Search Results (1,959)

This study presents a comprehensive multi-parameter analysis of seismo-ionospheric responses to the Mw 7.7 Myanmar earthquake on 28 March 2025, using GNSS-based Total Electron Content (TEC) data, seismic b-value trends, and acoustic gravity wave (AGW) signatures. A significant negative TEC anomaly (~30 TECU below the statistical threshold) was detected on 25 March, three days before the mainshock under geomagnetically quiet conditions, indicating a lithospheric origin. Concurrent variations in the Ionospheric Disturbance Index (IDI) and Rate of TEC Index (ROTI) indicate pronounced background departures and enhanced short-term variability during the preparation phase. Temporal b-value analysis shows a consistent decline from 1.12 to 0.58 across the 30-year to 6-month windows, with the lowest values clustering near the epicenter, indicating progressive stress accumulation. Spatial b-value mapping further reveals a low b-value zone overlapping the region of TEC depletion, while the Relative Seismic Hazard Index (RSHI) highlights high-hazard zones aligned with the epicentral area. Kernel density estimation (KDE) supports this coupling by showing a dominant low-b, low-vTEC cluster, consistent with linked lithospheric stress and ionospheric depletion. Overall, the integrated GNSS and seismic analyses demonstrate the value of multi-domain observations for characterizing earthquake preparation processes, highlighting a coherent physical linkage between crustal stress accumulation and ionospheric depletion that can enhance short-term seismic hazard assessment. Full article

Background: It is well known that the university period is an important stage for young adults, involving significant academic and psychosocial adjustments. Students with greater Mental Health Literacy (MHL), which is defined as the knowledge, beliefs, and skills individuals have regarding mental health and mental illness, are better able to identify difficulties, seek help, and adopt healthier coping strategies. This study aims to describe the MHL levels of undergraduate health students and identify associated factors related to academic life, mental health and psychological state. Methods: A cross-sectional, self-administered, web-based survey was conducted using a non-probability sampling strategy among undergraduate students in health-related degrees at a Portuguese higher-education institution. Data was collected using a general characterization questionnaire and the following instruments: MHL Questionnaire, Academic Life Satisfaction, Subjective Happiness Scale, Psychological Well-Being Scale (PWBS), and Depression Anxiety Stress Scale. Bivariate and linear regression analyses were employed to identify factors associated with MHL. Results: A total of 306 students (79% female, mean age = 21.6 years; 59% nursing students) participated. The median MHL score was 70 (range: 30–80). The linear regression model explained 17.5% of the variance in MHL. Higher MHL levels were associated with having the course as a first choice, holding a previous degree, reporting taking psychotropic medication use (which may reflect previous mental health service utilization), and higher levels of psychological well-being. Conclusions: This study provides evidence on factors associated with MHL among undergraduate health students, suggesting that higher MHL is associated with greater psychological well-being, highlighting the potential importance of integrating strategies to promote MHL and psychological well-being in health and nursing education. However, these findings should be interpreted with caution due to the single-institution convenience sample, potential self-selection and reporting biases, and cross-sectional design, which limits causal inferences. Full article

Textile-to-textile recycling is strongly constrained by upstream pre-processing, where post-consumer clothing must be identified, separated, and prepared under high variability in materials, appearance, and contamination. This paper presents a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic literature review of intelligent and automated technologies for textile recycling pre-processing covering the interval between 2015 to 2025. After screening and quality assessment, 21 primary studies published between 2020 and 2025 were included. The literature is synthesized across three task families: (i) identificationof fiber/material, composition, or color; (ii) sorting, considered only when explicit separation strategies are defined to operationalize identification outcomes into routing actions or output streams; and (iii) contaminant detection and/or removal, targeting non-recyclable items. Results show that identification dominates the field (19/21 studies), supported by Red–Green–Blue (RGB) and red–green–blue plus depth (RGB-D) imaging and material-signature sensing, including near-infrared (NIR) spectroscopy, hyperspectral imaging (HSI), and Raman spectroscopy. In contrast, sorting as a defined separation stage is less frequent (4/21), and contaminant-related automation remains sparse (3/21). Most studies are validated in laboratory conditions, with limited semi-industrial evidence, highlighting a persistent perception-to-action gap. Overall, the review indicates that robust separation strategies, representative datasets, and end-to-end system integration remain key bottlenecks for scalable automated textile recycling pre-processing. Full article

Background: Multiple myeloma remains an incurable malignancy in which patients ultimately experience relapses and refractory disease despite therapeutic advances. Triple-class exposed relapsed/refractory MM (TCE/RRMM) patients represent both a population with high unmet clinical needs and a substantial economic burden for the Italian National Health Service (NHS). Recently, BCMA-CD3 bispecific antibodies, including elranatamab and teclistamab, have expanded the treatment options for these patients. This study aimed to evaluate the cost-effectiveness of elranatamab versus teclistamab in adults with TCE/RRMM from the Italian NHS perspective. Methods: A partitioned survival model with three health states—progression-free survival, post-progression survival, and death—was developed over a 25-year lifetime horizon, adopting weekly cycles and a 3% annual discount rate. Clinical data for elranatamab were derived from the MagnetisMM-3 trial, while teclistamab outcomes were estimated through matching-adjusted indirect comparisons using MajesTEC-1 data. Direct medical costs included those associated with drug acquisition and administration, disease management, adverse event management, and end-of-life care. Utility values were obtained from EQ-5D-based assessments in MagnetisMM-3. Deterministic and probabilistic sensitivity analyses were performed to test model robustness. Results: Elranatamab yielded 3.31 life-years and 2.33 QALYs per patient, compared with 1.83 life-years and 1.27 QALYs for teclistamab. Total lifetime costs were lower for elranatamab (EUR 153,337) versus teclistamab (EUR 224,610), generating EUR 71,273 in savings. Thus, elranatamab is considered to be dominant, due to its higher efficacy and lower cost. Furthermore, the robustness of these findings was confirmed through sensitivity analyses. Conclusions: From the Italian NHS perspective, elranatamab represents a clinically superior and economically favorable option for patients with TCE/RRMM. The obtained results support its value and sustainability within the national treatment landscape. Full article

The present study investigated the community composition and dynamics of freshwater biofouling on fiberglass inland waterway vessel (IWV) models coated with two commercial antifouling paints deployed in the Danube River (Serbia) for a total of five months. Biofouling was characterized using visual observations, in situ optical microscopy, the rapid ATP bioluminescence method, dry biomass measurements, and analyses of phototrophic and fungal communities. Based on the results, Hard Racing TecCel demonstrated the highest suppression of biofouling, with the lowest biomass accumulation and reduced algal diversity. At all stages of biofouling, diatoms dominated the phototrophic community, comprising 123 taxa. Achnanthidium minutissimum and Gomphonella olivacea were shown to be persistent hull colonizers, while Cyanobacteriophyta and Chlorophyta had reduced presence. Overall, the results highlight a slower progression of freshwater biofouling compared to marine systems and emphasize the need for the development of tailored antifouling strategies for IWVs to reduce environmental impact and operational costs. Full article

This study addresses a gap in the literature by explicitly linking responsive web design frameworks to concrete cybersecurity vulnerabilities, moving beyond traditional discussions of usability and device compatibility to incorporate security-by-design principles in contemporary frontend development. The research adopts a qualitative comparative approach and considers five widely used responsive design frameworks: Bootstrap, Tailwind CSS, Foundation, Pure CSS, and Skeleton. These frameworks were selected based on criteria such as maturity, adoption, and architectural diversity. Three research questions guide the analysis: the identification of cybersecurity risks associated with responsive design frameworks, the extent to which these risks vary across frameworks, and the mitigation strategies required to address them. The findings confirm that most critical vulnerabilities originate outside the frontend layer, reinforcing the separation between presentation and backend logic. However, the results demonstrate that frameworks significantly influence the security risk profile, particularly regarding cross-site scripting, dependency management, and configuration practices. Modern utility-first frameworks shift security concerns toward the build pipeline and toolchain, while minimalistic and abandoned frameworks introduce risks related to obsolescence and unpatched “forever-day” vulnerabilities. The study concludes that frontend security depends less on framework choice alone and more on governance, continuous maintenance, and the systematic adoption of secure development and DevSecOps practices. Full article

Global Positioning System (GPS)-derived ionospheric electron concentration measurements provide a powerful observational framework for seismo-electromagnetic studies, enabling quantitative investigation of lithosphere–atmosphere–ionosphere coupling processes through statistically detectable perturbations in ionospheric electron concentration. We analyze GPS-derived Vertical Total Electron Content (VTEC) variations associated with the 14 August 2021 Haiti earthquake ($M_w$ 7.2) and the 11 January 2022 Cyprus earthquake ($M_w$ 6.6) using data from nearby International GNSS (Global Navigation Satellite System) Service (IGS) stations located within their respective earthquake preparation zones. VTEC time series spanning 45 days before and 7 days after each event are processed to remove the diurnal component, yielding residuals that isolate short-term ionospheric variability. Anomaly detection is performed using three statistical frameworks: a Gaussian mean, standard deviation model, a robust median/median absolute deviation (MAD) model, and a distribution-free quantile-based model. Daily “occurrence” and “energy” indices are constructed to quantify the frequency and cumulative strength of detected anomalies, respectively. While the indices exhibit similar temporal patterns across all methods, they indicate frequent anomaly detection, limiting statistical selectivity. To address this, both indices are normalized by their median values and filtered using a 95% quantile threshold, retaining only extreme deviations. This procedure substantially reduces background fluctuations and isolates a small number of statistically significant anomaly peaks. For both earthquakes, enhanced anomaly activity is identified in the weeks preceding the events, whereas post-event peaks coincide with periods of elevated meteorological and geomagnetic activity. The results demonstrate that normalization combined with robust statistical methods is essential for discriminating significant ionospheric TEC anomalies from background variability. Full article

Computed Tomography (CT) is a widely used imaging modality that employs X-rays and computational reconstruction to visualize internal anatomy. Although higher radiation doses produce higher-quality images, they also increase long-term cancer risk, motivating the use of low-dose protocols. However, low-dose CT data inherently suffer from elevated Poisson–Gaussian noise, necessitating effective denoising strategies. In myocardial CT perfusion (CTP) imaging, this challenge is compounded by residual cardiac motion, which misaligns consecutive time points and impairs accurate estimation of perfusion maps for diagnosing coronary artery disease. Traditional approaches typically treat these two problems, noise and motion, separately, denoising the reconstructed images first or applying the registration first. Such serial pipelines often degrade clinically significant features; e.g., denoising may destroy structural details essential for registration, while motion correction can distort subtle intensity cues needed for noise modelling. To overcome these limitations, we propose a unified deep learning framework that performs noise suppression and motion correction jointly for low-dose myocardial CTP. The method integrates two complementary components through a parallel ensemble strategy: (i) a modified Fast and Flexible Denoising Network (FFDNet) that incorporates noise-level maps to mitigate blended noise effectively, and (ii) a CNN-based registration model, extended with Time Enhancement Curve (TEC) correction and 4D physiological consistency constraints to estimate temporally coherent and anatomically plausible motion fields. By combining their outputs without iterative dependencies, the proposed framework produces motion-corrected and denoised CTP sequences in a single unified processing step, thereby better preserving myocardial structure and perfusion dynamics than conventional serial pipelines. The model has been evaluated using both reference-based (MSE, PSNR, SSIM, PCC, Noise Variance, TRE) and no-reference (NIQE, FID, KID, AUC) image quality metrics, supplemented by expert human assessment. Results demonstrate that jointly learning noise characteristics and motion patterns enables restoration of low-dose CTP images while minimizing feature corruption, thereby advancing the clinical utility of low-dose myocardial CTP imaging. Full article

Increasing attention to environmental performance in construction materials has intensified the need for robust Life Cycle Assessment (LCA) studies on bituminous waterproofing systems. This study addresses the lack of comparative LCAs based on primary data for hot-applied Elastomeric Modified Bitumen (EMB) and cold-applied Bitumen Emulsion (EMBE), two widely used materials with contrasting application methods and environmental profiles. While EMB has been moderately covered in the literature, this study contributes uniquely by providing one of the first LCAs based on primary data for EMBE, a formulation that is increasingly adopted in the construction sector but still underexplored in environmental assessments. The primary industrial data were combined with international LCI datasets (Ecoinvent) to model environmental impacts using SimaPro 9.4.0.3. Results show that EMBE demonstrates better climate performance (611 kg CO₂ eq/t) but is more sensitive to specific additives, especially resins and plasticizers, which significantly increase Ozone Depletion Potential and photochemical ozone formation. The Environmental Product Declaration (EPD) survey analysis further highlights the influence of recycled content, cold mix technologies, and production energy sources on environmental performance. The findings indicate that the selection of waterproofing materials should consider not only technical performance but also the distribution of environmental impacts across the life cycle. Full article

The authentication of wines with Protected Designation of Origin (PDO) status is a key requirement for quality assurance, traceability, and consumer trust, particularly in traditional wine-producing regions such as the Douro Demarcated Region (Portugal). Among the certification criteria, the reliable identification of grape varieties remains technically challenging, especially when rapid and non-destructive analytical approaches are required. In this study, Fourier-transform infrared spectroscopy coupled with chemometric analysis was evaluated as a rapid screening approach for the differentiation of monovarietal Douro wines produced under standardized microvinification conditions. Twenty-one monovarietal wines were analyzed using mid-infrared spectra (1800–1000 cm⁻¹) and classification models were developed using Partial Least Squares Discriminant Analysis (PLS-DA). The PLS-DA models showed preliminary discriminatory capacity, with apparent error rates of 10.2% for calibration and 19.3% under leave-one-out cross-validation. The results indicate that FTIR-ATR spectroscopy combined with chemometrics captures chemically relevant spectral variability associated with grape varietal differences and shows potential as a rapid exploratory screening approach within PDO traceability frameworks. Although the study is based on a limited number of biological replicates from a single vintage and sub-region, the findings provide a methodological baseline for future multi-vintage and multi-region investigations aimed at consolidating FTIR-based approaches for varietal authentication of Douro wines. Full article

In this work, a food-compatible bioprocess was evaluated for the production of yeast single-cell protein from mezcal agave bagasse. Bagasse was enzymatically hydrolyzed at 10% (w/v) solids (pH 4.8, 50 °C, 24 h) using commercial enzymes. The resulting liquid was clarified by activated charcoal adsorption and filtration to obtain a hydrolysate suitable for submerged fermentation. Enzymatic hydrolysis released reducing sugars in the range of 11–17 g/L. Saccharomyces cerevisiae was cultivated on the clarified hydrolysate under submerged conditions using both flask-scale and 2 L stirred-tank bioreactor experiments. Trials were performed at flask scale with initial sugars at 8, 17, and 50 g/L, and at 2 L stirred-tank bioreactor scale with initial sugars at 20.68 g/L (R1) and 16.30 (R2) g/L. At the flask scale, final biomass concentrations increased with initial sugar level. Values reached 6.18 ± 0.27, 8.02 ± 0.55, and 9.28 ± 0.10 g/L, while crude protein remained below 10% (3.40 ± 0.15 to 8.69 ± 0.09 g/100 g dry weight). In contrast, bioreactor cultivation resulted in higher protein enrichment, with protein contents over 40% under both oxygen regimes (41.71 ± 0.47 to 45.80 ± 0.43 g/100 g dry weight). Overall, the findings support enzymatic hydrolysis coupled with controlled submerged fermentation as a scalable approach for valorizing agave bagasse into protein-enriched yeast biomass. Full article

The ability to assess molecular binding kinetics in real time is critical for advancing our understanding of molecular interactions in biochemical and biotechnological systems. This work presents a novel optical tweezer (OT)-based method to monitor molecular affinity in real time, focusing on the high-affinity streptavidin–biotin system as a model. Transparent poly(methyl methacrylate) (PMMA) microparticles functionalized with streptavidin were trapped before, during, and after binding with biotinylated bovine serum albumin (biotin–BSA), enabling the analysis of forward-scattered signals to detect nanoscale changes in particle size. By applying the Power Spectral Density method, the friction coefficient of individual particles was calculated, allowing for real-time tracking of binding dynamics and the estimation of the association rate constant ($k_{\mathrm{on}}\approx {10}^6{\mathrm{M}}^{-1}{\mathrm{s}}^{-1}$). These results are consistent with literature values and demonstrate the potential of this OT-based approach for non-invasive, label-free detection of molecular interactions. Compared to existing techniques, such as atomic force microscopy and cantilever-based sensors, this method offers significant advantages, including real-time monitoring, adaptability to different bioaffinity systems, and compatibility with miniaturized setups. This work establishes a foundation for using OT-based tools to monitor high-affinity molecular interactions in real time. While demonstrated here using biotinylated BSA as a model ligand, future studies will explore the method’s applicability to smaller ligands and more subtle surface modifications. Full article

Background: Hepatocellular carcinoma (HCC) stands as a prevalent global health issue with increasing incidence and mortality rates. Hepatocellular carcinoma (HCC) exhibits profound molecular and clinical heterogeneity, which limits the effectiveness of current therapeutic strategies. Circadian rhythm disruption has been implicated in metabolic reprogramming, proliferation, and immune modulation in cancer, but its role in shaping HCC heterogeneity remains poorly defined. Methods: Four public HCC transcriptomic cohorts (TCGA-LIHC, CHCC, LIRI, LICA) were integrated using RMA normalization and ComBat for batch correction. Consensus clustering based on 31 core circadian clock genes (CCGs) identified robust molecular subtypes. Multi-omics characterization—including genomic alterations, pathway activity (GSEA/GSVA), immune microenvironment profiling (CIBERSORT, EPIC, MCP-counter, xCell), and drug-sensitivity prediction (pRRophetic/oncoPredict)—was performed to delineate subtype-specific biological properties. A nine-gene CCG-based RiskScore model was constructed using LASSO Cox regression to internally validate subtype robustness and intra-subtype risk stratification. Results: Using consensus clustering of 31 core CCGs in TCGA-LIHC and three independent validation cohorts (CHCC, LIRI, LICA), we identified three reproducible subtypes—Cluster-1 (metabolic–quiescent), Cluster-2 (transition–intermediate), and Cluster-3 (proliferation–inflammatory)—which were recapitulated across cohorts and showed distinct overall survival (Cluster-3 worst; log-rank p values significant across datasets). Multi-omic characterization revealed that Cluster-3 exhibits the highest tumor mutational burden and CNV burden with enrichment of TP53/AXIN1/TERT alterations, strong activation of cell-cycle, E2F, and G2M programs, and an immune-hot yet immunosuppressed microenvironment enriched for TAMs, Tregs and MDSCs. By contrast, Cluster-1 shows relative genomic stability, dominant hepatic metabolic signatures (fatty-acid oxidation, bile-acid and xenobiotic metabolism) and an immune-cold phenotype. Single-cell mapping linked ALAS1 expression to malignant hepatocytes predominating in Cluster-1, whereas NONO and CSNK1D localized to stromal (CAFs/TECs) and both malignant/immune compartments respectively in Cluster-3, providing a cellular mechanism for subtype-specific metabolism, angiogenesis and immune modulation. Finally, a nine-gene CCG-based RiskScore validated prognostic stratification and drug-sensitivity predictions indicated subtype-specific therapeutic vulnerabilities (notably increased predicted TKI sensitivity in Cluster-3). Conclusion: In conclusion, this study proposes a robust circadian rhythm-based molecular classification of hepatocellular carcinoma, revealing three biologically and clinically distinct subtypes characterized by divergent genomic alterations, metabolic programs, immune microenvironment states, and prognostic patterns. By integrating bulk and single-cell transcriptomic data, we identify subtype-specific roles of key circadian regulators—including ALAS1, NONO, and CSNK1D—in shaping tumor metabolism, proliferation, stromal remodeling, and immune suppression. These findings highlight circadian dysregulation as a potential upstream factor associated with HCC heterogeneity and provide a conceptual framework for developing subtype-tailored mechanistic studies and circadian-informed therapeutic strategies. Full article

On 6 February 2023, two major earthquakes occurred approximately nine hours apart in the Pazarcık (M_w = 7.7) and Elbistan (M_w = 7.6) districts of Kahramanmaraş, Türkiye. These devastating earthquakes caused extensive damage in many cities in the region. Kahramanmaraş and Malatya were among the cities most severely affected. Therefore, Kahramanmaraş and Malatya were chosen for this study. The aim was to investigate the effects of the earthquakes on the city centers of the selected cities. Reinforced concrete buildings of four, six, eight, and 10 floors were designed in accordance with Türkiye seismic codes. Linear response spectrum analysis (LRSA) was applied to these buildings according to the Türkiye Earthquake Code 2018 (TEC-2018), the Türkiye Earthquake Code 2007 (TEC-2007), and the Eurocode 8 (EC8). In addition, acceleration records of the 2023 Kahramanmaraş earthquakes were obtained from accelerometer stations near the city centers of the aforementioned cities. Nonlinear time history analysis (NTHA) was performed on sample buildings using these acceleration records. As a result of these analyses, base shear forces and roof displacements were obtained. Design acceleration spectra were obtained according to the Türkiye earthquake codes and the Eurocode 8. Acceleration spectra of the earthquakes were also obtained based on the acceleration records of the earthquakes. A comparison was made between TEC-2007, TEC-2018, and EC8 based on the LRSA calculation results. The calculations show that TEC-2018 is more detailed and realistic than TEC-2007. Furthermore, it was determined that the values obtained according to EC8 were greater than the values obtained according to TEC-2007 and TEC-2018. The results obtained from LRSA and NTHA were compared. It was determined that the results obtained from NTHA were much larger than those obtained from LRSA. Full article

For any robotic application, accurate sensor calibration is crucial. In the case of mobile platforms or flying drones, a sensor commonly utilized is the Inertial Measurement Unit (IMU). Current approaches to the calibration of IMU-equipped robotic systems focus on sensor-to-sensor calibration, meaning a second sensor is necessary for the calibration process. Furthermore, a great number of those rely on integrating the sensor measurements to obtain its pose, which leads to integration errors. In this work, we present a method for the extrinsic calibration of IMUs in robotic systems, which avoids the errors originating from IMU integration by instead taking a derivative approach using Savitzky–Golay filters. The proposed calibration method estimates the transformation between an IMU sensor and its parent frame in the system’s kinematic chain by minimizing the differences between derived linear accelerations and angular velocities and those measured by the sensor. Simulated data is used to establish a ground truth against which the calibration results are compared. Results indicate that the proposed method achieves a higher accuracy than the alternatives it is compared against, while also showing the method can be applied to industrial-grade IMUs. Full article