Profiles of Mathematics Anxiety Among 15-Year-Old Students: A Cross-Cultural Study Using Multi-Group Latent Profile Analysis

Using PISA 2012 data, the present study explored profiles of mathematics anxiety (MA) among 15-year old students from Finland, Korea, and the United States to determine the similarities and differences of MA across the three national samples by applying a multi-group latent profile analysis (LPA). The major findings were that (a) three MA profiles were found in all three national samples, i.e., Low MA, Mid MA, and High MA profile, and (b) the percentages of students classified into each of the three MA profiles differed across the Finnish, Korean, and American samples, with United States having the highest prevalence of High MA, and Finland the lowest. Multi-group LPA also provided clear and useful latent profile separation. The High MA profile demonstrated significant poorer mathematics performance and lower mathematics interest, self-efficacy, and self-concept than the Mid and Low MA profiles. Same differences appeared between the Mid and Low MA profiles. The implications of the findings seem clear: (1) it is possible that there is some relative level of universality in MA among 15-year old students which is independent of cultural context; and (2) multi-group LPA could be a useful analytic tool for research on the study of classification and cultural differences of MA

A Comparison Study of Tie Non-response Treatments in Social Networks Analysis

Analysis of social network data often faces the problem of tie non-response. Recent studies show that the results of social network analyses can be severely biased if tie non-response was ignored. To overcome the problems created by tie non-response, several treatments were proposed in the literature: complete-case approach, unconditional mean imputation, reconstruction, and multiple imputation. In this paper we assessed the impact of tie non-response on social network analysis and investigated the performance of four treatments to handle tie non-response. The simulation results showed that ignoring tie non-response data in network analysis could underestimate the degree and centralization of social networks depending on the types of network and the proportion of missing ties. We also found that unconditional mean imputation was the best tie non-response treatment. Multiple imputation could successfully correct for tie non-response in a few specific situations. Complete case approach and reconstruction, however, were not recommended. We advocate the importance of further research to better understand consequences of tie non-response in social networks analysis and to provide statistical guidance to researchers to tackle this problem in the field

Freemium Strategy in Competitive App Markets: Maintaining Profitability with Product Fit Uncertainty

Today’s app market is highly competitive and rapidly growing. This study considers two app developers with substitutable apps and a continuum of consumers with heterogeneous preferences. Each developer decides between offering a paid app and offering a free basic app with in-app purchases. Because of product fit uncertainty, consumers are not sure about the degree of misfit between the app and their preferences, and that uncertainty can be reduced by trying the free basic app. Using a game-theoretic modeling framework, we analyze how product fit uncertainty affects competing developers’ profits and examine the scenario in which each developer offers a paid app or a free basic app with in-app purchases in a duopoly setting. The analytical results suggest that the developer can offer a free basic app for consumer learning even if the app is not underestimated. When either developer offers a free trial, the developer with the higher-quality app will prefer higher product fit uncertainty, but the one with the lower-quality app will favor lower product fit uncertainty. Additionally, as the app quality increases or consumer preferences become stronger, developers may switch the strategy depending on the reduction in product fit uncertainty caused by the free trial. Our study establishes the usefulness of the freemium strategy beyond the contexts analyzed in the literature. The results explain empirical observations of the app market and provide recommendations about the information disclosure of fit attributes in a competitive market

Outsourcing Information Security: The Role of Information Leakage in Outsourcing Decisions

Emerging research regarding the economics of outsourcing information security recommends that firms utilize full outsourcing due to its cost advantages but ignore the risk of information leakage. In our model, we take the information leakage into account, and show that it is necessary for firm to assess the risk before outsourcing. Next, we divide a firm’s business operations into core business and non-core business operations and introduce a partial outsourcing strategy. We find that the security quality of partial outsourcing is always lower. Subsequently, we demonstrate the conditions for selecting from among three security strategies, i.e., in-house development, partial outsourcing and full outsourcing. Based on our results, in high-risk information leakage environments, we do not recommend outsourcing. We further demonstrate that outsourcing security of non-core business is an alternative strategy when the risk of information leakage is not high. A firm should shift from outsourcing to developing security protection in-house as the percentage of information assets utilized for core business increases. In addition, our results show that outsourcing information security of only core business is a strictly dominated strategy

SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model

The success of the Segment Anything Model (SAM) demonstrates the significance of data-centric machine learning. However, due to the difficulties and high costs associated with annotating Remote Sensing (RS) images, a large amount of valuable RS data remains unlabeled, particularly at the pixel level. In this study, we leverage SAM and existing RS object detection datasets to develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS. SAMRS totally possesses 105,090 images and 1,668,241 instances, surpassing existing high-resolution RS segmentation datasets in size by several orders of magnitude. It provides object category, location, and instance information that can be used for semantic segmentation, instance segmentation, and object detection, either individually or in combination. We also provide a comprehensive analysis of SAMRS from various aspects. Moreover, preliminary experiments highlight the importance of conducting segmentation pre-training with SAMRS to address task discrepancies and alleviate the limitations posed by limited training data during fine-tuning. The code and dataset will be available at https://github.com/ViTAE-Transformer/SAMRS.Comment: Accepted by NeurIPS 2023 Datasets and Benchmarks Trac

The SpeakIn System Description for CNSRC2022

This report describes our speaker verification systems for the tasks of the CN-Celeb Speaker Recognition Challenge 2022 (CNSRC 2022). This challenge includes two tasks, namely speaker verification(SV) and speaker retrieval(SR). The SV task involves two tracks: fixed track and open track. In the fixed track, we only used CN-Celeb.T as the training set. For the open track of the SV task and SR task, we added our open-source audio data. The ResNet-based, RepVGG-based, and TDNN-based architectures were developed for this challenge. Global statistic pooling structure and MQMHA pooling structure were used to aggregate the frame-level features across time to obtain utterance-level representation. We adopted AM-Softmax and AAM-Softmax combined with the Sub-Center method to classify the resulting embeddings. We also used the Large-Margin Fine-Tuning strategy to further improve the model performance. In the backend, Sub-Mean and AS-Norm were used. In the SV task fixed track, our system was a fusion of five models, and two models were fused in the SV task open track. And we used a single system in the SR task. Our approach leads to superior performance and comes the 1st place in the open track of the SV task, the 2nd place in the fixed track of the SV task, and the 3rd place in the SR task.Comment: 4 page

Analysis of skin influence in identification of heroin using singular value decomposition

AbstractIn this paper, the influence of skin in energy-dispersive X-ray diffraction (EDXRD) spectrum of heroin was studied using singular value decomposition (SVD). The spectra of pure heroin, skin and heroin covered by skin were organized as matrices for SVD after truncation and smoothing. It was demonstrated that the two largest singular values and their corresponding left and right singular vectors of each matrix could reconstruct the matrix in the permissible error and contained enough information of the matrix. We extracted the two largest singular values of each matrix as two dimensions of the feature point of the corresponding spectrum. The feature points of different samples were clustered and a linear relationship was proved to be between and movement of feature point and thickness of component of skin, such as fat and muscle. This indicated that the method of SVD may be suitable for identification of heroin covered by skin

Preparation and Characterization of Folate Targeting Magnetic Nanomedicine Loaded with Cisplatin

We used Aldehyde sodium alginate (ASA) as modifier to improve surfactivity and stability of magnetic nanoparticles, and folate acid (FA) as targeting molecule. Fe3O4 nanoparticles were prepared by chemical coprecipitation method. FA was activated and coupled with diaminopolyethylene glycol (NH2-PEG-NH2). ASA was combined with Fe3O4 nanoparticles, and FA-PEG was connected with ASA by Schiff’s base formation. Then Cl- in cisplatin was replaced by hydroxyl group in ASA, and FA- and ASA-modified cisplatin-loaded magnetic nanomedicine (CDDP-FA-ASA-MNPs) was prepared. This nanomedicine was characterized by transmission electron microscopy, dynamic lighterring scattering, phase analysis light scattering and vibrating sample magnetometer. The uptake of magnetic nanomedicine by nasopharyngeal and laryngeal carcinoma cells with folate receptor positive or negative expression were observed by Prussian blue iron stain and transmission electron microscopy. We found that CDDP-FA-ASA-MNPs have good water-solubility and stability. Mean diameter of Fe3O4 core was 8.17 ± 0.24 nm, hydrodynamic diameters was 110.90±1.70 nm, and zeta potential was -26.45±1.26 mV. Maximum saturation magnetization was 22.20 emu/g. CDDP encapsulation efficiency was 49.05±1.58% (mg/mg), and drug loading property was 14.31±0.49% (mg/mg). In vitro, CDDP-FA-ASA-MNPs were selectively taken up by HNE-1 cells and Hep-2 cells, which express folate receptor positively

The influence of X-ray wavelength and the simulative human skin and muscle obstruction on the detection of human body-hidden drugs by non-intrusive X-ray diffraction method

AbstractIn order to detect the body-hidden drugs non-intrusively and rapidly, the influence of the X-ray wavelength and covering of the simulative skin and muscle on the detection of methamphetamine sample by synchrotron radiation X-ray diffraction (SR-XRD) technique have been investigated. Synchrotron radiation based X-ray with three different wavelengths (1.29 Å, 1.54 Å, 1.80 Å) has been chosen as the X-ray source. The results indicate that the intensities as well as the number of the diffraction peaks of methamphetamine sample covered by simulative muscle decreased with the increasing of the X-ray wavelength from 1.29 Åto 1.80 Å. In addition, the intensities of the diffraction peaks for methamphetamine will be seriously affected by the covered simulative skin or muscle due to the X-ray absorption. Furthermore, the absorption of X-ray by the simulative muscle seems much stronger than that of the simulative skin. Moreover, the specific molecular structure of the methamphetamine sample has been obtained by X-ray diffraction method

A laser-engraved wearable sensor for sensitive detection of uric acid and tyrosine in sweat

Wearable sweat sensors have the potential to provide continuous measurements of useful biomarkers. However, current sensors cannot accurately detect low analyte concentrations, lack multimodal sensing or are difficult to fabricate at large scale. We report an entirely laser-engraved sensor for simultaneous sweat sampling, chemical sensing and vital-sign monitoring. We demonstrate continuous detection of temperature, respiration rate and low concentrations of uric acid and tyrosine, analytes associated with diseases such as gout and metabolic disorders. We test the performance of the device in both physically trained and untrained subjects under exercise and after a protein-rich diet. We also evaluate its utility for gout monitoring in patients and healthy controls through a purine-rich meal challenge. Levels of uric acid in sweat were higher in patients with gout than in healthy individuals, and a similar trend was observed in serum