Data collected from truck payload management systems at various surface mines shows that the payload variance is significant and must be considered in analysing the mine productivity,energy consumption,greenhouse gas ...Data collected from truck payload management systems at various surface mines shows that the payload variance is significant and must be considered in analysing the mine productivity,energy consumption,greenhouse gas emissions and associated cost.Payload variance causes significant differences in gross vehicle weights.Heavily loaded trucks travel slower up ramps than lightly loaded trucks.Faster trucks are slowed by the presence of slower trucks,resulting in‘bunching’,production losses and increasing fuel consumptions.This paper simulates the truck bunching phenomena in large surface mines to improve truck and shovel systems’efficiency and minimise fuel consumption.The study concentrated on completing a practical simulation model based on a discrete event method which is most commonly used in this field of research in other industries.The simulation model has been validated by a dataset collected from a large surface mine in Arizona state,USA.The results have shown that there is a good agreement between the actual and estimated values of investigated parameters.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
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
In 2024,the world witnessed further transformation and instability,marked by protracted and intensified geopolitical conflicts,repeated attempts to decouple and sever supply chains,and the rapid rise of the Global Sou...In 2024,the world witnessed further transformation and instability,marked by protracted and intensified geopolitical conflicts,repeated attempts to decouple and sever supply chains,and the rapid rise of the Global South.It has become all the more clear where the once-in-a-century transformations are heading.In 2024,China acted on the blueprint drawn up at the Third Plenary Session of the 20th Central Committee of the Communist Party of China(CPC),and made big strides in deepening reform comprehensively.Marking the 75th anniversary of the founding of New China,China carried forward its great cause of national development and pressed ahead with Chinese modernization with vigor and determination.展开更多
InAs/AlAs superlattice structures have significant potential for application in low-noise avalanche photodetectors.With their performance in practical applications linked to the fundamental physical properties of carr...InAs/AlAs superlattice structures have significant potential for application in low-noise avalanche photodetectors.With their performance in practical applications linked to the fundamental physical properties of carrier relaxation time,this study investigated the carrier relaxation times of InAs/AlAs superlattices across various monolayers,temperatures,and carrier concentrations.Our investigation indicated that relaxation times span several tens of picoseconds,confirming that high-quality interfaces do not significantly reduce relaxation times in the way defect states might.Moreover,our study demonstrates that adjustments to the superlattice period can effectively modulate both the bandgap and carrier relaxation times,potentially impacting the performance of avalanche photodiodes by altering the electron-phonon interaction pathways and bandgap width.We established that lower temperatures contribute to an increase in the bandgap and the suppression of high-frequency optical phonon vibrations,thereby lengthening the relaxation times.Additionally,our observations indicate that in InAs/AlAs superlattices,the relaxation time increases as the excitation power increases,owing to the phonon bottleneck effect.These insights into InAs/AlAs superlattice carrier dynamics highlight their applicability in enhancing avalanche photodetectors,and may contribute to the optimized design of superlattices for specific applications.展开更多
Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative diseases.However,little is k...Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative diseases.However,little is known about the long-term stability of these biomarker proteins in plasma samples stored at-80°C.We aimed to explore how storage time would affect the diagnostic accuracy of these biomarkers using a large cohort.Plasma samples from 229 cognitively unimpaired individuals,encompassing healthy controls and those experiencing subjective cognitive decline,as well as 99 patients with cognitive impairment,comprising those with mild cognitive impairment and dementia,were acquired from the Sino Longitudinal Study on Cognitive Decline project.These samples were stored at-80°C for up to 6 years before being used in this study.Our results showed that plasma levels of Aβ42,Aβ40,neurofilament light chain,and glial fibrillary acidic protein were not significantly correlated with sample storage time.However,the level of total tau showed a negative correlation with sample storage time.Notably,in individuals without cognitive impairment,plasma levels of total protein and tau phosphorylated protein threonine 181(p-tau181)also showed a negative correlation with sample storage time.This was not observed in individuals with cognitive impairment.Consequently,we speculate that the diagnostic accuracy of plasma p-tau181 and the p-tau181 to total tau ratio may be influenced by sample storage time.Therefore,caution is advised when using these plasma biomarkers for the identification of neurodegenerative diseases,such as Alzheimer's disease.Furthermore,in cohort studies,it is important to consider the impact of storage time on the overall results.展开更多
This paper aims to define the concept of time and justify its properties within the universal context, shedding new light on the nature of time. By employing the concept of the extrinsic universe, the paper explains t...This paper aims to define the concept of time and justify its properties within the universal context, shedding new light on the nature of time. By employing the concept of the extrinsic universe, the paper explains the observable universe as the three-dimensional surface of a four-dimensional 3-sphere (hypersphere), expanding at the speed of light. This expansion process gives rise to what we perceive as time and its associated aspects, providing a novel interpretation of time as a geometric property emerging from the dynamics of the universe’s expansion. The work offers insights into how this extrinsic perspective can address phenomena such as the universe’s accelerated expansion and dark matter, aligning the model with current observational data.展开更多
In this article, a finite volume element algorithm is presented and discussed for the numerical solutions of a time-fractional nonlinear fourth-order diffusion equation with time delay. By choosing the second-order sp...In this article, a finite volume element algorithm is presented and discussed for the numerical solutions of a time-fractional nonlinear fourth-order diffusion equation with time delay. By choosing the second-order spatial derivative of the original unknown as an additional variable, the fourth-order problem is transformed into a second-order system. Then the fully discrete finite volume element scheme is formulated by using L1approximation for temporal Caputo derivative and finite volume element method in spatial direction. The unique solvability and stable result of the proposed scheme are proved. A priori estimate of L2-norm with optimal order of convergence O(h2+τ2−α)where τand hare time step length and space mesh parameter, respectively, is obtained. The efficiency of the scheme is supported by some numerical experiments.展开更多
Time series forecasting is important in the fields of finance,energy,and meteorology,but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data.In this paper,we prop...Time series forecasting is important in the fields of finance,energy,and meteorology,but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data.In this paper,we propose the FractalNet-LSTM model,which combines fractal convolutional units with recurrent long short-term memory(LSTM)layers to model time series efficiently.To test the effectiveness of the model,data with complex structures and patterns,in particular,with seasonal and cyclical effects,were used.To better demonstrate the obtained results and the formed conclusions,the model performance was shown on the datasets of electricity consumption,sunspot activity,and Spotify stock price.The result showed that the proposed model outperforms traditional approaches at medium forecasting horizons and demonstrates high accuracy for data with long-term and cyclical dependencies.However,for financial data with high volatility,the model’s efficiency decreases at long forecasting horizons,indicating the need for further adaptation.The findings suggest further adaptation.The findings suggest that integrating fractal properties into neural network architecture improves the accuracy of time series forecasting and can be useful for developing more accurate and reliable forecasting systems in various industries.展开更多
基金CRC MiningThe University of Queensland for their financial support for this study
文摘Data collected from truck payload management systems at various surface mines shows that the payload variance is significant and must be considered in analysing the mine productivity,energy consumption,greenhouse gas emissions and associated cost.Payload variance causes significant differences in gross vehicle weights.Heavily loaded trucks travel slower up ramps than lightly loaded trucks.Faster trucks are slowed by the presence of slower trucks,resulting in‘bunching’,production losses and increasing fuel consumptions.This paper simulates the truck bunching phenomena in large surface mines to improve truck and shovel systems’efficiency and minimise fuel consumption.The study concentrated on completing a practical simulation model based on a discrete event method which is most commonly used in this field of research in other industries.The simulation model has been validated by a dataset collected from a large surface mine in Arizona state,USA.The results have shown that there is a good agreement between the actual and estimated values of investigated parameters.
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.
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.
文摘In 2024,the world witnessed further transformation and instability,marked by protracted and intensified geopolitical conflicts,repeated attempts to decouple and sever supply chains,and the rapid rise of the Global South.It has become all the more clear where the once-in-a-century transformations are heading.In 2024,China acted on the blueprint drawn up at the Third Plenary Session of the 20th Central Committee of the Communist Party of China(CPC),and made big strides in deepening reform comprehensively.Marking the 75th anniversary of the founding of New China,China carried forward its great cause of national development and pressed ahead with Chinese modernization with vigor and determination.
基金supported by the Science and Technology Innovation Program of Hunan Province(Grant No.2021RC4026)。
文摘InAs/AlAs superlattice structures have significant potential for application in low-noise avalanche photodetectors.With their performance in practical applications linked to the fundamental physical properties of carrier relaxation time,this study investigated the carrier relaxation times of InAs/AlAs superlattices across various monolayers,temperatures,and carrier concentrations.Our investigation indicated that relaxation times span several tens of picoseconds,confirming that high-quality interfaces do not significantly reduce relaxation times in the way defect states might.Moreover,our study demonstrates that adjustments to the superlattice period can effectively modulate both the bandgap and carrier relaxation times,potentially impacting the performance of avalanche photodiodes by altering the electron-phonon interaction pathways and bandgap width.We established that lower temperatures contribute to an increase in the bandgap and the suppression of high-frequency optical phonon vibrations,thereby lengthening the relaxation times.Additionally,our observations indicate that in InAs/AlAs superlattices,the relaxation time increases as the excitation power increases,owing to the phonon bottleneck effect.These insights into InAs/AlAs superlattice carrier dynamics highlight their applicability in enhancing avalanche photodetectors,and may contribute to the optimized design of superlattices for specific applications.
基金supported by the National Key Research&Development Program of China,Nos.2021YFC2501205(to YC),2022YFC24069004(to JL)the STI2030-Major Project,Nos.2021ZD0201101(to YC),2022ZD0211800(to YH)+2 种基金the National Natural Science Foundation of China(Major International Joint Research Project),No.82020108013(to YH)the Sino-German Center for Research Promotion,No.M-0759(to YH)a grant from Beijing Municipal Science&Technology Commission(Beijing Brain Initiative),No.Z201100005520018(to JL)。
文摘Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative diseases.However,little is known about the long-term stability of these biomarker proteins in plasma samples stored at-80°C.We aimed to explore how storage time would affect the diagnostic accuracy of these biomarkers using a large cohort.Plasma samples from 229 cognitively unimpaired individuals,encompassing healthy controls and those experiencing subjective cognitive decline,as well as 99 patients with cognitive impairment,comprising those with mild cognitive impairment and dementia,were acquired from the Sino Longitudinal Study on Cognitive Decline project.These samples were stored at-80°C for up to 6 years before being used in this study.Our results showed that plasma levels of Aβ42,Aβ40,neurofilament light chain,and glial fibrillary acidic protein were not significantly correlated with sample storage time.However,the level of total tau showed a negative correlation with sample storage time.Notably,in individuals without cognitive impairment,plasma levels of total protein and tau phosphorylated protein threonine 181(p-tau181)also showed a negative correlation with sample storage time.This was not observed in individuals with cognitive impairment.Consequently,we speculate that the diagnostic accuracy of plasma p-tau181 and the p-tau181 to total tau ratio may be influenced by sample storage time.Therefore,caution is advised when using these plasma biomarkers for the identification of neurodegenerative diseases,such as Alzheimer's disease.Furthermore,in cohort studies,it is important to consider the impact of storage time on the overall results.
文摘This paper aims to define the concept of time and justify its properties within the universal context, shedding new light on the nature of time. By employing the concept of the extrinsic universe, the paper explains the observable universe as the three-dimensional surface of a four-dimensional 3-sphere (hypersphere), expanding at the speed of light. This expansion process gives rise to what we perceive as time and its associated aspects, providing a novel interpretation of time as a geometric property emerging from the dynamics of the universe’s expansion. The work offers insights into how this extrinsic perspective can address phenomena such as the universe’s accelerated expansion and dark matter, aligning the model with current observational data.
文摘In this article, a finite volume element algorithm is presented and discussed for the numerical solutions of a time-fractional nonlinear fourth-order diffusion equation with time delay. By choosing the second-order spatial derivative of the original unknown as an additional variable, the fourth-order problem is transformed into a second-order system. Then the fully discrete finite volume element scheme is formulated by using L1approximation for temporal Caputo derivative and finite volume element method in spatial direction. The unique solvability and stable result of the proposed scheme are proved. A priori estimate of L2-norm with optimal order of convergence O(h2+τ2−α)where τand hare time step length and space mesh parameter, respectively, is obtained. The efficiency of the scheme is supported by some numerical experiments.
文摘Time series forecasting is important in the fields of finance,energy,and meteorology,but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data.In this paper,we propose the FractalNet-LSTM model,which combines fractal convolutional units with recurrent long short-term memory(LSTM)layers to model time series efficiently.To test the effectiveness of the model,data with complex structures and patterns,in particular,with seasonal and cyclical effects,were used.To better demonstrate the obtained results and the formed conclusions,the model performance was shown on the datasets of electricity consumption,sunspot activity,and Spotify stock price.The result showed that the proposed model outperforms traditional approaches at medium forecasting horizons and demonstrates high accuracy for data with long-term and cyclical dependencies.However,for financial data with high volatility,the model’s efficiency decreases at long forecasting horizons,indicating the need for further adaptation.The findings suggest further adaptation.The findings suggest that integrating fractal properties into neural network architecture improves the accuracy of time series forecasting and can be useful for developing more accurate and reliable forecasting systems in various industries.
