Gravity wave activity and dissipation in the height range from the low stratosphere to the low thermosphere(25–115 km)covering latitudes between 50°S and 50°N are statistically studied by using 9-year(Janua...Gravity wave activity and dissipation in the height range from the low stratosphere to the low thermosphere(25–115 km)covering latitudes between 50°S and 50°N are statistically studied by using 9-year(January 22,2002–December 31,2010)SABER/TIMED temperature data.We propose a method to extract realistic gravity wave fluctuations from the temperature profiles and treat square temperature fluctuations as GW activity.Overall,the gravity wave activity generally increases with height.Near the equator(0°–10°),the gravity wave activity shows a quasi-biennial variation in the stratosphere(below 40 km)while from 20°to 30°,it exhibits an annual variation below 40 km;in low latitudes(0°–30°)between the upper stratosphere and the low thermosphere(40–115 km),the gravity wave activity shows a semi-annual variation.In middle latitudes(40°–50°),the gravity wave activity has a clear annual variation below 85 km.In addition,we observe a four-monthly variation with peaks occurring usually in April,August,December in the northern hemisphere and in February,June,October in the southern hemisphere,respectively,above 85 km in middle latitudes,which has been seldom reported in gravity wave activity.In order to study the dissipation of gravity wave propagation,we calculate the gravity wave dissipation ratio,which is defined as the ratio of the gravity wave growth scale height to the atmosphere density scale height.The height variation of the dissipation ratio indicates that strong gravity wave dissipation mainly concentrates in the three height regions:the stratosphere(30–60 km),the mesopause(around 85 km)and the low thermosphere(above 100 km).Besides,gravity wave energy enhancement can be also observed in the background atmosphere.展开更多
The wavenumber spectral components WN4 at the mesosphere and low thermosphere(MLT)altitudes(70–10 km)and in the latitude range between±45°are obtained from temperature data(T)observed by the Sounding of the...The wavenumber spectral components WN4 at the mesosphere and low thermosphere(MLT)altitudes(70–10 km)and in the latitude range between±45°are obtained from temperature data(T)observed by the Sounding of the Atmosphere using Broadband Emission Radiometry(SABER)instruments on board the National Aeronautics and Space Administration(NASA)’s Thermosphere–Ionosphere–Mesosphere Energetics and Dynamics(TIMED)spacecraft during the 11-year solar period from 2002 to 2012.We analyze in detail these spectral components WNk and obtain the main properties of their vertical profiles and global structures.We report that all of the wavenumber spectral components WNk occur mainly around 100 km altitude,and that the most prominent component is the wavenumber spectral component WN4 structure.Comparing these long duration temperature data with results of previous investigations,we have found that the yearly variation of spectral component WN4 is similar to that of the eastward propagating non-migrating diurnal tide with zonal wavenumber 3(DE3)at the low latitudes,and to that of the semi-diurnal tide with zonal wavenumber 2(SE2)at the mid-latitudes:the amplitudes of the A4 are larger during boreal summer and autumn at the low-latitudes;at the mid-latitudes the amplitudes have a weak peak in March.In addition,the amplitudes of component WN4 undergo a remarkable short period variation:significant day-to-day variation of the spectral amplitudes A4 occurs primarily in July and September at the low-latitudes.In summary,we conclude that the non-migrating tides DE3 and SE2 are likely to be the origins,at the low-latitudes and the mid-latitudes in the MLT region,respectively,of the observed wavenumber spectral component WN4.展开更多
This study identified castor oil and phosphate ester as effective retarders through setting time,tensile,and flexural tests,and determined their optimal dosages.The mechanism by which phosphate ester affects the setti...This study identified castor oil and phosphate ester as effective retarders through setting time,tensile,and flexural tests,and determined their optimal dosages.The mechanism by which phosphate ester affects the setting time of polyurethane was further investigated using molecular dynamics simulations.Fourier transform infrared spectroscopy was also employed to systematically study the physical and chemical interactions between phosphate esters and polyurethane materials.The results demonstrate that a 1%concentration of phosphate ester provides the most effective retarding effect with minimal impact on the strength of polyurethane.When phosphate ester is added to the B component of the two-component polyurethane system,its interaction energy with component A decreases,as do the diffusion coefficient and aggregation degree of component B on the surface of component A.This reduction in interaction slows the setting time.Additionally,the addition of phosphate ester to polyurethane leads to the disappearance or weakening of functional groups,indicating competitive interactions within the phosphate ester components that inhibit the reaction rate.展开更多
Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the origin...Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
Ten years of SABER/TIMED temperature data are used to analyze the global structure and seasonal variations of the migrating 6-h tide from the stratosphere to the lower thermosphere. The amplitudes of the migrating 6-h...Ten years of SABER/TIMED temperature data are used to analyze the global structure and seasonal variations of the migrating 6-h tide from the stratosphere to the lower thermosphere. The amplitudes of the migrating 6-h tide increase with altitudes. In the stratosphere, the migrating 6-h tide peaks around 35°N/S. The climatologically annual mean of the migrating 6-h tide clearly shows the manifestation of the(4, 6) Hough mode between 70 and 90 km that peaks at the equator and near 35°N/S. Above 90 km, the 6-h tide shows more than one Hough mode with the(4, 6) mode being the dominant one. The migrating 6-h tide is stronger in the southern hemisphere. Annual, semiannual, 4-, and 3-month oscillations are the four dominant seasonal variations of the tidal amplitude. In the stratosphere and stratopause, the spring enhancement of the 6-h tide at middle latitudes is the most conspicuous feature. From the mesosphere to the lower thermosphere, the tidal amplitude at low latitudes is gradually in the scale of that at middle latitudes and exhibits different temporal variations at different altitudes and latitudes. Both ozone heating in the stratosphere and the background atmosphere probably affect the generation and the seasonal variations of the migrating 6-h tide. In addition, the non-linear interaction between different tidal harmonics is another possible mechanism.展开更多
It is shown that time asymmetry is essential for deriving thermodynamic law and arises from the turnover of energy while reducing its information content and driving entropy increase. A dynamically interpreted princip...It is shown that time asymmetry is essential for deriving thermodynamic law and arises from the turnover of energy while reducing its information content and driving entropy increase. A dynamically interpreted principle of least action enables time asymmetry and time flow as a generation of action and redefines useful energy as an information system which implements a form of acting information. This is demonstrated using a basic formula, originally applied for time symmetry/energy conservation considerations, relating time asymmetry (which is conventionally denied but here expressly allowed), to energy behaviour. The results derived then explained that a dynamic energy is driving time asymmetry. It is doing it by decreasing the information content of useful energy, thus generating action and entropy increase, explaining action-time as an information phenomenon. Thermodynamic laws follow directly. The formalism derived readily explains what energy is, why it is conserved (1st law of thermodynamics), why entropy increases (2nd law) and that maximum entropy production within the restraints of the system controls self-organized processes of non-linear irreversible thermodynamics. The general significance of the principle of least action arises from its role of controlling the action generating oriented time of nature. These results contrast with present understanding of time neutrality and clock-time, which are here considered a source of paradoxes, intellectual contradictions and dead-end roads in models explaining nature and the universe.展开更多
BACKGROUND Meniscal tears are one of the most common knee injuries.After the diagnosis of a meniscal tear has been made,there are several factors physicians use to guide clinical decision-making.The influence of time ...BACKGROUND Meniscal tears are one of the most common knee injuries.After the diagnosis of a meniscal tear has been made,there are several factors physicians use to guide clinical decision-making.The influence of time between injury and isolated meniscus repair on patient outcomes is not well described.Assessing this relationship is important as it may influence clinical decision-making and can add to the preoperative patient education process.We hypothesized that increasing the time from injury to meniscus surgery would worsen postoperative outcomes.AIM To investigate the current literature for data on the relationship between time between meniscus injury and repair on patient outcomes.METHODS PubMed,Academic Search Complete,MEDLINE,CINAHL,and SPORTDiscus were searched for studies published between January 1,1995 and July 13,2023 on isolated meniscus repair.Exclusion criteria included concomitant ligament surgery,incomplete outcomes or time to surgery data,and meniscectomies.Patient demographics,time to injury,and postoperative outcomes from each study were abstracted and analyzed.RESULTS Five studies met all inclusion and exclusion criteria.There were 204(121 male,83 female)patients included.Three of five(60%)studies determined that time between injury and surgery was not statistically significant for postoperative Lysholm scores(P=0.62),Tegner scores(P=0.46),failure rate(P=0.45,P=0.86),and International Knee Documentation Committee scores(P=0.65).Two of five(40%)studies found a statistically significant increase in Lysholm scores with shorter time to surgery(P=0.03)and a statistically significant association between progression of medial meniscus extrusion ratio(P=0.01)and increasing time to surgery.CONCLUSION Our results do not support the hypothesis that increased time from injury to isolated meniscus surgery worsens postoperative outcomes.Decision-making primarily based on injury interval is thus not recommended.展开更多
This paper presents a comparative study of ARIMA and Neural Network AutoRegressive (NNAR) models for time series forecasting. The study focuses on simulated data generated using ARIMA(1, 1, 0) and applies both models ...This paper presents a comparative study of ARIMA and Neural Network AutoRegressive (NNAR) models for time series forecasting. The study focuses on simulated data generated using ARIMA(1, 1, 0) and applies both models for training and forecasting. Model performance is evaluated using MSE, AIC, and BIC. The models are further applied to neonatal mortality data from Saudi Arabia to assess their predictive capabilities. The results indicate that the NNAR model outperforms ARIMA in both training and forecasting.展开更多
Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health data by comparing the perform...Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health data by comparing the performance of traditional linear time series models, namely Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, and Moving Average (MA) against neural network architectures. The primary goal is to evaluate the effectiveness of these models in predicting healthcare outcomes using patient records, specifically the Cancerpatient.xlsx dataset, which tracks variables such as patient age, symptoms, genetic risk factors, and environmental exposures over time. The proposed strategy involves training each model on historical patient data to predict age progression and other related health indicators, with performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. Our findings reveal that neural networks consistently outperform ARIMA and SARIMA by capturing non-linear patterns and complex temporal dependencies within the dataset, resulting in lower forecasting errors. This research highlights the potential of neural networks to enhance predictive accuracy in healthcare applications, supporting better resource allocation, patient monitoring, and long-term health outcome predictions.展开更多
In the present paper, we study the finite time domain dynamics of a scalar field interacting with external sources. We expand both the scalar field and the corresponding Hamiltonian in annihilation and creation operat...In the present paper, we study the finite time domain dynamics of a scalar field interacting with external sources. We expand both the scalar field and the corresponding Hamiltonian in annihilation and creation operators and evaluate the relevant path integral. So, we get the Green function within a finite time interval. We apply the solution to the relevant Cauchy problem and further, we study the dynamics of scalar fields coupled with electromagnetic fields via perturbative methods.展开更多
The acquisition of neutron time spectrum data plays a pivotal role in the precise quantification of uranium via prompt fission neutron uranium logging(PFNUL).However,the impact of the detector dead-time effect remains...The acquisition of neutron time spectrum data plays a pivotal role in the precise quantification of uranium via prompt fission neutron uranium logging(PFNUL).However,the impact of the detector dead-time effect remains paramount in the accurate acquisition of the neutron time spectrum.Therefore,it is imperative for neutron logging instruments to establish a dead-time correction method that is not only uncomplicated but also practical and caters to various logging sites.This study has formulated an innovative equation for determining dead time and introduced a dead-time correction method for the neutron time spectrum,called the“dual flux method.”Using this approach,a logging instrument captures two neutron time spectra under disparate neutron fluxes.By carefully selecting specific“windows”on the neutron time spectrum,the dead time can be accurately ascertained.To substantiate its efficacy and discern the influencing factors,experiments were conducted utilizing a deuterium-tritium(D-T)neutron source,a Helium-3(3He)detector,and polyethylene shielding to collate and analyze the neutron time spectrum under varying neutron fluxes(at high voltages).The findings underscore that the“height”and“spacing”of the two windows are the most pivotal influencing factors.Notably,the“height”(fd)should surpass 2,and the“spacing”twd should exceed 200μs.The dead time of the 3 He detector determined in the experiment was 7.35μs.After the dead-time correction,the deviation of the decay coefficients from the theoretical values for the neutron time spectrum under varying neutron fluxes decreased from 12.4%to within 5%.Similarly,for the PFNUL instrument,the deviation in the decay coefficients decreased from 22.94 to 0.49%after correcting for the dead-time effect.These results demonstrate the exceptional efficacy of the proposed method in ensuring precise uranium quantification.The dual flux method was experimentally validated as a universal approach applicable to pulsed neutron logging instruments and holds immense significance for uranium exploration.展开更多
This paper presents an optimized strategy for multiple integrations of photovoltaic distributed generation (PV-DG) within radial distribution power systems. The proposed methodology focuses on identifying the optimal ...This paper presents an optimized strategy for multiple integrations of photovoltaic distributed generation (PV-DG) within radial distribution power systems. The proposed methodology focuses on identifying the optimal allocation and sizing of multiple PV-DG units to minimize power losses using a probabilistic PV model and time-series power flow analysis. Addressing the uncertainties in PV output due to weather variability and diurnal cycles is critical. A probabilistic assessment offers a more robust analysis of DG integration’s impact on the grid, potentially leading to more reliable system planning. The presented approach employs a genetic algorithm (GA) and a determined PV output profile and probabilistic PV generation profile based on experimental measurements for one year of solar radiation in Cairo, Egypt. The proposed algorithms are validated using a co-simulation framework that integrates MATLAB and OpenDSS, enabling analysis on a 33-bus test system. This framework can act as a guideline for creating other co-simulation algorithms to enhance computing platforms for contemporary modern distribution systems within smart grids concept. The paper presents comparisons with previous research studies and various interesting findings such as the considered hours for developing the probabilistic model presents different results.展开更多
A performer breathes fire during Chinese New Year celebrations at Binondo district,considered the world’s oldest Chinatown,on January 29 in Manila,Philippines.The celebrations,lasting approximately 15 days,were fille...A performer breathes fire during Chinese New Year celebrations at Binondo district,considered the world’s oldest Chinatown,on January 29 in Manila,Philippines.The celebrations,lasting approximately 15 days,were filled with traditional activities such as family gatherings,lion dances,and the exchange of red envelopes,joining the vibrant cultural event observed by Chinese communities worldwide.展开更多
基金supported by the National Basic Research Program of China(Grant No.2012CB825605)the National Natural Science Foundation of China(Grants Nos.41174126+6 种基金4082501341221003 and 40974082)the Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20100141110020)the Ocean Public Welfare Scientific Research Project of the State Oceanic Administration of the People’s Republic of China(Grant No.201005017)a China Meteorological Administration(Grant No.GYHY201106011)the Open Programs of State Key Laboratory of Space Weatherthe Fundamental Research Funds for the Central Universities
文摘Gravity wave activity and dissipation in the height range from the low stratosphere to the low thermosphere(25–115 km)covering latitudes between 50°S and 50°N are statistically studied by using 9-year(January 22,2002–December 31,2010)SABER/TIMED temperature data.We propose a method to extract realistic gravity wave fluctuations from the temperature profiles and treat square temperature fluctuations as GW activity.Overall,the gravity wave activity generally increases with height.Near the equator(0°–10°),the gravity wave activity shows a quasi-biennial variation in the stratosphere(below 40 km)while from 20°to 30°,it exhibits an annual variation below 40 km;in low latitudes(0°–30°)between the upper stratosphere and the low thermosphere(40–115 km),the gravity wave activity shows a semi-annual variation.In middle latitudes(40°–50°),the gravity wave activity has a clear annual variation below 85 km.In addition,we observe a four-monthly variation with peaks occurring usually in April,August,December in the northern hemisphere and in February,June,October in the southern hemisphere,respectively,above 85 km in middle latitudes,which has been seldom reported in gravity wave activity.In order to study the dissipation of gravity wave propagation,we calculate the gravity wave dissipation ratio,which is defined as the ratio of the gravity wave growth scale height to the atmosphere density scale height.The height variation of the dissipation ratio indicates that strong gravity wave dissipation mainly concentrates in the three height regions:the stratosphere(30–60 km),the mesopause(around 85 km)and the low thermosphere(above 100 km).Besides,gravity wave energy enhancement can be also observed in the background atmosphere.
基金The present work is supported by National Science Foundation of China(41604138,41427901,41621063,41474133,41674158,41874179,41322030).
文摘The wavenumber spectral components WN4 at the mesosphere and low thermosphere(MLT)altitudes(70–10 km)and in the latitude range between±45°are obtained from temperature data(T)observed by the Sounding of the Atmosphere using Broadband Emission Radiometry(SABER)instruments on board the National Aeronautics and Space Administration(NASA)’s Thermosphere–Ionosphere–Mesosphere Energetics and Dynamics(TIMED)spacecraft during the 11-year solar period from 2002 to 2012.We analyze in detail these spectral components WNk and obtain the main properties of their vertical profiles and global structures.We report that all of the wavenumber spectral components WNk occur mainly around 100 km altitude,and that the most prominent component is the wavenumber spectral component WN4 structure.Comparing these long duration temperature data with results of previous investigations,we have found that the yearly variation of spectral component WN4 is similar to that of the eastward propagating non-migrating diurnal tide with zonal wavenumber 3(DE3)at the low latitudes,and to that of the semi-diurnal tide with zonal wavenumber 2(SE2)at the mid-latitudes:the amplitudes of the A4 are larger during boreal summer and autumn at the low-latitudes;at the mid-latitudes the amplitudes have a weak peak in March.In addition,the amplitudes of component WN4 undergo a remarkable short period variation:significant day-to-day variation of the spectral amplitudes A4 occurs primarily in July and September at the low-latitudes.In summary,we conclude that the non-migrating tides DE3 and SE2 are likely to be the origins,at the low-latitudes and the mid-latitudes in the MLT region,respectively,of the observed wavenumber spectral component WN4.
基金Funded by the National Natural Science Foundation of China(No.52370128)the Fundamental Research Funds for the Central Universities(No.2572022AW54)。
文摘This study identified castor oil and phosphate ester as effective retarders through setting time,tensile,and flexural tests,and determined their optimal dosages.The mechanism by which phosphate ester affects the setting time of polyurethane was further investigated using molecular dynamics simulations.Fourier transform infrared spectroscopy was also employed to systematically study the physical and chemical interactions between phosphate esters and polyurethane materials.The results demonstrate that a 1%concentration of phosphate ester provides the most effective retarding effect with minimal impact on the strength of polyurethane.When phosphate ester is added to the B component of the two-component polyurethane system,its interaction energy with component A decreases,as do the diffusion coefficient and aggregation degree of component B on the surface of component A.This reduction in interaction slows the setting time.Additionally,the addition of phosphate ester to polyurethane leads to the disappearance or weakening of functional groups,indicating competitive interactions within the phosphate ester components that inhibit the reaction rate.
基金supported in part by the Interdisciplinary Project of Dalian University(DLUXK-2023-ZD-001).
文摘Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金supported by the Chinese Academy of Sciences(Grant No.KZZD-EW-01-2)the National Natural Science Foundation of China(Grant Nos.41331069,41274153)+2 种基金the National Basic Research Program of China(Grant No.2011CB811405)the Specialized Research Fund for State Key Laboratories of Chinaperformed by Numerical Forecast Modelling R&D and VR System of State Key Lab.of Space Weather and Special HPC workstand of Chinese Meridian Project
文摘Ten years of SABER/TIMED temperature data are used to analyze the global structure and seasonal variations of the migrating 6-h tide from the stratosphere to the lower thermosphere. The amplitudes of the migrating 6-h tide increase with altitudes. In the stratosphere, the migrating 6-h tide peaks around 35°N/S. The climatologically annual mean of the migrating 6-h tide clearly shows the manifestation of the(4, 6) Hough mode between 70 and 90 km that peaks at the equator and near 35°N/S. Above 90 km, the 6-h tide shows more than one Hough mode with the(4, 6) mode being the dominant one. The migrating 6-h tide is stronger in the southern hemisphere. Annual, semiannual, 4-, and 3-month oscillations are the four dominant seasonal variations of the tidal amplitude. In the stratosphere and stratopause, the spring enhancement of the 6-h tide at middle latitudes is the most conspicuous feature. From the mesosphere to the lower thermosphere, the tidal amplitude at low latitudes is gradually in the scale of that at middle latitudes and exhibits different temporal variations at different altitudes and latitudes. Both ozone heating in the stratosphere and the background atmosphere probably affect the generation and the seasonal variations of the migrating 6-h tide. In addition, the non-linear interaction between different tidal harmonics is another possible mechanism.
文摘It is shown that time asymmetry is essential for deriving thermodynamic law and arises from the turnover of energy while reducing its information content and driving entropy increase. A dynamically interpreted principle of least action enables time asymmetry and time flow as a generation of action and redefines useful energy as an information system which implements a form of acting information. This is demonstrated using a basic formula, originally applied for time symmetry/energy conservation considerations, relating time asymmetry (which is conventionally denied but here expressly allowed), to energy behaviour. The results derived then explained that a dynamic energy is driving time asymmetry. It is doing it by decreasing the information content of useful energy, thus generating action and entropy increase, explaining action-time as an information phenomenon. Thermodynamic laws follow directly. The formalism derived readily explains what energy is, why it is conserved (1st law of thermodynamics), why entropy increases (2nd law) and that maximum entropy production within the restraints of the system controls self-organized processes of non-linear irreversible thermodynamics. The general significance of the principle of least action arises from its role of controlling the action generating oriented time of nature. These results contrast with present understanding of time neutrality and clock-time, which are here considered a source of paradoxes, intellectual contradictions and dead-end roads in models explaining nature and the universe.
文摘BACKGROUND Meniscal tears are one of the most common knee injuries.After the diagnosis of a meniscal tear has been made,there are several factors physicians use to guide clinical decision-making.The influence of time between injury and isolated meniscus repair on patient outcomes is not well described.Assessing this relationship is important as it may influence clinical decision-making and can add to the preoperative patient education process.We hypothesized that increasing the time from injury to meniscus surgery would worsen postoperative outcomes.AIM To investigate the current literature for data on the relationship between time between meniscus injury and repair on patient outcomes.METHODS PubMed,Academic Search Complete,MEDLINE,CINAHL,and SPORTDiscus were searched for studies published between January 1,1995 and July 13,2023 on isolated meniscus repair.Exclusion criteria included concomitant ligament surgery,incomplete outcomes or time to surgery data,and meniscectomies.Patient demographics,time to injury,and postoperative outcomes from each study were abstracted and analyzed.RESULTS Five studies met all inclusion and exclusion criteria.There were 204(121 male,83 female)patients included.Three of five(60%)studies determined that time between injury and surgery was not statistically significant for postoperative Lysholm scores(P=0.62),Tegner scores(P=0.46),failure rate(P=0.45,P=0.86),and International Knee Documentation Committee scores(P=0.65).Two of five(40%)studies found a statistically significant increase in Lysholm scores with shorter time to surgery(P=0.03)and a statistically significant association between progression of medial meniscus extrusion ratio(P=0.01)and increasing time to surgery.CONCLUSION Our results do not support the hypothesis that increased time from injury to isolated meniscus surgery worsens postoperative outcomes.Decision-making primarily based on injury interval is thus not recommended.
文摘This paper presents a comparative study of ARIMA and Neural Network AutoRegressive (NNAR) models for time series forecasting. The study focuses on simulated data generated using ARIMA(1, 1, 0) and applies both models for training and forecasting. Model performance is evaluated using MSE, AIC, and BIC. The models are further applied to neonatal mortality data from Saudi Arabia to assess their predictive capabilities. The results indicate that the NNAR model outperforms ARIMA in both training and forecasting.
文摘Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health data by comparing the performance of traditional linear time series models, namely Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, and Moving Average (MA) against neural network architectures. The primary goal is to evaluate the effectiveness of these models in predicting healthcare outcomes using patient records, specifically the Cancerpatient.xlsx dataset, which tracks variables such as patient age, symptoms, genetic risk factors, and environmental exposures over time. The proposed strategy involves training each model on historical patient data to predict age progression and other related health indicators, with performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. Our findings reveal that neural networks consistently outperform ARIMA and SARIMA by capturing non-linear patterns and complex temporal dependencies within the dataset, resulting in lower forecasting errors. This research highlights the potential of neural networks to enhance predictive accuracy in healthcare applications, supporting better resource allocation, patient monitoring, and long-term health outcome predictions.
文摘In the present paper, we study the finite time domain dynamics of a scalar field interacting with external sources. We expand both the scalar field and the corresponding Hamiltonian in annihilation and creation operators and evaluate the relevant path integral. So, we get the Green function within a finite time interval. We apply the solution to the relevant Cauchy problem and further, we study the dynamics of scalar fields coupled with electromagnetic fields via perturbative methods.
基金supported by the National Natural Science Foundation of China(No.42374226)Jiangxi Provincial Natural Science Foundation(Nos.20232BAB201043 and 20232BCJ23006)+2 种基金Nuclear Energy Development Project(20201192-01)National Key Laboratory of Uranium Resource Exploration-Mining and Nuclear Remote Sensing(ECUT)(2024QZ-TD-09)Fundamental Science on Radioactive Geology and Exploration Technology Laboratory(2022RGET20).
文摘The acquisition of neutron time spectrum data plays a pivotal role in the precise quantification of uranium via prompt fission neutron uranium logging(PFNUL).However,the impact of the detector dead-time effect remains paramount in the accurate acquisition of the neutron time spectrum.Therefore,it is imperative for neutron logging instruments to establish a dead-time correction method that is not only uncomplicated but also practical and caters to various logging sites.This study has formulated an innovative equation for determining dead time and introduced a dead-time correction method for the neutron time spectrum,called the“dual flux method.”Using this approach,a logging instrument captures two neutron time spectra under disparate neutron fluxes.By carefully selecting specific“windows”on the neutron time spectrum,the dead time can be accurately ascertained.To substantiate its efficacy and discern the influencing factors,experiments were conducted utilizing a deuterium-tritium(D-T)neutron source,a Helium-3(3He)detector,and polyethylene shielding to collate and analyze the neutron time spectrum under varying neutron fluxes(at high voltages).The findings underscore that the“height”and“spacing”of the two windows are the most pivotal influencing factors.Notably,the“height”(fd)should surpass 2,and the“spacing”twd should exceed 200μs.The dead time of the 3 He detector determined in the experiment was 7.35μs.After the dead-time correction,the deviation of the decay coefficients from the theoretical values for the neutron time spectrum under varying neutron fluxes decreased from 12.4%to within 5%.Similarly,for the PFNUL instrument,the deviation in the decay coefficients decreased from 22.94 to 0.49%after correcting for the dead-time effect.These results demonstrate the exceptional efficacy of the proposed method in ensuring precise uranium quantification.The dual flux method was experimentally validated as a universal approach applicable to pulsed neutron logging instruments and holds immense significance for uranium exploration.
文摘This paper presents an optimized strategy for multiple integrations of photovoltaic distributed generation (PV-DG) within radial distribution power systems. The proposed methodology focuses on identifying the optimal allocation and sizing of multiple PV-DG units to minimize power losses using a probabilistic PV model and time-series power flow analysis. Addressing the uncertainties in PV output due to weather variability and diurnal cycles is critical. A probabilistic assessment offers a more robust analysis of DG integration’s impact on the grid, potentially leading to more reliable system planning. The presented approach employs a genetic algorithm (GA) and a determined PV output profile and probabilistic PV generation profile based on experimental measurements for one year of solar radiation in Cairo, Egypt. The proposed algorithms are validated using a co-simulation framework that integrates MATLAB and OpenDSS, enabling analysis on a 33-bus test system. This framework can act as a guideline for creating other co-simulation algorithms to enhance computing platforms for contemporary modern distribution systems within smart grids concept. The paper presents comparisons with previous research studies and various interesting findings such as the considered hours for developing the probabilistic model presents different results.
文摘A performer breathes fire during Chinese New Year celebrations at Binondo district,considered the world’s oldest Chinatown,on January 29 in Manila,Philippines.The celebrations,lasting approximately 15 days,were filled with traditional activities such as family gatherings,lion dances,and the exchange of red envelopes,joining the vibrant cultural event observed by Chinese communities worldwide.
