This paper introduces a method for modeling the entire aggregated electric vehicle(EV)charging process and analyzing its dispatchable capabilities.The methodology involves developing a model for aggregated EV charging...This paper introduces a method for modeling the entire aggregated electric vehicle(EV)charging process and analyzing its dispatchable capabilities.The methodology involves developing a model for aggregated EV charging at the charging station level,estimating its physical dispatchable capability,determining its economic dispatchable capability under economic incentives,modeling its participation in the grid,and investigating the effects of different scenarios and EV penetration on the aggregated load dispatch and dispatchable capability.The results indicate that using economic dispatchable capability reduces charging prices by 9.7%compared to physical dispatchable capability and 9.3%compared to disorderly charging.Additionally,the peak-to-valley difference is reduced by 64.6%when applying economic dispatchable capability with 20%EV penetration and residential base load,compared to disorderly charging.展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
The purpose of this study was to describe and analyze how and why contradictions recurrently arise regarding how incoming emergency calls at dispatch centers should be assessed,sorted,and handled.The study is based on...The purpose of this study was to describe and analyze how and why contradictions recurrently arise regarding how incoming emergency calls at dispatch centers should be assessed,sorted,and handled.The study is based on data from documents,study visits,and interviews with representatives from dispatch operators in a Swedish context.The results identify competing alarm and healthcare logics that are incompatible,which leads to contradictions between the national and regional approaches and,consequently,makes collaboration more difficult and affects the precision of emergency call assessment.The study also shows that the assessments of emergency calls are governed more by which logic is applied than by the assessor's formal competence.The study highlights the importance of feedback and subsequent analysis from the health service to the initial call assessment stage,which can improve the alarm logic's weaknesses with over-triage,without extending the response time.We also focused on the mechanisms required to develop and maintain a long-term sustainable alarm function.展开更多
BACKGROUND Surgery is the first choice of treatment for patients with colorectal cancer.Traditional open surgery imparts great damage to the body of the patient and can easily cause adverse stress reactions.With the c...BACKGROUND Surgery is the first choice of treatment for patients with colorectal cancer.Traditional open surgery imparts great damage to the body of the patient and can easily cause adverse stress reactions.With the continuous development of medical technology,laparoscopic minimally invasive surgery has shown great advantages for the treatment of patients with celiac disease.AIM To investigate the short-term efficacy of laparoscopic radical surgery and traditional laparotomy for the treatment of colorectal cancer,and the differences in the risk analysis of unplanned reoperation after operation.METHODS As the research subjects,this study selected 100 patients with colorectal cancer who received surgical treatment at the Yulin First Hospital from January 2018 to January 2022.Among them,50 patients who underwent laparoscopic radical resection were selected as the research group and 50 patients who underwent traditional laparotomy were selected as the control group.Data pertaining to clinical indexes,gastrointestinal hormones,nutrition indexes,the levels of inflammatory factors,quality of life,Visual Analog Scale score,and the postoperative complications of the two groups of patients before and after treatment were collected,and the therapeutic effects in the two groups were analyzed and compared.RESULTS Compared with the control group,perioperative bleeding,peristalsis recovery time,and hospital stays were significantly shorter in the research group.After surgery,the levels of gastrin(GAS)and motilin(MTL)were decreased in both groups,and the fluctuation range of GAS and MTL observed in the research group was significantly lower than that recorded in the control group.The hemoglobin(Hb)levels increased after surgery,and the level of Hb in the research group was significantly higher compared with the control group.After the operation,the expression levels of tumor necrosis factor-α,interleukin-6,and C-reactive protein and the total incidence of complications were significantly lower in the research group compared with the control group.One year after the operation,the quality of life of the two groups was greatly improved,with the quality of life in the research group being significantly better.CONCLUSION Laparoscopy was effective for colorectal surgery by reducing the occurrence of complications and inflammatory stress reaction;moreover,the quality of life of patients was significantly improved,which warrants further promotion.展开更多
The integration of deep learning into smart grid operations addresses critical challenges in dynamic load forecasting and optimal dispatch amid increasing renewable energy penetration.This study proposes a hybrid LSTM...The integration of deep learning into smart grid operations addresses critical challenges in dynamic load forecasting and optimal dispatch amid increasing renewable energy penetration.This study proposes a hybrid LSTM-Transformer architecture for multi-scale temporal-spatial load prediction,achieving 28%RMSE reduction on real-world datasets(CAISO,PJM),coupled with a deep reinforcement learning framework for multi-objective dispatch optimization that lowers operational costs by 12.4%while ensuring stability constraints.The synergy between adaptive forecasting models and scenario-based stochastic optimization demonstrates superior performance in handling renewable intermittency and demand volatility,validated through grid-scale case studies.Methodological innovations in federated feature extraction and carbon-aware scheduling further enhance scalability for distributed energy systems.These advancements provide actionable insights for grid operators transitioning to low-carbon paradigms,emphasizing computational efficiency and interoperability with legacy infrastructure.展开更多
Styrene-butadiene-styrene(SBS)modified asphalt(SA)has long found effective applications in road construction materials.When combined with fillers,SBS-modified asphalt has demonstrated promising resistance to fatigue c...Styrene-butadiene-styrene(SBS)modified asphalt(SA)has long found effective applications in road construction materials.When combined with fillers,SBS-modified asphalt has demonstrated promising resistance to fatigue cracking caused by temperature fluctuations and aging.In this study,molybdenum disulfide(MoS_(2))and polyphosphoric acid(PPA)were ground in naphthenic oil(NO)and subjected to mechanical activation to create PPAmodified MoS_(2),referred to as OMS-PPA.By blending various ratios of OMS-PPA with SBS-modified asphalt,composite-modified asphalts were successfully developed to enhance their overall properties.To assess the mechanical characteristics and stability of these modified asphalts,various methods were employed,including penetration factor,flow activation energy,fluorescence microscopy,and dynamic shear rheology.Additionally,the short-term aging performance was evaluated using Fourier transform infrared(FTIR)spectroscopy and nanoindentation tests.The results revealed a 3.7%decrease in the penetration-temperature coefficient for SAOMS compared to SA,while 1-SA-OMS-PPA showed an even greater reduction of 7.1%.Furthermore,after short-term aging,carboxyl group generation in SA increased by 5.93%,while SA-OMS exhibited a smaller rise of 1.36%,and 1-SA-OMS-PPA saw an increase of just 0.93%.The study also highlighted significant improvements in the hardness of these materials.The hardness change ratio for SA-OMS decreased by 43.08%,while the ratio for 1-SA-OMS-PPA saw a notable reduction of 65.16% compared to unmodified SA.These findings suggest that OMS-PPA contributed to improvements in temperature sensitivity,particle dispersibility,and resistance to shortterm aging in asphalts.The results hold significant promise for the future development of advanced asphalt-based materials with potential high-value applications in flexible pavements for highways.展开更多
BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk...BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.展开更多
The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power grids.This complexity necessi...The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power grids.This complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly desirable.In the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization problem.The proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs effectively.This study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the MAED.The PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence characteristics.Furthermore,the convergence efficiency of the PPA is significantly influenced by the parameter c.To address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational models.The computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch problems.The simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.展开更多
With the increasing penetration of renewable energy in power systems,grid structures and operational paradigms are undergoing profound transformations.When subjected to disturbances,the interaction between power elect...With the increasing penetration of renewable energy in power systems,grid structures and operational paradigms are undergoing profound transformations.When subjected to disturbances,the interaction between power electronic devices and dynamic loads introduces strongly nonlinear dynamic characteristics in grid voltage responses,posing significant threats to system security and stability.To achieve reliable short-term voltage stability assessment under large-scale renewable integration,this paper innovatively proposes a response-driven online assessment method based on energy function theory.First,energy modeling of system components is performed based on energy function theory,followed by analysis of energy interaction mechanisms during voltage instability.To address the challenge of traditional energy functions in online applications,a convolutional neural network-long short-term memory(CNNLSTM)hybrid artificial Intelligence approach is introduced.By quantifying the contribution of each energy component to voltage stability,key energy terms are identified.The measurable electrical quantities corresponding to these key energies serve as inputs,while the energy at the voltage unstable equilibrium point(UEP)obtained from offline simulations is used as both the energy threshold and the output of the artificial intelligence model,enabling the construction of an artificial intelligence model for energy threshold prediction.The measurable electrical quantities corresponding to these key energies serve as inputs,while the energy at the unstable equilibrium point(UEP)obtained from offline simulations acts as the output,enabling the construction of an artificial intelligence model for energy threshold prediction.Real-time response data are fed into the model to predict the system's instantaneous energy threshold,which is then compared with the transient energy at fault clearance to evaluate stability.Validation on both a 3-machine,10-bus system and the New England 10-machine,39-bus system confirms the method's adaptability and accuracy.The simulation results demonstrate that the proposed short-term voltage stability assessment model outperforms other methods in both accuracy and computational efficiency.展开更多
Countries worldwide are advocating for energy transition initiatives to promote the construction of low-carbon energy systems.The low voltage ride through(LVRT)characteristics of renewable energy units and commutation...Countries worldwide are advocating for energy transition initiatives to promote the construction of low-carbon energy systems.The low voltage ride through(LVRT)characteristics of renewable energy units and commutation failures in line commutated converter high voltage direct current(LCC-HVDC)systems at the receiving end leads to short-term power shortage(STPS),which differs from traditional frequency stability issues.STPS occurs during the generator’s power angle swing phase,before the governor responds,and is on a timescale that is not related to primary frequency regulation.This paper addresses these challenges by examining the impact of LVRT on voltage stability,developing a frequency response model to analyze the mechanism of frequency instability caused by STPS,deriving the impact of STPS on the maximum frequency deviation,and introducing an energy deficiency factor to assess its impact on regional frequency stability.The East China Power Grid is used as a case study,where the energy deficiency factor is calculated to validate the proposed mechanism.STPS is mainly compensated by the rotor kinetic energy of the generators in this region,with minimal impact on other regions.It is concluded that the energy deficiency factor provides an effective explanation for the spatial distribution of the impact of STPS on system frequency.展开更多
With the intensification of the energy crisis and the worsening greenhouse effect,the development of sustainable integrated energy systems(IES)has become a crucial direction for energy transition.In this context,this ...With the intensification of the energy crisis and the worsening greenhouse effect,the development of sustainable integrated energy systems(IES)has become a crucial direction for energy transition.In this context,this paper proposes a low-carbon economic dispatch strategy under the green hydrogen certificate trading(GHCT)and the ladder-type carbon emission trading(CET)mechanism,enabling the coordinated utilization of green and blue hydrogen.Specifically,a proton exchange membrane electrolyzer(PEME)model that accounts for dynamic efficiency characteristics,and a steam methane reforming(SMR)model incorporating waste heat recovery,are developed.Based on these models,a hydrogen production–storage–utilization framework is established to enable the coordinated deployment of green and blue hydrogen.Furthermore,the gas turbine(GT)unit are retrofitted using oxygenenriched combustion carbon capture(OCC)technology,wherein the oxygen produced by PEME is employed to create an oxygen-enriched combustion environment.This approach reduces energy waste and facilitates low-carbon power generation.In addition,the GHCT mechanism is integrated into the system alongside the ladder-type CET mechanism,and their complementary effects are investigated.A comprehensive optimization model is then formulated to simultaneously achieve carbon reduction and economic efficiency across the system.Case study results show that the proposed strategy reduces wind curtailment by 7.77%,carbon emissions by 65.98%,and total cost by 12.57%.This study offers theoretical reference for the low-carbon,economic,and efficient operation of future energy systems.展开更多
Objectives:This study aimed to clarify the short-term symptoms,duration,and influencing factors in people recovering from coronavirus disease 2019(COVID-19)after China’s dynamic zero-COVID-19 policy was implemented i...Objectives:This study aimed to clarify the short-term symptoms,duration,and influencing factors in people recovering from coronavirus disease 2019(COVID-19)after China’s dynamic zero-COVID-19 policy was implemented in December 2022.Methods:We included data from a large-scale on-line survey conducted in China between January 14 and February 1,2023.Participants were individuals of all ages.Chi-squared tests and multivariate logistic regression analyses were performed to identify factors associated with different symptoms.Results:Overall,21,012 patients from seven regions of China were included in this study(female:71.22%).For most patients,the period from symptom onset to a negative nucleic acid test result was≤10 days(72.33%).The distribution of symptoms varied at different times,with respiratory(1-4 weeks)and psychocardiology(5-8 weeks)symptoms being the most common.Multivariate analysis identified male sex,no comorbidity,and living in northeast and northwest China(compared with central China)as independent factors associated with a lower risk of symptoms,while age(41-60 years)was a possible risk factor(compared with 18-40 years).Conclusions:Short-term respiratory and psychocardiology symptoms were the most common after COVID-19 recovery.Sex,age,geographical region,and comorbidities were potential influencing factors for the development of short-term symptoms.展开更多
This paper deeply explores the application strategies of short-term cost curves in the field of economics.Firstly,it elaborates on the basic theories and constituent elements of short-term cost curves.By drawing and a...This paper deeply explores the application strategies of short-term cost curves in the field of economics.Firstly,it elaborates on the basic theories and constituent elements of short-term cost curves.By drawing and analyzing the shortterm cost curve graphs,it presents the internal relationship between costs and output.Then,it focuses on researching its application strategies in multiple aspects such as enterprise production decisions,market pricing,and industry competition analysis.展开更多
In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle ...In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle position,and actual power,lagged features were generated to capture temporal dependencies.Among 24 evaluated models,the ensemble bagging approach achieved the best performance,with R^(2) values of 0.89 at 0 min and 0.75 at 60 min.Shapley Additive exPlanations(SHAP)analysis revealed that while wind speed is the primary driver for short-term predictions,air temperature and nacelle position become more influential at longer forecasting horizons.These findings underscore the reliability of short-term predictions and the potential benefits of integrating hybrid AI and probabilistic models for extended forecasts.Our work contributes a robust and explainable framework to support Sri Lanka’s renewable energy transition,and future research will focus on real-time deployment and uncertainty quantification.展开更多
Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causin...Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causing magnetic storms.Consequently,it is very important to accurately predict the time period of solar flares.This paper proposes a flare prediction model,based on physical images of active solar regions.We employ X-ray flux curves recorded directly by the Geostationary Operational Environmental Satellite,used as input data for the model,allowing us to largely avoid the influence of accidental errors,effectively improving the model prediction efficiency.A model based on the X-ray flux curve can predict whether there will be a flare event within 24 hours.The reverse can also be verified by the peak of the X-ray flux curve to see if a flare has occurred within the past 24 hours.The True Positive Rate and False Positive Rate of the prediction model,based on physical images of active regions are 0.6070 and 0.2410 respectively,and the accuracy and True Skill Statistics are 0.7590 and 0.5556.Our model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records,providing a simple method for solar flare prediction.展开更多
The economic dispatch problem(EDP) of microgrids operating in both grid-connected and isolated modes within an energy internet framework is addressed in this paper. The multi-agent leader-following consensus algorithm...The economic dispatch problem(EDP) of microgrids operating in both grid-connected and isolated modes within an energy internet framework is addressed in this paper. The multi-agent leader-following consensus algorithm is employed to address the EDP of microgrids in grid-connected mode, while the push-pull algorithm with a fixed step size is introduced for the isolated mode. The proposed algorithm of isolated mode is proven to converge to the optimum when the interaction digraph of microgrids is strongly connected. A unified algorithmic framework is proposed to handle the two modes of operation of microgrids simultaneously, enabling our algorithm to achieve optimal power allocation and maintain the balance between power supply and demand in any mode and any mode switching. Due to the push-pull structure of the algorithm and the use of fixed step size,the proposed algorithm can better handle the case of unbalanced graphs, and the convergence speed is improved. It is documented that when the transmission topology is strongly connected and there is bi-directional communication between the energy router and its neighbors, the proposed algorithm in composite mode achieves economic dispatch even with arbitrary mode switching.Finally, we demonstrate the effectiveness and superiority of our algorithm through numerical simulations.展开更多
Background Lip reading uses lip images for visual speech recognition.Deep-learning-based lip reading has greatly improved performance in current datasets;however,most existing research ignores the significance of shor...Background Lip reading uses lip images for visual speech recognition.Deep-learning-based lip reading has greatly improved performance in current datasets;however,most existing research ignores the significance of short-term temporal dependencies of lip-shape variations between adjacent frames,which leaves space for further improvement in feature extraction.Methods This article presents a spatiotemporal feature fusion network(STDNet)that compensates for the deficiencies of current lip-reading approaches in short-term temporal dependency modeling.Specifically,to distinguish more similar and intricate content,STDNet adds a temporal feature extraction branch based on a 3D-CNN,which enhances the learning of dynamic lip movements in adjacent frames while not affecting spatial feature extraction.In particular,we designed a local–temporal block,which aggregates interframe differences,strengthening the relationship between various local lip regions through multiscale convolution.We incorporated the squeeze-and-excitation mechanism into the Global-Temporal Block,which processes a single frame as an independent unitto learn temporal variations across the entire lip region more effectively.Furthermore,attention pooling was introduced to highlight meaningful frames containing key semantic information for the target word.Results Experimental results demonstrated STDNet's superior performance on the LRW and LRW-1000,achieving word-level recognition accuracies of 90.2% and 53.56%,respectively.Extensive ablation experiments verified the rationality and effectiveness of its modules.Conclusions The proposed model effectively addresses short-term temporal dependency limitations in lip reading,and improves the temporal robustness of the model against variable-length sequences.These advancements validate the importance of explicit short-term dynamics modeling for practical lip-reading systems.展开更多
BACKGROUND Elsberg syndrome is a type of postinfectious lumbosacral radiculitis typically tri-ggered by neurotropic viruses and manifests as bladder/bowel dysfunction,saddle sensory disturbances(including hypoesthesia...BACKGROUND Elsberg syndrome is a type of postinfectious lumbosacral radiculitis typically tri-ggered by neurotropic viruses and manifests as bladder/bowel dysfunction,saddle sensory disturbances(including hypoesthesia,hyperesthesia,or dyse-sthesia),and variable neurological deficits.Typically self-limiting,it often res-ponds to antiviral and neurotropic therapies.However,in patients with comorbi-dities that confer susceptibility to peripheral neuropathy(e.g.,diabetes mellitus),timely escalation to neuromodulation strategies,such as spinal cord stimulation,may be warranted to optimize functional outcomes when conservative measures are inadequate.CASE SUMMARY A 60-year-old male with diabetes mellitus presented with severe bladder and bowel dysfunction persisting for more than two months,followed by left gluteal and perianal(saddle area)herpes zoster eruption that was accompanied by significant neuropathic pain.Following a suboptimal response to conservative therapy,the patient underwent implantation of a short-term spinal cord stimu-lation.Following a 10-day trial of continuous tonic stimulation,the percutaneous electrode lead was removed.The patients experienced no surgical complications,and after the procedure,the patient achieved complete restoration of bladder and bowel function and significant pain alleviation.Two-month follow-up confirmed sustained full recovery.CONCLUSION Early implementation of short-term spinal cord stimulation represents a pro-mising therapeutic approach for promoting neurological recovery in patients with Elsberg syndrome refractory to conservative management,especially those with predisposing comorbidities such as diabetes mellitus.展开更多
Deep learning plays a vital role in real-life applications, for example object identification, human face recognition, speech recognition, biometrics identification, and short and long-term forecasting of data. The ma...Deep learning plays a vital role in real-life applications, for example object identification, human face recognition, speech recognition, biometrics identification, and short and long-term forecasting of data. The main objective of our work is to predict the market performance of the Dhaka Stock Exchange (DSE) on day closing price using different Deep Learning techniques. In this study, we have used the LSTM (Long Short-Term Memory) network to forecast the data of DSE for the convenience of shareholders. We have enforced LSTM networks to train data as well as forecast the future time series that has differentiated with test data. We have computed the Root Mean Square Error (RMSE) value to scrutinize the error between the forecasted value and test data that diminished the error by updating the LSTM networks. As a consequence of the renovation of the network, the LSTM network provides tremendous performance which outperformed the existing works to predict stock market prices.展开更多
To address the issues of unclear carbon responsibility attribution,insufficient renewable energy absorption,and simplistic carbon trading mechanisms in integrated energy systems,this paper proposes an electricheat-hyd...To address the issues of unclear carbon responsibility attribution,insufficient renewable energy absorption,and simplistic carbon trading mechanisms in integrated energy systems,this paper proposes an electricheat-hydrogen integrated energy system(EHH-IES)optimal scheduling model considering carbon emission stream(CES)and wind-solar accommodation.First,the CES theory is introduced to quantify the carbon emission intensity of each energy conversion device and transmission branch by defining carbon emission rate,branch carbon intensity,and node carbon potential,realizing accurate tracking of carbon flow in the process of multi-energy coupling.Second,a stepped carbon pricing mechanism is established to dynamically adjust carbon trading costs based on the deviation between actual carbon emissions and initial quotas,strengthening the emission reduction incentive.Finally,a lowcarbon economic dispatch model is constructed with the objectives of minimizing operation cost,carbon trading cost,wind-solar curtailment penalty cost,and energy loss.Simulation results show that compared with the traditional economic dispatch scheme 3,the proposed schemel reduces carbon emissions by 53.97%and wind-solar curtailment by 68.89%with a 16.10%increase in total cost.This verifies that the model can effectively improve clean energy utilization and reduce carbon emissions,achieving low-carbon economic operation of EHH-IES,with CES theory ensuring precise carbon flow tracking across multi-energy links.展开更多
基金State Grid Henan Power Company Science and Technology Project‘Key Technology and Demonstration Application of Multi-Domain Electric Vehicle Aggregated Charging Load Dispatch’(5217L0240003).
文摘This paper introduces a method for modeling the entire aggregated electric vehicle(EV)charging process and analyzing its dispatchable capabilities.The methodology involves developing a model for aggregated EV charging at the charging station level,estimating its physical dispatchable capability,determining its economic dispatchable capability under economic incentives,modeling its participation in the grid,and investigating the effects of different scenarios and EV penetration on the aggregated load dispatch and dispatchable capability.The results indicate that using economic dispatchable capability reduces charging prices by 9.7%compared to physical dispatchable capability and 9.3%compared to disorderly charging.Additionally,the peak-to-valley difference is reduced by 64.6%when applying economic dispatchable capability with 20%EV penetration and residential base load,compared to disorderly charging.
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
文摘The purpose of this study was to describe and analyze how and why contradictions recurrently arise regarding how incoming emergency calls at dispatch centers should be assessed,sorted,and handled.The study is based on data from documents,study visits,and interviews with representatives from dispatch operators in a Swedish context.The results identify competing alarm and healthcare logics that are incompatible,which leads to contradictions between the national and regional approaches and,consequently,makes collaboration more difficult and affects the precision of emergency call assessment.The study also shows that the assessments of emergency calls are governed more by which logic is applied than by the assessor's formal competence.The study highlights the importance of feedback and subsequent analysis from the health service to the initial call assessment stage,which can improve the alarm logic's weaknesses with over-triage,without extending the response time.We also focused on the mechanisms required to develop and maintain a long-term sustainable alarm function.
文摘BACKGROUND Surgery is the first choice of treatment for patients with colorectal cancer.Traditional open surgery imparts great damage to the body of the patient and can easily cause adverse stress reactions.With the continuous development of medical technology,laparoscopic minimally invasive surgery has shown great advantages for the treatment of patients with celiac disease.AIM To investigate the short-term efficacy of laparoscopic radical surgery and traditional laparotomy for the treatment of colorectal cancer,and the differences in the risk analysis of unplanned reoperation after operation.METHODS As the research subjects,this study selected 100 patients with colorectal cancer who received surgical treatment at the Yulin First Hospital from January 2018 to January 2022.Among them,50 patients who underwent laparoscopic radical resection were selected as the research group and 50 patients who underwent traditional laparotomy were selected as the control group.Data pertaining to clinical indexes,gastrointestinal hormones,nutrition indexes,the levels of inflammatory factors,quality of life,Visual Analog Scale score,and the postoperative complications of the two groups of patients before and after treatment were collected,and the therapeutic effects in the two groups were analyzed and compared.RESULTS Compared with the control group,perioperative bleeding,peristalsis recovery time,and hospital stays were significantly shorter in the research group.After surgery,the levels of gastrin(GAS)and motilin(MTL)were decreased in both groups,and the fluctuation range of GAS and MTL observed in the research group was significantly lower than that recorded in the control group.The hemoglobin(Hb)levels increased after surgery,and the level of Hb in the research group was significantly higher compared with the control group.After the operation,the expression levels of tumor necrosis factor-α,interleukin-6,and C-reactive protein and the total incidence of complications were significantly lower in the research group compared with the control group.One year after the operation,the quality of life of the two groups was greatly improved,with the quality of life in the research group being significantly better.CONCLUSION Laparoscopy was effective for colorectal surgery by reducing the occurrence of complications and inflammatory stress reaction;moreover,the quality of life of patients was significantly improved,which warrants further promotion.
文摘The integration of deep learning into smart grid operations addresses critical challenges in dynamic load forecasting and optimal dispatch amid increasing renewable energy penetration.This study proposes a hybrid LSTM-Transformer architecture for multi-scale temporal-spatial load prediction,achieving 28%RMSE reduction on real-world datasets(CAISO,PJM),coupled with a deep reinforcement learning framework for multi-objective dispatch optimization that lowers operational costs by 12.4%while ensuring stability constraints.The synergy between adaptive forecasting models and scenario-based stochastic optimization demonstrates superior performance in handling renewable intermittency and demand volatility,validated through grid-scale case studies.Methodological innovations in federated feature extraction and carbon-aware scheduling further enhance scalability for distributed energy systems.These advancements provide actionable insights for grid operators transitioning to low-carbon paradigms,emphasizing computational efficiency and interoperability with legacy infrastructure.
基金financially supported by the Key Research and Development Program of Hubei Province(Nos.2022BCA077 and 2022BCA082).
文摘Styrene-butadiene-styrene(SBS)modified asphalt(SA)has long found effective applications in road construction materials.When combined with fillers,SBS-modified asphalt has demonstrated promising resistance to fatigue cracking caused by temperature fluctuations and aging.In this study,molybdenum disulfide(MoS_(2))and polyphosphoric acid(PPA)were ground in naphthenic oil(NO)and subjected to mechanical activation to create PPAmodified MoS_(2),referred to as OMS-PPA.By blending various ratios of OMS-PPA with SBS-modified asphalt,composite-modified asphalts were successfully developed to enhance their overall properties.To assess the mechanical characteristics and stability of these modified asphalts,various methods were employed,including penetration factor,flow activation energy,fluorescence microscopy,and dynamic shear rheology.Additionally,the short-term aging performance was evaluated using Fourier transform infrared(FTIR)spectroscopy and nanoindentation tests.The results revealed a 3.7%decrease in the penetration-temperature coefficient for SAOMS compared to SA,while 1-SA-OMS-PPA showed an even greater reduction of 7.1%.Furthermore,after short-term aging,carboxyl group generation in SA increased by 5.93%,while SA-OMS exhibited a smaller rise of 1.36%,and 1-SA-OMS-PPA saw an increase of just 0.93%.The study also highlighted significant improvements in the hardness of these materials.The hardness change ratio for SA-OMS decreased by 43.08%,while the ratio for 1-SA-OMS-PPA saw a notable reduction of 65.16% compared to unmodified SA.These findings suggest that OMS-PPA contributed to improvements in temperature sensitivity,particle dispersibility,and resistance to shortterm aging in asphalts.The results hold significant promise for the future development of advanced asphalt-based materials with potential high-value applications in flexible pavements for highways.
文摘BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.
基金funded by Guangxi Science and Technology Base and Talent Special Project,grant number GuiKeAD20159077Foundation of Guilin University of Technology,grant number GLUTQD2018001.
文摘The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power grids.This complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly desirable.In the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization problem.The proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs effectively.This study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the MAED.The PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence characteristics.Furthermore,the convergence efficiency of the PPA is significantly influenced by the parameter c.To address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational models.The computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch problems.The simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.
基金the State Grid Shanxi Electric Power Company Science and Technology Project“Smart distribution network with a high proportion of distributed wind storage adaptability assessment and improvement strategy research”(520530230024).
文摘With the increasing penetration of renewable energy in power systems,grid structures and operational paradigms are undergoing profound transformations.When subjected to disturbances,the interaction between power electronic devices and dynamic loads introduces strongly nonlinear dynamic characteristics in grid voltage responses,posing significant threats to system security and stability.To achieve reliable short-term voltage stability assessment under large-scale renewable integration,this paper innovatively proposes a response-driven online assessment method based on energy function theory.First,energy modeling of system components is performed based on energy function theory,followed by analysis of energy interaction mechanisms during voltage instability.To address the challenge of traditional energy functions in online applications,a convolutional neural network-long short-term memory(CNNLSTM)hybrid artificial Intelligence approach is introduced.By quantifying the contribution of each energy component to voltage stability,key energy terms are identified.The measurable electrical quantities corresponding to these key energies serve as inputs,while the energy at the voltage unstable equilibrium point(UEP)obtained from offline simulations is used as both the energy threshold and the output of the artificial intelligence model,enabling the construction of an artificial intelligence model for energy threshold prediction.The measurable electrical quantities corresponding to these key energies serve as inputs,while the energy at the unstable equilibrium point(UEP)obtained from offline simulations acts as the output,enabling the construction of an artificial intelligence model for energy threshold prediction.Real-time response data are fed into the model to predict the system's instantaneous energy threshold,which is then compared with the transient energy at fault clearance to evaluate stability.Validation on both a 3-machine,10-bus system and the New England 10-machine,39-bus system confirms the method's adaptability and accuracy.The simulation results demonstrate that the proposed short-term voltage stability assessment model outperforms other methods in both accuracy and computational efficiency.
基金funded by the Technology Project of State Grid Corporation of China(Research on Safety and Stability Evaluation and Optimization Enhancement Technology of Flexible Ultra High Voltage Multiterminal DC System Adapting to the Background of“Sand and Gobi Deserts”),grant number J2024003。
文摘Countries worldwide are advocating for energy transition initiatives to promote the construction of low-carbon energy systems.The low voltage ride through(LVRT)characteristics of renewable energy units and commutation failures in line commutated converter high voltage direct current(LCC-HVDC)systems at the receiving end leads to short-term power shortage(STPS),which differs from traditional frequency stability issues.STPS occurs during the generator’s power angle swing phase,before the governor responds,and is on a timescale that is not related to primary frequency regulation.This paper addresses these challenges by examining the impact of LVRT on voltage stability,developing a frequency response model to analyze the mechanism of frequency instability caused by STPS,deriving the impact of STPS on the maximum frequency deviation,and introducing an energy deficiency factor to assess its impact on regional frequency stability.The East China Power Grid is used as a case study,where the energy deficiency factor is calculated to validate the proposed mechanism.STPS is mainly compensated by the rotor kinetic energy of the generators in this region,with minimal impact on other regions.It is concluded that the energy deficiency factor provides an effective explanation for the spatial distribution of the impact of STPS on system frequency.
基金supported by National Natural Science Foundation of China(52477101)Natural Science Foundation of Jiangsu Province(BK20210932).
文摘With the intensification of the energy crisis and the worsening greenhouse effect,the development of sustainable integrated energy systems(IES)has become a crucial direction for energy transition.In this context,this paper proposes a low-carbon economic dispatch strategy under the green hydrogen certificate trading(GHCT)and the ladder-type carbon emission trading(CET)mechanism,enabling the coordinated utilization of green and blue hydrogen.Specifically,a proton exchange membrane electrolyzer(PEME)model that accounts for dynamic efficiency characteristics,and a steam methane reforming(SMR)model incorporating waste heat recovery,are developed.Based on these models,a hydrogen production–storage–utilization framework is established to enable the coordinated deployment of green and blue hydrogen.Furthermore,the gas turbine(GT)unit are retrofitted using oxygenenriched combustion carbon capture(OCC)technology,wherein the oxygen produced by PEME is employed to create an oxygen-enriched combustion environment.This approach reduces energy waste and facilitates low-carbon power generation.In addition,the GHCT mechanism is integrated into the system alongside the ladder-type CET mechanism,and their complementary effects are investigated.A comprehensive optimization model is then formulated to simultaneously achieve carbon reduction and economic efficiency across the system.Case study results show that the proposed strategy reduces wind curtailment by 7.77%,carbon emissions by 65.98%,and total cost by 12.57%.This study offers theoretical reference for the low-carbon,economic,and efficient operation of future energy systems.
基金funded by the Young Scientists Fund of the National Natural Science Foundation of China under 82305433,82305437.
文摘Objectives:This study aimed to clarify the short-term symptoms,duration,and influencing factors in people recovering from coronavirus disease 2019(COVID-19)after China’s dynamic zero-COVID-19 policy was implemented in December 2022.Methods:We included data from a large-scale on-line survey conducted in China between January 14 and February 1,2023.Participants were individuals of all ages.Chi-squared tests and multivariate logistic regression analyses were performed to identify factors associated with different symptoms.Results:Overall,21,012 patients from seven regions of China were included in this study(female:71.22%).For most patients,the period from symptom onset to a negative nucleic acid test result was≤10 days(72.33%).The distribution of symptoms varied at different times,with respiratory(1-4 weeks)and psychocardiology(5-8 weeks)symptoms being the most common.Multivariate analysis identified male sex,no comorbidity,and living in northeast and northwest China(compared with central China)as independent factors associated with a lower risk of symptoms,while age(41-60 years)was a possible risk factor(compared with 18-40 years).Conclusions:Short-term respiratory and psychocardiology symptoms were the most common after COVID-19 recovery.Sex,age,geographical region,and comorbidities were potential influencing factors for the development of short-term symptoms.
文摘This paper deeply explores the application strategies of short-term cost curves in the field of economics.Firstly,it elaborates on the basic theories and constituent elements of short-term cost curves.By drawing and analyzing the shortterm cost curve graphs,it presents the internal relationship between costs and output.Then,it focuses on researching its application strategies in multiple aspects such as enterprise production decisions,market pricing,and industry competition analysis.
文摘In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle position,and actual power,lagged features were generated to capture temporal dependencies.Among 24 evaluated models,the ensemble bagging approach achieved the best performance,with R^(2) values of 0.89 at 0 min and 0.75 at 60 min.Shapley Additive exPlanations(SHAP)analysis revealed that while wind speed is the primary driver for short-term predictions,air temperature and nacelle position become more influential at longer forecasting horizons.These findings underscore the reliability of short-term predictions and the potential benefits of integrating hybrid AI and probabilistic models for extended forecasts.Our work contributes a robust and explainable framework to support Sri Lanka’s renewable energy transition,and future research will focus on real-time deployment and uncertainty quantification.
基金partially supported by the National Key R&D Program of China (2022YFE0133700)the National Natural Science Foundation of China(12273007)+4 种基金the Guizhou Provincial Excellent Young Science and Technology Talent Program (YQK[2023]006)the National SKA Program of China (2020SKA0110300)the National Natural Science Foundation of China(11963003)the Guizhou Provincial Basic Research Program (Natural Science)(ZK[2022]143)the Cultivation project of Guizhou University ([2020]76).
文摘Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causing magnetic storms.Consequently,it is very important to accurately predict the time period of solar flares.This paper proposes a flare prediction model,based on physical images of active solar regions.We employ X-ray flux curves recorded directly by the Geostationary Operational Environmental Satellite,used as input data for the model,allowing us to largely avoid the influence of accidental errors,effectively improving the model prediction efficiency.A model based on the X-ray flux curve can predict whether there will be a flare event within 24 hours.The reverse can also be verified by the peak of the X-ray flux curve to see if a flare has occurred within the past 24 hours.The True Positive Rate and False Positive Rate of the prediction model,based on physical images of active regions are 0.6070 and 0.2410 respectively,and the accuracy and True Skill Statistics are 0.7590 and 0.5556.Our model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records,providing a simple method for solar flare prediction.
基金supported by the National Natural Science Foundation of China(62103203)
文摘The economic dispatch problem(EDP) of microgrids operating in both grid-connected and isolated modes within an energy internet framework is addressed in this paper. The multi-agent leader-following consensus algorithm is employed to address the EDP of microgrids in grid-connected mode, while the push-pull algorithm with a fixed step size is introduced for the isolated mode. The proposed algorithm of isolated mode is proven to converge to the optimum when the interaction digraph of microgrids is strongly connected. A unified algorithmic framework is proposed to handle the two modes of operation of microgrids simultaneously, enabling our algorithm to achieve optimal power allocation and maintain the balance between power supply and demand in any mode and any mode switching. Due to the push-pull structure of the algorithm and the use of fixed step size,the proposed algorithm can better handle the case of unbalanced graphs, and the convergence speed is improved. It is documented that when the transmission topology is strongly connected and there is bi-directional communication between the energy router and its neighbors, the proposed algorithm in composite mode achieves economic dispatch even with arbitrary mode switching.Finally, we demonstrate the effectiveness and superiority of our algorithm through numerical simulations.
基金Supported by the National Key Research and Development Program of China(2023YFC3306201)the National Natural Science Foundation of China(61772125)the Fundamental Research Funds for the Central Universities(N2317004).
文摘Background Lip reading uses lip images for visual speech recognition.Deep-learning-based lip reading has greatly improved performance in current datasets;however,most existing research ignores the significance of short-term temporal dependencies of lip-shape variations between adjacent frames,which leaves space for further improvement in feature extraction.Methods This article presents a spatiotemporal feature fusion network(STDNet)that compensates for the deficiencies of current lip-reading approaches in short-term temporal dependency modeling.Specifically,to distinguish more similar and intricate content,STDNet adds a temporal feature extraction branch based on a 3D-CNN,which enhances the learning of dynamic lip movements in adjacent frames while not affecting spatial feature extraction.In particular,we designed a local–temporal block,which aggregates interframe differences,strengthening the relationship between various local lip regions through multiscale convolution.We incorporated the squeeze-and-excitation mechanism into the Global-Temporal Block,which processes a single frame as an independent unitto learn temporal variations across the entire lip region more effectively.Furthermore,attention pooling was introduced to highlight meaningful frames containing key semantic information for the target word.Results Experimental results demonstrated STDNet's superior performance on the LRW and LRW-1000,achieving word-level recognition accuracies of 90.2% and 53.56%,respectively.Extensive ablation experiments verified the rationality and effectiveness of its modules.Conclusions The proposed model effectively addresses short-term temporal dependency limitations in lip reading,and improves the temporal robustness of the model against variable-length sequences.These advancements validate the importance of explicit short-term dynamics modeling for practical lip-reading systems.
基金Supported by the Science and Technology Department of Sichuan Province,No.2023YFS0255。
文摘BACKGROUND Elsberg syndrome is a type of postinfectious lumbosacral radiculitis typically tri-ggered by neurotropic viruses and manifests as bladder/bowel dysfunction,saddle sensory disturbances(including hypoesthesia,hyperesthesia,or dyse-sthesia),and variable neurological deficits.Typically self-limiting,it often res-ponds to antiviral and neurotropic therapies.However,in patients with comorbi-dities that confer susceptibility to peripheral neuropathy(e.g.,diabetes mellitus),timely escalation to neuromodulation strategies,such as spinal cord stimulation,may be warranted to optimize functional outcomes when conservative measures are inadequate.CASE SUMMARY A 60-year-old male with diabetes mellitus presented with severe bladder and bowel dysfunction persisting for more than two months,followed by left gluteal and perianal(saddle area)herpes zoster eruption that was accompanied by significant neuropathic pain.Following a suboptimal response to conservative therapy,the patient underwent implantation of a short-term spinal cord stimu-lation.Following a 10-day trial of continuous tonic stimulation,the percutaneous electrode lead was removed.The patients experienced no surgical complications,and after the procedure,the patient achieved complete restoration of bladder and bowel function and significant pain alleviation.Two-month follow-up confirmed sustained full recovery.CONCLUSION Early implementation of short-term spinal cord stimulation represents a pro-mising therapeutic approach for promoting neurological recovery in patients with Elsberg syndrome refractory to conservative management,especially those with predisposing comorbidities such as diabetes mellitus.
文摘Deep learning plays a vital role in real-life applications, for example object identification, human face recognition, speech recognition, biometrics identification, and short and long-term forecasting of data. The main objective of our work is to predict the market performance of the Dhaka Stock Exchange (DSE) on day closing price using different Deep Learning techniques. In this study, we have used the LSTM (Long Short-Term Memory) network to forecast the data of DSE for the convenience of shareholders. We have enforced LSTM networks to train data as well as forecast the future time series that has differentiated with test data. We have computed the Root Mean Square Error (RMSE) value to scrutinize the error between the forecasted value and test data that diminished the error by updating the LSTM networks. As a consequence of the renovation of the network, the LSTM network provides tremendous performance which outperformed the existing works to predict stock market prices.
文摘To address the issues of unclear carbon responsibility attribution,insufficient renewable energy absorption,and simplistic carbon trading mechanisms in integrated energy systems,this paper proposes an electricheat-hydrogen integrated energy system(EHH-IES)optimal scheduling model considering carbon emission stream(CES)and wind-solar accommodation.First,the CES theory is introduced to quantify the carbon emission intensity of each energy conversion device and transmission branch by defining carbon emission rate,branch carbon intensity,and node carbon potential,realizing accurate tracking of carbon flow in the process of multi-energy coupling.Second,a stepped carbon pricing mechanism is established to dynamically adjust carbon trading costs based on the deviation between actual carbon emissions and initial quotas,strengthening the emission reduction incentive.Finally,a lowcarbon economic dispatch model is constructed with the objectives of minimizing operation cost,carbon trading cost,wind-solar curtailment penalty cost,and energy loss.Simulation results show that compared with the traditional economic dispatch scheme 3,the proposed schemel reduces carbon emissions by 53.97%and wind-solar curtailment by 68.89%with a 16.10%increase in total cost.This verifies that the model can effectively improve clean energy utilization and reduce carbon emissions,achieving low-carbon economic operation of EHH-IES,with CES theory ensuring precise carbon flow tracking across multi-energy links.
