Search Results (12,939)

This paper, positioned within the study of immigrant language varieties in Canada, examines mismo si (‘even if’), an understudied grammatical feature of Spanish in contact with French in Montreal. The phenomenon is analyzed cross-linguistically and within the theoretical framework of Distributed Morphology, approaching it from two complementary perspectives: (1) a sociolinguistic analysis of Spanish-French bilinguals in Montreal, and (2) a formal investigation of its structural properties. Mismo si, equivalent to Standard Spanish aunque and incluso si (‘even though’), is a lexical transfer from the French conjunction même si and conveys a concessive meaning. The evidence shows that this structure constitutes a distinctive linguistic adaptation to the bilingual sociolinguistic environment of Montreal. The article is organized in two sections: the first presents a qualitative and quantitative analysis of mismo si occurrences in the COLEM corpus (Corpus Oral de la Lengua Española en Montreal), while the second offers a formal examination of this contact-induced structure. Full article

In this study, linear and nonlinear parametric models (M1–M6) were jointly evaluated alongside machine learning (ML)-based approaches to achieve reliable and interpretable prediction of the penetration rate (ROP) of tunnel boring machines (TBMs). The analyses incorporate key geomechanical and structural variables, including the brittleness index (BI), uniaxial compressive strength (UCS), mean spacing of weakness planes (DPW), the angle between the tunnel axis and weakness planes (α), and Brazilian tensile strength (BTS). The coefficients of the parametric models were optimized using the Differential Evolution (DE) algorithm. Variable effects were systematically examined through Jacobian-based elasticity analysis under both original and standardized data scenarios. The results indicate that the M6 model, which explicitly incorporates interaction terms, delivers superior predictive accuracy and a more balanced, physically meaningful representation of variable contributions compared to widely used parametric formulations reported in the literature. While the dominant influence of BI and UCS on ROP is consistently preserved across all models, the indirect contributions of variables such as DPW and BTS are more clearly revealed in M6 owing to its interaction-based structure. Model performance improves systematically with increasing complexity, with the coefficient of determination (R²) rising from 0.62 for M1 to 0.69 for M6. Relative to the linear model, M6 achieves a 9.07% reduction in RMSE and a 10.48% increase in R², while providing additional improvements of 2.47% in RMSE and 2.37% in R² compared with the closest competing model. ML-based variable importance analyses are largely consistent with the parametric findings, highlighting BI and α in tree-based models, and UCS and α in SVM and GAM frameworks. Notably, the GAM exhibits the highest predictive performance under both data scenarios. Overall, the integrated use of parametric and ML approaches establishes a robust hybrid modeling framework that enables highly accurate and engineering-interpretable prediction of TBM penetration rate. Full article

Sosnovsky’s hogweed (Heracleum sosnowskyi Manden.) is an invasive plant species widely distributed across Eastern Europe and Russia that poses a serious threat to human health due to its pronounced phototoxic properties. Contact with the plant sap followed by exposure to solar ultraviolet (UV) radiation frequently results in phytophotodermatitis, which is characterized by erythema, blistering, ulceration, and persistent hyperpigmentation. The development of these photochemical injuries—most notably furanocoumarins—act as potent photosensitizers and induce cellular and DNA damage upon UV activation. This review provides an integrated overview of the geographical spread and invasiveness of H. sosnowskyi, the chemical composition of its biologically active metabolites, and the molecular mechanisms underlying hogweed-induced skin injury. Particular emphasis is placed on the photochemical transformations of furanocoumarins, including psoralens and their photooxidation products, such as 1,2-dioxetanes, which generate reactive oxygen species and DNA crosslinks. In addition, the review examines other compounds derived from hogweed biomass—including furan derivatives, aromatic compounds, fatty acids, sterols, and their oxidative products—that may contribute to phototoxic and cytotoxic effects. Clinical manifestations of hogweed-induced burns, their classification, symptomatology, and current therapeutic approaches are critically discussed, highlighting the absence of standardized treatment guidelines. Rather than serving as a purely clinical or botanical survey, this review frames Sosnovsky’s hogweed injury as a solar-light-activated photochemical hazard, tracing the sequence from environmental sunlight exposure through molecular photochemistry to biological tissue damage. By integrating chemical, biological, and dermatological perspectives, the review aims to clarify injury mechanisms and support the development of more effective preventive and mitigation strategies under real-world exposure conditions. Full article

Background/Objectives: Acne vulgaris is a chronic inflammatory skin disorder characterized by sebaceous gland hyperactivity, follicular hyperkeratinization, proliferation of Cutibacterium acnes (C. acnes), and subsequent inflammation. The development of effective therapeutics necessitates reliable preclinical models that accurately replicate key pathological aspects of the human disease. Methods: In this study, we established an inflammatory acne model in Wistar rats via the intradermal injection of live C. acnes into the ear pinnae and thoroughly characterized its temporal dynamics of the induced inflammation. Utilizing this model, we evaluated the protective efficacy of a whole-cell inactivated C. acnes vaccine (HI-C. acnes) formulated with adjuvants WS03 or MA107b. Results: Inflammation peaked between days 1 and 3 post-infection, manifesting as pronounced erythema, ear swelling, increased ear thickness, elevated bacterial load, and significant upregulation of pro-inflammatory cytokines (IL-6, IL-1β, and MCP-1). Histopathological examination revealed extensive neutrophil infiltration and microabscess formation, while immunohistochemistry confirmed localized overexpression of TNF-α, IL-1β, and CXCL1 within the lesional tissue. Inflammatory manifestations gradually subsided by day 5 and were fully resolved by day 7, which coincided with complete bacteria clearance and normalization of pro-inflammatory cytokine levels. Vaccinated rats developed significantly higher C. acnes-specific IgG titers and, upon challenge, exhibited markedly reduced ear swelling, diminished bacterial burden, and suppressed expression of key inflammatory mediators compared to control groups, indicating that vaccine-induced protection is associated with humoral immunity. Conclusions: Collectively, our standardized and quantifiable rat ear inflammation model provides a robust platform for mechanistic investigations and preclinical assessment of novel anti-acne vaccines and therapeutic agents. Full article

Background: Subclinical respiratory impairment among non-smokers in regions with haze-affected regions is still under-recognized, particularly in low- and middle-income countries (LMICs). This study assessed the prevalence of subclinical respiratory impairment among non-smoking adults and examined its determinants and associations with health-related quality of life (HRQoL) in Chiang Mai, Thailand. Methods: In this cross-sectional study, 244 non-smoking adults (18–65 years) from three rural districts underwent standardized spirometry and completed the Thai WHOQOL-BREF-26. Subclinical impairment was defined as an FEV₁/FVC < 0.70 or FVC < 80% predicted in the absence of symptoms. Demographic, occupational, and environmental information was obtained through structured questionnaires. Statistical analyses included non-parametric tests, univariate linear regression, and logistic regression. Results: A total of 37 participants (15.2%) had subclinical respiratory impairment. No demographic, occupational, or environmental factors such as sex, age, BMI category, agricultural work, marital status, and self-reported pollution exposure were found to be independently linked to impaired lung function. There was no correlation between spirometry indices and any WHOQOL-BREF domain. Elderly participants (>50 years) reported a higher level of physical and psychological HRQoL. Those with a higher Body Mass Index (BMI) were more likely to have a lower environmental quality of life. Farmers reported a better QoL, while women reported a lower QoL than men. Conclusions: Subclinical respiratory impairment occurs frequently in non-smoking rural adults exposed to haze pollution in Chiang Mai, and isn’t presently assessed by general HRQoL instruments. These findings support early spirometry screening for asymptomatic adults in polluted regions, as well as more stringent air cleanliness strategies to prevent the evolution towards overt respiratory pathology. Full article

This study examines the conceptualization of sustainability in heritage tourism in Saudi Arabia following the introduction of the Saudi Vision 2030 program and the country’s opening to tourism in 2019, both of which aim to diversify the economy and promote cultural heritage. A scoping review methodology based on the Arksey & O’Malley framework has been adopted; data were charted according to the Joanna Briggs Institute (JBI) charting method based on the PRISMA-ScR reporting protocol. Publications from 2019 to 2025 were systematically collected from the database and manual research, resulting in 25 fully accessible studies that met the inclusion criteria. Data were analyzed thematically, revealing six main areas of investigation, encompassing both sustainability outcomes and cross-cutting implementation enablers: heritage conservation and tourism development, architecture and urban planning, policy and governance, community engagement, marketing and technology, and geoheritage and environmental sustainability. The findings indicate that Saudi research in this field is primarily qualitative, focusing on ecological aspects. The studies reveal limited integration of social and technological dimensions, with significant gaps identified in standardized sustainability indicators, longitudinal monitoring, policy implementation, and digital heritage tools. The originality of this study lies in its comprehensive mapping of Saudi heritage tourism sustainability research, highlighting emerging gaps and future agendas. The results also provide a roadmap for policymakers, managers, and scholars to enhance governance policies, community participation, and technological integration, which can contribute to sustainable tourism development in line with Saudi Vision 2030 goals, thereby fostering international competitiveness while preserving cultural and natural heritage. Full article

Air quality management in greenhouses is critical to safeguarding plant health and occupational safety, yet conventional filtration methods often fall short in performance and sustainability. These enclosed environments are prone to the accumulation of bioaerosols, including fungi, bacteria, pollen, and dust particles, which can compromise crop productivity and pose health risks to workers. This review explores recent advancements in air filtration technologies for controlled environments such as greenhouses, where airborne particulate matter, bioaerosols, and volatile organic compounds (VOCs) present ongoing challenges. Special focus is given to the development of filtration media based on electrospun nanofibers, which offer high surface area, tunable porosity, and low airflow resistance. The use of biodegradable polymers in these systems to support environmental sustainability is examined, along with electrospinning techniques that enable precise control over fiber morphology and functionalization. Antimicrobial enhancements are discussed, including inorganic agents such as metal nanoparticles and bio-based options like essential oils. Essential oils, known for their broad-spectrum antimicrobial properties, are assessed for their potential in long-term, controlled-release applications through nanofiber encapsulation. Overall, this paper highlights the potential of integrating sustainable materials, innovative fiber fabrication techniques, and nature-derived antimicrobials to advance air filtration performance while meeting ecological and health-related standards. Full article

Background and Objectives: Sternocleidomastoid (SCM) dysfunction is commonly implicated in several musculoskeletal conditions. Accordingly, shear-wave elastography has been used to characterize SCM stiffness in asymptomatic and clinical cohorts. However, the only reproducibility study available reported limited reliability, so clinical interpretations should be made with caution. Therefore, this study revisits key methodological aspects of that protocol to assess intra-examiner reliability and includes two examiners with different levels of expertise to evaluate inter-examiner reliability. Materials and Methods: A longitudinal observational study was conducted, recruiting twenty-five asymptomatic participants. Two examiners with different experience levels participated in this study after following structured training. For each side, images were obtained in immediate succession in the sequence experienced–novice–experienced–novice (with side order randomized), using an ROI spanning full muscle thickness, stabilizing approximately 10 s before freezing to record Young’s modulus and shear-wave speed. Results: Inter-examiner agreement was good–excellent: single-measurement ICCs were 0.77–0.86, improving to 0.79–0.87 when averaging two trials, which also reduced the standard error of measurement (SEM) and minimal detectable changes (MDCs). Between-examiner mean differences were small and nonsignificant (p ≥ 0.068). Intra-examiner reliability was excellent (ICC ≈ 0.93–0.94) with small absolute errors. Precision was high (SEM ~5–6 kPa; 0.22 m/s), and MDCs were ~15–16 kPa and ~0.60 m/s, with no trial-to-trial bias (all p ≥ 0.311). Conclusions: The revised protocol showed excellent intra-examiner repeatability and good–excellent inter-examiner reliability with minimal bias. Averaging two acquisitions improved precision, while a single operator optimized longitudinal stability. Full article

The construction industry is undergoing a rapid digital transformation, with Digital Twins (DTs) and Extended Reality (XR) as two emerging technologies with great potential. Despite their potential, there are several challenges regarding DT and XR use in construction projects, including implementation barriers, interoperability issues, system complexity, and a lack of standardized frameworks. This study presents a systematic literature review (SLR) of DT and XR technologies—including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—in the construction industry. The study analyzes 52 peer-reviewed articles identified using the Web of Science database to explore thematic findings. Key findings highlight DT and XR applications for safety training, real-time monitoring, predictive maintenance, lifecycle management, renovation or demolition, scenario risk assessment, and education. The SLR also identifies core enabling technologies such as Building Information Modeling (BIM), Internet of Things (IoT), Big Data, and XR devices, while uncovering persistent challenges including interoperability, high implementation costs, and lack of standardization. The study highlights how integrating DTs and XR can improve construction by making it smarter, safer, and more efficient. It also suggests areas for future research to overcome current challenges and help increase the use of these technologies. The primary contribution of this study lies in deepening the understanding of DT and XR technologies by examining them through the lenses of their benefits as well as drivers for and challenges to their adoption. This enhanced understanding provides a foundation for exploring integrated DT and XR applications to advance innovation and efficiency in the construction sector. Full article

This study analyses the variety of the language used in textbooks for teaching Italian as a second/foreign language. These books use a language much closer to written than to spoken Italian and do not consider its varieties, providing examples and exercises with a “neutral” standard that speakers rarely use in everyday speech. The aim of this study is to provide a critical review of pronunciation sections in current L2 Italian textbooks, in the light of a renewed and growing interest in the study of the Italian language, not only by students with a migrant background in Italy, but also by second and third-generation emigrants who want to learn Italian to recover their roots. Thirty-two Italian textbooks were examined, considering some geolinguistic variables. The general tendency seems to be the introduction of some neo-standard Italian features. As far as the phonetic–phonological level is concerned, this is probably still insufficient because of the complexity of the Italian linguistic repertoire. Our analysis further suggests the inadequacy of notions such as (neo-)standard Italian for teaching purposes in the linguistic space of global Italian. Full article

Blockchain is increasingly applied in the agri-food sector to enhance traceability, data integrity, and accountability. However, its broader role in food system sustainability remains insufficiently characterized, particularly when examined against global megatrends shaping future agri-food transitions. This paper investigates how blockchain technology can reinforce sustainable, inclusive, and resilient food systems under the effect of major global megatrends. A structured literature review of peer-reviewed and industry sources was conducted to identify evidence on blockchain-enabled improvements in transparency, certification, and supply chain coordination. Complementary analysis of a curated dataset of European and international pilot implementations evaluated technological architectures, governance models, and demonstrated performance outcomes. Additionally, stakeholder-based foresight activities and scenarios representing alternative blockchain adoption pathways, developed within the TRUSTyFOOD project (GA: 101060534), were used to examine the interconnection between blockchain adoption and megatrends. Evidence from the literature and pilot cases indicates that blockchain can strengthen product-level traceability and improve verification of sustainability and safety claims. Cross-case analysis also reveals persistent constraints, including heterogeneous technical standards, limited interoperability, high deployment costs for smallholders, and governance risks arising from consortium-led platforms. Blockchain can function as an enabling digital layer for sustainable and resilient food systems and should be embedded in wider, participatory strategies that align digital innovation with long-term sustainability and equity goals in the agri-food sector. Full article

Industrial packaging systems exert substantial environmental pressures, including material resource depletion, greenhouse gas emissions, and the accumulation of post-consumer waste. As global supply chains expand and sustainability regulations intensify, demand for environmentally responsible packaging solutions continues to rise. This study evaluates the environmental footprint of industrial packaging by integrating recent developments in life cycle assessment (LCA), ecological footprint (EF) methodologies, material innovations, and circular economy models. The assessment examines the sustainability performance of conventional and alternative packaging materials, plastics, aluminum, corrugated cardboard, and polylactic acid (PLA). Findings indicate that although corrugated cardboard is renewable, it still presents a measurable environmental burden, with evidence suggesting that incorporating solar energy into production can reduce its footprint by more than 12%. PLA-based trays demonstrate promising environmental performance when sourced from renewable feedstocks and directed to appropriate composting systems. Despite these advancements, several systemic challenges persist, including ecological overshoot in industrial regions where EF may exceed local biocapacity limitations in waste management infrastructure, and significant economic trade-offs. Transportation-related emissions and scalability constraints for bio-based materials further hinder large-scale adoption. Existing research suggests that integrating sustainable packaging across supply chains could meaningfully reduce environmental impacts. Achieving this transition requires coordinated cross-sector collaboration, standardized policy frameworks, and embedding advanced environmental criteria into packaging design and decision-making processes. Full article

Objective: We examine whether a finite-range scalar–tensor modification of gravity can be simultaneously compatible with cosmological background data, galaxy rotation curves, and local/astrophysical consistency tests, while satisfying the luminal gravitational-wave propagation constraint ($c_T=1$) implied by GW170817 at low redshifts. Methods: We formulate the model at the level of an explicit covariant action and derive the corresponding field equations; for cosmological inferences, we adopt an effective background closure in which the late-time dark-energy density is modulated by a smooth activation function characterized by a length scale $\lambda$ and amplitude $ϵ$. We constrain this background model using Pantheon+, DESI Gaussian Baryon Acoustic Oscillations (BAOs), and a Planck acoustic-scale prior, including an explicit $\Lambda$CDM comparison. We then propagate the inferred characteristic length by fixing $\lambda$ in the weak-field Yukawa kernel used to model 175 SPARC galaxy rotation curves with standard baryonic components and a controlled spherical approximation for the scalar response. Results: The joint background fit yields ${\Omega }_m=0.293\pm 0.007$, $\lambda =7.{69}_{-1.71}^{+1.85}$$\mathrm{Mpc}$, and $H_0=72.33\pm 0.50$$\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$. With $\lambda$ fixed, the baryons + scalar model describes the SPARC sample with a median reduced chi-square of ${\chi }_{\nu }^2=1.07$; for a 14-galaxy subset, this model is moderately preferred over the standard baryons + NFW halo description in the finite-sample information criteria, with a mean $\Delta$AICc outcome in favor of the baryons + scalar model (≈2.8). A Vainshtein-type screening completion with $\Lambda =1.3\times {10}^{-8}$ eV satisfies Cassini, Lunar Laser Ranging, and binary pulsar bounds while keeping the kpc scales effectively unscreened. For linear growth observables, we adopt a conservative General Relativity-like baseline (${\mu }_0=0$) and show that current $f{\sigma }_8$ data are consistent with ${\mu }_0\simeq 0$ for our best-fit background; the model predicts $S_8=0.791$, consistent with representative cosmic-shear constraints. Conclusions: Within the present scope (action-level weak-field dynamics for galaxy modeling plus an explicitly stated effective closure for background inference), the results support a mutually compatible characteristic length at the Mpc scale; however, a full perturbation-level implementation of the covariant theory remains an issue for future work, and the role of cold dark matter beyond galaxy scales is not ruled out. Full article

Objective: This study aimed to compare the accuracy of two large language models (LLMs)—ChatGPT (version 3.5) and Google Gemini (formerly Bard)—in answering dental caries-related multiple-choice questions (MCQs) using a simulated student examination framework across seven examination lengths. Materials and Methods: A total of 125 validated dental caries MCQs were extracted from Dental Decks and Oxford University Press question banks. Seven examination groups were constructed with varying question counts (25, 35, 45, 55, 65, 75, and 85 questions). For each group, 100 simulations were generated per LLM (ChatGPT and Gemini), resulting in 1400 simulated examinations. Each simulated student received a unique randomized subset of questions. MCQs were answered by each LLM using a standardized prompt to minimize ambiguity. Outcomes included mean score, passing rate (≥60%), and performance differences between LLMs. Statistical analyses included independent t-tests, one-way ANOVA within each LLM, and two-way ANOVA examining interactions between LLM type and question count. Results: Across all seven examination formats, Gemini significantly outperformed ChatGPT (p < 0.001). Gemini achieved higher passing rates and higher mean scores in every examination length. One-way ANOVA revealed significant score variation with increasing exam length for both LLMs (p < 0.05). Two-way ANOVA demonstrated significant main effects of LLM type and question count, with no significant interaction. Randomization had no measurable effect on Gemini performance but influenced ChatGPT scores. Conclusions: Gemini demonstrated superior accuracy and higher passing rates compared to ChatGPT in all simulated examination formats. While both LLMs struggled with complex caries-related content, Gemini provided more reliable performance across question quantities. Educators should exercise caution in relying on LLMs for automated assessment or self-study, and future research should evaluate human–AI hybrid models and LLM performance across broader dental domains. Full article

The exponential growth of multimodal orthopedic data, ranging from longitudinal Electronic Health Records to high-resolution musculoskeletal imaging, has rendered manual analysis insufficient. This has established Large Language Models (LLMs) as algorithmically necessary for managing healthcare complexity. However, their deployment in high-stakes surgical environments presents a fundamental algorithmic paradox: while generic foundation models possess vast reasoning capabilities, they often lack the precise, protocol-driven domain knowledge required for safe orthopedic decision support. This review provides a structured synthesis of the emerging algorithmic frameworks required to build modern clinical AI assistants. We deconstruct current methodologies into their core components: large-language-model adaptation, multimodal data fusion, and standardized data interoperability pipelines. Rather than proposing a single proprietary architecture, we analyze how recent literature connects specific algorithmic choices such as the trade-offs between full fine-tuning and Low-Rank Adaptation to their computational costs and factual reliability. Furthermore, we examine the theoretical architectures required for ‘agentic’ capabilities, where AI systems integrate outputs from deep convolutional neural networks and biosensors. The review concludes by outlining the unresolved challenges in algorithmic bias, security, and interoperability that must be addressed to transition these technologies from research prototypes to scalable clinical solutions. Full article