Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,inclu...Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,including malware analysis and protocol fuzzing.However,existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery,resulting in imprecise and incomplete reconstructions.In this paper,we propose ProRE,a novel method for reconstructing protocol field structures based on program execution slice embedding.ProRE extracts code slices from protocol parsing at runtime,converts them into embedding vectors using a data flow-sensitive assembly language model,and performs hierarchical clustering to recover complete protocol field structures.Evaluation on two datasets containing 12 protocols shows that ProRE achieves an average F1 score of 0.85 and a cophenetic correlation coefficient of 0.189,improving by 19%and 0.126%respectively over state-of-the-art methods(including BinPRE,Tupni,Netlifter,and QwQ-32B-preview),demonstrating significant superiority in both accuracy and completeness of field structure recovery.Case studies further validate the effectiveness of ProRE in practical malware analysis scenarios.展开更多
Rwanda secured access to one of the world’s most lucrative agricultural markets this month when it finalised a trade protocol allowing fresh avocado exports to China,a deal that could fundamentally alter the trajecto...Rwanda secured access to one of the world’s most lucrative agricultural markets this month when it finalised a trade protocol allowing fresh avocado exports to China,a deal that could fundamentally alter the trajectory of the country’s trade.展开更多
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
Hyperpolarization of nuclear spins is crucial for advancing nuclear magnetic resonance and quantum information technologies,as nuclear spins typically exhibit extremely low polarization at room temperature due to thei...Hyperpolarization of nuclear spins is crucial for advancing nuclear magnetic resonance and quantum information technologies,as nuclear spins typically exhibit extremely low polarization at room temperature due to their small gyromagnetic ratios.A promising approach to achieving high nuclear spin polarization is transferring the polarization of electrons to nuclear spins.The nitrogen-vacancy(NV)center in diamond has emerged as a highly effective medium for this purpose,and various hyperpolarization protocols have been developed.Among these,the pulsed polarization(PulsePol)method has been extensively studied due to its robustness against static energy shifts of the electron spin.In this work,we present a novel polarization protocol and uncover a family of magic sequences for hyperpolarizing nuclear spins,with PulsePol emerging as a special case of our general approach.Notably,we demonstrate that some of these magic sequences exhibit significantly greater robustness compared to the PulsePol protocol in the presence of finite half𝜋pulse duration of the protocol,Rabi and detuning errors.This enhanced robustness positions our protocol as a more suitable candidate for hyper-polarizing nuclear spins species with large gyromagnetic ratios and also ensures better compatibility with high-efficiency readout techniques at high magnetic fields.Additionally,the generality of our protocol allows for its direct application to other solid-state quantum systems beyond the NV center.展开更多
Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variat...Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition(VMD)and Channel Attention Mechanism.First,Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power.Second,the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition(VMD).Finally,the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM model.In this model,the convolutional neural network(CNN)and channel attention mechanism dynamically adjust the weights while capturing the spatial features of the input data to improve the discriminative ability of key features.The extracted data is then fed into the bidirectional long short-term memory network(BiLSTM)to capture the time-series features,and the final output is the prediction result.The verification is conducted using a dataset from a distributed photovoltaic power station in the Northwest region of China.The results show that compared with other prediction methods,the method proposed in this paper has a higher prediction accuracy,which helps to improve the proportion of distributed PV access to the grid,and can guarantee the safe and stable operation of the power grid.展开更多
This article discusses the evolving real-world practice using nitazoxanide,nonsteroidal anti-inflammatory drugs(NSAIDs)and/or azithromycin(Kelleni’s protocol)to manage the evolving manifestations of severe acute resp...This article discusses the evolving real-world practice using nitazoxanide,nonsteroidal anti-inflammatory drugs(NSAIDs)and/or azithromycin(Kelleni’s protocol)to manage the evolving manifestations of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)Omicron EG.5.1,its descendant HV.1 as well as BA.2.86 and its descendant JN.1 subvariants in Egypt in 2024.These subvariants are well-known for their highly evolved immune-evasive properties and the manifestations include some peculiar manifestations as persistent cough besides high fever in young children as well as high fever,persistent severe cough,change of voice,loss of taste and smell,epigastric pain,nausea,vomiting,diarrhea,generalized malaise and marked bone aches in adults including the high-risk groups.It’s suggested that the ongoing SARS-CoV-2 evolution is continuing to mostly affect the high-risk groups of patients,to some of whom we’ve also successfully prescribed nitazoxanide and/or NSAIDs for post-exposure prophylaxis of all household contacts.We also continue to recommend starting the immune-modulatory antiviral Kelleni’s protocol as soon as possible in the course of infection and adjusting it in a personalized manner to be more aggressive from the beginning for the high risk patients,at least until the currently encountered surge of infections subsides.展开更多
Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,w...Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.展开更多
The care of a patient involved in major trauma with exsanguinating haemorrhage is time-critical to achieve definitive haemorrhage control,and it requires coordinated multidisciplinary care.During initial resuscitation...The care of a patient involved in major trauma with exsanguinating haemorrhage is time-critical to achieve definitive haemorrhage control,and it requires coordinated multidisciplinary care.During initial resuscitation of a patient in the emergency department(ED),Code Crimson activation facilitates rapid decisionmaking by multi-disciplinary specialists for definitive haemorrhage control in operating theatre(OT)and/or interventional radiology(IR)suite.Once this decision has been made,there may still be various factors that lead to delay in transporting the patient from ED to OT/IR.Red Blanket protocol identifies and addresses these factors and processes which cause delay,and aims to facilitate rapid and safe transport of the haemodynamically unstable patient from ED to OT,while minimizing delay in resuscitation during the transfer.The two processes,Code Crimson and Red Blanket,complement each other.It would be ideal to merge the two processes into a single protocol rather than having two separate workflows.Introducing these quality improvement strategies and coor-dinated processes within the trauma framework of the hospitals/healthcare systems will help in further improving the multi-disciplinary care for the complex trauma patients requiring rapid and definitive haemorrhage control.展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
Protocol Reverse Engineering(PRE)is of great practical importance in Internet security-related fields such as intrusion detection,vulnerability mining,and protocol fuzzing.For unknown binary protocols having fixed-len...Protocol Reverse Engineering(PRE)is of great practical importance in Internet security-related fields such as intrusion detection,vulnerability mining,and protocol fuzzing.For unknown binary protocols having fixed-length fields,and the accurate identification of field boundaries has a great impact on the subsequent analysis and final performance.Hence,this paper proposes a new protocol segmentation method based on Information-theoretic statistical analysis for binary protocols by formulating the field segmentation of unsupervised binary protocols as a probabilistic inference problem and modeling its uncertainty.Specifically,we design four related constructions between entropy changes and protocol field segmentation,introduce random variables,and construct joint probability distributions with traffic sample observations.Probabilistic inference is then performed to identify the possible protocol segmentation points.Extensive trials on nine common public and industrial control protocols show that the proposed method yields higher-quality protocol segmentation results.展开更多
BACKGROUND Surgery is the first choice of treatment for patients with colorectal cancer.Traditional open surgery imparts great damage to the body of the patient and can easily cause adverse stress reactions.With the c...BACKGROUND Surgery is the first choice of treatment for patients with colorectal cancer.Traditional open surgery imparts great damage to the body of the patient and can easily cause adverse stress reactions.With the continuous development of medical technology,laparoscopic minimally invasive surgery has shown great advantages for the treatment of patients with celiac disease.AIM To investigate the short-term efficacy of laparoscopic radical surgery and traditional laparotomy for the treatment of colorectal cancer,and the differences in the risk analysis of unplanned reoperation after operation.METHODS As the research subjects,this study selected 100 patients with colorectal cancer who received surgical treatment at the Yulin First Hospital from January 2018 to January 2022.Among them,50 patients who underwent laparoscopic radical resection were selected as the research group and 50 patients who underwent traditional laparotomy were selected as the control group.Data pertaining to clinical indexes,gastrointestinal hormones,nutrition indexes,the levels of inflammatory factors,quality of life,Visual Analog Scale score,and the postoperative complications of the two groups of patients before and after treatment were collected,and the therapeutic effects in the two groups were analyzed and compared.RESULTS Compared with the control group,perioperative bleeding,peristalsis recovery time,and hospital stays were significantly shorter in the research group.After surgery,the levels of gastrin(GAS)and motilin(MTL)were decreased in both groups,and the fluctuation range of GAS and MTL observed in the research group was significantly lower than that recorded in the control group.The hemoglobin(Hb)levels increased after surgery,and the level of Hb in the research group was significantly higher compared with the control group.After the operation,the expression levels of tumor necrosis factor-α,interleukin-6,and C-reactive protein and the total incidence of complications were significantly lower in the research group compared with the control group.One year after the operation,the quality of life of the two groups was greatly improved,with the quality of life in the research group being significantly better.CONCLUSION Laparoscopy was effective for colorectal surgery by reducing the occurrence of complications and inflammatory stress reaction;moreover,the quality of life of patients was significantly improved,which warrants further promotion.展开更多
“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian services.The dynamic nature of F...“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian services.The dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol development.Over the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high mobility.This paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and 2023.The research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant publications.The research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in FANETs.When compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance capabilities.These protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive capabilities.This comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in FANETs.Moreover,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.展开更多
Based on the observation data of hourly precipitation and the dual polarization data of the new generation of weather radar in Yushu area from 2022 to 2023,the radar characteristics of 18 short-term heavy precipitatio...Based on the observation data of hourly precipitation and the dual polarization data of the new generation of weather radar in Yushu area from 2022 to 2023,the radar characteristics of 18 short-term heavy precipitation processes were studied through mathematical statistics and comparative analysis of cases.There were four radar detection ranges:within 50 km,within 100 km,within 150 km and beyond 150 km.The evolution laws of echo intensity,echo top height,vertically integrated liquid water(VIL),radial velocity,and dual polarization parameters at different distances were mainly analyzed.The results show that there were significant differences in the radar characteristics of short-term heavy precipitation at different detection distances.Moreover,dense flat large particles,echo parameters with specific thresholds,and appropriate movement speed were important conditions for the occurrence of short-term heavy precipitation.Meanwhile,the radar determination thresholds for short-term heavy precipitation at various detection distances were extracted to provide a scientific basis for the near-term forecast and early warning of short-term heavy precipitation in Yushu area.展开更多
Styrene-butadiene-styrene(SBS)modified asphalt(SA)has long found effective applications in road construction materials.When combined with fillers,SBS-modified asphalt has demonstrated promising resistance to fatigue c...Styrene-butadiene-styrene(SBS)modified asphalt(SA)has long found effective applications in road construction materials.When combined with fillers,SBS-modified asphalt has demonstrated promising resistance to fatigue cracking caused by temperature fluctuations and aging.In this study,molybdenum disulfide(MoS_(2))and polyphosphoric acid(PPA)were ground in naphthenic oil(NO)and subjected to mechanical activation to create PPAmodified MoS_(2),referred to as OMS-PPA.By blending various ratios of OMS-PPA with SBS-modified asphalt,composite-modified asphalts were successfully developed to enhance their overall properties.To assess the mechanical characteristics and stability of these modified asphalts,various methods were employed,including penetration factor,flow activation energy,fluorescence microscopy,and dynamic shear rheology.Additionally,the short-term aging performance was evaluated using Fourier transform infrared(FTIR)spectroscopy and nanoindentation tests.The results revealed a 3.7%decrease in the penetration-temperature coefficient for SAOMS compared to SA,while 1-SA-OMS-PPA showed an even greater reduction of 7.1%.Furthermore,after short-term aging,carboxyl group generation in SA increased by 5.93%,while SA-OMS exhibited a smaller rise of 1.36%,and 1-SA-OMS-PPA saw an increase of just 0.93%.The study also highlighted significant improvements in the hardness of these materials.The hardness change ratio for SA-OMS decreased by 43.08%,while the ratio for 1-SA-OMS-PPA saw a notable reduction of 65.16% compared to unmodified SA.These findings suggest that OMS-PPA contributed to improvements in temperature sensitivity,particle dispersibility,and resistance to shortterm aging in asphalts.The results hold significant promise for the future development of advanced asphalt-based materials with potential high-value applications in flexible pavements for highways.展开更多
BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk...BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.展开更多
With the increasing penetration of renewable energy in power systems,grid structures and operational paradigms are undergoing profound transformations.When subjected to disturbances,the interaction between power elect...With the increasing penetration of renewable energy in power systems,grid structures and operational paradigms are undergoing profound transformations.When subjected to disturbances,the interaction between power electronic devices and dynamic loads introduces strongly nonlinear dynamic characteristics in grid voltage responses,posing significant threats to system security and stability.To achieve reliable short-term voltage stability assessment under large-scale renewable integration,this paper innovatively proposes a response-driven online assessment method based on energy function theory.First,energy modeling of system components is performed based on energy function theory,followed by analysis of energy interaction mechanisms during voltage instability.To address the challenge of traditional energy functions in online applications,a convolutional neural network-long short-term memory(CNNLSTM)hybrid artificial Intelligence approach is introduced.By quantifying the contribution of each energy component to voltage stability,key energy terms are identified.The measurable electrical quantities corresponding to these key energies serve as inputs,while the energy at the voltage unstable equilibrium point(UEP)obtained from offline simulations is used as both the energy threshold and the output of the artificial intelligence model,enabling the construction of an artificial intelligence model for energy threshold prediction.The measurable electrical quantities corresponding to these key energies serve as inputs,while the energy at the unstable equilibrium point(UEP)obtained from offline simulations acts as the output,enabling the construction of an artificial intelligence model for energy threshold prediction.Real-time response data are fed into the model to predict the system's instantaneous energy threshold,which is then compared with the transient energy at fault clearance to evaluate stability.Validation on both a 3-machine,10-bus system and the New England 10-machine,39-bus system confirms the method's adaptability and accuracy.The simulation results demonstrate that the proposed short-term voltage stability assessment model outperforms other methods in both accuracy and computational efficiency.展开更多
The inherent black-box nature of machine learning(ML) models limits their interpretability and broader application in heavy precipitation forecasting. Evaluating the reliability of these models involves analyzing the ...The inherent black-box nature of machine learning(ML) models limits their interpretability and broader application in heavy precipitation forecasting. Evaluating the reliability of these models involves analyzing the link between predictions and predictors. In this study, ERA5 reanalysis data, CERES satellite observations, and ground-based meteorological observatories were utilized to compile more comprehensive multi-type predictors for developing a Bayesian optimized XGBoost model for the nowcasting of heavy precipitation in the Guangdong-Hong Kong-Macao Greater Bay Area during the pre-summer rainy season. A comparison of model performance with different combinations of input features and classical machine learning algorithms demonstrated that the Bayesian optimized XGBoost model achieved the best overall performance, with an average Critical Success Index of 68.30%. Permutation Importance(PI) and shapley Additive Explanations(SHAP) methods were utilized to interpret feature effects in heavy precipitation forecasting. The results indicated that precipitable water vapor(PWV), cloud, relative humidity, and seasonal and diurnal variables had more significant effects on the model output as individual features. Furthermore, the collective influence of derivatives from PWV and meteorological parameters(e.g., temperature, relative humidity, pressure and dew point temperature)showed a significant enhancement over their individual impacts, indicating synergistic interactions among these predictors.Applying explainable artificial intelligence(XAI) to ML models helps understand how models utilize features for forecasting, enhances the reliability of forecasts, and guides feature selection and the mitigation of overfitting phenomena.展开更多
Countries worldwide are advocating for energy transition initiatives to promote the construction of low-carbon energy systems.The low voltage ride through(LVRT)characteristics of renewable energy units and commutation...Countries worldwide are advocating for energy transition initiatives to promote the construction of low-carbon energy systems.The low voltage ride through(LVRT)characteristics of renewable energy units and commutation failures in line commutated converter high voltage direct current(LCC-HVDC)systems at the receiving end leads to short-term power shortage(STPS),which differs from traditional frequency stability issues.STPS occurs during the generator’s power angle swing phase,before the governor responds,and is on a timescale that is not related to primary frequency regulation.This paper addresses these challenges by examining the impact of LVRT on voltage stability,developing a frequency response model to analyze the mechanism of frequency instability caused by STPS,deriving the impact of STPS on the maximum frequency deviation,and introducing an energy deficiency factor to assess its impact on regional frequency stability.The East China Power Grid is used as a case study,where the energy deficiency factor is calculated to validate the proposed mechanism.STPS is mainly compensated by the rotor kinetic energy of the generators in this region,with minimal impact on other regions.It is concluded that the energy deficiency factor provides an effective explanation for the spatial distribution of the impact of STPS on system frequency.展开更多
Objectives:This study aimed to clarify the short-term symptoms,duration,and influencing factors in people recovering from coronavirus disease 2019(COVID-19)after China’s dynamic zero-COVID-19 policy was implemented i...Objectives:This study aimed to clarify the short-term symptoms,duration,and influencing factors in people recovering from coronavirus disease 2019(COVID-19)after China’s dynamic zero-COVID-19 policy was implemented in December 2022.Methods:We included data from a large-scale on-line survey conducted in China between January 14 and February 1,2023.Participants were individuals of all ages.Chi-squared tests and multivariate logistic regression analyses were performed to identify factors associated with different symptoms.Results:Overall,21,012 patients from seven regions of China were included in this study(female:71.22%).For most patients,the period from symptom onset to a negative nucleic acid test result was≤10 days(72.33%).The distribution of symptoms varied at different times,with respiratory(1-4 weeks)and psychocardiology(5-8 weeks)symptoms being the most common.Multivariate analysis identified male sex,no comorbidity,and living in northeast and northwest China(compared with central China)as independent factors associated with a lower risk of symptoms,while age(41-60 years)was a possible risk factor(compared with 18-40 years).Conclusions:Short-term respiratory and psychocardiology symptoms were the most common after COVID-19 recovery.Sex,age,geographical region,and comorbidities were potential influencing factors for the development of short-term symptoms.展开更多
This paper deeply explores the application strategies of short-term cost curves in the field of economics.Firstly,it elaborates on the basic theories and constituent elements of short-term cost curves.By drawing and a...This paper deeply explores the application strategies of short-term cost curves in the field of economics.Firstly,it elaborates on the basic theories and constituent elements of short-term cost curves.By drawing and analyzing the shortterm cost curve graphs,it presents the internal relationship between costs and output.Then,it focuses on researching its application strategies in multiple aspects such as enterprise production decisions,market pricing,and industry competition analysis.展开更多
文摘Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,including malware analysis and protocol fuzzing.However,existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery,resulting in imprecise and incomplete reconstructions.In this paper,we propose ProRE,a novel method for reconstructing protocol field structures based on program execution slice embedding.ProRE extracts code slices from protocol parsing at runtime,converts them into embedding vectors using a data flow-sensitive assembly language model,and performs hierarchical clustering to recover complete protocol field structures.Evaluation on two datasets containing 12 protocols shows that ProRE achieves an average F1 score of 0.85 and a cophenetic correlation coefficient of 0.189,improving by 19%and 0.126%respectively over state-of-the-art methods(including BinPRE,Tupni,Netlifter,and QwQ-32B-preview),demonstrating significant superiority in both accuracy and completeness of field structure recovery.Case studies further validate the effectiveness of ProRE in practical malware analysis scenarios.
文摘Rwanda secured access to one of the world’s most lucrative agricultural markets this month when it finalised a trade protocol allowing fresh avocado exports to China,a deal that could fundamentally alter the trajectory of the country’s trade.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金supported by the National Natural Science Foundation of China (Grant Nos.12475012,62461160263 for P.W.,and 62276171 for H.L.)Quantum Science and Technology-National Science and Technology Major Project of China (Project No.2023ZD0300600 for P.W.)+3 种基金Guangdong Provincial Quantum Science Strategic Initiative (Grant Nos.GDZX240-3009 and GDZX2303005 for P.W.)Guangdong Basic and Applied Basic Research Foundation (Grant No.2024-A1515011938 for H.L.)Shenzhen Fundamental ResearchGeneral Project (Grant No.JCYJ20240813141503005 for H.L.)the Talents Introduction Foundation of Beijing Normal University (Grant No.310432106 for P.W.)。
文摘Hyperpolarization of nuclear spins is crucial for advancing nuclear magnetic resonance and quantum information technologies,as nuclear spins typically exhibit extremely low polarization at room temperature due to their small gyromagnetic ratios.A promising approach to achieving high nuclear spin polarization is transferring the polarization of electrons to nuclear spins.The nitrogen-vacancy(NV)center in diamond has emerged as a highly effective medium for this purpose,and various hyperpolarization protocols have been developed.Among these,the pulsed polarization(PulsePol)method has been extensively studied due to its robustness against static energy shifts of the electron spin.In this work,we present a novel polarization protocol and uncover a family of magic sequences for hyperpolarizing nuclear spins,with PulsePol emerging as a special case of our general approach.Notably,we demonstrate that some of these magic sequences exhibit significantly greater robustness compared to the PulsePol protocol in the presence of finite half𝜋pulse duration of the protocol,Rabi and detuning errors.This enhanced robustness positions our protocol as a more suitable candidate for hyper-polarizing nuclear spins species with large gyromagnetic ratios and also ensures better compatibility with high-efficiency readout techniques at high magnetic fields.Additionally,the generality of our protocol allows for its direct application to other solid-state quantum systems beyond the NV center.
基金supported by the Inner Mongolia Power Company 2024 Staff Innovation Studio Innovation Project“Research on Cluster Output Prediction and Group Control Technology for County-Wide Distributed Photovoltaic Construction”.
文摘Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition(VMD)and Channel Attention Mechanism.First,Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power.Second,the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition(VMD).Finally,the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM model.In this model,the convolutional neural network(CNN)and channel attention mechanism dynamically adjust the weights while capturing the spatial features of the input data to improve the discriminative ability of key features.The extracted data is then fed into the bidirectional long short-term memory network(BiLSTM)to capture the time-series features,and the final output is the prediction result.The verification is conducted using a dataset from a distributed photovoltaic power station in the Northwest region of China.The results show that compared with other prediction methods,the method proposed in this paper has a higher prediction accuracy,which helps to improve the proportion of distributed PV access to the grid,and can guarantee the safe and stable operation of the power grid.
文摘This article discusses the evolving real-world practice using nitazoxanide,nonsteroidal anti-inflammatory drugs(NSAIDs)and/or azithromycin(Kelleni’s protocol)to manage the evolving manifestations of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)Omicron EG.5.1,its descendant HV.1 as well as BA.2.86 and its descendant JN.1 subvariants in Egypt in 2024.These subvariants are well-known for their highly evolved immune-evasive properties and the manifestations include some peculiar manifestations as persistent cough besides high fever in young children as well as high fever,persistent severe cough,change of voice,loss of taste and smell,epigastric pain,nausea,vomiting,diarrhea,generalized malaise and marked bone aches in adults including the high-risk groups.It’s suggested that the ongoing SARS-CoV-2 evolution is continuing to mostly affect the high-risk groups of patients,to some of whom we’ve also successfully prescribed nitazoxanide and/or NSAIDs for post-exposure prophylaxis of all household contacts.We also continue to recommend starting the immune-modulatory antiviral Kelleni’s protocol as soon as possible in the course of infection and adjusting it in a personalized manner to be more aggressive from the beginning for the high risk patients,at least until the currently encountered surge of infections subsides.
基金This work is supported by the National Key Research and Development Program of China(No.2023YFB4203000)the National Natural Science Foundation of China(No.U22A20178)
文摘Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.
文摘The care of a patient involved in major trauma with exsanguinating haemorrhage is time-critical to achieve definitive haemorrhage control,and it requires coordinated multidisciplinary care.During initial resuscitation of a patient in the emergency department(ED),Code Crimson activation facilitates rapid decisionmaking by multi-disciplinary specialists for definitive haemorrhage control in operating theatre(OT)and/or interventional radiology(IR)suite.Once this decision has been made,there may still be various factors that lead to delay in transporting the patient from ED to OT/IR.Red Blanket protocol identifies and addresses these factors and processes which cause delay,and aims to facilitate rapid and safe transport of the haemodynamically unstable patient from ED to OT,while minimizing delay in resuscitation during the transfer.The two processes,Code Crimson and Red Blanket,complement each other.It would be ideal to merge the two processes into a single protocol rather than having two separate workflows.Introducing these quality improvement strategies and coor-dinated processes within the trauma framework of the hospitals/healthcare systems will help in further improving the multi-disciplinary care for the complex trauma patients requiring rapid and definitive haemorrhage control.
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
文摘Protocol Reverse Engineering(PRE)is of great practical importance in Internet security-related fields such as intrusion detection,vulnerability mining,and protocol fuzzing.For unknown binary protocols having fixed-length fields,and the accurate identification of field boundaries has a great impact on the subsequent analysis and final performance.Hence,this paper proposes a new protocol segmentation method based on Information-theoretic statistical analysis for binary protocols by formulating the field segmentation of unsupervised binary protocols as a probabilistic inference problem and modeling its uncertainty.Specifically,we design four related constructions between entropy changes and protocol field segmentation,introduce random variables,and construct joint probability distributions with traffic sample observations.Probabilistic inference is then performed to identify the possible protocol segmentation points.Extensive trials on nine common public and industrial control protocols show that the proposed method yields higher-quality protocol segmentation results.
文摘BACKGROUND Surgery is the first choice of treatment for patients with colorectal cancer.Traditional open surgery imparts great damage to the body of the patient and can easily cause adverse stress reactions.With the continuous development of medical technology,laparoscopic minimally invasive surgery has shown great advantages for the treatment of patients with celiac disease.AIM To investigate the short-term efficacy of laparoscopic radical surgery and traditional laparotomy for the treatment of colorectal cancer,and the differences in the risk analysis of unplanned reoperation after operation.METHODS As the research subjects,this study selected 100 patients with colorectal cancer who received surgical treatment at the Yulin First Hospital from January 2018 to January 2022.Among them,50 patients who underwent laparoscopic radical resection were selected as the research group and 50 patients who underwent traditional laparotomy were selected as the control group.Data pertaining to clinical indexes,gastrointestinal hormones,nutrition indexes,the levels of inflammatory factors,quality of life,Visual Analog Scale score,and the postoperative complications of the two groups of patients before and after treatment were collected,and the therapeutic effects in the two groups were analyzed and compared.RESULTS Compared with the control group,perioperative bleeding,peristalsis recovery time,and hospital stays were significantly shorter in the research group.After surgery,the levels of gastrin(GAS)and motilin(MTL)were decreased in both groups,and the fluctuation range of GAS and MTL observed in the research group was significantly lower than that recorded in the control group.The hemoglobin(Hb)levels increased after surgery,and the level of Hb in the research group was significantly higher compared with the control group.After the operation,the expression levels of tumor necrosis factor-α,interleukin-6,and C-reactive protein and the total incidence of complications were significantly lower in the research group compared with the control group.One year after the operation,the quality of life of the two groups was greatly improved,with the quality of life in the research group being significantly better.CONCLUSION Laparoscopy was effective for colorectal surgery by reducing the occurrence of complications and inflammatory stress reaction;moreover,the quality of life of patients was significantly improved,which warrants further promotion.
基金support the findings of this study are openly available in(Scopus database)at www.scopus.com(accessed on 07 January 2025).
文摘“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian services.The dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol development.Over the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high mobility.This paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and 2023.The research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant publications.The research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in FANETs.When compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance capabilities.These protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive capabilities.This comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in FANETs.Moreover,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.
文摘Based on the observation data of hourly precipitation and the dual polarization data of the new generation of weather radar in Yushu area from 2022 to 2023,the radar characteristics of 18 short-term heavy precipitation processes were studied through mathematical statistics and comparative analysis of cases.There were four radar detection ranges:within 50 km,within 100 km,within 150 km and beyond 150 km.The evolution laws of echo intensity,echo top height,vertically integrated liquid water(VIL),radial velocity,and dual polarization parameters at different distances were mainly analyzed.The results show that there were significant differences in the radar characteristics of short-term heavy precipitation at different detection distances.Moreover,dense flat large particles,echo parameters with specific thresholds,and appropriate movement speed were important conditions for the occurrence of short-term heavy precipitation.Meanwhile,the radar determination thresholds for short-term heavy precipitation at various detection distances were extracted to provide a scientific basis for the near-term forecast and early warning of short-term heavy precipitation in Yushu area.
基金financially supported by the Key Research and Development Program of Hubei Province(Nos.2022BCA077 and 2022BCA082).
文摘Styrene-butadiene-styrene(SBS)modified asphalt(SA)has long found effective applications in road construction materials.When combined with fillers,SBS-modified asphalt has demonstrated promising resistance to fatigue cracking caused by temperature fluctuations and aging.In this study,molybdenum disulfide(MoS_(2))and polyphosphoric acid(PPA)were ground in naphthenic oil(NO)and subjected to mechanical activation to create PPAmodified MoS_(2),referred to as OMS-PPA.By blending various ratios of OMS-PPA with SBS-modified asphalt,composite-modified asphalts were successfully developed to enhance their overall properties.To assess the mechanical characteristics and stability of these modified asphalts,various methods were employed,including penetration factor,flow activation energy,fluorescence microscopy,and dynamic shear rheology.Additionally,the short-term aging performance was evaluated using Fourier transform infrared(FTIR)spectroscopy and nanoindentation tests.The results revealed a 3.7%decrease in the penetration-temperature coefficient for SAOMS compared to SA,while 1-SA-OMS-PPA showed an even greater reduction of 7.1%.Furthermore,after short-term aging,carboxyl group generation in SA increased by 5.93%,while SA-OMS exhibited a smaller rise of 1.36%,and 1-SA-OMS-PPA saw an increase of just 0.93%.The study also highlighted significant improvements in the hardness of these materials.The hardness change ratio for SA-OMS decreased by 43.08%,while the ratio for 1-SA-OMS-PPA saw a notable reduction of 65.16% compared to unmodified SA.These findings suggest that OMS-PPA contributed to improvements in temperature sensitivity,particle dispersibility,and resistance to shortterm aging in asphalts.The results hold significant promise for the future development of advanced asphalt-based materials with potential high-value applications in flexible pavements for highways.
文摘BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.
基金the State Grid Shanxi Electric Power Company Science and Technology Project“Smart distribution network with a high proportion of distributed wind storage adaptability assessment and improvement strategy research”(520530230024).
文摘With the increasing penetration of renewable energy in power systems,grid structures and operational paradigms are undergoing profound transformations.When subjected to disturbances,the interaction between power electronic devices and dynamic loads introduces strongly nonlinear dynamic characteristics in grid voltage responses,posing significant threats to system security and stability.To achieve reliable short-term voltage stability assessment under large-scale renewable integration,this paper innovatively proposes a response-driven online assessment method based on energy function theory.First,energy modeling of system components is performed based on energy function theory,followed by analysis of energy interaction mechanisms during voltage instability.To address the challenge of traditional energy functions in online applications,a convolutional neural network-long short-term memory(CNNLSTM)hybrid artificial Intelligence approach is introduced.By quantifying the contribution of each energy component to voltage stability,key energy terms are identified.The measurable electrical quantities corresponding to these key energies serve as inputs,while the energy at the voltage unstable equilibrium point(UEP)obtained from offline simulations is used as both the energy threshold and the output of the artificial intelligence model,enabling the construction of an artificial intelligence model for energy threshold prediction.The measurable electrical quantities corresponding to these key energies serve as inputs,while the energy at the unstable equilibrium point(UEP)obtained from offline simulations acts as the output,enabling the construction of an artificial intelligence model for energy threshold prediction.Real-time response data are fed into the model to predict the system's instantaneous energy threshold,which is then compared with the transient energy at fault clearance to evaluate stability.Validation on both a 3-machine,10-bus system and the New England 10-machine,39-bus system confirms the method's adaptability and accuracy.The simulation results demonstrate that the proposed short-term voltage stability assessment model outperforms other methods in both accuracy and computational efficiency.
基金Science and Technology Development Fund of Macao Special Administrative Region (0009/2024/RIB1)Guangdong Major Project of Basic and Applied Basic Research Foundation(2020B0301030004)。
文摘The inherent black-box nature of machine learning(ML) models limits their interpretability and broader application in heavy precipitation forecasting. Evaluating the reliability of these models involves analyzing the link between predictions and predictors. In this study, ERA5 reanalysis data, CERES satellite observations, and ground-based meteorological observatories were utilized to compile more comprehensive multi-type predictors for developing a Bayesian optimized XGBoost model for the nowcasting of heavy precipitation in the Guangdong-Hong Kong-Macao Greater Bay Area during the pre-summer rainy season. A comparison of model performance with different combinations of input features and classical machine learning algorithms demonstrated that the Bayesian optimized XGBoost model achieved the best overall performance, with an average Critical Success Index of 68.30%. Permutation Importance(PI) and shapley Additive Explanations(SHAP) methods were utilized to interpret feature effects in heavy precipitation forecasting. The results indicated that precipitable water vapor(PWV), cloud, relative humidity, and seasonal and diurnal variables had more significant effects on the model output as individual features. Furthermore, the collective influence of derivatives from PWV and meteorological parameters(e.g., temperature, relative humidity, pressure and dew point temperature)showed a significant enhancement over their individual impacts, indicating synergistic interactions among these predictors.Applying explainable artificial intelligence(XAI) to ML models helps understand how models utilize features for forecasting, enhances the reliability of forecasts, and guides feature selection and the mitigation of overfitting phenomena.
基金funded by the Technology Project of State Grid Corporation of China(Research on Safety and Stability Evaluation and Optimization Enhancement Technology of Flexible Ultra High Voltage Multiterminal DC System Adapting to the Background of“Sand and Gobi Deserts”),grant number J2024003。
文摘Countries worldwide are advocating for energy transition initiatives to promote the construction of low-carbon energy systems.The low voltage ride through(LVRT)characteristics of renewable energy units and commutation failures in line commutated converter high voltage direct current(LCC-HVDC)systems at the receiving end leads to short-term power shortage(STPS),which differs from traditional frequency stability issues.STPS occurs during the generator’s power angle swing phase,before the governor responds,and is on a timescale that is not related to primary frequency regulation.This paper addresses these challenges by examining the impact of LVRT on voltage stability,developing a frequency response model to analyze the mechanism of frequency instability caused by STPS,deriving the impact of STPS on the maximum frequency deviation,and introducing an energy deficiency factor to assess its impact on regional frequency stability.The East China Power Grid is used as a case study,where the energy deficiency factor is calculated to validate the proposed mechanism.STPS is mainly compensated by the rotor kinetic energy of the generators in this region,with minimal impact on other regions.It is concluded that the energy deficiency factor provides an effective explanation for the spatial distribution of the impact of STPS on system frequency.
基金funded by the Young Scientists Fund of the National Natural Science Foundation of China under 82305433,82305437.
文摘Objectives:This study aimed to clarify the short-term symptoms,duration,and influencing factors in people recovering from coronavirus disease 2019(COVID-19)after China’s dynamic zero-COVID-19 policy was implemented in December 2022.Methods:We included data from a large-scale on-line survey conducted in China between January 14 and February 1,2023.Participants were individuals of all ages.Chi-squared tests and multivariate logistic regression analyses were performed to identify factors associated with different symptoms.Results:Overall,21,012 patients from seven regions of China were included in this study(female:71.22%).For most patients,the period from symptom onset to a negative nucleic acid test result was≤10 days(72.33%).The distribution of symptoms varied at different times,with respiratory(1-4 weeks)and psychocardiology(5-8 weeks)symptoms being the most common.Multivariate analysis identified male sex,no comorbidity,and living in northeast and northwest China(compared with central China)as independent factors associated with a lower risk of symptoms,while age(41-60 years)was a possible risk factor(compared with 18-40 years).Conclusions:Short-term respiratory and psychocardiology symptoms were the most common after COVID-19 recovery.Sex,age,geographical region,and comorbidities were potential influencing factors for the development of short-term symptoms.
文摘This paper deeply explores the application strategies of short-term cost curves in the field of economics.Firstly,it elaborates on the basic theories and constituent elements of short-term cost curves.By drawing and analyzing the shortterm cost curve graphs,it presents the internal relationship between costs and output.Then,it focuses on researching its application strategies in multiple aspects such as enterprise production decisions,market pricing,and industry competition analysis.
