, the location parameter, and the sample size is developed to construct confidence intervals for the Weibull location parameter. Thereafter, we compare it with other two interval estimation approaches by the performances of the coverage probability and the average length via simulations and a real application. The results show that the proposed method outperforms the pivot quantity (PQ) method and the bias-corrected and accelerated (Bca) bootstrap method in small and medium samples in terms of coverage probability.

A Cross-Channel Dense Connection and Multi-Scale Dual Aggregated Attention Network for Hyperspectral Image Classification

Hyperspectral image classification (HSIC) is one of the most important research topics in the field of remote sensing. However, it is difficult to label hyperspectral data, which limits the improvement of classification performance of hyperspectral images in the case of small samples. To alleviate this problem, in this paper, a dual-branch network which combines cross-channel dense connection and multi-scale dual aggregated attention (CDC_MDAA) is proposed. On the spatial branch, a cross-channel dense connections (CDC) module is designed. The CDC can effectively combine cross-channel convolution with dense connections to extract the deep spatial features of HSIs. Then, a spatial multi-scale dual aggregated attention module (SPA_MDAA) is constructed. The SPA_MDAA adopts dual autocorrelation for attention modeling to strengthen the differences between features and enhance the ability to pay attention to important features. On the spectral branch, a spectral multi-scale dual aggregated attention module (SPE_MDAA) is designed to capture important spectral features. Finally, the spatial spectral features are fused, and the classification results are obtained. The experimental results show that the classification performance of the proposed method is superior to some state-of-the-art methods in small samples and has good generalization.

Comparison of small sample methods for Handshape Recognition

Automatic Sign Language Translation (SLT) systems can be a great asset to improve the communication with and within deaf communities. Currently, the main issue preventing effective translation models lays in the low availability of labelled data, which hinders the use of modern deep learning models.
 SLT is a complex problem that involves many subtasks, of which handshape recognition is the most important. We compare a series of models specially tailored for small datasets to improve their performance on handshape recognition tasks. We evaluate Wide-DenseNet and few-shot Prototypical Network models with and without transfer learning, and also using Model-Agnostic Meta-Learning (MAML).
 Our findings indicate that Wide-DenseNet without transfer learning and Prototipical Networks with transfer learning provide the best results. Prototypical networks, particularly, are vastly superior when using less than 30 samples, while Wide-DenseNet achieves the best results with more samples. On the other hand, MAML does not improve performance in any scenario. These results can help to design better SLT models.

Fast inspection and accurate recognition of target objects for astronaut robots through deep learning

Target object detection based on deep learning and image processing technology is widely used in robot astronauts. However, existing detection methods have limitations in detection speed and accuracy due to the complex space environment (such as uneven illumination and particle radiation) and inadequate training samples. Inspired by the neural network structure and transfer learning, a deep learning detection method for small samples in complex environments is proposed. A depthwise separable convolution is added to a feature fusion network to reduce the number of parameters in image output feature mapping, and a linear bottleneck inverted residual structure is introduced into a backbone feature extraction network to reduce the computation and memory requirements during feature extraction. As a result, a backbone feature extraction–fusion network structure is established to solve the problem in detection speed. A squeeze-and-excitation (SE) attention module is introduced in front of the head, and an SE detector is constructed to improve the detection accuracy in a spatially complex environment by dynamically assigning image channel weights to highlight the target object features in blurred images. The learning efficiency and accuracy of the network model in the small sample case in this paper are addressed by incorporating the transfer learning idea and establishing the evaluation function of learning samples. Experimental results show that the proposed algorithm enables astronaut robots to detect object rapidly and accurately in complex environments. The average speed (frames per second) and accuracy of detection under 2200 training samples are 45.19 and 93.14%, respectively.

Real-time classification for Φ-OTDR vibration events in the case of small sample size datasets

Efficient classification of vibration signals detected by phase-sensitive optical time domain reflectometer (Φ-OTDR) based on small samples is an effective method to reduce the false alarm rate without GPU or large data sets. This paper proposes a fiber optic system vibration event recognition method based on a combination of image segmentation pre-processing, texture, statistical, morphological feature extraction, and weighted support vector machine (WSVM), which can effectively classify-five types of vibration events in high-speed railway perimeter intrusion detection with small sample data and no parallel processing units. Erosion and dilation operations are applied to vibration signal image feature enhancement in image pre-processing. The vibration signal region and background are separated by the maximum inter-class variance method, then 35 features of the vibration signal region are calculated and finally employed to construct a WSVM. Experiments show that the method achieves 99 FPS and 98.8% accuracy on the test set with 330 vibration images as the training set to build the model without GPU and in the presence of interference signals. It provides a generalized Φ-OTDR vibration event recognition method for small samples.

Calculation of the Rate of Reaching the Standard of Beijing Citizens' Scientific Quality During the 14th Five Year Plan Period——Based on the L-Q GM(1,1)

As one of the four centers, Beijing is an important part of the national innovation system and an important driving force of social development. The scientific quality of Beijing citizens has become an important yardstick of contemporary society. By interpolating the discontinuous sample data of Beijing citizens' scientific quality compliance rate from 2015 to 2020 and using the grey system GM (1,1) model improved by the L-Q method to calculate, the Beijing citizens' scientific quality compliance rate from 2021 to 2025 is obtained. The results show that the Beijing citizens' scientific quality compliance rate is steadily rising, and will exceed 30% from 2024 and reach 35.26% by 2025, This is far beyond the target that the proportion of Chinese citizens with scientific quality should exceed 15% by 2025 and the long-term goal of 2035 in the outline of the action plan for the scientific quality of the whole people (2021-2035). At the same time, this paper gives suggestions to improve Beijing's science popularization work: measure the development trend of the compliance rate with the small sample method to guide the work, promote the coordinated development of new infrastructure and traditional infrastructure in the field of science popularization, strengthen the systematic promotion of science popularization, and pay attention to the accurate supply of science popularization services.

An Improved Small-Sample Method Based on APSO-LSSVM for Gas-Bearing Probability Distribution Prediction From Multicomponent Seismic Data

Multicomponent seismic data contain abundant reservoir information and have significant advantages for reservoir prediction. However, due to limited exploration in some areas, the available data samples are limited. Hence, it is important to develop new methods that can identify the complex relationship between oil-gas-bearing property and the multicomponent seismic response with limited samples. The least-squares support vector machine (LSSVM) model is suitable for solving small-sample and optimization problems. However, the parameter optimization of the LSSVM kernel function affects the prediction results. Therefore, a hybrid artificial intelligence model based on adaptive particle swarm optimization (APSO) and LSSVM was developed for multicomponent seismic reservoir prediction. Different kernel functions of the LSSVM model were analyzed to determine which one had the best predictive performance, and this initial model was used for optimization. The problem of the PSO falling into the local minimum was alleviated by adaptively adjusting the inertia weight and velocity coefficients. The kernel function parameters of the LSSVM model were globally searched via APSO, and the APSO-LSSVM model for reservoir distribution prediction was obtained. Finally, the gas-bearing probability distribution in the area with few wells was predicted using the multicomponent seismic data and the APSO-LSSVM model. The results showed that APSO had a faster convergence speed in parameter optimization than PSO. Additionally, the prediction results of the proposed hybrid artificial intelligence model (APSO-LSSVM) were more accurate than those of the LSSVM alone. This model achieved good prediction results with a small number of samples, providing a new method for gas reservoir prediction in areas with insufficient exploration degree.

Small sample and efficient crop pest recognition method based on transfer learning and data transformation

In order to solve the problem of long training time and large samples required by traditional image recognition model, a method of crop pest recognition based on transfer learning and data conversion was proposed. It takes CNN models such as Inception V3, VGG16, ResNet as the backbone structure. And the transfer learning was used to improve the model effect. The original picture data was expanded through the transformation of flip, rotation, scale, crop, translation and shading. Based on the data of 11 common pests such as white grub, east asian locust and whitefly etc., the model training and recognition was carried out. The result shows that, the accuracy of transfer learning model is higher than that of non-transfer learning model. The Inception V3 model performs well of all, the recognition accuracy is more than 98.94%. Through the analysis of cross entropy and confusion matrix, data transformation is helpful to improve the accuracy of the model with small sample.

Single-Mode Electromagnetic Resonance Rewarming for the Cryopreservation of Samples with Large Volumes: A Numerical and Experimental Study.

Rapid and uniform rewarming has been proved to be beneficial, and sometimes indispensable for the survival of cryopreserved biomaterials, inhibiting ice-recrystallization-devitrification and thermal stress-induced fracture (especially in large samples). To date, the convective water bath remains the gold standard rewarming method for small samples in the clinical settings, but it failed in the large samples (e.g., cryopreserved tissues and organs) due to damage caused by the slow and nonuniform heating. A single-mode electromagnetic resonance (SMER) system was developed to achieve ultrafast and uniform rewarming for large samples. In this study, we investigated the heating effects of the SMER system and compared the heating performance with water bath and air warming. A numerical model was established to further analyze the temperature change and distribution at different time points during the rewarming process. Overall, the SMER system achieved rapid heating at 331.63 ± 8.59°C min-1 while limiting the maximum thermal gradient to <9°C min-1, significantly better than the other two warming methods. The experimental results were highly consistent, indicating SMER is a promising rewarming technology for the successful cryopreservation of large biosamples.

Evaluating the performance of Bayesian and restricted maximum likelihood estimation for stepped wedge cluster randomized trials with a small number of clusters

BackgroundStepped wedge trials are an appealing and potentially powerful cluster randomized trial design. However, they are frequently implemented with a small number of clusters. Standard analysis methods for these trials such as a linear mixed model with estimation via maximum likelihood or restricted maximum likelihood (REML) rely on asymptotic properties and have been shown to yield inflated type I error when applied to studies with a small number of clusters. Small-sample methods such as the Kenward-Roger approximation in combination with REML can potentially improve estimation of the fixed effects such as the treatment effect. A Bayesian approach may also be promising for such multilevel models but has not yet seen much application in cluster randomized trials.MethodsWe conducted a simulation study comparing the performance of REML with and without a Kenward-Roger approximation to a Bayesian approach using weakly informative prior distributions on the intracluster correlation parameters. We considered a continuous outcome and a range of stepped wedge trial configurations with between 4 and 40 clusters. To assess method performance we calculated bias and mean squared error for the treatment effect and correlation parameters and the coverage of 95% confidence/credible intervals and relative percent error in model-based standard error for the treatment effect.ResultsBoth REML with a Kenward-Roger standard error and degrees of freedom correction and the Bayesian method performed similarly well for the estimation of the treatment effect, while intracluster correlation parameter estimates obtained via the Bayesian method were less variable than REML estimates with different relative levels of bias.ConclusionsThe use of REML with a Kenward-Roger approximation may be sufficient for the analysis of stepped wedge cluster randomized trials with a small number of clusters. However, a Bayesian approach with weakly informative prior distributions on the intracluster correlation parameters offers a viable alternative, particularly when there is interest in the probability-based inferences permitted within this paradigm.

The use of small volumes of test samples in subvisible particle testing by the light obscuration method

The light obscuration method described in the State Pharmacopoeia of the Russian Federation for subvisible particle testing, provides for preparation of a pooled sample with a minimum volume of 25 mL to be used in four measurements, each with 5.0 mL of the test sample. In the case of, for example, ready-to-use 0.2–0.3 mL pre-filled syringes, the method requires pooling the contents of a large number of products, which is economically costly. The use of small volumes of test samples in measurements by the light obscuration method is especially relevant for expensive medicines. Current particle counters allow for testing of 0.1 mL samples, but this requires assessment of the procedure’s accuracy. The aim of the study was to assess the accuracy of subvisible particle testing by the light obscuration method for small volumes of test samples. Materials and methods: we used an HIAC 9703+ liquid particle counter; particle count reference standards containing 0.998×106 particles/mL and 3.800 particles/mL; suspensions of standard latex particles with a known size (20 μm). Results: the study assessed the accuracy of subvisible particle determination by the light obscuration method for small test samples of 0.1‒0.5 mL: trueness was 96–100%; repeatability was 0.8–1.8%; linear correlation coefficients for the calculated versus theoretical number of particles were more than 0.999. The use of 0.1 mL test samples is impractical because of insufficient accuracy of the test results. The relative standard deviation of subvisible particle measurements obtained with 0.2–5.0 mL test samples did not exceed the measurement error of the instrument. The use of small test samples (0.2–1.0 mL) requires the use of a 1 mL sampling syringe. The study demonstrated the need for determination of the pre-run volume (not less than 0.1 mL). Comparative testing of standard (5.0 mL) and small (0.5 mL) samples of protein-based biological products showed comparable results. Conclusions: the study demonstrated that the light obscuration method could be used for small volumes of test samples.