Characterization and source identification of fine particulate matter in urban Beijing during the 2015 Spring Festival

The Spring Festival (SF) is the most important holiday in China for family reunion and tourism. During the 2015 SF an intensive observation campaign of air quality was conducted to study the impact of the anthropogenic activities and the dynamic characteristics of the sources. During the study period, pollution episodes frequently occurred with 12 days exceeding the Chinese Ambient Air Quality Standards for 24-h average PM2.5 (75 μg/m3), even 8 days with exceeding 150 μg/m3. The daily maximum PM2.5 concentration reached 350 μg/m3 while the hourly minimum visibility was <0.8 km. Three pollution episodes were selected for detailed analysis including chemical characterization and diurnal variation of the PM2.5 and its chemical composition, and sources were identified using the Positive Matrix Factorization model. The first episode occurring before the SF was characterized by more formation of SO42− and NO3− and high crustal enrichment factors for Ag, As, Cd, Cu, Hg, Pb, Se and Zn and seven categories of pollution sources were identified, whereby vehicle emission contributed 38% to the PM2.5. The second episode occurring during the SF was affected heavily by large-scale firework emissions, which led to a significant increase in SO42−, Cl−, OC, K and Ba; these emissions were the largest contributor to the PM2.5 accounting for 36%. During the third episode occurring after the SF, SO42−, NO3−, NH4+ and OC were the major constituents of the PM2.5 and the secondary source was the dominant source with a contribution of 46%. The results provide a detailed understanding on the variation in occurrence, chemical composition and sources of the PM2.5 as well as of the gaseous pollutants affected by the change in anthropogenic activities in Beijing throughout the SF. They highlight the need for limiting the firework emissions during China's most important traditional festival

Notice of Retraction: An Improved High-Density Sub Trajectory Clustering Algorithm

Retracted

Ultrathin Ti3C2T x (MXene) Nanosheet-Wrapped NiSe2 Octahedral Crystal for Enhanced Supercapacitor Performance and Synergetic Electrocatalytic Water Splitting

Abstract Metal selenides, such as NiSe2, have exhibited great potentials as multifunctional materials for energy storage and conversation. However, the utilization of pure NiSe2 as electrode materials is limited by its poor cycling stability, low electrical conductivity, and insufficient electrochemically active sites. To remedy these defects, herein, a novel NiSe2/Ti3C2T x hybrid with strong interfacial interaction and electrical properties is fabricated, by wrapping NiSe2 octahedral crystal with ultrathin Ti3C2T x MXene nanosheet. The NiSe2/Ti3C2T x hybrid exhibits excellent electrochemical performance, with a high specific capacitance of 531.2 F g−1 at 1 A g−1 for supercapacitor, low overpotential of 200 mV at 10 mA g−1, and small Tafel slope of 37.7 mV dec−1 for hydrogen evolution reaction (HER). Furthermore, greater cycling stabilities for NiSe2/Ti3C2T x hybrid in both supercapacitor and HER have also been achieved. These significant improvements compared with unmodified NiSe2 should be owing to the strong interfacial interaction between NiSe2 octahedral crystal and Ti3C2T x MXene, which provides enhanced conductivity, fast charge transfer as well as abundant active sites, and highlight the promising potentials in combinations of MXene with metal selenides for multifunctional applications such as energy storage and conversion

Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority

The problem of multiple DAGs sharing satellite constellation resources has gradually attracted widespread attention. Due to the limited computing resources and energy consumption of satellite networks, it is necessary to formulate a reasonable multi-DAG task scheduling scheme to ensure the fairness of each workflow under the premise of considering latency and energy consumption. Therefore, in this paper, we propose a multi-DAG satellite network task scheduling algorithm based on cache-composite priority under the Software-Defined Networking satellite network architecture. The basic idea of this algorithm lies in the DAG selection phase, where not only are task priorities computed but also the concept of fair scheduling is introduced, so as to prevent the excessively delayed scheduling of low-priority DAG tasks. In addition, the concept of public subtasks is introduced to reduce the system overhead caused by repetitive tasks. The experimental results show that the hybrid scheduling strategy proposed in this paper can meet the demand of DAG scheduling and improve the degree of task completion while effectively reducing the task latency and energy consumption

Collaborative Optimization Method for Multi-Train Energy-Saving Control with Urban Rail Transit Based on DRLDA Algorithm

With the traffic congestion problem deteriorating, people increasingly choose urban rail transit (URT) to travel. Although URT alleviates traffic congestion, the long-term operation of a large number of trains leads to huge energy consumption. In order to adapt the major social development concept of “Low carbon”, a multi-train energy-saving control collaborative optimization method is proposed in this paper. First, the composition of single train operating conditions is determined by the conversion of operating conditions between stations and the force changes under the premise of ensuring safe and on-time train operation. A single-train energy consumption calculation combinatorial optimization model with the dual control objectives of reducing passengers’ average waiting time as well as train traction energy consumption is established. The energy saving control strategy of a single train is investigated by ARMA-Radial Basis Function Neural Network (ARMA-RBFNN) and Genetic Algorithm (GA). Next, the queuing theory is introduced to analyze the variation in passenger waiting time for multiple trains at different arrival intervals. A Deep Reinforcement Learning (DRL) algorithm is designed to obtain the correlation among passenger waiting time, arrival interval and train stopping time. The optimization objective is to minimize the multi-train traction energy consumption and the average passenger waiting time while considering conditions such as train operating safety interval, speed limit, multiple operating state and single train energy-saving models, etc. Then, a multi-train cooperative energy-saving control model is proposed based on the Dragonfly Algorithm (DA). Finally, a case study of Beijing Metro Line 4 is conducted to illustrate the effectiveness of the proposed method. The results demonstrate that the total traction energy consumption and passenger waiting time are reduced by 3.1% and 5 s, respectively, compared with the method of independently optimizing the single-train control strategy. The findings can aid in the development of energy-saving strategies and also provide a basis for energy-saving operation control of multiple trains

Signal Parameters Estimation and Optimization Using Mobile Navigation Data

In the management and evaluation of traffic network, signal parameters are important for monitoring and evaluating the operation state and the traffic capacity of intersection. However, a wide range of real-time signal timing schemes lacks a clear and effective method. In this paper, we propose the signal parameter calculation method based on mobile navigation data. Then, the possibility of crossing intersection passing time of the stop line is studied. The time differences between passing times of different cycles are distributed periodically that several peaks appear cycle by cycle. The relationship between sampling rate and relative error is discussed. Combined with the distribution peak normality test, the appropriate distribution peak is selected through the actual case. The cycle lengths and effective red time parameters are calculated and compared with the known signal parameters. The result demonstrates the proposed method has high accuracy and provides data support for the research of the traffic management

Modelling Method on Dynamic Optimal Setting and Associated Control for Intermittent Bus Lane

A new type of dynamic bus lane not only ensures the strategy of bus priority but also significantly improves the spatiotemporal utilization of road resources and reduces the conflicting demands of buses and social vehicles on road resources. Firstly, the heterogeneity of bus arrival time is analysed according to the process of aggregation and dissipation of vehicle queues at intersections. Considering the correlation between the operating states of social vehicles and buses, a dynamic control strategy for bus lane based on the insertable interval of social vehicles is established. Secondly, combining the setting conditions of the intermittent bus lane control area with the correlation scenario of signal control at intersections and then according to the HCM 2010 vehicle delay equation and the BPR function, the associated optimized control model with the minimum total travel time consumption of the road section as the objective function is constructed. The global optimal control of the intermittent bus lane is realized through the computational experiment. Finally, the setting conditions and benefits of three lane organization schemes (including intersect (i.e., no bus priority), parallel, and intermittent bus lanes) are compared and analysed through case study. The results indicate that the intermittent priority bus lanes have opening hours, which can not only ensure bus priority but also expand the right way for social vehicles and make full use of road space resources, thus improving the overall traffic efficiency

Hydroxyl-Decorated Pt as a Robust Water-Resistant Catalyst for Catalytic Benzene Oxidation

In catalytic oxidation reactions, the presence of environmental water poses challenges to the performance of Pt catalysts. This study aims to overcome this challenge by introducing hydroxyl groups onto the surface of Pt catalysts using the pyrolysis reduction method. Two silica supports were employed to investigate the impact of hydroxyl groups: SiO2-OH with hydroxyl groups and SiO2-C without hydroxyl groups. Structural characterization confirmed the presence of Pt-Ox, Pt-OHx, and Pt0 species in the Pt/SiO2-OH catalysts, while only Pt-Ox and Pt0 species were observed in the Pt/SiO2-C catalysts. Catalytic performance tests demonstrated the remarkable capacity of the 0.5 wt % Pt/SiO2-OH catalyst, achieving complete conversion of benzene at 160 °C under a high space velocity of 60,000 h-1. Notably, the catalytic oxidation capacity of the Pt/SiO2-OH catalyst remained largely unaffected even in the presence of 10 vol % water vapor. Moreover, the catalyst exhibited exceptional recyclability and stability, maintaining its performance over 16 repeated cycles and a continuous operation time of 70 h. Theoretical calculations revealed that the construction of Pt-OHx sites on the catalyst surface was beneficial for modulating the d-band structure, which in turn enhanced the adsorption and activation of reactants. This finding highlights the efficacy of decorating the Pt surface with hydroxyl groups as an effective strategy for improving the water resistance, catalytic activity, and long-term stability of Pt catalysts

S100a9 deficiency accelerates MDS-associated tumor escape via PD-1/PD-L1 overexpression

In recent studies, the tolerable safety profile and positive bone marrow (BM) response suggest a beneficial use of anti-PD-1 agents in the treatment of Myelodysplastic Syndromes (MDS), but the underlying mechanism is still unknown. MDS is mainly characterized by ineffective hematopoiesis, which may contribute to inflammatory signaling or immune dysfunction. Our previous studies focused on inflammatory signaling, and the results showed that S100a9 expression was higher in low-risk MDS and lower in high-risk MDS. In this study, we combine the inflammatory signaling and immune dysfunction. SKM-1 cells and K562 cells co-cultured with S100a9 acquire apoptotic features. Moreover, we confirm the inhibitory effect of S100a9 on PD-1/PD-L1. Importantly, PD-1/PD-L1 blockade and S100a9 can both activate the PI3K/AKT/mTOR signaling pathway. The cytotoxicity is higher in lower-risk MDS-lymphocytes than in high-risk MDS-lymphocytes, and S100a9 partially rescues the exhausted cytotoxicity in lymphocytes. Our study demonstrates that S100a9 may inhibit MDS-associated tumor escape via PD-1/PD-L1 blockade through PI3K/AKT/mTOR signaling pathway activation. Our findings indicate the possible mechanisms by which anti-PD-1 agents may contribute to the treatment of MDS. These insights may provide mutation-specific treatment as a supplementary therapy for MDS patients with high-risk mutations, such as TP53, N-RAS or other complex mutations