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Non-point source pollution (NPSP) significantly affects regional water environments worldwide. Identifying critical source areas (CSAs) and implementing best management practices (BMPs) are essential to prevent and control NPSP. This study used the Soil and Water Assessment Tool model to simulate and evaluate the characteristics of NPSP in the Gongjiang Watershed, a typical, hilly mountainous area in Southern China. The study identified CSAs of NPSP using total nitrogen (TN), total phosphorus (TP), water quality level, and population density factors. The reduction effect of different BMPs on the NPSP load was evaluated, and the cost-effectiveness (CE) of various BMPs was evaluated for the control of NPSP in the watershed. The interannual variation in NPSP load was high from 2013 to 2018, with the largest pollution load in 2016 (wet year) and the smallest in 2018 (dry year). The pollution load fluctuated throughout the year and was related to fertilization and precipitation runoff. The spatial heterogeneity of NPSP was large, with TN and TP losses showing a spatial pattern of high in the south, low in the north, and highest in the center. The CSAs of the NPSP covered 1566.66 km2, representing 9.77 % of the total watershed area. However, TN and TP loads accounted for 19.39 % and 23.53 % of total watershed production. The order of effectiveness of individual management measures in reducing TN was stubble coverage (SC) > vegetative buffer strips (VBS) > terrace engineering (TE) > grassed waterways (GW) > 20 % fertilizer reduction (FR20) > 10 % fertilizer reduction (FR10). The order of effectiveness of TP reduction was SC > VBS > TE > FR20 > GW > FR10. Combined management measures were much more effective in reducing TN and TP than individual measures, with an average increase in TN and TP reduction of 20.59 % and 25.23 %, respectively. Based on the results of the CE evaluation of BMPs, a combination of FR20, GW, and VBS was more effective than individual measures. We suggest a combination of FR20, GW, and VBS measures for effective NPSP prevention and control in Southern China’s typical, hilly mountainous areas.

Abstract Objective Smoking was a major risk factor for chronic obstructive pulmonary disease (COPD). This study plan to explore the mechanism of Polyphyllin B in lung injury induced by cigarette smoke (CSE) in COPD. Methods Network pharmacology and molecular docking were applied to analyze the potential binding targets for Polyphyllin B and COPD. Commercial unfiltered CSE and LPS were used to construct BEAS-2B cell injury in vitro and COPD mouse models in vivo, respectively, which were treated with Polyphyllin B or fecal microbiota transplantation (FMT). CCK8, LDH and calcein-AM were used to detect the cell proliferation, LDH level and labile iron pool. Lung histopathology, Fe3+ deposition and mitochondrial morphology were observed by hematoxylin–eosin, Prussian blue staining and transmission electron microscope, respectively. ELISA was used to measure inflammation and oxidative stress levels in cells and lung tissues. Immunohistochemistry and immunofluorescence were applied to analyze the 4-HNE, LC3 and Ferritin expression. RT-qPCR was used to detect the expression of FcRn, pIgR, STAT3 and NCOA4. Western blot was used to detect the expression of Ferritin, p-STAT3/STAT3, NCOA4, GPX4, TLR2, TLR4 and P65 proteins. 16S rRNA gene sequencing was applied to detect the gut microbiota. Results Polyphyllin B had a good binding affinity with STAT3 protein, which as a target gene in COPD. Polyphyllin B inhibited CS-induced oxidative stress, inflammation, mitochondrial damage, and ferritinophagy in COPD mice. 16S rRNA sequencing and FMT confirmed that Akkermansia and Escherichia_Shigella might be the potential microbiota for Polyphyllin B and FMT to improve CSE and LPS-induced COPD, which were exhausted by the antibiotics in C + L and C + L + P mice. CSE and LPS induced the decrease of cell viability and the ferritin and LC3 expression, and the increase of NCOA4 and p-STAT3 expression in BEAS-2B cells, which were inhibited by Polyphyllin B. Polyphyllin B promoted ferritin and LC3II/I expression, and inhibited p-STAT3 and NCOA4 expression in CSE + LPS-induced BEAS-2B cells. Conclusion Polyphyllin B improved gut microbiota disorder and inhibited STAT3/NCOA4 pathway to ameliorate lung tissue injury in CSE and LPS-induced mice.

The Karst plateau region has a unique natural erosion environment and sharp human–land conflicts. This study selected Bijie City, Northwest Guizhou, as the study area. To quantitatively analyze the human and natural impacts on soil erosion in this area, this paper evaluated the anthropogenic and natural soil erosion based on the Revised Universal Soil Loss Equation (RUSLE) coupled with the Quantitative Analytical Model (QAM). The results showed the following: (1) the total soil erosion modulus in the study area showed an increasing trend: 37.86 t/(ha·a) in 2010, 42.12 t/(ha·a) in 2015, and 48.67 t/(ha·a) in 2020; (2) human activities reduced soil erosion, with an anthropogenic soil erosion modulus of −13.79 t/(ha·a) in 2015 and −17.36 t/(ha·a) in 2020, indicating that human activities, such as projects of returning farmland to forests and rocky desertification control, played a key role in decreasing soil erosion in the study area.; and (3) the percentage of the area of soil erosion deterioration dominated by natural factors (AGN) is gradually decreasing, 89.47% in 2015 and 81.85% in 2020; the percentage of the area of soil erosion deterioration dominated by human activities (AGH) is increasing from 6.17% in 2015 to 13.80% in 2020; and the percentage of the area of soil erosion mitigation caused by human activities (ALH) and the area of soil erosion not affected by natural and human activities (NNH) showed no significant change. This result suggests more attention should be paid to the area of AGH to control soil erosion. This study analyzed the roles of natural factors as well as human activities in the Karst plateau, enriched the application scope of the QAM, and provided new ideas for theoretical research in this field.

Net ecosystem productivity (NEP) serves as a pivotal metric for quantitatively elucidating the carbon sink function of terrestrial ecosystems. As a prototype county for the development of an ecological civilization in China, the quantitative estimation of the ecotypic county’s ecosystem carbon sink capacity holds immense significance in comprehending the carbon cycle and facilitating the sustainable advancement of regional ecosystems. This study undertook the estimation of NEP in Wuning County from 2000 to 2020, employing a fusion of multi-source remote sensing data, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the improved Carnegie-Ames-Stanford Approach model, and the soil respiration model. Furthermore, we delved into the differences in NEP across various types of land cover. In addition, we employed the Theil-Sen Median trend analysis and Mann-Kendall test to discern the spatio-temporal trends of NEP. The findings indicated the following: (1) The downscaled NDVI generated by STARFM exhibited a remarkable consistency with Landsat NDVI overall (R2 > 0.95, P grassland > cropland. The application of STARFM has provided valuable insights into the methodology for precise delineation of spatio-temporal dynamics of NEP at the county scale. The outcomes of this study have furnished support for implementing climate change mitigation strategies in ecologically-oriented counties and the bottom-up promotion of China's carbon peaking and carbon neutrality goals.

Investigating large-scale spatial patterns of ecosystem services (ESs) and their underlying drivers can greatly contribute to policies-making and regional sustainability development. With water yield (WY), soil conservation (SC), and carbon sequestration (CS) as representative ESs, we aim to quantify their spatial patterns in the Yangtze River Economic Belt, China, and to identify their driving factors, and to formulate sound environmental management strategies. Spatial geography and socioeconomic data from 2000 to 2020 were mined and a range of research methods, including multiscale geographic weighted regression, self-organizing maps, and linear discriminant analysis, were employed for such a purpose. Annual average WY, SC, and CS were 403 mm, 9897 t·km−1, and 1071 g·CO2·m−2. The three ESs examined exhibit spatial heterogeneity. WY exhibited significant patterns of variation along the north–south gradient, while SC and CS exhibited significant variation along the topographic gradient. In the context of high correlation of driving factors among ESs, WY and SC exhibited a greater sensitivity to natural factors (such as precipitation), while CS demonstrated a height sensitivity to human activities in addition to vegetation cover. Spatial heterogeneity is pronounced among the main driving factors of ESs. Three threshold equations were established to describe the manner in which driving factors of different regional ecosystem services undergo transformations, equations possessed a high level of credibility in this study (coincidence > 80 %). This study reveals spatial variations in ecosystem services and their natural and socio-economic drivers. More specifically, we quantitatively validated the threshold in the expression of ecosystem service drivers, establishing a strong scientific foundation for regional ecosystem conservation and management.

Abstract Catalysis has been regarded as an effective strategy to mitigate sluggish reaction kinetics and serious shuttle effect of Li–S batteries. Herein, a spherical structure consists of ultrathin layered Ti3C2T x -TiN heterostructures (MX-TiN) through in-situ nitridation method is reported. Through controllable nitridation, highly conductive TiN layer grew on the surface and close coupled with interior MXene to form unique 2D heterostructures. The ultrathin heterostructure with only several nanometers in thickness enables outstanding ability to shorten electrons diffusion distance during electrochemical reactions and enlarge active surface with abundant adsorptive and catalytic sites. Moreover, the (001) surface of TiN is dominated by metallic Ti–3d states, which ensures fast transmitting electrons from high conductive MX-TiN matrix and thus guarantees efficient catalytic performance. Calculations and experiments demonstrate that polysulfides are strongly immobilized on MX-TiN, meanwhile the bidirectional reaction kinetics are catalytically enhanced by reducing the conversion barrier between liquid LiPSs and solid Li2S2/Li2S. As a result, the S/MX-TiN cathode achieves excellent long-term cyclability with extremely low-capacity fading rate of 0.022% over 1000 cycles and remarkable areal capacity of 8.27 mAh cm−2 at high sulfur loading and lean electrolytes. Graphical abstract

This work aims to address the image classification problem under open-set protocol: classes in test set do not appear in the training set. Intuitively, convolutional Neural Network (CNN) with softmax loss is a straight-forward solution. However, the unknown class (is not predefined in the training set) makes the boundaries of intra-class and inter-class more blurred, which brings more challenges for image classification. Although some softmax variants, such as center loss, CosFace loss etc., focus on learning discriminative features by minimizing the intra-class distance, they do not explicitly maximize inter-class distance, which is more important for open-set problem verified by our experiments. Besides, even though deep metric learning, such as with the contrastive loss and the triplet loss, can learn discriminative features of intra-class and inter-class, it needs a time-consuming image sampling process during training. In this paper, we propose a novel normalized maximal margin (NMM) loss for open-set image classification, which not only explicitly minimizes intra-class distance and maximizes inter-class distance, but also defines their margins. Specially, after analyzing the advantage of angular space that the softmax loss normalized by the feature and weights through geometric interpretation, we make NMM work in angular space. Then, the validity of NMM for discriminative features learning is demonstrated from the view of geometric interpretation as well. After that, we innovatively determine the upper bound of inter-class margin by theoretical analysis. Finally, extensive experiments are conducted on popular datasets: CIFAR-100 (object recognition), ImageNet (image classification), LFW (face recognition) and MSMT17 (person re-identification) to verify the effectiveness of NMM. The experimental results show that NMM achieves very competitive performance.

Periodically data plane update is a necessary task in Software Defined Networking(SDN). Most prior approaches update many flows without considering which flows should be updated. Hence, most of them get long update delay. However, due to the dramatic increase of data arriving in data centers in the coming 5G networks, flows to be updated should be carefully chosen. Otherwise, unsatisfactory update delay may occur, which will make the route after the reconfiguration invalid for most flows and even bring about a poor user experience. In this paper, we present a Timely Route Update Scheme(TRUS). Different from previously proposed update approaches, TRUS not only meets the real-time route update demand for data centers, but also guarantees bandwidth requirements of delay-sensitive flows. Further, it can avoid the congestion on “hot” links in the update process. Simulation results verify the effectiveness of our scheme and demonstrate it can largely cut down the route update latency while obtaining a good network performance when comparing with some former approaches.

Intrusion detection can identify unknown attacks from network traffics and has been an effective means of network security. Nowadays, existing methods for network anomaly detection are usually based on traditional machine learning models, such as KNN, SVM, etc. Although these methods can obtain some outstanding features, they get a relatively low accuracy and rely heavily on manual design of traffic features, which has been obsolete in the age of big data. To solve the problems of low accuracy and feature engineering in intrusion detection, a traffic anomaly detection model BAT is proposed. The BAT model combines BLSTM (Bidirectional Long Short-term memory) and attention mechanism. Attention mechanism is used to screen the network flow vector composed of packet vectors generated by the BLSTM model, which can obtain the key features for network traffic classification. In addition, we adopt multiple convolutional layers to capture the local features of traffic data. As multiple convolutional layers are used to process data samples, we refer BAT model as BAT-MC. The softmax classifier is used for network traffic classification. The proposed end-to-end model does not use any feature engineering skills and can automatically learn the key features of the hierarchy. It can well describe the network traffic behavior and improve the ability of anomaly detection effectively. We test our model on a public benchmark dataset, and the experimental results demonstrate our model has better performance than other comparison methods.

Rogue nodes in the Internet of vehicles (IoV) bring traffic congestion, vehicle collision accidents and other problems, which will cause great social losses. Therefore, rogue node discovery plays an important role in building secure IoV environments. Existing machine learning-based rogue node detection methods rely too much on historical data, and these methods may lead to long network delay and slow detection speed. Moreover, methods based on Roadside Units (RSUs) have poor performance if the number of RSUs is insufficient. Based on the widespread presence of ground vehicles, we propose a rogue node detection scheme based on the fog network formed by roadside parked vehicles. To achieve efficient rogue node discovery, a fog network composed of stable roadside parked vehicles is dynamically established, in which each fog node firstly collects the information of moving vehicles on the road in its coverage range, and then fog nodes use the U-test method to determine the rogue nodes in parallel, so as to find the bad nodes efficiently. Simulation results show that the proposed algorithm has higher detection accuracy and stability than the other rogue node detection schemes.

Natural disasters bring huge loss of life and property to human beings. Unmanned aerial vehicles (UAVs) own the advantages of high mobility, high flexibility, and rapid deployment, and are important equipment during post-disaster rescue. However, UAVs usually have restricted battery and computing power. They are not fit for performing compute-intensive tasks during rescue. Since there are widespread parking resources in a city, multiple parked vehicles working together to compute the applications from UAVs in a post-disaster rescue is investigated to ensure the quality of experience (QoE) of the UAVs. To execute uploaded task effectively, surviving parked vehicles within the monitoring range of an UAV are arranged into a cluster as much as possible. Then, the task execution cost is analyzed. Furthermore, a deep reinforcement learning (DRL)-based offloading policy is constructed, which interacts with the environment in an intelligent way to achieve optimization goals. The simulation experiments show that the proposed offloading scheme has a higher task completion rate and a lower task execution cost than other baselines schemes.

Actuators and sensors are two types of important nodes in wireless sensor and actuator networks (WSANs). Actuators consume a large amount of energy for driving themselves move, handling events, communicating with each other, and computation. Hence, the sensors and actuators all suffer from a serious energy-constrained problem in WSANs. Recently, many excellent energy recharging works are proposed for wireless sensor networks (WSNs). However, the wireless energy delivery issue for WSANs is rarely addressed. In this paper, by deeply analyzing the characteristics of WSANs, we discuss the wireless mobile recharging issue for WSANs and present a novel actuator failure avoidance online charging scheme (AFAC). To prevent as many sensors failure as possible while ensuring no actuator failure, AFAC always tries to select the node that not only makes no actuator failure but also brings about the least number of nonfunctional sensors as the next target. Furthermore, since charging the actuators takes a long time, maximum charging threshold is set for actuators to prevent sensor nodes failure in the network. The simulation results demonstrate that our scheme gets the lowest sensor failure ratio with low charging cost and charging delay in comparison with other wireless charging schemes for WSANs.

Abstract SnS, is a promising anode material for lithium ion batteries (LIBs) and sodium ion batteries (SIBs), however, undergoes poor cyclic lifespan due to its huge volume changes and bad electroconductivity. Here, a modified CVD method is used to directly grow graphene‐like carbon film on the surface of SnS nanosheet arrays which are supported by Co‐, N‐modified porous carbon fibers (CCF@SnS@G). In the strategy, the SnS nanosheet arrays confined into the integrated carbon matrix containing porous carbon fibers and graphene‐like carbon film, perform a greatly improved electrochemical performance. In situ TEM experiments reveal that the vertical graphene‐like carbon film can not only protect the SnS nanosheet from destruction well and enhance the conductivity, but also transforms SnS nanosheet into ultrafine nanoparticles to promote the electrochemical kinetics. Systematic electrochemical investigations exhibit that the CCF@SnS@G electrode delivers a stable reversible capacity of 529 mAh g−1 at a high current density of 5 A g−1 for LIBs and 541.4 mAh g−1 at 2 A g−1 for SIBs, suggesting its good potential for anode electrodes.

An underdetermined direction of arrival (DOA) estimation method of wideband linear frequency modulated (LFM) signals is proposed without grid mismatch. According to the concentration property of LFM signal in the fractional Fourier (FRF) domain, the received sparse model of wideband signals with time-variant steering vector is firstly derived based on a coprime array. Afterwards, by interpolating virtual sensors, a virtual extended uniform linear array (ULA) is constructed with more degrees of freedom, and its covariance matrix in the FRF domain is recovered by employing sparse matrix reconstruction. Meanwhile, in order to avoid the grid mismatch problem, the modified atomic norm minimization is used to retrieve the covariance matrix with the consecutive basis. Different from the existing methods that approximately assume the frequency and the steering vector of the wideband signals are time-invariant in every narrowband frequency bin, the proposed method not only can directly solve more DOAs of LFM signals than the number of physical sensors with time-variant frequency and steering vector, but also obtain higher resolution and more accurate DOA estimation performance by the gridless sparse reconstruction. Simulation results demonstrate the effectiveness of the proposed method.

The basic operation of a Delay Tolerant Sensor Network (DTSN) is to finish pervasive data gathering in networks with intermittent connectivity, while the publish/subscribe (Pub/Sub for short) paradigm is used to deliver events from a source to interested clients in an asynchronous way. Recently, extension of Pub/Sub systems in DTSNs has become a promising research topic. However, due to the unique frequent partitioning characteristic of DTSNs, extension of a Pub/Sub system in a DTSN is a considerably difficult and challenging problem, and there are no good solutions to this problem in published works. To ad apt Pub/Sub systems to DTSNs, we propose CED, a community-based event delivery protocol. In our design, event delivery is based on several unchanged communities, which are formed by sensor nodes in the network according to their connectivity. CED consists of two components: event delivery and queue management. In event delivery, events in a community are delivered to mobile subscribers once a subscriber comes into the community, for improving the data delivery ratio. The queue management employs both the event successful delivery time and the event survival time to decide whether an event should be delivered or dropped for minimizing the transmission overhead. The effectiveness of CED is demonstrated through comprehensive simulation studies.

In vehicular ad hoc networks (VANETs), message dissemination to one or more vehicles is very challenging, due to frequent network disconnections and uncertain locations of the destination vehicles. Deploying roadside units (RSUs) is a possible solution to overcome the challenges, but it often requires a large amount of investment. In this paper, we propose the idea of Parking Backbone, which does not need any RSUs but leverages a virtual overlay network formed by outside parked vehicles to track vehicles and to disseminate messages between moving vehicles. Our scheme consists of three parts. At first, to each road, parked vehicles both at roadside and off street are grouped into a cluster as far as possible. An urban overlay network is established based on this type of clusters for data transmission. Secondly, to a specific vehicle, a daily mobility model is established, to determine its location through a corresponding location prediction algorithm. Finally, a novel message delivery scheme is designed to efficiently transmit messages to destination vehicles through the proposed virtual overlay network. Thanks to the extensive and stable outside parking in cities, once grouped into the overlay structure, data transmission can be easily achieved over the Parking Backbone. Extensive simulation results prove that our scheme achieves high performance in message dissemination.

Information interaction is a crucial part of modern transportation activities. In this paper, we propose the idea of PASS: a parking-lot-assisted carpool method over vehicular ad hoc networks (VANETs). PASS aims at optimizing transport utilization by the carpooling among car drivers who cover a part of the same traveling route. With wireless device enabled in the vehicle, a user can easily get matched vehicles information and then express his travel demands via radio queries over VANETs to the corresponding driver. The driver can decide whether to provide carpooling services or not. We investigate the main challenges in PASS design, via the parking lot to collect vehicle trajectories, via accelerator sensor to sense vehicle's movement, establish a routing tree to delivery vehicle trajectory information to nearby parking lots, and design a suitable matching scheme to match the target vehicle in VANETs. Finally, simulation results prove that PASS is effective and efficient in carpooling among vehicle drivers.

Data dissemination is the fundamental operation in vehicular ad hoc networks (VANETs); for example, after an accident or congestion is detected by the corresponding sensors mounted on the vehicles, an alert message should be swiftly disseminated to the vehicles moving towards the affected areas. However, the unique characteristics of VANETs, such as high mobility of vehicle nodes, intermittent connectivity, and rapidly dynamic topology, make data dissemination over them extremely challenging. Motivated by the fact that there are large amounts of roadside parked vehicles in urban areas, this paper proposes a parking-based data dissemination scheme for VANETs. Data to be disseminated are buffered at the roadside parked vehicle, which continuously provides data dissemination services for the vehicles passing by. We analyze the challenging issues in achieving parking-based data dissemination and provide possible solution for each issue. Theoretical results illustrate the effectiveness of our approach, and simulation results based on a real city map and realistic traffic situations show that the proposed data dissemination paradigm achieves a higher delivery ratio with lower network load and reasonable delivery delay.

A reasonable evaluation index system is very important for the scientific assessment of the water environment carrying capacity (WECC). The framework system of driving force-pressure-state response-economic benefit (DPSRE) and one-vote veto mechanism were proposed, which was used to construct an evaluation index system for WECC. The analytic hierarchy process (AHP) was employed to analyze the WECC of Changxing County in Taihu Lake Basin, China, during the years from 2010 to 2018 and different water periods (wet, flat, and dry) in 2018. The results showed that the WECC of Changxing County fluctuatingly increased from 2010 to 2018 and the evaluation grade varied from critical overload to loadable, with the maximum value of 0.71 (loadable) in 2016 and the minimum value of 0.50 (overload) in 2015 and 2018. In addition, the WECC was loadable during the wet (0.75) and flat (0.74) water period, while overload in the dry water period (0.50) in 2018. Through analysis the influence weights of indicators, development and utilization ratio of water resources, domestic sewage treatment compliance rate, output tax of per unit emission right, vegetation coverage rate of riverbank, aquatic plants coverage rate of river were the major indicators affecting the WECC. By analysis the evaluation results, the improvement of WECC was attributed to the increase of economic benefit subsystem. The water quality compliance rate of regional exit section (S0 ) did not reach 100% was the real reason for the overload status of WECC in 2015, 2018 and the dry water period (2018). Ecological degradation of river and point and non-point source pollution were the most constraints to the WECC in Changxing County. [ABSTRACT FROM AUTHOR]