The present study investigates the use of generative artificial intelligence (AI) tools by pre-service teachers (PSTs) in lesson planning for a middle-school English as a foreign language (EFL) class, aiming to address gaps and inform teacher training. The case study examined PSTs in a South Korean university course who were tasked with creating lesson plans using generative AI to aid in lesson plan development for a middle school lesson that incorporated generative AI. Data were analyzed thematically, and results revealed that generative AI was used in topic selection, material creation, lesson organization, and language checking. While generative AI facilitated efficiency and creativity, challenges emerged, including the quality of outputs and limited incorporation of effective pedagogical strategies. These findings indicate a need for targeted training in prompt engineering, ethical considerations, pedagogy, and collaborative practices to enhance PSTs’ generative AI competencies. This study contributes to teacher education programs by providing insights into the practical integration of generative AI in pedagogical practices.

Forecasting port container throughput is crucial due to its impact on economic development. Socio-economic factors, which introduce uncertainty, are increasingly integrated into throughput forecasting. The complexity of common multivariate forecasting models significantly affects accuracy, yet few studies compare their performance on the same time series for throughput modeling. This study implements, evaluates, and compares the performance of eight multivariate forecasting models for port throughput within a proposed multiple-input single-output (MISO) system, chosen for their frequent use in container throughput research. It investigates two data preprocessing approaches: Random Forest Variable Importance Method (RF-VIM) and a Multi Lagged Value approach. The comparison uses six error metrics: normalized root mean squared error, mean absolute error, mean absolute percentage error, mean error, and root mean percentage error. Performances are discussed, and recommendations for adopting a suitable model are provided.

The second generation of stars in the globular clusters (GCs) of the Milky Way (MW) exhibit unusually high N, Na, or Al, compared to typical Galactic halo stars at similar metallicities. The halo field stars enhanced with such elements are believed to have originated in disrupted GCs or escaped from existing GCs. We identify such stars in the metallicity range −3.0 < [Fe/H] < 0.0 from a sample of ∼36,800 giant stars observed in the Sloan Digital Sky Survey and Large Sky Area Multi-Object Fiber Spectroscopic Telescope survey, and present their dynamical properties. The N-rich population (NRP) and N-normal population (NNP) among our giant sample do not exhibit similarities in either in their metallicity distribution function (MDF) or dynamical properties. We find that, even though the MDF of the NRP looks similar to that of the MW’s GCs in the range of [Fe/H] < −1.0, our analysis of the dynamical properties does not indicate similarities between them in the same metallicity range, implying that the escaped members from existing GCs may account for a small fraction of our N-rich stars, or the orbits of the present GCs have been altered by the dynamical friction of the MW. We also find a significant increase in the fraction of N-rich stars in the halo field in the very metal-poor (VMP; [Fe/H] < −2.0) regime, comprising up to ∼20% of the fraction of the N-rich stars below [Fe/H] = −2.5, hinting that partially or fully destroyed VMP GCs may have in some degree contributed to the Galactic halo. A more detailed dynamical analysis of the NRP reveals that our sample of N-rich stars do not share a single common origin. Although a substantial fraction of the N-rich stars seem to originate from the GCs formed in situ, more than 60% of them are not associated with those of typical Galactic populations, but probably have extragalactic origins associated with Gaia Sausage/Enceladus, Sequoia, and Sagittarius dwarf galaxies, as well as with presently unrecognized progenitors.

In the powder bed fusion (PBF) process, a 3D shape is formed by the continuous stacking of very fine powder layers using computer-aided design (CAD) modeling data, following which laser irradiation can be used to fuse the layers forming the desired product. In this method, the main process parameters for manufacturing the desired 3D products are laser power, laser speed, powder form, powder size, laminated thickness, and laser diameter. Stainless steel (STS) 316L exhibits excellent strength at high temperatures, and is also corrosion resistant. Due to this, it is widely used in various additive manufacturing processes, and in the production of corrosion-resistant components with complicated shapes. In this study, rectangular specimens have been manufactured using STS 316L powder via the PBF process. Further, the effect of heat treatment at 800 °C on the microstructure and hardness has been investigated.

Since the outbreak of coronavirus disease 2019 (COVID-2019), the infection has spread worldwide due to the highly contagious nature of severe acute syndrome coronavirus (SARS-CoV-2). To manage SARS-CoV-2, the development of diagnostic assays that can quickly and accurately identify the disease in patients is necessary. Currently, nucleic acid-based testing and serology-based testing are two widely used approaches. Of these, nucleic acid-based testing with quantitative reverse transcription-PCR (RT-qPCR) using nasopharyngeal (NP) and/or oropharyngeal (OP) swabs is considered to be the gold standard. Recently, the use of saliva samples has been considered as an alternative method of sample collection. Compared to the NP and OP swab methods, saliva specimens have several advantages. Saliva specimens are easier to collect. Self-collection of saliva specimens can reduce the risk of infection to healthcare providers and reduce sample collection time and cost. Until recently, the sensitivity and accuracy of the data obtained using saliva specimens for SARS-CoV-2 detection was controversial. However, recent clinical research has found that sensitive and reliable data can be obtained from saliva specimens using RT-qPCR, with approximately 81% to 95% correspondence with the data obtained from NP and OP swabs. These data suggest that self-collected saliva is an alternative option for the diagnosis of COVID-19.

Oral lichen planus (OLP) is a chronic inflammatory disease observed in approximately 0.5–2.2% of the population, and it is recognized as a premalignant lesion that can progress into oral squamous cell carcinoma (OSCC). The rate of malignant transformation is approximately 1.09–2.3%, and the risk factors for malignant transformation are age, female, erosive type, and tongue site location. Malignant transformation of OLP is likely related to the low frequency of apoptotic phenomena. Therefore, apoptosis-related genetic factors, like p53, BCL-2, and BAX are reviewed. Increased p53 expression and altered expression of BCL-2 and BAX were observed in OLP patients, and the malignant transformation rate in these patients was relatively higher. The involvement of microRNA (miRNA) in the malignant transformation of OLP is also reviewed. Because autophagy is involved in cell survival and death through the regulation of various cellular processes, autophagy-related genetic factors may function as factors for malignant transformation. In OLP, decreased levels of ATG9B mRNA and a higher expression of IGF1 were observed, suggesting a reduction in cell death and autophagic response. Activated IGF1-PI3K/AKT/mTor cascade may play an important role in a signaling pathway related to the malignant transformation of OLP to OSCC. Recent research has shown that miRNAs, such as miR-199 and miR-122, activate the cascade, increasing the prosurvival and proproliferative signals.

We present an analysis of the chemical abundances and kinematics of six low-mass dwarf stars, previously claimed to be candidate hypervelocity stars (HVSs). We obtained moderate-resolution ($R\sim6000$) spectra of these stars to estimate the abundances of several chemical elements (Mg, Si, Ca, Ti, Cr, Fe, and Ni), and derived their space velocities and orbital parameters using proper motions from the \gaia\ Data Release 2. All six stars are shown to be bound to the Milky Way, and in fact are not even considered high-velocity stars with respect to the Galactic rest frame. Nevertheless, we attempt to characterize their parent Galactic stellar components by simultaneously comparing their element abundance patterns and orbital parameters with those expected from various Galactic stellar components. We find that two of our program stars are typical disk stars. For four stars, even though their kinematic probabilistic membership assignment suggests membership in the Galactic disk, based on their distinct orbit l properties and chemical characteristics, we cannot rule out exotic origins as follows. Two stars may be runaway stars from the Galactic disk. One star has possibly been accreted from a disrupted dwarf galaxy or dynamically heated from a birthplace in the Galactic bulge. The last object may be either a runaway disk star or has been dynamically heated. Spectroscopic follow-up observations with higher resolution for these curious objects will provide a better understanding of their origin.