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Transnational youth frequently navigate multiple languages and continually negotiate not only affiliation, but also the legitimacy of the languages they use within changing linguistic hierarchies. However, their educational experiences are often framed through fragmented classroom practices, deficit-based assessments, and nationally bounded curricular frameworks. In this paper, I respond by theorizing Fictional Linguistic Landscapes (FLL) as a transdisciplinary pedagogical approach that utilizes fiction and participatory cultural practices to position language learning as a form of semiotic design, critical inquiry, and identity (re)work. Grounded in linguistic landscape studies, multiliteracies pedagogy, and fan-based meaning-making, FLL positions learners as world-builders and allows them to experiment with visibility, hierarchy, and language(s) in safe fictional environments. This study outlines the four-phase FLL in Second Language Teaching and Learning (L2TL) cycle and provides five pedagogical design spaces to address issues of raciolinguistic valuation, deficit institutional representations, affective harm, peer-level marginalization, and translocal or return migrant identity negotiation. Rather than viewing imagination as an outcome of teaching, FLLinL2TL structures it as a necessary process for learning, linking creative production to explicit linguistic objectives and reflective justification. I conclude by discussing implications for classroom practice, teacher education, and future research on the potential of the FLLinL2TL approach in transnational education research. Full article

High-fidelity imaging in extreme low light is challenged by ultra-low signal-to-noise ratios. We propose a hybrid noise modeling framework integrating physical priors with generative adversarial networks (GANs). The method simulates photon shot noise via Poisson distribution and incorporates readout, row, and quantization noise. A multi-layer perceptron (MLP) dynamically maps ISO levels to noise intensities in logarithmic space, followed by a residual U-Net for non-linear refinement. Results on the SID datasets show that our method outperforms state-of-the-art approaches in terms of Average Kullback–Leibler Divergence (AKLD). Denoising networks trained on our synthetic noise achieve performances comparable to those trained on real-world paired datasets. Full article

Accurate wind flow prediction is essential for various applications, including the placement of wind turbines and a multitude of environmental assessments. Traditionally this can be achieved by using time-consuming computational fluid dynamics (CFD) simulations on reanalysis data. This study explores the performance of an autoencoder (AE) and a variational autoencoder (VAE) in approximating downscaled wind speed and direction using real-world reanalysis data and reference geo- and vegetation data. The AE model was trained for 2000 epochs and demonstrates the ability to replicate wind patterns with a mean absolute error (MAE) of approximately −0.9. However, the AE model exhibited a consistent underestimation of wind speeds and a directional shift of approximately 10 degrees compared to CFD reference simulations. The VAE model produced visually improved results, capturing complex wind flow structures more accurately than the AE model. It mainly achieves better local accuracy and a reduced variance of the results. The overall result suggests that while autoencoders can approximate wind flow patterns, challenges remain in capturing the full variability of wind speeds and directions with sufficient precision. The study highlights the importance of balancing reconstruction accuracy and latent space regularization in VAE models. Future work should focus on optimizing model architecture and training strategies to enhance accuracy, prediction reliability and generalizability across diverse wind conditions and various locations. Full article

Fuzzy multi-criteria decision-making (MCDM) methods remain popular for addressing decision-making problems involving uncertainty and explainability. However, decisions are usually made using data with different dimensions or even modalities. Therefore, existing MCDM methods incorporate various normalization techniques in order to transform attribute values into dimensionless quantities, ensuring the robustness and reliability of the decision-making results. Nevertheless, these normalization techniques may affect the ranking of alternatives. This study therefore proposes a novel method to investigate the effect of various normalization techniques on fuzzy MCDM methods. The study introduces a novel method for creating a fuzzy decision-making matrix using Tukey’s fences method, enabling the evaluation of alternatives using attributes under uncertain conditions. This method was evaluated in the context of web service quality assessments involving multi-dimensional and random variable attributes. The study demonstrated that Vector and Linear normalization techniques yield similar alternative rankings when using fuzzy MCDM methods, whereas rankings differ when Non-linear normalization techniques are applied. We believe that the current study will allow researchers and practitioners to address various practical uncertain decision-making problems with multi-dimensional attributes, thus promoting the digital transformation of complex, real-world decision-making issues. Full article

Purpose: HER2-targeted therapy has been incorporated into the standard neoadjuvant treatment (NAT) regimen for HER2-positive early-stage breast cancer, yet a subset of patients have shown a limited pathological response. This study aimed to evaluate clinicopathological factors associated with NAT sensitivity and to develop a predictive model. Methods: This retrospective study included 13,004 HER2-positive breast cancer patients from the National Cancer Database (2010–2022) who received neoadjuvant chemotherapy plus HER2-targeted therapy. Pathological complete response (pCR) was defined as no residual invasive carcinoma in the breast and axillary lymph nodes (ypT0/is, ypN0). NAT sensitivity was additionally defined using clinical-to-pathologic stage migration according to the AJCC 8th edition criteria. Baseline characteristics and overall survival (OS) were compared between NAT-sensitive and NAT-insensitive groups. A multivariable logistic regression model was developed based on age, clinical T stage, clinical N stage, histologic subtype, tumor grade, and hormone receptor (HR) status. Model performance was assessed using the area under the receiver operating characteristic curve and calibration curves. Results: Among the patients included, 3660 (28.1%) achieved pCR. Based on the predefined stage-based criteria, 10,451 (80.4%) were classified as NAT-sensitive and 2553 (19.6%) as NAT-insensitive. NAT-insensitive patients were older and more likely to present with clinical T1c and node-negative disease, whereas NAT-sensitive patients more frequently had higher clinical T and N stages. HR-positive and lower tumor grades were significantly associated with treatment insensitivity. NAT-insensitive patients demonstrated significantly worse OS compared with NAT-sensitive patients (p < 0.001). The predictive model showed acceptable discrimination with AUCs of 0.762 in the training cohort and 0.776 in the validation cohort, demonstrating good calibration. Conclusions: NAT sensitivity in HER2-positive early-stage breast cancer exhibited substantial biological and clinical heterogeneity in real-world practice. A younger age, higher clinical stage, invasive ductal histology, higher tumor grade, and HR-negative status were associated with improved responses. A predictive model based on routinely available baseline variables demonstrated reasonable performance for estimating treatment sensitivity, supporting its potential utility for baseline risk stratification pending external validation. Full article

Incretin-based pharmacotherapy has rapidly transformed obesity management. However, despite its efficacy, gastrointestinal (GI) adverse events (AEs) are common and represent a major driver of treatment discontinuation. Symptoms such as nausea, vomiting, acid reflux, diarrhea, and constipation, not only impair the quality of life, but also compromise adherence, thereby limiting the real-world effectiveness of these agents. Targeted nutritional strategies may play a pivotal role in mitigating these symptoms and supporting sustained treatment. However, most clinical trials have relied on generalized lifestyle advice combined with hypocaloric dietary prescriptions, with limited integration of structured, mechanism-based nutritional counseling tailored to the physiological actions of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dual glucose-dependent insulinotropic polypeptide (GIP)/GLP-1 RAs. Consequently, practical guidance for clinicians and dietitians remains fragmented. The present review synthesizes the available evidence on GI AEs associated with incretin-based therapies and examines whether structured, targeted nutritional management can meaningfully reduce symptom burden. We also outline key monitoring strategies and focus on important clinical aspects for physicians and dietitians, aiming to optimize patient outcomes. In addition, we provide detailed information on the spectrum of GI AEs to guide effective management and limit intolerance. By bridging pharmacology with applied clinical nutrition, we aim to provide a pragmatic framework for improving tolerability, sustaining adherence, and translating trial efficacy into durable real-world effectiveness. Full article

The development and execution of prospective inner and outer space missions require focusing on the use of many small space vehicles operating in swarms with multiple informational, navigational, and mission-oriented interactions among themselves. Such missions involve providing communication and surveillance services, facilitating distributed material production in space, and conducting research expeditions to explore the resources and environments of new worlds. The cornerstone technology for operating distributed space systems is propulsion. Among a range of propulsion technologies—from using pressurized cold gases to implementing laser beams to generate thrust—certain methods stand out for application in small spacecraft. This paper provides a summary of space-operated propulsion, emphasizing the reasons for the more frequent adoption of one technology over another. The discussion on propulsion trends is complemented by examining the physical, engineering, production, operational, and societal rationale behind these choices. The findings reinforce the trend toward transitioning to fully electric satellites. This review serves as a means for reevaluating global propulsion trends and guiding the future development of inner and outer space propulsion-assisted economies effectively. Full article

China is one of the world’s major lychee producers, and the fruit’s soft texture, small size, and thin peel make non-destructive robotic harvesting particularly challenging. Accurate fruit detection, branch segmentation, and precise picking-point localization are critical for enabling automated harvesting in complex natural orchard environments. This study proposes an integrated perception framework for lychee harvesting that combines object detection, density-based clustering, and semantic segmentation. An improved YOLO11s-based detection network incorporating SimAM attention, CMUNeXt feature enhancement, and MPDIoU loss is developed to enhance robustness under illumination variation, occlusion, and scale changes. The proposed detector achieves a precision of 84.3%, recall of 73.2%, and mAP of 81.6%, outperforming baseline models. Density-based clustering is employed to group individual detections into fruit clusters. Comparative experiments demonstrate that MeanShift achieves the highest clustering consistency, with an average Adjusted Rand Index (ARI) of 0.768, outperforming k-means and other baselines. An improved DeepLab v3+ semantic segmentation network with a ResDenseFocal backbone and Focal Loss is designed for accurate branch extraction under complex backgrounds. Finally, a rule-based geometric picking-point localization algorithm is formulated in the image coordinate system by integrating detection, clustering, and branch segmentation results. Experimental validation demonstrates that the proposed framework can reliably localize picking points in two-dimensional images under natural orchard conditions. The proposed method provides a practical perception solution for intelligent lychee harvesting and establishes a foundation for future 3D robotic manipulation and field deployment. Full article

Addressing the low efficiency associated with single-frequency serial acquisition in urban exploration using traditional electrical resistivity tomography (ERT) instruments, this study introduces a Frequency Division Multiplexing Electrical Resistivity Tomography (FDM-ERT) method and hardware system. By utilizing transmission modules that simultaneously output AC excitation signals at distinct frequencies, coupled with receiver modules that enable multi-channel parallel acquisition and data transmission, the system achieves a “one-time layout, multi-frequency synchronous measurement” workflow. Laboratory tests under controlled conditions and preliminary field tests conducted at the Xiangjiang River beach demonstrate that this method maintains relatively high consistency with traditional single-frequency measurements. The relative error of apparent resistivity across frequency points remains below 2%, with an inversion root mean square error (RMSE) of 0.4%. Furthermore, the multi-frequency synchronous mode reduces total measurement time by approximately 66.7%. While these results were obtained in relatively controlled environments, they substantiate the core feasibility of the FDM-ERT system for multi-frequency synchronous measurement, providing a certain hardware foundation for subsequent validation and optimization in complex, real-world urban settings. Full article

This paper introduces the Unit Exponential Delay Time Distribution (UEDTD), a two-parameter model for data with support in the unit interval $(0,1)$. The model is derived using two distinct approaches: transformation method applied to the Exponential Delay Time Distribution (EDTD), which itself arises as the convolution of two independent exponential random variables, and product convolution method of two independent power-function random variables that connects UEDTD to Pareto distribution, offering additional interpretability and giving rise to several exact and efficient algorithms for generating random samples. The limit distribution is examined with derivation of key statistical properties. The order statistics with interesting asymptotic results for extremes distribution are discussed and formulated. A reparameterization for the model is suggested to improve estimation stability and formulation with maximum likelihood approach employed for parameter inference. A simulation study demonstrates the consistency and efficiency of the estimators across various sample sizes and parameter configurations. The practical applicability of the UEDTD is demonstrated through a real-world dataset, where it shows superior performance compared to established unit distributions, confirming the utility of the UEDTD for modeling proportional data in applied research. Full article

Background/Objectives: The COVID-19 pandemic has brought about significant clinical challenges in regard to digestive systems, as well as causing complications such as pancreatitis and biliary infections. Whether diabetes mellitus (DM) contributes to both an increased risk for these complications and mortality amongst COVID-19 patients remains to be investigated. This study aimed to illuminate any possible outcomes, including pancreatitis, cholangitis, cholecystitis and all-cause mortality, among COVID-19 patients with and without pre-existing type 2 diabetes mellitus (T2DM), using real-world data taken from a multinational electronic health record database. Methods: A retrospective cohort study based upon data taken from the database of the TriNetX Global Collaborative Network was conducted. We included patients from the database who had been diagnosed with COVID-19 from January 2020 to December 2023. Enrolled subjects were divided into two cohorts: COVID-19 patients with pre-existing T2DM who had had at least two medical visits, and those without T2DM. Propensity score matching was performed using 68 baseline variables. Outcomes were evaluated within 90 days following COVID-19 diagnosis, with patients with prior relevant diagnoses being excluded. Risk analyses, Kaplan–Meier survival estimates, and hazard ratios were calculated as the outcomes. Results: The incidence of acute pancreatitis was significantly higher in the DM+ group when compared to the DM– group (Hazard ratio (HR) = 1.307; 95% confidence interval (CI) 1.048–1.630, p = 0.017) and mortality (HR = 1.141; 95% CI 1.102–1.181, p < 0.05) by Kaplan–Meier analysis. Risk of cholecystitis (HR = 1.264; 95% CI 1.042–1.533, p = 0.017) was borderline increased, and cholangitis was not significant (HR 0.847, 95% CI 0.583–1.230) Conclusions: In COVID-19 patients, pre-existing T2DM is independently associated with increased risks of acute pancreatitis and mortality. Full article

The primary aim of this study is to develop a comprehensive theoretical model for creating digital content that enhances speech formation in toddlers aged 1–3, based on children’s folklore. This model seeks to integrate pedagogical, psychological, and cultural elements to offer a balanced and age-appropriate digital learning experience for young children. The study employed a systematic literature review using Creswell’s seven-step process, which involved identifying relevant research, reviewing and analyzing 22 peer-reviewed studies published between 2019 and 2023, and synthesizing their findings. VOSviewer version 1.6.18, a bibliometric visualization tool, was used to conduct a keyword co-occurrence analysis, identifying key concepts and trends in digital content creation for toddlers. The systematic review adhered to the PRISMA framework to ensure rigor in the selection and analysis of the included studies, which spanned fields such as education, psychology, and pediatric development. The study identified several key dimensions necessary for developing an effective theoretical model of digital content creation for toddlers: The content must be age-appropriate and consider the unique cognitive, linguistic, and developmental needs of toddlers. Children’s folklore plays a crucial role in language development, offering culturally rich and rhythmically engaging material for young learners. The model must address the balance between screen time and real-world interactions, ensuring that digital engagement does not replace essential real-life learning experiences. Ensuring the psychological and physiological safety of digital content is paramount, requiring the exclusion of inappropriate or harmful material and the inclusion of interactive, engaging content that supports speech development. The study concludes that a well-designed model for digital content creation, rooted in children’s folklore, can significantly enhance speech development in toddlers. Such a model must not only support language acquisition but also reflect cultural heritage, promote safe digital environments, and encourage a balance between digital and real-world interactions. By integrating the findings from various disciplines, this theoretical model provides a holistic framework that can guide the development of high-quality digital content aimed at supporting early childhood language development in the digital age. Full article

Pharmaceutical regulation plays a central role in protecting public health by governing clinical trials, market authorization, and post-marketing safety monitoring throughout the medicine life cycle. While substantial literature describes established systems, particularly the United States Food and Drug Administration (FDA), Japan’s Pharmaceuticals and Medical Devices Agency (PMDA), and the European medicines regulatory network coordinated by the European Medicines Agency (EMA) together with national competent authorities, comparative analyses that integrate both mature authorities, emerging regulators and transnational harmonization networks remain limited. This narrative review draws on primary regulator/network documentation and targeted peer-reviewed literature to compare core regulatory functions across jurisdictions, including approval pathways and evidentiary expectations, inspection and good manufacturing practice (GMP) oversight, transparency practices, and pharmacovigilance and risk-management approaches. Across regions, we observe increasing convergence in scientific expectations through initiatives such as the International Council for Harmonisation (ICH) and reliance and work-sharing models, alongside persistent differences in legal mandates, resourcing, timelines, and data requirements. These differences are most consequential for complex products (e.g., advanced therapies) and in crisis settings, where emergency or conditional authorizations amplify the need for strong lifecycle monitoring, real-world evidence governance, and cross-border communication. We conclude by outlining opportunities to strengthen regulatory resilience and equity through fit-for-purpose harmonization, investment in enabling infrastructure, and future work on interoperable data systems, signal detection, and coordinated post-marketing evaluation. Full article

Control charts are widely used in the industrial world to monitor the average and variability of production processes. Max-Half-Mchart is a multivariate control chart that is not particularly effective in handling many outliers. This research aims to develop a control chart that is more resistant to outliers by using Minimum Regularized Covariance Determinant (MRCD). MRCD is a development of the MCD method, which is better at dealing with ‘fat data,” namely, situations in which the number of variables is greater than the number of observations. The performance of a robust Max-Half-Mchart control chart based on MRCD was evaluated using the average run length (ARL) against shifts in the process mean, process variance, and simultaneous shifts. A comparison was also made of the outlier detection accuracy between the robust Max-Half-Mchart based on MRCD and the standard Max-Half-Mchart. Simulation results demonstrated that the MRCD-based robust chart is most sensitive to simultaneous shifts in the mean and variance, significantly outperforming the conventional method in “de-masking” process deviations. The robust framework maintains higher accuracy and AUC levels even at extreme contamination stages of 30% to 40% outliers, where traditional charts typically fail. A practical application to cement quality data further substantiated these findings, as the robust chart successfully identified 14 out-of-control signals (comprising the mean, variability, and simultaneous shifts), whereas the conventional chart detected none. These results indicate that the MRCD-based Max-Half-Mchart offers a more reliable and responsive quality monitoring system for complex industrial datasets. Full article

Objectives: This study aimed to investigate the usefulness and safety of sentinel lymph node (SLN) mapping in comparison to other types of lymph node dissection in patients with uterine corpus cancers. Methods: Retrospective data from 161 patients subjected to uterine corpus cancer staging with SLN mapping with indocyanine green (ICG) dye were collected. Results: SLN procedure was associated with a complication rate of 0%, a median number of dissected lymph nodes of 2 (range 0–13), and a median hospitalization following surgery of 5 (range:2–23) days. Systemic lymphadenectomy and one-sided pelvic lymph node resection were associated with the highest percentage of complications (12% and 25%; p = 0.0030), while the post-surgery course was uneventful for the selective lymphadenectomy group and SLN. Complication rates were the highest in patients with obesity and severe obesity (5.1% and 9.1%, respectively). The number of lymph nodes resected dropped numerically with increasing BMI. Successful ICG injection and SLN mapping were significantly more frequent in SLN procedures. Conclusions: Our study showed that SLN mapping was characterized by a low complication rate and short hospitalization following surgery, and obesity appeared to be related to a higher complication rate. Tailored surgical strategies and individualized patient selection are crucial for the success of SLN mapping; therefore, factors associated with successful SLN mapping with ICG need further exploration. Full article