Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across vari...Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across various domains.However,the deployment of such models in resource-constrained environments presents a unique set of challenges that require innovative solutions.Resource-constrained environments encompass scenarios where computing resources,memory,and energy availability are restricted.To empower sentiment analysis in resource-constrained environments,we address the crucial need by leveraging lightweight pre-trained models.These models,derived from popular architectures such as DistilBERT,MobileBERT,ALBERT,TinyBERT,ELECTRA,and SqueezeBERT,offer a promising solution to the resource limitations imposed by these environments.By distilling the knowledge from larger models into smaller ones and employing various optimization techniques,these lightweight models aim to strike a balance between performance and resource efficiency.This paper endeavors to explore the performance of multiple lightweight pre-trained models in sentiment analysis tasks specific to such environments and provide insights into their viability for practical deployment.展开更多
This paper presents our endeavors in developing the large-scale, ultra-high-resolution E3SM Land Model (uELM), specifically designed for exascale computers furnished with accelerators such as Nvidia GPUs. The uELM is ...This paper presents our endeavors in developing the large-scale, ultra-high-resolution E3SM Land Model (uELM), specifically designed for exascale computers furnished with accelerators such as Nvidia GPUs. The uELM is a sophisticated code that substantially relies on High-Performance Computing (HPC) environments, necessitating particular machine and software configurations. To facilitate community-based uELM developments employing GPUs, we have created a portable, standalone software environment preconfigured with uELM input datasets, simulation cases, and source code. This environment, utilizing Docker, encompasses all essential code, libraries, and system software for uELM development on GPUs. It also features a functional unit test framework and an offline model testbed for comprehensive numerical experiments. From a technical perspective, the paper discusses GPU-ready container generations, uELM code management, and input data distribution across computational platforms. Lastly, the paper demonstrates the use of environment for functional unit testing, end-to-end simulation on CPUs and GPUs, and collaborative code development.展开更多
Launch safety of explosive charges has become an urgent problem to be solved by all countries in the world aslaunch situation of ammunition becomes consistentlyworse.However, the existing numericalmodels have differen...Launch safety of explosive charges has become an urgent problem to be solved by all countries in the world aslaunch situation of ammunition becomes consistentlyworse.However, the existing numericalmodels have differentdefects. This paper formulates an efficient computational model of the combustion of an explosive charge affectedby a bottom gap in the launch environment in the context of the material point method. The current temperatureis computed accurately from the heat balance equation, and different physical states of the explosive charges areconsidered through various equations of state. Microcracks in the explosive charges are described with respectto the viscoelastic statistical crackmechanics (Visco–SCRAM) model. Themethod for calculating the temperatureat the bottomof the explosive charge with respect to the bottomgap is described. Based on this combustionmodel,the temperature history of a Composition B (COMB) explosive charge in the presence of a bottom gap is obtainedduring the launch process of a 155-mm artillery. The simulation results show that the bottom gap thickness shouldbe no greater than 0.039 cm to ensure the safety of the COM B explosive charge in the launch environment. Thisconclusion is consistent with previous results and verifies the correctness of the proposed model. Ultimately, thispaper derives amathematical expression for themaximumtemperature of the COMB explosive chargewith respectto the bottomgap thickness (over the range of 0.00–0.039 cm), and establishes a quantitative evaluationmethod forthe launch safety of explosive charges.The research results provide some guidance for the assessment and detectionof explosive charge safety in complex launch environments.展开更多
Considering the urban characteristics, a customized multi-scale numerical modeling system is established to simulate the urban meteorological environment. The system mainly involves three spatial scales: the urban sca...Considering the urban characteristics, a customized multi-scale numerical modeling system is established to simulate the urban meteorological environment. The system mainly involves three spatial scales: the urban scale, urban sub-domain scale, and single to few buildings scale. In it, different underlying surface types are employed, the building drag factor is used to replace its roughness in the influence on the urban wind field, the effects of building distribution, azimuth and screening of shortwave radiation are added, and the influence of anthropogenic heating is also taken into account. All the numerical tests indicate that the simulated results are reasonably in agreement with the observational data, so the system can be used to simulate the urban meteorological environment. Making use of it, the characteristics of the meteorological environment from the urban to urban sub-domain scales, even the among-buildings scale, can be recognized. As long as the urban planning scheme is given, the corresponding simulated results can be obtained so as to meet the need of optimizing urban planning.展开更多
In this research, the residential environment index system and evaluation model were established by means of subjective and objective methods. The methodology for establishing the evaluation system for residential env...In this research, the residential environment index system and evaluation model were established by means of subjective and objective methods. The methodology for establishing the evaluation system for residential environment was first analyzed; then the subjective evaluation data-base was established by questionnaire survey; and at the same time, the objective evaluation data-base was constructed by Geographic Information System (GIS); and then the related equation system between subjective and objective system was developed by multiple regression analysis. This research could benefit evaluation of the residential environment quality for various purposes, and also provide important rudimentary data-base for the development and improvement of residential environment for officials. Furthermore, the index system and evaluation model established in this research could construct a strong relation between subjective evaluation and objective data; and thus could provide a comprehensive, efficient and effective methodology for the evaluation of residential environment.展开更多
Collision avoidance decision-making models of multiple agents in virtual driving environment are studied. Based on the behavioral characteristics and hierarchical structure of the collision avoidance decision-making i...Collision avoidance decision-making models of multiple agents in virtual driving environment are studied. Based on the behavioral characteristics and hierarchical structure of the collision avoidance decision-making in real life driving, delphi approach and mathematical statistics method are introduced to construct pair-wise comparison judgment matrix of collision avoidance decision choices to each collision situation. Analytic hierarchy process （AHP） is adopted to establish the agents＇ collision avoidance decision-making model. To simulate drivers＇ characteristics, driver factors are added to categorize driving modes into impatient mode, normal mode, and the cautious mode. The results show that this model can simulate human＇s thinking process, and the agents in the virtual environment can deal with collision situations and make decisions to avoid collisions without intervention. The model can also reflect diversity and uncertainly of real life driving behaviors, and solves the multi-objective, multi-choice ranking priority problem in multi-vehicle collision scenarios. This collision avoidance model of multi-agents model is feasible and effective, and can provide richer and closer-to-life virtual scene for driving simulator, reflecting real-life traffic environment more truly, this model can also promote the practicality of driving simulator.展开更多
An environmental capacity model for the petroleum hydrocarbon pollutions （PHs） in Jiaozhou Bay is constructed based on field surveys, mesocosm, and parallel laboratory experiments. Simulated results of PHs seasonal ...An environmental capacity model for the petroleum hydrocarbon pollutions （PHs） in Jiaozhou Bay is constructed based on field surveys, mesocosm, and parallel laboratory experiments. Simulated results of PHs seasonal successions in 2003 match the field surveys of Jiaozhou Bay resaonably well with a highest value in July. The Monte Carlo analysis confirms that the variation of PHs concentration significantly correlates with the river input. The water body in the bay is reasonably subjected to self-purification processes, such as volatilization to the atmosphere, biodegradation by microorganism, and transport to the Yellow Sea by water exchange. The environmental capacity of PHs in Jiaozhou Bay is 1500 tons per year IF the seawater quality criterion （Grade Ⅰ/Ⅱ, 0.05 mgLl） in the region is to be satisfied. The contribution to self-purification by volatilization, biodegradation, and transport to the Yellow Sea accounts for 48%, 28%, and 23%, respectively, which make these three processes the main ways of PHs purification in Jiaozhou Bay.展开更多
This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy ...This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.展开更多
According to the Fick＇s second law of diffusion, six analytical solutions of chloride profile in concrete were studied and discussed with regard to different boundary and initial conditions. In those analytical solut...According to the Fick＇s second law of diffusion, six analytical solutions of chloride profile in concrete were studied and discussed with regard to different boundary and initial conditions. In those analytical solutions, the most prevailing error-function solution which is based on semi-infinite assumption is the simple one, but may under-estimate the chloride content in concrete and over-rate the life time prediction of concrete structures. The experimental results show that compared with other solutions, the chloride content in concrete predicted by error-function model is the minimum, and the calculation difference produced by different analytical models should not be ignored. The influence of models on chloride content prediction is more than other environment and material coefficients in some time. In order to get a more realistic prediction model, modification to error-function model is suggested based on analysis and calculation examples concerning the boundary and edge effect.展开更多
In this article we specify an individual-based foraging swarm （i.e., group of agents） model with individuals that move in an n-dimensional multi-obstacle environment. The motion of each individual （i） is determine...In this article we specify an individual-based foraging swarm （i.e., group of agents） model with individuals that move in an n-dimensional multi-obstacle environment. The motion of each individual （i） is determined by three factors： i） attraction to the local object position （x^-io＋） which is decided by the local information about the individuals＇ position that individual i can find; ii） repulsion from the other individuals on short distances; and iii） attraction to the global object position （xgoal） or repulsion from the obstacles in the environment, The emergent behavior of the swarm motion is the result of a balance between inter-individual interaction and the simultaneous interactions of the swarm members with their environment. We study the stability properties of the collective behavior of the swarm based on Lyapunov stability theory. The simulations show that the swarm can converge to goal regions and diverge from obstacle regions of the environment while maintaining cohesive.展开更多
As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an ...As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings.Our proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive framework.This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.We evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated attacks.Our research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive quality.Our model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security paradigms.Through our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of IoT.Our model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat landscape.This achievement marks a significant stride towards protecting the integrity of IoT infrastructure,ensuring operational resilience,and building privacy in this groundbreaking technology.展开更多
Model updating for aircraft in a high temperature environment(HTE)is proposed based on the hierarchical method.With this method,the problem can be decomposed into temperature field updating and dynamic structural upda...Model updating for aircraft in a high temperature environment(HTE)is proposed based on the hierarchical method.With this method,the problem can be decomposed into temperature field updating and dynamic structural updating.In order to improve the estimation accuracy,the model updating problem is turned into a multi-objective optimization problem by constructing the objective function which combined with residues of modal frequency and effective modal mass.Then the metamodeling,support vector regression(SVR)is introduced to improve the optimization efficiency,and the solution can be determined by adaptive weighted-sum method(AWS).Finally,the proposed method is tested on a finite element(FE)model of a reentry vehicle model.The results show that the multi-objective model updating method in HTE can identify the input parameters of the temperature field and structure with good accuracy.展开更多
An enriched environment is used as a behavio ral intervention therapy that applies sensory,motor,and social stimulation,and has been used in basic and clinical research of va rious neurological diseases.In this study,...An enriched environment is used as a behavio ral intervention therapy that applies sensory,motor,and social stimulation,and has been used in basic and clinical research of va rious neurological diseases.In this study,we established mouse models of photothrombotic stroke and,24 hours later,raised them in a standard,enriched,or isolated environment for 4 weeks.Compared with the mice raised in a standard environment,the cognitive function of mice raised in an enriched environment was better and the pathological damage in the hippocampal CA1 region was remarkably alleviated.Furthermore,protein expression levels of tumor necrosis factor receptor-associated factor 6,nuclear factorκB p65,interleukin-6,and tumor necrosis factorα,and the mRNA expression level of tumor necrosis factor receptor-associated factor 6 were greatly lower,while the expression level of miR-146a-5p was higher.Compared with the mice raised in a standard environment,changes in these indices in mice raised in an isolated environment were opposite to mice raised in an enriched environment.These findings suggest that different living environments affect the hippocampal inflammatory response and cognitive function in a mouse model of stro ke.An enriched environment can improve cognitive function following stroke through up-regulation of miR-146a-5p expression and a reduction in the inflammatory response.展开更多
Computer vision-based inspection methods show promise for automating post-earthquake building inspections.These methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected ...Computer vision-based inspection methods show promise for automating post-earthquake building inspections.These methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected images.Nevertheless,assessing the damage′s impact on structural safety requires localizing damage to specific building components with known design and function.This paper proposes a BIM-based automated inspection framework to provide context for visual surveys.A deep learning-based semantic segmentation algorithm is trained to automatically identify damage in images.The BIM automatically associates any identified damage with specific building components.Then,components are classified into damage states consistent with component fragility models for integration with a structural analysis.To demonstrate the framework,methods are developed to photorealistically simulate severe structural damage in a synthetic computer graphics environment.A graphics model of a real building in Urbana,Illinois,is generated to test the framework;the model is integrated with a structural analysis to apply earthquake damage in a physically realistic manner.A simulated UAV survey is flown of the graphics model and the framework is applied.The method achieves high accuracy in assigning damage states to visible structural components.This assignment enables integration with a performance-based earthquake assessment to classify building safety.展开更多
This study presented a multi-objective linear fractional inventory (LFI) problem with generalised intuitionistic fuzzy numbers. In modelling, the authors have assumed the ambiances where generalised trapezoidal intuit...This study presented a multi-objective linear fractional inventory (LFI) problem with generalised intuitionistic fuzzy numbers. In modelling, the authors have assumed the ambiances where generalised trapezoidal intuitionistic fuzzy numbers (GTIFNs) used to handle the uncertain information in the data. Then, the given multi-objective generalised intuitionistic fuzzy LFI model was transformed into its equivalent deterministic linear fractional programming problem by employing the possibility and necessity measures. Finally, the applicability of the model is demonstrated with a numerical example and the sensitivity analysis under several parameters is investigated to explore the study.展开更多
Battlefield environment simulation process is an important part of battlefield environment information support, which needs to be built around the task process. At present, the interoperability between battlefield env...Battlefield environment simulation process is an important part of battlefield environment information support, which needs to be built around the task process. At present, the interoperability between battlefield environment simulation system and command and control system is still imperfect, and the traditional simulation data model cannot meet war fighters’ high-efficient and accurate understanding and analysis on battlefield environment’s information. Therefore, a kind of task-orientated battlefield environment simulation process model needs to be construed to effectively analyze the key information demands of the command and control system. The structured characteristics of tasks and simulation process are analyzed, and the simulation process concept model is constructed with the method of object-orientated. The data model and formal syntax of GeoBML are analyzed, and the logical model of simulation process is constructed with formal language. The object data structure of simulation process is defined and the object model of simulation process which maps tasks is constructed. In the end, the battlefield environment simulation platform modules are designed and applied based on this model, verifying that the model can effectively express the real-time dynamic correlation between battlefield environment simulation data and operational tasks.展开更多
This paper, taking Hexi Corridor as an example, analyzes the altemating intimidation and the dynamic evolving relation between urbanization and eco-environment in arid area of West China. We argue that the harmonious ...This paper, taking Hexi Corridor as an example, analyzes the altemating intimidation and the dynamic evolving relation between urbanization and eco-environment in arid area of West China. We argue that the harmonious development system of the urbanization and eco-environment would go through four phases： rudimentary symbiotic phase, harmonious developmental phase, utmost increasing phase and spiral type rising phase. Throughout the four phases, the elements of the system would influence each other, coerce each other, and complete the spiral type rising process from low-grade symbiosis to high-grade harmony together. The study on Hexi Corridor shows that the urbanization level in Hexi Corridor has increased gradually from 1985 to 2003 accompanied with the fluctuations of eco-environment state. The response of eco-environment to urbanization has been evident, but lagged behind the urbanization course. At present, the harmonious development system in Hexi Corridor was in its harmonious developmental phase. However, the coupling degree has increased quickly and approached 90 yet, which is signaling that the system is about to enter the utmost increasing phase, and the ecological crisis will enter the latent period. We have found that the coupling degree can well reflect the interactive coercing and dynamic evolving situation between urbanization and eco-environment in Hexi Corridor. From the temporal change of the coupling degree, it can be concluded that urbanization sometimes needs to pay a certain cost for the damage of the eco-environment in its initial stages, but as the urbanization continues, the state of the eco-environment would be meliorated.展开更多
Temporal geographic information system(t_GIS) is a kind of computer information system that can display,process and analyze the micro_format distribution of temporal_spatial information of real world.It includes both ...Temporal geographic information system(t_GIS) is a kind of computer information system that can display,process and analyze the micro_format distribution of temporal_spatial information of real world.It includes both spatial geographic information and temporal information,and can analyze both the static geographic information and the dynamic geographic information. Three models based on t_GIS for environment dynamics,namely,the mechanism method,the experience method and the mixed method are given. t_GIS based on the environment dynamic model has more new functions than traditional GIS,such as fast I/O,inquiry,static/dynamic display and visible analysis of spatial and temporal sequence information,especially it can display the image of the evolution in the past,current and future environment through the extrapolation method within its defining region.The velocity for analogue display can be accelerated by setting up time_varying_area_function for position and attribute. Dynamic environmental information analysis systems based on t_GIS are applicable to almost all the fields related to management,display and analysis of local environmental dynamic information.For this reason,some considerations to construct distinct information systems have been enumerated in this paper,such as,the analysis information system for terrain evolution,the analysis information system for the condition of water and fertilizer of farmland,analysis and the evaluation information system for ecological environment,and the analysis information system for distribution changes of population of China,etc.展开更多
The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic developm...The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic development.This study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining industry.The IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable communication.The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and LoRaWAN.Intensive field tests are conducted in various opencast mines to investigate coverage potential and signal attenuation.ZigBee is tested in the Tadicherla open-cast coal mine in India.Similarly,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal attenuation.Extensive field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MAPE)of 0.249 and Scatter Index(SI)of 2.723.In the O2O scenario,the Ericsson model showed superior performance,with the highest R^(2)value of 0.959,supported by strong correlation metrics:NRMSE of 0.026,MSE of 8.685,MAPE of 0.685,Mean Absolute Deviation(MAD)of 20.839 and SI of 2.194.For the LoRaWAN protocol,the Cost-231 model achieved the highest R^(2)value of 0.921 in the I2O scenario,complemented by the lowest metrics:NRMSE of 0.018,MSE of 1.324,MAPE of 0.217,MAD of 9.218 and SI of 1.238.In the O2O environment,the Okumura-Hata model achieved the highest R^(2)value of 0.978,indicating a strong fit with metrics NRMSE of 0.047,MSE of 27.807,MAPE of 27.494,MAD of 37.287 and SI of 3.927.This advancement in reliable communication networks promises to transform the opencast landscape into networked signal attenuation.These results support decision-making for mining needs and ensure reliable communications even in the face of formidable obstacles.展开更多
Objective To predict the impact of MF radiation on human health. Methods The vertical distribution of field intensity was estimated by analogism on the basis of measured values from simulation measurement. Results A k...Objective To predict the impact of MF radiation on human health. Methods The vertical distribution of field intensity was estimated by analogism on the basis of measured values from simulation measurement. Results A kind of analogism on the basis of geometric proportion decay pattern is put forward in the essay. It showed that with increasing of height the field intensity increased according to geometric proportion law. Conclusion This geometric proportion prediction model can be used to estimate the impact of MF radiation on inhabited environment, and can act as a reference pattern in predicting the environmental impact level of MF radiation.展开更多
文摘Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across various domains.However,the deployment of such models in resource-constrained environments presents a unique set of challenges that require innovative solutions.Resource-constrained environments encompass scenarios where computing resources,memory,and energy availability are restricted.To empower sentiment analysis in resource-constrained environments,we address the crucial need by leveraging lightweight pre-trained models.These models,derived from popular architectures such as DistilBERT,MobileBERT,ALBERT,TinyBERT,ELECTRA,and SqueezeBERT,offer a promising solution to the resource limitations imposed by these environments.By distilling the knowledge from larger models into smaller ones and employing various optimization techniques,these lightweight models aim to strike a balance between performance and resource efficiency.This paper endeavors to explore the performance of multiple lightweight pre-trained models in sentiment analysis tasks specific to such environments and provide insights into their viability for practical deployment.
文摘This paper presents our endeavors in developing the large-scale, ultra-high-resolution E3SM Land Model (uELM), specifically designed for exascale computers furnished with accelerators such as Nvidia GPUs. The uELM is a sophisticated code that substantially relies on High-Performance Computing (HPC) environments, necessitating particular machine and software configurations. To facilitate community-based uELM developments employing GPUs, we have created a portable, standalone software environment preconfigured with uELM input datasets, simulation cases, and source code. This environment, utilizing Docker, encompasses all essential code, libraries, and system software for uELM development on GPUs. It also features a functional unit test framework and an offline model testbed for comprehensive numerical experiments. From a technical perspective, the paper discusses GPU-ready container generations, uELM code management, and input data distribution across computational platforms. Lastly, the paper demonstrates the use of environment for functional unit testing, end-to-end simulation on CPUs and GPUs, and collaborative code development.
基金the Natural Science Foundation of Heilongjiang Province,China(LH2019A008).
文摘Launch safety of explosive charges has become an urgent problem to be solved by all countries in the world aslaunch situation of ammunition becomes consistentlyworse.However, the existing numericalmodels have differentdefects. This paper formulates an efficient computational model of the combustion of an explosive charge affectedby a bottom gap in the launch environment in the context of the material point method. The current temperatureis computed accurately from the heat balance equation, and different physical states of the explosive charges areconsidered through various equations of state. Microcracks in the explosive charges are described with respectto the viscoelastic statistical crackmechanics (Visco–SCRAM) model. Themethod for calculating the temperatureat the bottomof the explosive charge with respect to the bottomgap is described. Based on this combustionmodel,the temperature history of a Composition B (COMB) explosive charge in the presence of a bottom gap is obtainedduring the launch process of a 155-mm artillery. The simulation results show that the bottom gap thickness shouldbe no greater than 0.039 cm to ensure the safety of the COM B explosive charge in the launch environment. Thisconclusion is consistent with previous results and verifies the correctness of the proposed model. Ultimately, thispaper derives amathematical expression for themaximumtemperature of the COMB explosive chargewith respectto the bottomgap thickness (over the range of 0.00–0.039 cm), and establishes a quantitative evaluationmethod forthe launch safety of explosive charges.The research results provide some guidance for the assessment and detectionof explosive charge safety in complex launch environments.
基金sponsored by the Key Project(96-920-34-07)of the Ministry of Science and Technology,Chinathe Nationa1 Natura1 Science Foundation of China(40333027).
文摘Considering the urban characteristics, a customized multi-scale numerical modeling system is established to simulate the urban meteorological environment. The system mainly involves three spatial scales: the urban scale, urban sub-domain scale, and single to few buildings scale. In it, different underlying surface types are employed, the building drag factor is used to replace its roughness in the influence on the urban wind field, the effects of building distribution, azimuth and screening of shortwave radiation are added, and the influence of anthropogenic heating is also taken into account. All the numerical tests indicate that the simulated results are reasonably in agreement with the observational data, so the system can be used to simulate the urban meteorological environment. Making use of it, the characteristics of the meteorological environment from the urban to urban sub-domain scales, even the among-buildings scale, can be recognized. As long as the urban planning scheme is given, the corresponding simulated results can be obtained so as to meet the need of optimizing urban planning.
文摘In this research, the residential environment index system and evaluation model were established by means of subjective and objective methods. The methodology for establishing the evaluation system for residential environment was first analyzed; then the subjective evaluation data-base was established by questionnaire survey; and at the same time, the objective evaluation data-base was constructed by Geographic Information System (GIS); and then the related equation system between subjective and objective system was developed by multiple regression analysis. This research could benefit evaluation of the residential environment quality for various purposes, and also provide important rudimentary data-base for the development and improvement of residential environment for officials. Furthermore, the index system and evaluation model established in this research could construct a strong relation between subjective evaluation and objective data; and thus could provide a comprehensive, efficient and effective methodology for the evaluation of residential environment.
基金supported by National Basic Research Program (973 Program,No.2004CB719402)National Natural Science Foundation of China (No.60736019)Natural Science Foundation of Zhejiang Province, China(No.Y105430).
文摘Collision avoidance decision-making models of multiple agents in virtual driving environment are studied. Based on the behavioral characteristics and hierarchical structure of the collision avoidance decision-making in real life driving, delphi approach and mathematical statistics method are introduced to construct pair-wise comparison judgment matrix of collision avoidance decision choices to each collision situation. Analytic hierarchy process （AHP） is adopted to establish the agents＇ collision avoidance decision-making model. To simulate drivers＇ characteristics, driver factors are added to categorize driving modes into impatient mode, normal mode, and the cautious mode. The results show that this model can simulate human＇s thinking process, and the agents in the virtual environment can deal with collision situations and make decisions to avoid collisions without intervention. The model can also reflect diversity and uncertainly of real life driving behaviors, and solves the multi-objective, multi-choice ranking priority problem in multi-vehicle collision scenarios. This collision avoidance model of multi-agents model is feasible and effective, and can provide richer and closer-to-life virtual scene for driving simulator, reflecting real-life traffic environment more truly, this model can also promote the practicality of driving simulator.
基金supported by the Science Fund Projects of Shandong Province (No.ZR2010DM005)National Key Technology Research and Development Program (No. 2010BAC69B01)+1 种基金Scientific and Technical Projects of Shandong Province on Environmental Protection ‘The source, capacity, and technology study of total control of pollutants in Shandong Province’Science and Technology Development Plan of Qingdao (No. 11-2-3-66-nsh and No. 11-2-1-18-hy)
文摘An environmental capacity model for the petroleum hydrocarbon pollutions （PHs） in Jiaozhou Bay is constructed based on field surveys, mesocosm, and parallel laboratory experiments. Simulated results of PHs seasonal successions in 2003 match the field surveys of Jiaozhou Bay resaonably well with a highest value in July. The Monte Carlo analysis confirms that the variation of PHs concentration significantly correlates with the river input. The water body in the bay is reasonably subjected to self-purification processes, such as volatilization to the atmosphere, biodegradation by microorganism, and transport to the Yellow Sea by water exchange. The environmental capacity of PHs in Jiaozhou Bay is 1500 tons per year IF the seawater quality criterion （Grade Ⅰ/Ⅱ, 0.05 mgLl） in the region is to be satisfied. The contribution to self-purification by volatilization, biodegradation, and transport to the Yellow Sea accounts for 48%, 28%, and 23%, respectively, which make these three processes the main ways of PHs purification in Jiaozhou Bay.
基金The National Natural Science Foundation of China (32371993)The Natural Science Research Key Project of Anhui Provincial University(2022AH040125&2023AH040135)The Key Research and Development Plan of Anhui Province (202204c06020022&2023n06020057)。
文摘This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.
基金Funded by the National Key Technology R&D Program(No.2011BAG07B04)
文摘According to the Fick＇s second law of diffusion, six analytical solutions of chloride profile in concrete were studied and discussed with regard to different boundary and initial conditions. In those analytical solutions, the most prevailing error-function solution which is based on semi-infinite assumption is the simple one, but may under-estimate the chloride content in concrete and over-rate the life time prediction of concrete structures. The experimental results show that compared with other solutions, the chloride content in concrete predicted by error-function model is the minimum, and the calculation difference produced by different analytical models should not be ignored. The influence of models on chloride content prediction is more than other environment and material coefficients in some time. In order to get a more realistic prediction model, modification to error-function model is suggested based on analysis and calculation examples concerning the boundary and edge effect.
基金This work was supported by the National Natural Science Foundation of China (No. 60574088).
文摘In this article we specify an individual-based foraging swarm （i.e., group of agents） model with individuals that move in an n-dimensional multi-obstacle environment. The motion of each individual （i） is determined by three factors： i） attraction to the local object position （x^-io＋） which is decided by the local information about the individuals＇ position that individual i can find; ii） repulsion from the other individuals on short distances; and iii） attraction to the global object position （xgoal） or repulsion from the obstacles in the environment, The emergent behavior of the swarm motion is the result of a balance between inter-individual interaction and the simultaneous interactions of the swarm members with their environment. We study the stability properties of the collective behavior of the swarm based on Lyapunov stability theory. The simulations show that the swarm can converge to goal regions and diverge from obstacle regions of the environment while maintaining cohesive.
基金funding from Deanship of Scientific Research in King Faisal University with Grant Number KFU241648.
文摘As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings.Our proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive framework.This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.We evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated attacks.Our research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive quality.Our model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security paradigms.Through our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of IoT.Our model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat landscape.This achievement marks a significant stride towards protecting the integrity of IoT infrastructure,ensuring operational resilience,and building privacy in this groundbreaking technology.
基金supported by the National Natural Science Foundation of China(No.11472132)the Fundamental Research Funds for Central University (No. NJ20160050)the Fundamental Research Funds for Central University(No.NJ2016098)
文摘Model updating for aircraft in a high temperature environment(HTE)is proposed based on the hierarchical method.With this method,the problem can be decomposed into temperature field updating and dynamic structural updating.In order to improve the estimation accuracy,the model updating problem is turned into a multi-objective optimization problem by constructing the objective function which combined with residues of modal frequency and effective modal mass.Then the metamodeling,support vector regression(SVR)is introduced to improve the optimization efficiency,and the solution can be determined by adaptive weighted-sum method(AWS).Finally,the proposed method is tested on a finite element(FE)model of a reentry vehicle model.The results show that the multi-objective model updating method in HTE can identify the input parameters of the temperature field and structure with good accuracy.
基金financially the National Natural Science Foundation of China,No.82072533the China Postdoctoral Science Foundation,No.2017M621675+1 种基金Huxin Foundation of Jiangsu Key Laboratory of Zoonosis of China,No.HX2003Yangzhou Science and Technology Development Plan Project of China,No.YZ2020201(all to XW)。
文摘An enriched environment is used as a behavio ral intervention therapy that applies sensory,motor,and social stimulation,and has been used in basic and clinical research of va rious neurological diseases.In this study,we established mouse models of photothrombotic stroke and,24 hours later,raised them in a standard,enriched,or isolated environment for 4 weeks.Compared with the mice raised in a standard environment,the cognitive function of mice raised in an enriched environment was better and the pathological damage in the hippocampal CA1 region was remarkably alleviated.Furthermore,protein expression levels of tumor necrosis factor receptor-associated factor 6,nuclear factorκB p65,interleukin-6,and tumor necrosis factorα,and the mRNA expression level of tumor necrosis factor receptor-associated factor 6 were greatly lower,while the expression level of miR-146a-5p was higher.Compared with the mice raised in a standard environment,changes in these indices in mice raised in an isolated environment were opposite to mice raised in an enriched environment.These findings suggest that different living environments affect the hippocampal inflammatory response and cognitive function in a mouse model of stro ke.An enriched environment can improve cognitive function following stroke through up-regulation of miR-146a-5p expression and a reduction in the inflammatory response.
基金Financial support for this research was provided in part by the US Army Corps of Engineers through a subaward from the University of California,San Diego,USA。
文摘Computer vision-based inspection methods show promise for automating post-earthquake building inspections.These methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected images.Nevertheless,assessing the damage′s impact on structural safety requires localizing damage to specific building components with known design and function.This paper proposes a BIM-based automated inspection framework to provide context for visual surveys.A deep learning-based semantic segmentation algorithm is trained to automatically identify damage in images.The BIM automatically associates any identified damage with specific building components.Then,components are classified into damage states consistent with component fragility models for integration with a structural analysis.To demonstrate the framework,methods are developed to photorealistically simulate severe structural damage in a synthetic computer graphics environment.A graphics model of a real building in Urbana,Illinois,is generated to test the framework;the model is integrated with a structural analysis to apply earthquake damage in a physically realistic manner.A simulated UAV survey is flown of the graphics model and the framework is applied.The method achieves high accuracy in assigning damage states to visible structural components.This assignment enables integration with a performance-based earthquake assessment to classify building safety.
文摘This study presented a multi-objective linear fractional inventory (LFI) problem with generalised intuitionistic fuzzy numbers. In modelling, the authors have assumed the ambiances where generalised trapezoidal intuitionistic fuzzy numbers (GTIFNs) used to handle the uncertain information in the data. Then, the given multi-objective generalised intuitionistic fuzzy LFI model was transformed into its equivalent deterministic linear fractional programming problem by employing the possibility and necessity measures. Finally, the applicability of the model is demonstrated with a numerical example and the sensitivity analysis under several parameters is investigated to explore the study.
基金The National Natural Science Foundation of China(41271393).
文摘Battlefield environment simulation process is an important part of battlefield environment information support, which needs to be built around the task process. At present, the interoperability between battlefield environment simulation system and command and control system is still imperfect, and the traditional simulation data model cannot meet war fighters’ high-efficient and accurate understanding and analysis on battlefield environment’s information. Therefore, a kind of task-orientated battlefield environment simulation process model needs to be construed to effectively analyze the key information demands of the command and control system. The structured characteristics of tasks and simulation process are analyzed, and the simulation process concept model is constructed with the method of object-orientated. The data model and formal syntax of GeoBML are analyzed, and the logical model of simulation process is constructed with formal language. The object data structure of simulation process is defined and the object model of simulation process which maps tasks is constructed. In the end, the battlefield environment simulation platform modules are designed and applied based on this model, verifying that the model can effectively express the real-time dynamic correlation between battlefield environment simulation data and operational tasks.
基金NationalNaturalScience Emphases Foundation ofChina,No.40335049NationalNaturalScience Foundation ofChina,No.40471059
文摘This paper, taking Hexi Corridor as an example, analyzes the altemating intimidation and the dynamic evolving relation between urbanization and eco-environment in arid area of West China. We argue that the harmonious development system of the urbanization and eco-environment would go through four phases： rudimentary symbiotic phase, harmonious developmental phase, utmost increasing phase and spiral type rising phase. Throughout the four phases, the elements of the system would influence each other, coerce each other, and complete the spiral type rising process from low-grade symbiosis to high-grade harmony together. The study on Hexi Corridor shows that the urbanization level in Hexi Corridor has increased gradually from 1985 to 2003 accompanied with the fluctuations of eco-environment state. The response of eco-environment to urbanization has been evident, but lagged behind the urbanization course. At present, the harmonious development system in Hexi Corridor was in its harmonious developmental phase. However, the coupling degree has increased quickly and approached 90 yet, which is signaling that the system is about to enter the utmost increasing phase, and the ecological crisis will enter the latent period. We have found that the coupling degree can well reflect the interactive coercing and dynamic evolving situation between urbanization and eco-environment in Hexi Corridor. From the temporal change of the coupling degree, it can be concluded that urbanization sometimes needs to pay a certain cost for the damage of the eco-environment in its initial stages, but as the urbanization continues, the state of the eco-environment would be meliorated.
文摘Temporal geographic information system(t_GIS) is a kind of computer information system that can display,process and analyze the micro_format distribution of temporal_spatial information of real world.It includes both spatial geographic information and temporal information,and can analyze both the static geographic information and the dynamic geographic information. Three models based on t_GIS for environment dynamics,namely,the mechanism method,the experience method and the mixed method are given. t_GIS based on the environment dynamic model has more new functions than traditional GIS,such as fast I/O,inquiry,static/dynamic display and visible analysis of spatial and temporal sequence information,especially it can display the image of the evolution in the past,current and future environment through the extrapolation method within its defining region.The velocity for analogue display can be accelerated by setting up time_varying_area_function for position and attribute. Dynamic environmental information analysis systems based on t_GIS are applicable to almost all the fields related to management,display and analysis of local environmental dynamic information.For this reason,some considerations to construct distinct information systems have been enumerated in this paper,such as,the analysis information system for terrain evolution,the analysis information system for the condition of water and fertilizer of farmland,analysis and the evaluation information system for ecological environment,and the analysis information system for distribution changes of population of China,etc.
文摘The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic development.This study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining industry.The IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable communication.The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and LoRaWAN.Intensive field tests are conducted in various opencast mines to investigate coverage potential and signal attenuation.ZigBee is tested in the Tadicherla open-cast coal mine in India.Similarly,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal attenuation.Extensive field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MAPE)of 0.249 and Scatter Index(SI)of 2.723.In the O2O scenario,the Ericsson model showed superior performance,with the highest R^(2)value of 0.959,supported by strong correlation metrics:NRMSE of 0.026,MSE of 8.685,MAPE of 0.685,Mean Absolute Deviation(MAD)of 20.839 and SI of 2.194.For the LoRaWAN protocol,the Cost-231 model achieved the highest R^(2)value of 0.921 in the I2O scenario,complemented by the lowest metrics:NRMSE of 0.018,MSE of 1.324,MAPE of 0.217,MAD of 9.218 and SI of 1.238.In the O2O environment,the Okumura-Hata model achieved the highest R^(2)value of 0.978,indicating a strong fit with metrics NRMSE of 0.047,MSE of 27.807,MAPE of 27.494,MAD of 37.287 and SI of 3.927.This advancement in reliable communication networks promises to transform the opencast landscape into networked signal attenuation.These results support decision-making for mining needs and ensure reliable communications even in the face of formidable obstacles.
文摘Objective To predict the impact of MF radiation on human health. Methods The vertical distribution of field intensity was estimated by analogism on the basis of measured values from simulation measurement. Results A kind of analogism on the basis of geometric proportion decay pattern is put forward in the essay. It showed that with increasing of height the field intensity increased according to geometric proportion law. Conclusion This geometric proportion prediction model can be used to estimate the impact of MF radiation on inhabited environment, and can act as a reference pattern in predicting the environmental impact level of MF radiation.
