Small Test Samples Research Articles

PENGEMBANGAN MEDIA PEMBELAJARAN KARTU PINTAR BERBASIS VIDEO ANIMASI TIGA DIMENSI DENGAN PENGINTEGRASIAN KEARIFAN LOKAL UNTUK PENINGKATAN KOSAKATA BAHASA JAWA SISWA KELAS III SDN PETOMPON 01 SEMARANG

The use of Javanese vocabulary at SDN Petompon 01 Semarang was not optimal. This wasbecause children locked practice in speaking Javanese and also the use of learning media is stillminimal. Responding to this phenomenon, researchers developed smart card learning media basedon Three-Dimensional Animation Videos with the integration of local wisdom to fosterenthusiasm and increase the use of Javanese vocabulary in the school. The research was carriedout on 2 July - 24 August 2023 at SDN Pentompon 01 Semarang. This research used R&D(Research and Development) with a qualitative approach. The research population was allstudents in class IIIA at SDN Petompon 01 Semarang, totaling 16 students. Small group trialresearch sample testing was carried out in class IIIB using a purposive sampling technique bytaking 6 students based on class ranking, namely three students in the top rank, two students inthe middle rank, and one student in the bottom rank. Large group trials were carried out in ClassIIIA. The dependent variable of this research was increasing Javanese vocabulary, while theindependent variable is developing smart card learning media based on Three-DimensionalAnimation Videos with the integration of local wisdom. The data collection techniques used wereobservation, interviews, documentation, questionnaires and tests. The instruments used areobservation guidelines, interview guidelines, questionnaire sheets, and test questions. Productdata analysis techniques and initial data analysis consist of a normality test and homogeneity testas well as final data analysis consisting of the N-Gain Test and also the T-test. The researchresults showed that an average of 61 pretest results and posttest scores showed an increase in theaverage student score of 67.5333 with a percentage increase of 41% in the pretest results and 44%in the posttest results with details of 10 students who did not complete. and 6 students completedit so it can be concluded that three-dimensional animated video-based learning media is quitefeasible and effective for use in learning Javanese

Fatigue Life Data Fusion Method of Different Stress Ratios Based on Strain Energy Density.

To accurately evaluate the probabilistic characteristics of the fatigue properties of materials with small sample data under different stress ratios, a data fusion method for torsional fatigue life under different stress ratios is proposed based on the energy method. A finite element numerical modeling method is used to calculate the fatigue strain energy density during fatigue damage. Torsional fatigue tests under different stresses and stress ratios are carried out to obtain a database for research. Based on the test data, the Wt-Nf curves under a single stress ratio and different stress ratios are calculated. The reliability of the models is illustrated by the scatter band diagram. More than 85% of points are within ±2 scatter bands, indicating that the fatigue life under different stress ratios can be represented by the same Wt-Nf curve. Furthermore, P-Wt-Nf prediction models are established to consider the probability characteristics. According to the homogeneity of the Wt-Nf model under different stress ratios, we can fuse the fatigue life data under different stress ratios and different strain energy densities. This data fusion method can expand the small sample test data and reduce the dispersion of the test data between different stress ratios. Compared with the pre-fusion data, the standard deviations of the post-fusion data are reduced by a maximum of 21.5% for the smooth specimens and 38.5% for the notched specimens. And more accurate P-Wt-Nf curves can be obtained to respond to the probabilistic properties of the data.

Intelligent prediction of wave loads based on multi-source data-driven methods

ABSTRACT High-fidelity wave-load tests is pivotal in the design and development of modern ship structures. Nonetheless, the prohibitive cost of conducting such tests limits their applicability. A wave-load data fusion method was implemented using the model test data from a typical displacement-type ship to generalise the small sample test, and a multi-source data characterisation and model parameter optimisation method were established to achieve efficient wave-load (wave frequency and slamming superposition) prediction. Using multi-source data, a multi-fidelity wave-load prediction (MFWLP) model was constructed by combining numerical computation, regular wave tests (RWT), and irregular wave test (IWT) data based on transfer learning with a secondary correction wave load prediction under irregular wave conditions. The model was effectively employed in a ship wave-load engineering case. This study reveals that the adopted MFWLP model demonstrates adequate extrapolation and prediction capabilities, even with limited high-fidelity test samples. The discrepancy between the nonlinear wave load prediction and model test was found to be within 15%. Therefore, the proposed method can serve as a foundation for rapidly assessing ship wave loads across various sea conditions, especially under high sea states.

Numerical Analysis of Crack Propagation in an Aluminum Alloy under Random Load Spectra

This study develops a rapid algorithm coupled with the finite element method to predict the fatigue crack propagation process and select the enhancement factor for the equivalent random load spectrum of accelerated fatigue tests. The proposed algorithm is validated by several fatigue tests of an aluminum alloy under the accelerated random load spectra. In the validation process, two kinds of panels with different geometries and sizes are used to calculate the stress intensity factor, critical crack length, and crack propagation life. The simulated and experimental findings indicate that when the aluminum alloy is in a low plasticity state, the crack propagation life exhibits a linear relationship with the acceleration factor. When the aluminum alloy is in a high plasticity state, this study proposes an empirical formula to calculate the equivalent stress intensity factor and crack propagation life. The normalized empirical formula is independent of the geometry and size of different samples, although the fracture processes are different in the two kinds of panels used in our study. Overall, the numerical method proposed in this paper can be applied to predict the fatigue crack propagation life for the random spectrum of large samples based on the results of the simulated accelerated crack propagation process and the accelerated fatigue tests of small samples to reduce the cost and time of the testing.

Failure analysis of an AISI 310 stainless steel beam reinforcement fracture during service in a rolling beam furnace

This work investigated the premature failure of a beam reinforcement made of austenitic stainless steel AISI 310 used in the entrance of a rolling beam furnace working continuously at cycles from 650 to 850 °C. Fracture surface and microstructural investigations allowed to identify the fracture mechanism as fatigue failure. The fracture was probably initiated at the oxidized grain boundaries at an early stage of service life, when no microstructure degradation was present, while most of the fatigue crack propagation occurred after the precipitation of secondary phases. The microstructure investigation revealed severe microstructure degradation with the presence of intermetallic sigma (σ) phase (area fraction = 19 %) and M23C6 carbides (area fraction = 1 %), which were formed during the high-temperature exposure. Cracking of σ phases and decohesion at σ /austenite matrix interfaces were observed near the fracture and in the small punch test samples. Such discontinuities provide an easy path for crack propagation.

Analysis of the Interference Effects in CMOS Image Sensors Caused by Strong Electromagnetic Pulses

With the electromagnetic environment becoming increasingly complex, it is crucial to address the risk posed by electromagnetic pulse, which critically impairs the performance and reliability of electronic systems based on complementary metal oxide semiconductor (CMOS) image sensors. In this context, research on the failure types of CMOS image sensors in a high-power electromagnetic environment, caused by strong electromagnetic pulses and the rapid evaluation method of interference immunity, has garnered significant interest. This paper conducts electromagnetic pulse simulation experiments on CMOS image sensors to first study their failure types, such as image abnormalities and functional interruption, and then identify the corresponding failure criteria. Furthermore, this study builds on the small sample test evaluation method to investigate the interference threshold of functional interruptions in CMOS image sensors by calculating the failure probability at different field strengths. The obtained data were combined with the Weibull distribution function for fitting, the results of which found the interference threshold to be at 40.4 kV/m. The findings of this study provide a basis for evaluating the survivability of CMOS image sensors and their associated reinforcement technology in high-power electromagnetic environments.

Bayesian estimation of equipment reliability with normal-type life distribution based on multiple batch tests

Abstract The test of new equipment is usually carried out in multiple batches according to the task schedule and test results. Constrained by the test environment, cost, and other factors, the amount of reliability test data in each batch is relatively limited, which brings difficulties to the accurate equipment reliability estimation work. For the reliability simulation tests conducted before each batch tests, it is particularly important to make full use of each batch tests information and simulation tests information to estimate the reliability of the equipment for small sample tests. This study takes the common normal-type life distribution equipment as the research object, and selects the normal-inverse gamma distribution as the equipment life parameters prior distribution based on the Bayesian method. Combined with the system contribution, the fusion weights of each batch tests information are determined and all the batch tests information is fused. Finally, the estimation of equipment reliability based on multiple batch tests is completed. The research results show that this method can integrate the information of each batch test and simulation test, overcome the problem of insufficient information of single batch tests, and provide an effective analytical tool for equipment reliability estimation.

RMST-based multiple contrast tests in general factorial designs.

Several methods in survival analysis are based on the proportional hazards assumption. However, this assumption is very restrictive and often not justifiable in practice. Therefore, effect estimands that do not rely on the proportional hazards assumption are highly desirable in practical applications. One popular example for this is the restricted mean survival time (RMST). It is defined as the area under the survival curve up to a prespecified time point and, thus, summarizes the survival curve into a meaningful estimand. For two-sample comparisons based on the RMST, previous research found the inflation of the type I error of the asymptotic test for small samples and, therefore, a two-sample permutation test has already been developed. The first goal of the present paper is to further extend the permutation test for general factorial designs and general contrast hypotheses by considering a Wald-type test statistic and its asymptotic behavior. Additionally, a groupwise bootstrap approach is considered. Moreover, when a global test detects a significant difference by comparing the RMSTs of more than two groups, it is of interest which specific RMST differences cause the result. However, global tests do not provide this information. Therefore, multiple tests for the RMST are developed in a second step to infer several null hypotheses simultaneously. Hereby, the asymptotically exact dependence structure between the local test statistics is incorporated to gain more power. Finally, the small sample performance of the proposed global and multiple testing procedures is analyzed in simulations and illustrated in a real data example.

A novel fatigue design modeling method under small-sample test data with generalized fiducial theory

Understanding the correlation between the fatigue life of engineering materials and the applied stress is a crucial aspect of reliability and safety design. However, small data observations frequently occur in fatigue testing due to factors like time and budget limitations, as well as constraints related to the availability of testing materials and resources. In this article, fiducial inference method is employed for modeling P-S-N curves to deal with scenarios with small sample sizes. Fiducial inference can be seen as a procedure that provides a measure within a parameter space while requiring fewer assumptions than Bayesian inference (no prior). The performance of the proposed method is evaluated by comparing it with the ISO method using statistical simulation data and aluminum alloy 2524-T3 data. In scenarios with ample data (no less than 15 observations at each of the four stress levels), the proposed fiducial-based method yields results comparable to those obtained through the ISO method. When dealing with small-sample data (around 2–4 observations for each of the four stress levels) and medium-sample data (about 5–10 observations per stress level), the proposed fiducial-based method consistently outperforms the ISO method in terms of the quantile scores (also known as pinball loss). This shows the advantageous performance of the fiducial method under conditions of limited data availability. Besides, the consistency in performance across varying data size scenarios underscores the reliability and robustness of the fiducial-based approach in estimating probabilistic S-N curves.

CONSTRUÇÃO E VALIDAÇÃO DE UM BANCO DE ITENS SOBRE AUTOMEDICAÇÃO DE RISCO

ABSTRACT Objective: to construct an item bank to measure risk self-medication and assess its content validity. Method: this is a methodological study carried out from May to October 2022, with two phases: 1) item bank elaboration in the light of medication literacy and Theory of Planned Behavior based on a scoping review; and 2) content validity by twenty-two health experts. The Content Validity Index, Content Validity Ratio, binomial exact test for small samples and intraclass correlation coefficient were calculated. Results: Risk Self-Medication construct item elaboration was based on a broad review of solidified national and international publications in the health area. The definitions were approved by judges, with their respective domains. In the first version, the bank contained 136 items. Two rounds of analysis were carried out with judges, which resulted in the removal of 87 items. The final version presented 49 items, distributed across three domains: Medication literacy; Behavioral intention; and Behavior. The total Content Validity Index was 0.89, with excellent reliability (0.964). There was significant disagreement in the attribution of scores among judges (p>0.05) in some items. Conclusion: the item bank has satisfactory content. It is recommended to undergo semantic analysis and subsequent structure validity.

Aluminum waste in alumina cement production (Mikhailovka)

Alumina cement is a rapidly hardening hydraulic binder containing low-basic calcium aluminates in the final product. The alumina cement production by melting is widespread. Melting of the mixture consisting of bauxite and limestone is carried out in the temperature range of 1500 to 1600 °C, which entails enormous energy consumption and, accordingly, the higher cost of resulting products. The paper studies the possibility of reducing the melting point as well as the cost of the main mixture component bauxite via its replacement by aluminum-containing waste.Purpose: Suggest the dependence between the mixture composition and the Al2O3 content in the initial aluminum waste; determine the best annealing temperature for alumina clinker; study strength properties of the resulting alumina cement and the effect of introduced limestone on the main quality index.Methodology: The chemical composition of the aluminum waste (sludge and powder), raw mixtures, and resulting clinker are detected in laboratory conditions using spectrophotometry and flame photometry methods in accordance with the state standard. X-ray phase analysis of waste and clinker is based on the powder diffraction method and conducted on the Thermo Scientific ARL X'TRA Powder Diffractometer. Physical and mechanical tests are carried out in accordance with the state standard poly-fraction sand.Research findings: Laboratory studies show that aluminum waste (sludge and powder), consisting of gibbsite and boehmite in combination with limestone, is suitable for the production of alumina clinker. Depending on the initial Al2O3 content, their content in the raw mixture ranges from 60 to 75 %.Value: Depending on the composition, the fest annealing temperature for alumina clinker, is 1350 to 1400 ℃; the mineralogical composition of clinkers consists of 85–90 % calcium aluminates. Alumina cement strength determined in small test samples having normal density and the ratio 1:0, is high after 2 days and ranges from 33.7 to 40.6 MPa. The addition of 5 % crystalline limestone into cement accelerates the initial rate of hardening and improves the strength from 33.7 to 53.2 MPa after 2 days due to the additional formation of 3CaO·Al2O3·CaCO3·11H2O.

Development and Validation of the Home Hospice Care Needs Questionnaire for the Dying Old Adult (HHCNQ-DE) in Mainland China.

Context: Home hospice needs assessment is the key to the development of home hospice services. Therefore, there is a need to develop assessment tools in mainland China to meet the comprehensive needs of the old adult at the end of life. Objective: To develop and validate a tool to assess Chinese mainland palliative care needs of the dying old adult at home. Methods: Using the Harmony Nursing Theory as a theoretical framework, through reference to the China home hospice care needs scale, literature research, in-depth conversations with the dying old adult, and group discussions among members of the research team, 43 original questionnaire items were preliminarily drafted. A 31-item predictive questionnaire was formed through 2 rounds of expert review and small sample testing. From April to September 2023, 199 old adult at the end of life at home in Jinzhou, Liaoning Province were investigated to determine the reliability and validity of the questionnaire. Results: The final questionnaire included 31 items. Exploratory factor analysis extracted 5 common factors, and the cumulative variance contribution rate was 68.811%; the Cronbach's alpha coefficient of the total questionnaire was 0.832, and the re-test reliability was .806; the content validity index of the questionnaire was 0.982, and the content validity index of the items was 0.83-1.00. Conclusion: The construction method of the HHCNQ-DE is scientific and reliable, with good reliability and validity, and it can be used as a tool to measure the needs of home hospice care in mainland China in the future.

Subject Matter Complexity and Disclosure Channel Richness

ABSTRACT Despite the increase in and diversity of disclosure channels available, our understanding of how managers incorporate channel features into their disclosure decisions remains incomplete. I provide evidence that managers choose relatively rich channels that offer multiple cues, opportunities for interaction, and linguistic diversity (i.e., the earnings call, as compared to the press release) to communicate complex information. The positive relation between disclosure channel richness and subject matter complexity persists in both a document-level analysis and a small sample test examining disclosure channel choice from all possible disclosure channels. I provide some evidence that deviating from the complexity/richness matching strategy is associated with a muted market response to firms’ quarterly disclosures. The results are consistent with managers choosing disclosure channels to reduce investors’ information processing costs. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G14; M41.

Development of Educational Media based Film Animation Podtoon on National Income Material

This research uses research and development (R&D) methods using the ADDIE model. For the small test sample, 10 college students were randomly selected. Porton Movie. tests on college students. Data analysis techniques used are quantitative and qualitative data analysis techniques. The results of research and development are educational materials based on Poodton cartoons that can be accessed from smartphones through YouTube and Google Drive applications which can also be downloaded offline. In the practical test, students also gave a positive response with an average overall score of 5.80 included in the very practical category as well as an effectiveness test using the t-test table for significance levels α = 5% = 0.05 and df = 9, then obtained t at t hit = 17.72 and t tab = 1.833 with the conclusion The value of t hit > t tab, so it is said that there is effectiveness (significance) The development of introductory teaching materials for macroeconomics based on poodton animated films with a significance level of 5% is very effective.

Reliability Assessment for a Spacecraft Landing Buffer Mechanism Based on Small Sample Data

The landing buffer mechanism (LBM) is a crucial component of aerospace landing explorations, determining the success of the entire mission. Reliability demonstration tests (RDTs) are required to ensure the LBM’s performance meets the design index. However, the RDTs of the LBM often encounter limited samples and zero-failure data, making traditional binominal test programs and reliability assessment methods based on the maximum likelihood theory unsuitable. This paper introduces a novel small-sample test verification method for the LBM, which transforms traditional binominal tests into more informative metrological tests by measuring the buffer strokes of the LBM’s subsystems. Furthermore, a confidence limit theory for the product of subsystem reliability is developed in conjunction with the reliability series model of the LBM. This theory can use the measurement data of subsystems to assess the confidence limit of the LBM’s reliability. An actual engineering application demonstrates that the proposed method can effectively handle zero-failure data and verifies that the LBM’s reliability exceeds 0.9999 with a confidence of 0.9.

An LM Test for the Conditional Independence between Regressors and Factor Loadings in Panel Data Models with Interactive Effects

A huge literature on modeling cross-sectional dependence in panels has been developed using interactive effects (IE). One area of contention is the hypothesis concerned with whether the regressors and factor loadings are correlated or not. Under the null hypothesis that they are conditionally independent, we can still apply the consistent and robust two-way fixed effects estimator. As an important specification test we develop an LM test for both static and dynamic panels with IE. Simulation results confirm the satisfactory performance of the LM test in small samples. We demonstrate its usefulness with an application to a total of 22 datasets, including static panels with a small T and dynamic panels with serially correlated factors, providing convincing evidence that the null hypothesis is not rejected in

Testing higher and infinite degrees of stochastic dominance for small samples: A Bayesian approach

This study proposes a distribution-free Bayesian procedure that detects infinite degrees of stochastic dominance (SD∞) between two random outcomes and then seeks a finite degree k≥1 of stochastic dominance (SDk). Based on small samples, we construct four-choice Bayesian tests by combining an encompassing prior Bayesian model with the Dirichlet process priors that discriminate between SD∞ and SDk of one random variable over the other with non-dominance or equality between them. We use Monte Carlo simulations to evaluate the novel Bayesian tests for SDk and SD∞ and compare them to the subsampling and bootstrap significance tests for SDk. Our simulation shows that the Bayesian tests for SDk outperform the significance tests for small samples, especially for detecting non-stochastic dominance. This study shows that the test for SD∞ is an accurate decision-making tool when using small samples.

Long-term life stress mapping algorithm of the deep-sea pressurized spherical shell based on digital-twin technology

Aiming at the difficulty of the global stress field monitoring of the deep-sea pressurized spherical shell in real time, this paper proposes a long-term life stress field reverse mapping algorithm based on digital-twin technology. Specifically, a high-fidelity FEM model is established based on the titanium alloy data, structure construction data and model test data. The Elastic modulus E and Poisson's ratio υ are selected as the structure characteristic parameters based on the preliminary small sample test results. The sensitivity analysis is carried out. And then, stress monitoring location selection results is proposed considering the factors such as mutual independence, installation conditions, and economic costs. The Long-Short Term Memory network (LSTM) together with the FEM results is tailored and employed to build the reverse mapping model. The mapping error is below 1% and compared to the FEM model, the efficiency of the proposed model is improved by more than 60 times.

Assessment of multi-dimensional joint probability distribution for uncertain mechanical strength parameters under small sample test data

The multi-dimensional joint probability distribution of uncertain mechanical strength parameters is crucial for reliability analysis in geotechnical engineering. However, due to the limited number of test data samples, the analysis faces significant challenges. This paper presents a modeling method for joint probability distribution based on multidimensional Gaussian copula, which is suitable for small sample conditions. Firstly, the copula theory is introduced, and a construction method for multidimensional Gaussian copula is proposed. Secondly, measurement data for 192 sets of coal seam roof strength parameters were collected from 24 coal mines in China. For the four-dimensional Gaussian copula, analyses of goodness of fit and simulation error were performed using the Pearson method, Kendall method, and Spearman method, respectively. Finally, the impact of different copula functions on the fitting capability of the original data is discussed. The results indicate that the established multi-dimensional Gaussian copula joint distribution model possesses an advantage in characterizing the uncertainty of relevant parameters. This model also offers an effective approach for studying the uncertainty of coal mine strength parameters. Different construction methods for multidimensional Gaussian copulas yield varying simulation outcomes. The Pearson method exhibits the most favorable fitting effect for constructing correlation parameters. The two rank correlation methods have the advantages of broad applicability, excellent fitting effect, and minimal variation in simulation error. For the bivariate copula fitting ability of uncertain parameters, the Gaussian Copula exhibits the best fitting ability under both positive and negative correlation scenarios. When the correlation is weak, Plackett copula and Frank copula can also serve as alternative candidate copula functions. These findings offer a significant theoretical foundation for conducting reliability analysis of a coal seam roof under limited test data conditions.

Experimental study on structural damage identification of multi-sensor separated channel network

Structural damage identification, the core driver of structural health monitoring (SHM), has gradually shifted to data-driven models in recent years. For the characteristics of multi-sensors in the SHM system of buildings, this paper proposes a neural network structure for damage identification called multi-sensor separated channel network (MSSCN). It is dedicated to extracting damage sensitive features from a single measurement channel while maintaining the spatial information of the sensor, thus completely decoupling intra-channel time and inter-channel spatial features. The proposed framework is validated by applying to the Qatar University Grandstand Simulator (QUGS) with a decentralized sensor arrangement. Further, we study vertical structures with strong channel correlation via experiments simulating linear and nonlinear multiple complex operating conditions to identify damage in transmission tower. Based on the above, it is demonstrated that the proposed model exhibits good diagnostic capability and performs competitive in noise immunity and small sample tests.