Search Results (2,764)

This study develops an ontology-based decision support framework to enhance sustainable polymer recycling within the circular economy. The framework, constructed in Protégé (OWL 2), systematically captures polymer categories with emphasis on polyethylene terephthalate (PET), polylactic acid (PLA), and rigid polyvinyl chloride (PVC) as well as recycling processes, waste classifications, and sustainability indicators such as carbon footprint. Semantic reasoning was implemented using the Semantic Web Rule Language (SWRL) and SPARQL Protocol and RDF Query Language (SPARQL) to infer optimal material flows and sustainable pathways. Validation through a UK industrial case study confirmed both the framework’s applicability and highlighted barriers to large-scale recycling, including performance gaps between virgin and recycled polymers. The comparative analysis showed carbon footprints of 2.8 kg CO₂/kg for virgin PET, 1.5 kg CO₂/kg for PLA, and 2.1 kg CO₂/kg for PVC, underscoring material-specific sustainability challenges. Validation through a UK industrial case study further highlighted additive complexity in PVC as a major barrier to large scale recycling. Bibliometric and thematic analyses conducted in this study revealed persistent gaps in sustainability metrics, lifecycle assessment, and semantic support for circular polymer systems. By integrating these insights, the proposed framework provides a scalable, data-driven tool for evaluating and optimising polymer lifecycles, supporting industry transitions toward resilient, circular, and net-zero material systems. Full article

The construction industry significantly impacts environmental degradation, making sustainable practices like green building construction projects (GBCPs) essential. Although GBCPs offer substantial benefits, they also come with unique risks related to their sustainable nature and common construction challenges. Research on GBCP risks is often fragmented, lacks proper classification, and misses a global perspective, with insufficient focus on empirical assessment and risk mitigation strategies. This study addresses these gaps by systematically identifying risks associated with GBCPs, empirically assessing them using data from global experts, and proposing mitigation strategies. Utilising reliability tests, descriptive statistics, one-sample t-tests, hypothesis testing, and correlation analysis, 42 risk factors were determined and assigned to nine groups: legal and regulatory, technical, financial, material-related, design, schedule and planning, communication and awareness, performance and operational, and environmental. Green product certification and re-evaluation charges, client finance difficulties, the high cost of green materials and equipment, the absence of qualified project teams, and additional expenditures for green building design and construction are the top five concerns. The study also identifies 45 mitigation strategies, enhancing understanding of GBCP risks and guiding stakeholders in effective risk management and sustainable construction practices. Full article

Marine pollution incidents pose significant threats to marine ecosystems and coastal communities across Pacific Island nations, necessitating advanced predictive capabilities for effective environmental management. This study analyzes 8133 marine pollution incidents from 2001–2014 across 25 Pacific Island nations to develop predictive models for pollution type classification, hotspot identification, and seasonal pattern forecasting. Our analysis reveals Papua New Guinea as the dominant pollution hotspot, experiencing 51.9% of all regional incidents, with plastic waste dumping comprising 78.8% of pollution events and exhibiting pronounced seasonal peaks during June (coinciding with critical fish breeding periods). Machine learning classification achieved 99.1% accuracy in predicting pollution types, with material composition emerging as the strongest predictor, followed by seasonal timing and geographic location. Temporal analysis identified distinct seasonal dependencies, with June representing peak pollution activity (755 average incidents), coinciding with vulnerable marine ecological periods. The predictive framework successfully distinguishes between persistent geographic hotspots and episodic pollution events, enabling targeted conservation interventions during high-risk periods. These findings demonstrate that pollution type and location are highly predictable from environmental and temporal variables, providing marine conservationists with tools to anticipate when and where pollution will most likely impact fish populations and ecosystem health. The study establishes the first comprehensive baseline for Pacific Island marine pollution patterns and validates machine learning approaches for proactive pollution monitoring, offering scalable solutions for protecting ocean ecosystems and supporting evidence-based policy formulation across the region. Full article

Mechanical exfoliation remains the most common method for producing high-quality two-dimensional (2D) materials, but its inherently low yield requires screening large numbers of samples to identify usable flakes. Efficient optimization of the exfoliation process demands scalable methods to analyze deposited material across extensive datasets. While machine learning clustering techniques have demonstrated ~95% accuracy in classifying 2D material thicknesses from optical microscopy images, current tools are limited by slow processing speeds and heavy reliance on manual user input. This work presents an open-source, GPU-accelerated software platform that builds upon existing classification methods to enable high-throughput analysis of 2D material samples. By leveraging parallel computation, optimizing core algorithms, and automating preprocessing steps, the software can quantify flake coverage and thickness across uncompressed optical images at scale. Benchmark comparisons show that this implementation processes over 200× more pixel data with a 60× reduction in processing time relative to the original software. Specifically, a full dataset of2916 uncompressed images can be classified in 35 min, compared to an estimated 32 h required by the baseline method using compressed images. This platform enables rapid evaluation of exfoliation results across multiple trials, providing a practical tool for optimizing deposition techniques and improving the yield of high-quality 2D materials. Full article

Background and Objectives: The growing academic interest within the biomedical sciences regarding the diagnostic capabilities of multimodal language models, such as ChatGPT-4o, is clear. However, their ability to interpret oral clinical images remains insufficiently explored. This exploratory pilot study aimed to provide preliminary observations about the diagnostic validity of ChatGPT-4o in identifying oral squamous cell carcinoma (OSCC), oral leukoplakia (OL), and oral lichen planus (OLP) using only clinical photographs, without the inclusion of additional clinical data. Materials and Methods: Two general dentists selected 23 images of oral lesions suspected to be OSCC, OL, or OLP. ChatGPT-4o was asked to provide a probable diagnosis for each image on 30 occasions, generating a total of 690 responses. The responses were then evaluated against the reference diagnosis set up by an expert to calculate sensitivity, specificity, predictive values, and the area under the ROC curve. Results: ChatGPT-4o demonstrated high specificity across the three conditions (97.1% for OSCC, 100% for OL, and 96.1% for OLP), correctly classifying 90% of OSCC cases (AUC = 0.81). However, this overall accuracy was largely driven by correct negative classifications, while the clinically relevant sensitivity for OSCC was only 65%. In spite of that, sensitivity was highly variable: 60% for OL and just 25% for OLP, which limits its usefulness in a clinical setting for ruling out these conditions. The model achieved positive predictive values of 86.7% for OSCC and 100% for OL. Given the small dataset, these findings should be interpreted only as preliminary evidence. Conclusions: ChatGPT-4o demonstrates potential as a complementary tool for the screening of OSCC in clinical oral images. Nevertheless, the pilot nature of this study and the reduced sample size highlight that larger, adequately powered studies (with several hundred cases per pathology) are needed to obtain robust and generalizable results. Nevertheless, its sensitivity remains insufficient, as a significant proportion of true cases were missed, underscoring that the model cannot be relied upon as a standalone diagnostic tool. Full article

Coffee processing generates large volumes of spent coffee grounds (SCGs), which contain 30–40% hemicellulose, 8.6–13.3% cellulose, and 25–33% lignin, making them a promising lignin-rich filler for biocomposites. Conventional wood composites rely on urea-formaldehyde (UF), melamine–urea–formaldehyde (MUF), and phenol–formaldehyde resins (PF), which dominate 95% of the market. Although formaldehyde emissions from these resins can be mitigated through strict hygiene standards and technological measures, concerns remain due to their classification as category 1B carcinogens under EU regulations. In this study, fiber-based biocomposites were fabricated from thermomechanical wood fibers, SCGs, and ammonium lignosulfonate (ALS). SCGs and ALS were mixed in a 1:1 ratio and incorporated at 40–75% of the oven-dry fiber mass. Hot pressing was performed at 150 °C under 1.1–1.8 MPa to produce panels with a nominal density of 750 kg m⁻³, and we subsequently tested them for their physical properties (density, water absorption (WA), and thickness swelling (TS)), mechanical properties (modulus of elasticity (MOE), modulus of rupture (MOR), and internal bond (IB) strength), and thermal behavior and biodegradation performance. A binder content of 50% yielded MOE ≈ 2707 N mm⁻² and MOR ≈ 22.6 N mm⁻², comparable to UF-bonded medium-density fiberboards (MDFs) for dry-use applications. Higher binder contents resulted in reduced strength and increased WA values. Thermogravimetric analysis (TGA/DTG) revealed an inorganic residue of 2.9–8.5% and slower burning compared to the UF-bonded panels. These results demonstrate that SCGs and ALS can be co-utilized as a renewable, formaldehyde-free adhesive system for manufacturing wood fiber composites, achieving adequate performance for value-added practical applications while advancing sustainable material development. Full article

Fractional Kelvin–Voigt (FKV) oscillators describe vibrations in viscoelastic structures with memory effects, leading to dynamics that are often more complex than those of classical harmonic oscillators. Since the harmonic oscillator is a simple, widely known, and broadly applied model, it is natural to ask under which conditions the dynamics of an FKV oscillator can be reliably approximated by a classical harmonic oscillator. In this work, we develop practical tools for such analysis by deriving approximate formulas that relate the parameters of an FKV oscillator to those of a best-fitting harmonic oscillator. The fitting is performed by minimizing a so-called divergence coefficient, a discrepancy measure that quantifies the difference between the responses of the FKV oscillator and its harmonic counterpart, using a genetic algorithm. The resulting data are then used to identify functional relationships between FKV parameters and the corresponding frequency and damping ratio of the approximating harmonic oscillator. The quality of these approximations is evaluated across a broad range of FKV parameters, leading to the identification of parameter regions where the approximation is reliable. In addition, we establish an empirical criterion that separates oscillatory from non-oscillatory FKV systems and employ statistical tools to validate both this classification and the accuracy of the proposed formulas over a wide parameter space. The methodology supports simplified modeling of viscoelastic dynamics and may contribute to applications in structural vibration analysis and material characterization. Full article

Objective: Neurosurgical interventions for medically refractory hypertonia (MRH) benefit both patients and their caregivers. Concurrent severe rotatory scoliosis and fusion constructs can make traditional microsurgical rhizotomy and navigated radiofrequency ablation (RFA) peripheral rhizotomy technically infeasible. We report the first case series of lumbosacral endoscopic ventral–dorsal rhizotomy (eVDR) in patients with MRH, and highlight this novel, minimally invasive, safe, and effective technique. Material and Methods: We retrospectively reviewed our single institution series of four patients with advanced hypertonia, gross motor function classification scale (GMFCS) 5, and severe rotatory scoliosis who underwent an eVDR using a flexible endoscope. We report demographics, operative characteristics, and outcomes. Results: Four patients underwent bilateral L1-S1 eVDR. Two patients had spastic quadriplegia and two had mixed spastic and dystonic hypertonia. Mean operative time was 225 ± 11 min and mean estimated blood loss (EBL) was 28.8 ± 26.2 mLs. Average length of stay was 2.75 days (range = 1–5 days), and average follow-up was 5.75 months (range = 3–9 months). All patients had significant decrease in bilateral lower extremity modified Ashworth Scale (mAS) scores (median decrease = 3, interquartile range [IQR] = 1; Wilcoxon rank-sum test z = −2.3, p = 0.02). The median decrease in Barry–Albright Dystonia Scale (BADS) scores for both patients with dystonia was 8 (IQR = 0). Two patients had minor perioperative events; none required additional surgery. All parents reported improvement in caregiving metrics. Conclusions: eVDR offers a safe and effective approach for tone reduction in patients with MRH and severe rotatory scoliosis and/or fusion hardware, which disallows traditional approaches. Full article

Tool wear is a major challenge in sheet-metal forming, as it directly affects product quality and process stability. Reliable monitoring of tool-wear conditions is therefore essential, yet it remains challenging due to limited data availability and uncertainties in manufacturing conditions. To this end, this study evaluates different strategies for developing robust machine learning models under data scarcity for fluctuating manufacturing conditions: a 1D-CNN using time-series data (baseline model), a 1D-CNN with signal fusion of force and acceleration signals, and a 2D-CNN based on Gramian Angular Field (GAF) transformation. Experiments are conducted using inline data from a blanking process with varying material thicknesses and varying availability of training data. The results show that the fusion model achieved the highest improvement (up to 93.2% with the least training data) compared to the baseline model (78.3%). While the average accuracy of the 2D-CNN was comparable to that of the baseline model, its performance was more consistent, with a reduced standard deviation of 5.4% compared to 9.2%. The findings underscore the benefits of sensor fusion and structured signal representation in enhancing classification robustness. Full article

This paper presents a relevant and timely study on the application of thermal loaded digital shearography for crack classification in glass fiber reinforced plastic (GFRP) structures, particularly air-cooled condenser (ACC) fan blades. A thermal loaded digital shearography system was applied to measure strain concentration caused by the cracks at different fatigue cycles. A thermomechanical model was introduced to estimate the heating temperature and the time to ensure heat can reach to the desired depth and that both shallow and deep cracks can be detected. In order to correlate the information of strain concentration in the shearograms to the different stages of cracks, fatigue testing with dynamic three-point bending was conducted. The fatigue tests demonstrated how the strain concentration evolved in the shearograms, while the crack developed from the early (no noticeable strain concentration), to the middle (strain concentration is forming), to the late stage (significant strain concentration is found). The relationships between the degrees of strain concentration in the shearograms and the different stages of cracks can be obtained from testing of the artificial cracks. Using the rules and experimental results obtained from artificial samples, digital shearography was applied to classify the crack stages in parts of ACC fan blades from industry. The combination of artificial crack testing, fatigue loading experiments, and validation with CT scans demonstrates a comprehensive approach and provides potential guidance for industry to determine criticality and maintenance criteria. Full article

Tissue repair is a significant challenge in biomedical research. Traditional treatments face limitations such as donor shortage, high costs, and immune rejection. Recently, magnetic-responsive materials, particularly magnetic nanoparticles have been introduced into tissue engineering due to their ability to respond to external magnetic fields, generating electrical, thermal, and mechanical effects. These effects enable precise regulation of cellular behavior and promote tissue regeneration. Compared to traditional physical stimulation, magnetic-responsive material-mediated stimulation offers advantages such as non-invasiveness, deep tissue penetration, and high spatiotemporal precision. This review summarizes the classification, fabrication, magnetic effects and applications of magnetic-responsive materials, focusing on their mechanisms and therapeutic effects in neural and bone tissue engineering, and discusses future directions. Full article

Introduction. We hypothesized changes in the elasticity in quadriceps and patella tendon before and after total knee arthroplasty would be correlated with a post-operative range of motion after total knee arthroplasty. To prospectively assess the post-operative range of motion after total knee arthroplasty, logistic regression was adopted with elasticity in the quadriceps and patella tendons were measured using shear wave elastography (SWE). Materials and Methods. From March 2021 to June 2021, SWE was performed on 95 patients (86 women; aged 57–85, mean 70.62 ± 5.49 years) preoperatively and 2 days after total knee arthroplasty. Elasticity at quadriceps and patellar tendons were measured with full flexion and extension using SWE. Based on the range of motion after surgery at 56 days, we divided the patients into two groups (Group A > 120 degrees; group B < 120 degrees). Using a logistic regression algorithm, classification between groups was performed. For the input of algorithm, patient information, the elasticity of quadriceps and patella tendons preoperatively and two days after total knee arthroplasty were used. Results. The accuracy of predicting group using only patient information was 62%, whereas using only elasticity was 68%. Furthermore, combining information and elasticity before and after surgery at 2 days, accuracy, sensitivity, specificity was 79%, 92%, 56%. Conclusions. Combined with patient information, elasticity measured by SWE at pre-op and early post-op periods could be effective to predict the performance of postoperative ROM. This algorithm could provide direction for rehabilitation. Full article

Identifying the material composition of objects is crucial for many recycling sector applications. Traditionally, object classification relies either on hyperspectral imaging (HSI), which analyses the chemometric properties of objects to infer material types, or on RGB imaging, which captures an object’s visual appearance and compares it to a reference sample. While both approaches have their strengths, each also suffers from limitations, particularly in challenging scenarios such as robotic municipal waste sorting, where objects are often heavily deformed or contaminated with various forms of dirt, complicating material recognition. This work presents a novel method for material-based object classification that jointly exploits HSI and RGB imaging. The proposed approach aims to mitigate the weaknesses of each technique while amplifying their respective advantages. It involves the real-time alignment of HSI and RGB data streams to ensure reliable result correlation, alongside a machine learning framework that learns to exploit the strengths and compensate for the weaknesses of each modality across different material types. Experimental validation on a municipal waste sorting facility demonstrates that the combined HSI–RGB approach significantly outperforms the individual methods, achieving robust and accurate classification even in highly challenging conditions. Full article

As heritage cognition evolves, aligning conceptual understanding with conservation strategies becomes essential for effective practice. This study develops the Heritage Cognition–Conservation Paradigm Correspondence Framework, a methodological tool designed to evaluate the alignment between heritage cognition and conservation paradigms. Methodologically, the framework is constructed through document analysis, conceptual classification, and framing co-construction. Building on a critical review of the development trajectory of heritage conservation, it integrates four cognitive phases and three conservation paradigms into a dual-axis matrix, operationalized through six analytical dimensions for heritage cognition and four for conservation paradigms. The framework is subsequently applied through a case study of Traditional Chinese Villages, demonstrating its diagnostic capacity and analytical utility. The case study reveals a significant misalignment: while official discourse reflects pluralistic heritage thinking (within the most advanced, fourth cognitive phase), conservation practice remains rooted in value-based logics and material-based approaches (within the initial paradigms). This misalignment stems from fragmented object recognition, form-focused objectives, and top–down governance structures that marginalize local agency and overlook cultural processes as the heritage nature of those villages. By establishing and operationalizing the correspondence framework, this study provides a transferable tool for diagnosing cognition–practice disjunctions across heritage contexts. Beyond its empirical findings, the study advances a methodological contribution for heritage conservation and advocates a strategic shift toward process-oriented, community-embedded approaches that emphasize cultural continuity, reframed objectives, and participatory governance. Full article

Background and Objectives: The severe systemic response to urinary tract infections known as urosepsis is associated with significant morbidity and mortality rates. The neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) are simple blood tests that could be useful in predicting the outcome of sepsis. Materials and Methods: A prospective observational study was conducted at a tertiary care hospital, where our team studied 223 patients with urosepsis. The patients underwent Sepsis-3 criteria-based urosepsis and septic shock stratification followed by survivor and non-survivor classification. Clinical scores (Sequential Organ Failure Assessment-SOFA, National Early Warning Score-NEWS), laboratory markers (NLR, PLR, PCT-procalcitonin), and patient outcomes were then analysed. Results: An admission NLR ≥ 13 was a strong predictor of septic shock (adjusted Odds Ratio (OR) 2.10, 95% Confidence Interval (CI) 1.25–3.54) and in-hospital mortality (adjusted OR 2.45, 95% CI 1.40–4.28). While the prognostic value of the PLR remained moderate, the NLR demonstrated superior predictive power. As easily measurable biomarkers, the NLR and PLR provide valuable information to help clinicians identify at-risk patients during the early stages of urosepsis. Conclusions: The NLR is an independent predictor with high predictive value for both septic shock and mortality, performing as well as established clinical scores. The combination of these parameters with clinical assessments could lead to better early decisions and improved outcomes for patients with urosepsis. Full article