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A city-based framework identifies wild Hedychium species suitable for near-nature urban landscaping in China

Space–time variable-order fractional analysis of nonlinear longitudinal wave propagation in magneto-electro-elastic materials

Smart city traffic optimization using IoD and IoT integration

Automating the assessment of quality indicators using a clinical data warehouse: a pilot study on door-to-imaging time in stroke management

POLAR-DETR: Polarized occlusion-aware local-global attention real-time detection transformer for total laboratory automation

Green DOE based RP-HPLC method for the simultaneous determination of Azelastine and Losartan in spiked human plasma samples

Abstract A green RP-HPLC approach coupled with spectrofluorometric detection was established for the simultaneous estimation of the co-administered drugs Azelastine (AZL) and Losartan (LOS) in spiked human plasma samples. Preliminary trials were carried out for determination of the Critical Method Parameters (CMPs) and Critical Quality Attributes (CQAs). Design of Experiment (DOE) was developed relying on the use of Central Composite Design (CCD) for the optimization of conditions to establish a simple, rapid, cost-effective and environmentally benign approach. The chromatographic separation was based on using a mobile phase composed of methanol: acetonitrile: 0.02M phosphate buffer of pH 3.25 with 0.05% tri-ethylamine (60: 1.5: 38.5, v/v/v) at a flow rate of 1.2 mL/min and injection volume of 10µL. The fluorescence detection was carried out at 245 nm/400 nm and 290 nm/360 nm for estimation of LOS and AZL, respectively. The retention times of AZL and LOS were 8.9 ± 0.2 min and 13.4 ± 0.1 min, respectively. The developed method was validated according to ICH Guidelines with linear relationship in concentration ranges of 0.10–50.0µg/mL for AZL and 0.30–40.0µg/mL for LOS, while in spiked plasma samples the concentration ranges were 0.025–0.07µg/mL for AZL and 0.025–0.10 µg/mL for LOS. The developed approach was successfully applied for the quantitation of both drugs in spiked human plasma samples. Complex MoGAPI and AGREE methodologies were used for greenness assessment.

A latent class analysis of cardiometabolic risk factors and the predicted prevalence of subclinical atherosclerosis in middle-aged Swedish adults

Abstract Previous research on cardiometabolic risk has mostly used a variable-centred approach, assessing risk factors separately or in predefined combinations. This study used a probabilistic modelling approach to identify distinct cardiometabolic risk classes and estimate the predicted prevalence of subclinical atherosclerosis. The analysis included 28,307 middle-aged adults from the Swedish CArdioPulmonary bioImage Study (2013–2018), linked to national registers. Eleven risk factors were assessed: smoking, alcohol consumption, sodium and fibre intake, physical activity, stress, waist circumference, triglycerides, HDL-cholesterol, blood pressure, and fasting glucose. Subclinical atherosclerosis was defined using coronary artery calcium (CAC) scores and the presence of carotid plaque. A three-step latent class analysis identified four cardiometabolic risk classes: “low fibre intake and normolipidemia” (55.2%, Class 1), “high sodium intake and normolipidemia” (12.8%, Class 2), “unhealthy lifestyle and heightened metabolic risk” (10.1%, Class 3), and “unhealthy lifestyle and high metabolic risk” (21.9%, Class 4). Predicted mean CAC scores ranged from 42.6 (Class 2, 95% CI 39.0–46.3) to 92.1 (Class 4, 95% CI 86.2–98.0). Predicted carotid plaque prevalence ranged from 51.6% (Class 2, 95% CI 50.6–52.6) to 60.8% (Class 4, 95% CI 59.8–61.9). Latent classes offered a complementary descriptive framework beyond single risk factors, supporting more tailored prevention according to risk profiles.

Contactin-2 protects against aortic valve calcification via osteogenic differentiation inhibition

Medicinal and aromatic plants as climate-smart crops: case studies on Pelargonium graveolens and Viola odorata under Egyptian conditions

Protective role of lycopene against salinity-induced oxidative stress in Medicago sativa L. seedlings

Radar-based inspiratory-to-expiratory time ratio estimation: a validation study

Abstract Respiration is a key indicator of health and wellbeing, with metrics such as respiratory rate (RR), inspiratory time (TI), expiratory time (TE), and the inspiratory-to-expiratory time (I:E) ratio offering insights into conditions ranging from acute life-threatening and chronic diseases to symptom management. While traditional methods already measure these parameters with high accuracy, they still require contact-based sensors, limiting their practicality for continuous monitoring. This study evaluates radar as a non-contact alternative by validating multiple radar-derived respiratory metrics against impedance pneumography measurements in 30 healthy volunteers at rest. Synchronous recordings from both modalities were analysed to assess agreement across methods using descriptive statistics, scatter plots, modified Bland-Altman plots, and equivalence testing (TI: ±0.3 s, TE: ±0.3 s, RR: ±2 brpm, I:E ratio: ±0.2). Equivalence testing indicated high correlation ( p  ≤ 0.001***) across all metrics, with 81.8% (TI), 77.6% (TE), 97.2% (RR), and 85.7% (I:E ratio) of values within predefined bounds. These findings highlight radar’s potential for continuous respiratory monitoring, particularly in medical fields where minimizing patient burden is essential as in palliative, post anaesthesia, and intensive care settings.

A spam detection model based on the discriminative TF-IDF belief rule base

Abstract Novel spam with rapidly evolving content faces a scarcity of labeled data in its early stages. Yet, current detection models rely heavily on large datasets and high-dimensional features, leading to poor generalization and opaque decisions when data is scarce. This opacity hinders error tracing and limits their use in early threat detection and response. The belief rule base (BRB), as an expert system, demonstrates effective learning under small-sample conditions, and its rule-based reasoning mechanism provides decision interpretability. However, high-dimensional features may cause combination explosion. To address these issues, a BRB spam detection model based on the Discriminative term frequency-inverse document frequency (TF-IDF) method (DTI-BRB) is proposed in this paper. By discriminating whether terms are more indicative of ham or spam, the Discriminative TF-IDF method converts raw text into low-dimensional features, thereby effectively resolving the combination explosion problem inherent in the traditional BRB model. Through two case studies under small-sample conditions, the effectiveness of the proposed model is validated. With only 200 samples, it achieves accuracies of 91.5% and 95.5% in the two cases, respectively, exhibiting excellent predictive performance and interpretability.

Tailored Phosphate Leaving Groups Direct Pathway-Dependent Self-Assembly

Physical model study on the mechanism of floor heave for the deep-buried roadway excavated in soft rock of gently inclined thin strata

Preventing chick culling in the poultry industry with a new biomarker for rapid in ovo gender screening

Abstract Chicken eggs are one of the most consumed foods worldwide. However, the practice of chicken culling in the poultry industry involves unnecessary animal suffering and finding a way to put an end to this has become a societal priority. One approach that has been propagated as acceptable is based on the selection of female eggs early in the incubation process and the devitalization of the male eggs. It is with this objective in mind that we searched for a biomarker for early gender screening in eggs. Applying an untargeted mass spectrometry approach, we profiled allantoic fluid of different day-old eggs and identified the feature 3-[(2-aminoethyl)sulfanyl]butanoic acid (ASBA) as a strong biomarker for in-ovo gender prediction for day-9 old embryos. In the present work, we describe the identification of ASBA as a new biomarker in allantoic fluid for gender screening and the optimization of a high throughput assay using acoustic droplet ejection-mass spectrometry (ADE-MS). Special attention is given to the optimization of ADE-MS compatible liquid handling and the development of the data processing to ensure a reliable gender prediction. We have been able to accurately determine the gender of day-9 eggs in a cohort of 154 samples with a prediction accuracy of 95.5%, with a throughput of 1800 samples per hour for the prototype, which may vary in production systems.

Optimization of drawing parameters based on top-coal flow law in thick-seam caving mining

Research and implementation of intelligent clothing personalized customization system based on deep learning

Abstract This study presents an intelligent personalized garment customization system that integrates deep learning methodologies. The system employs a microservices architecture to unify four core modules: body measurement data extraction, style preference learning, virtual try-on visualization, and design recommendation generation. We propose a novel CNN-Transformer-GAN architecture, specifically tailored for personalized garment design tasks, achieving exceptional accuracy. Experimental results demonstrate that the system attains a mean absolute error (MAE) of 0.38 cm in body measurement, an accuracy of 87.4% in style matching, and a response time of 285 ms. Compared to existing approaches, the proposed system improves measurement accuracy by 38.7% and delivers visualization quality comparable to metaverse-based systems. To evaluate user experience, we conducted two complementary studies: (1) a controlled single-blind user study with 120 participants, which yielded satisfaction scores between 4.42 and 4.65 across recommendation accuracy, interface usability, and visualization quality; and (2) a large-scale deployment test involving 250 users, which reported an average overall satisfaction score of 4.55 out of 5.0. This research helps bridge the gap between artificial intelligence and personalized fashion design, advancing resource-efficient customization and better alignment with consumer needs in the apparel industry. By integrating state-of-the-art deep learning techniques with responsive user preference modeling, the system offers an innovative solution for intelligent garment customization.

Human stem cell-derived neurons establish functional inhibitory–excitatory cortical circuits in a chimeric transplantation model

Transcriptional remodeling of cardiomyocytes and fibroblasts during post-myocardial infarction recovery

Abstract Myocardial infarction (MI) results from reduced coronary blood flow, leading to oxygen deprivation and impaired systolic and diastolic function, which increases the risk of cardiac arrhythmias. Various cardiac cell types respond to this stress to preserve heart function, but the precise, cell-type-specific mechanisms remain poorly understood. To investigate these responses, we performed single-nucleus RNA sequencing (snRNA-seq) on left ventricular tissue from mouse hearts at baseline (Day 0) and at 1 and 4 weeks post-MI. This enabled us to characterize transcriptional changes across major cardiac cell types. We observed significant shifts in the transcriptional states of cardiomyocytes (CMs) and fibroblasts (FBs) cell populations following MI. CMs showed a major transcriptional modulation from healthy to diseased state during early chronic phase of post-MI, however, the recovery phenotype was observed during the late chronic phase, suggesting a natural compensatory response of CMs against the ischemic stress. FBs exhibited dynamic transcriptional changes consistent with roles in post-MI healing and fibrosis. In addition, inferred alterations in cell-cell communication networks highlighted changes in intercellular signaling pathways, shedding light on disrupted crosstalk in the injured heart. Together, our findings provide a comprehensive transcriptional landscape of cardiac cell populations, especially CMs and FBs, following MI and identify potential molecular targets for therapeutic intervention.

Self-supervised non-dominated sorted model for co-clustering

Abstract Co-clustering is widely used for data analysis that independently reveals the clustering structures of rows and columns while also identifying their inter-relationships, which renders it more informative than conventional one-way clustering methods. Co-clustering is to not only cluster the samples and features of original data, but also mine the relationship between samples and features, and this is naturally a multi-objective problem. However, researchers frequently utilize the method of single-objective optimization to solve the co-clustering issue, while disregarding its multi-objective nature, and the side information in the original data is also ignored. To address these problems, we propose a self-supervised non-dominated sorted model for co-clustering (SNSC), which is represented by a group of multi-objective functions. The model not only perfectly aligns with the multi-objective nature of co-clustering tasks but also utilizes the supervised information in the original data. The objective function group consists of four objective functions acting on the original data and similarity matrix respectively. The heuristic initialization method with self-supervised properties is used in conjunction with the random initialization method, which improves the efficiency of the model and reduces the likelihood of converging to local optima. The overall model remains unsupervised, as all the supervised information is derived from the original data. Further, the algorithm for the SNSC model is designed by using the idea of the genetic algorithm, which is theoretically supported, and the complexity analysis of the algorithm is given. Finally, experiments on 12 datasets and 5 comparison algorithms show that the SNSC algorithm has significant advantages.