Browse Articles

Beyond distortions: a benchmark for subjective evaluation of image rendering quality

Design and evaluation of bayesian optimized hybrid deep learning model for forecasting crop yields using climate dynamics

Thermal performance and economic viability of a finned, inclined latent heat thermal energy storage unit: a numerical study

The effect of simvastatin on orthopaedic biomaterial induced inflammation

Abstract The most common indications for lower limb revision arthroplasty are aseptic loosening of the implant and adverse reactions to particulate debris, both of which are driven by host immune responses to orthopaedic biomaterials. Pharmacoepidemiologic studies suggest that statins may improve lower limb arthroplasty survival, potentially through pleiotropic anti-inflammatory effects, but the biology underpinning this remains unclear. The aim of this study was to investigate the effect of simvastatin on orthopaedic bio-material-induced inflammation in-vitro. Gene expression was measured by qPCR and protein secretion was measured by ELISA and Meso scale discovery assays. In THP-1 macrophages, co-culture with simvastatin significantly abrogated cobalt-mediated increases in IL-8 gene expression in addition to IL-8, IL-1β, CCL3, CCL4 and CCL20 protein secretion. Simvastatin also inhibited zirconium oxide-mediated levels of CCL2 and CCL4, as well as alumina oxide-mediated increases in CCL2. These novel findings demonstrate that statins can significantly reduce orthopaedic biomaterial-induced expression of pro-inflammatory cytokines in-vitro. As statins are widely used in clinical practice and inexpensive, this provides an exciting basis for future work to leverage statins to reduce the impact of a burgeoning demand for revision arthroplasty on patients.

Microplastic contamination in soils and its influence on consistency (Atterberg) limits

Hydrogen-driven digital transactions market under carbon oracles and green transportation in energy sustainable societies under social stakeholders

Abstract The decarbonization of urban energy communities increasingly requires coordinated integration of hydrogen, electricity, heat, and mobility under market-regulated environments. This study develops a hydrogen-driven digital transactions market embedded within a clustered, integrated energy hub architecture, where digital transactions markets, such as carbon emission trading (CET) and green certificate trading (GCT) mechanisms, are endogenously incorporated into operational scheduling. The framework coordinates hydrogen-diversified utilization, dual electric–hydrogen transportation systems, multi-vector storage, and renewable generation under carbon accounting constraints and social multi-stakeholder interactions. A decentralized multi-carrier optimization model is formulated to minimize system-wide scheduling cost while integrating CET/GCT revenues directly into dispatch decisions. Uncertainties in renewable generation, demand, and electricity prices are modeled using an inexact probabilistic stochastic programming approach with scenario generation and reduction. To extend evaluation beyond economic performance, a hydrogen-centric eco-social welfare layer comprising ten normalized indicators is introduced, quantifying emission mitigation, accessibility, equity, cost relief, and public acceptance. The model is validated on a four-hub clustered configuration under baseline and stress-test scenarios, including demand surges, renewable shortfalls, hydrogen price shocks, and market price fluctuations. Results demonstrate effective coordination between hydrogen production, storage, and mobility demand, with demand-side flexibility reducing operational costs by more than 16% in selected hubs. Carbon and certificate oracles market participation improves financial performance while enhancing emission compliance. Sensitivity analysis confirms robustness under combined worst-case disturbances. The proposed framework establishes a unified operational market structure that links hydrogen diversification, digital carbon-regulated transactions, and measurable eco-social welfare within sustainable urban energy systems.

Optimization and effectiveness assessment of hydraulic fracturing target layers using multi-source information fusion

Synthesis, structural characterization, and dual DNA/HSA binding of novel Palladium(II) violurate complex with selective p53/Caspase-3-mediated anticancer activity

Abstract A novel palladium(II) complex with the violurate ligand, [Pd(H₂L)₂], was synthesized and characterized. Molar conductivity (15.55 Ω -1 cm² mol -1 ) confirmed its non-electrolytic nature, and ESI-MS showed a molecular ion peak at m/z 417.57 matching [C₈H₄N₆O₈Pd]⁺. PXRD structural analysis revealed a triclinic crystal system (space group P-1) with τ₄ = 0.04, confirming square-planar geometry around Pd(II), with Pd–O bond lengths ranging from 2.065 to 2.178 Å. Thermal analysis showed three decomposition stages (41.00%, 15.00%, and 18.50% mass losses) with palladium metal as the final residue. The complex exhibited DNA binding constants of K b = 1.694 × 10⁴ M -1 (UV-Vis) and 2.870 × 10 7 M -1 (fluorescence) with ΔG = -24.12 and − 42.94 kJ mol -1 , respectively, indicating groove binding mode as confirmed by viscosity measurements. HSA binding constants were K b = 2.784 × 10 5 M -1 (UV-Vis) and 6.546 × 10 9 M -1 (fluorescence). Cytotoxicity assays against MDA-MB-231, HCT-116, and HepG-2 cell lines gave IC₅₀ values of 29.42, 38.11, and 24.68 µM, respectively, with selectivity indices (1.99, 1.78, and 1.29) higher than cisplatin. The complex induced late apoptosis (8.46-fold increase) via p53/caspase-3 upregulation (4.46-fold and 3.96-fold, respectively). DFT calculations showed reduced HOMO-LUMO gap (5.406 eV vs. 6.517 eV for free ligand), correlating with enhanced bioactivity. These findings present [Pd(H₂L)₂] complex as a promising dual-targeting anticancer candidate. Unlike conventional Pd(II) complexes that target DNA via intercalation or N/S coordination, our complex employs an unusual O₄ coordination sphere from violurate ligands, demonstrating a non-intercalative groove-binding mode and a p53/caspase-3 mediated apoptosis with enhanced selectivity index.

Sward structure driven by light interception controls grazing efficiency in BRS Zuri guinea grass pastures

Abstract The hypothesis was that variations in pre-grazing light interception (LI) would result in distinct pasture structures, directly affecting the frequency and severity of defoliation by grazing animals. This study evaluated the effects of four LI levels (80, 85, 90, and 95%) on the structural characteristics of zuri grass and on defoliation dynamics during grazing. Sixteen paddocks were grazed for 24 h, and defoliation was assessed in 18 tillers per paddock at 4, 8, and 24 h. The evaluated variables included grazed area, frequency (FD) and severity of defoliation (SD) by morphological component, bite rate (TB), and number of steps between feeding stations (PE). Both FD and SD increased linearly with increasing LI, particularly for expanded and expanding leaves. Conversely, TB and PE decreased linearly as LI increased, with reductions of 4.74 bites min⁻¹ and 0.15 steps, respectively, indicating lower foraging effort under higher LI. Pastures managed at 90 and 95% LI promoted greater forage mass above the post-grazing residue target and supported higher instantaneous stocking rates, resulting in greater forage utilization efficiency. In contrast, LI levels of 80 and 85% induced more intense foraging behavior and reduced utilization efficiency. Managing zuri grass at 90–95% LI is recommended to optimize pasture structure and maximize forage utilization efficiency in grazing systems.

Stabilization of HIF-1α using the prolyl hydroxylase inhibitor roxadustat reduces gastrointestinal graft-versus-host disease

The impact of ethnicity on longitudinal humoral and cellular immune responses to SARS-CoV-2 booster vaccination

Abstract Ethnic differences in SARS-CoV‑2 vaccine immunogenicity have been reported after primary vaccination, but it remains unclear whether these disparities persist following booster doses or whether patterns of immune boosting and waning differ across ethnic groups. In this longitudinal study of healthcare workers enrolled in the BE‑DIRECT cohort, we evaluated humoral and cellular immune responses before booster vaccination, 3–4 weeks afterwards, and at approximately six months. Anti‑spike and neutralising antibodies, as well as T‑cell ELISpot responses to spike peptides, were quantified, and changes across boosting and waning phases were examined using multivariable regression adjusting for demographic factors, vaccine regimen, prior infection and baseline immune measures. South Asian participants demonstrated substantially higher post‑booster anti‑spike titres than White participants, although titres converged by six months. After adjustment, South Asian individuals exhibited greater increases in total anti-spike and neutralising antibody titres, while decline in antibody titres between post-booster and six-month post-booster sampling points were similar across ethnic groups. T‑cell responses during the boosting phase did not differ, but decline of S2‑specific and overall spike‑specific T‑cell responses between post-booster and six-month post-booster sampling points occurred more slowly in South Asians. These findings indicate that South Asian healthcare workers mount stronger early booster‑induced antibody responses and exhibit more sustained T‑cell immunity, highlighting the need to understand underlying mechanisms and implications for vaccine effectiveness.

Predicting the flood susceptibility under land use and climate change scenarios using deep learning algorithms

Abstract Flood risk in semi-arid, snow-fed basins is increasingly influenced by both land-use and climate change, yet their combined future effects remain poorly quantified. This study predicts future flood generation potential (FGP) under combined land-use and climate scenarios in the Gharesou Watershed (Iran) using deep learning. Future land-use (2034–2054) was simulated via Markov chain, and climate variables (temperature, precipitation) under three SSP scenarios were downscaled using the change-factor method. FGP was mapped using CNN, MLP, and DNN algorithms, validated against observed discharge data. By 2054, natural vegetation is projected to decline by 20.9% of the watershed area, while agricultural and residential lands expand. Temperature rises by 3.5–4.5 °C, and although annual maximum precipitation declines, extreme events become more frequent. Under the optimized CNN model, high- to very-high-risk zones expand from 62% to 87% of the watershed. This study provides the first quantitative attribution of future flood risk in a snow-fed semi-arid basin, identifying land-use change as the dominant driver (about60-70% of increased risk) and climate change as an intensifier (about 30–40%). These results indicate that protecting natural vegetation and restricting land-use conversion in high-risk zones are more urgent than climate adaptation alone. Proactive policies (restoring rangelands/forests, integrating climate scenarios into spatial planning, and enforcing land-use regulations) are essential to enhance watershed resilience.

A one-pot biplex RPA-Cas assay for sensitive detection of Mycobacterium tuberculosis from tongue swabs

Production and performance of a 172Hf/172Lu generator

Study on acoustic emission and infrared radiation characteristics of coal combination with different tectonic coal thickness

Relationship between triglyceride-glucose index and muscle strength in middle-aged and older Chinese adults without chronic diseases: a nationwide longitudinal cohort study

White cell - platelet ratio: A strong indicator for early mortality in liver cirrhosis patients with esophagogastric varices

Abstract Esophagogastric varices (EGV) in liver cirrhosis patients within the intensive care unit (ICU) is a significant medical concern. This study aims to develop and validate a machine learning (ML) model to predict the early mortality of those patients. Medical information was extracted from Intensive Care (MIMIC)-IV database, and 793 cirrhotic patients accompanied with EGV were enrolled, randomly assigned to the training group and the test group in a 7:3 ratio. For external validation, 100 cirrhotic patients with EGV hospitalized in ICU in our institution were retrospectively analyzed, The least absolute shrinkage and selection operator (LASSO) method and Logistic Regression(LR) analysis were applied for variable selection and predictive signature building, and four predictive models - LR, Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF) were conducted, and their performance in predicting 28-day all-cause mortality in the patients was evaluated using area under the receiver operating characteristic (AUROC), and decision curve analysis (DCA). five predictors associated with 28-day all-cause mortality in cirrhotic patients with EGV were identified based on LASSO and regression analysis, including MELD score, SOFA score, admission age, esophagogastric Variceal bleeding (EVB) and white cell-platelet ratio Z-score (WPR Z-score). Forest Plot and survival analysis showed WPR Z-score is strongly associated with 28-day all-cause mortality in those patients. The model based on LR showed the best predictive performance in the training set and test set with AUROC (0.833, 95% CI: 0.793–0.873) vs. (0.854, 95% CI༚0.795–0.913). For external validation, AUROC was (0.882, 95% CI༚0.795–0.924). LASSO-based predictive model, especially the LR model, showed promise in predicting early mortality in critically ill patients with cirrhosis and EGV. WPR Z-score showed strong association with early mortality in those patients.

Seismic attributes analysis and petrophysical modeling for reservoir characterization and prospect identification in the I/R oil fields, Murzuq Basin, Libya

Multicomponent synthesis of novel spiro-indoline-3,4′-pyran derivatives: in silico and in vitro study

Comparison of CT attenuation and DEXA-derived bone mineral density in porcine femur specimens: an ex vivo methodological feasibility study

Abstract Dual-energy X-ray absorptiometry (DEXA) is the clinical reference standard for bone mineral density assessment, while computed tomography (CT) may enable opportunistic evaluation from routinely acquired scans. This methodological feasibility study evaluated whether AI-assisted CT segmentation can generate regional attenuation measurements corresponding to DEXA-derived measurements. Thirty porcine femur specimens underwent repeated DEXA and CT imaging across 75 observation entries under controlled experimental conditions, including sequential hydrochloric acid exposure cycles. AI-based semantic segmentation implemented in MONAI automatically delineated proximal femoral regions corresponding to DEXA regions of interest. Correlation and reliability analyses were performed at specimen and region levels. AI segmentation achieved high performance across anatomical regions (Dice > 0.84). CT-derived measurements correlated strongly with DEXA at the specimen level (Pearson r  = .78) and showed moderate but consistent region-level correlations ( r  = .64). Reliability was excellent, with ICC values ranging from 0.978 to 0.988. Automated CT-based attenuation analysis provides reproducible regional measurements that correlate with DEXA in a controlled setting. The proposed phantom-independent framework enables scalable and standardized extraction of CT-derived data, supporting potential application in larger or retrospective datasets, while not replacing calibrated quantitative CT approaches.