Statistical Pattern Research Articles

Epistemological and ethical issues of evidence-based concept in medicine

Despite of active implementation of the concept of “evidence-based medicine” into all areas of medical education, clinical science and practical healthcare, the epistemological problems of this new scientific reality currently remain underdeveloped in the philosophy of science. Aim: to identify the epistemological and ethical problems inherent in the evidence-based approach in medicine. Methods: The epistemological and ethical aspects of the concept of evidence-based medicine were considered. Results: At the current stage, evidence-based medicine reflects a transitional stage from the classical to the non-classical research paradigm. In this regard, the epistemological and ethical aspects of the concept of evidence-based medicine require further study and critical reflection. Conclusions: The discourse of evidence adopted in medicine today is aimed at increasing the objectivity of medical knowledge from practical point of view. However, this approach is not without significant drawbacks, including the overestimation of statistical patterns at the expense of “individual clinical differences”, the implicit primacy of phenomenological settings over the theoretical layer in the postulated structure of the scientific program, the unjustifiably rapid doctrinalization of the approach and the stigmatization of its opponents.

Fuzzy-ER Net: Fuzzy-based Efficient Residual Network-based lung cancer classification

Globally, Lung Cancer (LC) continues to be the primary cause of cancer-related death. Effective diagnosis is essential to save the lives of people. Nevertheless, manual Computed Tomography (CT) scan analysis takes more time and is inaccurate. The principal intention of this paper is to establish a hybrid Fuzzy-based Efficient Residual Network (Fuzzy-ER Net) for LC classification. The prime phase is the acquisition of input CT images from the database and the obtained CT image is sent to the pre-processing stage where noise is eradicated utilizing a Double bilateral filter. Thereafter, segmentation of the lung lobe is done by using a Dual-Attention V-network (DAV-Net). Moreover, feature extraction is performed, where features that are extracted include area, irregularity index, Local Vector Pattern (LVP), Local Gabor XOR Pattern (LGXP), and Statistical Fuzzy Local Binary Pattern (SFLBP). Eventually, LC classification is done by utilizing the proposed hybrid Fuzzy-ER Net. Here, the proposed Fuzzy-ER Net is newly devised by assimilating fuzzy concepts, EfficientNet, and Deep Residual Network (DRN). Additionally, the evaluation of the Fuzzy-ER Net on the basis of various metrics shows that it achieved maximum accuracy, True Positive Rate (TPR), of 93.2 % and 94.8 %, minimum False Positive Rate (FPR) is 5.7 %, maximum precision of 92.6 %, and maximum F-measure of 93.7 %.

Automated lithofacies classification: A comprehensive machine learning approach in Shushan Basin reservoirs, Western Desert, Egypt

The application of machine learning serves as a pivotal tool for petroleum geologists in facies classification. This new workflow distinguishes itself from existing classifiers by leveraging hidden statistical patterns in logging data to present a few recognizable clustering options for geologists. These choices are guided by other geological data sources, allowing geologists to retain the dimensional locations of chosen clusters for identification in other wells lacking these additional sources. The classification technique maximizes the value of conventional logging data (gamma ray, resistivity, density, neutron and sonic) for discerning rock typing, porosity ranking, fluid content, highlighting similar petrographic characteristics and elements composition, facilitating the inference of porosity and permeability degrees with high confidence.The workflow is designed in this study to predict siltstone, shale, limestone, basaltic intrusions, and coal, accurately identifies various sandstone sub-facies, differentiates between tight and hydrocarbon-bearing sandstone across four wells, with blind validation on a separate well. The classification is validated using Litho Scanner tool, petrography thin sections, and laboratory analysis.This comprehensive approach demonstrates the efficiency and applicability of the methodology, marking significant advancements in facies classification within petroleum geology.

“A Little More Gravitas”: Why Correctional Health Care Workers Value Third-Party Certifications

ABSTRACT Some people who work in correctional health care (CHC) choose to obtain third-party professional certifications. Prior research indicates that, in the aggregate, rates of certification correspond to rates of litigation; that is, increases in states’ numbers of CHC certifications tend to follow from increases in the number of lawsuits incarcerated people file against them. These statistical patterns, however, do not reveal what perceived value certification offers to the individuals who pursue it. To address this open question, we conducted interviews with 33 certified CHC workers. These interviews revealed two major themes in individuals’ ideas about the value of certification. First, certification constitutes a source of external legitimacy, helping people counteract the stigma associated with working in corrections and claim professional validity and social esteem. Second, certification provides workplace credibility, helping people assert expertise and access career opportunities in their working lives. These findings add new depth to social scientific understanding of how people manage working in stigmatized occupational settings and navigate the conflicting imperatives of care and punishment that characterize correctional environments.

The algorithmic nature of song-sequencing: statistical regularities in music albums

Based on a review of anecdotal beliefs, we explored statistical patterns of track-sequencing within a large set of released music albums. We found that songs with high levels of valence, energy and loudness are more likely to be positioned at the beginning of each album. We also found that transitions between consecutive tracks tend to alternate between increases and decreases of valence and energy. These findings were used to build a system which automates the process of album-sequencing. Our results and hypothesis have both practical and theoretical applications. Practically, sequencing regularities can be used to inform playlist generation systems. Theoretically, we show that professional musicians and music producers have significant levels of agreement about how to determine the order of tracks in their albums.

MEASUREMENT AND CHARACTERISTIC ANALYSIS OF SKELETAL GEOMETRIC PARAMETERS IN CHINESE HUMANS

Problem: Significant differences exist between the body dimensions of Chinese and Western individuals, and these differences affect the injury characteristics of passengers in traffic accidents. Most current parametric finite element human models are based on Western body data, which does not adequately assess the injury risks for Chinese individuals. Therefore, it is imperative to establish a digital model that reflects the anatomical features of Chinese humans. This requires comprehensive anthropometric data specific to the Chinese population. Aim: This study aims to collect and analyze skeletal geometric parameters from various representative regions in China across different age groups to fill the gap in Chinese anthropometric data. Additionally, it seeks to explore regional and age-related differences in these parameters, providing a scientific basis for developing finite element models that reflect the characteristics of the Chinese population. Methods: Clinical CT data of skeletal geometric parameters were collected from 224 individuals across seven representative regions of China, including parameters of the head, chest, and lower limbs. Descriptive statistics, K-W tests, and U-tests were used to analyze regional differences. Spearman correlation and linear regression analyses were employed to assess the impact of age. Furthermore, weighted averages and weighted variances of skeletal geometric parameters were calculated to reflect the characteristics of individuals from various regions across the country, which will be applied in the development of Chinese human models. Results: The results indicate significant regional differences in the skeletal geometric parameters of Chinese individuals, exhibiting regional patterns between the north–south and east–west areas. While skeletal geometric parameters showed significant differences with age, no clear statistical pattern was observed. Conclusion: This study systematically extracts and analyzes the skeletal geometric parameters of the Chinese population from regional and age perspectives for the first time. These data will be applied in the development of Chinese human models, providing crucial support for improving passenger safety in traffic accidents, enhancing vehicle safety design, and establishing anthropometric standards suitable for the Chinese context.

Vibration monitoring of masonry bridges to assess damage under changing temperature

Structural Health Monitoring (SHM) is of utmost importance for the preservation and safe operation of historical arch bridges. This paper presents the development of a SHM strategy aimed at the model-based damage assessment of masonry bridges using frequency data. Structural damage induces natural frequency changes that are strictly related to the damage location. Consequently, a numerical model capable of reproducing the intact dynamic characteristics should allow to simulate damage scenarios, including the observed one, with the anomaly localisation being performed through the similarity between the experimentally detected frequency changes and the numerically simulated ones. The proposed methodology is based on the availability of an appropriate knowledge of the investigated structure, allowing to define a Finite Element (FE) model that accurately reproduces the system dynamic characteristics. Hence, the SHM strategy involves: (a) the use of the calibrated model to simulate different damage scenarios, so that a Damage Location Reference Matrix (DLRM) is defined through the associated frequency shifts; (b) the damage detection through statistical pattern recognition of vibration data; (c) the damage localisation through the comparison between the identified frequency changes and those defined in the DLRM matrix. Pseudo-experimental monitoring data, referring to a historical masonry viaduct, were generated and used to exemplify the reliability and accuracy of the developed algorithms in detecting and localizing damage.

Validating pretrained language models for content quality classification with semantic-preserving metamorphic relations

Context:Utilizing pretrained language models (PLMs) has become common practice in maintaining the content quality of question-answering (Q&A) websites. However, evaluating the effectiveness of PLMs poses a challenge as they tend to provide local optima rather than global optima. Objective:In this study, we propose using semantic-preserving Metamorphic Relations (MRs) derived from Metamorphic Testing (MT) to address this challenge and validate PLMs. Methods:To validate four selected PLMs, we conducted an empirical experiment using a publicly available dataset comprising 60000 data points. We defined three groups of Metamorphic Relations (MRGs), consisting of thirteen semantic-preserving MRs, which were then employed to generate “Follow-up” testing datasets based on the original “Source” testing datasets. The PLMs were trained using a separate training dataset. A comparison was made between the predictions of the four trained PLMs for “Source” and “Follow-up” testing datasets in order to identify instances of violations, which corresponded to inconsistent predictions between the two datasets. If no violation was found, it indicated that the PLM was insensitive to the associate MR; thereby, the MR can be used for validation. In cases where no violation occurred across the entire MRG, non-violation regions were identified and supported simulation metamorphic testing. Results:The results of this study demonstrated that the proposed MRs could effectively serve as a validation tool for content quality classification on Stack Overflow Q&A using PLMs. One PLM did not violate the “Uppercase conversion” MRG and the “Duplication” MRG. Furthermore, the absence of violations in the MRGs allowed for the identification of non-violation regions, confirming the ability of the proposed MRs to support simulation metamorphic testing. Conclusion:The experimental findings indicate that the proposed MRs can validate PLMs effectively and support simulation metamorphic testing for PLMs. However, further investigations are required to enhance the semantic comprehension and common sense knowledge of PLMs and explore highly informative statistical patterns of PLMs, in order to improve their overall performance.

Assessment of spatial variability of agrochemical properties of agro-gray soil when planning a feld experiment with mineral fertilizers

The purpose of the work is to investigate the statistical parameters and patterns of variability of agrochemical indicators of agro–gray soil, elements of the structure of the spring wheat crop within the plots of the experimental field. Experimental data were obtained on the basis of observations conducted on agricultural landscapes Sukhobuzimskoye land areas in the Krasnoyarsk forest-steppe of the Krasnoyarsk geomorphological district, located borders the Chulym-Yenisei denudation plateau in the southwestern outskirts of Central Siberia. The results of the intra-field heterogeneity of the agrochemical properties of the agro-gray soil and the elements of the structure of the spring wheat crop on the site intended for field experiment are presented. The key statistical and geostatistical values of the intrafield heterogeneity are determined. The statistics of agrochemical indicators and the results of crop data from the plots intended for the placement of repetitions are analyzed. The materials of the agrochemical survey of the agro-gray soil revealed a low humus content. The level of spatial variation of the indicator corresponded to the average. The scope of variation is represented by values covering only one gradation of the evaluation scale. The entire studied area was characterized by a slightly acidic reaction of the medium according to the average pH values. Its variation in the 0-20 cm soil layer indicated uniformity of distribution in space. The level of variability of agrochemical indicators decreased in the following series: yield (40 %) > humus (19 %) > P2O5 (13 %) > K2O (10 %) > pH2O (4 %). According to the generalization of geostatistical information, the analyzed site is classified as unaligned in terms of soil fertility. In general, the soil fertility of the studied area had a significant variation, which determined the choice of elements of the field experience methodology. When laying out field experience, it is necessary to choose the appropriate elements of the methodology: a randomized method of placing repetitions, rectangular or elongated forms of plots.

Regionalized multiple-point statistical simulation for calibrating process-based geologic models to seismic data

Calibrating process-based geologic models to seismic data is critical and has been challenging for decades. The traditional approach to data calibration involves tuning the model input parameters by trial-and-error or through an automated inverse procedure. This can improve the model calibration to data but can hardly reach a fully satisfactory result. We adopt a multiple-point statistics (MPS) approach where a process-based geologic model is used as a training image for statistical pattern recognition. First, we define a rock-physics model from the process-based geologic model and derive its seismic attributes through seismic forward modeling. Then, we use the process-based model and its seismic attributes as coupled training images for geologic pattern recognition and regeneration under seismic data constraints. The method differs from the conventional MPS method in several ways: (1) the training image is a process-based geologic model of the reservoir of interest, thus defined on the same grid of the reservoir model; (2) the training image is generally nonstationary but there is no need to partition the nonstationary training image into pseudostationary ones; (3) the geologic facies and seismic constraint are related through seismic forward modeling instead of statistical inference, thus there is no need to convert seismic data to facies proportion or probability; (4) multiple-point statistics are based on Bayes’ law and Gaussian kernel approximation of conditional probability instead of a somehow arbitrary probability combination scheme or a heuristic rule; and (5) the method does not involve an iterative optimization procedure. Therefore, it also differs from the neural-network-based machine-learning approach, where the data conditioning is achieved through an iterative optimization procedure. These differences make our method advantageous for calibrating process-based geologic models. The two examples with synthetic data illustrate the effectiveness of the method. The first one is derived from a satellite image of a river delta, and the second one from the Stanford VI reservoir model.

Robust entropy rate estimation for nonstationary neuronal calcium spike trains based on empirical probabilities

Objective. Temporal patterns in neuronal spiking encode stimulus uncertainty, and convey information about high-level functions such as memory and cognition. Estimating the associated information content and understanding how that evolves with time assume significance in the investigation of neuronal coding mechanisms and abnormal signaling. However, existing estimators of the entropy rate, a measure of information content, either ignore the inherent nonstationarity, or employ dictionary-based Lempel-Ziv (LZ) methods that converge too slowly for one to study temporal variations in sufficient detail. Against this backdrop, we seek estimates that handle nonstationarity, are fast converging, and hence allow meaningful temporal investigations.Approach. We proposed a homogeneous Markov model approximation of spike trains within windows of suitably chosen length and an entropy rate estimator based on empirical probabilities that converges quickly.Main results. We constructed mathematical families of nonstationary Markov processes with certain bi/multi-level properties (inspired by neuronal responses) with known entropy rates, and validated the proposed estimator against those. Further statistical validations were presented on data collected from hippocampal (and primary visual cortex) neuron populations in terms of single neuron behavior as well as population heterogeneity. Our estimator appears to be statistically more accurate and converges faster than existing LZ estimators, and hence well suited for temporal studies.Significance. The entropy rate analysis revealed not only informational and process memory heterogeneity among neurons, but distinct statistical patterns in neuronal populations (from two different brain regions) under basal and post-stimulus conditions. Taking inspiration, we envision future large-scale studies of different brain regions enabled by the proposed tool (estimator), potentially contributing to improved functional modeling of the brain and identification of statistical signatures of neurodegenerative diseases.

Algorithm for controlling the operation of energy-efficient gas production points

During the operation of gas distribution networks, the gas pressure when moving to the consumer decreases at the gas distribution point (GDP). When the gas pressure at the hydraulic fracturing site decreases, a decrease in the temperature of the transported gas is observed (Joule-Thompson effect or adiabatic expansion). As a result of a decrease in gas temperature, condensate may form in the form of liquid fractions and crystalline hydrates. As a result, it becomes possible that the operation of the hydraulic fracturing pressure regulator will deviate from the planned mode with possible condensate adhesion on the walls, impulse tubes and cavities of the regulator, affecting the operation of instrumentation, contamination of filters, etc. Traditionally, this is dealt with by maintaining the required gas temperature through partial combustion of the transported gas with heating of the hydraulic fracturing room and gas. But always, when burning gas, certain volumes of it were consumed, and therefore funds, and at the same time gas combustion products entered the atmosphere. In addition, gas combustion, that is, the use of open fire, increased the risks of hydraulic fracturing. In this paper, it is proposed another algorithm for the gas preparation process when supplying it to the consumer: to abandon additional gas costs and the disadvantages indicated with the combustion procedure, while maintaining the necessary quality of gas preparation for its further use using a simple conversion of bypass lines with two vortex channels. The basis for this method of gas preparation was a series of works by the Department of Physics on the theoretical substantiation of statistical patterns in the formation of directed molecular flows. The proposed physical statistics is based on the universal mechanism of long-range wave action (ULWM), the selective nature of which at the stage of continuous streamers determines both the speed of the wave component and the loss of rotational degrees of freedom of molecular motion. Thanks to this, we observe thermal effects inherent in the Ranque-Hilsch effect. On this physical basis, an explanation is given for the operation of both vortex tubes and a number of atmospheric phenomena, including tornadoes and the appearance of lightning. It is on the basis of ULWM that a description of the physical essence of the operation of two vortex channels (condensation and decondensation) is given with a gain in energy and quality of preparation of the gas mixture.

Harnessing probabilistic neural network with triple tree seed algorithm-based smart enterprise quantitative risk management framework

Enterprise risk management (ERM) frameworks convey vital principles that help create a consistent risk management culture, irrespective of employee turnover or industry standards. Enterprise Management System (EMS) are becoming a popular research area for assuring a company’s long-term success. Statistical pattern recognition, federated learning, database administration, visualization technology, and social networking are all used in this field, which includes artificial intelligence (AI), data science, and statistics. Risk assessment in EMS is critical for enterprise decision-making to be effective. Recent advancements in AI, machine learning (ML), and deep learning (DL) concepts have enabled the development of effective risk assessment models for EMS. This special issue seeks groundbreaking research articles that showcase the application of applied probability and statistics to interdisciplinary studies. This study offers Improved Metaheuristics with a Deep Learning Enabled Risk Assessment Model (IMDLRA-SES) for Smart Enterprise Systems. Using feature selection (FS) and DL models, the provided IMDLRA-SES technique estimates business risks. Preprocessing is used in the IMDLRA-SES technique to change the original financial data into a usable format. In addition, an FS technique based on oppositional lion swarm optimization (OLSO) is utilized to find the best subset of features. In addition, the presence or absence of financial hazards in firms is classified using the triple tree seed algorithm (TTSA) with a probabilistic neural network (PNN) model. The TTSA is used as a hyperparameter optimizer to improve the efficiency of the PNN-based categorization. An extensive set of experimental evaluations is performed on German and Australian credit datasets to illustrate the IMDLRA-SES model’s improved performance. The performance validation of the IMDLRA-SES model portrayed a superior accuracy value of 95.70% and 96.09% over existing techniques.

Vector Rhetoric: GPT’s Rhetorical Agency

ABSTRACT The growing capabilities of large language models (LLMs) pose important questions for rhetorical theory and pedagogy. This article offers an overview of how LLMs like GPT work and a consideration of whether they should be considered rhetorical agents. To answer this question, the article considers structural and argumentative similarities in classical theorizations of rhetoric and the philosophy of Wilfrid Sellars. GPT’s particular method of encoding statistical patterns in language gives it some rudimentary semantics and reliably generates acceptable natural language output, so it should be considered to have a degree of rhetorical agency. But it is also badly limited by its restriction to written text, and an analysis of its interface shows that much of its rhetorical savvy is caused by the highly restricted rhetorical situation created by the ChatGPT interface.

Performance of a Large‐Language Model in scoring construction management capstone design projects

AbstractGrading is one of the most relevant hurdles for instructors, diverting instructor's focus on the development of engaging learning activities, class preparation, and attending to students' questions. Institutions and instructors are continuously looking for alternatives to reduce educators' time required on grading, frequently, resulting in hiring teaching assistants whose inexperience and frequent rotation can lead to inconsistent and subjective evaluations. Large Language Models (LLMs) like GPT‐4 may alleviate grading challenges; however, research in this field is limited when dealing with assignments requiring specialized knowledge, complex critical thinking, subjective, and creative. This research investigates whether GPT‐4's scores correlate with human grading in a construction capstone project and how the use of criteria and rubrics in GPT influences this correlation. Projects were graded by two human graders and three training configurations in GPT‐4: no detailed criteria, paraphrased criteria, and explicit rubrics. Each configuration was tested through 10 iterations to evaluate GPT consistency. Results challenge GPT‐4's potential to grade argumentative assignments. GPT‐4's score correlates slightly better (although poor overall) with human evaluations when no additional information is provided, underscoring the poor impact of the specificity of training materials for GPT scoring in this type of assignment. Despite the LLMs' promises, their limitations include variability in consistency and reliance on statistical pattern analysis, which can lead to misleading evaluations along with privacy concerns when handling sensitive student data. Educators must carefully design grading guidelines to harness the full potential of LLMs in academic assessments, balancing AI's efficiency with the need for nuanced human judgment.

Quantitative analysis of student learning progress in online curses during the COVID-19 Pandemic

Abstract The pandemic of COVID-19 forced the transition of education to online platforms. This shift allowed for a more in-depth study of the learning process of students, as it provided access to information about their activities and interactions with the course’s contents [1-3]. In this work, we apply quantitative methods to analyze the student learning progress of students for two first year courses of the B.Sc. degree in Physics (Universidad Católica del Norte). The data are collected from activity logs of digital platforms during Academic year 2021, which were employed to characterize the student learning habits along these processes (experience, selectivity and workflow). Statistical analysis is based on the distribution of the time spent on learning activities, such as the daily time distribution and the cumulative attention per online materials. The results show that the learning process of students in online courses is complex and adaptive. We report statistical patterns similar to those found in financial markets. These empirical results provide evidence about the importance of economic principles and methods in the understanding of the teaching-learning process.

“To Use or Not to Use?” A Mixed-Methods Study on the Determinants of EFL College Learners’ Behavioral Intention to Use AI in the Distributed Learning Context

Artificial intelligence (AI) offers new possibilities for English as a foreign language (EFL) learners to enhance their learning outcomes, provided that they have access to AI applications. However, little is written about the factors that influence their intention to use AI in distributed EFL learning contexts. This mixed-methods study, based on the technology acceptance model (TAM), examined the determinants of behavioral intention to use AI among 464 Chinese EFL college learners. As to quantitative data, a structural equation modelling (SEM) approach using IBM SPSS Amos (Version 24) produced some important findings. First, it was revealed that perceived ease of use significantly and positively predicts perceived usefulness and attitude toward AI. Second, attitude toward AI significantly and positively predicts behavioral intention to use AI. However, contrary to the TAM assumptions, perceived usefulness does not significantly predict either attitude toward AI or behavioral intention to use AI. Third, mediation analyses suggest that perceived ease of use has a significant and positive impact on students’ behavioral intention to use AI through their attitude toward AI, rather than through perceived usefulness. As to qualitative data, semi-structured interviews with 15 learners, analyzed by the software MAXQDA 2022, provide a nuanced understanding of the statistical patterns. This study also discusses the theoretical and pedagogical implications and suggests directions for future research.

Developmental changes in the word co-occurrences of Spanish–English bilingual children with and without developmental language disorder

Purpose The study aimed to understand how bilingual children with typical language development (TLD) and those with developmental language disorder (DLD) use frequent word co-occurrences in their narratives. Method We studied the change over time in the word co-occurrences used by 30 Spanish-English bilingual children with and without DLD (experimental group). An additional normative group consisted of 98 TLD Spanish-English bilingual first graders. Children narrated two Spanish and two English stories in kindergarten and first grade. Employing a Python program on the transcribed narratives, we extracted all adjacent two-word and three-word co-occurrences. From the normative group, the 90 most frequently occurring two-word and 90 most frequently occurring three-word co-occurrences were extracted. The type and tokens of word co-occurrences each child in the experimental group produced out of the 180 identified word co-occurrences were analysed. Result Overall, children at first grade produced more word co-occurrences types than in kindergarten. Children with DLD used fewer types of word co-occurrences but produced them as often as than their TLD peers. Children with DLD increased their word co-occurrences from kindergarten to first grade at the same rate although at a lower frequency. Children in both groups produced similar types and tokens of word co-occurrences in both Spanish and English, except tokens of two word co-occurrences. Children produced two word co-occurrences more often in English than in their Spanish narratives. Conclusion The results shed light on children with DLD’s deficits in production of word co-occurrences, indirectly reflecting possible deficits in statistical pattern detection.

Factors Affecting Customer's Purchase Decision of Makeup Products From Local Brands On TikTok Live Streams

Social media platforms have drastically reshaped communication, content sharing, and consumption in the fast-evolving digital era. TikTok, a prominent platform, has gained substantial influence in Indonesia, becoming a significant force in the business world. This study examines the factors that affect consumer purchase decisions for local makeup products during TikTok live-streaming sessions. Utilizing a mixed-methods approach, the research integrates qualitative and quantitative methods to understand consumer behavior comprehensively. Qualitative data is analyzed through manual coding, offering detailed insights into consumer perceptions and motivations. Quantitative data is assessed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to identify statistical relationships and patterns. The study involves a minimum of 12 participants for qualitative analysis and 200 for quantitative analysis, focusing on beauty enthusiasts familiar with TikTok's live-streaming features. The research aims to uncover how live streaming influences purchasing decisions and provides actionable insights for local beauty brands. By leveraging TikTok's interactive capabilities, the findings will help these brands enhance their marketing strategies and engage more effectively with their target audience. This study contributes to understanding the impact of social media on consumer behavior and offers practical recommendations for maximizing the potential of live streaming in the beauty industry.

A Mixed-Methods Investigation into the Interplay Between Supportive Work Environment, Achievement Emotions, and Teaching for Creativity as Perceived by Chinese EFL Teachers.

Teaching for creativity (TfC) has recently received increased attention in English-as-a-foreign-language (EFL) contexts. Previous studies confirmed that TfC links with achievement emotions (AEs) in this context. Additionally, a supportive work environment (SWE) has been shown to be associated with AEs. However, relationships among all three variables - SWE, AEs, and TfC - have not been adequately explored in EFL contexts, nor have the underlying bases for these links been elucidated. In this study, we adopted a mixed-methods approach to investigate the complex associations between SWE, AEs (anxiety, pride, enjoyment, and anger), and TfC as perceived by 441 Chinese EFL teachers. Quantitative results revealed that SWE, four AEs, and TfC were significantly correlated, with small to large effect sizes. Furthermore, the four AEs collectively mediated the relationship between SWE and TfC in our structural equation model. Qualitative interviews with 15 EFL teachers provided nuanced insights into possible reasons for these statistical patterns. We discuss the theoretical and pedagogical implications of these findings and suggest directions for future research.