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A new class of functions is defined by expressing analytic characterizations of starlike function involving logarithm. To make this study more versatile, we redefine and study the class involving an operator associated with $q$ -hypergeometric function. Estimates of the initial coefficients and Fekete-Szegő inequality of the functions, which belong to the defined function class, are our main results.

While academic literature offers various models of tourism competitiveness, their specific application to sports tourism in the unique contexts of developing nations remains fragmented and under-theorized, with no prior systematic mapping of evidence from these settings. This scoping review protocol outlines a systematic methodology to comprehensively map and synthesize the existing literature on the competitiveness of sports tourism destinations, with a specific focus on evidence from developing countries. The primary research question is: What are the key determinants and conceptualizations of competitiveness for sports tourism destinations in developing countries? Guided by the Joanna Briggs Institute (JBI) Scoping Review Framework and reported per PRISMA-ScR guidelines, the review will employ inclusive eligibility criteria (Population: sports tourism destinations; Concept: competitiveness factors; Context: developing countries), search academic and grey literature sources, extract data on definitions, determinants, models, challenges, strategies, and evidence, and conduct inductive thematic analysis to identify patterns and gaps. The findings aim to consolidate existing knowledge, identify key determinants and gaps, and establish a foundational understanding to guide future research, policy formulation, and industry practices in the sports tourism domain, particularly for resource-constrained settings.

Optical Genome Mapping (OGM) is an emerging technology in clinical laboratories for identifying copy number and structural variations in the DNA of patients with haematological malignancies. A critical initial step is the isolation of ultra-high molecular weight genomic DNA (UHMW gDNA), which typically requires 1.5 million white blood cells. However, this cell number is not always achievable in clinical practice due to various limitations. For instance, diagnostic analysis of multiple myeloma (MM) is should be performed on CD138-positive cells derived from bone marrow aspirates (BMA), where both the sample volume and the number of CD138-positive cells This method describes a customized protocol which enables isolation of UHMW gDNA starting from as few as 500 000 cells, while still resulting in DNA of sufficient quality and quantity to perform OGM and collect at least 1500 Gbp of data.

Depression affects over 280 million people worldwide, with neurological patients particularly prone to medication-induced episodes. Conventional diagnostic approaches rely on subjective evaluations, limiting reproducibility and consistency in clinical settings. This study proposes an interpretable deep learning framework for objective depression detection using EEG signals. We hypothesize that combining EEG-based features with explainable artificial intelligence can provide both high accuracy and transparency in diagnosis. The model was trained on EEG data from 232 neurological patients, achieving 98 % classification accuracy. Interpretability was enhanced through SHAP (SHapley Additive exPlanations) analysis, which identified clinically meaningful EEG biomarkers such as the delta/alpha ratio and theta band power. This paper highlights the following contributions: Integration of EEG features with a lightweight deep learning model for depression detection High diagnostic accuracy achieved while maintaining interpretability for clinicians An objective tool that is compatible with existing EEG infrastructure, supporting clinical adoption These results show that our framework bridges predictive performance with interpretability, offering a transparent and scalable EEG-based diagnostic tool. We conclude that this approach can complement clinical decision-making, reducing dependence on subjective evaluation and enabling more consistent, data-driven mental health care.

Image Captioning is a crucial task that enables systems to generate descriptive sentences for visual content. Though image captioning systems bloom at the intersection of Computer Vision and Natural Language Processing, these models act mostly as black boxes offering little or no insight into how captions are derived. We present a novel explainable image captioning framework that integrates a Convolutional Neural Network encoder with a Transformer decoder. Attention-based heatmaps are used to explain the visuals offering transparency in the decision making process. The method evaluates captioning quality and interpretability on the MS COCO dataset using BLEU, METEOR, CIDER and SPICE. The method enhances the trustworthiness and transparency, making it reliable for applications like healthcare, education, security, surveillance and forecasting. A reproducible method for integrating visual explainability into image captioning exploring transformer decoder attention maps. The method contributes to the growing body of eXplainable AI (XAI) by addressing the transparency gap in vision-language models Balance performance with interpretability paving the way for more transparent and trustworthy AI systems.

Antiepileptic drugs are considered contaminants of emerging concern in water and are resistant to conventional wastewater treatment processes. Therefore, their presence has been detected in surface waters, and their elimination/degradation requires effective treatment methods. In this research, ultrasound-based methods (e.g., sonolysis, sono-Fenton, and sono-photo-Fenton) were addressed in the degradation of antiepileptic drug primidone at laboratory scale. A high-frequency ultrasound (at 578 kHz and 20.4 W) was applied. Then, Fe²⁺ ions (5 mg l^-1) and a UVA lamp (4 W) were added to the sonochemical reactor. After 75 min of treatment, the sono-photo-Fenton method showed better degradation efficiency (93 %) than the sono-Fenton (83 %) and sonolysis (62 %) methods. Finally, the effectiveness of the degradation method by sono-photo-Fenton was tested in simulated pharmaceutical wastewater, degrading 72 % of primidone at 75 min of treatment, indicating matrix effect plays a role in the degradation (which could be a potential application of ultrasound hybridized with the photo-Fenton process).•Three ultrasound-based treatment methods were applied to degrade primidone in water.•The sono-photo-Fenton method degraded 93 % of primidone during 75 min of treatment.•The matrix influence on primidone degradation by sono-photo-Fenton was evaluated.

This article presents the development of LOSARI, a novel R-based statistical software designed to facilitate students' self-regulated learning (SRL) in statistics courses. LOSARI can be accessed online without installation and allows students to perform statistical analyses through a point-and-click interface without coding. It integrates several innovative features: interactive video tutorials embedded in the analysis environment, real-time error notifications that guide students in correcting mistakes, and automatic interpretation of results to support independent learning. The software was validated through a student satisfaction survey using the End-User Computing Satisfaction (EUCS) model, which indicated that most users had positive perceptions of LOSARI and found it effective for learning statistics outside the classroom. Possible extensions and enhancements are also discussed.•A structured process for developing LOSARI as an R-based statistical learning tool.•Introduction of key features, including interactive video tutorials, real-time error notifications, and automatic interpretation.•Validation method through student satisfaction measurement and comparison with manual statistical coding.