Search Results (8,616)

In building measurement using terrestrial laser scanners (TLSs), acquired 3D point clouds (3DPCs) often contain significant reflection artifacts caused by reflective glass surfaces. Such reflection artifacts significantly degrade the performance of downstream applications. This study proposes a novel strategy, called GRASS, to remove these reflection artifacts. Specifically, candidate glass points are identified based on multi-echo returns caused by glass components. These potential glass regions are then refined through planar segmentation using geometric constraints. Then, we trace laser beam trajectories to identify the reflection affected zones based on the estimated glass planes and scanner positions. Finally, reflection artifacts are identified using dual criteria: (1) Reflection symmetry between artifacts and their source entities across glass components. (2) Geometric similarity through a 3D deep neural network. We evaluate the effectiveness of the proposed solution across a variety of 3DPC datasets and demonstrate that the method can reliably estimate multiple glass regions and accurately identify virtual points. Furthermore, both qualitative and quantitative evaluations confirm that GRASS outperforms existing methods in removing reflection artifacts by a significant margin. Full article

Objective: We analyzed nanoparticle regulation research to examine the evolution of regulatory frameworks, identify major thematic structures, and evaluate current challenges in the governance of rapidly advancing nanotechnologies. By drawing parallels with the historical development of radiation regulation, the study aimed to contextualize emerging regulatory strategies and derive lessons for future governance. Methods: A total of 9095 PubMed-indexed articles published between January 2015 and October 2025 were analyzed using text mining, keyword frequency analysis, and topic modeling. Preprocessed titles and abstracts were transformed into a TF-IDF (Term Frequency–Inverse Document Frequency) document–term matrix, and NMF (Non-negative Matrix Factorization) was applied to extract semantically coherent topics. Candidate topic numbers (K = 1–12) were evaluated using UMass coherence scores and qualitative interpretability criteria to determine the optimal topic structure. Results: Six major research topics were identified, spanning energy and sensor applications, metal oxide toxicity, antibacterial silver nanoparticles, cancer nano-therapy, and nanoparticle-enabled drug and mRNA delivery. Publication output increased markedly after 2019 with interdisciplinary journals driving much of the growth. Regulatory considerations were increasingly embedded within experimental and biomedical research, particularly in safety assessment and environmental impact analyses. Conclusions: Nanoparticle regulation matured into a dynamic multidisciplinary field. Regulatory efforts should prioritize adaptive, data-informed, and internationally harmonized frameworks that support innovation while ensuring human and environmental safety. These findings provide a data-driven overview of how regulatory thinking was evolved alongside scientific development and highlight areas where future governance efforts were most urgently needed. Full article

Underwater image enhancement remains challenging due to wavelength-dependent absorption, spatially varying scattering, and non-uniform illumination, which jointly cause severe color distortion, contrast degradation, and structural information loss. To address these issues, we propose UCS-Net, a degradation-aware dual-path framework that exploits the complementarity between global and local representations. A spatial color balance module first stabilizes the chromatic distribution of degraded inputs through a learnable gray-world-guided normalization, mitigating wavelength-induced color bias prior to feature extraction. The network then adopts a dual-branch architecture, where a hierarchical Swin Transformer branch models long-range contextual dependencies and global color relationships, while a multi-scale residual convolutional branch focuses on recovering local textures and structural details suppressed by scattering. Furthermore, a multi-scale attention fusion mechanism adaptively integrates features from both branches in a degradation-aware manner, enabling dynamic emphasis on global or local cues according to regional attenuation severity. A hue-preserving reconstruction module is finally employed to suppress color artifacts and ensure faithful color rendition. Extensive experiments on UIEB, EUVP, and UFO benchmarks demonstrate that UCS-Net consistently outperforms state-of-the-art methods in both full-reference and non-reference evaluations. Qualitative results further confirm its effectiveness in restoring fine structural details while maintaining globally consistent and visually realistic colors across diverse underwater scenes. Full article

The maritime transport sector’s reliance on fossil-based fuels remains a major contributor to global greenhouse gas emissions, underscoring the urgent need for sustainable alternatives such as marine biofuels. Thermochemical pyrolysis of biomass and plastic waste represents a promising route for producing renewable and recycled marine fuel feedstocks. This review provides an integrated analysis of the full production and upgrading chain, encompassing pyrolysis of lignocellulosic biomass and polymer-derived resources, catalytic upgrading, and qualitative evaluation of product distribution and yield trends. Particular emphasis is placed on montmorillonite-based catalysts as naturally abundant, low-cost, and environmentally benign alternatives to conventional zeolites. The review systematically examines the influence of key montmorillonite modification strategies, including acid activation, pillaring, and ion-exchanged, on acidity, textural properties, and catalytic performance in catalytic cracking and hydrodeoxygenation processes. The analysis shows that catalyst modification strongly governs the yield, selectivity, and reproducibility of biofuels. By adopting this integrated perspective, the review extends beyond existing works focused on isolated upgrading steps or zeolitic catalysts. Key research gaps are identified, particularly regarding long-term catalyst stability, deep deoxygenation of real bio-oils, and compliance with marine fuel standards. Full article

Cell nucleus segmentation in microscopy images is an initial step in the quantitative analysis of imaging data, which is crucial for diverse biological and biomedical applications. While traditional machine learning methodologies have demonstrated limitations, recent advances in U-Net models have yielded promising improvements. However, it is noteworthy that these models perform well on balanced datasets, where the ratio of background to foreground pixels is equal. Within the realm of microscopy image segmentation, state-of-the-art models often encounter challenges in accurately predicting small foreground entities such as nuclei. Moreover, the majority of these models exhibit large parameter sizes, predisposing them to overfitting issues. To overcome these challenges, this study introduces a novel architecture, called mA-UNet, designed to excel in predicting small foreground elements. Additionally, a data preprocessing strategy inspired by road segmentation approaches is employed to address dataset imbalance issues. The experimental results show that the MIoU score attained by the mA-UNet model stands at $95.50\%$, surpassing the nearest competitor, UNet++, on the 2018 Data Science Bowl dataset. Ultimately, our proposed methodology surpasses all other state-of-the-art models in terms of both quantitative and qualitative evaluations. The mA-UNet model is also implemented in VHDL on the Zynq UltraScale+ FPGA, demonstrating its ability to perform complex computations with minimal hardware resources, as well as its efficiency and scalability on advanced FPGA platforms. Full article

Short-form video platforms increasingly shape students’ media practices, yet little is known about the pedagogical value of student-generated videos in university mathematics. This study examined an intervention in a first-year mathematics course at a European university in which students produced 1–2 min explanatory videos solving integration problems and subsequently engaged in peer evaluation of selected exemplars. A mixed-methods design was employed, combining coursework and final examination scores with interview data. No statistically significant performance gains were observed; however, strong positive correlations between coursework, final examination and overall grade indicated stable achievement patterns across assessment points. Qualitative analysis suggested that the process of producing short instructional videos encouraged students to reflect on explanatory clarity, peer perspectives, and the communication of mathematical reasoning, despite linguistic and technical challenges. Overall, the findings provide exploratory insights into how student-generated videos can be integrated into undergraduate mathematics courses as a low-stakes instructional activity supporting reflective engagement and peer-oriented explanation. This study contributes to the scholarship of teaching and learning (SoTL) in STEM education by offering an empirically grounded account of a media-based, peer-oriented learning activity in a university mathematics context. Full article

Informal learning increasingly occurs in digital environments, where citizens access, evaluate and apply knowledge outside of formal education. In the context of disaster preparedness, such informal learning is crucial for promoting individual and collective self-protection. This study examines how disaster preparedness knowledge is represented in German-language online resources, and how these materials can be categorised from an adult education perspective. An exploratory mixed-methods design combining expert-guided sampling, a qualitatively developed coding scheme, large-scale web scraping and AI-assisted classification was employed. A total of 7305 webpages were analysed in terms of actor type, topic, media format, and didactic design. The findings suggest that government and commercial organisations dominate the online preparedness landscape, with limited contributions from civil society and individuals. Thematically, most resources focus on general preventive measures and checklists, whereas scenario-specific and procedural content is underrepresented. Didactically rich and interactive formats are rare, with most materials relying on static, text-based communication. From an adult education perspective, these results suggest a gap between raising awareness and active learning. While online resources offer easy access to preparedness knowledge, they rarely facilitate deeper understanding, participation or collaborative learning. Methodologically, the study illustrates how AI-assisted analysis can combine qualitative interpretive depth with computational scalability in educational research. Full article

Selenium (Se) is an antioxidant essential trace element influencing inflammatory and immune pathways. This systematic review aimed to evaluate the role of maternal Se status during pregnancy in miscarriage risk. A systematic search of PubMed and Embase up to July 2024 was conducted to identify relevant original research studies in English. Available evidence was qualitatively synthesized and predefined sources of bias were assessed. Of 2345 studies identified, 421 full texts were assessed and 14 were included, encompassing 2309 pregnancies. Despite notable methodological limitations across several studies, current evidence indicates that maternal blood Se concentrations are lower among women who experience miscarriage compared to those with uncomplicated pregnancies. Findings regarding placental Se levels were inconsistent, but important methodological issues were noted. Environmental Se exposure was investigated in a single low-powered study, which did not demonstrate a statistically significant association. Potential interactions between Se status, co-exposure to other environmental or lifestyle factors, and effect modification remain insufficiently explored. Adequate maternal Se status during early gestation may reduce miscarriage risk by mitigating oxidative stress and ferroptosis, supporting immune regulation, and modulating thyroid autoimmunity and function. However, causal inference cannot be established due to the absence of randomized interventional evidence. Full article

Practical training and recruitment strategies are critical for the sustainable implementation of psychosocial interventions. However, few studies have examined how to prepare community partners and doctoral students to support culturally adapted psycho-oncology interventions. This pre-pilot study aims first to evaluate two distinct training programs and recruitment procedures, and second to explore preliminary pre-post outcomes of the Caregiver-Patients Support to Cope with Advanced Cancer (CASA) intervention, using the Consolidated Framework for Implementation Research (CFIR). Three clinical psychology graduate students received CASA training, and two community partners completed Recruitment training. We present descriptive pre- and post-assessments, along with qualitative feedback, for both training and institutional (Puerto Rico Biobank) and community-based recruitment outcomes. A related-samples nonparametric analysis examined pre- and post-CASA intervention signals. Results indicated knowledge gains among doctoral students (pre-test M = 3.3; post-test M = 9.3) and community partners (pre-test M = 4.5; post-test M = 9.5). Preliminary outcomes revealed significant improvements in spiritual well-being (Z = −2.618, p = 0.009) and quality of life (Z = −2.957, p = 0.003) and a reduction in depressive (Z = −2.764, p = 0.006), anxiety (Z = −2.667, p = 0.008), and distress (Z = −2.195, p = 0.028) symptoms following CASA. Of 26 recruited dyads, institutional referrals enrolled 16 dyads (61.5%), while community partners referred 10 dyads with a 90.9% success rate. Findings support the feasibility of both training and CASA exploratory outcomes suggest meaningful psychosocial benefits for Latino dyads coping with advanced cancer. Combining institutional infrastructure with community engagement may enhance sustainability and equitable access to psycho-oncology care. Full article

This article examines the failure of the two most recent temporary housing programs implemented in Spain following two major disasters: the 2011 Lorca earthquake and the 2021 La Palma volcanic eruption. Despite differing hazard typologies, both cases resulted in incomplete and ultimately unsuccessful housing programs, with only 13 of the 60 planned units built in Lorca and 121 of the 200 planned units delivered in La Palma. Using a qualitative comparative case study approach, the research analyzes governance decisions, housing design, and implementation processes to assess their impact on the sustainability of post-disaster temporary housing. The analysis adopts the five dimensions of sustainability—environmental, economic, social, cultural, and institutional—as an integrated analytical framework for evaluating public management performance in post-disaster temporary housing. The findings show that early decision-making, shaped by political urgency, technical misjudgments, and the absence of adaptive governance, led to severe delays, cost overruns, inadequate and energy-inefficient construction, and the formation of marginalized settlements. This study concludes that the lack of regulatory frameworks, legal instruments, and operational protocols for temporary housing in Spain was a determining factor in both failures, generating vulnerability, prolonging recovery processes, and undermining sustainability across all five dimensions. By drawing lessons from these cases, this article contributes to debates on resilient and sustainable post-disaster recovery and highlights the urgent need for integrated regulatory frameworks for temporary housing in Spain. Full article

Background/Objective: Recent North American guidelines suggest that CT is indicated for further evaluation where ultrasound (US) is negative, although the negative predictive value (NPV) of ultrasound in adult patients when the appendix is not seen remains unclear. To assess the negative predictive value (NPV) of ultrasound in adult patients when the appendix is not seen. Methods: A scoping review of MEDLINE and EMBASE was performed from inception to 13 May 2025 using PRISMA-ScR guidelines to identify studies evaluating the outcome of adult patients where the appendix is not seen on ultrasound, with preference for studies where there were no secondary signs of acute appendicitis (right lower quadrant free fluid, abscess, ileus, echogenic fat or regional lymphadenopathy). Original studies with at least 10 patients were included in the review. The reference standard included a combination of clinical follow-up, CT and/or pathology. Data synthesis was provided as a qualitative review of the existing literature. Results: Six studies were included in the review. The number of included patients range from 12 to 179 with a mean age of 29–38 years. Few studies reported the patient BMI. NPVs ranged from 80 to 90% for all indeterminate ultrasounds and 83 to 95% for studies where secondary signs of appendicitis were excluded (90 to 95% when non-surgical reference standards were included). Two studies reported NPVs of 96–100% when the pre-test probability was low. Conclusions: The NPV of indeterminate ultrasound for adult patients with right lower quadrant pain and no secondary signs of appendicitis is likely ≥90%. When combined with a low clinical suspicion, the NPV is likely >95%. The appropriateness of a subsequent CT indication when the appendix is not visualized on ultrasound should be determined on an individualized basis. Full article

This paper presents a scalable and low-cost Retrieval Augmented Generation (RAG) architecture designed to enhance learning in university-level courses, with a particular focus on supporting students from economically disadvantaged backgrounds. Recent advances in large language models (LLMs) have demonstrated considerable potential in educational contexts; however, their adoption is often limited by computational costs and the need for stable broadband access, issues that disproportionately affect low-income learners. To address this challenge, we propose a lightweight, mobile, and friendly RAG system that integrates the LLaMA language model with the Milvus vector database, enabling efficient on device retrieval and context-grounded generation using only modest hardware resources. The system was implemented in a university-level Data Mining course and evaluated over four semesters using a quasi-experimental design with randomized assignment to experimental and control groups. Students in the experimental group had voluntary access to the RAG assistant, while the control group followed the same instructional schedule without exposure to the tool. The results show statistically significant improvements in academic performance for the experimental group, with p < 0.01 in the first semester and p < 0.001 in the subsequent three semesters. Effect sizes, measured using Hedges g to account for small cohort sizes, increased from 0.56 (moderate) to 1.52 (extremely large), demonstrating a clear and growing pedagogical impact over time. Qualitative feedback further indicates increased learner autonomy, confidence, and engagement. These findings highlight the potential of mobile RAG architectures to deliver equitable, high-quality AI support to students regardless of socioeconomic status. The proposed solution offers a practical engineering pathway for institutions seeking inclusive, scalable, and resource-efficient approaches to AI-enhanced education. Full article

Periapical lesions (PALs) are a common sequela of pulpal pathology, and accurate radiographic detection is essential for successful endodontic diagnosis and treatment outcome. With recent advancements in Artificial Intelligence (AI), deep learning systems have shown remarkable potential to enhance the diagnostic accuracy of PALs. This study highlights recent evidence on the use of AI-based systems in detecting PALs across various imaging modalities. These include intraoral periapical radiographs (IOPAs), panoramic radiographs (OPGs), and cone-beam computed tomography (CBCT). A literature search was conducted for peer-reviewed studies published from January 2021 to July 2025 evaluating artificial intelligence for detecting periapical lesions on IOPA, OPGs, or CBCT. PubMed/MEDLINE and Google Scholar were searched using relevant MeSH terms, and reference lists were hand screened. Data were extracted on imaging modality, AI model type, sample size, subgroup characteristics, ground truth, and outcomes, and then qualitatively synthesized by imaging modality and clinically relevant moderators (i.e., lesion size, tooth type and anatomical surroundings, root-filling status and effect on clinician’s performance). Thirty-four studies investigating AI models for detecting periapical lesions on IOPA, OPG, and CBCT images were summarized. Reported diagnostic performance was generally high across radiographic modalities. The study results indicated that AI assistance improved clinicians’ performance and reduced interpretation time. Performance varied by clinical context: it was higher for larger lesions and lower around complex surrounding anatomy, such as posterior maxilla. Heterogeneity in datasets, reference standards, and metrics limited pooling and underscores the need for external validation and standardized reporting. Current evidence supports the use of AI as a valuable diagnostic platform adjunct for detecting periapical lesions. However, well-designed, high-quality randomized clinical trials are required to assess the potential implementation of AI in the routine practice of periapical lesion diagnosis. Full article

The current debate on digital education is at a crossroads between the need for technological innovation and the growing concern about the impact of passive screen use. In this context, identifying sustainable pedagogical models that integrate Information and Communication Technologies (ICT) in a meaningful and inclusive way is an urgent need. This article presents a case study of the Amara Berri System (ABS), aiming to analyze how inclusive and sustainable digital innovation is operationalized within the system and whether teachers’ length of service is associated with the implementation and perceived impact of inclusive ICT practices. The investigation is based on a mixed-methods sequential design. A questionnaire was administered to a sample of 292 teachers to collect data on their practices and perceptions. Subsequently, a focus group with eight teachers was conducted to further explore the meaning of their practices. Quantitative results show that the implementation and positive evaluation of inclusive ICT practices correlate significantly with teachers’ seniority within the system, which suggests that the model is formative in itself. Qualitative analysis shows that ICTs are not an end in themselves within the ABS, but an empowering tool for the students. The “Audiovisual Media Room”, managed by students, functions as a space for social and creative production that gives technology a pedagogical purpose. The study concludes that the sustainability of digital innovation requires coherence with the pedagogical project. Findings offer valuable implications for the design of teacher training contexts that foster the integration of technology within a framework of truly inclusive education. Full article

Road infrastructure underpins Nigeria’s economic competitiveness, yet Public–Private Partnership (PPP) performance is constrained not by inadequate legislation but by persistent weaknesses in enforcement and governance. Transparency deficits across procurement, design management, certification, and toll-revenue reporting have produced chronic delays, cost overruns, and declining public trust. This study offers the first empirical investigation of blockchain–Building Information Modelling (BIM) integration as a transparency-enhancing mechanism within Nigeria’s PPP road sector, focusing on Lagos State. Using a qualitative design, ten semi-structured interviews with stakeholders across the PPP lifecycle were thematically analysed to diagnose systemic governance weaknesses and assess the contextual feasibility of digital innovations. Findings reveal entrenched opacity rooted in weak enforcement, discretionary decision-making, and informal communication practices—including biased bidder evaluations, undocumented design alterations, manipulated certifications, and toll-revenue inconsistencies. While respondents recognised BIM’s potential to centralise project information and blockchain’s capacity for immutable records and smart-contract automation, they consistently emphasised that technological benefits cannot be realised absent credible institutional foundations. The study advances an original theoretical contribution: the Codify–Condition–Capacitate framework, which explains the institutional preconditions under which digital governance tools can improve transparency. This framework argues that effectiveness depends on: codifying digital standards and legal recognition; conditioning enforcement mechanisms to reduce discretionary authority; and capacitating institutions through targeted training and phased pilots. The research generates significant practical implications for policymakers in Nigeria and comparable developing contexts seeking institution-aligned digital transformation. Methodological rigour was ensured through purposive sampling, thematic saturation assessment, and documented analytical trails. Full article