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Electrocatalytic hydrogen peroxide (H 2 O 2 ) production via the two-electron oxygen reduction reaction is a promising alternative to the energy-intensive and high-pollution anthraquinone oxidation process. However, developing advanced electrocatalysts with high H 2 O 2 yield, selectivity, and durability is still challenging, because of the limited quantity and easy passivation of active sites on typical metal-containing catalysts, especially for the state-of-the-art single-atom ones. To address this, we report a graphene/mesoporous carbon composite for high-rate and high-efficiency 2e − oxygen reduction catalysis. The coordination of pyrrolic-N sites -modulates the adsorption configuration of the *OOH species to provide a kinetically favorable pathway for H 2 O 2 production. Consequently, the H 2 O 2 yield approaches 30 mol g −1 h −1 with a Faradaic efficiency of 80% and excellent durability, yielding a high H 2 O 2 concentration of 7.2 g L −1 . This strategy of manipulating the adsorption configuration of reactants with multiple non-metal active sites provides a strategy to design efficient and durable metal-free electrocatalyst for 2e − oxygen reduction. A graphene/mesoporous carbon composite presents rapid and efficient H2O2 electrosynthesis capability by two-electron oxygen reduction catalysis which is facilitated by the presence of multiple pyrrolic nitrogen dopants within the material.

The report aims at providing an analysis of the effects of ECE on the cognitive and social development of children in the long run based on the statistical analysis. In this study, various educational programs have been analyzed to see their effectiveness on children’s development, thus helping to identify the most important factors that influence the educational outcome. The research strategy that is used in this study involves the integration of the longitudinal data and cross-sectional surveys to evaluate the efficacy of ECE on cognitive skills, social behavior, and health status. Longitudinal design allows following the children’s development over several years, thus giving a broad understanding of the effects of ECE. The quantitative data that were collected test included scores, standard parent and teacher questionnaires, and observational measures to give a complete picture of the effects of ECE. The results show the importance of early intervention by showing great improvements in language development, literacy, numeracy, pro-social skills like cooperation, empathy and self-regulation of children who are enrolled in ECE programs. Furthermore, this study also emphasis on the benefits of early childhood education such as, better academic performance, better job opportunities as well as, better mental health. These results provide a basis for policy recommendations which suggest that there should be increased emphasis on the improvement of the quality of ECE programs so that everyone has access to them.

To explore the isotopic composition of precipitation in the monsoon region of offshore China, this paper takes Hong Kong, China as the study area. Based on the Global Network of Isotope in Precipitation (GNIP), the data about hydrogen and oxygen stable isotopes in the precipitation of Hong Kong from 1961 to 2022 were collected, from which its time variation trend was obtained via linear regression. Further, the distribution characteristics and influencing factors of hydrogen and oxygen stable isotopes in the precipitation of Hong Kong were analyzed. According to the results, the precipitation isotopes and d-excess in Hong Kong have no significant inter-annual variability. The seasonal variation of precipitation isotope and d-excess is monthly apparent, both of which are lower in the rainy season and higher in the dry season. In addition, the seasonal periodicity of isotope and d-excess proves that the main source of precipitation is marine water vapor, and the source of water vapor controlling precipitation in Hong Kong remains stable as a whole. The global meteoric water line in Hong Kong is δD=8.17δ18O+11.82, which is very close to the global one. Meanwhile, δ18O in precipitation is negatively correlated with the temperature, precipitation, and water vapor pressure. As the main driving force to control its isotope variation, precipitation conceals the effect of temperature. Taking Hong Kong as an example, the above research reveals some characteristics of monsoon regions in offshore China, which is of positive significance for further investigating the influencing factors of the hydrologic cycle and isotope change at regional and local scales in the future.

Defined traffic laws must be respected by all vehicles when driving on the road, including self-driving vehicles without human drivers. Nevertheless, the ambiguity of human-oriented traffic laws, particularly compliance thresholds, poses a significant challenge to the implementation of regulations on self-driving vehicles, especially in detecting illegal driving behaviors. To address these challenges, here we present a trigger-based hierarchical online monitor for self-assessment of driving behavior, which aims to improve the rationality and real-time performance of the monitoring results. Furthermore, the general principle to determine the ambiguous compliance threshold based on real driving behaviors is proposed, and the specific outcomes and sensitivity of the compliance threshold selection are analyzed. In this work, the effectiveness and real-time capability of the online monitor were verified using both Chinese human driving behavior datasets and real vehicle field tests, indicating the potential for implementing regulations in self-driving vehicles for online monitoring. Ambiguity in human-oriented traffic laws poses a significant challenge to the regulation of self-driving vehicles. Here, the authors present a trigger-based hierarchical online compliance monitor for self-assessment of self-driving vehicles using ambiguous compliance threshold selection principles.

The small area of a printed circuit board (PCB) results in densely distributed defects, leading to a lower detection accuracy, which subsequently impacts the safety and stability of the circuit board. This paper proposes a new YOLO-BFRV network model based on the improved YOLOv8 framework to identify PCB defects more efficiently and accurately. First, a bidirectional feature pyramid network (BIFPN) is introduced to expand the receptive field of each feature level and enrich the semantic information to improve the feature extraction capability. Second, the YOLOv8 backbone network is refined into a lightweight FasterNet network, reducing the computational load while improving the detection accuracy of minor defects. Subsequently, the high-speed re-parameterized detection head (RepHead) reduces inference complexity and boosts the detection speed without compromising accuracy. Finally, the VarifocalLoss is employed to enhance the detection accuracy for densely distributed PCB defects. The experimental results demonstrate that the improved model increases the mAP by 4.12% compared to the benchmark YOLOv8s model, boosts the detection speed by 45.89%, and reduces the GFLOPs by 82.53%, further confirming the superiority of the algorithm presented in this paper.

Ensuring nuclear safety has become of great significance as nuclear power is playing an increasingly important role in supplying worldwide electricity. β-ray monitoring is a crucial method, but commercial organic scintillators for β-ray detection suffer from high temperature failure and irradiation damage. Here, we report a type of β-ray scintillator with good thermotolerance and irradiation hardness based on a two-dimensional halide perovskite. Comprehensive composition engineering and doping are carried out with the rationale elaborated. Consequently, effective β-ray scintillation is obtained, the scintillator shows satisfactory thermal quenching and high decomposition temperature, no functionality decay or hysteresis is observed after an accumulated radiation dose of 10 kGy (dose rate 0.67 kGy h −1 ). Besides, the two-dimensional halide perovskite β-ray scintillator also overcomes the notorious intrinsic water instability, and benefits from low-cost aqueous synthesis along with superior waterproofness, thus paving the way towards practical application. Efficient radiation monitoring ensures safety in nuclear power, but beta-ray scintillators should be developed for use near a highly radioactive and hot reactor. Here, the authors report a two-dimensional halide perovskite-based beta-ray scintillator with high irradiation hardness and thermotolerance.

Cylinders are common in both industrial and daily settings. Accurate geometric fitting of their parameters, including position, orientation, and radius, is important in real-world perception tasks and industrial applications. At present, consumer-level RGB-D cameras provide three-dimensional (3D) point cloud data with acceptable accuracy and are widely adopted in various sensing applications. Consequently, this task is typically formulated as a geometric fitting problem based on point cloud data. However, point cloud data acquired from such sensors often contain noise, particularly when scanning curved surfaces, which directly degrades the performance of point cloud-based fitting methods. In this paper, we propose an edge–point cloud fusion approach for the geometric fitting of cylinder parameters from single-view RGB-D data. Our approach leverages two-dimensional (2D) image-domain edge constraints together with point cloud data, then fuses them in a unified formulation to jointly optimize cylinder parameters. By explicitly incorporating reliable edge information, our method effectively mitigates the effects of noise in point cloud data. We evaluate the proposed method using real-world RGB-D data, and the experimental results show that our approach achieves significant improvements in both accuracy and robustness.

The traditional architecture of the Dong and other ethnic minorities show cases unique architectural skills. Its conservation is significant for maintaining the diversity of national culture and transmitting traditional craftsmanship. This study used ABAQUS software to analyse the mechanical properties of two typical wind and rain bridge structures. The results show that the arch bridge structure of Model 2 is significantly better than the simply supported beam bridge structure of Model 1 in terms of load-bearing capacity. Specifically, the mid-span deflection of Model 2 is only approximately 7% of that of Model 1. In addition, the El-Centro seismic wave was selected as the excitation data for this study, and the time-history analysis was performed according to the 7-degree seismic fortification standard. The results demonstrate that the arch bridge structure of Model 2 also performs well in terms of seismic performance, with lower acceleration responses at both mid-span analysis points compared to that of Model 1. These findings provide a scientific basis for the structural optimisation of wind and rain bridges and offer valuable insights into the modern conservation and continuation of traditional architectural heritage.

Alzheimer's disease (AD) is a neurodegenerative disease that significantly impacts the quality of life of patients and their families. Neuroimaging-driven brain age prediction has been proposed as a potential biomarker to detect mental disorders, such as AD, aiding in studying its effects on functional brain networks. Previous studies have shown that individuals with AD display impaired resting-state functional connections. However, most studies on brain age prediction have used structural magnetic resonance imaging (MRI), with limited studies based on resting-state functional MRI (rs-fMRI). In this study, we applied a graph neural network (GNN) model on controls to predict brain ages using rs-fMRI in patients with AD. We compared the performance of the GNN model with traditional machine learning models. Finally, the model was also used to identify the critical brain regions in AD. The experimental results demonstrate that our GNN model can predict brain ages of normal controls using rs-fMRI data from the ADNI database. Moreover the differences between brain ages and chronological ages were more significant in AD patients than in normal controls. Our results also suggest that AD is associated with accelerated brain aging and that the GNN model based on resting-state functional connectivity is an effective tool for predicting brain age. Our study provides evidence that rs-fMRI is a promising modality for brain age prediction in AD research, and the GNN model proves to be effective in predicting brain age. Furthermore, the effects of the hippocampus, parahippocampal gyrus, and amygdala on brain age prediction are verified.

The development of biomaterials, stem cells and bioactive factors has led to cartilage tissue engineering becoming a promising tactic to repair cartilage defects. Various polymer three-dimensional scaffolds that provide an extracellular matrix (ECM) mimicking environment play an important role in promoting cartilage regeneration. In addition, numerous growth factors have been found in the regenerative process. However, it has been elucidated that the uncontrolled delivery of these factors cannot fully exert regenerative potential and can also elicit undesired side effects. Considering the complexity of the ECM, neither scaffolds nor growth factors can independently obtain successful outcomes in cartilage tissue engineering. Therefore, collectively, an appropriate combination of growth factors and scaffolds have great potential to promote cartilage repair effectively; this approach has become an area of considerable interest in recent investigations. Of late, an increasing trend was observed in cartilage tissue engineering towards this combination to develop a controlled delivery system that provides adequate physical support for neo-cartilage formation and also enables spatiotemporally delivery of growth factors to precisely and fully exert their chondrogenic potential. This review will discuss the role of polymer scaffolds and various growth factors involved in cartilage tissue engineering. Several growth factor delivery strategies based on the polymer scaffolds will also be discussed, with examples from recent studies highlighting the importance of spatiotemporal strategies for the controlled delivery of single or multiple growth factors in cartilage tissue engineering applications. Keywords: polymer scaffold, growth factor, delivery, cartilage repair