Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience,this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects.A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020.The spatial-temporal evolution characteristics were analyzed using Kernel density estimation,standard deviation ellipse,and spatial Markov chain analysis,and the spatial Tobit model was introduced to discover the influencing factors.The results indicate the following:①Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend,with the center of gravity moving to the southwest,and the polarization phenomenon intensifying.②The urban resilience level in a region has certain spatial and geographical dependence,while the probability of urban resilience transfer differs in adjacent cities with different resilience levels.③Urban centrality,economic scale,openness level,and financial development promote urban resilience,whereas government scale significantly inhibits it.Finally,this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

Spatial-temporal heterogeneity of ecological quality changes across the Qinghai-Tibet Plateau under the influence of climate factors and human activities

Over the last few decades,the ecological quality of the Qinghai–Tibet Plateau(QTP)has significantly changed due to climate warming,humidification,and increasing human activities.Thus,evaluating this region's ecological quality and dominant factors is crucial for sustainable development.In this study,the changes in the ecological quality on the QTP from 2000 to 2020 were evaluated based on aggregated indices and Sen–MK trend analyses,and the dominant factors affecting the ecological quality of the QTP were quantitatively analyzed using decision tree classification.The results revealed that(1)the ecological quality of the QTP exhibited an overall high trend in the east and a low pattern in the west;(2)the ecological quality of the QTP significantly increased from 2000 to 2020,and human activities were the dominant factors causing this change;and(3)the changes in the ecological quality and dominant factors exhibited obvious spatiotemporal heterogeneity.The area with an improved ecological quality occurred mainly in the northern QTP region.It was governed by human activities and precipitation.In contrast,the area with a deteriorated ecological quality occurred largely in the southern QTP region and was dominated by human activities and temperature.The 2000–2010 period was the most significant period of heterogeneity regarding of ecological quality and its driving factors.(4)The change in the ecological quality was mainly affected by the synergistic relationship between human activities and climate change in this region,which encompassed multiple dominant factors.This study provides important information on the spatiotemporal heterogeneity of ecological quality change and its dominant factors on the QTP and offers systematic guidance for the planning and implementation of ecological protection projects.

Spatial-temporal distribution and geochemistry of highly evolved Mesozoic granites in Great Xing’an Range,NE China:Discriminant criteria and geological significance

Highly evolved granite is an important sign of the mature continent crust and closely associated with deposits of rare metals.In this work,the authors undertake systematically zircon U-Pb ages and whole rock elemental data for highly evolved granitic intrusions from the Great Xing’an Range(GXR),NE China,to elucidate their discriminant criteria,spatial-temporal distribution,differentiation and geodynamic mecha-nism.Geochemical data of these highly evolved granites suggest that high w(SiO_(2))(>70%)and differentiation index(DI>88)could be quantified indicators,while strong Eu depletion,high TE_(1,3),lowΣREE and low Zr/Hf,Nb/Ta,K/Rb could only be qualitative indicators.Zircon U-Pb ages suggest that the highly evolved gran-ites in the GXR were mainly formed in Late Mesozoic,which can be divided into two major stages:Late Ju-rassic-early Early Cretaceous(162-136 Ma,peak at 138 Ma),and late Early Cretaceous(136-106 Ma,peak at 126 Ma).The highly evolved granites are mainly distributed in the central-southern GXR,and display a weakly trend of getting younger from northwest to southeast,meanwhile indicating the metallogenic potential of rare metals within the central GXR.The spatial-temporal distribution,combined with regional geological data,indicates the highly evolved Mesozoic granites in the GXR were emplaced in an extensional environ-ment,of which the Late Jurassic-early Early Cretaceous extension was related to the closure of the Mongol-Okhotsk Ocean and roll-back of the Paleo-Pacific Plate,while the late Early Cretaceous extension was mainly related to the roll-back of the Paleo-Pacific Plate.

Spatial-temporal Divergence Characteristics and Driving Factors of Green Economic Efficiency in the Yangtze River Economic Belt of China

The spatial and temporal variation of green economic efficiency and its driving factors are of great significance for the con-struction of high-efficiency and low-consumption green development model and sustainable socio-economic development.The research focused on the Yangtze River Economic Belt(YREB)and employed the miniumum distance to strong efficient frontier DEA(MinDs)model to measure the green economic efficiency of the municipalities in the region between 2008 and 2020.Then,the spatial autocorrel-ation model was used to analyze the evolution characteristics of its spatial pattern.Finally,Geodetector was applied to reveal the drivers and their interactions on green economic efficiency.It is found that:1)the overall green economic efficiency of the YREB from 2008 to 2020 shows a W-shaped fluctuating upward trend,green economic efficiency is greater in the downstream and smallest in the upstream;2)the spatial distribution of green economic efficiency shows clustering characteristics,with multi-core clustering based on‘city clusters-central cities'becoming more obvious over time;the High-High agglomeration type is mainly clustered in Jiangsu and Zheji-ang,while the Low-Low agglomeration type is clustered in the western Sichuan Plateau area and southwestern Yunnan;3)from input-output factors,whether it is the YREB as a whole or the upper,middle and lower reaches regions,the economic development level,labor input,and capital investment are the leading factors in the spatial-temporal evolution of green economic efficiency,with the com-prehensive influence of economic development level and pollution index being the most important interactive driving factor;4)from so-cio-economic factors,information technology drivers such as government intervention,transportation accessibility,information infra-structure,and Internet penetration are always high impact influencers and dominant interaction factors for green economic efficiency in the YREB and the three major regions in the upper,middle and lower reaches.Accordingly,the article puts forward relevant policy re-commendations in terms of formulating differentiated green transformation strategies,strengthening network leadership and informa-tion technology construction and coordinating multi-factor integrated development,which could provide useful reference for promoting synergistic green economic efficiency in the YREB.

Adaptive spatial-temporal graph attention network for traffic speed prediction

Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.

Spatial-temporal Variation Characteristics of Water Quality in the Lower Reaches of the Nenjiang River

As an important river in the western part of Jilin Province,the lower reach of the Nenjiang River is an important wetland water source conservation area in Jilin Province.Within the watershed,it governs the Momoge Wetland,the Xianghai Wetland,and the Danjiang Wetland in Jilin Province.The main problem in the lower reaches of the Nenjiang River is the uneven distribution of water resources in time and space,and the intensification of land salinization.Zhenlai County and Da an City in the Nenjiang River Basin have sufficient surface water resources,with surface water as the drinking water source.Baicheng City and Tongyu County have scarce surface water resources,and both use groundwater as their domestic water source.The main polluted section in the basin is the Xianghai Reservoir,and the annual water quality evaluation is Class V.However,the water quality of the Tao er River,the main stream of the Nenjiang River,is significantly better than that of the Xianghai Reservoir.In order to better study the water environmental pollution situation in the Nenjiang River basin,monitoring data from five sections of non seasonal rivers in the basin from 2012 to 2021 were selected for studying water quality.This in-depth exploration of the water pollution status and river water quality change trends in the Nenjiang River basin is of great significance for future rural development,agricultural pattern transformation,and the promotion of water ecological civilization construction.

Spatial-temporal difference between nitrate in groundwater and nitrogen in soil based on geostatistical analysis

The study of temporal and spatial variations of nitrate in groundwater under different soil nitrogen environments is helpful to the security of groundwater resources in agricultural areas.In this paper,based on 320 groups of soil and groundwater samples collected at the same time,geostatistical analysis and multiple regression analysis were comprehensively used to conduct the evaluation of nitrogen contents in both groundwater and soil.From May to August,as the nitrification of groundwater is dominant,the average concentration of nitrate nitrogen is 34.80 mg/L;The variation of soil ammonia nitrogen and nitrate nitrogen is moderate from May to July,and the variation coefficient decreased sharply and then increased in August.There is a high correlation between the nitrate nitrogen in groundwater and soil in July,and there is a high correlation between the nitrate nitrogen in groundwater and ammonium nitrogen in soil in August and nitrate nitrogen in soil in July.From May to August,the area of low groundwater nitrate nitrogen in 0-5 mg/L and 5-10 mg/L decreased from 10.97%to 0,and the proportion of high-value area(greater than 70 mg/L)increased from 21.19%to 27.29%.Nitrate nitrogen is the main factor affecting the quality of groundwater.The correlation analysis of nitrate nitrogen in groundwater,nitrate nitrogen in soil and ammonium nitrogen shows that they have a certain period of delay.The areas with high concentration of nitrate in groundwater are mainly concentrated in the western part of the study area,which has a high consistency with the high value areas of soil nitrate distribution from July to August,and a high difference with the spatial position of soil ammonia nitrogen distribution in August.

STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.

Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China

Forest soil carbon is a major carbon pool of terrestrial ecosystems,and accurate estimation of soil organic carbon(SOC)stocks in forest ecosystems is rather challenging.This study compared the prediction performance of three empirical model approaches namely,regression kriging(RK),multiple stepwise regression(MSR),random forest(RF),and boosted regression trees(BRT)to predict SOC stocks in Northeast China for 1990 and 2015.Furthermore,the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified.A total of 82(in 1990)and 157(in 2015)topsoil(0–20 cm)samples with 12 environmental factors(soil property,climate,topography and biology)were selected for model construction.Randomly selected80%of the soil sample data were used to train the models and the other 20%data for model verification using mean absolute error,root mean square error,coefficient of determination and Lin's consistency correlation coefficient indices.We found BRT model as the best prediction model and it could explain 67%and 60%spatial variation of SOC stocks,in 1990,and 2015,respectively.Predicted maps of all models in both periods showed similar spatial distribution characteristics,with the lower SOC in northeast and higher SOC in southwest.Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods.SOC stocks were mainly stored under Cambosols,Gleyosols and Isohumosols,accounting for 95.6%(1990)and 95.9%(2015).Overall,SOC stocks increased by 471 Tg C during the past 25 years.Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories.The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks.Overall,our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.

Role of heterogenous microstructure and deformation behavior in achieving superior strength-ductility synergy in zinc fabricated via laser powder bed fusion

Zinc(Zn)is considered a promising biodegradable metal for implant applications due to its appropriate degradability and favorable osteogenesis properties.In this work,laser powder bed fusion(LPBF)additive manufacturing was employed to fabricate pure Zn with a heterogeneous microstructure and exceptional strength-ductility synergy.An optimized processing window of LPBF was established for printing Zn samples with relative densities greater than 99%using a laser power range of 80∼90 W and a scanning speed of 900 mm s−1.The Zn sample printed with a power of 80 W at a speed of 900 mm s−1 exhibited a hierarchical heterogeneous microstructure consisting of millimeter-scale molten pool boundaries,micrometer-scale bimodal grains,and nanometer-scale pre-existing dislocations,due to rapid cooling rates and significant thermal gradients formed in the molten pools.The printed sample exhibited the highest ductility of∼12.1%among all reported LPBF-printed pure Zn to date with appreciable ultimate tensile strength(∼128.7 MPa).Such superior strength-ductility synergy can be attributed to the presence of multiple deformation mechanisms that are primarily governed by heterogeneous deformation-induced hardening resulting from the alternative arrangement of bimodal Zn grains with pre-existing dislocations.Additionally,continuous strain hardening was facilitated through the interactions between deformation twins,grains and dislocations as strain accumulated,further contributing to the superior strength-ductility synergy.These findings provide valuable insights into the deformation behavior and mechanisms underlying exceptional mechanical properties of LPBF-printed Zn and its alloys for implant applications.

Dense Spatial-Temporal Graph Convolutional Network Based on Lightweight OpenPose for Detecting Falls

Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy.To solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose.Lightweight OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the network.The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in parallel.The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection accuracy.Two representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation benchmarks.The results show that the proposed model is superior to the current fall detection models.The accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.

Synergy of inside doped metals–Outside coated graphene to enhance hydrogen storage in magnesium-based alloys

Grain growth of magnesium(Mg)and its hydride is one of the main reasons for kinetic and capacity degradation during the hydrogen absorption and desorption cycles.To solve this problem,herein we propose a novel method involving synergistic effect of inside embedded metals and outside coated graphene to limit the growth of Mg and its hydride grains.The graphene coated Mg-Y-Al alloys were selected as a model system for demonstrating this positive effect where the Mg_(91)Y_(3)Al_(6)alloy was first prepared by rapidly solidified method and then high-pressure milled with 5 wt%graphene upon 5 MPa hydrogen gas for obtaining in-situ formed YAl_(2)and YH_(3)embedded in the MgH_(2)matrix with graphene shell(denoted as MgH_(2)-Y-Al@GR).In comparison to pure MgH_(2),the obtained MgH_(2)-Y-Al@GR composites deliver much better kinetics and more stable cyclic performance.For instance,the MgH_(2)-Y-Al@GR can release about 6.1 wt%H_(2)within 30 min at 300℃ but pure MgH_(2)only desorbs∼1.5 wt%H_(2).The activation energy for desorption of MgH_(2)-Y-Al@GR samples is calculated to be 75.3±9.1 kJ/mol that is much lower than approximately 160 kJ/mol for pure MgH_(2).Moreover,its capacity retention is promoted from∼57%of pure MgH_(2)to∼84%after 50th cycles without obvious particle agglomeration and grain growth.The synergistic effect of outside graphene coating with inside embedded metals which could provide a huge number of active sites for catalysis as well as inhibit the grain growth of Mg and its hydride is believed to be responsible for these.

Tuning synergy between nickel and iron in Ruddlesden-Popper perovskites through controllable crystal dimensionalities towards enhanced oxygenevolving activity and stability

Ni-Fe-based oxides are among the most promising catalysts developed to date for the bottleneck oxygen evolution reaction(OER)in water electrolysis.However,understanding and mastering the synergy of Ni and Fe remain challenging.Herein,we report that the synergy between Ni and Fe can be tailored by crystal dimensionality of Ni,Fe-contained Ruddlesden-Popper(RP)-type perovskites(La_(0.125)Sr_(0.875))n+1(Ni_(0.25)Fe_(0.75))nO3n+1(n=1,2,3),where the material with n=3 shows the best OER performance in alkaline media.Soft X-ray absorption spectroscopy spectra before and after OER reveal that the material with n=3 shows enhanced Ni/Fe-O covalency to boost the electron transfer as compared to those with n=1 and n=2.Further experimental investigations demonstrate that the Fe ion is the active site and the Ni ion is the stable site in this system,where such unique synergy reaches the optimum at n=3.Besides,as n increases,the proportion of unstable rock-salt layers accordingly decreases and the leaching of ions(especially Sr^(2+))into the electrolyte is suppressed,which induces a decrease in the leaching of active Fe ions,ultimately leading to enhanced stability.This work provides a new avenue for rational catalyst design through the dimensional strategy.

Tailoring the microenvironment of Ti sites in Ti-containing materials for synergizing with Au sites to boost propylene epoxidation

Au sites supported on Ti-containing materials(Au/Ti-containing catalyst)are currently considered as a promising catalyst for the propylene epoxidation owing to the synergistic effect that hydrogen peroxide species formed on Au sites diffuses to the Ti sites to form the Ti-hydroperoxo intermedi-ates and contributes to the formation of propylene oxide(PO).In principle,thermal treatment will significantly affect the chemical and physical structures of Ti-containing materials.Consequently,the synergy between tailored Ti sites with different surface properties and Au sites is highly expected to enhance the catalytic performance for the reaction.Herein,we systematically studied the intrinsic effects of different microenvironments around Ti sites on the PO adsorption/desorption and conversion,and then effectively improved the catalytic performance by tailoring the number of surface hydroxyl groups.The Ti^(Ⅵ) material with fewer hydroxyls stimulates a remarkable enhancement in PO selectivity and H_(2) efficiency compared to the Ti^(Ⅵ) material that possessed more hydroxyls,offering a 7-fold and 4-fold increase,respectively.As expected,the Ti^(Ⅵ+Ⅳ) and Ti^(Ⅳ) materials also exhibit a similar phenomenon to the Ti^(Ⅵ) materials through the same thermal treatment,which strongly supports that the Ti sites microenvironment is an important factor in suppressing PO con-version and enhancing catalytic performance.These insights could provide guidance for the rational preparation and optimization of Ti-containing materials synergizing with Au catalysts for propylene epoxidation.

Ultrasound-activated in situ click chemistry to trigger autophagosome tracking for enhanced autophagy blockade and synergistic cancer therapy

The blockade of cytoprotective autophagy has been demonstrated to effectively enhance the efficacy of sonodynamic therapy(SDT).However,the limited recogni-tion of antiautophagy agents for autophagosomes impedes the clinical application of autophagy inhibition.To efficiently deliver hydroxychloroquine(HCQ),an autophagy inhibitor,to autophagosomes,we utilized a strategy based on in situ click chemistry between sulfhydryl(-SH)and maleimide(Mal)groups to trigger autophagosomes tracking and suppress tumor growth synergistically.A cascade nanoreactor was synthesized by encapsulating Mal-modified HCQ(MHCQ)into a manganese porphyrin-based metal-organic framework with sonosensitizer proper-ties,followed by poly(ethylene glycol)ylated liposomal membrane coating.After ultrasound irradiation,SDT-induced apoptotic cells released damaged proteins with free-SH groups,which MHCQ rapidly captured in situ via a Mal-thiol click reaction.When autophagosomes actively wrapped damaged proteins for detoxifi-cation,they simultaneously internalized HCQ anchored on proteins.In this scenario,antiautophagy drugs could actively track intracellular autophagosomes instead of undergoing passive diffusion in the cytosol.The interaction between HCQ and autophagic vesicles was greatly enhanced,which strengthened the blocking effi-ciency of autophagy and resulted in complete cell death.Overall,this study with smart design provides a promising strategy for improving intracellular targeted delivery to autophagosomes,thereby enhancing antitumor therapy.

Trade-offs and synergies between ecosystem services in Yutian County along the Keriya River Basin,Northwest China

The Keriya River Basin is located in an extremely arid climate zone on the southern edge of the Tarim Basin of Northwest China,exhibiting typical mountain-oasis-desert distribution characteristics.In recent decades,climate change and human activities have exerted significant impacts on the service functions of watershed ecosystems.However,the trade-offs and synergies between ecosystem services(ESs)have not been thoroughly examined.This study aims to reveal the spatiotemporal changes in ESs within the Keriya River Basin from 1995 to 2020 as well as the trade-offs and synergies between ESs.Leveraging the Integrated Valuation of Ecosystem Services and Trade-offs(InVEST)and Revised Wind Erosion Equation(RWEQ)using land use/land cover(LULC),climate,vegetation,soil,and hydrological data,we quantified the spatiotemporal changes in the five principal ESs(carbon storage,water yield,food production,wind and sand prevention,and habitat quality)of the watershed from 1995 to 2020.Spearman correlation coefficients were used to analyze the trade-offs and synergies between ES pairs.The findings reveal that water yield,carbon storage,and habitat quality exhibited relatively high levels in the upstream,while food production and wind and sand prevention dominated the midstream and downstream,respectively.Furthermore,carbon storage,food production,wind and sand prevention,and habitat quality demonstrated an increase at the watershed scale while water yield exhibited a decline from 1995 to 2020.Specifically,carbon storage,wind and sand prevention,and habitat quality presented an upward trend in the upstream but downward trend in the midstream and downstream.Food production in the midstream showed a continuously increasing trend during the study period.Trade-off relationships were identified between water yield and wind and sand prevention,water yield and carbon storage,food production and water yield,and habitat quality and wind and sand prevention.Prominent temporal and spatial synergistic relationships were observed between different ESs,notably between carbon storage and habitat quality,carbon storage and food production,food production and wind and sand prevention,and food production and habitat quality.Water resources emerged as a decisive factor for the sustainable development of the basin,thus highlighting the intricate trade-offs and synergies between water yield and the other four services,particularly the relationship with food production,which warrants further attention.This research is of great significance for the protection and sustainable development of river basins in arid areas.

Trade-Offs and Synergie Effects of Regulating Ecosystem Services along an Climatic Gradient in Slovakia

This study aims to assess the synergies and trade-offs of regulating ecosystem services. Ecosystem services are non-linearly interconnected and changes in one service can positively or negatively affect another. We evaluated ecosystem services based on biophysical indicators using an expert scoring system that determines the corresponding soil functions and is part of the existing databases available in Slovakia. This methodological combination enabled us to provide unique mapping and assessment of ecosystem services within Slovakia. Correlation analysis between individual regulating ecosystem services and climate regions, slope, texture, and altitude confirm the statistically significant influence of climate and slope in all agricultural land, arable land, and grassland ecosystems. Statistically significant synergistic effects were established between the regulation of the water regime and the regulation of soil erosion within each climate region, apart from the very warm climate region. Only in a very warm climate region was potential of regulating ecosystem services mutually beneficial for soil erosion control and soil cleaning potential (immobilization of inorganic pollutants).

医科达Synergy医用电子直线加速器故障维修六例

放射治疗是利用放射线治疗肿瘤的医疗方式[1]。医用电子直线加速器作为医院开展放射治疗的重要医疗设备,在肿瘤治疗中发挥重要作用。同时,医用电子直线加速器的技术结构复杂,组成模块、系统、零部件多,集成度高,设备具有“唯一性”、故障风险大等特点,如出现故障不能及时解决,可影响患者治疗效果,严重时甚至会造成不可逆性的重大损伤[2]。目前,医科达Synergy医用电子直线加速器的市场占有率较高,其通信模块较多、运动结构相对复杂,故障率较高。本研究以我院在用的医科达Synergy医用电子直线加速器为例,对其结构原理及6例故障维修过程进行总结分析,以供同行借鉴和参考。