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
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 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
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
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru...Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.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
Objective To investigate the co-effect of Demand-control-support （DCS） model and Effort-reward Imbalance （ERI） model on the risk estimation of depression in humans in comparison with the effects when they are used...Objective To investigate the co-effect of Demand-control-support （DCS） model and Effort-reward Imbalance （ERI） model on the risk estimation of depression in humans in comparison with the effects when they are used respectively. Methods A total of 3 632 males and 1 706 females from 13 factories and companies in Henan province were recruited in this cross-sectional study. Perceived job stress was evaluated with the Job Content Questionnaire and Effort-Reward Imbalance Questionnaire （Chinese version）. Depressive symptoms were assessed by using the Center for Epidemiological Studies Depression Scale （CES-D）. Results DC （demands/job control ratio） and ERI were shown to be independently associated with depressive symptoms. The outcome of low social support and overcommitment were similar. High DC and low social support （SS）, high ERI and high overcommitment, and high DC and high ERI posed greater risks of depressive symptoms than each of them did alone. ERI model and SS model seem to be effective in estimating the risk of depressive symptoms if they are used respectively. Conclusion The DC had better performance when it was used in combination with low SS. The effect on physical demands was better than on psychological demands. The combination of DCS and ERI models could improve the risk estimate of depressive symptoms in humans.展开更多
Oestrogens are not exclusive to the female gender but occur in moderate circulating levels of 25-70 pg ml^-1 in men, compared to 44- 153 pg ml^-1 in women. Arising from aromatisation of testosterone （T）, oestrogen i...Oestrogens are not exclusive to the female gender but occur in moderate circulating levels of 25-70 pg ml^-1 in men, compared to 44- 153 pg ml^-1 in women. Arising from aromatisation of testosterone （T）, oestrogen is considered to have many opposing physiological functions and the progressive T decline in the aging male is associated with relative and/or absolute increase in serum oestradiol （E2）. Sexual disinterest and erectile dysfunction （ED） in the elderly may well be due to pathophysiological E2-T imbalance; the altered hormonal ratio may also explain the higher incidence of ED in hyperestrogenism or following exposure to environmental/plant oestrogens.展开更多
China’s financial conundrum arises from two sources: (1) its large trade (saving) surplus results in a currency mismatch because it is an immature creditor that cannot lend in its own currency. Instead foreign curren...China’s financial conundrum arises from two sources: (1) its large trade (saving) surplus results in a currency mismatch because it is an immature creditor that cannot lend in its own currency. Instead foreign currency claims (largely dollars) build up within domestic financial institutions. And (2) economists – both American and Chinese – mistakenly attribute the surpluses to an undervalued renminbi. To placate the United States, the result is a gradual appreciation of the renminbi against the dollar of 6% or more per year. This predictable appreciation since 2004, and the fall in US interest rates since mid 2007, not only attracts hot money inflows but inhibits private capital outflows from financing China’s huge trade surplus. This one-way bet in the foreign exchange markets can no longer be offset by relatively low interest rates in China compared to the United States, as had been the case in 2005-06. Thus, the People’s Bank of China (PBOC) now must intervene heavily to prevent the renminbi from ratcheting upwards – and so becomes the country’s sole international financial intermediary. Despite massive efforts by the PBOC to sterilize the monetary consequences of the reserve buildup, inflation in China is increasing, with excess liquidity that spills over into the world economy. China has been transformed from a deflationary force on American and European price levels into an inflationary one. Because of the currency mismatch, floating the RMB is neither feasible nor desirable – and a higher RMB would not reduce China’s trade surplus. Instead, monetary control and normal private-sector finance for the trade surplus require a return to a credibly fixed nominal yuan/dollar rate similar to that which existed between 1995 and 2004. But for any newly reset yuan/dollar rate to be credible as a monetary anchor, foreign "China bashing" to get the RMB up must end. Currency stabilization would allow the PBOC to regain monetary control and quash inflation. Only then can the Chinese government take decisive steps to reduce the trade (saving) surplus by tax cuts, increased social expenditures, and higher dividend payouts. But as long as the economy remains overheated, the government hesitates to take these trade-surplus-reduction measures because of their near-term inflationary consequences.展开更多
A better understanding of the regional disparity and imbalance characteristics of China's urbanization development is the important premise for constituting correct policy and strategy and promoting the healthy an...A better understanding of the regional disparity and imbalance characteristics of China's urbanization development is the important premise for constituting correct policy and strategy and promoting the healthy and sustainable development of urbanization in the 21st century. The regional differences of China's urbanization level have close relations with natural conditions of landform and climate etc.,the urbanization level reduces with the eleva-tion of topography and decrease of precipitation. According to the statistical data set of ur-banization in 1950-2006,the temporal change course of inter-provincial disparity of Chinese urbanization level since the founding of New China in 1949 was studied,and then the inter-regional and intra-regional disparities of urbanization development were analyzed by the Theil index and its nested decomposition method,to grasp the dynamic change of spatial disparities of China's urbanization level on the whole. Using the imbalance index model,the imbalance status of urban population distribution relative to total population,grain output,total agricultural output value,gross output value of industry,tertiary industrial output value as well as gross regional product was discussed,to hold the balance characteristics of urbanization development relative to the regional development conditions from the macroscopic scales.展开更多
In diabetes mellitus, the polyol pathway is highly active and consumes approximately 30% glucose in the body. This pathway contains 2 reactions catalyzed by aldose reductase(AR) and sorbitol dehydrogenase, respectivel...In diabetes mellitus, the polyol pathway is highly active and consumes approximately 30% glucose in the body. This pathway contains 2 reactions catalyzed by aldose reductase(AR) and sorbitol dehydrogenase, respectively. AR reduces glucose to sorbitol at the expense of NADPH, while sorbitol dehydrogenase converts sorbitol to fructose at the expense of NAD+, leading to NADH production. Consumption of NADPH, accumulation of sorbitol, and generation of fructose and NADH have all been implicated in the pathogenesis of diabetes and its complications. In this review, the roles of this pathway in NADH/NAD+redox imbalance stress and oxidative stress in diabetes are highlighted. A potential intervention using nicotinamide riboside to restore redox balance as an approach to fighting diabetes is also discussed.展开更多
An efficient compensation scheme combining a timedomain Gaussian elimination（GE） channel estimator and a frequency-domain GE equalizer is proposed for orthogonal frequency division multiplexing（OFDM） systems with ...An efficient compensation scheme combining a timedomain Gaussian elimination（GE） channel estimator and a frequency-domain GE equalizer is proposed for orthogonal frequency division multiplexing（OFDM） systems with frequencydependent in-phase and quadrature-phase（IQ） imbalances at both transmitter and receiver.Compared with the traditional least square and least mean square compensation schemes,the proposed compensation scheme achieves the same bit error rate as the ideal IQ branches by using only two training OFDM symbols instead of about 20 OFDM symbols.展开更多
A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a...A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.展开更多
BACKGROUND:Fluid and electrolyte balance is a key concept to understand for maintaining homeostasis,and for a successful treatment of many metabolic disorders.There are various regulating mechanisms for the equilibriu...BACKGROUND:Fluid and electrolyte balance is a key concept to understand for maintaining homeostasis,and for a successful treatment of many metabolic disorders.There are various regulating mechanisms for the equilibrium of electrolytes in organisms.Disorders of these mechanisms result in electrolyte imbalances that may be life-threatening clinical conditions.In this study we defined the electrolyte imbalance characteristics of patients admitted to our emergency department.METHODS:This study was conducted in the Emergency Department(ED) of Uludag University Faculty of Medicine,and included 996 patients over 18 years of age.All patients had electrolyte imbalance,with various etiologies other than traumatic origin.Demographic and clinical parameters were collected after obtaining informed consent from the patients.The ethical committee of the university approved this study.RESULTS:The mean age of the patients was 59.28±16.79,and 55%of the patients were male.The common symptoms of the patients were dyspnea(14.7%),fever(13.7%),and systemic deterioration(11.9%);but the most and least frequent electrolyte imbalances were hyponatremia and hypermagnesemia,respectively.Most frequent findings in physical examination were confusion(14%),edema(10%) and rales(9%);and most frequent pathological findings in ECG were tachycardia in24%,and atrial fibrillation in 7%of the patients.Most frequent comorbidity was malignancy(39%).Most frequent diagnoses in the patients were sepsis(11%),pneumonia(9%),and acute renal failure(7%).CONCLUSIONS:Electrolyte imbalances are of particular importance in the treatment of ED patients.Therefore,ED physicians must be acknowledged of their fluid-electrolyte balance dynamics and general characteristics.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
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.
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.
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 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.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01295).
文摘Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.
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.
基金funded by Henan Provincial Health Science and Technology Key Projects(201001009)National Science and Technology Infrastructure Program(2006BAI06B 08),China
文摘Objective To investigate the co-effect of Demand-control-support （DCS） model and Effort-reward Imbalance （ERI） model on the risk estimation of depression in humans in comparison with the effects when they are used respectively. Methods A total of 3 632 males and 1 706 females from 13 factories and companies in Henan province were recruited in this cross-sectional study. Perceived job stress was evaluated with the Job Content Questionnaire and Effort-Reward Imbalance Questionnaire （Chinese version）. Depressive symptoms were assessed by using the Center for Epidemiological Studies Depression Scale （CES-D）. Results DC （demands/job control ratio） and ERI were shown to be independently associated with depressive symptoms. The outcome of low social support and overcommitment were similar. High DC and low social support （SS）, high ERI and high overcommitment, and high DC and high ERI posed greater risks of depressive symptoms than each of them did alone. ERI model and SS model seem to be effective in estimating the risk of depressive symptoms if they are used respectively. Conclusion The DC had better performance when it was used in combination with low SS. The effect on physical demands was better than on psychological demands. The combination of DCS and ERI models could improve the risk estimate of depressive symptoms in humans.
文摘Oestrogens are not exclusive to the female gender but occur in moderate circulating levels of 25-70 pg ml^-1 in men, compared to 44- 153 pg ml^-1 in women. Arising from aromatisation of testosterone （T）, oestrogen is considered to have many opposing physiological functions and the progressive T decline in the aging male is associated with relative and/or absolute increase in serum oestradiol （E2）. Sexual disinterest and erectile dysfunction （ED） in the elderly may well be due to pathophysiological E2-T imbalance; the altered hormonal ratio may also explain the higher incidence of ED in hyperestrogenism or following exposure to environmental/plant oestrogens.
文摘China’s financial conundrum arises from two sources: (1) its large trade (saving) surplus results in a currency mismatch because it is an immature creditor that cannot lend in its own currency. Instead foreign currency claims (largely dollars) build up within domestic financial institutions. And (2) economists – both American and Chinese – mistakenly attribute the surpluses to an undervalued renminbi. To placate the United States, the result is a gradual appreciation of the renminbi against the dollar of 6% or more per year. This predictable appreciation since 2004, and the fall in US interest rates since mid 2007, not only attracts hot money inflows but inhibits private capital outflows from financing China’s huge trade surplus. This one-way bet in the foreign exchange markets can no longer be offset by relatively low interest rates in China compared to the United States, as had been the case in 2005-06. Thus, the People’s Bank of China (PBOC) now must intervene heavily to prevent the renminbi from ratcheting upwards – and so becomes the country’s sole international financial intermediary. Despite massive efforts by the PBOC to sterilize the monetary consequences of the reserve buildup, inflation in China is increasing, with excess liquidity that spills over into the world economy. China has been transformed from a deflationary force on American and European price levels into an inflationary one. Because of the currency mismatch, floating the RMB is neither feasible nor desirable – and a higher RMB would not reduce China’s trade surplus. Instead, monetary control and normal private-sector finance for the trade surplus require a return to a credibly fixed nominal yuan/dollar rate similar to that which existed between 1995 and 2004. But for any newly reset yuan/dollar rate to be credible as a monetary anchor, foreign "China bashing" to get the RMB up must end. Currency stabilization would allow the PBOC to regain monetary control and quash inflation. Only then can the Chinese government take decisive steps to reduce the trade (saving) surplus by tax cuts, increased social expenditures, and higher dividend payouts. But as long as the economy remains overheated, the government hesitates to take these trade-surplus-reduction measures because of their near-term inflationary consequences.
基金National Science and Technology Supporting Program of "the Eleventh Five-Year Plan",No.2006BAJ05A06 2006BAJ14B03-01Innovation Project of CAS, No.KZCX2-YW-307-02
文摘A better understanding of the regional disparity and imbalance characteristics of China's urbanization development is the important premise for constituting correct policy and strategy and promoting the healthy and sustainable development of urbanization in the 21st century. The regional differences of China's urbanization level have close relations with natural conditions of landform and climate etc.,the urbanization level reduces with the eleva-tion of topography and decrease of precipitation. According to the statistical data set of ur-banization in 1950-2006,the temporal change course of inter-provincial disparity of Chinese urbanization level since the founding of New China in 1949 was studied,and then the inter-regional and intra-regional disparities of urbanization development were analyzed by the Theil index and its nested decomposition method,to grasp the dynamic change of spatial disparities of China's urbanization level on the whole. Using the imbalance index model,the imbalance status of urban population distribution relative to total population,grain output,total agricultural output value,gross output value of industry,tertiary industrial output value as well as gross regional product was discussed,to hold the balance characteristics of urbanization development relative to the regional development conditions from the macroscopic scales.
基金National Institutes of Health,Grant/Award Number:R01NS079792UNTHSC Seed Grants,Grant/Award Number:RI10015 and RI10039
文摘In diabetes mellitus, the polyol pathway is highly active and consumes approximately 30% glucose in the body. This pathway contains 2 reactions catalyzed by aldose reductase(AR) and sorbitol dehydrogenase, respectively. AR reduces glucose to sorbitol at the expense of NADPH, while sorbitol dehydrogenase converts sorbitol to fructose at the expense of NAD+, leading to NADH production. Consumption of NADPH, accumulation of sorbitol, and generation of fructose and NADH have all been implicated in the pathogenesis of diabetes and its complications. In this review, the roles of this pathway in NADH/NAD+redox imbalance stress and oxidative stress in diabetes are highlighted. A potential intervention using nicotinamide riboside to restore redox balance as an approach to fighting diabetes is also discussed.
基金supported by the National Natural Science Fundation of China(6127123061172073)the Open Research Fund of National Mobile Communications Research Lab(2010D13)
文摘An efficient compensation scheme combining a timedomain Gaussian elimination（GE） channel estimator and a frequency-domain GE equalizer is proposed for orthogonal frequency division multiplexing（OFDM） systems with frequencydependent in-phase and quadrature-phase（IQ） imbalances at both transmitter and receiver.Compared with the traditional least square and least mean square compensation schemes,the proposed compensation scheme achieves the same bit error rate as the ideal IQ branches by using only two training OFDM symbols instead of about 20 OFDM symbols.
文摘A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.
文摘BACKGROUND:Fluid and electrolyte balance is a key concept to understand for maintaining homeostasis,and for a successful treatment of many metabolic disorders.There are various regulating mechanisms for the equilibrium of electrolytes in organisms.Disorders of these mechanisms result in electrolyte imbalances that may be life-threatening clinical conditions.In this study we defined the electrolyte imbalance characteristics of patients admitted to our emergency department.METHODS:This study was conducted in the Emergency Department(ED) of Uludag University Faculty of Medicine,and included 996 patients over 18 years of age.All patients had electrolyte imbalance,with various etiologies other than traumatic origin.Demographic and clinical parameters were collected after obtaining informed consent from the patients.The ethical committee of the university approved this study.RESULTS:The mean age of the patients was 59.28±16.79,and 55%of the patients were male.The common symptoms of the patients were dyspnea(14.7%),fever(13.7%),and systemic deterioration(11.9%);but the most and least frequent electrolyte imbalances were hyponatremia and hypermagnesemia,respectively.Most frequent findings in physical examination were confusion(14%),edema(10%) and rales(9%);and most frequent pathological findings in ECG were tachycardia in24%,and atrial fibrillation in 7%of the patients.Most frequent comorbidity was malignancy(39%).Most frequent diagnoses in the patients were sepsis(11%),pneumonia(9%),and acute renal failure(7%).CONCLUSIONS:Electrolyte imbalances are of particular importance in the treatment of ED patients.Therefore,ED physicians must be acknowledged of their fluid-electrolyte balance dynamics and general characteristics.
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.
