Bond-Order Time Series Analysis for Detecting Reaction Events in Ab Initio Molecular Dynamics Simulations.

Ab initio molecular dynamics is able to predict novel reaction mechanisms by directly observing the individual reaction events that occur in simulation trajectories. In this article, we describe an approach for detecting reaction events from simulation trajectories using a physically motivated model based on time series analysis of ab initio bond orders. We found that applying a threshold to the bond order was insufficient for accurate detection, whereas peak finding on the first time derivative resulted in significantly improved accuracy. The model is trained on a reference set of reaction events representing the ideal result given unlimited computing resources. Our study includes two model systems: a heptanylium carbocation that undergoes hydride shifts and an unsaturated iron carbonyl cluster that features CO ligand migration and bridging behavior. The results indicate a high level of promise for this analysis approach to be used in mechanistic analysis of reactive AIMD simulations more generally

Economic risk assessment of future debris flows by machine learning method

The economic analysis presented thus far serves to map risk levels in various catchments, offering effective guidance and scientific support for decision makers. By quantifying potential risks, decision makers can gain a better understanding of future challenges, enabling them to prioritize actions and optimize resource allocation in high-risk zones. This approach facilitates long-term urban planning, policy development, and the formulation of adaptation strategies to effectively reduce and manage identified risks. Moreover, the preparedness and emergency response system would be implemented accordingly. Despite these strengths, some limitations persist, suggesting room for improvement in the proposed methodology’s performance. The database of debris-flow occurrences needs further enrichment to refine the volume prediction model. Another potential area for enhancement lies in augmenting the physical vulnerability assessment with new data, considering additional building characteristics such as shape and the number of windows. Nevertheless, all these limitations cannot alter the fact that the proposed ML-based method represents a new tool for generating a map of economic risk caused by future debris-flow events. It also signifies a practical method to deliver accurate and reliable warnings to local residents about the risks posed by debris flows

Suggestions on the development strategy of shale gas in China

AbstractFrom the aspects of shale gas resource condition, main exploration and development progress, important breakthrough in key technologies and equipment, this paper systematically summarized and analyzed current situation of shale gas development in China and pointed out five big challenges such as misunderstandings, lower implementation degree and higher economic uncertainty of shale gas resource, and still no breakthrough in exploration and development core technologies and equipment for shale gas buried depth more than 3500 m, higher cost and other non-technical factors that restrict the development pace. Aiming at the above challenges, we put forward five suggestions to promote the shale gas development in China: (1) Make strategies and set goals according to our national conditions and exploration and development stages. That is, make sure to realize shale gas annual production of 20 × 109 m3, and strives to reach 30 × 109 m3. (2) Attach importance to the research of accumulation and enrichment geological theory and exploration & development key engineering technologies for lower production and lower pressure marine shale gas reservoir, and at the same time orderly promote the construction of non-marine shale gas exploration & development demonstration areas. (3) The government should introduce further policies and set special innovation funds to support the companies to carry out research and development of related technologies and equipment, especially to strengthen the research and development of technology, equipment and process for shale gas bellow 3500 m in order to achieve breakthrough in deep shale gas. (4) Continue to promote the geological theory, innovation in technology and management, and strengthen cost control on drilling, fracturing and the whole process in order to realize efficient, economic and scale development of China's shale gas. (5) Reform the mining rights management system, establish information platform of shale gas exploration and development data, and correctly guide the non-oil and gas companies to participate in shale gas exploration and development

A chloroplast retrograde signal, 3'-phosphoadenosine 5'-phosphate, acts as a secondary messenger in abscisic acid signaling in stomatal closure and germination

Organelle-nuclear retrograde signaling regulates gene expression, but its roles in specialized cells and integration with hormonal signaling remain enigmatic. Here we show that the SAL1-PAP (3′-phosphoadenosine 5′- phosphate) retrograde pathway interacts with abscisic acid (ABA) signaling to regulate stomatal closure and seed germination in Arabidopsis. Genetically or exogenously manipulating PAP bypasses the canonical signaling components ABA Insensitive 1 (ABI1) and Open Stomata 1 (OST1); priming an alternative pathway that restores ABA-responsive gene expression, ROS bursts, ion channel function, stomatal closure and drought tolerance in ost1-2. PAP also inhibits wild type and abi1-1 seed germination by enhancing ABA sensitivity. PAP-XRN signaling interacts with ABA, ROS and Ca2+; up-regulating multiple ABA signaling components, including lowly-expressed Calcium Dependent Protein Kinases (CDPKs) capable of activating the anion channel SLAC1. Thus, PAP exhibits many secondary messenger attributes and exemplifies how retrograde signals can have broader roles in hormone signaling, allowing chloroplasts to fine-tune physiological responsesCE140100008; DE14010114

A precipitation downscaling framework for regional warning of debris flow in mountainous areas

A timely warning system for debris-flow mitigation in mountainous areas is vital to decrease casualties. However, the lack of rainfall monitoring stations and coarse resolution of satellite-based observations pose challenges for developing such a debris-flow warning model in data-scarce areas. To offer an effective method for the generation of precipitation with fine resolution, a machine learning (ML) based approach is proposed to establish the relationship between precipitation and regional environmental factors (REVs), including normalized difference vegetation index (NDVI), digital elevation model (DEM), geolocations (longitude and latitude) and land surface temperature (LST). This approach enables the downscaling of 3B42 TRMM precipitation data, providing fine temporal and spatial resolution precipitation data. We use PERSIANN-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) data to calibrate the downscaled results using geographical differential analysis (GDA) before applying them in a case study in the Gyirong Zangbo Basin. After that, we calculate the rainfall thresholds of effective antecedent rainfall (Pe) - intraday rainfall (Po) based on the calibrated precipitation and integrate them into a susceptibility map to develop a debris-flow warning model. The results show that: (1) this ML-based approach can effectively achieve the downscaling of TRMM data; (2) calibrated TRMM data outperforms the original TRMM and downscaled TRMM data, reducing deviations by 55% and 57%; (3) the integrated model, incorporating rainfall thresholds, outperforms a single susceptibility map in providing debris-flow warnings. The developed warning model can offer dynamic warnings for debris flows that may have been missed by the original warning system at a regional scale

Notice of Violation of IEEE Publication Principles: Single-Phase Common-Ground-Type Transformerless PV Grid-Connected Inverters

This paper presents a family of novel common-ground-type transformerless photovoltaic (PV) grid-connected inverters, which requires only five power switches, one capacitor, and one filter. A simple dual-closed led-loop control is used to improve control stabilization and accuracy. The main advantages of proposed inverters are: 1) the leakage current is completely eliminated (unlike traditional topologies, which can only suppress leakage current); 2) the devices used are a few and the cost is low; 3) low loss and high efficiency; 4) the ability of realizing reactive power; and 5) there is no need for high DC input voltage compared with half-bridge-type topologies. The operating principle, modulation mode, and control strategy are introduced in detail. The performance of the proposed topology is compared with that of several traditional topologies. The leakage current suppression ability and efficiency of the proposed topology are superior to those of the traditional topologies. The model predictive control (MPC) is applied in the proposed topology, which is easy to realize and can accelerate the dynamic response. Finally, the simulation and experimental results of a 1-kVA prototype are given, which proves the validity of the proposed topology in PV grid-connected system

A hybrid machine-learning model to map glacier-related debris flow susceptibility along Gyirong Zangbo watershed under the changing climate

Gyirong serves as an important channel to Chine-Nepal Economic Corridor, which is also the only land route for China-Nepal trade since the 2015 earthquake. However, the Gyirong corridor suffers from glacier-related debris flow from every April to September because of the complex topographic features and the changing climate. Therefore, a susceptibility map in response to precipitation and temperature change is timely, not only to ensure the safe operation of this corridor, but also to provide decision-makers a guidance for hazard mitigation and environmental remediation. Conventional method is difficult to consider and link the meteorological factors (e.g. temperature and precipitation), topographies, ecological, geological conditions all together to produce the susceptibility map, as such, machine learning is utilised to conduct the analysis. Logistic Regression (LR) and Support Vector Machine (SVM) were firstly applied to evaluate their efficiency and effectiveness of the performance of producing the susceptibility map. In order to improve the fitting and prediction accuracy (ACC), genetic algorithm - support vector machine (GA-SVM) and certainty factor - genetic algorithm - support vector machine (CF-GA-SVM) were conducted based on the initial analysis results of receiver operating characteristics curve (ROC) and ACC. Through the analysis, it can be seen that over 61% of the study areas have a high susceptibility to debris flow, requiring an intensive attention from the local government. To further optimise the computational time, when dealing with small amounts of sample data, SVM is more efficient than LR, but CF-GA-SVM can achieve the highest AUC (Area Under Curve) and ACC values, 0.945 and 0.800, respectively. Overall, CF-GA-SVM model presents a relatively high robustness according to sensitivity analysis

The Interphase Influences on the Particle-Reinforced Composites with Periodic Particle Configuration

This work improved upon an effective micromechanical method to analyze the mechanical properties of three-dimensional particle-reinforced composites (PRC) with consideration of the interfacial debonding. By incorporating the interfacial debonding model, Mises yield criterion, and failure theory, the effects of particle shape, particle volume fraction, and loading condition on the mechanical properties are studied. A comparison of simulation results obtained from the established method and published experimental data is presented. Good consistency can be found in this study. On this basis, the interfacial cohesive strength and particle shape effects on the biaxial failure strength of particle-reinforced composites with interfacial debonding were also studied. The results revealed that both interfacial strength and particle shape have significant effects on biaxial tensile failure strength. However, the different interfacial strength influence on failure envelope can hardly be discerned in biaxial compressive loading