Browse Articles

Stabilizing sub-2 nm δ-Bi2O3 via strong lanthanide-oxide-support interaction for durable CO2 electroreduction to formate

Abstract Stabilizing metal oxides is a prerequisite for elucidating their intrinsic mechanistic roles and sustaining high electrocatalytic activity. Here, we synthesize a high-temperature-phase La 2 O 3 -socketed sub-2 nm δ-Bi 2 O 3 heterojunction (δ-Bi 2 O 3 /La 2 O 3 ) that suppresses Bi 3+ reduction to metallic Bi, achieving ≥95% formate Faradaic efficiency for ~200 hours in industrial-level electrolyzers. Electronic structure analyses reveal that strong electrostatic interactions between δ-Bi 2 O 3 and La 2 O 3 drive oxygen migration to the interface, contracting δ-Bi 2 O 3 domains and enhancing La–Bi d-p orbital hybridization. This structural relaxation stabilizes interfacial Bi–O–La linkages and electron-deficient Bi 2 O 3+x species under cathodic potentials, as confirmed by in situ X-ray absorption spectroscopy. Pourbaix diagrams and in situ infrared spectroscopy demonstrate that La 2 O 3 promotes water dissociation to form a hydroxylated δ-Bi 2 O 3 surface under working potentials, enhancing protonation propensity. Consequently, the energy barrier for the rate-determining step (*CO 2 → *HCOO) is lowered to +0.15 eV on δ-Bi 2 O 3 /La 2 O 3 , significantly lower than the +0.83 eV barrier on pristine δ-Bi 2 O 3 . This work establishes a sub-nanoscale oxide/oxide heterojunction strategy to stabilize high-valent metal sites, enabling sustainable electrochemical conversion.

The impact of specialized and diversified agglomeration in China’s manufacturing sector on energy efficiency

Unlocking energy-carbon-water synergies in global water supply system maintenance

Solamargine induces apoptosis and ferroptosis through the ROS/p38 MAPK signalling pathway in intrahepatic cholangiocarcinoma

Integrating common and rare variants improves polygenic risk prediction across diverse populations

Abstract PRSs predict complex traits by aggregating genetic effects across the genome, yet most models focus on common variants, overlooking rare variants that may contribute to hidden heritability. Here, we develop RICE, a PRS framework integrating both common and rare variants to improve genetic risk prediction across diverse ancestries. RICE constructs separate PRSs: for common variants, it integrates methods using ensemble learning; for rare variants, it uses gene-level testing with functional annotations and penalized regression. We evaluate RICE using simulated datasets and sequencing data from UK Biobank and All of Us, involving up to 740 million genetic variants from 361,939 individuals across diverse ancestries and 11 complex traits. In real data analysis, RICE improves predictive accuracy compared to leading common variant methods for traits with distinct rare variant architectures, particularly lipids and height. For lipid traits, incorporating rare variants increased R 2 by up to ~11.2% in Europeans and ~60.7% in African ancestry compared to common variant PRS alone. Notably, for lipid traits, RICE captures substantial predictive signal beyond established high-penetrance genes, validating its ability to leverage the broader polygenic architecture of rare variation.

Modeling and experimental evaluation of marotti oil biodiesel blends in a diesel engine using response surface methodology and artificial neural networks

Head-to-head comparison of fecal occult blood tests for colorectal neoplasia detection in Chinese outpatients: a multicenter diagnostic trial

Limited predictive value of Padua and Caprini scores for venous thromboembolism in AECOPD patients with atrial fibrillation

Task allocation method for anchoring robot with multiple drilling units and multiple tasks in coal mine roadways

Impact of construction and demolition wastes on the performance of sustainable LC3-based structural lightweight concrete

Abstract This study explores the possibility of producing sustainable structural lightweight concrete (LWC) based on limestone calcined clay cement (LC³) using waste from construction and demolition. The main innovation is the dual substitution of waste-derived materials for traditional LC³ constituents: crushed brick powder (CBP) was used in place of metakaolin (MK), and recycled concrete powder (RCP) was used in place of limestone powder (LSP). To achieve lower densities, nine concrete mixtures were created using crushed brick as both fine and coarse aggregates in addition to an air-entraining agent. Flowability, dry density, ultrasonic pulse velocity, compressive strength, resistance to magnesium sulfate attack and high temperatures (200 and 400 degrees Celsius), water absorption, and porosity were all assessed through an extensive experimental program. With only a small drop in 28-day compressive strength (5–8%) and a slight increase in water absorption (10–12%), the results showed that CBP is a very promising substitute for MK. All mixtures met the structural LWC requirements of DIN EN 1045-1 (dry density of 1650–1850 kg/m³ and strength > 24 MPa), but substituting RCP for LSP resulted in a more noticeable decrease in 28-day compressive strength (15–20%) and an increase in water absorption (13–18%). Additionally, after 180 days of sulfate exposure, all LC³ systems showed very little mass loss (< 0.7%) and maintained over 80% of their residual strength at 400 °C. According to the study, CBP and RCP can effectively and sustainably replace MK and LSP in LC³-based LWC, allowing for a 60% reduction in clinker while preserving structural integrity and promoting waste valorization.

Genotoxic potential of Dianthus superbus var. superbus and Petasites paradoxus (Retz.) Baumg. methanolic extracts in Chinese hamster ovary cells

Development of Nigella sativa essential oil loaded keratin/gelatin cryogel with antimicrobial and antioxidant properties for wound dressing

Investigation of losses on directivity improvement in the graphene reflectarray design

A novel risk stratification strategy for precision prevention of gastric cancer based on clinicopathological features and IGFBP7

Heterologous expression of a heme-dependent catalase from Avicennia marina confers multi-stress tolerance in Escherichia coli for biotechnological applications

Real-world platinum selection in immune checkpoint inhibitor-based therapy for recurrent or metastatic head and neck cancer: an exploratory multi-institutional retrospective study

Global neural oscillations underlie performance variability and attentional state fluctuations in humans

The obesity paradox in bone health: high prevalence of osteoporosis and its non-linear association with BMI among urban elderly in China

Experimental and machine learning-based comparison of swirling and conventional conical fluidized bed reactors for enhanced thermal performance

Abstract The current study presents an integrated methodology for improving the performance of conventional fluidized bed reactors using swirling flow via a blade distributor and conical shape. The primary objective of this study is to investigate the combined effect of swirling flow and conical geometry on bed flow and heat transfer, which has not been sufficiently studied, especially through experimental techniques supported by machine learning modeling able to predict performance. A blade distributor was employed in the SCFBR, while the bed heat transfer coefficient and surface particle temperature were estimated and measured at different inlet velocities ranging from 0.993 to 2.5 m/s. The study results emphasized that the SCFBR clearly outperforms the CFBR based on lower bed pressure drop, more uniform particle distribution, and higher heat transfer coefficient. The heat transfer coefficient of the SCFBR increased by 40% compared to that of the CFBR, corresponding to a more homogenized distribution of particle surface temperature. The machine learning findings elucidate the superiority of the Extra Trees model in modeling the bed pressure drop and heat transfer coefficient, achieving with an R² of 0.973, an RMSE of 52.11, and a MAE of 35.11 for the heat transfer coefficient and an R² of 0.965, an RMSE of 8.76, and a MAE of 2.27 for the bed pressure drop. The good agreement between the experimental and ML results demonstrated the reliability of the proposed methodology. This study emphasizes the importance of integrating swirling flow with conical geometry, supported ML modeling, and represents a promising method for developing thermally efficient fluidized bed reactors with low energy consumption for advanced industrial applications.

WS-SSA: workflow scheduling in cloud computing using salp swarm algorithm

Abstract Effective process scheduling is mandatory in cloud computing. It optimizes the use of resources, reduces the execution time, and reduces the operating cost. This study introduces a new workflow scheduling algorithm based on the salp swarm algorithm (SSA). This algorithm is a nature-based metaheuristic and is based on salp swimming behavior in the ocean. The proposed scheduler is based on the use of a single-objective fitness function to optimize the overall workflow makespan. It solves the NP-hard cloud scheduling problem, which involves effective mapping of tasks to heterogeneous virtual machines. The workflow execution is represented via a directed acyclic graph (DAG). The SSA is very efficient in search space exploration and is insensitive to parameter choices. To evaluate the quality of the proposed scheduler, several experiments were conducted via the WorkflowSim simulation framework. The makespan and energy consumption are taken as important performance indicators in the assessment. The WS-SSA was evaluated against FCFS, MCT, MIN-MIN, MAX-MIN, round robin, the WOA, the GA, and PSO under identical VM settings and workflow inputs to ensure fairness. It reduces the average makespan by up to 38% compared with classical methods and approximately 8.7% compared with the WOA and approximately 5–22% and 3–12% over the GA and PSO, respectively.