Search Results (850)

Historical document collections are increasingly available as digitised images and PDFs, but their conversion into reliable text remains affected by optical character recognition (OCR) errors, degraded pages, heterogeneous layouts, and domain-specific terminology. This study proposes a pathway-level framework for documenting and comparing conventional OCR, OCR followed by large language model (LLM)-assisted correction, and direct multimodal transcription. The framework is demonstrated using the Portuguese Agricultural and Forestry Surveys (1950–1958). A stratified validation sample of 45 pages was selected by visual quality, page type, and geographic coverage. Outputs were evaluated against manually verified reference transcriptions using content-normalised character error rate (CER) and word error rate (WER), document-condition analysis, paired statistical tests, and an entity-level semantic preservation assessment focused on place names, agricultural terms, and measurement expressions. Under the evaluated model and interface conditions, both LLM-based pathways produced lower mean CER and WER than the conventional OCR baseline, with the lowest values observed for direct multimodal transcription. Semantic preservation was also higher for the LLM-based pathways, although measurement expressions remained the most persistent risk, particularly in table-based pages. Downstream tasks were not directly evaluated. The findings support the framework as a method for validating text-extraction pathways before reuse, rather than establishing a universal ranking of tools. Full article

Improving the nutritional quality of food through advanced and sustainable agricultural practices has become a key objective of modern vegetable crop production. Emphasis is placed on increasing the content of health-promoting bioactive compounds, such as vitamins and polyphenols, particularly flavonoids whose accumulation is strongly affected by various biotic and abiotic stress factors. To mitigate stress-induced limitations and enhance plant performance, biostimulants are increasingly applied. Among them, Trichoderma spp. are widely recognized for their ability to promote plant growth and resilience, primarily through enzymatic activity and the production of bioactive metabolites. The aim of this study was to evaluate the potential of the native Trichoderma sp. strain STP8 to enhance the production of bioactive compounds through seed and soil applications at planting and 26 days after planting (DAP), applied individually or in combination. A spore suspension (4 × 10⁶ spores mL⁻¹) was used. The experiment was arranged in a randomized complete block design with five replicates. At harvest (43 DAP), dry matter, ascorbic acid, chlorophyll, and carotenoid contents were determined. Additionally, flavonoids and non-flavonoids, total phenolics, individual phenolic compounds, and antioxidant capacity were analyzed. Achieved results demonstrate that the effects of the native Trichoderma sp. strain STP8 on lettuce secondary metabolism and antioxidant properties are strongly dependent on the developmental stage at which inoculation is performed, providing further insight into the stage-specific interactions between plans and Trichoderma. Practically, a single application at planting proved to be the most effective strategy for enhancing the accumulation of bioactive compounds, indicating that optimized application timing may improve the efficacy of Trichoderma-based biostimulants, while avoiding unnecessary repeated applications. These findings support the potential use of native Trichoderma strains as sustainable tools for improving the nutritional and functional quality of lettuce. Further research integrating physiological, biochemical, and molecular analyses is required to elucidate the mechanisms by which the native Trichoderma sp. strain STP8 regulates the biosynthesis of bioactive compounds in lettuce. Full article

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder worldwide, affecting approximately 56.9 million people in 2021 and projected to reach 152 million by 2050. Its defining pathological features, amyloid-beta plaques and neurofibrillary tangles, accumulate for up to two decades before cognitive symptoms emerge, placing the preclinical and mild cognitive impairment (MCI) stages at the centre of the early detection problem. Despite this, current diagnostic practice in routine clinical settings remains unreliable, with post-mortem studies placing the specificity of clinical AD diagnosis between 44.3 and 70.8% even in specialist memory clinics. Machine learning (ML) and deep learning (DL) applied to neuroimaging and electrophysiological data have emerged as candidate tools for closing this diagnostic gap, yet whether the accuracy figures reported in published studies translate into clinically useful performance on independent data remains unresolved. This study presents a structured comparative review of machine learning and deep learning methods reported across four publicly available Alzheimer’s disease datasets, namely the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Open Access Series of Imaging Studies (OASIS), the OpenNeuro ds004504 electroencephalography (EEG) dataset, and the Kaggle Alzheimer’s magnetic resonance imaging (MRI) dataset. Thirteen model families are examined through the published literature rather than through new experiments, and for each model and dataset combination, the best accuracy reported in the source study is recorded alongside the model’s mathematical formulation. All performance figures reported in this abstract and throughout the paper are taken from the published studies reviewed, not from new experiments conducted by the authors. Across the reviewed studies, deep learning architectures pre-trained on ImageNet and fine-tuned on neuroimaging data are reported to produce the highest accuracy on MRI classification tasks. Residual Network (ResNet)-101 is reported at 98.21 percent on ADNI and 97.45 percent on OASIS, while the IncepRes fusion architecture reaches 98.35% on OASIS by combining multi-scale feature extraction from InceptionV3 with residual connectivity from ResNet152V2. Traditional machine learning classifiers remain competitive on tabular clinical and biomarker data, with Extreme Gradient Boosting (XGBoost) reaching 91% on ADNI multiclass features. Logistic Regression achieves 82 to 85% on binary ADNI classification and is the only classifier in this review that provides explicit per-feature prediction contributions without post hoc tooling. Gaussian Naïve Bayes achieves 80 to 83% on the same task. On the OpenNeuro EEG dataset, K-nearest neighbours (KNN) with singular value decomposition (SVD) entropy features achieves 91% binary accuracy, with feature engineering quality determining performance more reliably than classifier architecture. Eight principal findings emerge from the cross-dataset analysis. Binary classification consistently outperforms multiclass by 10 to 30% across all datasets, reflecting the genuine biological ambiguity of the mild cognitive impairment category. Dataset size and augmentation predict reported accuracy more reliably than model architecture. Ensemble methods outperform individual classifiers by 5 to 8% in nearly every imaging study. Deeper architectures can overfit small clinical cohorts. EEG models trail MRI models by approximately 10 to 15% on comparable binary tasks. Cross-dataset generalisation has not been systematically evaluated in most studies, and the few that have tested it report accuracy drops of 5 to 10% or more when models encounter data from different scanners or cohorts. Eight recurring limitations constrain the clinical utility of these findings. Small sample sizes and limited demographic diversity, severe class imbalance inflating raw accuracy metrics, poor cross-dataset generalisation driven by scanner heterogeneity, limited deep learning interpretability, the dominance of binary over multiclass tasks, the absence of longitudinal modelling despite available datasets, inadequate standardisation of preprocessing and evaluation protocols, and the signal-to-noise ratio constraints specific to EEG recordings of elderly patients collectively define the gap between benchmark performance and clinical readiness. Future work must prioritise multi-centre training cohorts, multimodal fusion architectures, longitudinal progression modelling, and standardised interpretability evaluation as non-optional requirements for any system intended for clinical deployment. Full article

Rural tourism destinations increasingly converge on standardized facilities and packaged landscapes, eroding the place-based distinctiveness that motivates rural travel. Although previous research has distinguished affective and cognitive experiential mechanisms, it remains unclear whether these pathways are differentially associated with sustainability-oriented and loyalty-related behavioral intentions. Addressing this gap, the present study examines how tourists’ perceptions of rural destinations are associated with these two forms of behavioral response. Building on the established distinction between affective and cognitive experiential mechanisms, two perceptual systems are linked to parallel affective and cognitive experiential routes to examine their distinct behavioral associations. An affective route, in which perceived locality and authenticity foster place attachment, and a cognitive route, in which perceptions of planning quality foster tourist satisfaction, are expected to specialize by outcome. Survey data were collected from 411 visitors to rural tourism destinations in Hainan, China, and analyzed using a latent-variable structural equation model with bias-corrected bootstrap tests of competing indirect effects. The results reveal a clear distinction between the two behavioral systems. Locality and authenticity are positively associated with place attachment, which in turn is associated with sustainable behavioral intention, whereas planning quality is positively associated with tourist satisfaction, which is associated with revisit and recommendation intention. The competing-mediation analysis further indicates that locality is associated with sustainable behavioral intention primarily through place attachment rather than satisfaction, and this affective pathway remains consistent across visitor-origin subgroups. Given the cross-sectional design, these relationships should be interpreted as associations rather than evidence of causality. The findings re-conceptualize locality as a measurable tourist perception and demonstrate that sustainability-oriented behavioral intention and loyalty-related intention represent distinct behavioral outcomes associated with different experiential pathways. For destination planners and managers, the findings suggest that strengthening authentic rural character may support sustainability-oriented behaviors, whereas improving the planning and service environment may encourage revisit and recommendation intentions. Full article

Selecting appropriate plant material is essential for successful vegetable production, as it directly affects product quality, market value, and consumer satisfaction. While major vegetable species already have stable markets, there is growing demand for innovative, healthy, and sustainable products, driving continuous development in the sector. This study provides an overview of novel vegetable products, ranging from native resources to recent innovations in widely consumed crops. Special emphasis is placed on neglected species, particularly edible halophytes, which offer high nutritional value and strong potential under climate change conditions. The study also examines unconventional plants not traditionally used as vegetables, which can contribute to diversifying diets and supporting local economies. At the same time, the vegetable industry continues to innovate by developing new products from common species, taking advantage of their variability in size, shape, color, and taste, including the rising trend of miniaturized vegetables. In addition, the concepts of “superfoods” and functional foods are critically analyzed to clarify their respective significance and distinguishing characteristics. The role of branding is also highlighted due to its increasing influence on consumer preferences. Overall, the study integrates contributions from farmers, industry, and researchers to expand vegetable diversity, promote innovation, and enhance product quality and consumer satisfaction. Full article

Concrete placing surface flatness is an important quality indicator affecting thickness control, finishing efficiency, and subsequent construction quality. Although point-cloud-based flatness inspection has been widely studied for completed floors, walls, slabs, and precast components, near-real-time detection of small local unevenness on temporary concrete placing surfaces during construction remains insufficiently investigated, especially under the effects of point-cloud noise, local slope, incomplete coverage, and baseline false alarms. To address this gap, this study proposes a LiDAR-based automatic workflow that combines multi-frame median fusion, local reference-plane fitting, threshold-based deviation judgment, and visualized output. Indoor validation was conducted using an empty-ground baseline, 20 cm × 20 cm square plates, and 50 cm × 5 cm strip plates with thicknesses of 3, 5, and 10 mm. The average over-limit point ratio was 2.165% ± 0.192% for the empty-ground baseline. For 3, 5, and 10 mm square plates, the ratios were 2.536% ± 0.370%, 3.811% ± 0.638%, and 4.657% ± 0.850%, respectively; for strip plates, they were 2.269% ± 0.059%, 2.937% ± 0.443%, and 3.347% ± 0.435%, respectively. Compared with the baseline, the 5 and 10 mm square plates increased the ratio by 1.646 and 2.492 percentage points, while the 5 and 10 mm strip plates increased it by 0.772 and 1.182 percentage points. These results show that the workflow provides a clear thickness-dependent response for square targets and a detectable but weaker response for narrow strip targets. Scientifically, the study demonstrates how local threshold-scale unevenness can be distinguished from baseline point-cloud fluctuations. In application, it provides preliminary perception support for future online flatness inspection and automatic screeding assistance. Full article

Introduction: Chronic dermatological diseases that appear in adolescence, such as acne vulgaris, atopic dermatitis, alopecia areata and psoriasis vulgaris, not only cause physical symptoms but also significantly affect young people’s quality of life, mental state, self-esteem and social relationships. Social media, especially information spread by influencers, significantly influences adolescents’ body image, health-related attitudes and even the quality of the physician-patient relationship. The aim of our study was to explore the relationships between dermatology-related quality of life, media use, health values, body image and self-esteem among adolescents with chronic dermatological diseases. Methods: In our cross-sectional, quantitative study, we used validated questionnaires (DLQI, EQ-5D-5L, IRVS, Attitude Scale, STAI-Y2, SWLS-H, Rosenberg Scale, BAT), which we supplemented with a media consumption questionnaire of our own design. Structured data collection took place between October 2024 and March 2025, with the participation of 208 adolescents aged 11–18. Data analysis was performed using SPSS 26.0 (Spearman’s correlation, Mann–Whitney test). Differences were considered significant at p < 0.05. Ethical approval: BM/22429-1/2024. Results: Acne vulgaris was the most common diagnosis (65%), followed by atopic dermatitis (22%) and psoriasis (11%). Over a quarter of the adolescents (27%) followed influencers who provided skincare advice. The mean daily screen time was 4.5 h, with 3.7 h on smartphones. A longer screen time was significantly correlated with poorer dermatological quality of life. Greater dermatology-related quality of life impairment (higher DLQI scores) was associated with poorer general quality of life (EQ-5D-5L). Following skincare-related influencers was significantly associated with dermatology-related quality of life and anxiety. Conversely, stronger health values were significantly linked to more favourable health behaviors. Conclusions: In this sample, greater dermatology-related quality-of-life impairment was associated with poorer psychosocial outcomes. Longer screen time was associated with poorer dermatology-related quality of life and less favourable psychosocial outcomes. The novelty of our study lies in the use of a self-developed media consumption questionnaire, which is suitable for the complex mapping of psychological and quality-of-life factors in adolescents. Full article

The quick transition of the healthcare industry to digital during the era of the Internet of Medical Things and Artificial Intelligence has ignited the demand for frameworks for data sharing while retaining safety and patient privacy. Centralized learning models place potentially sensitive patient data at risk of leakage, regulatory violation, and cyber-attacks which undermine receptivity and responsible ownership of big medical data. Federated learning is a novel paradigm that allows patients from various healthcare entities to train machine learning models while maintaining the ability to leverage their data without sharing their direct data. This study proposes a systematic literature review of approaches of privacy-preserving federated learning frameworks in healthcare applications. Following PRISMA guidelines, searches were conducted across Web of Science, Scopus, IEEE Xplore, ScienceDirect, PubMed, and ACM Digital Library with predefined query strings, explicit inclusion/exclusion criteria, and quality appraisal procedures. A total of 80 peer-reviewed studies, published from January 2015 to December 2025, were included in this systematic review, which examined cryptographic, architectural and algorithmic methods including differential privacy, homomorphic encryption, and Secure Multi-Party Computation, along with integrations using blockchain to enhance trust and confidence in distributed healthcare systems. The findings indicate a gradual shift towards hybrid privacy-preserving federated learning architectures which combined multiple security mechanisms to improve trust, confidentiality and robustness. Although significant progress has been achieved, the real-world deployment of such systems is heavily affected due to the challenges in communication efficiency, non-IID data distribution, adversarial attacks, and regulatory requirements. This research highlights future research directions for scalable, explainable and interoperable federated architectures that strike an optimal balance of privacy, utility and system performance for next-gen health intelligence. Trial registration: PROSPERO (CRD420261401073). Full article

Greenhouse gas emissions and air pollution are closely interconnected environmental challenges with major implications for human health and global sustainability. Many of the activities that drive climate change also release pollutants such as nitrogen dioxide, sulphur dioxide, carbon monoxide, and particulate matter, which directly affect air quality and population health. This review synthesises current evidence on the main sources of greenhouse gas emissions and atmospheric pollutants, the atmospheric processes that influence air quality, and the epidemiological evidence linking air pollution exposure to adverse health outcomes. The paper also discusses the public health co-benefits of climate mitigation strategies, including the transition to cleaner energy systems, sustainable transport policies, and urban environmental interventions. Finally, the review places air pollution and climate change within the broader framework of human security, highlighting their implications for health security, environmental stability, food systems, and economic resilience. By integrating perspectives from environmental epidemiology, public health, and global environmental governance, this review provides a multidisciplinary overview of the links between greenhouse gas emissions, air quality, and human well-being, and underscores the importance of coordinated policy responses to address these interconnected challenges. Full article

This study examines the relationship between Environmental, Social, and Governance (ESG) performance and financing and pricing outcomes in green and conventional bond markets in China over the period of 2017–2024. Drawing on signaling theory, information asymmetry theory, and market segmentation theory, the study argues that the role of ESG performance differs across bond types because green and conventional bonds operate within different institutional and informational environments. Using a comparative analysis of green and conventional bonds, the findings show that ESG performance is more strongly and consistently associated with conventional bond characteristics, particularly in relation to issuance amount, yield to maturity, and credit spreads. In contrast, ESG effects in green bonds are weaker and less consistent, suggesting that investors place greater emphasis on certification mechanisms, environmental project objectives, and sustainability-related bond characteristics than on broader issuer-level ESG disclosures. The findings also suggest that ESG information does not affect all debt instruments in the same way or always functions as a purely risk-reducing signal. In the Chinese market, stronger ESG exposure may also be associated with transition risks, regulatory pressures, and sector-specific sustainability challenges, particularly in conventional bond markets. Overall, the results indicate that the financial relevance of ESG performance depends not only on firm characteristics but also on the institutional and informational environment of the financial instrument itself. The findings remain robust across alternative model specifications and sensitivity analyses, providing additional confidence in the reported differences between green and conventional bond markets. The study contributes to the sustainable finance literature by showing that the pricing relevance of ESG information is instrument-specific rather than uniform across debt markets. It also provides practical implications for regulators, investors, and issuers by highlighting the importance of disclosure quality, transparency standards, and external verification mechanisms in strengthening investor confidence and reducing potential greenwashing risks in sustainable finance markets. Full article

Urban green spaces (UGSs) are considered crucial for enhancing older adults’ subjective well-being. However, limited studies have explored the synergistic effects of UGS quality and quantity on satisfaction across green spaces, residential environments, and life domains, making it challenging to uncover the multifaceted sustainable benefits of UGSs on older adults’ subjective well-being. This study drew on multi-source data and place attachment theory to depict neighborhood-accessible UGS quantity (provision, accessibility, and visibility) and quality (cognition, behavior, and affect). Through the geographical visualization of bivariate SHapley Additive exPlanations (SHAP) interaction values extracted from the trained eXtreme Gradient Boosting (XGBoost) model, and the comparison of bivariate SHAP maps with univariate SHAP maps, the study revealed the nonlinear geographic associations between UGS quantity and quality and three types of satisfaction. The results showed that when UGS quantity and quality coexisted, variations in the impact of quantity on older adults’ satisfaction were associated with quality differences. The gain effect of quality on quantity was more significant in areas with limited green space within a 500 m buffer zone. UGSs made a direct contribution to green space satisfaction, while their indirect association with life satisfaction surpassed that of residential satisfaction due to their provision of emotional qualities. This study calls for neighborhood green planning aimed at improving older adults’ subjective well-being, which should shift focus from quantity to quality and balance the relationship between quantity and quality based on regional characteristics. Full article

Oral health has significant implications for pulmonary outcomes, particularly among individuals with dysphagia who are at risk for aspiration. Moreover, oral health and condition affect nutrition accessibility and status. Inadequate oral hygiene promotes bacterial colonization, plaque accumulation, and aspiration-related respiratory complications. This narrative review aimed to explore current evidence and expert perspectives across palliative medicine, pulmonary and critical care, and dentistry on the role of oral hygiene in supporting pulmonary health and maintaining opportunities for oral nutrition. A comprehensive literature search was conducted through the Texas Tech University Health Sciences Center digital library using Cochrane Library (Wiley), EBSCO Discovery, Embase, Ovid databases, PubMed, SCOPUS, ScienceDirect, Web of Science, and Google Scholar between 14 January 2026 and 1 April 2026. From 1287 identified records, 70 studies were selected to be highlighted in the manuscript after duplicate removal and eligibility screening. Relevant literature was reviewed to examine associations among dysphagia, oral health and condition, oral hygiene and care protocols, feeding route, salivary composition and function, and respiratory outcomes. Emphasis was placed on studies addressing pneumonia, oral versus tube feeding, and evidence-based oral care practices. Findings indicate that pneumonia, depression, and mortality rates are higher in patients receiving tube feeding compared to oral feeding. Evidence-based oral care practices inclusive of mechanical plaque disruption, oral cleansing products (Chlorhexidine, hydrogen peroxide, and sodium bicarbonate), and structured oral hygiene protocols can reduce pulmonary consequences of aspiration and support safer/least risk oral intake. Saliva plays a pivotal role in plaque breakdown, microbial defense, and host immunity; oral feeding helps to preserve salivary function. Results of this review highlight the importance of oral hygiene in both restorative and palliative care contexts. This review establishes a framework for embedding oral cleansing agents and protocols into a nutrition-focused health care infrastructure. Based on the literature analysis and inter- and multidisciplinary clinical expertise of the author group, the manuscript proposes consensus statements intended as expert guidance rather than formal clinical practice guidelines. Adherence to best practices in oral care can mitigate pulmonary consequences of aspiration amid dysphagia, make oral nutrition more accessible and comfortable, sustain opportunities for least risk oral feeding across diagnoses and health care settings, and improve quality of life for patients with dysphagia amid life-limiting illness. Full article

With the continuous deepening of green building concepts and the sustained advancement of research on health-oriented design, increasing attention has been paid to the impact of architectural space on users’ psychological perception and behavioral outcomes. In China, the rapid development of urban cultural facilities and the growing emphasis on high-quality public cultural spaces have made concert halls an important context for examining how architectural environments shape user experience. In recent years, relevant studies have gradually expanded from energy conservation, function, and technical performance evaluation to discussion of the subjective experience of the architectural environment and its psychological effects. As a typical type of cultural building, the concert hall is an important place for music communication and artistic experience, and its spatial environment may also influence users’ state of immersion and emotional resonance. However, existing studies mostly focus on the acoustic quality, visual characteristics, and functional organization of concert halls, and still lack a systematic empirical explanation of how restorative design influences user satisfaction through psychological mechanisms. Using survey data from 972 users of six representative concert halls in six Chinese cities, this study constructs a theoretical model with perceived restorative design as the independent variable, flow experience and musical resonance as mediating variables, and user satisfaction as the dependent variable, aiming to broaden the understanding of the internal mechanism through which restorative design affects user satisfaction. The results show that: (1) perceived restorative design is positively associated with user satisfaction; (2) flow experience and musical resonance respectively play mediating roles between perceived restorative design and user satisfaction; and (3) flow experience and musical resonance respectively play a chain mediating role between perceived restorative design and user satisfaction. This study enriches the applied research on restorative design in the field of cultural architecture, reveals the psychological path through which restorative design in concert halls affects user satisfaction, and expands the theoretical boundaries of research on architectural environment experience. The conclusions provide a theoretical basis for optimizing the design of concert hall buildings and improving user experience, and also offer practical insights for the human-centered and high-quality development of cultural buildings in the context of green building. Full article

In biodiversity-rich borderlands, some humanitarian settlements are rapidly expanding. This creates a profound conflict: refugees need a place to live, and ecosystems need protection. However, how settlement growth spatially affects the ecology surrounding protected areas remains understudied. This study takes as an example the city of Teknaf in Bangladesh, one of the world’s largest refugee gathering areas, to explore how settlement expansion changes the ecological structure and function of protected area boundaries, with a focus on two questions: Are there critical spatial thresholds? What is the role of climate feedback mechanisms? We build an analysis framework that integrates several types of data: multitemporal remote sensing images, land-use changes, ecological indicators (NDVI, LST, HQ), landscape pattern indices, gradient analysis, and 2036 simulations based on the business-as-usual scenario. Through this framework, we identify the ecological threshold at the junction of settlements and forests within the Teknaf Wildlife Sanctuary. The expansion of settlements has turned the landscape, which was originally dominated by vegetation, into fragmented hard patches. At the same time, the habitat is severely degraded, and heat stress intensifies. Notably, a critical transition zone emerges at approximately 300–500 m from the protected area boundary, where landscape fragmentation intensifies, habitat quality declines, and heat stress reaches its peak, highlighting a spatial hotspot of ecological vulnerability. If there are no intervention measures, future scenario simulations show that the continued expansion of settlements will only isolate protected areas and accelerate ecological degradation. On the basis of gradient analysis for spatial diagnosis, we propose a zoning management framework and regeneration landscape strategy with the direct goal of coordinating ecological protection and humanitarian needs in crisis-prone border areas. Full article

Selective laser sintering (SLS) enables the fabrication of polymer components with intricate geometries; however, surface roughness remains a concern that affects performance and quality. This study systematically examines the influence of build orientation (X, Y, Z) and part location (inner, middle, outer) on surface roughness of Polyamide 12 (PA12) components. The results show that components built in the Y orientation and placed in the inner chamber exhibited the lowest surface roughness (Sa = 9.88 ± 0.16 µm). In contrast, Z-oriented components at the outer edge showed the highest (Sa = 11.49 ± 0.27 µm), indicating a 10.13% variation. SEM analysis showed smoother morphology and reduced porosity in the inner regions, while DSC confirmed higher crystallinity in samples with improved finish. These findings highlight the role of thermal stability and spatial positioning within the build chamber in controlling surface characteristics, emphasizing the importance of orientation and placement strategies for improving the quality of PA12 components fabricated by SLS. Full article