Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
BACKGROUND:Electrolyte imbalance is common following traumatic brain injury(TBI)and can significantly impact patient outcomes.We aimed to explore the occurrence,patterns,and consequences of electrolyte imbalance in ad...BACKGROUND:Electrolyte imbalance is common following traumatic brain injury(TBI)and can significantly impact patient outcomes.We aimed to explore the occurrence,patterns,and consequences of electrolyte imbalance in adult patients with TBI.METHODS:A retrospective study was conducted from 2016 to 2021 at a level 1 trauma center among hospitalized TBI patients.On admission,the levels of serum electrolytes,including sodium,potassium,calcium,magnesium,and phosphate,were analyzed.Demographics,injury characteristics,and interventions were assessed.The primary outcome was the in-hospital mortality.Multivariate logistic regression analysis was performed to identify independent predictors of mortality in TBI patients.RESULTS:A total of 922 TBI patients were included in the analysis,of whom 902(98%)had electrolyte imbalance.The mean age of patients with electrolyte imbalance was 32.0±15.0 years.Most patients were males(94%).The most common electrolyte abnormalities were hypocalcemia,hypophosphatemia,and hypokalemia.The overall in-hospital mortality rate was 22%in the entire cohort.In multivariate logistic analysis,the predictors of mortality included age(odds ratio[OR]=1.029,95%confidence intervals[CI]:1.013-1.046,P<0.001),low GCS(OR=0.883,95%CI:0.816-0.956,P=0.002),high Injury Severity Score(ISS)scale(OR=1.051,95%CI:1.026-1.078,P<0.001),hypernatremia(OR=2.175,95%CI:1.196-3.955,P=0.011),hyperkalemia(OR=4.862,95%CI:1.222-19.347;P=0.025),low serum bicarbonate levels(OR=0.926,95%CI:0.868-0.988,P=0.020),high serum lactate levels(OR=1.128,95%CI:1.022-1.244,P=0.017),high glucose levels(OR=1.072,95%CI:1.014-1.133,P=0.015),a longer activated partial thromboplastin time(OR=1.054,95%CI:1.024-1.084,P<0.001)and higer international normalized ratio(INR)(OR=3.825,95%CI:1.592-9.188,P=0.003).CONCLUSION:Electrolyte imbalance is common in TBI patients,with the significant prevalence of hypocalcemia,hypophosphatemia,and hypokalemia.However,hypernatremia and hyperkalemia were associated with the risk of mortality,emphasizing the need for further research to comprehend electrolyte dynamics in TBI patients.展开更多
Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in ...Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in bank credit default datasets limits the predictive performance of traditional machine learning and deep learning models.To address this issue,this study employs the conditional variational autoencoder-Wasserstein generative adversarial network with gradient penalty(CVAE-WGAN-gp)model for oversampling,generating samples similar to the original default customer data to enhance model prediction performance.To evaluate the quality of the data generated by the CVAE-WGAN-gp model,we selected several bank loan datasets for experimentation.The experimental results demonstrate that using the CVAE-WGAN-gp model for oversampling can significantly improve the predictive performance in credit risk assessment problems.展开更多
This study demonstrates the complexity and importance of water quality as a measure of the health and sustainability of ecosystems that directly influence biodiversity,human health,and the world economy.The predictabi...This study demonstrates the complexity and importance of water quality as a measure of the health and sustainability of ecosystems that directly influence biodiversity,human health,and the world economy.The predictability of water quality thus plays a crucial role in managing our ecosystems to make informed decisions and,hence,proper environmental management.This study addresses these challenges by proposing an effective machine learning methodology applied to the“Water Quality”public dataset.The methodology has modeled the dataset suitable for providing prediction classification analysis with high values of the evaluating parameters such as accuracy,sensitivity,and specificity.The proposed methodology is based on two novel approaches:(a)the SMOTE method to deal with unbalanced data and(b)the skillfully involved classical machine learning models.This paper uses Random Forests,Decision Trees,XGBoost,and Support Vector Machines because they can handle large datasets,train models for handling skewed datasets,and provide high accuracy in water quality classification.A key contribution of this work is the use of custom sampling strategies within the SMOTE approach,which significantly enhanced performance metrics and improved class imbalance handling.The results demonstrate significant improvements in predictive performance,achieving the highest reported metrics:accuracy(98.92%vs.96.06%),sensitivity(98.3%vs.71.26%),and F1 score(98.37%vs.79.74%)using the XGBoost model.These improvements underscore the effectiveness of our custom SMOTE sampling strategies in addressing class imbalance.The findings contribute to environmental management by enabling ecology specialists to develop more accurate strategies for monitoring,assessing,and managing drinking water quality,ensuring better ecosystem and public health outcomes.展开更多
Sustainable Development Goal 2(SDG 2,zero hunger)highlights that global hunger and food insecurity have worsened since 2015,driven in part by growing imbalance.Addressing the challenge of achieving SDG 2 in the face o...Sustainable Development Goal 2(SDG 2,zero hunger)highlights that global hunger and food insecurity have worsened since 2015,driven in part by growing imbalance.Addressing the challenge of achieving SDG 2 in the face of rapid global population growth requires sustained attention to global and national cropland changes.Accurately quantifying the correlation between population and cropland area(i.e.,SDG 2.4.1 per capita cropland)and analyzing the trends of global cropland imbalance are essential for a comprehensive understanding of SDG 2.In this study,we utilized a new global 30 m land-cover dynamic dataset(GLC_FCS30D)to analyze cropland dynamics,quantify per capita cropland and its changes across various countries and levels of development.Our results indicate that the global cropland area expanded by 0.944 million km^(2)from 1985 to 2022,with an average expansion rate of 2.42×10^(4)km^(2)/yr.However,the global per capita cropland area decreased from 0.347 ha in 1985 to 0.217 ha in 2022,mainly due to a higher population increase of nearly 65%in the same period.In the context of globalization,cropland expansion and per capita cropland exhibited spatial imbalances globally,particularly in developing countries.Developing countries saw an increase in total cropland area by 7.09%but a significant decrease in per capita cropland area by 37.38%.From a temporal perspective,the global imbalance has been steadily increasing with the Gini index rising from 0.895 in 1985 to 0.909 in 2022.Consequently,this study reveals an increasing imbalance of global per capita cropland across various countries,which threatens the attainment of the targets of SDG 2.展开更多
This study investigates the distribution and imbalances of research funding in the field of Environmental Chemistry,utilizing application and funding data fromthe National Natural Science Foundation of China(NSFC)over...This study investigates the distribution and imbalances of research funding in the field of Environmental Chemistry,utilizing application and funding data fromthe National Natural Science Foundation of China(NSFC)over the past decade.The findings reveal significant regional disparities,with Eastern regions receiving over 70%of the national funding,while the Northeast accounts for only 4%to 6.5%.Additionally,the analysis shows notable differences in funding allocation among various research institutions,with a substantial portion of funds concentrated in a few leading institutions,leading to inequities across different types and levels of organizations.The impact of applicant gender on funding disparities is relatively minor;although female applicants have a slightly lower funding rate,the concentration of funds is marginally higher among females.Furthermore,the study highlights that key projects and talent-oriented initiatives,due to their significant funding concentration,exacerbate the existing imbalances.Overall,this research provides valuable insights for optimizing funding policies and advocates for a more equitable distribution of resources in Environmental Chemistry research,addressing the identified disparities.展开更多
This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning.This issue is caused by the class imbalance of the radio frequency interference samples in the traini...This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning.This issue is caused by the class imbalance of the radio frequency interference samples in the training dataset,and one solution is applied to improve the distribution of the training data by augmenting minority class samples using a deep convolutional generative adversarial network.Experi.mental results demonstrate that retraining the deep learning model with the newly generated dataset leads to a new fast radio burst classifier,which effectively reduces false positives caused by periodic wide-band impulsive radio frequency interference,thereby enhancing the performance of the search pipeline.展开更多
A system model is developed to describe the translational and rotational motion of an active-magnetic-bearing-suspended rigid rotor in a single-gimbal control moment gyro onboard a rigid satellite. This model strictly...A system model is developed to describe the translational and rotational motion of an active-magnetic-bearing-suspended rigid rotor in a single-gimbal control moment gyro onboard a rigid satellite. This model strictly reflects the motion characteristics of the rotor by considering the dynamic and static imbalance as well as the coupling between the gimbal＇s and the rotor＇s motion on a satellite platform. Adaptive auto-centering control is carefully constructed for the rotor with unknown dynamic and static imbalance. The rotor makes its rotation about the principal axis of inertia through identifying the small rotational angles between the geometric axis and the principal axis as well as the displacements from the geometric center to the mass center so as to tune a stabilizing controller composed of a decentralized PD controller with cross-axis proportional gains and high- and low-pass filters. The main disturbance in the wheel spinning can thereby be completely removed and the vibration acting on the satellite attenuated.展开更多
A pilot pattern across two orthogonal frequency division multiplexing OFDM symbols with a special structure is designed for the channel estimation of OFDM systems with inphase and quadrature IQ imbalances at the recei...A pilot pattern across two orthogonal frequency division multiplexing OFDM symbols with a special structure is designed for the channel estimation of OFDM systems with inphase and quadrature IQ imbalances at the receiver.A high-efficiency time-domain TD least square LS channel estimator and a low-complexity frequency-domain Gaussian elimination GE equalizer are proposed to eliminate IQ distortion.The former estimator can significantly suppress channel noise by a factor N/L＋1 over the existing frequency-domain FD LS where N and L＋1 are the total number of subcarriers and the length of cyclic prefix and the proposed GE requires only 2N complex multiplications per OFDM symbol.Simulation results show that by exploiting the TD property of the channel the proposed TD-LS channel estimator obtains a significant signal-to-noise ratio gain over the existing FD-LS one whereas the proposed low-complexity GE compensation achieves the same bit error rate BER performance as the existing LS one.展开更多
Rectifying the structural imbalance between the provision of and demand for rural public services can effectively boost the efficiency of public funds utilization and the level of public service provision. Based on th...Rectifying the structural imbalance between the provision of and demand for rural public services can effectively boost the efficiency of public funds utilization and the level of public service provision. Based on the findings of a field survey, this article presents a summary of the structural imbalance between the provision of and demand for rural public services. This paper holds that the structural imbalance is primarily reflected in the dislocation between provision and demand, the unsuitable mode of provision, the monolithic provision mechanism, the excessive focus on construction at the expense of governance and the overemphasis of counties and townships at the cost of villages. Such structural imbalance is principally because of the limited financial strength of government at the grass-roots level due to treasury centralization and the over-dependence of public services on special funds allocated by government at or above provincial level.展开更多
A model of fuel injection adjustment for balancing the 4-stroke six cylinder diesel engine coupling geneset is developed by detecting imbalance in operating engine by the frequency analysis of the crankshaft＇s speed ...A model of fuel injection adjustment for balancing the 4-stroke six cylinder diesel engine coupling geneset is developed by detecting imbalance in operating engine by the frequency analysis of the crankshaft＇s speed variation. In this work, the crankshaft is considered to be a rigid body, so that the variation of its angular speed could be directly correlated to the total gas-pressure torque. By analyzing only the lower harmonic orders, the speed variation spectrum can filter out the distortions produced by the dynamic response of the crankshaft. The information carried by these harmonic orders permits to establish correlations between measurements and the average gas pressure torque of the engine, and to detect imbalance and identify faulty cylinders. Detailed experimental reading are taken on diesel engine coupling genset on the test bed of Greaves Cotton Ltd Pane, India.展开更多
背景:肢体间不对称是人类生长发育过程中的一般现象,长期专项训练可使运动员肢体间不对称发生特异性适应。目的:回顾了体育运动中肢体间不对称的形成原因、表现形式及对运动能力的影响,并概述了相关评估方法及干预策略。方法:检索中国...背景:肢体间不对称是人类生长发育过程中的一般现象,长期专项训练可使运动员肢体间不对称发生特异性适应。目的:回顾了体育运动中肢体间不对称的形成原因、表现形式及对运动能力的影响,并概述了相关评估方法及干预策略。方法:检索中国知网、万方、PubMed及Web of Science数据库建库至2024年9月间收录的文献,中文检索词为“不对称,对称,失衡,平衡,力量,爆发力,跳跃,跑,人体测量学,运动损伤,运动能力,运动表现”,英文检索词为“asymmetry,asymmetries,asymmetric,asymmetrical,imbalance,strength,power,force,jump,sprint,athletic performance,anthropometry,injury”。排除重复发表、内容不相关及会议文献后,最终纳入131篇文献进行分析。结果与结论:(1)肢体间不对称可受遗传、任务、训练、损伤、疲劳和肢体偏好等因素影响,主要表现为解剖结构、力量表现、专项任务不对称等形式;(2)肢体间不对称增大可导致双侧同相位对称类运动能力受损,但与双侧异相位对称类运动能力间关系尚不明确;(3)训练干预可有效改善肢体间不对称,且单侧训练效果优于双侧,训练方式和内容选择应符合专项需求;(4)为进一步厘清肢体间不对称与运动能力间的关系,建议未来研究秉承肢体间不对称的“任务特异性”理念,规范研究设计,选择敏感测试手段与指标,统一计算方式以提供更多的高质量证据,并分类制定不同专项肢体间不对称的预警阈值标准。展开更多
As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security ...As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.展开更多
With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion det...With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
文摘BACKGROUND:Electrolyte imbalance is common following traumatic brain injury(TBI)and can significantly impact patient outcomes.We aimed to explore the occurrence,patterns,and consequences of electrolyte imbalance in adult patients with TBI.METHODS:A retrospective study was conducted from 2016 to 2021 at a level 1 trauma center among hospitalized TBI patients.On admission,the levels of serum electrolytes,including sodium,potassium,calcium,magnesium,and phosphate,were analyzed.Demographics,injury characteristics,and interventions were assessed.The primary outcome was the in-hospital mortality.Multivariate logistic regression analysis was performed to identify independent predictors of mortality in TBI patients.RESULTS:A total of 922 TBI patients were included in the analysis,of whom 902(98%)had electrolyte imbalance.The mean age of patients with electrolyte imbalance was 32.0±15.0 years.Most patients were males(94%).The most common electrolyte abnormalities were hypocalcemia,hypophosphatemia,and hypokalemia.The overall in-hospital mortality rate was 22%in the entire cohort.In multivariate logistic analysis,the predictors of mortality included age(odds ratio[OR]=1.029,95%confidence intervals[CI]:1.013-1.046,P<0.001),low GCS(OR=0.883,95%CI:0.816-0.956,P=0.002),high Injury Severity Score(ISS)scale(OR=1.051,95%CI:1.026-1.078,P<0.001),hypernatremia(OR=2.175,95%CI:1.196-3.955,P=0.011),hyperkalemia(OR=4.862,95%CI:1.222-19.347;P=0.025),low serum bicarbonate levels(OR=0.926,95%CI:0.868-0.988,P=0.020),high serum lactate levels(OR=1.128,95%CI:1.022-1.244,P=0.017),high glucose levels(OR=1.072,95%CI:1.014-1.133,P=0.015),a longer activated partial thromboplastin time(OR=1.054,95%CI:1.024-1.084,P<0.001)and higer international normalized ratio(INR)(OR=3.825,95%CI:1.592-9.188,P=0.003).CONCLUSION:Electrolyte imbalance is common in TBI patients,with the significant prevalence of hypocalcemia,hypophosphatemia,and hypokalemia.However,hypernatremia and hyperkalemia were associated with the risk of mortality,emphasizing the need for further research to comprehend electrolyte dynamics in TBI patients.
基金supported by National Key R&D Program of China(2022YFA1008000)the National Natural Science Foundation of China(12571297,12101585)+1 种基金the CAS Talent Introduction Program(Category B)the Young Elite Scientist Sponsorship Program by CAST(YESS20220125).
文摘Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in bank credit default datasets limits the predictive performance of traditional machine learning and deep learning models.To address this issue,this study employs the conditional variational autoencoder-Wasserstein generative adversarial network with gradient penalty(CVAE-WGAN-gp)model for oversampling,generating samples similar to the original default customer data to enhance model prediction performance.To evaluate the quality of the data generated by the CVAE-WGAN-gp model,we selected several bank loan datasets for experimentation.The experimental results demonstrate that using the CVAE-WGAN-gp model for oversampling can significantly improve the predictive performance in credit risk assessment problems.
文摘This study demonstrates the complexity and importance of water quality as a measure of the health and sustainability of ecosystems that directly influence biodiversity,human health,and the world economy.The predictability of water quality thus plays a crucial role in managing our ecosystems to make informed decisions and,hence,proper environmental management.This study addresses these challenges by proposing an effective machine learning methodology applied to the“Water Quality”public dataset.The methodology has modeled the dataset suitable for providing prediction classification analysis with high values of the evaluating parameters such as accuracy,sensitivity,and specificity.The proposed methodology is based on two novel approaches:(a)the SMOTE method to deal with unbalanced data and(b)the skillfully involved classical machine learning models.This paper uses Random Forests,Decision Trees,XGBoost,and Support Vector Machines because they can handle large datasets,train models for handling skewed datasets,and provide high accuracy in water quality classification.A key contribution of this work is the use of custom sampling strategies within the SMOTE approach,which significantly enhanced performance metrics and improved class imbalance handling.The results demonstrate significant improvements in predictive performance,achieving the highest reported metrics:accuracy(98.92%vs.96.06%),sensitivity(98.3%vs.71.26%),and F1 score(98.37%vs.79.74%)using the XGBoost model.These improvements underscore the effectiveness of our custom SMOTE sampling strategies in addressing class imbalance.The findings contribute to environmental management by enabling ecology specialists to develop more accurate strategies for monitoring,assessing,and managing drinking water quality,ensuring better ecosystem and public health outcomes.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFB3907403)the National Natural Science Foundation of China(Grant No.42201499)the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals(Grant No.CBAS2022ORP03).
文摘Sustainable Development Goal 2(SDG 2,zero hunger)highlights that global hunger and food insecurity have worsened since 2015,driven in part by growing imbalance.Addressing the challenge of achieving SDG 2 in the face of rapid global population growth requires sustained attention to global and national cropland changes.Accurately quantifying the correlation between population and cropland area(i.e.,SDG 2.4.1 per capita cropland)and analyzing the trends of global cropland imbalance are essential for a comprehensive understanding of SDG 2.In this study,we utilized a new global 30 m land-cover dynamic dataset(GLC_FCS30D)to analyze cropland dynamics,quantify per capita cropland and its changes across various countries and levels of development.Our results indicate that the global cropland area expanded by 0.944 million km^(2)from 1985 to 2022,with an average expansion rate of 2.42×10^(4)km^(2)/yr.However,the global per capita cropland area decreased from 0.347 ha in 1985 to 0.217 ha in 2022,mainly due to a higher population increase of nearly 65%in the same period.In the context of globalization,cropland expansion and per capita cropland exhibited spatial imbalances globally,particularly in developing countries.Developing countries saw an increase in total cropland area by 7.09%but a significant decrease in per capita cropland area by 37.38%.From a temporal perspective,the global imbalance has been steadily increasing with the Gini index rising from 0.895 in 1985 to 0.909 in 2022.Consequently,this study reveals an increasing imbalance of global per capita cropland across various countries,which threatens the attainment of the targets of SDG 2.
基金supported by the Major Project of Philosophy and Social Science Research of Jiangsu(No.2019SJZDA073).
文摘This study investigates the distribution and imbalances of research funding in the field of Environmental Chemistry,utilizing application and funding data fromthe National Natural Science Foundation of China(NSFC)over the past decade.The findings reveal significant regional disparities,with Eastern regions receiving over 70%of the national funding,while the Northeast accounts for only 4%to 6.5%.Additionally,the analysis shows notable differences in funding allocation among various research institutions,with a substantial portion of funds concentrated in a few leading institutions,leading to inequities across different types and levels of organizations.The impact of applicant gender on funding disparities is relatively minor;although female applicants have a slightly lower funding rate,the concentration of funds is marginally higher among females.Furthermore,the study highlights that key projects and talent-oriented initiatives,due to their significant funding concentration,exacerbate the existing imbalances.Overall,this research provides valuable insights for optimizing funding policies and advocates for a more equitable distribution of resources in Environmental Chemistry research,addressing the identified disparities.
基金supported by the Chinese Academy of Science"Light of West China"Program(2022-XBQNXZ-015)the National Natural Science Foundation of China(11903071)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China and administered by the Chinese Academy of Sciences。
文摘This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning.This issue is caused by the class imbalance of the radio frequency interference samples in the training dataset,and one solution is applied to improve the distribution of the training data by augmenting minority class samples using a deep convolutional generative adversarial network.Experi.mental results demonstrate that retraining the deep learning model with the newly generated dataset leads to a new fast radio burst classifier,which effectively reduces false positives caused by periodic wide-band impulsive radio frequency interference,thereby enhancing the performance of the search pipeline.
文摘A system model is developed to describe the translational and rotational motion of an active-magnetic-bearing-suspended rigid rotor in a single-gimbal control moment gyro onboard a rigid satellite. This model strictly reflects the motion characteristics of the rotor by considering the dynamic and static imbalance as well as the coupling between the gimbal＇s and the rotor＇s motion on a satellite platform. Adaptive auto-centering control is carefully constructed for the rotor with unknown dynamic and static imbalance. The rotor makes its rotation about the principal axis of inertia through identifying the small rotational angles between the geometric axis and the principal axis as well as the displacements from the geometric center to the mass center so as to tune a stabilizing controller composed of a decentralized PD controller with cross-axis proportional gains and high- and low-pass filters. The main disturbance in the wheel spinning can thereby be completely removed and the vibration acting on the satellite attenuated.
基金The Open Research Fund of National Mobile Communications Research Laboratory of Southeast University(No.2013D02)the Fundamental Research Funds for the Central Universities(No.30920130122004)the National Natural Science Foundation of China(No.61271230,61472190)
文摘A pilot pattern across two orthogonal frequency division multiplexing OFDM symbols with a special structure is designed for the channel estimation of OFDM systems with inphase and quadrature IQ imbalances at the receiver.A high-efficiency time-domain TD least square LS channel estimator and a low-complexity frequency-domain Gaussian elimination GE equalizer are proposed to eliminate IQ distortion.The former estimator can significantly suppress channel noise by a factor N/L＋1 over the existing frequency-domain FD LS where N and L＋1 are the total number of subcarriers and the length of cyclic prefix and the proposed GE requires only 2N complex multiplications per OFDM symbol.Simulation results show that by exploiting the TD property of the channel the proposed TD-LS channel estimator obtains a significant signal-to-noise ratio gain over the existing FD-LS one whereas the proposed low-complexity GE compensation achieves the same bit error rate BER performance as the existing LS one.
文摘Rectifying the structural imbalance between the provision of and demand for rural public services can effectively boost the efficiency of public funds utilization and the level of public service provision. Based on the findings of a field survey, this article presents a summary of the structural imbalance between the provision of and demand for rural public services. This paper holds that the structural imbalance is primarily reflected in the dislocation between provision and demand, the unsuitable mode of provision, the monolithic provision mechanism, the excessive focus on construction at the expense of governance and the overemphasis of counties and townships at the cost of villages. Such structural imbalance is principally because of the limited financial strength of government at the grass-roots level due to treasury centralization and the over-dependence of public services on special funds allocated by government at or above provincial level.
基金supported by the BCUD University of Pune,India under Grant No.BCUD/OSD/184 as two year contract & Greaves Cotton Ltd (DEU) Pune
文摘A model of fuel injection adjustment for balancing the 4-stroke six cylinder diesel engine coupling geneset is developed by detecting imbalance in operating engine by the frequency analysis of the crankshaft＇s speed variation. In this work, the crankshaft is considered to be a rigid body, so that the variation of its angular speed could be directly correlated to the total gas-pressure torque. By analyzing only the lower harmonic orders, the speed variation spectrum can filter out the distortions produced by the dynamic response of the crankshaft. The information carried by these harmonic orders permits to establish correlations between measurements and the average gas pressure torque of the engine, and to detect imbalance and identify faulty cylinders. Detailed experimental reading are taken on diesel engine coupling genset on the test bed of Greaves Cotton Ltd Pane, India.
文摘背景:肢体间不对称是人类生长发育过程中的一般现象,长期专项训练可使运动员肢体间不对称发生特异性适应。目的:回顾了体育运动中肢体间不对称的形成原因、表现形式及对运动能力的影响,并概述了相关评估方法及干预策略。方法:检索中国知网、万方、PubMed及Web of Science数据库建库至2024年9月间收录的文献,中文检索词为“不对称,对称,失衡,平衡,力量,爆发力,跳跃,跑,人体测量学,运动损伤,运动能力,运动表现”,英文检索词为“asymmetry,asymmetries,asymmetric,asymmetrical,imbalance,strength,power,force,jump,sprint,athletic performance,anthropometry,injury”。排除重复发表、内容不相关及会议文献后,最终纳入131篇文献进行分析。结果与结论:(1)肢体间不对称可受遗传、任务、训练、损伤、疲劳和肢体偏好等因素影响,主要表现为解剖结构、力量表现、专项任务不对称等形式;(2)肢体间不对称增大可导致双侧同相位对称类运动能力受损,但与双侧异相位对称类运动能力间关系尚不明确;(3)训练干预可有效改善肢体间不对称,且单侧训练效果优于双侧,训练方式和内容选择应符合专项需求;(4)为进一步厘清肢体间不对称与运动能力间的关系,建议未来研究秉承肢体间不对称的“任务特异性”理念,规范研究设计,选择敏感测试手段与指标,统一计算方式以提供更多的高质量证据,并分类制定不同专项肢体间不对称的预警阈值标准。
基金supported by the Key Project of Joint Fund of the National Natural Science Foundation of China“Research on Key Technologies and Demonstration Applications for Trusted and Secure Data Circulation and Trading”(U24A20241)the National Natural Science Foundation of China“Research on Trusted Theories and Key Technologies of Data Security Trading Based on Blockchain”(62202118)+4 种基金the Major Scientific and Technological Special Project of Guizhou Province([2024]014)Scientific and Technological Research Projects from the Guizhou Education Department(Qian jiao ji[2023]003)the Hundred-Level Innovative Talent Project of the Guizhou Provincial Science and Technology Department(Qiankehe Platform Talent-GCC[2023]018)the Major Project of Guizhou Province“Research and Application of Key Technologies for Trusted Large Models Oriented to Public Big Data”(Qiankehe Major Project[2024]003)the Guizhou Province Computational Power Network Security Protection Science and Technology Innovation Talent Team(Qiankehe Talent CXTD[2025]029).
文摘As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.
基金supported by the High-Level Talent Foundation of Jinling Institute of Technology(grant number.JIT-B-202413).
文摘With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.
