Search Results (179)

Modern aerospace engineering places increasing emphasis on materials that combine low weight with high mechanical performance. Fiber metal laminates (FMLs), which merge metal layers with fiber-reinforced composites, meet this demand by delivering improved fatigue resistance, impact tolerance, and environmental durability, often surpassing the performance of their constituents in demanding applications. Despite these advantages, inspecting such thin, layered structures remains a significant challenge, particularly when they are difficult or impossible to access. As with any new invention, they always come with challenges. This study examines the effectiveness of the fundamental anti-symmetric Lamb wave mode (A0) in detecting weak interfacial defects within Carall laminates, a type of hybrid fiber metal laminate (FML). Delamination detectability is analyzed in terms of strong wave dispersion observed downstream of the delaminated sublayer, within a region characterized by acoustic distortion. A three-dimensional finite element (FE) model is developed to simulate mode trapping and full-wavefield local displacement. The approach is validated by reproducing experimental results reported in prior studies, including the author’s own work. Results demonstrate that the A0 mode is sensitive to delamination; however, its lateral resolution depends on local position, ply orientation, and dispersion characteristics. Accurately resolving the depth and extent of delamination remains challenging due to the redistribution of peak amplitude in the frequency domain, likely caused by interference effects in the acoustically sensitive delaminated zone. Additionally, angular scattering analysis reveals a complex wave behavior, with most of the energy concentrated along the centerline, despite transmission losses at the metal-composite interfaces in the Carall laminate. The wave interaction with the leading and trailing edges of the delaminations is strongly influenced by the complex wave interference phenomenon and acoustic mismatched regions, leading to an increase in dispersion at the sublayers. Analytical dispersion calculations clarify how wave behavior influences the detectability and resolution of delaminations, though this resolution is constrained, being most effective for weak interfaces located closer to the surface. This study offers critical insights into how the fundamental anti-symmetric Lamb wave mode (A0) interacts with delaminations in highly attenuative, multilayered environments. It also highlights the challenges in resolving the spatial extent of damage in the long-wavelength limit. The findings support the practical application of A0 Lamb waves for structural health assessment of hybrid composites, enabling defect detection at inaccessible depths. Full article

A lack of access to healthy food has been a problem for low-income residents in many developed urban areas. Due to travel time and additional transportation costs, these residents often opt for unhealthy food rather than nutritious alternatives. This study examines the spatial distribution of food deserts in Mississauga—one of Canada’s most populous cities and a city with one of the highest diabetes rates in the Province of Ontario. Network analysis was employed to map the geographic inaccessibility to essential nutritious food, defined as residential areas that are beyond a 15-min walking distance from grocery stores. Socioeconomic indicators were integrated to identify and compare the regions that are socioeconomically disadvantaged and, therefore, most affected by food inaccessibility. The results reveal the presence of several food deserts spatially dispersed in Mississauga. The implications of these findings are discussed, with a focus on the relationship between food desert locations and the socioeconomic conditions of the affected residents. This study provides a practical, replicable approach for identifying food deserts that can be easily applied in other regions. The model developed offers valuable tools for policymakers and urban planners to address food desert issues, improving access to healthy food and positively impacting the health and well-being of affected populations. Full article

Cervical cancer (CC) remains a critical global health issue which disproportionately affects low- and middle-income countries. In Chile, the Arica and Parinacota region experiences high CC mortality and low Papanicolaou (Pap) test coverage, with indigenous Aymara women facing significant screening barriers. Understanding health beliefs surrounding CC prevention is essential for improving adherence, particularly in under-represented populations. This study assesses the psychometric properties of the CPC-28, an instrument measuring beliefs about CC and Pap testing, among Aymara women in Chile. A cross-sectional survey of 299 Aymara women (25–64) was conducted using stratified probabilistic sampling. Confirmatory factor analysis (CFA) confirmed the CPC-28’s six-factor latent structure, demonstrating strong model fit (CFI = 0.969, TLI = 0.965, RMSEA = 0.058). Reliability indices ranged from acceptable to excellent (α = 0.585–0.921; ω = 0.660–0.923). Moderate correlations emerged between severity, susceptibility, and perceived benefits of Pap testing, although CPC-28 results did not predict adherence. These findings support CPC-28’s validity evidence for Aymara women but highlight cultural influences on screening behaviors. Structural barriers, including language and healthcare inaccessibility, are likely to affect perceived susceptibility. Future research should explore indigenous perspectives and socio-cultural determinants of Pap testing, incorporating mixed-method approaches to identify culturally relevant interventions and improve screening adherence. Full article

Human papillomaviruses (HPVs), like many other viruses, are able to integrate their genomes into the host cellular genome. This integration can activate viral oncogenes or alter the function of cellular oncogenes and tumor suppressor genes, thereby increasing the likelihood of HPV-associated tumor development. In particular, HPV types 16 and 18 are responsible for over 70% of all cervical, anal, and oropharyngeal cancers worldwide, with rising incidence. Even more, high-resolution mapping of preferred integration sites using LR-Seq technologies offers deep insights into the molecular mechanisms of HPV integration. LR-Seq enables the detection of complex integration patterns, where the viral genome can be replicated and amplified into virus–host concatemers, including events within large structural variations or highly repetitive genomic regions. Furthermore, aligning LR-Seq data to the latest T2T reference genome (hs1) is necessary to provide new information about viral integration in genomic regions that were previously inaccessible, such as centromeres and other structurally complex repeat-rich loci. In this review, we provide insights into HPV genomic integration revealed by LR-Seq technologies, with a particular focus on how the use of the complete T2T reference genome enhances the detection of integration events in previously uncharacterized, repeat-rich regions of the human genome. Full article

The ability to walk is often lost after neural injury, leading to multiple secondary complications that reduce quality of life and increase healthcare costs. The current rehabilitation interventions primarily focus on restoring leg movements through intensive training on a treadmill or using robotic devices, but ignore engaging the arms. Several groups have recently shown that simultaneous arm and leg (A&L) cycling improves walking function and interlimb connectivity. These findings highlight the importance of neuronal pathways between the arm (cervical) and leg (lumbar) control regions in the spinal cord during locomotion, and emphasize the need for activating these pathways to improve walking after neural injury or disease. While the findings to date provide important evidence about actively including the arms in walking rehabilitation, these strategies have yet to be optimized. Moreover, improvements beyond A&L cycling alone may be possible with conjunctive targeted strategies to enhance spinal interlimb connectivity. The aim of this review is to highlight the current evidence for improvements in walking function and neural interlimb connectivity after neural injury or disease with cycling-based rehabilitation paradigms. Furthermore, strategies to enhance the outcomes of A&L cycling as a rehabilitation strategy are explored. These include the use of functional electrical stimulation-assisted cycling in acute care settings, utilizing non-invasive transcutaneous spinal cord stimulation to activate previously inaccessible circuitry in the spinal cord, and the use of paired arm and leg rehabilitation robotics. This review aims to consolidate the effects of exercise interventions that incorporate the arms on improved outcomes for walking, functional mobility, and neurological integrity, underscoring the importance of integrating the arms into the rehabilitation of walking after neurological conditions affecting sensorimotor function. Full article

Gravity anomalies play critical roles in geological analysis, geodynamic monitoring, and precise geoid modeling. Obtaining accurate gravity data is challenging, particularly in inaccessible or sparsely covered regions. This study evaluates machine learning (ML) methods—Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Ensemble of Trees—for predicting gravity anomalies in southeastern Kazakhstan and compares their effectiveness with traditional Kriging interpolation. A dataset, consisting of the simple Bouguer anomaly values, latitude, longitude, elevation, normal gravity, and terrain corrections derived from historical maps at a scale of 1:200,000, was utilized. Models were trained and validated using cross-validation techniques, with performance assessed by statistical metrics (RMSE, MAE, R²) and spatial error analysis. Results indicated that the Exponential GPR model demonstrated the highest predictive accuracy, outperforming other ML methods, with 72.9% of predictions having errors below 1 mGal. Kriging showed comparable accuracy and superior robustness against extreme errors. Most prediction errors from all methods were spatially associated with mountainous regions featuring significant elevation changes. While this study demonstrated the effectiveness of machine learning methods for gravity anomaly prediction, their accuracy decreases in complex terrain, indicating the need for further research to improve model performance in such environments. Full article

The accurate measurement of solar radiation is essential for applications in agriculture, renewable energy, and environmental monitoring. Traditional pyranometers provide high-precision readings but are often costly and inaccessible for large-scale deployment. This study explores the feasibility of using low-cost ambient light sensors combined with statistical and machine learning models based on linear, random forest, and support vector regressions to estimate solar irradiance. To achieve this, an Internet of Things-based system was developed, integrating the light sensors with cloud storage and processing capabilities. A dedicated solar radiation sensor (Davis 6450) served as a reference, and results were validated against meteorological API data. Experimental validation demonstrated a strong correlation between sensor-measured illuminance and solar irradiance using the random forest model, achieving a coefficient of determination (R²) of 0.9922, a root mean squared error (RMSE) of 44.46 W/m², and a mean absolute error (MAE) of 27.12 W/m². These results suggest that low-cost light sensors, when combined with data-driven models, offer a viable and scalable solution for solar radiation monitoring, particularly in resource-limited regions. Full article

Since 2011, five of Austria’s 29 known prehistoric pile dwellings have been part of the transnational UNESCO World Heritage Site “Prehistoric Pile Dwellings around the Alps”. These remarkable archaeological sites have been preserved for over 7000 years in lakes and moors. Due to their hidden location underwater or in the soil of bogs, the sites are inaccessible to the public, making it difficult to convey the full scope of this heritage. To address this, the national project “Surfaced—the digital pile dwellings” was launched, aiming to create a virtual bridge connecting the sites, collections, and exhibitions across Austria. It involved digitizing 500 objects, scanned in high resolution, and presenting them as 3D models in an open-access web application. The web application PfahlbauKompass allows users to explore these 3D models, view information about the artefacts and the sites, and create digital collections. It provides access to finds from national museums, regional heritage houses, and private collections. The project offers scientific potential as well as opportunities for virtual exhibitions and educational initiatives. It aims to preserve and visualize an essential part of Austria’s cultural heritage and was designed not only to archive finds but also to raise awareness of the hidden sites among the public. Full article

Nanobodies, or single-domain antibodies (V_HHs) from camelid heavy-chain-only antibodies, offer significant advantages in therapeutic and diagnostic applications due to their small size and ability to bind cryptic protein epitopes inaccessible to conventional antibodies. In this study, we examined nanobodies specific to regions of the SARS-CoV-2 spike glycoprotein, including the receptor-binding domain (RBD), N-terminal domain (NTD), and subunit 2 (S2). Using flow virometry, a high-throughput technique for viral quantification, we achieved the efficient detection of pseudotyped viruses expressing the spike glycoprotein. RBD-targeting nanobodies showed the most effective staining, followed by NTD-targeting ones, while S2-specific nanobodies exhibited limited resolution. The simple genetic structure of nanobodies enables the creation of multimeric formats, improving binding specificity and avidity. Bivalent V_HH-Fc constructs (V_HHs fused to the Fc region of human IgG) outperformed monovalent formats in resolving viral particles from background noise. However, S2-specific monovalent V_HHs demonstrated improved staining efficiency, suggesting their smaller size better accesses restricted antigenic sites. Furthermore, direct staining of cell supernatants was possible without virus purification. This versatile nanobody platform, initially developed for antiviral therapy against SARS-CoV-2, can be readily adapted for flow virometry applications and other diagnostic assays. Full article

In subtropical regions, typhoons and tropical storms can generate massive rainstorms resulting in thousands of landslides, often termed as Multiple-Occurrence of Regional Landslide Events (MORLE). Understanding the hazards, their location, and their triggering mechanism can help to mitigate exposure and potential impacts. Extreme rainfall events and earthquakes frequently trigger destructive landslides that cause extensive economic loss, numerous fatalities, and significant damage to natural resources. However, inventories of rainfall-induced landslides suggest that they occur frequently under climate change. This study proposed a semi-automated time series algorithm that integrates Sentinel-2 and Integrated Multi-satellite Retrievals for Global Precipitation Measurements (GPM-IMERG) data to detect rainfall-induced landslides. Pixel-wise NDVI time series data are analyzed to detect change points, which are typically associated with vegetation loss due to landslides. These NDVI abrupt changes are further correlated with the extreme rainfall events in the GPM-IMERG dataset, within a defined time window, to detect RIL. The algorithm is tested and evaluated eight previously published landslide inventories, including both those manually mapped and those derived from high-resolution satellite data. The landslide detection yielded an overall F1-score of 0.82 and a mean producer accuracy of 87%, demonstrating a substantial improvement when utilizing moderate-resolution satellite data. This study highlights the combination of using optical images and rainfall time series data to detect landslides in remote areas that are often inaccessible to field monitoring. Full article

This study utilized unmanned aerial systems (UAS) and terrestrial laser scanners (TLS) to develop a 3D numerical model of slope anchors and conduct a comprehensive analysis. Initial data were collected using a UAS with 4 K resolution, followed by a second dataset captured 6 months later with 8 K resolution after artificially damaging the anchor. The model analyzed damage factors such as cracks, destruction, movement, and settlement. Cracks smaller than 0.3 mm were detected with an error margin of ±0.05 mm. The maximum damaged area on the anchor head was within 3% of the designed value, and the volume of damaged regions was quantified. A combination analysis examined elevation differences on the anchor’s irregular bottom surface, resulting in an average difference at 20 points, reflecting ground adhesion. The rotation angle (<1°) and displacement of the anchor head were also measured. The study successfully extracted quantitative damage data, demonstrating the potential for an accurate assessment of anchor performance. The findings highlight the value of integrating UAS and TLS technologies for slope maintenance. By organizing these quantitative metrics into a database, this approach offers a robust alternative to traditional visual inspections, especially for inaccessible facilities, providing a foundation for enhanced safety evaluations. Full article

Slowly moving active landslides threaten infrastructure, particularly along highway routes traversing active mountainous ranges. Detecting and characterizing such landslides in highly elevated mountainous terrains is challenging due to their inaccessibility, wide area coverage, limited approaches, and the complex nature of mass movements. In this study, we processed Sentinel-1 Synthetic Aperture Radar data acquired from 2015 to 2024 to detect active landslides along the Karakoram Highway alternate route (Chitral-Gilgit) and the Karakoram Highway part (Gilgit-Khunjerab). We detected 1037 active landslides in the study region using phase gradient stacking and a deep learning network. Based on the detection, we applied time series InSAR analysis to reveal the velocity and deformation series for some large-scale landslides, revealing high displacement rates with line-of-sight velocities reaching up to −81 mm/yr. We validated our detections by comparing them with Google Earth imagery and the previously published landslide inventories along the Karakoram Highway. This study reveals the spatial distribution of active landslides along the uplifted mountainous terrain, highlighting potentially unstable zones, and offers insights into hazard mitigation and risk analysis, especially for less monitored economic roads in orogenic zones. Full article

Introduction: Additive manufacturing has emerged as a promising solution for improving the accessibility and affordability of upper limb prostheses. Despite the growing need, traditional prosthetic devices remain costly and often inaccessible, particularly in underserved regions. This review examines the current landscape of 3D-printed upper limb prostheses, focusing on their design, functionality, and cost-effectiveness. It aims to assess the potential of 3D-printing upper limb prostheses in addressing current accessibility barriers. Methods: A two-phase approach was used to analyze the literature on 3D-printed upper limb prostheses. The first phase involved a literature search using keywords related to 3D printing and upper limbs prostheses. The second phase included data collection from online platforms such as Enabling the Future, Thingiverse, and NIH 3D Print Exchange. Studies focusing on the design, fabrication, and clinical application of 3D-printed prostheses were included. The results were organized into categories based on design characteristics, kinematic features, and manufacturing specifications. Results: A total of 35 3D-printed upper limb prostheses were reviewed, with the majority being hand prostheses. Devices were categorized based on their range of motion, actuation mechanism, materials, cost, and assembly complexity. The e-NABLE open-source platform has played a significant role in the development and dissemination of these devices. Prostheses were classified into cost categories (low, moderate, and high), with 64% of models costing under USD 50. Most designs were rated as easy to moderate in terms of assembly, making them accessible for non-specialist users. Conclusions: Three-dimensional printing offers an effective, low-cost alternative to traditional prosthetic manufacturing. However, variability in design, a lack of standardized manufacturing protocols, and limited clinical validation remain challenges. Future efforts should focus on establishing standardized guidelines, improving design consistency, and validating the clinical effectiveness of 3D-printed prostheses to ensure their long-term viability as functional alternatives to traditional devices. Full article

The early detection of cancerous lesions is a challenging task given the cancer biology and the variability in tissue characteristics, thus rendering medical image analysis tedious and time-inefficient. In the past, conventional computer-aided diagnosis (CAD) and detection methods have heavily relied on the visual inspection of medical images, which is ineffective, particularly for large and visible cancerous lesions in such images. Additionally, conventional methods face challenges in analyzing objects in large images due to overlapping/intersecting objects and the inability to resolve their image boundaries/edges. Nevertheless, the early detection of breast cancer lesions is a key determinant for diagnosis and treatment. In this study, we present a deep learning-based technique for breast cancer lesion detection, namely blob detection, which automatically detects hidden and inaccessible cancerous lesions in unsupervised human breast histology images. Initially, this approach prepares and pre-processes data through various augmentation methods to increase the dataset size. Secondly, a stain normalization technique is applied to the augmented images to separate nucleus features from tissue structures. Thirdly, morphology operation techniques, namely erosion, dilation, opening, and a distance transform, are used to enhance the images by highlighting foreground and background pixels while removing overlapping regions from the highlighted nucleus objects in the image. Subsequently, image segmentation is handled via the connected components method, which groups highlighted pixel components with similar intensity values and assigns them to their relevant labeled components (binary masks). These binary masks are then used in the active contours method for further segmentation by highlighting the boundaries/edges of ROIs. Finally, a deep learning recurrent neural network (RNN) model automatically detects and extracts cancerous lesions and their edges from the histology images via the blob detection method. This proposed approach utilizes the capabilities of both the connected components method and the active contours method to resolve the limitations of blob detection. This detection method is evaluated on 27,249 unsupervised, augmented human breast cancer histology dataset images, and it shows a significant evaluation result in the form of a $98.82\%$ F1 accuracy score. Full article

We investigate the phenomenological effects of the nonholomorphic (NH) higgsino mass term, ${\mu }^{\prime }$, within the minimal supersymmetric standard model (MSSM) extended by a non-abelian flavor symmetry, referred to as the sNHSSM. This flavor symmetry enables a substantial reduction in the number of free parameters inherent to the MSSM, streamlining them from a large set to just eight. Our study explores the interplay between cold dark matter (CDM) relic density (${\mathsf{\Omega }}_{\mathrm{CDM}}{h}^2$) and the anomalous magnetic moment of the muon, ${(g-2)}_{\mu }$. We study correlations among the theoretical parameters that emerge from this interplay and are further constrained by experimental data such as the Higgs boson mass, B-physics observables, and the charge and color breaking minima constraints. Moreover, our findings reveal that incorporating the NH higgsino mass term opens up new regions of parameter space that were previously inaccessible. Full article