Search Results (1,119)

This work explores the key capabilities of emerging sensing technologies in the context of Structural Health Monitoring (SHM) of civil infrastructures, aiming to contribute to research on integrated and intelligent systems for more accessible and efficient monitoring solutions. As a case study, it focuses on the analysis of the static and dynamic behavior of the Edgar Cardoso stay-cable bridge during its rehabilitation, using fully customized transducers and equipment. The developed system integrates sensors capable of measuring accelerations, displacements, and temperature, which are connected to an autonomous data acquisition and transmission network. A digital interface was also developed to store, process, and visualize the collected data, enabling remote access for subsequent interpretation and analysis. The main contribution of this research lies in the use of optimized wireless monitoring systems with extended autonomy. This is achieved by employing edge computing techniques to minimize energy consumption during data transmission, as well as by managing the sleep modes of the sensor nodes. At same time, a methodology was proposed for the automatic and real-time estimation of axial forces in cables. This approach relies on the use of innovative edge computing tools, combined with the taut string theory as a simplified modelling framework. The results confirm the effectiveness of the developed system in achieving long-term operation without compromising monitoring performance. In addition, the developed system enabled the identification of the structure’s dynamic properties, particularly natural frequencies. The temperature profiles in critical sections, as well as displacements in the expansion joint were also measured and evaluated. The results demonstrate the potential of customized sensing solutions as effective tools for the management, maintenance, and long-term preservation of strategic infrastructures. Full article

Silicon is increasingly applied in agriculture to improve plant productivity under both abiotic and biotic stress constraints. Nevertheless, its mechanisms of action are often studied separately at the soil, plant, or microbiome levels, limiting a comprehensive understanding of its overall impact on agroecosystem functioning. This review proposes an integrated perspective of the soil–plant–microbiome continuum, linking silicon chemistry in soil solutions with the effects of silicon amendments on soil properties and the processes of uptake, transport, and deposition in the plants. We show that silicon bioavailability depends on maintaining a pool of dissolved silicon dominated by orthosilicic acid, regulated by mineral weathering, adsorption–desorption dynamics, polymerization, pH, iron and aluminum oxides, and organic matter. In soils, silicon inputs can improve structure, modulate acidity and cation exchange balances, influence nutrient availability, and reduce the mobility of certain metals. They may also affect enzymatic activities and microbial community composition. In plants, silicon uptake and transport, mediated by specific transporters, contribute to tissue silicification, the maintenance of leaf architecture, and the regulation of water, ionic, and redox homeostasis. These processes provide a basis for enhanced tolerance to drought, salinity, and metal toxicity, as well as biotic stress caused by pathogens and pests. Finally, we discuss key limitations to the agronomic application of silicon, including the diagnosis of the silicic status of soils, the choice of source and mode of application, and the genotypic variability of acquisition, as well as the need for multi-site tests and more robust mechanistic validations. This synthesis provides a coherent mechanistic framework to better define the conditions under which silicon can serve as a reliable tool for sustainable crop management under climate change. Full article

Reliable determination of radon adsorption properties in candidate adsorbents is essential for developing highly sensitive methods capable of measuring low ²²²Rn activity concentrations in air. Such measurements are increasingly important in environmental monitoring, climate research, and low-background experiments. Conventional approaches for determining the adsorption coefficient and heat of adsorption are labor- and time-intensive, limiting their suitability for comparative studies under identical conditions. Here, a recently proposed method is applied for the first time in a systematic comparative study. The approach couples solid-state nuclear track detectors (SSNTDs) with adsorbents that simultaneously act as radon collectors and alpha emitters, enabling fully parallel exposure and signal acquisition across multiple samples. Eight adsorbents—three activated carbon fabrics, two bulk activated carbons, and three synthetic zeolites—were evaluated simultaneously over a temperature range of 0–46.5 °C. Activated carbon fabrics exhibited the highest adsorption coefficients, with ACC-5092-10 reaching 11.8 ± 1.3 m³/kg at 20 °C. The heats of adsorption ranged from 24.8 ± 3.9 to 33.3 ± 5.0 kJ/mol, consistent with the literature values. For synthetic zeolites, the adsorption coefficient increased linearly with the Si:Al ratio. The influence of water content was further investigated for the five best-performing materials. The most hydrophobic material, zeolite SA-25 (Si:Al = 25), showed only a 25% reduction in adsorption coefficient under saturated humidity, whereas activated carbons exhibited strong suppression. These results demonstrate the practicality, sensitivity, and efficiency of the SSNTD–adsorbent method for comparative radon adsorption studies. Full article

Large Language Models (LLMs) are increasingly deployed across professional and societal domains, introducing security, privacy, and governance challenges beyond traditional software vulnerabilities. Despite extensive research on individual risk categories, a unified lifecycle-oriented perspective connecting architectural properties, adversarial threats, and governance implications remains limited. This review examines security and privacy risks associated with LLMs through a lifecycle framework covering data acquisition, model training, alignment procedures, deployment, and post-deployment interaction. The study synthesizes prior research to construct a taxonomy of threats including prompt injection, jailbreaking, adversarial manipulation, training-stage attacks, privacy leakage, and socio-technical misuse. Ethical issues such as hallucination, bias amplification, and malicious use are analyzed alongside governance and regulatory frameworks. Results indicate that vulnerabilities in LLM systems arise primarily from probabilistic generation mechanisms, large-scale data ingestion, and complex deployment ecosystems rather than isolated implementation defects. Classical software vulnerability models therefore provide only partial coverage of risks associated with generative AI systems. The review is grounded in the concept of the alignment gap to explain how discrepancies between training objectives and real-world interaction contribute to persistent vulnerabilities. The findings highlight the need for lifecycle-oriented defense-in-depth strategies combining technical safeguards, privacy-preserving training, runtime monitoring, and governance mechanisms to support responsible deployment of LLM-based systems. Full article

Underground utilities form an essential part of urban infrastructure, yet their importance often becomes apparent only when service disruptions occur. Excavation activities for maintenance, relocation, or new construction carry considerable risks, including utility strikes, project delays, worker injuries, and even fatalities. These risks are largely driven by incomplete or inaccurate information about the location, depth, or material properties of buried utilities. To address this challenge, this study proposes a comprehensive Building Information Modeling (BIM)-based framework for managing underground utilities throughout their lifecycles. The framework is structured into five key stages: data acquisition, data processing, modeling, system application, and data updating. A highway project was used as a case study to validate the proposed approach. The study involved the integrated modeling and visualization of the highway corridor, underground gas pipelines, and overground high-voltage transmission pylons using Autodesk Civil 3D, InfraWorks, and Navisworks. The developed model and workflow were subsequently reviewed with the client department. Application of the framework to a 5 km highway corridor identified five utility-road conflict points (three subsurface gas pipeline intersections and two overground pylon encroachments) that were not detectable from existing 2D records. Expert review by the client department confirmed that the BIM-based visualization and 4D simulation improved construction planning clarity and supported proactive utility relocation decisions. By simplifying information workflows and enabling collaboration among stakeholders, the proposed framework demonstrates strong potential to improve excavation safety, enhance decision-making, and support the wider adoption of BIM for underground utility management. Full article

Background/Objectives: Magnetic nanoparticles have emerged as powerful tools for biomedical imaging, targeted drug delivery, and hyperthermia therapy. Magnetic particle imaging (MPI) is among the most promising technologies built around its properties: a radiation-free, quantitative tomographic modality that detects superparamagnetic iron oxide nanoparticles (SPIONs) directly against a biologically silent background. This review synthesizes MPI’s physical principles, nanoparticle design strategies, and preclinical applications within the broader landscape of magnetic material engineering for biomedical use. Methods: A systematic review was conducted covering MPI signal generation and image reconstruction, nanoparticle core synthesis and surface coating approaches, and preclinical applications, spanning cell tracking, oncological imaging, vascular perfusion, neuroimaging, and MPI-guided theranostics. Studies were selected to provide quantitative benchmarks and direct comparisons with competing modalities where available. Results: MPI delivers signal-to-background ratios above 1000:1, iron-mass linearity at R² ≥ 0.99, regardless of tissue depth, and acquisition rates up to 46 volumes per second. Tracer architecture—encompassing single-core particles, multicore nanoflowers, and stimuli-responsive cluster designs—is the primary determinant of sensitivity, environmental robustness, and theranostic capability. Preclinical results include detection of cell populations in the low thousands, earlier ischaemia identification than diffusion-weighted MRI, real-time drug release quantification, and spatially confined tumour hyperthermia. Three translational bottlenecks are identified: the absence of a clinically approved tracer with optimal relaxation dynamics, hardware performance losses when scaling to human-bore systems, and overestimation of passive tumour accumulation in murine models. Conclusions: MPI illustrates how progress in magnetic material design directly expands clinical imaging and theranostic possibilities. Successful translation will require indication-driven, interdisciplinary development that integrates materials science, scanner engineering, and regulatory strategy in parallel. Full article

Conventional spatial frequency domain imaging (SFDI) based optical property inversion is inefficient, while deep learning methods suffer from heavy reliance on large-scale real datasets. To address this contradiction, a simulation-driven approach for subsurface fruit bruise discrimination was proposed. An SFDI simulation environment was built with Blender to generate 800 paired datasets of diffuse reflectance images and optical transport coefficients, overcoming the high cost and long cycle of real dataset acquisition. We designed the CBAM-GAN-U-Net model and adopted surface profile correction in the prediction method to eliminate curved surface-induced non-planar distortion, with the whole method validated on liquid phantoms, green apples and crown pears. This prediction method achieved high accuracy in predicting the reduced scattering coefficient μ_s′, with NMAE of 0.021 ± 0.007 (phantoms), 0.039 ± 0.012 (severely bruised green apples) and 0.044 ± 0.015 (severely bruised crown pears), outperforming U-Net and GANPOP. Based on the predicted μ_s′, a discrimination strategy combining coefficient of variation, mean ratio and receiver operating characteristic (ROC) curve analysis was adopted, attaining 100% accuracy for non-bruised/bruised fruit discrimination, with misclassification rates of 6% (green apples) and 8% (crown pears) for mild/severe bruise differentiation. This method enables accurate subsurface fruit bruise detection, providing a reliable technical solution for the fruit and vegetable industry and helping reduce postharvest supply chain losses. Full article

Dynamic response measurements support bridge monitoring and structural assessment because they are obtainable under operational loading and are sensitive to changes in stiffness, boundary conditions, and mass distribution. This article presents a structured scoping review of dynamic-response-based bridge monitoring and assessment. It covers damage-sensitive indicators, stiffness/capacity proxy inference, interpretation under operational and extreme loading, sensing with acquisition (contact, and indirect/drive-by), and data processing, machine learning and digital-twin integration for decision support. Evidence was identified through targeted searches in Scopus and The Lens with duplicate resolution in Zotero. The cited studies are compiled into a traceable evidence inventory linked to method families and decision objectives. The synthesis shows that global modal properties enable change screening but are highly confounded by environmental/operational variability. Localization and state characterization typically require denser or higher-fidelity sensing and signal conditioning. Finally, capacity-related inference using calibrated conversion models or machine learning (ML) surrogates remains context-bounded and validation-dependent. This review provides an end-to-end pipeline, evidence-maturity rubric, and conservative failure-mode checks with escalation logic that tie SHM outputs to inspection and analysis rather than direct condition declarations for bridge owners. This review is intentionally scoped and does not claim PRISMA-style comprehensiveness. Full article

The SH-SY5Y cell line is widely used as an in vitro model for pharmacological and molecular investigations of Parkinson’s disease (PD). The use of SH-SY5Y cells in PD research critically relies on their ability to differentiate into a mature, post-mitotic, dopaminergic (DAergic) neuronal phenotype. However, SH-SY5Y cells are inherently heterogeneous since they are firstly catecholaminergic cells and may express diverse phenotypic markers besides the DAergic ones. These properties seem to be determined by the differentiation protocol that is employed, thus meaning it is crucial to obtain proper cell types. This systematic review aims to discuss the main differentiation protocols used in PD research over the last 30 years. They include inducers such as retinoic acid (RA), the phorbol ester TPA, and the BDNF. Among the 514 studies that were screened, 249 employed these inducers. Then, we quantitatively report the ability of these protocols to differentiate SH-SY5Y cells in mature DAergic neurons, evaluating morphology, differentiation markers, and DAergic markers among the studies that specifically compared differentiated to undifferentiated SH-SY5Y cells (61 studies over 249). As our research shows, despite the highest usage of the RA differentiation protocol, the combination of RA with the BDNF inducer seems to increase the expression and the acquisition of a DAergic phenotype. Nevertheless, during this analysis, some limitations emerged, highlighting the intrinsic phenotypic heterogeneity of these cells, thereby limiting their suitability according to the specific biological question under investigation. A deep investigation into the literature about the molecular phenotypic features of differentiated SH-SY5Y cells may eventually help us to understand the advantages and disadvantages of each protocol that was employed, and adequately set experiments around the PD research. Full article

The mechanical properties and in situ stress conditions of coal reservoirs critically control the effectiveness of hydraulic fracturing, yet the continuous acquisition of relevant parameters at the well scale is often limited by logging data availability and quality. To address this, an integrated workflow combining machine learning-based parameter inversion with a fracturing suitability evaluation framework was proposed for coalbed methane (CBM) reservoirs. A supervised neural network model was developed to establish nonlinear relationships between conventional logs and key parameters, including Young’s modulus, Poisson’s ratio, and horizontal principal stresses. Based on these inverted parameters, a dimensionless Fracturing Index (FI) was constructed to comprehensively characterize coal fracturability by integrating brittleness, fracture toughness, and stress conditions, with a density-based constraint introduced to ensure mechanical consistency. Point-scale FI values within coal seams were upscaled to the well scale for inter-well comparison and regional evaluation. Results showed that FI varied relatively little within individual wells but markedly between wells, reflecting systematic inter-well variations in mechanical and stress conditions, consistent with spatial patterns revealed by cross-well profiles. Correlation analysis from over ten wells with both FI and treatment data demonstrated positive relationships between FI and breakdown pressure, injected fluid volume, and proppant volume, confirming its engineering relevance. Consequently, a four-level FI-based classification scheme was established to identify favorable zones across the study area. This FI framework provides a practical, interpretable tool for early-stage CBM development, offering quantitative guidance for well prioritization, stimulation design, and regional planning in unfractured areas. Full article

To accurately quantify the intrinsic absorption efficiency of bamboo leaves to the solar spectrum, we measured the reflectance and transmittance of leaves from 55 bamboo species cultivated at the same site, and developed a mathematical model to calculate the annual cumulative photon absorption of photosynthetically active radiation (PAR) per leaf. The results showed the following: (1) Bamboo leaf optical properties exhibited high instrumental and spatial measurement consistency, with transmittance not significantly fluctuating with changes in incident light intensity or quality. (2) Bamboo leaves exhibited significant spectral selective absorption characteristics, with stronger absorption of blue and red light and weaker absorption of green light; Phyllostachys vivax had the highest mean absorptance per unit area, while Chimonobambusa tumidinoda had the lowest. (3) The annual photon absorption per unit leaf area ranged from 1.83 × 10⁵ to 9.86 × 10⁵ μmol, with Phyllostachys iridescens being the lowest and Chimonobambusa marmorea the highest. The annual photon absorption per single leaf ranged from 1.84 × 10⁶ to 5.13 × 10⁷ μmol, with Indocalamus decorus achieving the highest total absorption due to its largest leaf area (114.9 cm²), while Bambusa multiplex var. riviereorum was the lowest. (4) All tested bamboo species showed consistent seasonal dynamics in photon absorption, with the highest in summer and lowest in winter. Although unit-area absorptance reflects the intrinsic light interception efficiency, leaf morphology has a substantial influence (explaining 99.56% of the variance) in determining total light acquisition per leaf. Full article

The majority of terrestrial plant species establish below-ground interconnections via arbuscular mycorrhizal (AM) mycelium, thereby forming extensive common mycorrhizal networks (CMNs). CMNs serve as critical infrastructure for nutrient acquisition, mediating soil nutrient capture and distribution. In nitrogen-fixing plants, phosphorus (P) transport is particularly dependent on functional CMNs. The rapid expansion in graphene oxide (GO) production and its broad application have raised significant ecological concerns, particularly regarding its potential impacts on terrestrial ecosystems. Despite these concerns, the impact of GO on P transport dynamics within legume–arbuscular mycorrhizal fungi (AMF) symbioses remains critically scarce. This study established a symbiotic system using the model nitrogen-fixing legume Medicago sativa L. and AMF. This experimental system enabled a comprehensive assessment of GO effects on rhizosphere P mobilization, plant P acquisition, CMNs architecture, fungal community composition, and expression of key P transporter genes. Our results demonstrated that high GO concentrations significantly altered rhizosphere properties, increasing pH while reducing organic acid content and alkaline phosphatase activity. Furthermore, GO exposure significantly inhibited root growth, mycorrhizal colonization rates, and plant P acquisition efficiency. Additionally, GO exposure altered AMF community composition, reduced rhizosphere microbial diversity, and suppressed P metabolism gene expression. Specifically, 0.6% GO induced significant downregulation of MsCS and GigmPT by 83.5% and 62.3%, respectively. This indicates that GO impairs plant P uptake by disrupting the core pathway involving GigmPT and MsCS, triggering P stress in M. sativa. Collectively, these findings provide compelling evidence that GO exposure disrupts legume–AMF symbiotic integrity, ultimately impairing P transport efficiency. Full article

Siderophores are high-affinity iron-chelating metabolites that underpin microbial survival in iron-limited environments and play central roles in metal homeostasis, ecological competition, and pathogenesis. Traditionally viewed as dedicated Fe(III) scavengers, siderophores are now recognized as structurally and functionally versatile coordination agents whose donor-set architectures—particularly catecholate and α-hydroxycarboxylate motifs—permit conditional interactions beyond iron. In iron- and boron-rich niches, especially marine and mildly alkaline systems where borate availability increases, certain siderophores are chemically capable of forming reversible borate complexes through cis-diol coordination. Although Fe(III) exhibits substantially higher thermodynamic affinity and remains the primary biological target, boron binding represents a predictable secondary property arising from shared oxygen-donor chemistry. This dynamic interplay allows siderophores to cycle between iron-bound, boron-bound, and apo states depending on local redox conditions, pH, and metal availability. Here, we synthesize current knowledge on the structural classes of microbial siderophores, their transport and regulatory mechanisms, and emerging evidence for boron coordination within catecholate and carboxylate systems. By integrating coordination chemistry with microbial ecology, we propose an expanded model in which siderophores function not only as iron acquisition molecules but also as modulators of boron speciation and environmental sensing. This functional plasticity positions siderophores at the intersection of iron and boron biogeochemical cycles and highlights new directions for understanding microbial adaptation in complex metal-rich environments. Full article

Background: Sarcopenia is an age-related syndrome characterized by progressive loss of skeletal muscle mass and strength, representing a major contributor to disability and increased mortality in older adults. Current diagnostic frameworks increasingly emphasize muscle quality alongside quantity, creating a clinical need for bedside tools that can objectively assess muscle mechanical properties. Shear-wave elastography (SWE), an ultrasound-based technique that quantifies muscle stiffness, has emerged as a promising biomechanical biomarker of muscle quality. Aim: This narrative review evaluates the evidence supporting SWE for assessing muscle quality and its association with aging, sarcopenia, and functional outcomes. Methods: We searched PubMed, Embase, and Web of Science (from January 2010 to December 2025) using terms related to elastography and sarcopenia. Based on relevance and methodological quality, approximately 50 key studies were selected for in-depth discussion and synthesis. Synthesis: Observational studies consistently demonstrate that SWE detects age-related reductions in muscle stiffness, which correlate significantly with declines in muscle strength and physical performance. Unlike conventional B-mode ultrasound, which primarily provides morphological parameters, SWE directly reflects intrinsic tissue mechanics, enabling more direct assessment of muscle quality. In high-risk populations such as patients with type 2 diabetes, reduced muscle stiffness is also associated with sarcopenia and poor functional outcomes. However, reported stiffness trends with aging remain heterogeneous, and validated diagnostic thresholds are lacking. Stiffness changes vary by muscle group, acquisition protocol, and loading state. Clinical implementation is currently limited by inter-device variability, operator dependence, and sensitivity to muscle loading conditions. Conclusions: Current evidence suggests that SWE holds promise as an adjunctive research tool for assessing muscle quality and risk stratification, but it is not yet ready for standalone clinical diagnosis due to methodological heterogeneity, lack of validated cutoffs, and limited longitudinal data. Future large-scale, longitudinal, multicenter studies with standardized protocols are needed to establish its definitive diagnostic utility. Full article

Phytoecdysteroids have garnered increasing interest due to their broad biological and pharmacological properties. The present study reports on the development and validation of a reliable liquid chromatography–mass spectrometry method for the detection and quantification of 20-hydroxyecdysone, turkesterone, and ponasterone. The optimized procedure improved ionization efficiency and chromatographic resolution through gradient elution using 0.1% formic acid in water and acetonitrile. Data acquisition in selective ion monitoring modes ensured high analytical precision, reproducibility, and sensitivity. The method demonstrated excellent linearity, accuracy, repeatability, and low detection limits, making it suitable for routine phytochemical and quality control applications. Application of the method to extracts from nutrient-rich superfoods, including kaniwa, spinach, quinoa, and asparagus, confirmed these plants as natural sources of phytoecdysteroids. Additionally, thirteen commercially available dietary supplements labeled as containing extracts of Rhaponticum carthamoides, Cyanotis arachnoidea, Ajuga turkestanica, or ecdysteroids were analyzed. Several products standardized to 80–95% ecdysterone contained substantially lower amounts than declared, with measured 20-hydroxyecdysone levels ranging from below the limit of detection to approximately 50 mg per capsule, whereas some non-standardized products exhibited moderate to high levels, reaching up to approximately 105 mg per capsule. Variability in turkesterone content was also observed among products marketed as standardized extracts. The method provides a simple, reliable, and accessible approach for the quantitative analysis of major phytoecdysteroids in complex plant matrices and dietary supplements. Its implementation may support phytochemical research, routine quality control, and anti-doping monitoring of ecdysteroid-containing products. Full article