Browse Articles

Analysis of functional and serviceability performance in sustainable yarns and socks by using multi-response optimization technique

A multihued sustainable appraisal of the electrochemical method for synchronized micro-estimation of the household drug Paracetamol with Aceclofenac or Dicyclomine

Abstract Paracetamol (PCT) is an analgesic and antipyretic that is consumed on a large scale and frequently formulated in fixed-dose combinations to enhance therapeutic efficacy. In this study, an Eco-benign differential pulse voltammetric method using an unmodified glassy carbon electrode (GCE) was developed for the simultaneous micro-estimation of PCT in two binary mixtures: with Aceclofenac (ACL) and with Dicyclomine (DIC). In the Britton–Robinson buffer (BRB) at pH 2, PCT and ACL exhibited well-resolved anodic peaks at 0.62 and 0.81 V, respectively, while at pH 9, PCT and DIC demonstrated oxidation peaks at 0.41 and 0.58 V, respectively. The validation of the proposed method was conducted in accordance with ICH guidelines and they are applied to pharmaceutical tablet formulations successfully. Linearity was achieved over concentration ranges of 0.2–25 µg·mL⁻¹ for PCT and ACL, and 1–25 µg·mL⁻¹ for PCT and DIC, with coefficients of correlation that exceed 0.9995. The method adheres to Green and White Analytical Chemistry principles by minimizing reagent consumption, analysis time and environmental impact. A multihued sustainability assessment was conducted using several greenness and sustainability metrics and compared with reported techniques. In addition, alignment with the United Nations Sustainable Development Goals was evaluated using the innovative Need, Quality and Sustainability (NQS) index. The results demonstrate that the proposed voltammetric approach is simple, rapid, cost-effective and suitable for routine quality control analysis of paracetamol-based binary mixtures.

Exudate compositions differ between the cover crops vetch and oat

Abstract Enhancing carbon (C) storage in soils through crop management can mitigate atmospheric CO₂ levels and improve soil health. Cover crops like bristle oat ( Avena strigosa ) and common vetch ( Vicia sativa ) may contribute to soil C sequestration via root biomass and rhizodeposition, including root exudates, but C storage potential at the species and varietal levels for these plants remain poorly documented, partly due to difficulties in measuring root exudation under field conditions. This study compared four cultivated varieties of bristle oat and common vetch, focusing on their root and exudate C content and metabolic profiles. Distinct metabolite profiles were identified mainly at the species levels. Oat roots had a reduced C content compared to vetch roots although they contained more amino acids, sugars, organic acids and specialized metabolites. On the contrary, C content was higher in oat exudates with more fatty acids and specialized metabolites while vetch exuded more sugars, organic acids and nucleotides. These results suggest that the mixture of these two plants can produce complementary C deposits, enhancing C storage and allowing the construction of a rich (microbial) soil ecosystem.

Preparation of a distillers’ grains derived lignin-chitosan adsorbent for enhanced distillery wastewater treatment

Contribution of non-linear internal waves to marine net primary production has been underestimated

Comparative greenness assessment for the simultaneous estimation of diclofenac and methocarbamol in their tablets applying synchronous fluorimetry

Abstract For treatment of muscle spasms associated pain, combination of nonsteroidal anti-inflammatory drugs like diclofenac (DIC) and muscle relaxants as methocarbamol (MET) is usually utilized. This work represents a novel, rapid, facile, sensitive, and selective first derivative synchronous fluorescence spectroscopy (FDSFS) for the simultaneous determination of DIC and MET in their combined tablets. Factors influencing method’s sensitivity were investigated, and the best findings were accomplished applying Δ λ = 60 nm and using water as a diluting solvent. Through applying the optimized experimental conditions, DIC showed a lower detection limit of 0.15 µg/mL and a quantitation limit of 0.30 µg/mL, while MET corresponding values were 0.03 µg/mL and 0.05 µg/mL. Diclofenac was measured at 288 nm, while methocarbamol was measured at 346 nm, exhibiting linearity over the concentration ranges of 0.3–2.5 and 0.05–5.0 µg/mL, respectively. Through application to several laboratory-prepared mixtures and commercial formulation, the suggested method’s applicability was determined. When comparing the proposed method to the reported HPLC method using the student’s t -test and F -ratio test, no discernible differences were found. Due to simplicity and economical advantage of the method, it can be applied in quality control laboratories for analysis of the studied drugs. The evaluation of the method’s eco-friendliness and greenness was also performed using Analytical GREEnness (AGREE), Green Analytical Procedure Index (GAPI) and Analytical Green Star Area (AGSA) metrics. Complete validation procedures were applied to the suggested approach in compliance with the International Conference on Harmonization’s criteria.

First detection of an H5N2 subtype of Influenza A virus detected in Charadrius collaris from the Brazilian Pantanal

Teaching activity design and psychological practice of music majors under a convolutional neural network and transformer module

Nudging with monthly feedback may help to reduce urinary catheter days and catheter-associated urinary tract infections

Experimental and Monte Carlo simulation study on photons shielding properties of ZrO2-reinforced polyester composites utilizing GEANT4 and MCNP codes

Alkalinity enrichment stimulates calcification and linear extension in Acropora cervicornis

Abstract Environmental change, disease, and a myriad of local stressors have led to worldwide declines in coral cover, demanding that restoration efforts scale with the magnitude of the crisis. Critical to this goal is the industrial-scale propagation of coral fragments. Preliminary evidence from aquarists suggests that elevating seawater alkalinity increases coral growth, and several papers have reported enhanced calcium carbonate deposition. Many questions remain, however, concerning the effectiveness on species targeted for restoration, optimal alkalinity range, and the manner in which growth is affected (e.g., skeleton extension versus density). Here, we investigate the effect of elevated alkalinity on total calcification and linear extension of the Caribbean coral, Acropora cervicornis . Corals were exposed to one of four alkalinity treatments for 33 days using a sodium bicarbonate/sodium carbonate solution. Elevated alkalinity significantly enhanced total calcification, increasing by 125% in the highest treatment. Linear extension was also significantly enhanced during the first half of the experiment (98%), but the effect was no longer detectable after week three when growth slowed in all groups, including the controls. These findings suggest that elevated alkalinity may accelerate coral propagation, representing an affordable and practical tool to enhance production efficiency in land-based coral restoration facilities.

Human-centric participation paradigm: exploring the enjoyment experience of cultural heritage virtual museum based on system usability and VR technology

TRIM29 promotes epithelial–mesenchymal transition, angiogenesis, and stromal remodeling in lung adenocarcinoma: integrated validation at histologic, transcriptomic, and protein levels

Validation of conformal prediction in cervical atypia classification

Abstract Deep learning based cervical cancer classification can potentially increase access to screening in low-resource regions. However, deep learning models are often overconfident and do not reliably reflect diagnostic uncertainty. Moreover, they are typically optimized to generate maximum-likelihood predictions, which fail to convey uncertainty or ambiguity in their results. Such challenges can be addressed using conformal prediction, a model-agnostic framework for generating prediction sets that contain likely classes for trained deep-learning models. The size of these prediction sets indicates model uncertainty, contracting as model confidence increases. However, existing validation of conformal prediction primarily focuses on whether the prediction set includes or covers the true class, often overlooking the presence of extraneous classes. We argue that prediction sets should be truthful and valuable to end users, ensuring that the listed likely classes align with human expectations rather than being overly relaxed and including false positives or unlikely classes. In this study, we comprehensively validate conformal prediction sets using expert annotation sets collected from multiple annotators. We evaluate three conformal prediction approaches applied to three deep-learning models trained for cervical atypia classification. Our expert annotation-based analysis reveals that conventional coverage-based validations overestimate performance and that current conformal prediction methods often produce prediction sets that are not well aligned with human labels. Additionally, we explore the capabilities of the conformal prediction methods in identifying ambiguous and out-of-distribution data.

A hybrid recommendation framework utilizing domain-adaptive RoBERTa embeddings for enhanced personalization in e-commerce

Abstract With the rapid growth of e-commerce and online platforms, delivering personalised and accurate recommendations remains a challenge due to sparse interaction data and diverse user interests. This paper proposes HyReC , a unique hybrid recommendation framework that integrates content-based and collaborative filtering while maintaining computational efficiency. Domain-adaptive RoBERTa embeddings are used to extract semantic representations from textual content, capturing user and item preferences from descriptions and reviews. A Deep Neural Network (DNN) model uses user-item interactions to generate latent behavioural embeddings, which are enriched behavioural statistical features such as mean rating, rating variance (standard deviation), interaction frequency, and skewness. Heterogeneous embeddings are fused using a Bahdanau attention mechanism , enabling the model to dynamically weight content, collaborative, and statistical signals. The fused representation is then used to generate recommendations through a Learning-to-Rank layer , depending on application scale. A model is trained using the Adam optimiser to ensure fast convergence and stable performance. Experimental evaluation on the Amazon Baby dataset demonstrates that HyReC achieves superior performance of 0.15 , MAE of 0.10 , MSE of 0.023 , R² of 0.98 , Pearson Correlation of 0.99 , MAPE of 1.5% , and F1-score of 0.98 , outperforming state-of-the-art models such as LSTM, RBM + KNN, GNN, and GAT. Experiments on benchmark datasets demonstrate that the proposed framework improves recommendation accuracy , diversity , and robustness compared to baseline models, effectively addressing data sparsity , user interest drift , and heterogeneous content .

A contrastive learning framework with adaptive feature fusion for brain tumor classification

Single cell profiling of ER stress in coronary artery disease and therapeutic mechanisms of Ginkgo biloba extract

A quantile-based composite ionospheric disturbance estimator for RTK positioning reliability

Abstract Reliable real-time kinematic (RTK) positioning is highly sensitive to short-term ionospheric irregularities and spatial electron density gradients, which may degrade ambiguity resolution and positioning integrity. Existing disturbance indicators typically rely on single-parameter metrics such as the Rate of Total Electron Content (TEC) Index (ROTI) or spatial TEC gradients considered independently, limiting their capability to characterize complex space–time ionospheric dynamics. We introduce a quantile-based composite ionospheric disturbance estimator designed for RTK positioning reliability assessment. The proposed framework integrates temporal ionospheric variability and spatial Vertical TEC (VTEC) gradient information into a unified risk indicator. Short-term ionospheric irregularities are characterized using rolling-median ROTI values, from which a high-quantile regional disturbance metric is extracted. Spatial ionospheric structure is quantified through interpolation of the VTEC field and computation of the gradient magnitude, followed by high-quantile extraction of gradient intensity. Both components are normalized using an adaptive quantile-based scaling scheme to ensure robustness against extreme values and regional statistical variability. The final RTK disturbance estimator is formulated as a weighted composite index combining normalized temporal and spatial disturbance measures. The method is fully reproducible and independent of absolute TEC magnitude, relying solely on GNSS-derived ionospheric observables. Validation using dense multi-frequency GNSS observations demonstrates that the composite estimator captures disturbance patterns not resolved by global ionospheric models and provides a physically interpretable risk score relevant for high-precision GNSS applications. The proposed approach offers a generalizable framework for ionospheric integrity monitoring and composite risk assessment in real-time GNSS positioning systems. The proposed approach provides a reproducible GNSS-based framework for composite disturbance monitoring. In this study, the method is validated using a mid-latitude regional CORS network (Latvia); extension to other ionospheric regimes and sparse networks requires further investigation.

A misclassification-aware explainable hybrid CNN-vision transformer framework for radiographic weld inspection

Screenathon 2.0: human–AI collaborative screening applied to patient-generated health data

Abstract Systematic reviews are essential for evidence-based research, yet the traditional screening process is time-consuming and difficult to scale. Human-only screening can introduce inconsistency, while fully automated approaches employing Large Language Models often lack the contextual judgement required for complex decisions. To address this, we introduce a crowd-based screening methodology that integrates human expertise with adaptive machine learning. The methods have been applied in the context of a large EU project where experts from 27 collaborating partners jointly screened 5842 papers across eleven disease topics related to patient-generated health data in a span of 2 days. Post-processing played a central role in ensuring data quality, including topic reallocation, targeted full-text verification, and noisy-label filtering. This Screenathon resulted in 487 records being labeled as relevant and 6,463 records as irrelevant. The number of records screened per participant ranged from 3 to 2496, with a mean of 216.4 records per screener ( SE  = 95.19). Exploratory analyses using survey results indicated increased trust in AI-assisted systematic reviewing after the event, along with generally positive evaluations of usability. The current Screenathon demonstrates that crowdsourced human–AI collaboration requires thoughtful training and calibration, together with strong post-processing safeguards.