Search Results (12,375)

With the development of polar regions and the deepening utilization of cold region resources, a large number of infrastructure projects are continuously being carried out. The freezing temperature of unsaturated soil is a critical factor governing the freezing depth and stability of foundations in cold regions or seasons. Concurrently, the supercooling state of soil significantly influences the assessment of its phase composition and physico-mechanical properties. This study employed physical experiments, theoretical formulas, and numerical simulations to reveal the influencing factors and underlying mechanisms of supercooling characteristics in unsaturated soils under controlled low-rate continuous cooling conditions. The results demonstrate that a reduced temperature gradient between the sample surface and the ambient environment correlates with a lower supercooling limit temperature and an extended supercooling duration. An excessively high cooling rate suppresses the supercooling phenomenon in the sample core due to boundary effects. In contrast, neither the temperature difference nor the external cooling rate exhibit a negligible influence on the freezing temperature. Analysis of the temperature–time curves reveals that the freezing process of silty clay is more stable, exhibiting fewer stepwise temperature declines during the phase change plateau, whereas mudstone shows heightened sensitivity to variations in the thermal gradient. Compared to conventional thermocouple measurements, the proposed methodology achieves an optimal balance between temporal efficiency and measurement accuracy. It not only enhances experimental controllability and data reliability, but also provides more scientific theoretical support and technical pathways for predicting freezing depth, designing foundation thermal systems, and preventing frozen ground disasters in cold region engineering. Full article

Background: Early detection of dental caries is essential for effective oral health management. Current diagnostic workflows rely heavily on radiographic imaging, which involves infrastructure requirements, workflow coordination, and resource considerations that may limit frequent use in high-throughput or resource-constrained settings. These contextual factors motivate exploration of adjunct screening concepts that could support front-end triage decisions within existing care pathways. This study evaluates, in simulation, whether modeled tooth-percussion response signals contain sufficient discriminative information to justify further translational and managerial investigation. Implementation costs, workflow optimization, and economic outcomes are not evaluated directly; rather, the objective is to assess whether the technical preconditions for a potentially scalable screening concept are satisfied under controlled in silico conditions. Methods: An in silico model of tooth percussion was developed in which enamel, dentin, and pulp/root structures were represented as a simplified layered mechanical system. Impulse responses generated from simulated tapping were used to compute the modeled surface-vibration response (enamel-layer displacement), which served as a proxy for a measurable percussion-related signal (e.g., contact vibration), rather than a recorded acoustic waveform. Carious conditions were simulated through depth-dependent reductions in stiffness and effective mass and increases in damping to represent enamel and dentin demineralization. A synthetic dataset of labeled simulated signals was generated under varying structural parameters and measurement-noise assumptions. Machine-learning models using Mel-frequency cepstral coefficient (MFCC) features were trained to classify healthy teeth, enamel caries, and dentin caries at a screening (triage) level. Results: Under baseline simulation conditions, the classifier achieved an overall accuracy of 0.97 with balanced macro-averaged F1-score (0.97). Misclassifications occurred primarily between healthy and enamel-caries categories, whereas dentin-caries cases were most consistently identified. When measurement noise and structural variability were increased, performance declined gradually, reaching approximately 0.90 accuracy under the most challenging simulated scenario. These results indicate that discriminative information is present within the modeled signals at a screening (triage) level, meaning that higher-risk categories can be distinguished probabilistically rather than with definitive diagnostic certainty. Sensitivity and specificity trade-offs were not optimized in this study, as the objective was to assess separability rather than to define clinical decision thresholds. Conclusions: Within the constraints of the in silico model, simulated tooth-percussion response signals demonstrated discriminative patterns between healthy, enamel caries, and dentin caries categories at a screening (triage) level. These findings establish technical plausibility under controlled simulation conditions and support further investigation of percussion-based screening as a potential adjunct to clinical assessment. From a healthcare management perspective, the present results address a prerequisite question—whether such signals contain sufficient information to justify translational research, rather than demonstrating workflow optimization, cost reduction, or system-level impact. Clinical validation, threshold optimization, and implementation studies are required before managerial or operational benefits can be evaluated. Full article

Fumonisin B1 (FB1) is a secondary metabolite produced by Fusarium species, exhibiting strong toxicity and classified as a Group 2B carcinogen by the International Agency for Research on Cancer. It poses a significant threat to both human and animal health. Therefore, developing a simple and reliable method for FB1 detection and analysis is imperative. In this study, a biosensor based on nucleic acid aptamers was developed, utilizing plasma-modified graphene oxide (mGO) as a fluorescence quencher for FB1 detection. This system leverages the interaction between mGO and FAM-APT (a nucleic acid aptamer labeled with 5-carboxyfluorescein, FAM), achieving fluorescence quenching through fluorescence resonance energy transfer (FRET) under excitation at 490 nm and emission at 520 nm. In the presence of FB1, FAM-APT specifically binds to FB1 and dissociates from the mGO surface, resulting in fluorescence recovery. Quantitative detection of FB1 was achieved by measuring the differential fluorescence intensity. The biosensor demonstrated excellent linearity over a concentration range of 10 to 5 × 10⁶ ng/L, with a detection limit (LOD) as low as 0.16 μg/L. Additionally, the sensor exhibited high specificity for FB1 among six common mycotoxins. In practical sample analysis, recovery rates ranged from 95.8% to 104.7% in corn samples and from 89.3% to 94.5% in rice samples. This aptamer-based biosensor features a simple structure, high sensitivity, and a wide detection range, providing important technical support for advancing mycotoxin research. Full article

Polytetrafluoroethylene (PTFE) is attractive for high-frequency communications but adheres very poorly to other materials due to its very low surface energy. Conventionally, surface treatments of PTFE are used to increase the polarity of the PTFE surface and enable bonding to materials with increased surface free energy. However, surface treatments are difficult to scale, can damage surfaces, and often lack reproducibility. Therefore, developing a material that can make PTFE adhere well to other materials without surface treatment is highly desirable. In this study, we aimed to develop a new material with strong adhesion to PTFE. We synthesized three polymer gels from dodecyl acrylate (DA) and 2-(dimethylamino) ethyl acrylate (DMAE): the homopolymer gels PDEAE and PDA, and the copolymer gel P(DEAE-co-DA). The copolymer gel P(DEAE-co-DA) exhibited high pressure-sensitive adhesion to PTFE, recording the highest adhesive strength (F = 430.0 N/m) and the highest peel energy (G = 713.4 J/m²) compared to the homopolymer gels PDEAE and PDA. Mechanical testing showed PDEAE had the greatest strength and toughness, PDA balanced stiffness and extensibility, and P(DEAE-co-DA) was the most flexible and extensible. The P(DEAE-co-DA) with the smoothest surface (Sz ≈ 0.176 µm) showed the highest F and G, implying that surface roughness did not contribute significantly to the interfacial adhesion between the gels and the PTFE. Based on the surface free energy ${\sigma }_s$ and work of adhesion $W_a$ values, the adhesive strength to PTFE was predicted to be PDEAE > P(DEAE-co-DA) > PDA, but the measured G in peel tests contradicted this, indicating that the gels’ viscoelastic deformation and energy dissipation dominate the measured F and G. The frequency-dependent viscoelastic data and relaxation times τ and activation energies $E_a$ suggested optimal adhesion requires a balance of adhesion (mobility for energy dissipation (short τ, low $E_a$)) and sufficient cohesion (high G′). P(DEAE-co-DA) achieved this balance, explaining its high measured F and G. With precise control of polymer chain mobility, the adhesion of P(DEAE-co-DA) gels can likely be improved further. Future work will employ block copolymerization and monomer-ratio control to tune molecular motion and enhance adhesion to PTFE. Full article

Sharm El-Luli, located along the southern Red Sea coast of Egypt, is a semi-enclosed, shallow, mangrove-fringed lagoon characterized by limited hydrodynamic exchange, high salinity, and low terrigenous input. This study investigates the influence of sediment properties, hydrodynamic gradients, and mangrove-associated microhabitats on the spatial distribution of benthic ostracod assemblages within this lagoonal system. Eighteen surface sediment samples (W1–W18) were collected along an onshore–offshore gradient and analyzed for ostracod composition, sediment texture, carbonate and organic matter content, and water parameters including temperature, salinity, dissolved oxygen, pH, redox potential, and total dissolved solids. Thirty-four ostracod taxa were identified, revealing a pronounced inner–outer ecological partitioning across the lagoon. Redundancy analysis (RDA) demonstrates that ostracod distribution is primarily controlled by substrate heterogeneity, organic enrichment, salinity, and conductivity-related variables. The inner, low-energy mangrove margin is dominated by Aglaiocypris triebeli, Paranesidea fracticorallicola, and Hiltermannicythere rubrimaris, reflecting stressed, low-diversity conditions associated with organic-rich sediments and restricted circulation. In contrast, mid- and outer-lagoon stations host more diverse assemblages dominated by Xestoleberis spp., Neonesidea schulzi, Loxocorniculum ghardaquensis, and Jugosocythereis borchersi, indicative of better-flushed environments with higher carbonate content and stable marine salinity. These results demonstrate that benthic ostracods respond sensitively to fine-scale environmental gradients in mangrove-fringed lagoons, underscoring their value for assessing ecological health and sedimentary dynamics in semi-enclosed Red Sea coastal systems. Full article

The Cairo Monorail System presents significant geotechnical challenges due to its integrated structural configuration and its alignment across heterogeneous soil conditions, including collapsible and swelling soils. This study investigates the foundation performance of the monorail through a combination of advanced site investigations, full-scale pile load testing under dry and wetted conditions, and finite-element modeling incorporating soil–structure interaction. Field load tests on large-diameter bored piles founded in collapsible soils demonstrated a pronounced increase in settlement and a reduction in stiffness following wetting, confirming the sensitivity of pile behavior to moisture variations. Three-dimensional numerical analyses of the integrated monorail system showed that differential settlements between adjacent columns are generally limited to less than 9 mm under serviceability loading conditions, satisfying passenger comfort requirements. Long-term coupled seepage–deformation analyses conducted using PLAXIS indicated that surface water infiltration into swelling soils may induce time-dependent monopile heave of approximately 10 mm over a 50-year design life, which remains within acceptable serviceability limits. The results demonstrate that detailed geotechnical characterization, combined with appropriate numerical modeling strategies, can effectively control differential deformation and long-term heave in continuous monorail systems, ensuring their operational safety and long-term performance. Full article

Early detection of cancer biomarkers in blood is critical for improving patient outcomes; however, conventional immunoassays often rely on complex instrumentation and are not well suited for point-of-care testing or multiplexed analysis. Herein, we present a dual-mode colorimetric–surface-enhanced Raman scattering (SERS) lateral flow immunoassay (LFIA) platform for multiplexed detection of cancer biomarkers, employing elongated rod-shaped silver nanoshells (ERNSs) as SERS nanotags. The ERNS features a rough Ag shell with internally incorporated Raman labeling compounds (RLCs), enabling plasmonic extinction for visual readout and strong SERS signals for quantitative analysis while preserving the external metal surfaces for efficient antibody conjugation. Leveraging these advantages, a multiplex LFIA capable of simultaneously detecting prostate-specific antigen (PSA) and carbohydrate antigen 19-9 (CA19-9) on a single strip was successfully demonstrated. Visual inspection enabled rapid discrimination of samples at or near clinically relevant cut-off levels, while Raman analysis achieved limits of detection of 8.0 × 10⁻³ ng/mL for PSA and 5.4 × 10⁻² U/mL for CA19-9, corresponding to approximately 500-fold and 685-fold lower concentrations than their respective clinical thresholds. This ERNS-based colorimetric–SERS LFIA integrates rapid screening and highly sensitive quantification within a single platform and offers a versatile nanoprobe design strategy for multiplex biomarker detection and liquid biopsy-based diagnostic applications, with potential relevance to point-of-care settings. Full article

Quaternary ammonium compounds (QACs) have dominated biocidal practice in cultural heritage conservation for decades, yet growing evidence of environmental persistence, aquatic ecotoxicity, and antimicrobial resistance induction has prompted the search for safer alternatives. Essential oils (EO) have emerged as promising bio-based biocides, though their environmental performance has rarely been quantified through rigorous life cycle approaches. This study presents a comparative Life Cycle Impact Assessment (LCIA) of EO-based and QAC-based biocidal formulations across representative conservation scenarios, following ISO 14040/14044 standards and the Environmental Footprint 3.1 methodology with USEtox^® 2.1 characterization factors. Three complementary functional units were employed: formulation-based, surface-based, and intervention-based. The results reveal a fundamental trade-off: EO-based systems exhibit 81% higher climate change impacts but 82–89% lower human toxicity and freshwater ecotoxicity impacts compared to QAC-based systems. Surface-normalized comparisons reduce the climate gap to 32%, while toxicity advantages remain robust across all sensitivity scenarios. Monte Carlo analysis confirms the robustness of toxicity findings (p > 99%), whereas climate comparisons remain scenario-dependent. These findings support context-dependent adoption of EO-based biocides in conservation practice and demonstrate that EO-related climate impacts are technically mitigable, while QAC toxicity is intrinsic to their molecular structure. Full article

The purpose of this article is to present a systematic reflection on my experience teaching international English as a Second Language (ESL) students about Indigenous rights and reconciliation in a year-long university ESL course in Canada during the COVID-19 pandemic. Teaching materials and activities originally aimed to engage students in a pre-political form of intercultural citizenship engagement with the historical struggles and contemporary realities of Indigenous communities in Canada. Over a six-week period, I engaged in a journaling process to (1) explore the opportunities and challenges of teaching this topic in an online course environment and (2) reflect on my attempts to support and challenge students to critically examine their views and assumptions about cultural diversity in Canada and within their own cultural contexts. A qualitative analysis of the reflective notes revealed that students’ engagement with the course activities designed for this theme was limited. While students completed all required tasks, their discussions and artifacts suggest that engaging with reconciliation from a distance constitutes a complex demand for their intercultural learning. This complexity was reflected in students’ reliance on surface-level engagement rather than sustained critical or dialogic exploration. These findings raise questions about the pedagogical framing of the activities, the temporal and experiential distance of the learning context, and the role these factors may have played in constraining students’ ability to meaningfully engage with reconciliation as a lived and ethically charged process. Drawing on scholarship addressing the ethical challenges of the teacher role and positionality in teaching sensitive topics within intercultural citizenship education (ICE), this article concludes with a reflexive discussion of instructional intentions, ethical tensions, and design considerations that may inform future intercultural citizenship pedagogy in similarly constrained teaching and learning contexts. Full article

Liver cancer continues to be a predominant cause of cancer-related mortality globally, primarily attributable to late diagnosis and a scarcity of dependable biomarkers for early identification. Raman spectroscopy has emerged as a valuable analytical instrument for liver cancer detection, providing rapid, label-free, and non-destructive molecular profiling of biological specimens. Raman-based methodologies can discern malignant from non-malignant conditions by analyzing small biochemical alterations in biofluids, including blood, urine, and exosomes, as well as in liver tissue, yielding unique spectrum fingerprints. Progress in chemometric analysis, including machine learning models and multivariate statistical methods, has significantly improved the diagnostic precision of Raman spectroscopy, attaining elevated sensitivity and specificity across numerous studies. Furthermore, the integration of complementary techniques, such as surface-enhanced Raman spectroscopy (SERS) and Raman optical activity (ROA) has broadened its prospects for clinical application. This review article elucidates the contemporary applications of Raman spectroscopy in the diagnosis of liver cancer, presents pivotal findings across various sample types, and examines the challenges and future prospects of building Raman-based platforms as dependable diagnostic instruments in oncology. Full article

Electrical Resistivity Tomography (ERT) has proven to be a highly sensitive geophysical method for characterizing the dynamics of seawater intrusion. This study uses tank experiments to simulate seawater intrusion, utilizing electrical resistivity tomography to monitor real-time changes in groundwater resistivity during the intrusion process. The objective is to quantitatively reveal the development and evolution mechanisms of seawater intrusion wedges in sandy aquifers, thereby establishing a real-time resistivity monitoring method for groundwater distribution and migration characteristics. This study improves resistivity imaging data processing methods, enhancing both efficiency and accuracy. The refined cross-hole ERT technique is applicable not only to meter-scale indoor experiments; its optimized forward and inverse algorithms can also be directly transferred to regional-scale field monitoring. Experimental results show that the average resistivity in the study area continuously decreases from 57 Ω·m in the initial freshwater state to 1.1 Ω·m at the intrusion stabilization point. Areas with resistivity values below 20 Ω·m corresponded exactly to the brine intrusion zone. The evolution of the freshwater-saltwater interface unfolded in three stages: Initially, the density difference (0.025 g/cm³) dominated, with the saltwater intrusion depth at the aquifer base reaching 0.45 m, significantly exceeding the 0.04 m penetration at the upper section. During the intermediate stage, the interface morphology differentiated into an “upper triangular, lower arc-shaped” configuration. The bottom intrusion distance increased to 1.65 m, and the thickness of the brackish-freshwater mixing zone expanded from 0.1 m to 0.3 m. In the final stage, the interface stabilized and began intruding toward the surface, establishing a new hydrodynamic equilibrium. In addition, the migration rate of saline water at the aquifer base gradually decreased from 6.25 × 10⁻⁴ cm/s initially to 1.16 × 10⁻⁵ cm/s at steady state. These results reflect the dynamic coupling process between seepage and dispersion and demonstrate that this method enables effective real-time monitoring of seawater intrusion development and conditions, as well as early warning capabilities. Full article

Extreme rainfall events often trigger landslides, debris flows, and sediment-laden floods that cause severe damage in built-up areas, yet sediment deposition is rarely quantified in hazard assessments. This study evaluates the capability of the physically based catchment model LISEMHazard to reconstruct sediment generation, transport, and deposition during Hurricane Maria (2017) in two catchments in Dominica (Coulibistrie and Grand Bay). Simulations were performed at 10 m resolution using rainfall, topography, soil, and land-use data. Model calibration and validation used mapped landslides and debris flows, field measurements of deposition height, and DEMs of Difference (DoDs). LISEMHazard reproduced the general magnitude of sediment volumes and the frequency–area distribution of medium and large landslides but showed poor ability to predict their exact locations and overestimated landslide depth and deposition height. Agreement between modeled and observed debris-flow patterns was good in major channels but weak in minor ones. Sensitivity analysis indicated that soil depth and cohesion dominate uncertainties, whereas saturated hydraulic conductivity and surface roughness exert minimal influence. Despite substantial data and model limitations, physically based modeling remains a practical approach for spatial estimation of sediment deposition needed for risk assessment, structural damage evaluation, and cleanup cost estimation. Full article

Land-use and land-cover change plays a critical role in shaping urban expansion patterns and modifying near-surface soil conditions, hydrological behaviour, and geomorphological stability in rapidly developing regions. This study presents a comparative modelling framework to analyze long-term land-use change and its implications for regional-scale geotechnical risk screening by integrating historical land-use classification, Markov transition analysis, and machine learning–based spatial simulation. Landsat imagery from 1985 and 2024 was classified using a Support Vector Machine approach, and future land-use projections for 2063 were generated using both the TerrSet Land Change Modeler (LCM) and the GeoFLUS model under identical transition demands. Spatial driving variables included topographic, hydrological, and accessibility-related factors that influence soil behaviour and urban suitability. The results reveal sustained urban expansion primarily driven by the systematic conversion of agricultural land into built-up surfaces, while forested areas and water bodies exhibit high class persistence, as indicated by dominant diagonal values in the Markov transition matrix. Although both models reproduce consistent directional trends, they generate distinct spatial allocation patterns, with LCM producing compact and centralized growth and GeoFLUS generating more spatially dispersed expansion. These differences lead to contrasting implications for potential settlement, flooding, and slope instability zones. By treating future land-use maps as alternative geotechnical screening scenarios rather than fixed predictions, this study demonstrates how model uncertainty can be incorporated into hazard-sensitive planning. The proposed framework supports preliminary geotechnical zoning and infrastructure planning by identifying robust development corridors and spatial uncertainty zones where detailed site investigations may be prioritized. The methodology is transferable to other rapidly urbanizing regions facing complex soil and geomorphological constraints. Full article

The global expansion of genetically modified (GM) crop cultivation has increased the demand for analytical platforms that can provide rapid, reliable, and cost-effective detec-tion of GM-derived ingredients to support traceability, regulatory compliance, and accu-rate labeling. Conventional molecular assays such as polymerase chain reaction (PCR) and isothermal amplification are highly sensitive and specific but depend on sophisticated instrumentation and trained personnel, limiting their applicability in field settings. Here, we present a label-free and amplification-free nanobiosensor based on citrate-capped gold nanoparticles (AuNPs) for the direct colorimetric detection of the Cry1Ac gene associated with the MON87701 soybean event, without the use of polymerase chain reaction (PCR) or any enzymatic nucleic acid amplification step. The assay relies on the localized surface plasmon resonance (LSPR) of AuNPs, which induces a red-to-purple color transition upon hybridization between complementary DNA strands. Critical reaction parameters, including NaCl concentration, AuNP size, and ionic strength, were optimized to enable selective and reproducible aggregation. Integration with a Support Vector Machine (SVM) algorithm enabled automated spectral classification and semi-quantitative discrimination of GM content levels. The optimized AuNP–SVM system achieved high sensitivity (limit of detection ≈ 2.5 ng μL⁻¹, depending on nanoparticle batch), strong specificity toward Cry1Ac-positive sequences, and reproducible classification accuracies exceeding 90%. By eliminating enzymatic amplification steps, the proposed platform significantly reduces assay time, operational complexity, and instrumentation requirements, making it suitable for rapid on-site GMO screening. Full article

Condition monitoring of rolling bearings is essential for ensuring the reliability of mechanical systems operating under severe or insufficient lubrication conditions. This study proposes a fault diagnosis framework that integrates tribological interpretation of wear phenomena, acoustic emission (AE) signal analysis, and machine learning, based on bearing life tests conducted under dry conditions as an accelerated wear environment to capture damage progression within a practical experimental time. Unlike conventional studies relying on artificially introduced defects, this work focuses on AE signals obtained from bearings in which damage initiates and progresses through actual wear processes. Life tests were conducted using deep groove ball bearings under two radial load conditions. The temporal evolution of the coefficient of friction, AE signals, and surface damage was analyzed. Although the coefficient of friction was the most sensitive indicator of wear progression, its direct measurement is impractical for in-service applications. Frequency-domain analysis revealed that AE counts per second and band-specific AE energy exhibit early changes consistent with the evolution of the friction coefficient. Using these physically interpretable AE features, a fully connected neural network was developed to classify bearing conditions into normal, early-stage damage, and damage progression. The proposed model achieved an average classification accuracy of approximately 85%, demonstrating the effectiveness of AE-based machine learning for bearing fault diagnosis under real wear progression conditions rather than artificial defect scenarios. Full article