摘要 : Nonlinear multimode optical systems have attracted substantial attention due to their rich physical properties. Complex interplay between the nonlinear effects and mode couplings makes it difficult to understand the collective dynamics of photons. Authors of recent studies have shown that such collective phenomena can be effectively described by Rayleigh-Jeans thermodynamics theory, which is a powerful tool for the study of nonlinear multimode photonic systems. These systems, in turn, offer a compelling platform for investigating fundamental issues in statistical physics, attributed to their tunability and the ability to access negative temperature regimes. However, to date, a theory for nonequilibrium transport and fluctuations is yet to be established. Here, we employ full counting statistics theory to study the nonequilibrium transport of particles and energy in nonlinear multimode photonic systems in both positive and negative temperature regimes. Furthermore, we discover that, in situations involving two reservoirs of opposite temperatures and chemical potentials, an intriguing phenomenon known as the loop current effect can arise, wherein the current in the positive energy sector runs counter to that in the negative energy sector. In addition, we numerically confirm that the fluctuation theorem remains applicable in optical thermodynamics systems across all regimes, from positive temperatures to negative ones. Our findings closely align with numerical simulations based on first-principles nonlinear wave equations. In this paper, we seek to deepen the understanding of irreversible nonequilibrium processes and statistical fluctuations in nonlinear many-body photonic systems which will enhance our grasp of collective phenomena of photons and foster a fruitful intersection between optics and statistical physics.

摘要 : EOC dominated the labile organic carbon pool in coastal wetland soil. Invasion of mudflats by Spartina alterniflora increased soil EOC and DOC. EOC and DOC decreased when Spartina marshes were converted into aquaculture ponds. SOC mineralization rate increased most strongly with increasing DOC. Latitudinal gradients in EOC and MBC suggest a temperature-dependent effect. Labile organic carbon (LOC) plays a pivotal role in soil biogeochemistry and ecological functions. China's coastal wetlands have been profoundly impacted due to plant invasion and land use change, but the effects on soil LOC quantity and composition are unclear. This study analyzed the soil LOC components—namely, dissolved organic carbon (DOC), easily oxidizable carbon (EOC), and microbial biomass carbon (MBC)—across twenty-one coastal wetlands in southeastern China. These wetlands underwent a uniform land cover transition from native mudflats (MFs) to Spartina alterniflora marshes (SAs), and eventually to aquaculture ponds (APs). The results indicated that EOC was the dominant component of soil organic carbon (SOC) (57.5%–61.6%), followed by MBC (3.5%–4.5%) and DOC (<0.5%). The transition from MFs to SAs led to a rise in mean EOC and DOC by 18.6% and 41.4%, respectively. Subsequent conversion of SAs to APs resulted in a reduction in mean EOC and DOC by 5.9% and 20.3%, respectively. MBC did not differ significantly among habitat types. Total nitrogen availability was the main driver of changes in LOC across both land cover change scenarios. The mineralization rate of SOC were more strongly correlated with DOC than EOC and MBC. Microbial turnover of EOC was temperature dependent across the geographical range. These finds highlighted that plant invasion and land use change affected LOC fractions and subsequent SOC stability and carbon emissions in coastal wetlands.

摘要 : Geological disasters, as a common occurrence, have a serious impact on social development in terms of their frequency of occurrence, disaster effects, and resulting losses. To effectively reduce the casualties, property losses, and social effects caused by various disasters, it is necessary to conduct real-time monitoring and early warning of various geological disaster risks. With the growing development of the information age, public attention to disaster relief, casualties, social impact effects, and other related situations has been increasing. Since social media platforms such as Weibo and Twitter contain a vast amount of real-time data related to disaster information before and after a disaster occurs, scientifically and effectively utilizing these data can provide sufficient and reliable information support for disaster relief, post-disaster recovery, and public appeasement efforts. As one of the techniques in natural language processing, the topic model can achieve precise mining and intelligent analysis of valuable information from massive amounts of data on social media to achieve rapid use of thematic models for disaster analysis after a disaster occurs, providing reference for post-disaster-rescue-related work. Therefore, this article first provides an overview of the development process of the topic model. Secondly, based on the technology utilized, the topic models were roughly classified into three categories: traditional topic models, word embedding-based topic models, and neural network-based topic models. Finally, taking the disaster data of "Dongting Lake breach" in Hunan, China as the research object, the application process and effectiveness of the topic model in urban geological disaster information mining were systematically introduced. The research results provide important references for the further practical innovation and expansion of the topic model in the field of disaster information mining.

摘要 : Determining soil categories in geotechnical engineering is critical to ensuring building safety, optimizing structural design, and controlling project costs. This study proposes a novel soil classification method based on an integrated machine-learning model. The integrated model synthesizes prediction results from four classification models, assigning weights to each model's probability outputs for various soil categories, then normalizes these aggregated probabilities to ensure that the predicted probabilities of the different soil categories add up to one, ultimately selecting the soil category with the highest normalized probability as the final classification. Initially, data from five distinct engineering projects located in Shanghai were collected, and 70% of data from each site was used to create a comprehensive dataset. It was then divided into training and testing datasets in a ratio of 8:2. Four classification algorithms, including decision tree, Random forest, KNN, and Adaboost, were trained using the training dataset. To enhance their collective predictive capability, Particle Swarm Optimization (PSO) was applied to fine-tune the weight coefficients among these models. The fitness function of PSO was defined as the integrated model's performance on the testing dataset. For the final stage of the study, the efficacy of the integrated model was subsequently validated using the remaining 30% of the data from each site. The experimental results show that the integrated model has high accuracy and robustness in multiple engineering sites compared with individual machine-learning models. In addition, the proposed integrated model exhibits significant superiority over traditional CPT-based direct methods. Overall, the integrated model shows accuracy and significant robustness across different project sites, highlighting its potential for application in geotechnical engineering.

摘要 : Among the products of the electrocatalytic reduction of carbon dioxide (CO2RR), CO is currently the most valuable product for industrial applications. However, poor stability is a significant obstacle to CO2RR. Therefore, we synthesized a series of bimetallic organic framework materials containing different ratios of tungsten to copper using a hydrothermal method and used them as precursors. The precursors were then subjected to pyrolysis at 800 °C under argon gas, and the M-N bimetallic sites were formed after 2 h. Loose porous structures favorable for electrocatalytic reactions were finally obtained. The material could operate at lower reduction potentials than existing catalysts and obtained higher Faraday efficiencies than comparable catalysts. Of these, the current density of WCu-C/N (W:Cu = 3:1) could be stabilized at 7.9 mA ‧ cm-2 and the FE of CO reached 94 % at a hydrogen electrode potential of -0.6 V (V vs. RHE). The novel materials made with a two-step process helped to improve the stability and selectivity of the electrocatalytic reduction of CO2 to CO, which will help to promote the commercial application of this technology.

摘要 : Hydrothermal and ultrasonic processes were used in this study to synthesize a single-atom Cu anchored on t-BaTiO3. The resulting material effectively employs vibration energy for the piezoelectric (PE) catalytic degradation of pollutants. The phase and microstructure of the sample were analyzed using X-ray diffraction (XRD) and scanning electron microscopy (SEM), and it was found that the sample had a tetragonal perovskite structure with uniform grain size. The nanomaterial achieved a considerable increase in tetracycline degradation rate (approximately 95 % within 7 h) when subjected to mechanical vibration. In contrast, pure BaTiO3 demonstrated a degradation rate of 56.7 %. A significant number of piezo-induced negative charge carriers, electrons, can leak out to the Cu-doped BaTiO3 interface due to Cu's exceptional conductivity. As a result, a single-atom Cu catalyst can facilitate the separation of these electrons, resulting in synergistic catalysis. By demonstrating a viable approach for improving ultrasonic and PE materials this research highlights the benefits of combining ultrasonic technology and the PE effect.

摘要 : In recent years, it has been difficult to remove excess N (mainly NO3–-N) in wastewater with a low ratio of biochemical oxygen demand/chemical oxygen demand. The present study combined the operation of electrochemical process (with a modified brass mesh electrode being used as the cathode) and anammox (anaerobic ammonium oxidation) to treat a high nitrate-simulated wastewater and investigated the overall operating parameters of the combined system. Results showed that the efficiency of the combined system for the removal of total nitrogen can be stabilized over 76.35% at an electrochemical feed of 300 mg/L NO3–-N, and the NO2–-N/NH4+-N (around 130 mg/L) in the effluent of the electrochemical part was kept around 1. Although specific anammox activity decreased from 142.07 to 129.23 mg/g VSS-d, the amount of secreted extracellular polymeric substances increased from an initial 152.10 to 204.12 mg/g VSS. The main functional bacteria were Candidatus Brocadia (2.80–8.84%) and Candidatus Jettenia (0.94–1.99%). Compared to traditional denitrification, the composite process lags behind but still holds a certain economic advantage over electrochemical nitrate removal counterpart.

摘要 : A multistep spatiotemporal forecasting (MSTF) network is developed through incorporating the graph convolutional network (GCN) and the long short-term memory (LSTM) network within a sequence-to-sequence (seq2seq) framework. The MSTF method can not only extract spatial and temporal information from the input data but also make multistep-ahead and continuous predictions. An MSTF-based harmful algal bloom (HAB) forecasting model is then formulated to predict the chlorophyll-a (Chl-a) concentration of the Dianchi Lake (China). The integrated gradients (IG) method is employed to interpret the trained MSTF model and quantify the attribution of each input dimension to the Chl-a prediction. Results indicate that (i) the coefficient of determination (R2) of the MSTF model in 24-h-ahead Chl-a prediction reaches 0.926, 28.4% higher than that of the traditional LSTM model; (ii) the ammonia nitrogen (12.3%), the total phosphorus (10.2%), the total nitrogen (9.9%), and the temperature (8.6%) are significant variables for Chl-a prediction; (iii) the spatial information from neighbor lake and river stations plays an important role in the HAB forecasting, with an average contribution of 35.0%; (iv) the proposed MSTF model is also skillful in the 72-h-ahead Chl-a prediction. Results presented highlight the importance of considering both spatial and temporal dependency of monitoring data in HAB forecasting and mechanism interpreting.

摘要 : A van der Waals (vdW) heterostructure is formed by combining multiple materials through vdW bonds. It can combine the advantages of electronic, optical, thermal, and magnetic properties of different 2D materials and has the potential to develop into the next generation of high-performance functional devices. Herein, the current research advances of vdW heterostructures are reviewed. First, current fabrication methods and physical structures of vdW heterostructures are summarized. The 2D/nD (n = 0, 1, 2, 3) mixed-dimensional heterostructures are discussed in detail. Second, a new type of vdW heterostructure is introduced based on two-dimensional electron gas with a nanoscale junction interface. Finally, the application prospects of vdW heterostructures in photoelectric and memory devices are further outlined by combing new applications in the neural networks. This review shows that vdW heterostructures have great advantages in high integration, energy harvesting, and logical operations, and it provides directions and suggestions for the future research and application of environmentally friendly, high-performance, and smart functional devices.