Search Results (2,453)

Virtual reality heritage experiences can be understood as multisensory interaction systems, yet how auditory, haptic, and gestural cues combine at the system level to shape electronic word-of-mouth (eWOM) intention remains insufficiently understood. Addressing this problem from a configurational systems perspective, this study applies fuzzy-set qualitative comparative analysis (fsQCA) to five auditable interaction cues (acoustic clarity, rhythmic drive, vibrotactile actuation level, gesture complexity, and compound gesture frequency) across a set of widely used VR cultural heritage applications. The results identify two sufficient system-level pathways to high eWOM intention: a rhythm-driven, low-burden pathway and a coordination-driven pathway characterized by clearer audio, stronger rhythmic structure, and tighter haptic and gestural action closure. Low eWOM intention is most consistently associated with weak cue interpretability, limited temporal drive, or unbalanced stimulation patterns, suggesting that isolated enhancement of single channels does not reliably translate into downstream sharing intentions. These findings reposition VR heritage design as a problem of configuring coherent multisensory interaction systems rather than maximizing individual stimuli. The study contributes a bounded, case-comparative account of how auditable cue bundles shape eWOM intention and offers system design guidance for resource-sensitive multisensory coordination in VR heritage applications. Full article

Transfer learning offers a practical way to improve neural machine translation in low-resource settings, but its effectiveness depends on both the choice of transfer language and the amount of target-language data available for adaptation. In this study, we examine these factors specifically for Polish–English translation using mBART. We evaluate Czech, Russian, and German as parent languages and extend the analysis with a combined Slavic parent model trained on Czech and Russian. The models are compared across 0-shot, 10-shot, 100-shot, 1k-shot, and 10k-shot settings. Within this Polish–English mBART setting, Czech provides the strongest zero-shot performance, while Russian and German improve substantially as Polish fine-tuning data increases and achieve the strongest results at higher shot levels. The paper therefore analyzes selected transfer-language configurations rather than a formally measured similarity variable. The results suggest that, in this setup, transfer-language choice matters most when no Polish supervision is available, whereas larger amounts of Polish data can compensate for weaker initial transfer alignment. Full article

Classical covert wireless communication is commonly formulated under a noise-only null hypothesis, in which a warden detects the presence of a transmission. In shared-spectrum settings with persistent legitimate traffic, however, a warden may already observe legitimate traffic and may therefore test whether an observation is statistically consistent with a legitimate signal class. Motivated by this regime, this paper studies mimicry covert communication in the post-demodulation OFDM resource-grid domain. A normalizing flow is trained on legitimate IEEE 802.11a NonHT-Data resource-grid observations, and covert bits are embedded by shared-key latent sign modulation, whose inner coordinatewise sign-flip rule preserves the standard Gaussian prior and thus the learned legitimate distribution under the ideal flow model. To improve message recovery under observation-domain perturbations, the framework further combines this inner embedding with a two-stage, two-state robustness-aware coordinate selector and a CRC-Polar outer code with reliability-weighted soft decoding. Experiments show that the coded design substantially improves message recovery over an uncoded repeated-sign baseline while keeping Willie-side discriminability low under both classifier-based and flow-density typicality tests. The study focuses on the learned post-demodulation resource-grid observation domain and leaves full over-the-air RF-chain validation for future work. Full article

Tuberculosis remains one of the leading infectious causes of morbidity and mortality worldwide, disproportionately affecting women of reproductive age, particularly in low- and middle-income countries. Tuberculosis during pregnancy represents a major clinical challenge, as physiological and immunological changes associated with pregnancy may obscure symptoms, delay diagnosis, and contribute to adverse maternal and perinatal outcomes. This narrative review provides an updated and clinically oriented overview of tuberculosis during pregnancy, with particular emphasis on diagnostic challenges, imaging strategies, microbiological testing, maternal–fetal complications, and therapeutic management. Key topics include symptom-based screening, tuberculin skin test and interferon gamma release assays, as well as molecular diagnostic methods such as GeneXpert Mycobacterium tuberculosis/Rifampicin (MTB/RIF) and Xpert MTB/RIF Ultra, chest radiography, computed tomography, and emerging biomarkers. We also discuss the impact of tuberculosis on pregnancy outcomes, including prematurity, low birth weight, maternal morbidity, and neonatal complications, as well as the particular challenges posed by human immunodeficiency virus HIV coinfection and multidrug-resistant tuberculosis. Current treatment strategies, preventive approaches, postpartum care, neonatal management, and Bacille Calmette–Guérin vaccination are reviewed in light of contemporary evidence and international recommendations. Finally, we highlight practical diagnostic algorithms, current evidence gaps, and priorities for future research aimed at improving maternal and neonatal outcomes in both high- and low-resource settings. Full article

This paper introduces CMA-YOLO, a network that integrates multi-scale features with dual attention mechanisms to address weak feature representation, low detection accuracy, and loss of fine-grained details in deep networks for wind turbine blade surface defect detection. First, we construct the C2MSA module by designing a Multi-scale Feature-enhanced Attention Convolution Mix (MS-ACmix) based on ACmix and embedding it into the C2PSA block. This lets the network capture local and global contextual features, strengthening multi-scale target recognition and lowering missed detections. Second, we devise a Monte Carlo Dual Attention (MCDA) mechanism combining random sampling with dual attention. This approach retains the regularization benefits of the Monte Carlo method while leveraging dual attention selection, enabling improved detection accuracy with low computational cost. Finally, we substitute the original downsampling layers in the backbone and neck with the ADown module. This lightweight design, together with efficient feature extraction and fusion, reduces fine-grained detail loss and improves defect detection capability. Quantitative results reveal that, compared to YOLO11n, CMA-YOLO yields improvements of 3.4% in mAP@0.5, 6.1% in mAP@0.5:0.95, and 8.8% in recall, with a 0.7 GFLOPs reduction in computational cost, thus validating the proposed algorithm. Overall, CMA-YOLO provides a lightweight and effective approach for inspecting blade surface defects on wind turbines operating in resource-limited settings. Full article

Healthy, inclusive, and environmentally supportive educational settings are increasingly recognised as relevant to children’s development, well-being, and equity. However, evidence on the physical environment in early childhood education remains fragmented across outdoor spaces, indoor spatial organisation, indoor environmental quality, materials, and contaminant-related conditions. This systematic review aimed to synthesise current evidence on the relationship between the physical environment of early childhood educational settings and multidimensional indicators of child well-being. The protocol was registered in PROSPERO, and the review followed PRISMA 2020 guidelines. Searches were conducted in Web of Science Core Collection, Scopus, ERIC, and APA PsycInfo. Methodological quality and risk of bias were assessed using ROBINS-I and JBI critical appraisal tools. Eighteen studies were included. Of these, 10 focused on outdoor spaces and schoolyards, five on indoor spaces and spatial organisation, and three on indoor environmental quality, materials, or contaminants. The findings suggest four main interpretive patterns: (i) expanding opportunities for participation through functionally diverse areas and materials; (ii) shaping coexistence and interaction through access to and distribution of resources; (iii) supporting sensory regulation; and (iv) sustaining environmental health and habitability. Overall, more favourable settings were associated with better indicators of activity and play, interaction and coexistence, and involvement and regulation. For indoor environmental quality studies, however, the evidence was mainly indirect, referring to environmental-health, comfort, exposure, or habitability indicators rather than direct child-level well-being outcomes. The certainty of the evidence was moderate to low due to methodological limitations, particularly confounding and selection bias in non-randomised intervention studies and imprecision in the measurement of environmental exposure in several cross-sectional studies. The findings may inform cautious reflection on spatial design, educational practice, and policy, but stronger recommendations require more robust study designs, reproducible exposure metrics, clearer distinction between direct and indirect well-being-related indicators, and comparable outcome measures. Full article

Background: Mitochondrial genomes provide valuable information on evolutionary relationships among organisms and on the selective pressures acting on energy metabolism, increasing their relevance in ecological and environmental genomics studies. Haemulon plumierii is a reef-associated fish distributed throughout the Gulf of Mexico and Caribbean Sea and has been proposed as a bioindicator species within the Mesoamerican Reef System. Methods: In this study, we present a high-quality mitochondrial genome of H. plumierii from the southeastern coast of Mexico generated using PacBio HiFi long-read sequencing. Results: The circular mitogenome is 16,823 bp long and contains the complete set of 37 canonical mitochondrial genes, including 13 protein-coding genes, 22 tRNAs, two rRNAs, and one control region (D-loop). The gene order, strand orientation, and tRNA secondary structures were consistent with the conserved vertebrate mitochondrial architecture. Comparative analyses with closely related haemulid species revealed conserved nucleotide composition patterns, negative GC skew values, strong AT enrichment within the D-loop, and highly conserved mitochondrial synteny. Phylogenetic reconstruction based on complete mitochondrial genomes placed H. plumierii firmly within the Haemulon clade. Selective pressure analyses revealed pervasive purifying selection acting on mitochondrial protein-coding genes, supported by low dN/dS ratios, high amino acid identity, constrained nucleotide diversity in cytochrome oxidase genes, and conserved codon usage patterns shaped primarily by AT-driven mutational bias. Pairwise genetic distance analyses further supported moderate interspecific divergence within Caribbean Haemulon species. Conclusions: Overall, the mitogenomic resource generated here provides an important evolutionary and functional framework for future phylogenetic, ecological, and environmental genomics studies in Caribbean reef fishes. Full article

Online hate speech and abusive language pose a growing challenge for content moderation, especially in multilingual settings and for low-resource languages such as Lithuanian. This paper investigates to what extent modern multilingual sentence embedding models can support accurate hate speech detection in Lithuanian, Russian, and English, and how their performance depends on downstream modeling choices and feature dimensionality. We introduce LtHate, a new Lithuanian hate speech corpus derived from news portals and social networks, and benchmark six modern multilingual encoders (gemma, qwen, bge, snow, jina, and e5) on LtHate, RuToxic, and EnSuperset using a unified Python pipeline. For each embedding type, we train both a one-class histogram-based anomaly detector (HBOS) and a two-class gradient-boosted tree ensemble (CatBoost), with and without Principal Component Analysis (PCA) compression to 32-dimensional feature vectors. Across all datasets, two-class supervised models consistently and substantially outperform one-class anomaly detection, with the best configurations achieving up to 78.8% accuracy (Kappa 0.58, AUC ROC 0.87) in Lithuanian (jina), 92.2% accuracy (Kappa 0.77, AUC ROC 0.97) in Russian (e5), and 76.9% accuracy (Kappa 0.48, AUC ROC 0.86) in English (e5). PCA compression deteriorates the discriminative power of CatBoost only slightly, with much more negative impact for the HBOS model. These results demonstrate how modern multilingual sentence embeddings combined with gradient-boosted decision trees provides robust machine learning solutions for multilingual hate speech detection applications. Full article

Urban farming microinterventions are small, place-based cultivation projects that operate under severe spatial and resource constraints yet can generate social learning and locally embedded resilience. The present paper examines how design decisions shape the effectiveness of such interventions through three design-led case studies: Blooming Structure (2018, Warsaw), a temporary hydroponic “laboratory” installation; Micro-cultivation (2018, Warsaw), a shopfront vertical demonstration farm; and Micro-cultivation 2 (2019), modular “cultivation furniture” for interiors and exhibition deployment. The analysis combines project documentation with practice-based observations and applies five interpretive dimensions: spatial fit, technical feasibility, communicative legibility, replicability, and social programming. Findings highlight that successful microinterventions align legible cultivation infrastructure with high visibility, accessibility and participatory formats that support skills transfer and copying-based scaling. Rather than offering universal claims about urban agriculture outcomes, the paper provides a reference set of design principles that may inform similar micro-scale interventions in other contexts, subject to local constraints. Limitations include the small sample size and the concentration on projects from Poland. Practically, the findings can support designers, municipalities, and civic organisations in structuring microinterventions as replicable, low-threshold prototypes and in aligning technical systems with maintenance capacity and public engagement. Full article

The rapid proliferation of online misinformation poses significant risks to democratic processes and public decision-making. However, existing machine learning and deep learning approaches often rely on large annotated datasets and exhibit limited robustness under severe class imbalance and low-resource conditions, particularly in Spanish-language contexts. To address this, this study proposes an interpretable and robust framework for misinformation detection under such constraints. A unified, linguistically grounded and data-centric pipeline is developed, integrating structured lexical, syntactic, and semantic features with synthetic minority augmentation, class-balanced ensemble learning, autoencoder-based representation learning, and active learning under data scarcity. Importantly, the framework systematically evaluates the interaction between these components within a reproducible experimental setting. Results demonstrate that the proposed approach achieves consistent improvements in macro-averaged F1 and minority-class recall compared to baseline models, while reducing performance variance across folds. Ensemble and augmentation strategies provide the most stable configurations, enhancing the detection of underrepresented classes. Moreover, the use of interpretable linguistic features allows predictions to be associated with discourse-level patterns, improving transparency. Consequently, the framework offers a reproducible, computationally efficient, and interpretable solution for misinformation detection in low-resource environments, supporting practical deployment and future multilingual extensions. Importantly, this study provides the first systematic analysis of the interaction between linguistic representations and imbalance mitigation strategies under extreme data scarcity. Full article

Cervical cancer remains a major global health challenge, particularly in low- and middle-income countries, where access to screening, vaccination, and timely treatment may be limited. Cervical cytology has played an important historical role in prevention, but it is labor-intensive, time-consuming, and subject to observer variability and limited sensitivity. In many contemporary screening programs, HPV testing is now used as the primary screening test, while cytology is used mainly for the triage of HPV-positive women. In recent years, artificial intelligence (AI), particularly deep learning (DL), has shown considerable potential in medical image analysis and computer-aided diagnosis. This review summarizes current applications of AI in cervical cytology and related diagnostic workflows, including automated and assisted slide screening, liquid-based cytology, the triage of equivocal or HPV-positive cases, and colposcopy support. Across these settings, AI-assisted systems may improve efficiency, standardization, and diagnostic consistency, and may reduce workload in resource-constrained environments. However, the evidence is heterogeneous, and important challenges remain, including the need for large and diverse datasets, prospective validation, regulatory approval, digital infrastructure, workflow integration, and the resolution of ethical and legal issues. AI should therefore be regarded as a promising adjunct to human expertise rather than a replacement in cervical cytology and related clinical diagnostic pathways. Full article

Background: Orbital rhabdomyosarcoma (RMS) is the most common primary pediatric malignant orbital tumor, typically curable with chemotherapy and radiation. Data regarding MRI chemotherapy response and long-term ophthalmologic outcomes remain limited in non-cooperative-group settings. Methods: We retrospectively reviewed children with primary orbital RMS treated at King Hussein Cancer Center (2002–2025) with vincristine, actinomycin-D, and cyclophosphamide (VAC). Pre-local-control MRI responses were classified as complete (CR), partial (PR), stable/minor (SD/MR), or progressive disease (PD). Survival and ophthalmologic outcomes were analyzed. Results: Twenty-two patients (median age 5.6 years) were included. All had localized disease (77% low-risk). All received VAC; 20 (91%) received radiotherapy (median 45 Gy). Pre-radiotherapy MRI showed 8 (36%) CR and 11 (50%) PR. Four patients (18%) died. Five-year event-free survival (EFS) and overall survival (OS) were 73% and 84%, respectively. Cataracts developed in 45% of the cohort (50% of irradiated patients) at a median of 39.1 months (range 9.4–95.1). At last assessment, visual acuity was good in 60%, moderate in 25%, and severely impaired in 15%. Conclusions: Excellent survival in orbital RMS is achievable in resource-stratified settings. Induction MRI progressive disease (PD) was associated with poor outcomes in this cohort and may represent an early prognostic signal warranting further validation in larger studies. Furthermore, the documented maximum cataract latency of 95 months suggests that the standard 5-year surveillance window is insufficient. These findings support extending ophthalmologic surveillance beyond the standard 5-year window. We propose, based on our retrospective institutional data, a 10-year functional survivorship framework. Full article

Recurrent pericarditis (RP) remains challenging, especially in tuberculosis (TB)-endemic regions where empirical anti-TB therapy is often unnecessarily prolonged. We report a 35-year-old woman with three RP episodes over six months, presenting with pleuritic chest pain, elevated inflammatory markers, and moderate-to-large pericardial effusion. Extensive infectious (including TB), autoimmune, and malignancy workups were negative. Cardiac magnetic resonance revealed persistent pericardial late gadolinium enhancement despite clinical remission. Whole-exome sequencing identified a heterozygous MEFV c.442G>C (p.Glu148Gln) variant, suggesting an autoinflammatory predisposition. Although the patient finally achieved sustained symptom-free status for six months on a standardized low-dose colchicine regimen, still over 10% of patients have recurrent symptoms receiving colchicine in addition to conventional anti-inflammatory therapy with aspirin or ibuprofen. This case highlights the shifting paradigm from an infection-centered to an autoinflammatory framework for RP in TB-endemic countries, underscores the role of MEFV variants in idiopathic recurrent pericarditis, and illustrates the real-world gap between genetic insights and therapeutic accessibility to IL-1 inhibitors in resource-limited settings. Full article

With the 2030 deadline for the United Nations Sustainable Development Goals (SDGs) approaching, there is a growing global urgency to identify innovative, scalable, and inclusive AI-based or AI-enabled solutions capable of accelerating progress across sectors. Yet the benefits of AI remain unevenly distributed, particularly in low-resource settings where limited infrastructure, cost barriers, and unequal access to skills constrain adoption. This paper explores how Tiny Machine Learning (TinyML)—a low-power, low-cost edge AI paradigm—offers a concrete technological pathway aligned with the principles of Frugal AI, providing accessible, energy-efficient, and context-adapted tools for sustainable development. We evaluate how participatory citizen science, when combined with TinyML, enables communities to co-create AI applications that address locally defined challenges in environmental monitoring, agriculture, and public health. Drawing on early outcomes from workshops, collaborative projects, and innovation competitions, the paper examines how TinyML-enabled participatory approaches cultivate technical skills, stimulate grassroots entrepreneurship, and generate prototypes suited to low-resource environments. Using a qualitative multiple-case study of 50 participatory TinyML initiatives across 22 countries, we analyse how frugal edge-AI practices support skills formation, prototype development, and early entrepreneurial engagement. The analysis identifies the pedagogical, technical, and institutional frameworks that support successful participatory AI initiatives, emphasizing open educational resources, cross-sector partnerships, and community-driven problem formulation. We introduce the Frugal Edge AI Lean Canvas to help innovators identify novelty, ethical implications, and measurable impact. TinyML-based participatory innovation offers a promising route for accelerating SDG progress by expanding who can create, deploy, and benefit from AI. Full article

This study investigated the effects of red grape pomace powder (GPP) on spontaneously fermented salamis produced from the meat of retired cows and young bulls of the Cinisara dairy breed. The use of GPP and meat from these animal categories was motivated by the valorization of low-commercial-value agri-food resources and the enhancement of sustainable local production chains. Plate count analyses showed typical fermentation dynamics, with lactic acid bacteria (LAB), coagulase-negative staphylococci, and yeasts reaching approximately 7 log CFU/g, and confirmed the absence of major foodborne pathogens. Illumina sequencing further characterized the bacterial community, identifying Latilactobacillus as the dominant genus at the end of ripening, with relative abundance (RA) of up to 65% in GPP-enriched trials. Physicochemical analyses showed progressive changes during ripening, including weight loss, pH decrease, color development, and increased proteolysis. GPP supplementation contributed to the stabilization of a*, chroma, and hue values, while reducing lightness during ripening. Oxidative stability measurements showed that GPP derived polyphenols effectively limited oxidative reactions, especially secondary lipid oxidation. GPP also modulated the volatile profile by increasing ester formation and introducing plant-derived compounds. Sensory evaluation revealed higher color intensity and aroma in enriched salamis, along with higher bitterness and lower structural homogeneity, especially in those produced from retired cows. Consumer surveys conducted in two retail settings indicated strong interest in this innovation, with over 80% of respondents willing to pay a 10–20% price premium. Overall, GPP emerges as a promising functional ingredient for enhancing, diversifying, and valorizing fermented salamis produced from dairy cattle meat, supporting both product innovation and sustainability-oriented strategies. Full article