Search Results (588)

Mechanical recycling of polypropylene (PP) is constrained by the heterogeneous properties of post-consumer feedstocks. Melt flow rate (MFR) is a key property relevant to processing, and it varies widely across packaging grades, which limits the quality and substitutability of recyclates. This study evaluates near-infrared hyperspectral imaging (NIR-HSI) for predicting MFR in post-consumer PP packaging. Eighty-two rigid PP samples (46 white, 36 clear) with MFR values between 2 and 108 g 10 min⁻¹ were collected from an Austrian material recovery facility. Thirteen different linear and non-linear regression models were examined using median and pixel-wise aggregated spectral representations across the samples. Tree-based models consistently achieved best performances with R² = 0.85, RMSE = 12.4 g 10 min⁻¹ on white samples and R² = 0.61, RMSE = 14.0 g 10 min⁻¹ on clear samples. On the combined sample set, R² = 0.66 and RMSE = 17.3 g 10 min⁻¹ were reached. Informative spectral regions correspond to typical bands of PP. Binary classification at different thresholds (6, 12, 30, 60 g 10 min⁻¹) were also examined and achieved balanced accuracies of 0.82–0.92. Median spectral representations consistently outperformed pixel-wise aggregation. Results demonstrate that NIR-HSI can support grade-directed sorting of post-consumer PP, particularly for opaque white samples, though heteroscedasticity at high MFR values and irreducible outliers represent inherent limitations. Full article

In recent years, with the widespread application of shortwave infrared (SWIR) spectroscopy in mineral identification and hydrothermal alteration studies, an increasing number of studies have attempted to integrate SWIR spectral data with machine learning approaches to fully exploit mineralization-related discriminative information embedded in high-dimensional spectral datasets. In this study, the Zhunuo porphyry copper deposit in Tibet was selected as the research target. SWIR drill core spectral data were systematically acquired, and a random forest (RF) machine learning model was applied to full-band SWIR spectra (1300–2500 nm) to conduct integrated analyses of copper grade regression and mineralization discrimination. A total of 2140 drill core samples were measured, with three replicate measurements per sample, yielding 6420 spectra. After standardized preprocessing and interpolation resampling, a unified spectral feature dataset was constructed for regression and classification analyses. SWIR spectral data are characterized by a large number of bands, strong inter-band correlations, and relatively limited sample sizes; under such conditions, model generalization ability and stability become critical factors in method selection. Based on ensemble learning, the random forest model constructs multiple decision trees and aggregates their predictions through voting or averaging, effectively reducing model variance and mitigating overfitting, and is therefore well suited for high-dimensional, small-sample, and highly correlated geological spectral datasets. In porphyry copper systems, the spectral characteristics of hydrothermal alteration minerals and mineralization intensity commonly exhibit complex nonlinear relationships, which can be effectively captured by random forest models without requiring predefined functional forms. The regression results indicate that accurate quantitative prediction of copper grade based solely on SWIR spectral data remains limited. In contrast, when a threshold-based binary classification was introduced using an industrial cutoff grade of 0.2% Cu, the model achieved an overall accuracy of 75%, an F1 score of 0.69, and an area under the ROC curve (AUC) of 0.80, demonstrating strong mineralization discrimination capability and stability. Overall, the integration of SWIR spectroscopy with machine learning methods provides an efficient, reliable, and geologically interpretable technical approach for early-stage exploration and detailed drill core interpretation in porphyry copper deposits. Full article

Reducing the environmental impact of road transport requires pavements that contribute to lower fuel consumption of vehicles and greenhouse gas emissions throughout their life cycle. Rolling resistance plays a key role in this context, while warm mix asphalt (WMA) technologies offer additional benefits by reducing energy use and emissions during production and construction. This study investigates the combined influence of aggregate type and aggregate gradation on the rolling resistance and functional performance of WMA wearing course mixtures. Ten laboratory-produced mixtures were designed, including dense-graded asphalt concrete (AC 11 VS) and stone mastic asphalt (SMA 8 S) with granite or dolomite aggregates, produced at reduced temperatures using a chemical WMA additive and polymer-modified bitumen PMB 45/80-65. Rolling resistance was evaluated using a laboratory energy loss method with two different tyres, along with assessments of volumetric properties, moisture resistance, surface macrotexture, and resistance to scuffing. The results indicate that aggregate gradation is the primary factor governing rolling resistance, and dense-graded mixtures exhibit lower energy loss due to their smoother surface texture. The aggregate type showed a secondary but consistent effect, with granite mixtures generally demonstrating slightly lower rolling resistance and improved resistance to surface degradation. In general, the findings confirm that WMA technologies can be effectively integrated into low-rolling-resistance asphalt mixtures, achieving reduced rolling resistance without compromising durability and thus supporting energy-efficient and sustainable pavement solutions. Full article

Introduction: Patient-reported outcomes (PROs) are increasingly valued in oncology for capturing treatment tolerability and quality of life, and they are emerging as important data sources for precision-medicine and AI-driven clinical workflows. While the EQ-5D-5L questionnaire remains a widely used standardized instrument, dynamic electronic PROs (ePROs) collected via mobile applications generate richer, higher-frequency longitudinal data. Their alignment with established PRO measures, however, is not well-understood, limiting their integration into routine care and downstream analytic applications. In the prospective OGIPRO trial (KEK-ZH 2021-D0051), patients with HER2-positive breast cancer reported well-being and symptoms via the Medidux ePRO platform alongside weekly EQ-5D-5L assessments. In this retrospective analysis, we used linear mixed-effects modeling to examine associations between: (i) dynamic ePRO well-being and the EQ-5D-5L visual analogue scale (VAS); (ii) dynamic ePRO symptom grades and EQ-5D-5L domain sums; (iii) ePRO symptom grades and EQ-5D-5L disutility using the EQ-5D-5L value set for Germany. Materials and Methods: The analytic dataset comprised 13,699 dynamic ePRO data points (3376 well-being ratings and 10,323 symptom grades across 91 symptom types) from 53 patients, forming high-frequency longitudinal patient trajectories. Of these, 252 and 226 time-aligned observations, respectively, were used for direct comparison with EQ-5D-5L VAS and domain scores. Results: Dynamic ePRO well-being showed strong agreement with EQ-5D-5L VAS (β = 1.061, 95% CI: 1.015–1.107), with low between-patient variability. In contrast, the agreement between aggregated ePRO symptom grades and EQ-5D-5L domain sums was weaker (β = 0.404, 95% CI: 0.307–0.501) and more heterogeneous across patients. The same applied to the agreement between ePRO symptom grades and EQ-5D-5L disutility (β = 0.213; 95% CI: 0.151–0.275). Discussion: Dynamic ePRO well-being aligns closely with EQ-5D-5L VAS scores, supporting its use as a pragmatic substitute in clinical and research settings. Aggregated symptom grades, however, showed limited concordance with EQ-5D-5L domains, indicating the need for more granular analyses on larger datasets. Conclusions: Overall, dynamic ePRO systems provide validated, high-resolution longitudinal patient data and represent a scalable foundation for patient monitoring and data-driven decision support in oncology, including future AI-based precision-medicine applications. Full article

Municipal solid waste incineration bottom ash (IBA), a major by-product of waste-to-energy plants, is typically landfilled or utilized as low-grade aggregate due to its low intrinsic reactivity and complex composition. This study systematically investigates the efficacy of chemical pretreatment in enhancing the cementitious behavior of IBA, specifically examining the effects of alkali type (Ca(OH)₂, NaOH, and Na₂CO₃) and pretreatment duration on reactivity, microstructure, and mechanical performance. The results indicate that Ca(OH)₂ activation provides the most significant enhancement; a one-day treatment yielded a 28-day strength activity index (H₂₈) of 76% and facilitated the formation of a compact microstructure rich in ettringite (AFt) and C-S-H gels. Conversely, NaOH and Na₂CO₃ treatments were less effective, leading to increased porosity and reduced strength attributed to charge imbalance and excessive carbonation, respectively. Prolonged alkaline treatment yielded diminishing returns, causing premature gel densification or excessive silicate depolymerization. Life-cycle assessment (LCA) revealed that Na₂CO₃ pretreatment entails the highest carbon footprint due to its high molar mass and energy-intensive production, whereas NaOH offers the highest CO₂ efficiency per unit of reactivity. Overall, Ca(OH)₂ represents a balanced strategy, combining strong activation potential, chemical compatibility, and moderate carbon emissions, thereby supporting the sustainable valorization of IBA in low-carbon cementitious systems. Full article

Waste concrete powder (WCP), characterized by low reactivity and limited utilization potential, is rapidly accumulating due to the increasing volume of demolition and recycling activities, creating significant environmental and resource challenges. Meanwhile, the shortage of natural fine aggregate (NFA) has become increasingly severe. To address these issues, this study develops a sustainable approach that utilizes WCP as the main raw material, together with fly ash (FA), ground granulated blast-furnace slag (GGBFS), ordinary Portland cement (OPC), and sulphoaluminate cement (SAC), to produce a WCP-based artificial fine aggregate (WAFA) through a cold-bonding process. The physical, mechanical, and microstructural properties of WAFA were systematically analyzed, and its concrete performance was evaluated by replacing NFA at 100% volume. The results show that WAFA exhibits a regular spherical morphology and, after grading adjustment, meets the Zone II sand requirements of GB/T 14684-2022. Increasing the cement content from 2% to 10% raises the 28-day single-particle compressive strength (SPCS) from 12.98 MPa to 23.08 MPa (a 77.8% increase), while enhancing WCP reactivity improves SPCS from 16.17 MPa to 22.80 MPa (a 29.1% increase). Higher cement content and WCP reactivity also promote the formation of C–S–H gel and ettringite (AFt), resulting in higher bulk density, reduced water absorption, and a denser microstructure. In concrete applications, WAFA substantially improves workability, with slump values exceeding those of NFA and recycled fine aggregate (RFA) concretes. Although WAFA concrete exhibits slightly lower compressive and splitting tensile strengths compared with NFA concrete, optimized mix design allows the achievement of target strength grades from C30 to C50, with the C50-W10-50 mixture showing the most favorable mechanical performance. In summary, WAFA shows potential for contributing to the high-value utilization of construction waste and the reduction in natural sand consumption. Full article

Background/Objectives: Workplace violence (WPV) is a major occupational concern in psychiatric settings, where mental health nurses (MHNs) are consistently identified as a high-risk professional group. Within this context, sexual violence (SV) remains understudied as a distinct phenomenon and is often embedded within aggregated measures of WPV. This systematic review aimed to synthesize the available evidence on SV against MHNs working in inpatient settings by: (1) describing its prevalence, forms, and characteristics; (2) examining psychological, occupational, and physical outcomes; and (3) identifying associated risk factors. Methods: This systematic review was conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD420251103606). A literature search was performed across PubMed, CINAHL, Scopus, Web of Science, and PsycInfo, supplemented by reference list checking and citation tracking. Peer-reviewed quantitative and qualitative studies published in English or Italian were eligible if they involved MHNs working in inpatient settings and addressed SV. Study selection, data extraction, and risk-of-bias assessment were conducted independently by two reviewers. A narrative synthesis following SWiM guidance was undertaken, and the certainty of evidence for statistically significant outcomes was assessed using the GRADE approach. Results: Twenty-five studies published between 2003 and 2025 were included. Definitions of SV varied substantially. Reported prevalence ranged from 0% to 68%, with verbal sexual harassment ranging from 19.5% to 53.4%, physical sexual harassment ranging from 14% to 42.9%, and sexual assault up to 18.6%. Evidence indicated associations between SV exposure and poorer quality of life, burnout, and days lost from work. The main risk factors included gender, age, education, work experience, employment type, acute psychiatric settings, night shifts, normalization of violence, and history of physical and sexual violence. Conclusions: SV against MHNs represents a relevant issue in psychiatric settings. Findings suggest significant psychological and occupational consequences. Standardized definitions and measurement, longitudinal research, and intervention studies are needed to inform effective prevention strategies and organizational responses. Full article

The spectral characteristics of hematite are critical for its remote sensing identification and inversion, but these characteristics are significantly influenced by particle size. Previous studies have primarily focused on particle size ranges (>40 µm) that have already been investigated and generally concluded that spectral reflectance in the near-infrared (NIR) band increases as particle size decreases. However, the potential “reversal” of this trend—specifically, a decrease in reflectance with decreasing particle size due to agglomeration effects—and its underlying mechanism at the micron and sub-micron scales remain unclear. To address this issue, six distinct particle size grades targeting the ultrafine scale were systematically prepared from high-purity hematite, with average diameters ranging from 37.5 µm down to 0.76 µm. Reflectance spectroscopy measurements were conducted to analyze spectral variations across the 350~2500 nm wavelength range. The experimental results showed that particle size had little influence on reflectance within the 350~1175 nm wavelength range. In contrast, significant dependence on particle size was observed in the 1175~2500 nm range, where a reversal of the reflectance trend occurred at a critical particle size of 15.41 µm. Specifically, reflectance increased with decreasing particle size above 15.41 µm. However, reflectance decreases dramatically when particle size falls below 15.41 µm due to increased agglomeration. This contrasts with the trend reported in previous studies. Mechanism analysis revealed that, within the 350~1175 nm range, the high complex refractive index of hematite resulted in minimal influence of particle size on reflectance. In the range of 1175~2500 nm, reflectance increased with decreasing particle size when the particle size exceeded 15.41 µm, a behavior primarily governed by particle scattering effects. Conversely, when the particle size decreased below 15.41 µm, the reflectance declined significantly with a further reduction in particle size, demonstrating a distinct trend reversal. This phenomenon is attributed to the low complex refractive index of hematite combined with a dramatic increase in particle aggregation effects as particle size decreases. These factors collectively increase the equivalent optical path length and intensify multiple absorption, leading to the observed decrease in reflectance. This study establishes the key control of agglomeration effects on the spectral behavior of fine hematite particles, providing crucial theoretical and experimental foundations for advancing high-precision, quantitative remote sensing inversion. Full article

Lignin, an abundant and renewable biopolymer, holds significant potential for asphalt modification owing to its unique aromatic structure and reactive functional groups. This review summarizes the main lignin preparation routes and key physicochemical attributes and assesses its applicability for enhancing asphalt performance. The physical incorporation of lignin strengthens the asphalt matrix, improving its viscoelastic properties and resistance to oxidative degradation. These enhancements are mainly attributed to the cross-linking effect of lignin’s polymer chains and the antioxidant capacity of its phenolic hydroxyl groups, which act as free-radical scavengers. At the mixture level, lignin-modified asphalt (LMA) exhibits improved aggregate bonding, leading to enhanced dynamic stability, fatigue resistance, and moisture resilience. Nevertheless, excessive lignin content can have a negative impact on low-temperature ductility and fatigue resistance at intermediate temperatures. This necessitates careful dosage optimization or composite modification with softeners or flexible fibers. Mechanistically, lignin disperses within the asphalt, where its polar groups adsorb onto lighter components to boost high-temperature performance, while its strong interaction with asphaltenes alleviates water-induced damage. Furthermore, life cycle assessment (LCA) studies indicate that lignin integration can substantially reduce or even offset greenhouse gas emissions through bio-based carbon storage. However, the magnitude of the benefit is highly sensitive to lignin production routes, allocation rules, and recycling scenarios. Although the laboratory research results are encouraging, there is a lack of large-scale road tests on LMA. There is also a lack of systematic research on the specific mechanism of how it interacts with asphalt components and changes the asphalt structure at the molecular level. In the future, long-term service-road engineering tests can be designed and implemented to verify the comprehensive performance of LMA under different climates and traffic grades. By using molecular dynamics simulation technology, a complex molecular model containing the four major components of asphalt and lignin can be constructed to study their interaction mechanism at the microscopic level. Full article

To provide essential information on geoheritage and geotourism potential in Jiangxi Province—a key region for geoheritage distribution in China—this study summarizes and categorizes the types, grades, and distribution characteristics of geoheritage within local communities. The primary analytical methods included average nearest neighbour analysis, kernel density estimation, and spatial autocorrelation to explore spatial distribution patterns. A total of 202 significant geoheritage sites were identified in Jiangxi Province. Furthermore, an evaluation index system was established using the entropy weight TOPSIS model to assess the geotourism potential of each city. The findings reveal the following: (1) Geoheritage sites in Jiangxi Province exhibit an overall aggregated spatial distribution, although clustering intensity varies among different geoheritage types and grades. (2) Considering both grade and category, the core distribution area of geoheritage is located in eastern Shangrao City, while global-level geoheritage sites are mainly concentrated in the Poyang Lake Plain. (3) Spatial autocorrelation analysis indicates that, except for global-level geoheritage sites, other geoheritage sites display significant spatial agglomeration with positive spatial correlation. Moreover, local-scale spatial association characteristics differ notably according to geoheritage type and grade. (4) The geotourism development potential across Jiangxi Province shows clear spatial differentiation, with higher potential concentrated in the eastern and southern regions. Full article

Next-generation human papillomavirus (HPV) vaccines encompass newly licensed and emerging formulations that employ alternative production platforms, expanded valency, or novel antigenic targets beyond conventional L1-based vaccines. These vaccines aim to address affordability challenges, supply limitations, and suboptimal vaccination coverage, particularly in low- and middle-income countries. This review aggregates current clinical, immunological, and programme-related evidence on newly licensed vaccines, including the World Health Organization (WHO)-prequalified bivalent formulations (Cecolin^® and Walrinvax^®), the quadrivalent Cervavac^®, and the Escherichia coli-derived nonavalent Cecolin 9^®, which received national licensure in 2025. Additionally, emerging high-valency candidates in Phase I–III trials—9-valent, 11-valent, and 14-valent formulations—are critically assessed. Clinical trials demonstrate that next-generation HPV vaccines provide robust protection; for example, Cecolin^® showed 100% efficacy against HPV-16/18-associated high-grade squamous intraepithelial lesions (HSIL) and up to 97.8% efficacy against persistent HPV infection, while Walrinvax^® demonstrated 78.6% protection against CIN2+ lesions. Cervavac^® showed non-inferior immunogenicity compared with established vaccines. While comparative analyses of efficacy, immunogenicity, and safety indicate that these vaccines are strong alternatives to established products, robust long-term effectiveness and real-world impact data remain essential before full clinical equivalence can be definitively established. Advances in L2-based platforms further aim to broaden cross-type protection, simplify manufacturing, and enable thermostable formulations, thereby enhancing applicability in resource-limited settings. Economic evaluations demonstrating favorable cost-effectiveness emphasize the essential role of next-generation vaccines in improving access and reducing inequity. Overall, innovations in valency, technology, and delivery strategies have the potential to significantly expand global HPV prevention coverage and accelerate progress toward cervical cancer elimination. Full article

Hemostasis depends on the coordinated interaction between platelets, coagulation factors, endothelium, and shear forces. Current point-of-care (POC) assays evaluate isolated components of haemostasis or operate under nearly static conditions, limiting their ability to reproduce physiological thrombus formation. In this study, we performed the technical validation of Smart Clot, a fully automated, microfluidic POC platform that quantifies platelet aggregation, fibrin formation, and total thrombus growth under controlled arterial shear using unmodified whole blood. Recalcified citrated blood was perfused over collagen at $\dot{\gamma }$_w = 300 s⁻¹. Dual-channel epifluorescence microscopy acquired platelet and fibrin(ogen) signals at 1 frame per second. Integrated Density time-series were fitted with a five-parameter logistic model; first derivatives and their integrals yielded standardized pseudo-volumes for platelets, fibrin(ogen), and total thrombus. Sixty-two healthy donors established reference distributions; one-hundred-thirteen patients on antithrombotic therapy assessed pharmacodynamic sensitivity. Platelet-derived parameters were approximately normally distributed, whereas fibrin(ogen) and total thrombus values followed log-normal patterns. Anticoagulants and antiplatelet agents produced graded, mechanism-consistent inhibition of all thrombus components. Cardiopulmonary bypass samples showed profound but transient suppression of platelet and fibrin activity. Across activity ranges, multiple statistical assessments supported high analytical repeatability. Smart Clot provides rapid, reproducible, flow-aware quantification of platelet–fibrin dynamics, capturing pharmacological modulation and peri-operative impairment with high sensitivity. These results support its potential as a next-generation POC assay for physiological hemostasis assessment. Full article

The construction industry is a major consumer of natural resources and a significant source of CO₂ emissions. Although numerous studies have addressed cement reduction through supplementary materials, the replacement of natural aggregates has received less attention despite its high environmental relevance. Practical application of recycled aggregate concrete remains limited due to complex classification and testing requirements. This study investigates the use of locally crushed construction and demolition waste as aggregate for new structural concrete with minimal on-site preparation. The goal was to maximize recycled material utilization while ensuring adequate performance. Demolition materials from normal- and high-strength concrete, 3D-printed concrete, and fired clay bricks were crushed using jaw and impact crushers, and the entire particle size curve was incorporated into new mixtures. Two compositions were tested: 50% and 75% recycled aggregate combined with natural quartz sand, without increasing cement content. Compressive strength and density were evaluated at 28 and 90 days. High-strength concrete waste provided strengths close to the reference mixture, while normal concrete and brick aggregates resulted in lower but still structural-grade concretes. The strengths achieved ranged between 35 MPa and 73 MPa, which is between 48% and 98% of the reference value, respectively. A linear relationship was found between density and compressive strength, enabling estimation from simple measurements. The results confirm that uncontaminated demolition waste can be efficiently reused on site with limited testing, supporting circular construction and reduced environmental impact. Full article

Polyurethane (PU) mixtures exhibit superior mechanical performance compared to traditional asphalt mixtures, owing to the excellent engineering properties of the PU binder. This study investigates the dynamic rheological properties of an open-graded polyurethane mixture (PUM–OGFC) in comparison with a dense-graded polyurethane mixture (PUM–AC). The time–temperature superposition principle and three rheological models (Standard Logistic Sigmoid (SLS), Generalized Logistic Sigmoid (GLS), and Havriliak–Negami (HN)) were employed to construct and analyze master curves. The results show that while PUM–AC possesses a higher dynamic modulus, PUM–OGFC exhibits a lower phase angle, indicating a more elastic response. Critically, PUM–OGFC demonstrated superior rutting resistance, as evidenced by its higher rutting parameter (|E*|/sin δ). Aggregate gradation significantly influenced all rheological properties. The master curve analysis further revealed that PUM–OGFC exhibits greater temperature sensitivity than PUM–AC. The SLS and GLS models provided excellent fits for both dynamic modulus and phase angle data, whereas the HN model was suitable only for dynamic modulus. In summary, the open-graded structure, when combined with a PU binder, creates a high-performance composite with an exceptional balance of elasticity and rutting resistance, showcasing its potential for demanding pavement applications. Full article

Background/Objectives: Diagnosing Rotator Cuff Tears (RCTs) via Magnetic Resonance Imaging (MRI) is clinically challenging due to complex 3D anatomy and significant interobserver variability. Traditional slice-centric Convolutional Neural Networks (CNNs) often fail to capture the necessary volumetric context for accurate grading. This study aims to develop and validate the Patient-Aware Vision Transformer (Pa-ViT), an explainable deep-learning framework designed for the automated, patient-level classification of RCTs (Normal, Partial-Thickness, and Full-Thickness). Methods: A large-scale retrospective dataset comprising 2447 T2-weighted coronal shoulder MRI examinations was utilized. The proposed Pa-ViT framework employs a Vision Transformer (ViT-Base) backbone within a Weakly-Supervised Multiple Instance Learning (MIL) paradigm to aggregate slice-level semantic features into a unified patient diagnosis. The model was trained using a weighted cross-entropy loss to address class imbalance and was benchmarked against widely used CNN architectures and traditional machine-learning classifiers. Results: The Pa-ViT model achieved a high overall accuracy of 91% and a macro-averaged F1-score of 0.91, significantly outperforming the standard VGG-16 baseline (87%). Notably, the model demonstrated superior discriminative power for the challenging Partial-Thickness Tear class (ROC AUC: 0.903). Furthermore, Attention Rollout visualizations confirmed the model’s reliance on genuine anatomical features, such as the supraspinatus footprint, rather than artifacts. Conclusions: By effectively modeling long-range dependencies, the Pa-ViT framework provides a robust alternative to traditional CNNs. It offers a clinically viable, explainable decision support tool that enhances diagnostic sensitivity, particularly for subtle partial-thickness tears. Full article