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Nocardiosis is an infectious disease caused by Nocardia spp., mainly affecting immunocompromised hosts. Nocardia infection is not common; especially Nocardia wallacei infection is even rarer. The patient, female, 61 years old, farmer, has been working in the field for a long time and has normal immune function. Her main clinical manifestation was persistent back pain. Chest-enhanced computed tomography showed pulmonary inflammation. Rare pathogen Nocardia wallacei was detected in alveolar lavage fluid using matrix-assisted laser destructive ionization time-of-flight mass spectrometry. She received treatment with linezolid and was discharged after her condition improved.

Snapshot spectral imaging technology enables the capture of complete spectral information of objects in an extremely short period of time, offering wide-ranging applications in fields requiring dynamic observations such as environmental monitoring, medical diagnostics, and industrial inspection. In the past decades, snapshot spectral imaging has made remarkable breakthroughs with the emergence of new computational theories and optical components. From the early days of using various spatial-spectral data mapping methods, they have evolved to later attempts to encode various dimensions of light, such as amplitude, phase, and wavelength, and then computationally reconstruct them. This review focuses on a systematic presentation of the system architecture and mathematical modeling of these snapshot spectral imaging techniques. In addition, the introduction of metasurfaces expands the modulation of spatial-spectral data and brings advantages such as system size reduction, which has become a research hotspot in recent years and is regarded as the key to the next-generation snapshot spectral imaging techniques. This paper provides a systematic overview of the applications of metasurfaces in snapshot spectral imaging and provides an outlook on future directions and research priorities.

Artificial intelligence (AI) provides new opportunities for K-12 English as foreign language (EFL) teachers to improve their teaching. To address the emerging trend of integrating AI into teaching, this study investigated EFL teachers' perceptions, knowledge, and behavioral intention to use AI to support teaching and learning of English in middle schools. This study combined relevant aspects of the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technological Pedagogical and Content Knowledge (TPACK) as the theoretical basis. A survey was conducted in an AI education demonstration district in China. This survey adopted a 5-point Likert scale which was developed from previous research and the interview records of EFL teachers. A total of 470 valid responses were collected. The reliability and validity of the scale were satisfied with eight constructs: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), AI language technological knowledge (AIL-TK), AI technological pedagogical knowledge (AI-TPK), AI-TPACK, and Behavioral Intention (BI). The results showed that the EFL teachers were positive with regard to the measured factors. PE, SI, AIL-TK, and AI-TPACK had significant positive predictive power on BI; and EE, FC, AI-TPK had indirect effects on BI. The complex interrelations were mapped out to provide educators and policymakers with a theoretically grounded scheme to foster teachers' BI to use AI in teaching.

The effective application of spherical video-based virtual reality (SVVR) in writing education depends on teachers' lesson design, which is deeply influenced by their technological pedagogical and content knowledge (TPACK). However, how teaching conceptions, as the fundamental viewpoint that influences teachers' teaching focuses, shape their TPACK remains uncertain. This study aimed to explore how teachers' conceptions shape their TPACK regarding SVVR-supported writing instruction. Twenty-one secondary school teachers participated in this study and conducted one semester of SVVR-supported writing lessons. Data were collected through semi-structured interviews, and the interview transcriptions were analyzed using content analysis to explore the association between the teaching conceptions and TPACK. Teaching conceptions were classified into three orientations, namely skill, community, and identity, to reflect the teachers' teaching focuses. TPACK was classified into three categories, namely Replacement, Amplification, and Transformation, to indicate the levels of integrating SVVR into the writing lessons. The results showed that teachers with students' identity-focused conceptions shaped their TPACK at the Transformation level of SVVR integration. Teachers with community-focused conceptions developed students' emotional connections with people and places through their TPACK for deeper writing. Teachers with skill-focused conceptions, on the other hand, shaped their TPACK at the Replacement level that replaced the existing teaching activities and resources with SVVR to teach students writing skills. The findings suggest that teachers may need to shift the conceptions of writing instruction toward identity orientation to develop transformative TPACK.

The crisis caused by COVID-19 has forced many vendors to consider alternative marketing strategies, such as live streaming on social commerce platforms. Social commerce live streaming involves using live streamers to endorse and sell products on their own social media accounts. Statistics show that this format can serve as a survival tool for vendors in exceptional circumstances. However, it is also a valuable opportunity to bring innovation and engagement to traditional e-commerce vendors. Based on a sample of 621 Chinese consumers, this empirical research proposes that features of live streamers (expertise, attractiveness, and humor) play an essential role in social commerce intention. At the same time, virtual friendship, emotional engagement, and platform attachment are antecedents of online purchasing intentions. It also contends that social presence interaction has a moderating effect on live s-commerce and that these elements together constitute a comprehensive model of live s-commerce. Our findings reveal that virtual friendship, platform attachment, and emotional engagement with live streamers are crucial for increasing buying intentions, and they confirm the mediation effect of social commerce intention and the moderating effect of social presence interaction. The practical and theoretical implications are pointed out to assist brands, managers, retailers, and e-marketplaces in developing effective marketing plans.

Advancement in virtual reality (VR) technology has enabled language learners to be engaged in immersive and interactive learning environments. Recent studies have revealed that the educational use of VR technology can improve students' writing achievement, behavioral engagement, and cognitive engagement in learning. However, the factors contributing to these improvements are not fully understood. Writing programs that foster a positive writing self-concept is helpful for students' development. In this study, adopting the self-determination theory as the theoretical lens, the affordances of VR contributing to students' self-concept in descriptive writing (DW) were explored through a newly adapted survey. Secondary school students (n = 300) who attended a VR-supported writing course completed the survey that measured their responses to five related factors: (1) autonomous observation; (2) relatedness; (3) self-efficacy; (4) satisfaction; and (5) writing self-concept. Exploratory and confirmatory factor analyses were conducted to validate the five-factor survey measurements. The results indicated that the construct validity and reliability of the survey were acceptable. A structural equation model was constructed to investigate the hypothesized relationships of the factors contributing to the students' writing self-concept. The results indicated self-efficacy and satisfaction directly predict students' writing self-concept, and relatedness indirectly predicts writing self-concept. These findings reveal the underlying mechanism which could inform educators who are keen on promoting or harnessing VR-supported learning and teaching in writing education.

With growing public concerns about ecological issues, it has been increasingly critical for corporations to enhance their green competitive advantages. Utilizing a natural‐resource‐based view of firm and human resource management (HRM) perspective, we propose that green HRM (GHRM) promotes firms' green competitive advantages by facilitating two kinds of green innovation (i.e., green product/process innovation). Via a two‐wave survey study of chief human resources officers (CHOs) and chief financial officers (CFOs) from 69 companies in China, we find support that GHRM positively links to green product and process innovations, each of which will, in turn, have a positive correlation with a green competitive advantage. Moreover, an indirectly positive linkage between GHRM and green competitive advantage via two types of green innovation is also proved. Our study expands current research by demonstrating a positive effect of GHRM in building green competitive advantage and by revealing two parallel mechanisms of such effect. [ABSTRACT FROM AUTHOR]

Ginkgo biloba L. is a rare dioecious species that is valued for its diverse applications and is cultivated globally. This study aimed to develop a rapid and effective method for determining the sex of a Ginkgo biloba. Green and yellow leaves representing annual growth stages were scanned with a hyperspectral imager, and classification models for RGB images, spectral features, and a fusion of spectral and image features were established. Initially, a ResNet101 model classified the RGB dataset using the proportional scaling–background expansion preprocessing method, achieving an accuracy of 90.27%. Further, machine learning algorithms like support vector machine (SVM), linear discriminant analysis (LDA), and subspace discriminant analysis (SDA) were applied. Optimal results were achieved with SVM and SDA in the green leaf stage and LDA in the yellow leaf stage, with prediction accuracies of 87.35% and 98.85%, respectively. To fully utilize the optimal model, a two-stage Period-Predetermined (PP) method was proposed, and a fusion dataset was built using the spectral and image features. The overall accuracy for the prediction set was as high as 96.30%. This is the first study to establish a standard technique framework for Ginkgo sex classification using hyperspectral imaging, offering an efficient tool for industrial and ecological applications and the potential for classifying other dioecious plants. [ABSTRACT FROM AUTHOR]