Browse Articles

Bayesian neural network-based policy effect prediction for green transformation of power business environment

Abstract Predicting how green policies reshape power business environments remains notoriously difficult. The underlying dynamics are nonlinear, the uncertainties substantial, and conventional models often fall short. This study develops a Bayesian neural network framework designed specifically for forecasting and optimizing green policy outcomes within the Fujian power system, placing particular weight on quantifying prediction uncertainty to support sound decision-making. Our methodology weaves together stochastic variational inference and multi-objective optimization, thereby capturing the channels through which policies transmit their effects to environmental outcomes. Drawing on empirical data spanning 2018–2024, we find that this approach outperforms standard machine learning techniques by roughly 4–5% points in prediction accuracy while delivering markedly better uncertainty calibration. Scenario analyses reveal that moderate-to-high policy intensity tends to achieve favorable cost-effectiveness, with renewable energy incentives, carbon pricing, and regulatory enforcement standing out as especially potent drivers of transformation. Perhaps more importantly for practitioners, the framework demonstrates that well-designed moderate-intensity strategies can surpass maximum-intensity approaches once diminishing returns enter the picture. By enabling joint assessment of environmental gains, economic efficiency, and operational stability under uncertainty, this work offers a practical foundation for evidence-based policy design—though readers should bear in mind that our validation remains grounded in the Fujian regional context.

Internal gelation-based alginate hydrogel films incorporating Quercus infectoria gall extract for multifunctional wound dressing applications

Chronic high-altitude exposure and cognitive health in Chinese college students: a 4-year longitudinal neuroimaging study

Molecular identification of bat fly species and associated Bartonella bacteria from Lopburi and Sa Kaeo Provinces in Thailand

Integrating circulating microRNAs with epidemiological factors enhances breast cancer detection across subtypes: the MCC-Spain study

Huperzine A improves neurological function in mice with intracerebral hemorrhage by alleviating neuroinflammation and ferroptosis

The small-cage induced sedentariness in male young rats: evidence from energy expenditure and glucose uptake

Response of sediment delivery ratio to water-sediment and riverbed boundary conditions during flood events in the lower yellow river since 2000

Abstract The sediment delivery ratio is greatly affected by the water-sediment and the riverbed boundary, which represent the river’s capacity to transport sediment under specified conditions. This study examines the response of the sediment delivery ratio to water-sediment and riverbed boundary conditions in the Lower Yellow River (LYR) since the operation of the Xiaolangdi Reservoir began. It evaluates the spatial-temporal variations of water-sediment and riverbed boundaries based on hydrological data and topographic data from 2000 to 2023. Based on the sediment transport rate equation, a theoretical equation for the sediment delivery ratio during flood events has been developed, thoroughly considering the effects of riverbed boundary conditions, including median particle size of bed sediment, river gradient, and width-to-depth ratio. The results show that the sediment delivery ratio negatively correlates with the incoming sediment coefficient, the median particle size of bed sediment, and the width-to-depth ratio. In contrast, it positively correlates with the water load variation coefficient and river gradient. In comparison to solely accounting for water and sediment conditions, incorporating the riverbed boundary into the theoretical equation results in a more precise alignment with the measured sediment delivery ratio data. This indicates that the riverbed boundary is a crucial factor influencing the sediment delivery ratio. Under the current boundary conditions, the Aishan to Lijin reach has the highest sediment transport capacity. To improve the sediment transport capacity of the Tiexie to Lijin reach, it is recommended to narrow the river width upstream of Gaocun and increase the width-to-depth ratio. The findings of this study provide essential scientific insights into the sediment transport capacity of alluvial rivers subjected to variations in water-sediment and riverbed boundary conditions, thereby offering important references for hydrological engineering and river management practices.

ACSL4 mediates ferroptosis to promote immunoglobulin A nephropathy progression: scRNA-seq analysis

Skeletal and dentoalveolar effects of different hyrax maxillary expansion protocols compared with novel magnetic expansion: a CBCT-based study

Short-term effects of meteorological factors on hand, foot, and mouth disease in Zhengzhou, China

Adaptive machine learning models for predictive maintenance in industrial internet of things (IIoT) systems

Gaze dynamics toward familiar and unfamiliar faces in prosopagnosia

Simulation-based optimization analysis of passenger flow organization in metro interchange stations using AnyLogic

Abstract In large-scale metro interchange stations, significant passenger flow volumes during peak hours are prone to induce pedestrian congestion phenomena, presenting operational challenges. Taking Metro S transfer station as the research object, this study constructs an AnyLogic pedestrian simulation platform based on the social force model to simulate the current passenger flow conditions in the station concourse area, identifying key bottleneck zones. By employing an optimization method that integrates passenger flow guidance with coordinated allocation of equipment resources, a dual-path passenger flow diversion mechanism is designed to alleviate congestion caused by intersecting passenger flow lines. The optimization results demonstrate that this approach can effectively mitigate peak-hour congestion while reducing passenger walking time and improving throughput efficiency. This offers decision support for passenger flow management in large metro transfer stations.

The deubiquitinating enzyme Otu1 releases substrates from the conserved initiation complex of the Cdc48/p97 ATPase for proteasomal degradation

Abstract Many eukaryotic proteins are modified with a polyubiquitin chain and then recruited to either the Cdc48 ATPase (p97 or VCP in mammals) or the 26S proteasome by conserved cofactors. They can then shuttle between the Cdc48 ATPase and the 26S proteasome before being degraded. How substrates avoid being trapped on the Cdc48 ATPase complex is incompletely understood, as they can undergo repeated cycles of translocation through the ATPase pore. Here, we show that the deubiquitinating enzyme (DUB) Otu1 (Yod1 in mammals) can break this futile cycle. Otu1 trims the ubiquitin chain of the substrate before its translocation through the Cdc48 pore is initiated, allowing transfer to the proteasome and subsequent degradation. A cryo-EM structure shows that the mammalian homolog Yod1 binds to p97 simultaneously with other Cdc48/p97 cofactors. As in the yeast system, polypeptide translocation through the ATPase pore is initiated by the unfolding of a ubiquitin molecule, suggesting that the mechanism of substrate processing is conserved in all eukaryotes.

Theoretical analysis of thermomechanical response for biological skin tissues

An intelligent approach for early smoke/fire detection using vision sensors in smart cities

Radiological characteristics in coronal and sagittal planes and short-term postoperative outcomes in patients with lumbar disc herniation accompanied by sciatic scoliosis

Bioanalysis of amphetamines in alternative matrices using a sensitive and validated liquid chromatography-tandem mass spectrometry method and its application to real samples

Abstract Amphetamines abuse remains a major concern in both clinical and forensic toxicology. While conventional matrices like blood and urine are commonly used, their detection window is limited. This study aimed to validate a UHPLC-ESI-MS/MS method for amphetamines detection across both conventional and alternative biological specimens and apply it to real patient samples. A validated UHPLC-ESI-MS/MS method was used to quantify amphetamines in whole blood, urine, oral fluid, scalp hair, and fingernail specimens collected from fifty amphetamine abusers. Method validation followed international guidelines, assessing parameters including linearity, sensitivity, precision, and carry-over. Decontamination protocols and wash analyses were performed to exclude external contamination for keratinized matrices. The method demonstrated excellent linearity (r² = 0.995–0.998) and sensitivity (LOD = 2 ng/mL or ng/mg) across all matrices. Oral fluid showed strong correlation with recent drug use and was a reliable non-invasive alternative to blood and urine. Hair yielded the highest amphetamine concentrations due to melanin binding, whereas nail showed lower levels but provided a complementary window for retrospective exposure. Simultaneous quantification of amphetamines in five matrices provides a comprehensive assessment of both recent and chronic drug use. Hair remains the superior matrix for long-term detection, but nail offers valuable supplementary information. Oral fluid emerged as a practical and reliable tool for recent exposure monitoring. This is the first study to report amphetamine concentrations in all five matrices from the same individuals, offering critical insights for toxicological interpretations.

Effects of skewing collision cells on transport properties in multiparticle collision dynamics simulations

Multiparticle collision dynamics (MPCD) is a mesoscale simulation technique that uses a simplified solvent to model hydrodynamic interactions. Rather than interacting through pairwise forces, MPCD solvent particles undergo momentum-exchanging collisions within spatially localized cells according to prescribed rules. The conventional MPCD algorithm employs cubic collision cells, but this choice is not optimal for systems that are most naturally described using skewed simulation boxes. Here, we investigate the behavior of an MPCD scheme in which the collision cells are aligned with the vectors that define a triclinic (parallelepiped) simulation box. We find that skewing the collision cells has a small but statistically significant impact on the transport properties of the pure solvent. Similar but more pronounced effects are found for nearly hard spheres in solution, including a significant decrease in their nominal self-diffusion coefficient and unphysical anisotropy in their self-diffusion tensor. Thus, our analysis indicates that skewed MPCD collision cells may result in spurious behavior and should be used with caution. We posit that these artifacts may be mitigated by grid-free schemes for placing particles into collision cells.