With the rapid development of UHV transmission technology, UHV cross-provincial power grid supporting new energy has become an important part of the power system. However, due to the imbalance of power demand and resource allocation in various provinces, the deviation of power distribution mode has become an important problem restricting the stable operation of cross-provincial power grid. This paper proposes a power distribution mode deviation processing model based on UHV cross-provincial power grid supporting new energy, aiming to improve the stability and reliability of power grid operation. First, this paper establishes a mathematical model of power distribution mode deviation by deeply analyzing the structure and operation characteristics of UHV cross- provincial power grid. Secondly, in view of the diversity and complexity of power distribution mode deviation, a deviation processing method based on multi-objective optimization algorithm is proposed. By balancing the power supply and demand relationship and optimizing the power distribution strategy, the risk caused by the deviation is effectively reduced. Finally, the effectiveness and feasibility of the proposed model and algorithm are verified by simulation. The results show that the model can significantly improve the operation efficiency and stability of the power grid under different load conditions.

Chlorate (ClO3-) are commonly identified oxyanions pollutant in the water environment, and catalytic hydrogenation reduction of oxyanions has emerged as a promising water treatment strategy. Supported Rh catalysts have been widely applied in the liquid phase catalytic hydrogenation due to their high activation capacity for H2, while exploring highly active and stable catalysts for different pollutants remains a great challenge. The transition metal Ni was introduced into the Rh-based catalyst Rh/CeO2 by the impregnation method to obtain the bimetallic catalyst RhNi10/CeO2, which was used in the catalytic hydrogenation reduction of ClO3-. In-situ DRIFTS identifies the highly dispersed Rh nanoparticles in the catalyst, and the strong metal-support interaction between the supported bimetallic Rh-Ni and CeO2 supports is determined by XPS characterization. RhNi10/ CeO2 exhibits an initial activity approximately 27 times higher than that of Rh/CeO2. Furthermore, theoretical calculations corroborate the experimental observation that when chlorate is adsorbed on the Rh surface, the energy released increases with the introduction of Ni. We reveal the enhanced reactivity of the RhNi10/CeO2 through changing ClO3-concentration, which renders adsorption on catalyst surface pathway sequencely more favorable. In addition, the reusability of the catalyst was tested by adding chloride ions (Cl-) and catalyst recycling. Results revealed that RhNi10/CeO2 maintained 100 % removal efficiency despite slight inhibition by Cl-(12 times the ClO3-concentration of 0.4 mM). After 5 cycles, the activity loss remained below 9 % with 100 % removal efficiency. The bimetallic synergy leads to increased resistance to chloride ion interference, providing high catalytic stability.

Aiming at the characteristics of ultra-high voltage supporting new energy power grid, this paper proposes a coordinated interactive optimization model of multiple green power entities. Based on the principle of game theory, this model constructs a multi-objective optimization framework to realize the coordinated dispatch of various green power entities in the inter-provincial transmission network. The model solves the dynamic game among multiple entities, and optimizes the power allocation strategy of each entity under the conditions of power balance constraints, power flow constraints, voltage constraints and line transmission capacity constraints to ensure the safe and stable operation of the power grid. This paper first analyzes the relationship between ultra-high voltage power grid and new energy supporting, establishes mathematical models of wind power clusters, photovoltaic clusters and energy storage, and clarifies the coordinated interaction mechanism between various entities. Subsequently, this paper proposes a collaborative optimization algorithm based on Nash equilibrium, and discusses in detail the solution process and its application in the multi-objective model. Finally, the effectiveness of the model is verified through the simulation analysis of the IEEE39 node system. The simulation results show that the proposed model can significantly improve the economic benefits of the system and the utilization rate of new energy, while effectively reducing the operating cost and network loss of the system.

Rainy weather significantly affects traffic efficiency and safety on freeways. This study investigates a safe speed limit model for freeways under rainy conditions, considering both human-driven vehicles (HVs) and automatic vehicles (AVs). The relationship between safe speed limits and rainfall intensity is quantified. The intelligent driver model (IDM) was selected as the basis for this study due to its complexity and applicability. By analyzing the effects of rainfall on model parameters, an improved IDM was proposed, optimizing the expected speed. Simulation results demonstrate that the proposed speed limit method effectively reduces collision rates, with AVs showing superior traffic efficiency, stability, and safety compared to HVs.

Precise construction of single and uniform active species in supported noble metal catalysts, for clarifying structure-activity relationships and optimizing catalytic activity, is essential but highly challenging. Here we have developed a defects-assisted adsorption combined with hydrogen-induced aggregation method for controllably fabricating uniform Rh species from single atoms to nanoclusters (1.1 nm) and ultrafine nanoparticles (2.1 nm) on defect-rich CeO2. For catalytic N2O decomposition, Rh nanocluster catalysts with nearly 100 % Rh exposure present superior activity, with a turnover frequency (137.4 h- 1 at 250 degrees C) 4.6 times higher than Rh nanoparticles catalysts and 148.8 times higher than Rh single atoms catalysts. Mechanism studies indicate different Rh species on the defect-rich CeO2 have various responses to O2, mainly due to electronic effect. Rh clusters act as the optimal active species owing to the presence of adjacent Rh atoms and positively charged Rh species, facilitating the transformation of intermediates and desorption of products, respectively. Besides, defects from CeO2 nanorods play crucial roles in the controlled catalyst synthesis process and the enhancement of catalytic activity. This work highlights that precisely constructing metal active sites with single-cluster species and appropriate electronic properties can achieve optimal catalytic performance in some structure-sensitive reactions.

Purpose This study aims to explore the factors influencing user acceptance of mobility-as-a-service (MaaS) platforms in Shenzhen and to provide recommendations for future implementation and development. Design/methodology/approach Using data from 232 valid questionnaires collected from Shenzhen residents, this study applies the Unified Theory of Acceptance and Use of Technology to construct a structural equation model for MaaS acceptance. Relevant hypotheses were proposed and tested using analysis of variance. Findings The results indicate that comfort attitude, effort expectancy and performance expectancy considerably and positively influence users’ willingness to accept MaaS platforms. Performance expectancy mediates the effect of effort expectancy on willingness to accept. Although social influence had a positive impact, it was not significant. Research limitations/implications This study is primarily limited to Shenzhen and may not fully reflect the acceptance of MaaS by residents in other cities or regions. Moreover, the research relies solely on survey data, which may be subject to self-reporting biases. Future research should expand to a broader geographical area and consider using actual behavioral data to enhance the reliability and generalizability of the results. Practical implications The findings are significantly relevant for policymakers and urban transport planners. Understanding the key factors influencing residents’ acceptance of MaaS, such as comfort and performance expectancy, can enhance the design and promotion of MaaS projects to achieve higher user attraction and satisfaction. Emphasizing the simplification of user interfaces and operation processes can improve user experience and foster widespread adoption. Social implications The study reveals that social influence has a minimal impact on MaaS acceptance, suggesting that public education and promotional strategies need to be redesigned to focus more on communicating personal benefits and specific advantages. Additionally, emphasizing the role of MaaS in increasing public transport usage and reducing private car dependency could help promote sustainable transportation development and improve urban traffic conditions. Originality/value This study provides a comprehensive analysis of the factors affecting MaaS acceptance in Shenzhen, offering new insights into user behavior within the context of urban mobility. This study contributes to the understanding of user acceptance in developing regions and proposes actionable recommendations, including pilot projects and service plans, to support the effective deployment of MaaS platforms in Shenzhen.

Traditional short-term traffic volume forecasting approaches make it difficult to predict the highly spatiotemporally coupled short-time traffic. To tackle the problem, this paper first proposes a variational modal algorithm (GWO-VMD) based on the optimization of the gray wolf search algorithm. It aims to decompose and reduce the noise of short-time traffic flows. Meanwhile, it reduces the intricacy of data sequences and enhances the regularity pattern. To address the insufficient utilization of spatiotemporal features, this paper presents an innovative deep-learning traffic prediction framework based on the stacking of multiple temporal trend-aware graph attention (TGA) layers and gated temporal convolution (GTC) layers, which are called trend-aware temporal graph neural network (TTGAN). TGA dynamically models the space-time relationships of traffic data, and GTC models the temporal characteristics of traffic data. The experimental findings demonstrate that the MAPE model, as presented, achieves a reduction of 9% and 2% compared to the AGCRN and GWNET models, respectively, in the domain of deep spatiotemporal graph modeling. Data decomposition and noise reduction are necessary to achieve accurate results. This model has superior performance in terms of mean absolute error (MAE), coefficient of determination (R2), and explained variance score (EVAR).

In order to solve the problem of all types of transaction settlement in multiple markets during the transitional stage of the spot market, a full transaction settlement platform is designed. We analyzed the main types of multi-market power trading business, designed the overall structure and functional framework of the full transaction settlement platform, analyzed the supporting technology of the platform, and designed the medium and long-term transaction mode and power spot transaction according to the basic mechanism of full transaction settlement Mode. This paper analyzes the linking mechanism between medium and long-term transactions and spot transactions, and explains the settlement issues of all types of transactions. Finally, according to the application of the platform in this paper, the application effect analysis is carried out, which illustrates the effectiveness of the platform application.

As urban expansion accelerates, travel distance and vehicle miles traveled continue to grow. Simultaneously, agricultural land is gradually giving way to urban construction, and ancient communities are transforming into urban villages as a result of being surrounded by urban land. Given the widespread distribution of urban villages in Chinese cities, which accommodate a large number of low-income groups, paying attention to the travel behavior of these groups is beneficial for promoting the fair development ofsociety. Therefore, this study utilized resident survey data from urban villages and commercial housing communities of Zhuhai in 2018 and built a structural equation modeling analysis framework to investigate the differences in the influence of the built environment (BE) on the travel behavior of these two types of housing. The results showed that the BE of urban villages had a significantly different impact on travel behavior compared to commercial housing. This discovery can help authorities better understand the impact of the BE of urban villages on travel distance, transit choices, and walking/cycling decisions. Moreover, this research is conducive to proposing targeted measures for sustainable transportation development in urban villages.

The surge in e-commerce has led to an increased demand for urban express services, requiring the strategic development of delivery networks that are both efficient and cost-effective. This study addresses a practical vehicle routing problem (VRP) in an urban express delivery network to minimize transportation costs. Specifically, it considers the implementation of backhaul discounts, a factor disregarded in the existing literature. This VRP is further complicated by various realistic constraints, including pickup and delivery, time windows, multiple trips, heterogeneous fleets, and docking capacity limitations, which make most general VRP solvers inapplicable. This study proposes a trip-based formulation to overcome this challenge and develop a tailored branch-and-price algorithm. Feasible trips are classified into four types to simplify the computation of backhaul discounts, thereby enhancing solution efficiency. Validation with real-world data from SF Express substantiates the efficacy of our method and yields insights for sustainable city logistics management. Moreover, our simplified column generation algorithm exhibits competitive performance, achieving optimal solutions expeditiously for the tested instances.

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Pt-based alumina catalysts doped with varying niobium contents (i.e., 0, 1.20, 2.84, and 4.73 wt

The pandemic, such as COVID-19, has greatly impacted route of container shipping optimization across the world. Faced with these new challenges, optimizing the existing route to reduce shipping costs is a pressing concern. Utilizing quantitative methods and IBM ILOG CPLEX solution, considering the impact of liner schedules on routes, this paper conducts an optimization study on target routes with the goal of minimizing operational costs. By employing these methods, the pandemic-related factors were ultimately quantified, known routes were optimized, and transportation costs were reduced. It is beneficial for optimizing container shipping routes in the context of the pandemic's influence.

A microgrid is a system that incorporates various decentralized power sources and manages the distribution of electricity. In microgrid, prosumers play a significant role in trading electricity, and it is essential to establish an effective power trading mechanism to incentivize their active participation. This study proposes a power trading mechanism for prosumers in microgrids, which incorporates blockchain technology to protect their rights and interests. The mechanism employs reinforcement learning to optimize trading decisions and develops a reputation mechanism to evaluate prosumers' trustworthiness based on their past transactions. The proposed strategy encourages prosumers to conduct more transactions with honest peers by introducing reputation value rewards into the benefit function. The simulation results indicate that the proposed strategy outperforms traditional approaches and reputation value significantly impacts prosumers' utility. Overall, the proposed strategy aims to promote honest transactions and enhance prosumers' participation in microgrid power transactions.

Osteochondral (OC) repair is an extremely challenging topic due to the complex biphasic structure and poor intrinsic regenerative capability of natural osteochondral tissue. In contrast to the current surgical approaches which yield only short-term relief of symptoms, tissue engineering strategy has been shown more promising outcomes in treating OC defects since its emergence in the 1990s. In particular, the use of multizonal scaffolds (MZSs) that mimic the gradient transitions, from cartilage surface to the subchondral bone with either continuous or discontinuous compositions, structures, and properties of natural OC tissue, has been gaining momentum in recent years. Scrutinizing the latest developments in the field, this review offers a comprehensive summary of recent advances, current hurdles, and future perspectives of OC repair, particularly the use of MZSs including bilayered, trilayered, multilayered, and gradient scaffolds, by bringing together onerous demands of architecture designs, material selections, manufacturing techniques as well as the choices of growth factors and cells, each of which possesses its unique challenges and opportunities.

Abstract Background The crosstalk between periodontal ligament stem cells (PDLSCs) and macrophages plays an important role in periodontal bone homeostasis. Metabolic reprogramming is necessary for osteoclastic differentiation of macrophages. However, whether PDLSCs exert immunomodulatory function via modulating the metabolic reprogramming of macrophages is unknown. Methods PDLSCs from healthy individuals (H-PDLSCs) and patients with periodontitis (I-PDLSCs) were collected, then the exosomes were respectively isolated (H-Exo, I-Exo). The functions of H-Exo and I-Exo on the osteoclast function and periodontitis treatment were compared. The molecular mechanism of H-Exo on periodontitis was detected by microRNA sequence. And the metabolic reprogramming of macrophages was analyzed by seahorse test and 13C-glucose tracer. Results The results indicated that H-Exo inhibited osteoclastic differentiation and bone resorption in vitro and in vivo, while I-Exo has no obvious inhibitory effects. miRNA sequencing revealed that miR-92a-3p was a key molecule involved in the immunomodulatory effects of H-Exo. H-Exo modulates mitochondrial dynamics and cellular metabolism of macrophages via the miR-92a-3p/MFN1/PKM2 axis. Conclusions This study offers valuable insight into the crosstalk between PDLSCs and macrophages in periodontal bone homeostasis. In addition, this study also confirms that Exo from PDLSCs can modulate macrophage mitochondria dynamic and metabolism, which is a new way for PDLSCs to exert its immunoregulatory function.

In order to improve the effectiveness of the medium and long-term market power purchase and sale strategy and enhance the trading efficiency of the electricity selling company, a medium- and long-term market power purchase and sale strategy considering the time-sharing power consumption deviation assessment is proposed. In the medium- and long-term market model of electricity, it will suffer from bias assessment when the actual electricity consumption of electricity users deviates from the amount of electricity traded in the medium- and long-term market. The purchasing cost of the power selling company is modeled and the assessment cost of time-sharing deviation of the power users is considered. On this basis, a model for evaluating the utility of electricity sales in the medium- and long-term market is proposed. If the benefit of purchasing and selling electricity exceeds its expected income, the purchasing and selling electricity strategy is effective; otherwise, you need to adjust your trading strategy. Finally, a numerical example is constructed based on the actual data of a provincial power grid to verify the effectiveness of the proposed strategy. The results of the example show that if the time-sharing deviation assessment is not considered, the electricity purchasing and selling strategy of the electricity selling company may lead to higher transaction risk.

Urban renewal provides opportunities to improve urban transport structures during the process of improving built environments. It is necessary to clarify the impact of different elements of the built environment on travel behaviors in the context of urban village renewal. This paper presents a microscopic perspective of individual travel behavior by proposing analytical frameworks to investigate travel behavior in terms of DiDi commuting trips. Considering the effect of spatial dependence, a Spatial Durbin Error Model was established, incorporating a spatial lag and spatial error. Traveling information was employed from the ride-sourcing company DiDi during the morning and evening peaks within in the urban village areas and workplaces of Shenzhen, and the variables of a built environment were scaled within travel analysis zones (TAZs). The results show that the impacts of the built environment on ride-sourced commuting were different between job and housing locations, with more influential factors in residential locations (urban villages). On the other hand, working locations had an influential magnitude more than twice that of residential locations. Alongside that, due to the spillover effect, it was more effective to hinder ride-sourced commuting and promote green traveling modes by increasing the number of bus stops. The findings provide some insights into transit-oriented urban renewal. Therefore, when transforming urban villages, an emphasis should be placed on the enhancement of transit availability, and the mixed land use could be considered last due to limited time and funds.