Browse Articles

Early extubation effects on postoperative outcomes in high-risk abdominal surgery based on propensity score matching

Separation-free artificial photosynthesis of concentrated hydrogen peroxide and value-added fuels over Ta atomic sites

Factors related to high sodium intake based on 24-hour urinary sodium excretion in population aged 50 years and older

People are more likely to cheat when they delegate tasks to AI

Design of a combined LED and rapid-injection NMR system for structure elucidations and kinetic analyses

Social maintenance masks induced aggression in zebrafish

Platelet activation plays a pro-inflammatory role in myasthenia gravis

The association between anxiety, activity performance and nomophobia in students

The assembly of a hybrid type IV secretion system by a Crohn’s disease-associated Escherichia coli strain

Competitive adsorption of pharmaceuticals and dissolved organic matter from hospital effluent on amine-functionalized periodic mesoporous organosilicas

Facile cascade-anchored synthesis of ultrahigh metal loading single-atom for significantly improved Fenton-like catalysis

Design and control of a continuum robot with switchable stiffness based on ball-and-socket joints

Leveraging catechol chemistry to tackle toughness-softness-work capacity tradeoff in reprogrammable liquid crystal actuators

An OpenSEES graphical user interface for structural dynamics and earthquake engineering instruction

Unlocking data in Klebsiella lysogens to predict capsular type-specificity of phage depolymerases

Multi-objective optimization of surface finish and VOC emissions in FFF 3D printing using ANN–NSGA-II approach

Abstract Fused filament fabrication (FFF) is a widely adopted 3D printing technique for the rapid, cost-effective, and customized fabrication of complex microfluidic channels using polylactic acid (PLA), particularly for drug delivery and biomedical applications. However, achieving optimal surface finish and minimizing volatile organic compound (VOC) emissions during printing remains a substantial challenge. The current study proposes a new approach by integrating a hybrid framework of artificial neural networks (ANN) with the non-dominated sorting genetic algorithm II (NSGA-II) to enhance both the quality and environmental safety of the FFF 3D printing process for microfluidic channel applications. The proposed approach optimizes four key process parameters such as layer thickness (LT), print speed (PS), material flow rate (MFR), and raster angle (RA) with a prime objective of obtaining an excellent surface finish with minimum VOC emissions. A customized FFF 3D printer embedded with cost-effective, high-sensitivity emission sensors was designed and developed to facilitate real-time monitoring, thereby offering a unique and economical capability not available in conventional commercial printers. Experimental data generated using central composite design (CCD) were employed to train a high-fidelity ANN model (R² > 95%), which functions as a surrogate model for NSGA-II-based multi-objective optimization. The ANN model demonstrated strong predictive accuracy with low mean squared error (MSE) and high correlation coefficients (R² = 0.9967 for training, 0.956 for validation, and 0.9261 for testing). The proposed ANN-NSGA-II framework identified Pareto-optimal solutions at LT (0.15 mm), PS (40 mm/s), MFR (100%), and RA (30º). The optimization results demonstrate effective control of process parameters, yielding the dual benefit of enhanced surface finish and reduced VOC emissions. The novelty of this work lies in integration of real-time emission sensing with predictive intelligence and evolutionary multi-objective optimization, ensuring high print quality while simultaneously advancing environmentally responsible additive manufacturing (AM) practices.

Author Correction: Fluctuating local polarization: a generic fingerprint for enhanced piezoelectricity in Pb-based and Pb-free perovskite ferroelectrics

Health risks from persistent heavy metal contamination in crops and water at an abandoned naturally revegetated galamsey site in Ghana

Influence of individual models and studies on quantitative mitigation findings in the IPCC Sixth Assessment Report

Abstract Quantitative mitigation findings based on emissions scenarios submitted to the Intergovernmental Panel on Climate Change (IPCC) play an authoritative role in climate policy and decision making. We analyse the impact of the uneven representation of models and modelling studies in the IPCC Sixth Assessment Report (AR6) on statistical values that are used to present quantitative mitigation findings. We find that several key AR6 findings are influenced considerably by the model with the most scenarios, including emissions reductions by 2030 and the decline in fossil fuels consistent with 1.5 °C, and we find that the year of net-zero greenhouse gas emissions is influenced considerably by both the model and the study with the most scenarios. We find that weighting by model- or study does not provide a straightforward solution and discuss three issues related to the use of database statistics to present emissions scenarios findings. Informed by the purpose of the IPCC and the kinds of insights that can be obtained from emissions scenarios, we suggest improvements to the assessment of emissions scenarios.

Quantifying blast damage and failure mode transition in reinforced concrete columns with a dimensionless model