This essay aims at an analysis of Murphy's failure of his quest for freedom from the aspect of narrative temporality whichwill focus on order, duration and frequency. From the perspective of temporal order, the es...This essay aims at an analysis of Murphy's failure of his quest for freedom from the aspect of narrative temporality whichwill focus on order, duration and frequency. From the perspective of temporal order, the essay will endeavor to probe into the pro-lepsis in the novel to analyse predetermined narrative and its function. Considering with temporal duration, this essay will penetratethe deceleration of narrative—scene and pause in Murphy to explore its thematic meaning. To investigate the temporal frequency,iterative narrative within text will be exemplified to discuss closed systems in the novel. Based on the analysis of the narrativity tem-porality in Murphy, the essay will focuses on that how its narrative strategy well embodies its theme—the predetermined failure ofMurphy's quest for freedom in closed systems.展开更多
Pioneering U-Pb isotopic studies by a small group of workers in the mid-late 1980s demonstrated the feasibility of using rare accessory mineral chronometers in mafic(gabbroic)intrusive rocks.These examples showed that...Pioneering U-Pb isotopic studies by a small group of workers in the mid-late 1980s demonstrated the feasibility of using rare accessory mineral chronometers in mafic(gabbroic)intrusive rocks.These examples showed that mafic layered intrusions and diabase/dolerite dyke swarms alike crystallized high-temperature展开更多
Mandarin在(pinyin:zài)is the most frequently used character in representing spatial and temporal relationship.Current studies mostly focus on its lexical meaning and syntactic structure while cognitive features o...Mandarin在(pinyin:zài)is the most frequently used character in representing spatial and temporal relationship.Current studies mostly focus on its lexical meaning and syntactic structure while cognitive features of its grammatical categories have been neglected.This paper investigates into the categorization of zài by conducting a morphosyntactic test among College English majors in China.The results show that:prototypes are organizing the grammatical categories of zài at all levels in terms of intra-categorial gradience;the semantic construal of zài construction could significantly influence the accuracy of the grammatical categorization of zài;the syntactic structure can provide viable cue for the identification of grammatical categories of zài;spatiality,temporality and the status of existing are three essential semantic features encoded by zài,the concurrence of which leads to various degree of inter-categorial vagueness,indicating a conflict between the rigid grammatical classification and the indeterminate nature of the grammatical functions of zai,suggesting the necessity to reconsider the efficacy of applying indiscriminately the Anglo-Saxon grammar into the study of Chinese spatial-temporal constructions.展开更多
OpenStreetMap(OSM)is a dataset in constant change and this dynamic needs to be better understood.Based on 12-year time series of seven OSM data contribution activities extracted from 20 large cities worldwide,we inves...OpenStreetMap(OSM)is a dataset in constant change and this dynamic needs to be better understood.Based on 12-year time series of seven OSM data contribution activities extracted from 20 large cities worldwide,we investigate the temporal dynamic of OSM data production,more specifically,the auto-and cross-correlation,temporal trend,and annual seasonality of these activities.Furthermore,we evaluate and compare nine different temporal regression methods for forecasting such activities in horizons of 1–4 weeks.Several insights could be obtained from our analyses,including that the contribution activities tend to grown linearly in a moderate intra-annual cycle.Also,the performance of the temporal forecasting methods shows that they yield in general more accurate estimations of future contribution activities than a baseline metric,i.e.the arithmetic average of recent previous observations.In particular,the well-known ARIMA and the exponentially weighted moving average methods have shown the best performances.展开更多
Sepsis remains a leading cause morbidity and mortality worldwide;effective targeted therapies remain elusive due to its inherent heterogeneity and dynamic temporal evolution.Existing frameworks often focus on either t...Sepsis remains a leading cause morbidity and mortality worldwide;effective targeted therapies remain elusive due to its inherent heterogeneity and dynamic temporal evolution.Existing frameworks often focus on either the diverse manifestations of sepsis or its progression over time,but fail to integrate these critical aspects.In this review,we propose a novel spatial-temporal framework that integrates both the heterogeneity and temporality of sepsis.The framework consists of two key dimensions:The cross-sectional(heterogeneity)dimension,which addresses pathogen variability,host factors,and pathogen-host interactions;The longitudinal(temporality)dimension,which explores the dynamic evolution of sepsis and the need for adaptive,real-time interventions.Given the complexity of multidimensional temporal data,big data techniques have the potential to integrate these data and decompose sepsis into distinct disease subtypes.Stratification facilitates the development of personalized therapeutic approaches tailored to specific subtypes.Moreover,methods,such as reinforcement learning,can track the dynamic transitions between these subtypes,enabling real-time adaptation of treatment strategies.展开更多
Question answering is an important problem that aims to deliver specific answers to questions posed by humans in natural language.How to efficiently identify the exact answer with respect to a given question has becom...Question answering is an important problem that aims to deliver specific answers to questions posed by humans in natural language.How to efficiently identify the exact answer with respect to a given question has become an active line of research.Previous approaches in factoid question answering tasks typically focus on modeling the semantic relevance or syntactic relationship between a given question and its corresponding answer.Most of these models suffer when a question contains very little content that is indicative of the answer.In this paper,we devise an architecture named the temporality-enhanced knowledge memory network(TE-KMN) and apply the model to a factoid question answering dataset from a trivia competition called quiz bowl.Unlike most of the existing approaches,our model encodes not only the content of questions and answers,but also the temporal cues in a sequence of ordered sentences which gradually remark the answer.Moreover,our model collaboratively uses external knowledge for a better understanding of a given question.The experimental results demonstrate that our method achieves better performance than several state-of-the-art methods.展开更多
The concept of time is encoded in language in different ways,which is reflected in temporal expressions,tenses,aspects and word order in languages.Some scholars claim that Chinese is a language characterized by tempor...The concept of time is encoded in language in different ways,which is reflected in temporal expressions,tenses,aspects and word order in languages.Some scholars claim that Chinese is a language characterized by temporality.This paper aims to explore what the temporality of Chinese refers to in both its narrow and broad senses,and the classification of temporality under the guidance of the iconicity of sequence as well as the word order principles.Moreover,some scholars hold a seemingly opposite view that Chinese is a language of spatiality—whether the two views are contradictory to each other is also discussed in the current study.The research mainly adopts the methods of literature analysis and retrospection.It is found that the temporality of Chinese mainly refers to the correspondence between the sequence of linguistic units and the temporal order of events or states represented in the conceptual world,and that temporality can be classified into natural temporality,cognitive temporality,and functional temporality.Then,this paper concludes that the viewpoint that Chinese is a language of temporality does not conflict with the opinion that Chinese is characterized by spatiality,as these two traits are concerned with two irrelevant aspects of language.However,it will posit that whether the property of temporality is consistent at every linguistic level in Chinese,even including characters,needs further exploration.展开更多
Brain lesions,such as those caused by stroke or traumatic brain injury(TBI),frequently result in persistent motor and cognitive impairments that significantly affect the individual patient's quality of life.Despit...Brain lesions,such as those caused by stroke or traumatic brain injury(TBI),frequently result in persistent motor and cognitive impairments that significantly affect the individual patient's quality of life.Despite differences in the mechanisms of injury,both conditions share a high prevalence of motor and cognitive impairments.These deficits show only limited natural recovery.展开更多
Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and dr...Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area.Based on the meteorological observations from 2000 to 2020,we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries(Kazakhstan,Uzbekistan,Kyrgyzstan,Turkmenistan,and Tajikistan)via Theil-Sen trend analysis and Geodetector modeling method,quantitatively revealing the influence of environmental factors,such as temperature,precipitation,and vegetation,on the frequency of dust weather.The results showed that:(1)dust weather in Central Asia was mainly distributed in a large''dust belt''extending from west to east from northern part of the Caspian lowland desert,and concentrated in basins,plains,and other low-altitude areas.Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia,with a maximum annual frequency of 21.9%;(2)strong dust weather in Central Asia has fluctuated and slightly decreased since 2001.The highest frequency(1.1%)occurred in spring(from March to June);(3)from 2000 to 2020,changes such as spot shifting and shrinking occurred in the four main source areas(north of the Aral Sea,Kyzylkum Desert,Karakum Desert,and Garabogazköl Bay region),where sandstorms occurred in Central Asia,and northern Caspian lowland desert became the most important low-emission dust source in Central Asia;and(4)the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia.This study provides a theoretical basis for sand prevention and sand control in Central Asia.In the future,Central Asia should focus on the rational utilization of land and water resources,and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area.展开更多
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
Brown spot(BS)of rice,caused by Bipolaris oryzae,is a serious concern that not only causes quantitative losses but also affects grain quality.To manage this disease,the use of resistant genetic sources and QTLs is an ...Brown spot(BS)of rice,caused by Bipolaris oryzae,is a serious concern that not only causes quantitative losses but also affects grain quality.To manage this disease,the use of resistant genetic sources and QTLs is an eco-friendly and economical option.In the current study,F_(3) progenies derived from a cross of susceptible parent PMS-18-B(PAU 10845-1-1-1-1)×resistant parent RP Path 77(RP patho-17)were used to identify potential QTLs linked to BS resistance and to associate this resistance with a temporal spike in defense-related enzymes.展开更多
Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese...Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.展开更多
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.展开更多
Majority of carbon emissions originate from fossil energy consumption,thus necessitating calculation and monitoring of carbon emissions from energy consumption.In this study,we utilized energy consumption data from Si...Majority of carbon emissions originate from fossil energy consumption,thus necessitating calculation and monitoring of carbon emissions from energy consumption.In this study,we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions.We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration.Furthermore,we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation,and we used the Geographically and Temporally Weighted Regression(GTWR)model to analyze the influencing factors of carbon emissions at this scale.The results of this study are as follows:(1)from 2000 to 2019,the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease,with an average annual growth rate of 4.24%.However,in recent years,it has stabilized,and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration;(2)carbon emissions exhibited significant spatial clustering,with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration;(3)factors such as GDP,population(Pop),urbanization rate(Ur),and industrialization structure(Ic)all showed a significant influence on carbon emissions;(4)the spatial heterogeneity of each influencing factor was evident.展开更多
Across four studies,we explore the impact of solitude on consumers’reliance on feelings versus reasons in decision making,along with the underlying mechanism and boundary conditions.The results indicate that solitude...Across four studies,we explore the impact of solitude on consumers’reliance on feelings versus reasons in decision making,along with the underlying mechanism and boundary conditions.The results indicate that solitude individuals(vs.non-solitude)would prefer feeling-based strategy in decision-making,resulting in a higher intention of choosing the affectively superior option over the cognitively superior option(Study 1).Self-focus plays the underlying mechanism in the solitude effect(Study 2).Moreover,we also examine two boundary conditions:motivation(Study 3)and temporal orientation(Study 4),which indicates that involuntary motivation and future orientation can mitigate the solitude effect on affective processing.These findings provide insights into consumers’judgments of product attributes and selection of decision-making strategies according to their situations.展开更多
As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir comput...As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir computing(RC).RC enables the processing of temporal information with minimal training costs,making it a promising approach for neuromorphic computing.However,current memristor devices of-ten suffer from limitations in dynamic conductance and temporal behavior,which affects their perfor-mance in these applications.In this study,we present a multilayered indium-tin-oxide(ITO)/ZnO/indium-gallium-zinc oxide(IGZO)/ZnO/ITO memristor fabricated via radiofrequency sputtering to explore its fil-amentary and nonfilamentary resistive switching(RS)characteristics.High-resolution transmission elec-tron microscopy confirmed the polycrystalline structure of the ZnO/IGZO/ZnO active layer.Dual-switching modes were demonstrated by controlling the current compliance(I_(CC)).In the filamentary mode,the memristor exhibited a large memory window(10^(3)),low-operating voltages(±2 V),excellent cycle-to-cycle stability,and multilevel switching with controlled reset-stop voltages,making it suitable for high-density memory applications.Nonfilamentary switching demonstrated stable on/off ratios above 10,en-durance up to 102 cycles,and retention suited for short-term memory.Key synaptic behaviors,such as paired-pulse facilitation(PPF),post-tetanic potentiation(PTP),and spike-rate dependent plasticity(SRDP)were successfully emulated by modulating pulse amplitude,width,and interval.Experience-dependent plasticity(EDP)was also demonstrated,further replicating biological synaptic functions.These tempo-ral properties were utilized to develop a 4-bit reservoir computing system with 16 distinct conductance states,enabling efficient information encoding.For image recognition tasks,convolutional neural net-work(CNN)simulations achieved a high accuracy of 98.45%after 25 training epochs,outperforming the accuracy achieved following artificial neural network(ANN)simulations(87.79%).These findings demon-strate that the multilayered memristor exhibits high performance in neuromorphic systems,particularly for complex pattern recognition tasks,such as digit and letter classification.展开更多
Tenebrionid beetles represent a crucial arthropod taxon in the Gobi desert ecosystems owing to their species richness and high biomass,both of which are essential for maintaining ecosystem health and stability.However...Tenebrionid beetles represent a crucial arthropod taxon in the Gobi desert ecosystems owing to their species richness and high biomass,both of which are essential for maintaining ecosystem health and stability.However,the spatiotemporal variations of tenebrionid beetle assemblages in the Gobi desert remain poorly understood.In this study,the monthly dynamics of tenebrionid beetles in the central part of the Hexi Corridor,Northwest China,a representative area of the Gobi desert ecosystems,were monitored using pitfall trapping during 2015-2020.The following results were showed:(1)monthly activity of tenebrionid beetles was observed from March to October,with monthly activity peaking in spring and summer,and monthly activity periods and peak of tenebrionid beetle species exhibited interspecific differences that varied from year to year;(2)spatial distribution of tenebrionid beetle community was influenced by structural factors.Specifically,at a spatial scale of 24.00 m,tenebrionid beetle community was strongly and positively correlated with the dominant species,with distinct spatial distribution patterns observed for Blaps gobiensis and Microdera kraatzi alashanica;(3)abundance of tenebrionid beetles was positively correlated with monthly mean precipitation and monthly mean temperature,whereas monthly abundance of B.gobiensis and M.kraatzi alashanica was positively correlated with monthly mean precipitation;and(4)the cover of Reaumuria soongarica(Pall.)Maxim.and Nitraria sphaerocarpa Maxim.had a positive influence on the number of tenebrionid beetles captured.In conclusion,monthly variation in precipitation significantly influences the community dynamic of tenebrionid beetles,with precipitation and shrub cover jointly determining the spatial distribution pattern of these beetles in the Gobi desert ecosystems.展开更多
The ultrafast laser-matter interaction is explored to induce new pioneering principles and technologies into the realms of fundamental science and industrial production.The local thermal melting and connection propert...The ultrafast laser-matter interaction is explored to induce new pioneering principles and technologies into the realms of fundamental science and industrial production.The local thermal melting and connection properties of the ultrafast laser welding technology offer a novel method for welding of diverse transparent materials,thus having wide range of potential applications in aerospace,opto-mechanical systems,sensors,microfluidic,optics,etc.In this comprehensive review,tuning the transient electron activation processes,high-rate laser energy deposition,and dynamic evolution of plasma morphology by the temporal/spatial shaping methods have been demonstrated to facilitate the transition from conventional homogeneous transparent material welding to the more intricate realm of transparent/metal heterogeneous material welding.The welding strength and stability are also improvable through the implementation of real-time,in-situ monitoring techniques and the prompt diagnosis of welding defects.The principles of ultrafast laser welding,bottleneck problems in the welding,novel welding methods,advances in welding performance,in-situ monitoring and diagnosis,and various applications are reviewed.Finally,we offer a forward-looking perspective on the fundamental challenges within the field of ultrafast laser welding and identify key areas for future research,underscoring the imperative need for ongoing innovation and exploration.展开更多
Temporal interference(TI)is a form of stimulation that epitomizes an innovative and non-invasive approach for profound neuromodulation of the brain,a technique that has been validated in mice.Yet,the thin cranial bone...Temporal interference(TI)is a form of stimulation that epitomizes an innovative and non-invasive approach for profound neuromodulation of the brain,a technique that has been validated in mice.Yet,the thin cranial bone structure of mice has a marginal influence on the effect of the TI technique and may not effectively showcase its effectiveness in larger animals.Based on this,we carried out TI stimulation experiments on rats.Following the TI intervention,analysis of electrophysiological data and immunofluorescence staining indicated the generation of a stimulation focus within the nucleus accumbens(depth,8.5 mm)in rats.Our findings affirm the viability of the TI methodology in the presence of thick cranial bones,furnishing efficacious parameters for profound stimulation with TI administered under such conditions.This experiment not only sheds light on the intervention effects of TI deep in the brain but also furnishes robust evidence in support of its prospective clinical utility.展开更多
文摘This essay aims at an analysis of Murphy's failure of his quest for freedom from the aspect of narrative temporality whichwill focus on order, duration and frequency. From the perspective of temporal order, the essay will endeavor to probe into the pro-lepsis in the novel to analyse predetermined narrative and its function. Considering with temporal duration, this essay will penetratethe deceleration of narrative—scene and pause in Murphy to explore its thematic meaning. To investigate the temporal frequency,iterative narrative within text will be exemplified to discuss closed systems in the novel. Based on the analysis of the narrativity tem-porality in Murphy, the essay will focuses on that how its narrative strategy well embodies its theme—the predetermined failure ofMurphy's quest for freedom in closed systems.
文摘Pioneering U-Pb isotopic studies by a small group of workers in the mid-late 1980s demonstrated the feasibility of using rare accessory mineral chronometers in mafic(gabbroic)intrusive rocks.These examples showed that mafic layered intrusions and diabase/dolerite dyke swarms alike crystallized high-temperature
文摘Mandarin在(pinyin:zài)is the most frequently used character in representing spatial and temporal relationship.Current studies mostly focus on its lexical meaning and syntactic structure while cognitive features of its grammatical categories have been neglected.This paper investigates into the categorization of zài by conducting a morphosyntactic test among College English majors in China.The results show that:prototypes are organizing the grammatical categories of zài at all levels in terms of intra-categorial gradience;the semantic construal of zài construction could significantly influence the accuracy of the grammatical categorization of zài;the syntactic structure can provide viable cue for the identification of grammatical categories of zài;spatiality,temporality and the status of existing are three essential semantic features encoded by zài,the concurrence of which leads to various degree of inter-categorial vagueness,indicating a conflict between the rigid grammatical classification and the indeterminate nature of the grammatical functions of zai,suggesting the necessity to reconsider the efficacy of applying indiscriminately the Anglo-Saxon grammar into the study of Chinese spatial-temporal constructions.
文摘OpenStreetMap(OSM)is a dataset in constant change and this dynamic needs to be better understood.Based on 12-year time series of seven OSM data contribution activities extracted from 20 large cities worldwide,we investigate the temporal dynamic of OSM data production,more specifically,the auto-and cross-correlation,temporal trend,and annual seasonality of these activities.Furthermore,we evaluate and compare nine different temporal regression methods for forecasting such activities in horizons of 1–4 weeks.Several insights could be obtained from our analyses,including that the contribution activities tend to grown linearly in a moderate intra-annual cycle.Also,the performance of the temporal forecasting methods shows that they yield in general more accurate estimations of future contribution activities than a baseline metric,i.e.the arithmetic average of recent previous observations.In particular,the well-known ARIMA and the exponentially weighted moving average methods have shown the best performances.
基金supported by grants from the Beijing Municipal Natural Science Foundation(No.L222019)CAMS Innovation Fund for Medical Sciences(CIFMS)(No.2024-I2M-C&T-C-002)+1 种基金National High Level Hospital Clinical Research Funding(Nos.2022-PUMCH-B-115 and 2022-PUMCH-D-005)National Key Research and Development Program of China(No.2024YFF1207104).
文摘Sepsis remains a leading cause morbidity and mortality worldwide;effective targeted therapies remain elusive due to its inherent heterogeneity and dynamic temporal evolution.Existing frameworks often focus on either the diverse manifestations of sepsis or its progression over time,but fail to integrate these critical aspects.In this review,we propose a novel spatial-temporal framework that integrates both the heterogeneity and temporality of sepsis.The framework consists of two key dimensions:The cross-sectional(heterogeneity)dimension,which addresses pathogen variability,host factors,and pathogen-host interactions;The longitudinal(temporality)dimension,which explores the dynamic evolution of sepsis and the need for adaptive,real-time interventions.Given the complexity of multidimensional temporal data,big data techniques have the potential to integrate these data and decompose sepsis into distinct disease subtypes.Stratification facilitates the development of personalized therapeutic approaches tailored to specific subtypes.Moreover,methods,such as reinforcement learning,can track the dynamic transitions between these subtypes,enabling real-time adaptation of treatment strategies.
基金supported by the National Basic Research Program(973)of China(No.2015CB352302)the National Natural Science Foundation of China(Nos.61625107,U1611461,U1509206,and 61402403)+2 种基金the Key Program of Zhejiang Province,China(No.2015C01027)the Chinese Knowledge Center for Engineering Sciences and Technologythe Fundamental Research Funds for the Central Universities,China
文摘Question answering is an important problem that aims to deliver specific answers to questions posed by humans in natural language.How to efficiently identify the exact answer with respect to a given question has become an active line of research.Previous approaches in factoid question answering tasks typically focus on modeling the semantic relevance or syntactic relationship between a given question and its corresponding answer.Most of these models suffer when a question contains very little content that is indicative of the answer.In this paper,we devise an architecture named the temporality-enhanced knowledge memory network(TE-KMN) and apply the model to a factoid question answering dataset from a trivia competition called quiz bowl.Unlike most of the existing approaches,our model encodes not only the content of questions and answers,but also the temporal cues in a sequence of ordered sentences which gradually remark the answer.Moreover,our model collaboratively uses external knowledge for a better understanding of a given question.The experimental results demonstrate that our method achieves better performance than several state-of-the-art methods.
文摘The concept of time is encoded in language in different ways,which is reflected in temporal expressions,tenses,aspects and word order in languages.Some scholars claim that Chinese is a language characterized by temporality.This paper aims to explore what the temporality of Chinese refers to in both its narrow and broad senses,and the classification of temporality under the guidance of the iconicity of sequence as well as the word order principles.Moreover,some scholars hold a seemingly opposite view that Chinese is a language of spatiality—whether the two views are contradictory to each other is also discussed in the current study.The research mainly adopts the methods of literature analysis and retrospection.It is found that the temporality of Chinese mainly refers to the correspondence between the sequence of linguistic units and the temporal order of events or states represented in the conceptual world,and that temporality can be classified into natural temporality,cognitive temporality,and functional temporality.Then,this paper concludes that the viewpoint that Chinese is a language of temporality does not conflict with the opinion that Chinese is characterized by spatiality,as these two traits are concerned with two irrelevant aspects of language.However,it will posit that whether the property of temporality is consistent at every linguistic level in Chinese,even including characters,needs further exploration.
基金supported by the Defitech Foundation(Morges,CH)to FCHthe Bertarelli Foundation-Catalyst program(Gstaad,CH)to FCH+2 种基金the Wyss Center for Bio and Neuroengineering the Lighthouse Partnership for AI-guided Neuromodulation to FCHthe Fonds de recherche du Quebec-Sante(FRQS#342969)to CEPthe Neuro X Postdoctoral Fellowship Program to CEP。
文摘Brain lesions,such as those caused by stroke or traumatic brain injury(TBI),frequently result in persistent motor and cognitive impairments that significantly affect the individual patient's quality of life.Despite differences in the mechanisms of injury,both conditions share a high prevalence of motor and cognitive impairments.These deficits show only limited natural recovery.
基金funded by the National Natural Science Foundation of China(42571311).
文摘Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area.Based on the meteorological observations from 2000 to 2020,we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries(Kazakhstan,Uzbekistan,Kyrgyzstan,Turkmenistan,and Tajikistan)via Theil-Sen trend analysis and Geodetector modeling method,quantitatively revealing the influence of environmental factors,such as temperature,precipitation,and vegetation,on the frequency of dust weather.The results showed that:(1)dust weather in Central Asia was mainly distributed in a large''dust belt''extending from west to east from northern part of the Caspian lowland desert,and concentrated in basins,plains,and other low-altitude areas.Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia,with a maximum annual frequency of 21.9%;(2)strong dust weather in Central Asia has fluctuated and slightly decreased since 2001.The highest frequency(1.1%)occurred in spring(from March to June);(3)from 2000 to 2020,changes such as spot shifting and shrinking occurred in the four main source areas(north of the Aral Sea,Kyzylkum Desert,Karakum Desert,and Garabogazköl Bay region),where sandstorms occurred in Central Asia,and northern Caspian lowland desert became the most important low-emission dust source in Central Asia;and(4)the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia.This study provides a theoretical basis for sand prevention and sand control in Central Asia.In the future,Central Asia should focus on the rational utilization of land and water resources,and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area.
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
基金supported by Punjab Agricultural University,Ludhiana,India,for providing the infrastructure and other facilities for conducting experiments.All other forms of support and financial assistance are duly acknowledged.
文摘Brown spot(BS)of rice,caused by Bipolaris oryzae,is a serious concern that not only causes quantitative losses but also affects grain quality.To manage this disease,the use of resistant genetic sources and QTLs is an eco-friendly and economical option.In the current study,F_(3) progenies derived from a cross of susceptible parent PMS-18-B(PAU 10845-1-1-1-1)×resistant parent RP Path 77(RP patho-17)were used to identify potential QTLs linked to BS resistance and to associate this resistance with a temporal spike in defense-related enzymes.
基金funded by the Deanship of Scientific Research and Libraries at Princess Nourah bint Abdulrahman University,through the“Nafea”Program,Grant No.(NP-45-082).
文摘Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.
基金funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
基金supported by the Humanities and Social Sciences Project of the Ministry of Education of the Peoples Republic(No.21YJCZH099)the National Natural Science Foundation of China(Nos.41401089 and 41741014)the Science and Technology Project of Sichuan Province(No.2023NSFSC1979).
文摘Majority of carbon emissions originate from fossil energy consumption,thus necessitating calculation and monitoring of carbon emissions from energy consumption.In this study,we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions.We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration.Furthermore,we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation,and we used the Geographically and Temporally Weighted Regression(GTWR)model to analyze the influencing factors of carbon emissions at this scale.The results of this study are as follows:(1)from 2000 to 2019,the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease,with an average annual growth rate of 4.24%.However,in recent years,it has stabilized,and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration;(2)carbon emissions exhibited significant spatial clustering,with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration;(3)factors such as GDP,population(Pop),urbanization rate(Ur),and industrialization structure(Ic)all showed a significant influence on carbon emissions;(4)the spatial heterogeneity of each influencing factor was evident.
文摘Across four studies,we explore the impact of solitude on consumers’reliance on feelings versus reasons in decision making,along with the underlying mechanism and boundary conditions.The results indicate that solitude individuals(vs.non-solitude)would prefer feeling-based strategy in decision-making,resulting in a higher intention of choosing the affectively superior option over the cognitively superior option(Study 1).Self-focus plays the underlying mechanism in the solitude effect(Study 2).Moreover,we also examine two boundary conditions:motivation(Study 3)and temporal orientation(Study 4),which indicates that involuntary motivation and future orientation can mitigate the solitude effect on affective processing.These findings provide insights into consumers’judgments of product attributes and selection of decision-making strategies according to their situations.
基金supported by the National R&D Pro-gram through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(Nos.RS-2024-00356939 and RS-2024-00405691).
文摘As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir computing(RC).RC enables the processing of temporal information with minimal training costs,making it a promising approach for neuromorphic computing.However,current memristor devices of-ten suffer from limitations in dynamic conductance and temporal behavior,which affects their perfor-mance in these applications.In this study,we present a multilayered indium-tin-oxide(ITO)/ZnO/indium-gallium-zinc oxide(IGZO)/ZnO/ITO memristor fabricated via radiofrequency sputtering to explore its fil-amentary and nonfilamentary resistive switching(RS)characteristics.High-resolution transmission elec-tron microscopy confirmed the polycrystalline structure of the ZnO/IGZO/ZnO active layer.Dual-switching modes were demonstrated by controlling the current compliance(I_(CC)).In the filamentary mode,the memristor exhibited a large memory window(10^(3)),low-operating voltages(±2 V),excellent cycle-to-cycle stability,and multilevel switching with controlled reset-stop voltages,making it suitable for high-density memory applications.Nonfilamentary switching demonstrated stable on/off ratios above 10,en-durance up to 102 cycles,and retention suited for short-term memory.Key synaptic behaviors,such as paired-pulse facilitation(PPF),post-tetanic potentiation(PTP),and spike-rate dependent plasticity(SRDP)were successfully emulated by modulating pulse amplitude,width,and interval.Experience-dependent plasticity(EDP)was also demonstrated,further replicating biological synaptic functions.These tempo-ral properties were utilized to develop a 4-bit reservoir computing system with 16 distinct conductance states,enabling efficient information encoding.For image recognition tasks,convolutional neural net-work(CNN)simulations achieved a high accuracy of 98.45%after 25 training epochs,outperforming the accuracy achieved following artificial neural network(ANN)simulations(87.79%).These findings demon-strate that the multilayered memristor exhibits high performance in neuromorphic systems,particularly for complex pattern recognition tasks,such as digit and letter classification.
基金funded by the National Natural Science Foundation of China(U23A2063)the Gansu Province Top-notch Leading Talents Project(E339040101)the National Natural Science Foundation of China(41771290,42377043,41773086).
文摘Tenebrionid beetles represent a crucial arthropod taxon in the Gobi desert ecosystems owing to their species richness and high biomass,both of which are essential for maintaining ecosystem health and stability.However,the spatiotemporal variations of tenebrionid beetle assemblages in the Gobi desert remain poorly understood.In this study,the monthly dynamics of tenebrionid beetles in the central part of the Hexi Corridor,Northwest China,a representative area of the Gobi desert ecosystems,were monitored using pitfall trapping during 2015-2020.The following results were showed:(1)monthly activity of tenebrionid beetles was observed from March to October,with monthly activity peaking in spring and summer,and monthly activity periods and peak of tenebrionid beetle species exhibited interspecific differences that varied from year to year;(2)spatial distribution of tenebrionid beetle community was influenced by structural factors.Specifically,at a spatial scale of 24.00 m,tenebrionid beetle community was strongly and positively correlated with the dominant species,with distinct spatial distribution patterns observed for Blaps gobiensis and Microdera kraatzi alashanica;(3)abundance of tenebrionid beetles was positively correlated with monthly mean precipitation and monthly mean temperature,whereas monthly abundance of B.gobiensis and M.kraatzi alashanica was positively correlated with monthly mean precipitation;and(4)the cover of Reaumuria soongarica(Pall.)Maxim.and Nitraria sphaerocarpa Maxim.had a positive influence on the number of tenebrionid beetles captured.In conclusion,monthly variation in precipitation significantly influences the community dynamic of tenebrionid beetles,with precipitation and shrub cover jointly determining the spatial distribution pattern of these beetles in the Gobi desert ecosystems.
基金supports from National Key R&D Program of China(Grant No.2023YFB4605500)National Natural Science Foundation of China(Grant No.52105498)+3 种基金Natural Science Foundation of Hunan Province(Grant No.2022JJ40597)the Science and Technology Innovation Program of Hunan Province(Grant No.2022RC1132)State Key Laboratory of Precision Manufacturing for Extreme Service Performance(Grant No.ZZYJKT2023-08)support in analyzing the status of ultrafast laser welding applications,as well as the corresponding project support(Grant No.HKF202400595).
文摘The ultrafast laser-matter interaction is explored to induce new pioneering principles and technologies into the realms of fundamental science and industrial production.The local thermal melting and connection properties of the ultrafast laser welding technology offer a novel method for welding of diverse transparent materials,thus having wide range of potential applications in aerospace,opto-mechanical systems,sensors,microfluidic,optics,etc.In this comprehensive review,tuning the transient electron activation processes,high-rate laser energy deposition,and dynamic evolution of plasma morphology by the temporal/spatial shaping methods have been demonstrated to facilitate the transition from conventional homogeneous transparent material welding to the more intricate realm of transparent/metal heterogeneous material welding.The welding strength and stability are also improvable through the implementation of real-time,in-situ monitoring techniques and the prompt diagnosis of welding defects.The principles of ultrafast laser welding,bottleneck problems in the welding,novel welding methods,advances in welding performance,in-situ monitoring and diagnosis,and various applications are reviewed.Finally,we offer a forward-looking perspective on the fundamental challenges within the field of ultrafast laser welding and identify key areas for future research,underscoring the imperative need for ongoing innovation and exploration.
基金supported by the National Key Research and Development Program Project(2021YFC2400203)the Shaanxi Province Key Research and Development Program Project(2023-YBSF-120)+1 种基金the Shandong Provincial Natural Science Foundation(ZR2024QF287)the National Natural Science Foundation of China(31972907).
文摘Temporal interference(TI)is a form of stimulation that epitomizes an innovative and non-invasive approach for profound neuromodulation of the brain,a technique that has been validated in mice.Yet,the thin cranial bone structure of mice has a marginal influence on the effect of the TI technique and may not effectively showcase its effectiveness in larger animals.Based on this,we carried out TI stimulation experiments on rats.Following the TI intervention,analysis of electrophysiological data and immunofluorescence staining indicated the generation of a stimulation focus within the nucleus accumbens(depth,8.5 mm)in rats.Our findings affirm the viability of the TI methodology in the presence of thick cranial bones,furnishing efficacious parameters for profound stimulation with TI administered under such conditions.This experiment not only sheds light on the intervention effects of TI deep in the brain but also furnishes robust evidence in support of its prospective clinical utility.
