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
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
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 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
A psychobiological model of the etiopathology of bipolar disorder is proposed. Based on genetic-epigenetic and chronobiological factors a hyperintentional personality structure, if faced with non-feasible intentional ...A psychobiological model of the etiopathology of bipolar disorder is proposed. Based on genetic-epigenetic and chronobiological factors a hyperintentional personality structure, if faced with non-feasible intentional programs in the environment, suffers from inner and outer stress. This stress situation leads to imbalances in information processing in glial-neuronal synaptic units, called tripartite synapses. In depression the overexpression of astrocytic receptors and of gap junctions in the astroglial network causes a prolonged information processing which affects the behavior generating systems in the brainstem reticular formation. Because the activation of the behavior generating systems is protracted, they are unable to select an appropriate mode of behavior (e.g. communicating, eating, working, sleeping, etc.) from sensory information in real time. Inversely, in mania astrocytic receptors and gap junctions are underexpressed causing a shortened synaptic information processing with rapid changes in behavior. Switching may represent a coping-attempt with depression by mania and vice versa. Towards a comprehensive model of the pathophysiology of bipolar disorder the role of microglia and their devastating effects on glial-neuronal interactions are outlined. Finally, the testing of the model is discussed.展开更多
The paper aims to investigate the current account imbalances in the context of an overview of macroeconomic fundamentals after the liberalization process in Turkey. Two main questions discussed here are： （1） What i...The paper aims to investigate the current account imbalances in the context of an overview of macroeconomic fundamentals after the liberalization process in Turkey. Two main questions discussed here are： （1） What is the link between liberalization and current account imbalances; （2） What kind of mechanisms ensured this link to become a vicious circle. The period after 1989, Turkey was characterized by significant fluctuations in macroeconomic activity by the implementation of liberalization policies. Once financial liberalization is adopted, Turkey faced with a new challenge： large current account deficits. On the other hand, foreign capital inflows aggravated a lending boom. Because of excessive risk taken by banks, interest rates began to rise. As mentioned above, the paper studies Turkey＇s liberalization process with a number of indicators that point to a fragility of the external balance： unhealthy structure of financial sector, particularly banking sector, large fiscal imbalances, low savings and investment rates, unstable GDP growth. Domestic structural features combining with macroeconomic policy stance and political factors are examined as well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
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
This paper examines the inherent relationship between the global imbalance and the financial crisis from historical review and a survey of the literature.This paper sets up a two-country model featured by monetary heg...This paper examines the inherent relationship between the global imbalance and the financial crisis from historical review and a survey of the literature.This paper sets up a two-country model featured by monetary hegemony showing that the financial crisis of 2008 is interrelated with the United States’ expansionary monetary policy and the hegemony of the U.S.dollar.This paper then analyses the impact of the crisis and the policy responses,focusing on the preconditions for China’s economic recovery.Through an international comparison,we argue that one of the Great Depression’s lessons is that the exorbitant government intervention in some areas was harmful and that the necessary condition for China’s recovery is economic flexibility,namely,resilient market mechanisms.展开更多
Multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) scheme has been considered as the most promising physical-layer architecture for the future wireless systems to provide high-spee...Multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) scheme has been considered as the most promising physical-layer architecture for the future wireless systems to provide high-speed communications. However, the performance of the MIMO-OFDM system may be degraded by in-phase/quadrature-phase (I/Q) imbalances caused by component imperfections in the analog front-ends of the transceivers. I/Q imbalances result in inter-carrier interference (ICI) in OFDM systems and cause inaccurate estimate of the channel state information (CSI), which is essential for diversity combining at the MIMO receiver. In this paper, we propose a novel approach to analyzing a MIMO-OFDM wireless communication system with I/Q imbalances over multi-path fading channels. A virtual channel is proposed as the combination of multi-path fading channel effects and I/Q imbalances at the transmitter and receiver. Based on this new approach, the effects of the channel and I/Q imbalances can be jointly estimated, and the influence of channel estimation error due to I/Q imbalances can be greatly reduced. An optimal minimal mean square error (MMSE) estimator and a low-complexity least square (LS) estimator are employed to estimate the joint coefficients of the virtual channel, which are then used to equalize the distorted signals. System performance is theoretically analyzed and verified by simulation experiments under different system configurations. The results show that the proposed method can significantly improve system performance that is close to the ideal case in which I/Q are balanced and the channel state information is known at the receiver.展开更多
China’s ballooning current account surplus has caused a plethora of adverse effects on the healthy development of its economy.Based on an in-depth analysis of the contributory factors to the swelling current account ...China’s ballooning current account surplus has caused a plethora of adverse effects on the healthy development of its economy.Based on an in-depth analysis of the contributory factors to the swelling current account surplus,this paper purports to demonstrate theoretically and empirically that while the chronic savings-consumption imbalance is an important contributor to China’s huge trade surplus,the fundamental underlying contributor is the income structure and savings structure imbalance stemming from the disproportionate increase in retained earnings relative to stagnant wage bills.Corporate retained earnings keep growing rapidly because corporate profit margins are"overstated"and state-owned enterprises"do not pay dividends."Only when these issues are resolved at the institutional level can the savings rate be reduced to an appropriate level with domestic demand boosted to eliminate excess trade surpluses and fundamentally fix internal and external economic imbalances.展开更多
To the Editor:Kidney plays a pivotal role in maintaining electrolyte balance,which is essential for the normal functioning of many metabolic and physiological processes.Electrolyte imbalances(EIs)are common manifestat...To the Editor:Kidney plays a pivotal role in maintaining electrolyte balance,which is essential for the normal functioning of many metabolic and physiological processes.Electrolyte imbalances(EIs)are common manifestations of renal dysfunction and can have potentially life-threatening consequences.Mixed EIs,defined as the coexistence of two or more EIs in a single patient,are frequently observed in nephrology practice.Previous studies have typically treated different EIs as separate entities and explored their independent associations with prognosis.However,the combined prognostic impact of coexisting EIs remains unclear.This knowledge gap highlights the need for a more comprehensive understanding of mixed EIs,particularly in patients with renal disease(RD),in which electrolyte dysregulation is both a characteristic feature and an essential therapeutic target.展开更多
Against the backdrop of geopolitical conflicts and major power competition,the US and several Western nations have claimed that China has significant"overcapacity"in manufacturing and have imposed various ta...Against the backdrop of geopolitical conflicts and major power competition,the US and several Western nations have claimed that China has significant"overcapacity"in manufacturing and have imposed various tariff and nontariff trade measures to shield domestic industries.Using data from publicly listed companies,this study conducted an industry-level analysis of investment and capacity expansion in China's manufacturing sector:We find that the growth in manufacturing investment is currently driven mainly by the"new trio"(electric vehicles,solar cells,and lithium batteries),and most enterprises have begun market-oriented clearing.However,the new trio involved in investment and capacity expansion accounts for only a small portion of China's manufacturing sector.The US narrative regarding China's overcapacity is therefore fundamentally misleading.From the perspective of major power dynamics,the essence of the overcapacity narrative lies in China-US trade imbalances,which are,in turn,a consequence of macroeconomic imbalances between the two nations.Insufficient demand in China and excess demand in the US form the underlying impetus behind the trade imbalances.展开更多
The nutritional status of rubber trees(Hevea brasiliensis)is inseparable from the production of natural rubber.Nitrogen(N)and potassium(K)levels in rubber leaves are 2 crucial criteria that reflect the nutritional sta...The nutritional status of rubber trees(Hevea brasiliensis)is inseparable from the production of natural rubber.Nitrogen(N)and potassium(K)levels in rubber leaves are 2 crucial criteria that reflect the nutritional status of the rubber tree.Advanced hyperspectral technology can evaluate N and K statuses in leaves rapidly.However,high bias and uncertain results will be generated when using a small size and imbalance dataset to train a spectral estimaion model.A typical solution of laborious long-term nutrient stress and high-intensive data collection deviates from rapid and flexible advantages of hyperspectral tech.Therefore,a less intensive and streamlined method,remining information from hyperspectral image data,was assessed.展开更多
We analyze global and euro area imbalances by focusing on China and Germany as large surplus and creditor countries. In the 2000s, domestic reforms expanded the effective labor force, restrained wages, shifted income ...We analyze global and euro area imbalances by focusing on China and Germany as large surplus and creditor countries. In the 2000s, domestic reforms expanded the effective labor force, restrained wages, shifted income toward profits and increased corporate saving. As a result, the Chinese and German current account surpluses widened, and that of Germany has proven more persistent, with subdued domestic investment. China is an early-stage creditor, holding a short equity position and a longposition in safe debt. Germany＇s balanced net debt and equity claims mark it as a mature creditor thatprovides insurance to the rest of the world. China pays to lay off equity risk, while Germany, by contrast, harvests a moderate yield on its net claims. In both economies, the shortfall of the net international investment position from cumulated current account surpluses arises from exchange rate changes, asymmetric valuation gains, and, in Germany＇s case, credit losses.展开更多
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
Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ...Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.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
Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from...Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.展开更多
With the development of sharded blockchains,high cross-shard rates and load imbalance have emerged as major challenges.Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates...With the development of sharded blockchains,high cross-shard rates and load imbalance have emerged as major challenges.Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates.Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads.Meanwhile,because of the coupling between consensus and execution,a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution.However,we observe that transaction partitioning and subsequent consensus do not require actual account data but only need to determine the relative partition order between shards.Therefore,we propose a novel sharded blockchain,called HATLedger,based on Hybrid Account and Transaction partitioning.First,HATLedger proposes building a future transaction graph to detect upcoming hotspot accounts and making more precise account partitioning to reduce transaction cross-shard rates.In the event of an impending overload,the source shard employs simulated partition transactions to specify the partition order across multiple target shards,thereby rapidly partitioning the pending transactions.The target shards can reach consensus on received transactions without waiting for account data.The source shard subsequently sends the account data to the corresponding target shards in the order specified by the previously simulated partition transactions.Based on real transaction history from Ethereum,we conducted extensive sharding scalability experiments.By maintaining low cross-shard rates and a relatively balanced load distribution,HATLedger achieves throughput improvements of 2.2x,1.9x,and 1.8x over SharPer,Shard Scheduler,and TxAllo,respectively,significantly enhancing efficiency and scalability.展开更多
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.
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.
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A psychobiological model of the etiopathology of bipolar disorder is proposed. Based on genetic-epigenetic and chronobiological factors a hyperintentional personality structure, if faced with non-feasible intentional programs in the environment, suffers from inner and outer stress. This stress situation leads to imbalances in information processing in glial-neuronal synaptic units, called tripartite synapses. In depression the overexpression of astrocytic receptors and of gap junctions in the astroglial network causes a prolonged information processing which affects the behavior generating systems in the brainstem reticular formation. Because the activation of the behavior generating systems is protracted, they are unable to select an appropriate mode of behavior (e.g. communicating, eating, working, sleeping, etc.) from sensory information in real time. Inversely, in mania astrocytic receptors and gap junctions are underexpressed causing a shortened synaptic information processing with rapid changes in behavior. Switching may represent a coping-attempt with depression by mania and vice versa. Towards a comprehensive model of the pathophysiology of bipolar disorder the role of microglia and their devastating effects on glial-neuronal interactions are outlined. Finally, the testing of the model is discussed.
文摘The paper aims to investigate the current account imbalances in the context of an overview of macroeconomic fundamentals after the liberalization process in Turkey. Two main questions discussed here are： （1） What is the link between liberalization and current account imbalances; （2） What kind of mechanisms ensured this link to become a vicious circle. The period after 1989, Turkey was characterized by significant fluctuations in macroeconomic activity by the implementation of liberalization policies. Once financial liberalization is adopted, Turkey faced with a new challenge： large current account deficits. On the other hand, foreign capital inflows aggravated a lending boom. Because of excessive risk taken by banks, interest rates began to rise. As mentioned above, the paper studies Turkey＇s liberalization process with a number of indicators that point to a fragility of the external balance： unhealthy structure of financial sector, particularly banking sector, large fiscal imbalances, low savings and investment rates, unstable GDP growth. Domestic structural features combining with macroeconomic policy stance and political factors are examined as well
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.
文摘This paper examines the inherent relationship between the global imbalance and the financial crisis from historical review and a survey of the literature.This paper sets up a two-country model featured by monetary hegemony showing that the financial crisis of 2008 is interrelated with the United States’ expansionary monetary policy and the hegemony of the U.S.dollar.This paper then analyses the impact of the crisis and the policy responses,focusing on the preconditions for China’s economic recovery.Through an international comparison,we argue that one of the Great Depression’s lessons is that the exorbitant government intervention in some areas was harmful and that the necessary condition for China’s recovery is economic flexibility,namely,resilient market mechanisms.
文摘Multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) scheme has been considered as the most promising physical-layer architecture for the future wireless systems to provide high-speed communications. However, the performance of the MIMO-OFDM system may be degraded by in-phase/quadrature-phase (I/Q) imbalances caused by component imperfections in the analog front-ends of the transceivers. I/Q imbalances result in inter-carrier interference (ICI) in OFDM systems and cause inaccurate estimate of the channel state information (CSI), which is essential for diversity combining at the MIMO receiver. In this paper, we propose a novel approach to analyzing a MIMO-OFDM wireless communication system with I/Q imbalances over multi-path fading channels. A virtual channel is proposed as the combination of multi-path fading channel effects and I/Q imbalances at the transmitter and receiver. Based on this new approach, the effects of the channel and I/Q imbalances can be jointly estimated, and the influence of channel estimation error due to I/Q imbalances can be greatly reduced. An optimal minimal mean square error (MMSE) estimator and a low-complexity least square (LS) estimator are employed to estimate the joint coefficients of the virtual channel, which are then used to equalize the distorted signals. System performance is theoretically analyzed and verified by simulation experiments under different system configurations. The results show that the proposed method can significantly improve system performance that is close to the ideal case in which I/Q are balanced and the channel state information is known at the receiver.
基金funded and supported by the China Reform Foundation and the Ministry of Commerce
文摘China’s ballooning current account surplus has caused a plethora of adverse effects on the healthy development of its economy.Based on an in-depth analysis of the contributory factors to the swelling current account surplus,this paper purports to demonstrate theoretically and empirically that while the chronic savings-consumption imbalance is an important contributor to China’s huge trade surplus,the fundamental underlying contributor is the income structure and savings structure imbalance stemming from the disproportionate increase in retained earnings relative to stagnant wage bills.Corporate retained earnings keep growing rapidly because corporate profit margins are"overstated"and state-owned enterprises"do not pay dividends."Only when these issues are resolved at the institutional level can the savings rate be reduced to an appropriate level with domestic demand boosted to eliminate excess trade surpluses and fundamentally fix internal and external economic imbalances.
基金supported by the grants from Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences(No.2024-I2M-C&T-B-011)National College Student Innovation and Entrepreneurship Training Program(No.2023zglc06001).
文摘To the Editor:Kidney plays a pivotal role in maintaining electrolyte balance,which is essential for the normal functioning of many metabolic and physiological processes.Electrolyte imbalances(EIs)are common manifestations of renal dysfunction and can have potentially life-threatening consequences.Mixed EIs,defined as the coexistence of two or more EIs in a single patient,are frequently observed in nephrology practice.Previous studies have typically treated different EIs as separate entities and explored their independent associations with prognosis.However,the combined prognostic impact of coexisting EIs remains unclear.This knowledge gap highlights the need for a more comprehensive understanding of mixed EIs,particularly in patients with renal disease(RD),in which electrolyte dysregulation is both a characteristic feature and an essential therapeutic target.
文摘Against the backdrop of geopolitical conflicts and major power competition,the US and several Western nations have claimed that China has significant"overcapacity"in manufacturing and have imposed various tariff and nontariff trade measures to shield domestic industries.Using data from publicly listed companies,this study conducted an industry-level analysis of investment and capacity expansion in China's manufacturing sector:We find that the growth in manufacturing investment is currently driven mainly by the"new trio"(electric vehicles,solar cells,and lithium batteries),and most enterprises have begun market-oriented clearing.However,the new trio involved in investment and capacity expansion accounts for only a small portion of China's manufacturing sector.The US narrative regarding China's overcapacity is therefore fundamentally misleading.From the perspective of major power dynamics,the essence of the overcapacity narrative lies in China-US trade imbalances,which are,in turn,a consequence of macroeconomic imbalances between the two nations.Insufficient demand in China and excess demand in the US form the underlying impetus behind the trade imbalances.
基金supported by the High-level Talent Project of Natural Science Foundation of Hainan Province(No.321RC468)the Key R&D project of Hainan Province(ZDYF2022GXJS008)+1 种基金the National Natural Science Foundation of China(No.32060413)the Innovation Research Team Project of Natural Science Foundation of Hainan Province(No.320CXTD431).
文摘The nutritional status of rubber trees(Hevea brasiliensis)is inseparable from the production of natural rubber.Nitrogen(N)and potassium(K)levels in rubber leaves are 2 crucial criteria that reflect the nutritional status of the rubber tree.Advanced hyperspectral technology can evaluate N and K statuses in leaves rapidly.However,high bias and uncertain results will be generated when using a small size and imbalance dataset to train a spectral estimaion model.A typical solution of laborious long-term nutrient stress and high-intensive data collection deviates from rapid and flexible advantages of hyperspectral tech.Therefore,a less intensive and streamlined method,remining information from hyperspectral image data,was assessed.
文摘We analyze global and euro area imbalances by focusing on China and Germany as large surplus and creditor countries. In the 2000s, domestic reforms expanded the effective labor force, restrained wages, shifted income toward profits and increased corporate saving. As a result, the Chinese and German current account surpluses widened, and that of Germany has proven more persistent, with subdued domestic investment. China is an early-stage creditor, holding a short equity position and a longposition in safe debt. Germany＇s balanced net debt and equity claims mark it as a mature creditor thatprovides insurance to the rest of the world. China pays to lay off equity risk, while Germany, by contrast, harvests a moderate yield on its net claims. In both economies, the shortfall of the net international investment position from cumulated current account surpluses arises from exchange rate changes, asymmetric valuation gains, and, in Germany＇s case, credit losses.
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.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia Grant No.KFU253765.
文摘Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.
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 Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[RS-2021-II211341,Artificial Intelligence Graduate School Program(Chung-Ang University)],and by the Chung-Ang University Graduate Research Scholarship in 2024.
文摘Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.
基金funded by the National Key Research and Development Program of China(Grant No.2024YFE0209000)the NSFC(Grant No.U23B2019)。
文摘With the development of sharded blockchains,high cross-shard rates and load imbalance have emerged as major challenges.Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates.Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads.Meanwhile,because of the coupling between consensus and execution,a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution.However,we observe that transaction partitioning and subsequent consensus do not require actual account data but only need to determine the relative partition order between shards.Therefore,we propose a novel sharded blockchain,called HATLedger,based on Hybrid Account and Transaction partitioning.First,HATLedger proposes building a future transaction graph to detect upcoming hotspot accounts and making more precise account partitioning to reduce transaction cross-shard rates.In the event of an impending overload,the source shard employs simulated partition transactions to specify the partition order across multiple target shards,thereby rapidly partitioning the pending transactions.The target shards can reach consensus on received transactions without waiting for account data.The source shard subsequently sends the account data to the corresponding target shards in the order specified by the previously simulated partition transactions.Based on real transaction history from Ethereum,we conducted extensive sharding scalability experiments.By maintaining low cross-shard rates and a relatively balanced load distribution,HATLedger achieves throughput improvements of 2.2x,1.9x,and 1.8x over SharPer,Shard Scheduler,and TxAllo,respectively,significantly enhancing efficiency and scalability.
