Browse Articles

Integrated high density seismic imaging and structural characterization of the Yahu deep brine system, Qaidam Basin

Navigating Lipid Nanostructure Design Space Through Continuous Microfluidic Automation

ABSTRACT The versatility of nanoscale lipid particles has positioned them as the scaffold of choice for biomedical delivery, synthetic membrane engineering, and fundamental biophysical exploration. Across these fields, rational particle design has become a major bottleneck. Lipid composition, stoichiometry, size, morphology, phase behavior, and biophysical properties combine into an enormous, high‑dimensional space that is inherently difficult to explore, making conventional optimization approaches slow and limited. Identifying optimal lipid compositions in this vast space necessitates high‐throughput screening based on efficient low‐cost automation. Here we introduce a high‐throughput microfluidic platform for rapid screening of lipid nanoparticles, offering precise, programmable control over composition and morphology. The system integrates on‐chip microfluidic metering of lipid stocks with continuous particle self‐assembly, followed by robotic collection into 96‐well plates. This workflow enables the generation of over 200 unique formulations per hour, delivering a several‐orders‐of‐magnitude increase in throughput relative to conventional approaches. We apply the platform to systematically map biophysical space at unprecedented resolution, validate compositional trends in transfection efficiency, and identify non‐lamellar formulations with optimal functional performance. By coupling scalable synthesis with automated screening, we expect this platform to provide a robust foundation for data‐driven and AI‐integrated discovery of self‐assembled nanomaterials including lipid, polymeric, and biomolecular assemblies.

Explainable hybrid deep learning for automated cervical cytology classification

Abstract Accurate and explainable automated analysis of cervical cytology remains a major challenge for artificial intelligence (AI)-assisted cervical cancer screening, particularly in low-resource settings where access to expert cytotechnologists is limited. Although deep convolutional neural networks (CNNs) have substantially improved classification accuracy, their limited interpretability hinders clinical trust and routine adoption. Conversely, neuro-fuzzy systems provide transparent decision-making but lack the ability to learn complex cytomorphological representations directly from image data. Here, we present PapsAI XNet , a novel explainable hybrid deep learning framework that integrates attention-guided CNN feature extraction with an Adaptive Neuro-Fuzzy Inference System (ANFIS) to simultaneously achieve high diagnostic accuracy and interpretable decision-making. PapsAI XNet incorporates a Channel Attention Module and feature normalization strategy to enhance morphological feature discrimination and stabilize downstream fuzzy inference. A compact ANFIS rule base initialized through data-driven clustering enables transparent reasoning while avoiding the rule explosion commonly associated with high-dimensional neuro-fuzzy systems. The framework was evaluated on the publicly available Herlev dataset comprising seven cervical cytology classes and benchmarked against ResNet-18 and MobileNetV2 using accuracy, sensitivity, specificity, precision, F1-score, receiver operating characteristic (ROC) analysis, computational complexity, and explainability analyses. PapsAI XNet achieved an overall accuracy of 97.8% , sensitivity of 96.4% , specificity of 98.6% , precision of 97.1% , and F1-score of 96.7% , significantly outperforming both benchmark CNN architectures. The greatest improvements were observed for mild and moderate dysplasia, where gradual morphological transitions frequently challenge conventional deep learning models. Class-wise area under the ROC curve exceeded 0.97 for all abnormal cytological categories. Furthermore, learned Gaussian membership functions, fuzzy-rule surfaces, and rule activation patterns provided clinically meaningful explanations linking deep morphological features to diagnostic outcomes while maintaining a lightweight computational footprint suitable for real-time deployment. These findings demonstrate that integrating attention-guided deep feature learning with interpretable neuro-fuzzy reasoning enables accurate, explainable, and computationally efficient cervical cytology classification. PapsAI XNet provides a practical framework for trustworthy AI-assisted cervical cancer screening and represents an important step toward clinically deployable explainable AI for digital cytopathology, particularly in resource-constrained healthcare systems.

Intermediate‐Site Anchoring Ligands Enable Robust Nonlayered Interfacial Passivation for Efficient and Stable Air‐Processed Perovskite Solar Cells

Abstract The complex moisture–oxygen environment in air places stringent demands on surface passivation for air‐processed perovskite solar cells. However, most conventional ammonium ligand‐based passivation, which binds to the perovskite surface through a terminal site, often induces ligand intercalation, elevates interfacial resistance, and compromises environmental stability, thereby limiting efficient device fabrication under ambient conditions. In this study, we report a robust, ligand‐based, intermediate‐site anchoring strategy for nonlayered interfacial passivation using a series of choline derivatives. The thioacyl sulfur coordinates strongly with under‐coordinated Pb 2+ sites, while iodide counter‐anions assist in halide vacancy healing, collectively forming a thermally robust and electronically homogeneous top interface. The surface passivation homogenizes surface potential, optimizes band alignment, relaxes residual strain, and suppresses trap‐assisted recombination and halide migration. Consequently, the resulting perovskite solar cells achieve a power conversion efficiency (PCE) of 26.54%, the highest value for air‐processed n–i–p PSCs reported so far. These devices also retained over 90% PCE after 2000 h at 65°C and 90% under continuous maximum power point tracking for 1000 h (AM 1.5G, 40°C ± 1°C), with projected T 80 lifetimes of ∼9800 h under illumination and ∼11 000 h under thermal aging, among the most stable air‐processed perovskite solar cells reported to date.

Comparative assessment of geotechnical and microstructural transformations in clayey soil stabilised with wheat straw ash, pottery waste, and lime

Abstract Expansive clayey soils show high plasticity, swelling and low bearing resistance, limiting pavement and foundation use. This study compares wheat straw ash (WSA), pottery waste (PW) and lime for stabilising high-plasticity clay through performance-based optimisation and mechanism interpretation. Raw materials were characterised using particle-size distribution, index properties, XRF and SEM-EDX, and mixes were prepared on a total dry mass basis. Initial dosage ranges of WSA: 5–20%, PW: 5–30% and lime: 1–6% were screened. Optimum contents were selected using a threshold-based multi-criteria approach based on differential free swell (DFS), pH, consistency limits and compaction behaviour, identifying 12.5% WSA, 22% PW and 4% lime. WSA eliminated DFS and reduced plasticity index to 7.6, mainly through active-clay dilution, pore filling and partial matrix densification. PW reduced plasticity index to 7.01 and improved behaviour through angular-particle packing, interlocking and load transfer. Lime reduced plasticity index to 6.43 and produced the strongest response due to high alkalinity, fabric modification and calcium-assisted bonding. Compaction showed increased optimum moisture content and reduced maximum dry density for WSA and lime, while PW maintained better compactability. Unconfined compressive strength and soaked California bearing ratio tests evaluated the optimum mixes. At 28 days, WSA, PW and lime achieved strength values of 768.3, 869.65 and 1245 kPa and CBR values of 4.29, 4.80 and 9.36, respectively. SEM and XRD observations were consistent with distinct stabilisation responses: active-clay dilution and pore filling for WSA, granular skeleton modification for PW, and calcium-assisted physicochemical modification for lime. The findings show that WSA and PW can serve as useful waste-derived alternatives, although lime remained the most effective mechanical stabiliser.

An updated systematic review, meta-analysis, and network meta-analysis of breast cancer incidence after metabolic and bariatric surgery

Explainable deep learning for early sepsis detection from ICU time-series data using XAI techniques

Abstract Sepsis is one of the most deadly illnesses with a high risk of mortality. Consequently, identifying it at the beginning of illness symptoms is crucial and plays a key role in improving patient outcomes. This study presents a customized solution for the early detection of sepsis with an emphasis on the use of interpretability and explainability techniques, utilizing a range of machine learning approaches and interpretable artificial intelligence methods. The database on which this research study is based has many problems; the main ones being large data gaps and class disparities. Employing robust methods, precise categorizations, and rigorous computations, Approximately 12 diverse models were developed and optimized. With ROC-AUC indicators of 0.9566 and 0.9595 and F1 scores of 0.85 and 0.85 respectively, Bidirectional Long Short-Term Memory (BiLSTM) and Temporal Convolutional Network (TCN) models performed better than conventional models in terms of sepsis prediction. These two approaches have shown remarkable progress in detecting clinical patterns while avoiding false negative results—an essential aspect of the medical field. To assess model performance and offer clear insights into model predictions, interpretation-based techniques were employed. This improved clinical confidence and facilitated well-informed decisions in crucial medical diagnoses.

Computational discovery of bioactive flavonoids from Ziziphus mauritiana via integrated ADME prediction, molecular docking, DFT calculation and molecular dynamics simulation

Impacts of anthropogenic and environmental stressors on biotic communities in Al-Mahmoudia Canal, Egypt: a seasonal assessment of water quality and plankton dynamics

Abstract Al-Mahmoudia Canal is crucial for water supply in Egypt’s Al-Beheira and Alexandria governorates, but it faces ecological challenges from pollution. This study examines plankton dynamics and water quality across four seasons at seven sites, analyzing microbiological contamination, heavy metals, turbidity, and nutrients. Phytoplankton abundance peaked in spring at Station 4, with seasonal variations linked to temperature and nutrients. Zooplankton showed seasonal diversity, with increased turbidity and nutrient levels in summer and autumn due to organic pollution. Heavy metal and microbial contamination also fluctuated seasonally, with fecal coliform levels exceeding safety limits. The study highlights pollution hotspots and emphasizes the need for ongoing monitoring and management to ensure sustainable water resource use in the Nile Delta.

Genotype-specific responses of six grapevine cultivars to in vitro propagation

Unified digital biomarker platform for wearable devices assuring integrity, heterogeneity, and scalability

Degradation-aware PSO–MPC energy management for second-life battery prosumer microgrids

Abstract The growing retirement of electric vehicle lithium-ion batteries has created significant opportunities for their reuse in stationary energy storage systems. However, integrating second-life batteries (SLBs) into residential photovoltaic (PV)-based prosumer microgrids remains challenging due to heterogeneous initial state-of-health (SoH) conditions, accelerated degradation behavior, and uncertain remaining useful life. This paper proposes a degradation-aware energy management framework for SLB-integrated prosumer microgrids based on the joint use of particle swarm optimization (PSO) and model predictive control (MPC). The proposed framework consists of four coordinated stages: PSO-based capacity sizing, hybrid AI-based forecasting of PV generation, electricity prices, and load demand, SoH-aware adaptive battery management within the MPC layer, and net present value (NPV)-based battery replacement decision-making. Within the control layer, the adaptive battery management mechanism updates state-of-charge limits, C-rate constraints, and degradation penalty coefficients according to real-time SoH evolution, thereby mitigating excessive capacity fade while preventing economically inefficient underutilization. In addition, a three-stage replacement strategy is developed to determine whether continued operation, SLB, or fresh battery replacement yields the highest long-term economic benefit. The proposed method is evaluated through long-horizon simulations using 2024 meteorological, market, and demand data for five residential prosumers in Antalya, Türkiye. The results show that the framework achieves a fleet-average self-consumption ratio of 71.8%, a renewable energy penetration factor of 37.6%, and a cost of energy of 0.0722 $/kWh, while sustaining battery utilization and enabling lifecycle-aware operation under realistic uncertainty. These findings demonstrate that the proposed PSO–MPC framework provides a computationally tractable approach that achieved stable convergence and feasible MPC solve times (2.1–3.4 s per 72-hour horizon) across all 20 simulated years under the five-prosumer Antalya configuration for degradation-aware energy management of SLB-based microgrids.

Experimental and numerical investigation of longitudinal ventilation velocity and heat release rate on temperature distribution in highway tunnel fires

Abstract Although tunnel fires are relatively infrequent, the unique, elongated, and nearly enclosed structure of tunnels exacerbates fire hazards, and their occurrence can result in catastrophic losses, necessitating effective fire safety management. This study examines the characteristics of smoke spread and temperature attenuation in highway tunnel fires under varying heat release rates and longitudinal ventilation velocities, specifically focusing on conditions of traffic congestion. By utilizing a combination of scaled-model experiments and analytical modeling, the research highlights that smoke generated in tunnel fires travels along the tunnel length, with its dispersion heavily influenced by both heat release rates and the presence of congested vehicles. In scenarios without ventilation, smoke ascends to the tunnel crown and propagates symmetrically toward both ends, while the movement of congested vehicles does not significantly affect smoke transport. The introduction of longitudinal ventilation significantly mitigates upstream smoke movement, leading to reduced maximum temperatures near the fire source and preventing smoke recirculation, especially critical during traffic congestion when evacuation routes may be limited. The study identifies critical wind speeds necessary for effective smoke management, revealing that these speeds increase with higher heat release rates. Initial increases in ventilation velocity may temporarily elevate temperatures due to enhanced combustion; however, further increases result in a predominant cooling effect that lowers overall tunnel temperatures. Additionally, the temperature distribution is asymmetrical, with downstream temperatures rising as heat release rates increase. A temperature decay model for tunnel crowns was developed, demonstrating a strong correlation with experimental data and underscoring the effectiveness of longitudinal ventilation in managing high temperatures and controlling smoke flow during tunnel fire incidents, particularly in congested traffic conditions.

Optimization model of tunnel construction machine groups configuration based on neural optimization machine

An elastic–plastic model considering the dilatancy behavior of granular materials under proportional stress paths

Prognostic value of serum anion gap in pulmonary embolism independent of lactate: evidence from a natural experiment of missing lactate data

A fused Cuckoo Search Algorithm and African Vultures Optimization for optimising the patch of wearable microstrip patch antenna

Association of platelet to highdensity lipoprotein cholesterol ratio (PHR) with stroke and its subtypes: a large prospective cohort study

Utility and safety of a self-developed patent chest strap in endoscopic transaxillary thyroid surgery: a prospective comparative effectiveness study

Abstract A novel patent chest-strap was developed for use in endoscopic transaxillary thyroid surgery. We conducted a phase prospective study in China to evaluate its utility and safety. This phase 1 study evaluates the utility, safety, and effectiveness of a novel patent chest-strap in a real-world clinical patient population. This single-center, open-label, prospective, randomized study was conducted from July 1, 2025, to March 31, 2026, to evaluate the long-term safety and efficacy of a patented chest strap (PCS) in patients undergoing endoscopic transaxillary thyroidectomy. Patients older than 18 years who underwent the procedure with or without the chest strap were enrolled. The primary endpoints included postoperative drainage outcomes and total usage of the PCS, which served as measures of effectiveness. Secondary endpoints focused on strap-related complaints, assessed in comparison with traditional bandages (TB), to evaluate safety. A total of 108 patients were included in the PCS group and 126 in the TB group. In the PCS group, the mean age was 43.5 ± 16.2 years, and 61 patients (56.48%) were female. Compared with the TB group, the PCS group showed statistically significant differences in height (1.67 ± 0.03 m vs. 1.73 ± 0.04 m; P  < 0.00001), weight, and body mass index ( P  < 0.0001). The mean postoperative drainage volume on the first day was 50 mL in the PCS group and 48.5 ± 9.19 mL in the TB group. Drainage volumes differed significantly between groups on the second day (36 ± 8.49 mL vs. 44 ± 12.73 mL; P  < 0.001) and the third day (35 ± 7.07 mL vs. 25 mL; P  < 0.001). The total postoperative drainage volume was lower in the PCS group than in the TB group (111.92 ± 13.44 mL vs. 122.5 ± 24.75 mL). Complications were more frequent in the TB group. Skin allergy occurred in 40 patients (31.75%) in the TB group compared with 9 patients (8.33%) in the PCS group. The patent chest-strap has the postoperative benefits for patients with endoscopic transanxillary thyroidectomy, and suffered fewer ratio of complications. This phase real-world study performed a positive feedback for a self-developed clinical patent transformation achievement in China. Registration : Chinese Clinical Trial Registry ( https://www.medicalresearch.org.cn/ ) ChiCTR2500107867 (Registered data: 15 August, 2025)

Normal CURE and RURE values for displacement encoding with stimulated echoes (DENSE) at 1.5 Tesla MRI

Abstract Assessment of left ventricular (LV) dyssynchrony is important for diagnosing and guiding therapy in heart failure. Displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI) quantifies myocardial dyssynchrony but lacks robust reference values. Forty healthy Caucasian volunteers (18–60 years) with no history of cardiac diseases underwent CMR at 1.5T MRI scanner using 2D spiral cine DENSE research sequence. Three short-axis slices (basal, mid-ventricular, apical) were acquired from the LV. Phase images were processed with open-source DENSEanalysis software to measure circumferential (E cc ) and radial (E rr ) strains, from which circumferential uniformity estimate (CURE) and radial uniformity estimate (RURE) were computed. Participants were categorized by sex and age into groups (18–30, 31–41, and 42–60 years). DENSE-derived strain values were also compared to those obtained using MRI feature tracking (FT). Males and females differed in average E cc (− 18 ± 4 vs. − 19 ± 5%, p  = 0.01) and E rr (44 ± 20 vs. 36 ± 22%, p  = 0.001), with no differences due to age. The apical segment of RURE was higher in males ( p  = 0.04), and age did not affect RURE. CURE showed no sex‑ or age‑related variation. The lower limit of normal was 0.80 for CURE and 0.64 for RURE. When FT was compared to DENSE, mean E cc was similar (− 20% vs. − 19%), though patient-level agreement was moderate (Intra class correlation (ICC) = 0.588, p  = 0.013); FT showed lower E rr (mean 34% vs. 42%; ICC 0.304, p  = 0.062). CURE demonstrated stable values across age and sex in healthy volunteers, supporting its use as a robust marker of LV dyssynchrony with a normal threshold of ≥ 0.80. In contrast, RURE showed greater variability, although a lower reference limit of ≥ 0.64 was established.