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
BACKGROUND Total hip arthroplasty(THA)has increased along with an increasing demand for improved quality of life.Combined with prolonged life expectancy,the number of revision surgeries is expected to increase.Stress ...BACKGROUND Total hip arthroplasty(THA)has increased along with an increasing demand for improved quality of life.Combined with prolonged life expectancy,the number of revision surgeries is expected to increase.Stress shielding is a significant issue with traditional femoral stems used in THA,making revision surgeries particularly challenging in younger patients.This has sparked renewed interest in studying safety and functional outcomes of short metaphyseal femoral stems,which have the potential to alleviate these challenges and simplify revision surgeries.AIM To evaluate the long-term outcomes of short-stem THA.METHODS A total of 124 hips that underwent THA using the short femoral stem(TRILOCK®Depuy)between May 2006 and November 2008 were included in this study.Patients were followed for a period of 15 years.Outcomes were assessed in terms of pain relief,hip joint range of motion,improvement in mobility,and functional outcomes using the modified Harris Hip Score,Oxford hip score,and Western Ontario and McMaster Universities Osteoarthritis index score.RESULTS A total of 124 hips in 98 patients were evaluated.Significant improvements in functional outcomes were observed over the 15-year follow-up period,with no cases of subsidence,implant loosening,or complications necessitating revision surgery.The only complication reported was heterotopic ossification in 1 patient.CONCLUSION Short metaphyseal stems provide better functional outcomes with early mobilization,and its long-term follow-up without subsidence,implant loosening,or proximal femoral bone loss simplifies revision surgery in younger patients.展开更多
Solar radiation data forecasting algorithms are important,especially in developing countries,as vast solar power plants cannot measure reliable and constant solar irradiance.The challenges of solar irradiance predicti...Solar radiation data forecasting algorithms are important,especially in developing countries,as vast solar power plants cannot measure reliable and constant solar irradiance.The challenges of solar irradiance prediction may be resolved by machine learning using weather datasets.This study emphasises the daily and monthly global solar radiation data predictions of three locations,Pretoria,Bloemfontein,and Vuwani,at different provinces in South Africa with various solar radiation distributions.The study evaluated five different machine learning models.Forecasting models were established to evaluate global solar radiation,focusing on input data.The selected forecast models are centered on their ability to perform with time series data.These models use five years of data from meteorological parameters,such as global horizontal irradiance(GHI),relative humidity,wind speed and ambient temperature between 1 January 2018 and 31 December 2022.The datasets from these meteorological parameters are utilised for training and testing the employed algorithms,which are examined using five statistical metrics.Moreover,the inconsistency of the solar irradiance time series was equally assessed using the clearness index.The results from this study demonstrate that the R2 value recording 0.866 datasets in Bloemfontein of random forest algorithm presents the highest performance during the training processes for all models studied,while the random tree in Vuwani showed the lowest performance of R2 of 0.210 with other algorithms in testing processes.Additionally,the maximum solar radiation was found in December for both Pretoria and Bloemfontein,recorded as 5.347 and 5.844 kWh/m2/day,respectively,while it was 4.692 kWh/m2/day at Vuwani in January.Similarly,the average clearness index of 0.605,0.657 and 0.533 are obtained at Pretoria,Bloemfontein,and Vuwani,respectively.Among the three sites under study,the solar radiation and clearness index are higher in Bloemfontein.Therefore,the proposed algorithms could be used conveniently for short-and long-term solar power plants in South Africa.展开更多
Over the past few years,Malware attacks have become more and more widespread,posing threats to digital assets throughout the world.Although numerous methods have been developed to detect malicious attacks,these malwar...Over the past few years,Malware attacks have become more and more widespread,posing threats to digital assets throughout the world.Although numerous methods have been developed to detect malicious attacks,these malware detection techniques need to be more efficient in detecting new and progressively sophisticated variants of malware.Therefore,the development of more advanced and accurate techniques is necessary for malware detection.This paper introduces a comprehensive Dual-Channel Attention Deep Bidirectional Long Short-Term Memory(DCADBiLSTM)model for malware detection and riskmitigation.The Dual Channel Attention(DCA)mechanism improves themodel’s capability to concentrate on the features that aremost appropriate in the input data,which reduces the false favourable rates.The Bidirectional Long,Short-Term Memory framework helps capture crucial interdependence from past and future circumstances,which is essential for enhancing the model’s understanding of malware behaviour.As soon as malware is detected,the risk mitigation phase is implemented,which evaluates the severity of each threat and helps mitigate threats earlier.The outcomes of the method demonstrate better accuracy of 98.96%,which outperforms traditional models.It indicates the method detects and mitigates several kinds of malware threats,thereby providing a proactive defence mechanism against the emerging challenges in cybersecurity.展开更多
BACKGROUND The World Health Organization defined long coronavirus disease 2019(COVID-19)as the continuation or development of new symptoms 3 months after the initial severe acute respiratory syndrome coronavirus 2 inf...BACKGROUND The World Health Organization defined long coronavirus disease 2019(COVID-19)as the continuation or development of new symptoms 3 months after the initial severe acute respiratory syndrome coronavirus 2 infection,with these symptoms lasting for at least 2 months with no other explanation.AIM To evaluate the potential laboratory and instrumental findings(short-term and long-term)resulting from COVID-19.METHODS This longitudinal observational COVID-19 cohort study(March 1,2020-March 1,2021)was carried out on patients≥18 years old who were admitted to the University Hospitals of Pisa,Siena and Careggi and the Azienda USL Toscana Nord Ovest,Sud Est and USL Centro Toscana and were subjected to follow-up.Follow-up was conducted between 0 day and 89 days,90 days and 179 days,180 days and 269 days,270 days and 359 days,and more than 360 days after hospitalization.RESULTS Of 2887 patients(58.5%males,average age 66.2 years)hospitalized in the study period(March 1,2020-March 1,2021)carrying out at least one follow-up examination within 12 months of discharge,a total of 1739 patients(705 males,average age 66 years)underwent laboratory tests,of whom 714 patients(470 males,average age 63 years)underwent spirometry.Some laboratory test results remained above the threshold even at follow-up beyond 360 days(C-reactive protein:36%,fibrin degradation fragment:48.8%,gamma-glutamyl transferase:16.8%),while others showed a return to normal range more quickly in almost all patients.Alterations in liver enzymes,hematocrit,hemoglobin,lymphocytes and neutrophils were associated with the risk of requiring oxygen therapy or forced expiratory volume in one second/forced vital capacity alterations at follow-up.CONCLUSION Alterations in liver enzymes,hematocrit or hemoglobin,lymphocytes and neutrophils were associated with risk outcomes(need for oxygen therapy or spirometry alterations).These imbalanced conditions may contribute to pulmonary dysfunction.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 increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production,and it has become a challenging task to enhance the accuracy of coal burst disaster predic...The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production,and it has become a challenging task to enhance the accuracy of coal burst disaster prediction.To address the issue of insufficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction,this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory(Bi-LSTM)network.The method involves three main modules,including microseismic spatio-temporal characteristic indicators construction,temporal prediction model,and spatial prediction model.To validate the effectiveness of the proposed method,engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia,focusing on 13 high-energy microseismic events with energy levels greater than 105 J.In terms of temporal prediction,the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions,and there is no false alarm detected throughout the entire testing period.Moreover,compared to the traditional threshold-based coal burst temporal prediction method,the accuracy of the proposed method is increased by 38.5%.In terms of spatial prediction,the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions,3 medium hazard predictions,and 4 weak hazard predictions.展开更多
The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through acceler...The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through accelerated life testing.In the absence of lifetime data,the hidden long-term correlation between performance degradation data is challenging to mine effectively,which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method.To address this problem,a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed.Firstly,a nonlinear health indicator(HI)calculation method based on kernel principal component analysis(KPCA)and exponential weighted moving average(EWMA)is designed.Then,using the raw vibration data and HI,a multi-layer perceptron(MLP)neural network is trained to further calculate the HI of the online bearing in real time.Furthermore,The bidirectional long short-term memory model(BiLSTM)optimized by particle swarm optimization(PSO)is used to mine the time series features of HI and predict the remaining service life.Performance verification experiments and comparative experiments are carried out on the XJTU-SY bearing open dataset.The research results indicate that this method has an excellent ability to predict future HI and remaining life.展开更多
Short-chain fatty acids,metabolites produced by the fermentation of dietary fiber by gut microbiota,have garnered significant attention due to their correlation with neurodegenerative diseases,particularly Parkinson’...Short-chain fatty acids,metabolites produced by the fermentation of dietary fiber by gut microbiota,have garnered significant attention due to their correlation with neurodegenerative diseases,particularly Parkinson’s disease.In this review,we summarize the changes in short-chain fatty acid levels and the abundance of short-chain fatty acid-producing bacteria in various samples from patients with Parkinson’s disease,highlighting the critical role of gut homeostasis imbalance in the pathogenesis and progression of the disease.Focusing on the nervous system,we discuss the molecular mechanisms by which short-chain fatty acids influence the homeostasis of both the enteric nervous system and the central nervous system.We identify key processes,including the activation of G protein-coupled receptors and the inhibition of histone deacetylases by short-chain fatty acids.Importantly,structural or functional disruptions in the enteric nervous system mediated by these fatty acids may lead to abnormalα-synuclein expression and gastrointestinal dysmotility,which could serve as an initiating event in Parkinson’s disease.Furthermore,we propose that short-chain fatty acids help establish communication between the enteric nervous system and the central nervous system via the vagal nerve,immune circulation,and endocrine signaling.This communication may shed light on their potential role in the transmission ofα-synuclein from the gut to the brain.Finally,we elucidate novel treatment strategies for Parkinson’s disease that target short-chain fatty acids and examine the challenges associated with translating short-chain fatty acid-based therapies into clinical practice.In conclusion,this review emphasizes the pivotal role of short-chain fatty acids in regulating gut-brain axis integrity and their significance in the pathogenesis of Parkinson’s disease from the perspective of the nervous system.Moreover,it highlights the potential value of short-chain fatty acids in early intervention for Parkinson’s disease.Future research into the molecular mechanisms of short-chain fatty acids and their synergistic interactions with other gut metabolites is likely to advance the clinical translation of innovative short-chain fatty acid-based therapies for Parkinson’s disease.展开更多
Deep learning enables neural networks to improve prediction performance through data supplementation.In financial time series forecasting,however,such data-driven approaches can encounter limitations where additional ...Deep learning enables neural networks to improve prediction performance through data supplementation.In financial time series forecasting,however,such data-driven approaches can encounter limitations where additional data degrade performance,contrary to common expectations.While more data can still be beneficial,it may introduce systemic concept drift due to the complex nonstationarities of stock price index time series,thereby exacerbating overfitting.One such drift is memory inconsistency:locally measured long memories fluctuate over time,alternately approaching and deviating from the random walk condition.We address this problem by typifying memory inconsistencies into two simplified forms:long-term dependentto-independent(D2I)and long-term independent-to-dependent(I2D)inconsistencies.The first experiment,which uses U.S.stock price indices,suggests that additional training examples may lead to performance deterioration of long short-term memory(LSTM)networks,especially when memory inconsistencies are prominent.Since stock markets are influenced by numerous unknown dynamics,the second experiment,which uses simulated mean-reverting time series derived from the fractional Ornstein–Uhlenbeck(fOU)process,is conducted to focus solely on challenges arising from memory inconsistencies.The experimental results demonstrate that memory inconsistencies disrupt the performance of LSTM networks.Theoretically,additional errors from D2I and I2D inconsistencies increase as the time lag increases.Since LSTM networks are inherently recurrent,causing information from distant steps to attenuate,they fail to effectively capture memory inconsistencies in practical offline learning schemes.Nonetheless,transplanting pretrained memory-consistent gate parameters into the LSTM model partially mitigates the performance deterioration caused by memory inconsistencies,suggesting that memory augmentation strategies have the potential to overcome this problem.As such a memory augmentation method,we propose the Gate-of-Gates(GoG)model,which extends the capacity of LSTM gates and demonstrates that it can mitigate additional errors arising from memory inconsistencies.展开更多
Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,w...Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.展开更多
Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict...Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict key performance indicators(PIs)of pavement,namely the international roughness index(IRI)and rutting depth(RD).Subsequently,we propose a comprehensive performance indicator for the pavement quality index(PQI),which leverages the highway performance assessment standard method,entropy weight method,and fuzzy comprehensive evaluation method.This indicator can evaluate the overall performance condition of the pavement.The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks,called MnRoad and RIOHTrack.Six variables are used as predictors,including temperature,precipitation,total traffic volume,asphalt surface layer thickness,pavement age,and maintenance condition.Furthermore,wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy.In comparison to a traditional autoregressive integrated moving average(ARIMAX)model,the proposed LSTM model performs better in terms of PI prediction and resiliency to noise.Finally,the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.展开更多
Founded in September 2020,the International SparkLink Alliance(iSLA)now has approximately 1,200 members in diverse sectors including terminals,homes,vehicles,manufacturing,transportation,finance and healthcare.The iSL...Founded in September 2020,the International SparkLink Alliance(iSLA)now has approximately 1,200 members in diverse sectors including terminals,homes,vehicles,manufacturing,transportation,finance and healthcare.The iSLA has established a technical standards system for wireless short-range communication covering full-stack standards such as the end-to-end protocol system.展开更多
This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-t...This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-term memory(LSTM)neural network model is proposed to monitor the operational state of the converter and accurately detect faults as they occur.By sampling and processing a large number of thyristor converter operation data,the LSTM model is trained to identify and detect abnormal state,and the power supply health status is monitored.Compared with traditional methods,LSTM model shows higher accuracy and abnormal state detection ability.The experimental results show that this method can effectively improve the reliability and safety of the thyristor converter,and provide a strong guarantee for the stable operation of the nuclear fusion reactor.展开更多
Hepatocellular carcinoma(HCC)remains one of the most prevalent and lethal malignancies worldwide.Long non-coding RNAs(lncRNAs)have emerged as crucial regulators of gene expression and cancer progression,yet the functi...Hepatocellular carcinoma(HCC)remains one of the most prevalent and lethal malignancies worldwide.Long non-coding RNAs(lncRNAs)have emerged as crucial regulators of gene expression and cancer progression,yet the functional diversity of RP11-derived lncRNAs—originally mapped to bacterial artificial chromosome(BAC)clones from the Roswell Park Cancer Institute—has only recently begun to be appreciated.This mini-review aims to systematically synthesize current findings on RP11-derived lncRNAs in HCC,outlining their genomic origins,molecular mechanisms,and biological significance.We highlight their roles in metabolic reprogramming,microRNA network modulation,and tumor progression,as well as their diagnostic and prognostic value in tissue and serum-based analyses.Finally,we discuss therapeutic opportunities and propose future directions to translate RP11-derived lncRNAs into clinically actionable biomarkers and targets for precision liver cancer therapy.展开更多
A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a force...A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.展开更多
Since 2000, the French National Radioactive Waste Management Agency （ANDRA） has been constructing an Underground Research Laboratory （URL） at Bure （east of the Paris Basin） to perform experiments in order to obt...Since 2000, the French National Radioactive Waste Management Agency （ANDRA） has been constructing an Underground Research Laboratory （URL） at Bure （east of the Paris Basin） to perform experiments in order to obtain in situ data necessary to demonstrate the feasibility of geological repository in the Callovo- Oxfordian claystone. An important experimental program is planned to characterize the response of the rock to different drift construction methods, Before 2008, at the main level of the laboratory, most of the drifts were excavated using pneumatic hammer and supported with rock bolts, sliding steel arches and fiber shotcrete. Other techniques, such as road header techniques, stiff and flexible supports, have also been used to characterize their impacts. The drift network is developed following the in situ major stresses. The parallel drifts are separated enough so as they can be considered independently when their hydromechanical （HM） behaviors are compared. Mine-by experiments have been performed to measure the HM response of the rock and the mechanical loading applied to the support system due to the digging and after excavation. Drifts exhibit extensional （mode I） and shear fractures （modes II and III） induced by excavation works. The extent of the induced fracture networks depends on the drift orientation versus the in situ stress field. This paper describes the drift convergence and deformation in the surrounding rock walls as function of time and the impact of different support methods on the rock mass behavior. An observation based method is finally applied to distinguish the instantaneous and time-dependent parts of the rock mass deformation around the drifts.展开更多
A new method was presented to discuss the respective roles of short-and long-range interactions in protein folding.It's based on an off-lattice model,which is also being called as toy model.Simulated annealing alg...A new method was presented to discuss the respective roles of short-and long-range interactions in protein folding.It's based on an off-lattice model,which is also being called as toy model.Simulated annealing algorithm was used to search its native conformation.When it is applied to analysis proteins 1agt and 1aho,we find that helical segment cannot fold into native conformation without the influence of long-range interactions.That's to say that long-range interactions are the main determinants in protein folding.展开更多
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BACKGROUND Total hip arthroplasty(THA)has increased along with an increasing demand for improved quality of life.Combined with prolonged life expectancy,the number of revision surgeries is expected to increase.Stress shielding is a significant issue with traditional femoral stems used in THA,making revision surgeries particularly challenging in younger patients.This has sparked renewed interest in studying safety and functional outcomes of short metaphyseal femoral stems,which have the potential to alleviate these challenges and simplify revision surgeries.AIM To evaluate the long-term outcomes of short-stem THA.METHODS A total of 124 hips that underwent THA using the short femoral stem(TRILOCK®Depuy)between May 2006 and November 2008 were included in this study.Patients were followed for a period of 15 years.Outcomes were assessed in terms of pain relief,hip joint range of motion,improvement in mobility,and functional outcomes using the modified Harris Hip Score,Oxford hip score,and Western Ontario and McMaster Universities Osteoarthritis index score.RESULTS A total of 124 hips in 98 patients were evaluated.Significant improvements in functional outcomes were observed over the 15-year follow-up period,with no cases of subsidence,implant loosening,or complications necessitating revision surgery.The only complication reported was heterotopic ossification in 1 patient.CONCLUSION Short metaphyseal stems provide better functional outcomes with early mobilization,and its long-term follow-up without subsidence,implant loosening,or proximal femoral bone loss simplifies revision surgery in younger patients.
文摘Solar radiation data forecasting algorithms are important,especially in developing countries,as vast solar power plants cannot measure reliable and constant solar irradiance.The challenges of solar irradiance prediction may be resolved by machine learning using weather datasets.This study emphasises the daily and monthly global solar radiation data predictions of three locations,Pretoria,Bloemfontein,and Vuwani,at different provinces in South Africa with various solar radiation distributions.The study evaluated five different machine learning models.Forecasting models were established to evaluate global solar radiation,focusing on input data.The selected forecast models are centered on their ability to perform with time series data.These models use five years of data from meteorological parameters,such as global horizontal irradiance(GHI),relative humidity,wind speed and ambient temperature between 1 January 2018 and 31 December 2022.The datasets from these meteorological parameters are utilised for training and testing the employed algorithms,which are examined using five statistical metrics.Moreover,the inconsistency of the solar irradiance time series was equally assessed using the clearness index.The results from this study demonstrate that the R2 value recording 0.866 datasets in Bloemfontein of random forest algorithm presents the highest performance during the training processes for all models studied,while the random tree in Vuwani showed the lowest performance of R2 of 0.210 with other algorithms in testing processes.Additionally,the maximum solar radiation was found in December for both Pretoria and Bloemfontein,recorded as 5.347 and 5.844 kWh/m2/day,respectively,while it was 4.692 kWh/m2/day at Vuwani in January.Similarly,the average clearness index of 0.605,0.657 and 0.533 are obtained at Pretoria,Bloemfontein,and Vuwani,respectively.Among the three sites under study,the solar radiation and clearness index are higher in Bloemfontein.Therefore,the proposed algorithms could be used conveniently for short-and long-term solar power plants in South Africa.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant No.(IPP:421-611-2025).
文摘Over the past few years,Malware attacks have become more and more widespread,posing threats to digital assets throughout the world.Although numerous methods have been developed to detect malicious attacks,these malware detection techniques need to be more efficient in detecting new and progressively sophisticated variants of malware.Therefore,the development of more advanced and accurate techniques is necessary for malware detection.This paper introduces a comprehensive Dual-Channel Attention Deep Bidirectional Long Short-Term Memory(DCADBiLSTM)model for malware detection and riskmitigation.The Dual Channel Attention(DCA)mechanism improves themodel’s capability to concentrate on the features that aremost appropriate in the input data,which reduces the false favourable rates.The Bidirectional Long,Short-Term Memory framework helps capture crucial interdependence from past and future circumstances,which is essential for enhancing the model’s understanding of malware behaviour.As soon as malware is detected,the risk mitigation phase is implemented,which evaluates the severity of each threat and helps mitigate threats earlier.The outcomes of the method demonstrate better accuracy of 98.96%,which outperforms traditional models.It indicates the method detects and mitigates several kinds of malware threats,thereby providing a proactive defence mechanism against the emerging challenges in cybersecurity.
基金Supported by Regione Toscana,No.D55H20000210002.
文摘BACKGROUND The World Health Organization defined long coronavirus disease 2019(COVID-19)as the continuation or development of new symptoms 3 months after the initial severe acute respiratory syndrome coronavirus 2 infection,with these symptoms lasting for at least 2 months with no other explanation.AIM To evaluate the potential laboratory and instrumental findings(short-term and long-term)resulting from COVID-19.METHODS This longitudinal observational COVID-19 cohort study(March 1,2020-March 1,2021)was carried out on patients≥18 years old who were admitted to the University Hospitals of Pisa,Siena and Careggi and the Azienda USL Toscana Nord Ovest,Sud Est and USL Centro Toscana and were subjected to follow-up.Follow-up was conducted between 0 day and 89 days,90 days and 179 days,180 days and 269 days,270 days and 359 days,and more than 360 days after hospitalization.RESULTS Of 2887 patients(58.5%males,average age 66.2 years)hospitalized in the study period(March 1,2020-March 1,2021)carrying out at least one follow-up examination within 12 months of discharge,a total of 1739 patients(705 males,average age 66 years)underwent laboratory tests,of whom 714 patients(470 males,average age 63 years)underwent spirometry.Some laboratory test results remained above the threshold even at follow-up beyond 360 days(C-reactive protein:36%,fibrin degradation fragment:48.8%,gamma-glutamyl transferase:16.8%),while others showed a return to normal range more quickly in almost all patients.Alterations in liver enzymes,hematocrit,hemoglobin,lymphocytes and neutrophils were associated with the risk of requiring oxygen therapy or forced expiratory volume in one second/forced vital capacity alterations at follow-up.CONCLUSION Alterations in liver enzymes,hematocrit or hemoglobin,lymphocytes and neutrophils were associated with risk outcomes(need for oxygen therapy or spirometry alterations).These imbalanced conditions may contribute to pulmonary dysfunction.
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 Research and Development Program(2022YFC3004603)the Jiangsu Province International Collaboration Program-Key National Industrial Technology Research and Development Cooperation Projects(BZ2023050)+1 种基金the Natural Science Foundation of Jiangsu Province(BK20221109)the National Natural Science Foundation of China(52274098).
文摘The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production,and it has become a challenging task to enhance the accuracy of coal burst disaster prediction.To address the issue of insufficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction,this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory(Bi-LSTM)network.The method involves three main modules,including microseismic spatio-temporal characteristic indicators construction,temporal prediction model,and spatial prediction model.To validate the effectiveness of the proposed method,engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia,focusing on 13 high-energy microseismic events with energy levels greater than 105 J.In terms of temporal prediction,the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions,and there is no false alarm detected throughout the entire testing period.Moreover,compared to the traditional threshold-based coal burst temporal prediction method,the accuracy of the proposed method is increased by 38.5%.In terms of spatial prediction,the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions,3 medium hazard predictions,and 4 weak hazard predictions.
基金supported by the National Key Research and Development Project(Grant Number 2023YFB3709601)the National Natural Science Foundation of China(Grant Numbers 62373215,62373219,62073193)+2 种基金the Key Research and Development Plan of Shandong Province(Grant Numbers 2021CXGC010204,2022CXGC020902)the Fundamental Research Funds of Shandong University(Grant Number 2021JCG008)the Natural Science Foundation of Shandong Province(Grant Number ZR2023MF100).
文摘The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through accelerated life testing.In the absence of lifetime data,the hidden long-term correlation between performance degradation data is challenging to mine effectively,which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method.To address this problem,a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed.Firstly,a nonlinear health indicator(HI)calculation method based on kernel principal component analysis(KPCA)and exponential weighted moving average(EWMA)is designed.Then,using the raw vibration data and HI,a multi-layer perceptron(MLP)neural network is trained to further calculate the HI of the online bearing in real time.Furthermore,The bidirectional long short-term memory model(BiLSTM)optimized by particle swarm optimization(PSO)is used to mine the time series features of HI and predict the remaining service life.Performance verification experiments and comparative experiments are carried out on the XJTU-SY bearing open dataset.The research results indicate that this method has an excellent ability to predict future HI and remaining life.
基金supported by the National Key R&D Program of China,No.2021YFC2501200(to PC).
文摘Short-chain fatty acids,metabolites produced by the fermentation of dietary fiber by gut microbiota,have garnered significant attention due to their correlation with neurodegenerative diseases,particularly Parkinson’s disease.In this review,we summarize the changes in short-chain fatty acid levels and the abundance of short-chain fatty acid-producing bacteria in various samples from patients with Parkinson’s disease,highlighting the critical role of gut homeostasis imbalance in the pathogenesis and progression of the disease.Focusing on the nervous system,we discuss the molecular mechanisms by which short-chain fatty acids influence the homeostasis of both the enteric nervous system and the central nervous system.We identify key processes,including the activation of G protein-coupled receptors and the inhibition of histone deacetylases by short-chain fatty acids.Importantly,structural or functional disruptions in the enteric nervous system mediated by these fatty acids may lead to abnormalα-synuclein expression and gastrointestinal dysmotility,which could serve as an initiating event in Parkinson’s disease.Furthermore,we propose that short-chain fatty acids help establish communication between the enteric nervous system and the central nervous system via the vagal nerve,immune circulation,and endocrine signaling.This communication may shed light on their potential role in the transmission ofα-synuclein from the gut to the brain.Finally,we elucidate novel treatment strategies for Parkinson’s disease that target short-chain fatty acids and examine the challenges associated with translating short-chain fatty acid-based therapies into clinical practice.In conclusion,this review emphasizes the pivotal role of short-chain fatty acids in regulating gut-brain axis integrity and their significance in the pathogenesis of Parkinson’s disease from the perspective of the nervous system.Moreover,it highlights the potential value of short-chain fatty acids in early intervention for Parkinson’s disease.Future research into the molecular mechanisms of short-chain fatty acids and their synergistic interactions with other gut metabolites is likely to advance the clinical translation of innovative short-chain fatty acid-based therapies for Parkinson’s disease.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2023S1A5A8077102).
文摘Deep learning enables neural networks to improve prediction performance through data supplementation.In financial time series forecasting,however,such data-driven approaches can encounter limitations where additional data degrade performance,contrary to common expectations.While more data can still be beneficial,it may introduce systemic concept drift due to the complex nonstationarities of stock price index time series,thereby exacerbating overfitting.One such drift is memory inconsistency:locally measured long memories fluctuate over time,alternately approaching and deviating from the random walk condition.We address this problem by typifying memory inconsistencies into two simplified forms:long-term dependentto-independent(D2I)and long-term independent-to-dependent(I2D)inconsistencies.The first experiment,which uses U.S.stock price indices,suggests that additional training examples may lead to performance deterioration of long short-term memory(LSTM)networks,especially when memory inconsistencies are prominent.Since stock markets are influenced by numerous unknown dynamics,the second experiment,which uses simulated mean-reverting time series derived from the fractional Ornstein–Uhlenbeck(fOU)process,is conducted to focus solely on challenges arising from memory inconsistencies.The experimental results demonstrate that memory inconsistencies disrupt the performance of LSTM networks.Theoretically,additional errors from D2I and I2D inconsistencies increase as the time lag increases.Since LSTM networks are inherently recurrent,causing information from distant steps to attenuate,they fail to effectively capture memory inconsistencies in practical offline learning schemes.Nonetheless,transplanting pretrained memory-consistent gate parameters into the LSTM model partially mitigates the performance deterioration caused by memory inconsistencies,suggesting that memory augmentation strategies have the potential to overcome this problem.As such a memory augmentation method,we propose the Gate-of-Gates(GoG)model,which extends the capacity of LSTM gates and demonstrates that it can mitigate additional errors arising from memory inconsistencies.
基金This work is supported by the National Key Research and Development Program of China(No.2023YFB4203000)the National Natural Science Foundation of China(No.U22A20178)
文摘Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.
基金supported by the National Key Research and Development Program of China(No.2021YFB2600300).
文摘Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict key performance indicators(PIs)of pavement,namely the international roughness index(IRI)and rutting depth(RD).Subsequently,we propose a comprehensive performance indicator for the pavement quality index(PQI),which leverages the highway performance assessment standard method,entropy weight method,and fuzzy comprehensive evaluation method.This indicator can evaluate the overall performance condition of the pavement.The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks,called MnRoad and RIOHTrack.Six variables are used as predictors,including temperature,precipitation,total traffic volume,asphalt surface layer thickness,pavement age,and maintenance condition.Furthermore,wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy.In comparison to a traditional autoregressive integrated moving average(ARIMAX)model,the proposed LSTM model performs better in terms of PI prediction and resiliency to noise.Finally,the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.
文摘Founded in September 2020,the International SparkLink Alliance(iSLA)now has approximately 1,200 members in diverse sectors including terminals,homes,vehicles,manufacturing,transportation,finance and healthcare.The iSLA has established a technical standards system for wireless short-range communication covering full-stack standards such as the end-to-end protocol system.
基金supported by the Open Fund of Magnetic Confinement Fusion Laboratory of Anhui Province(No.2024AMF04003)the Natural Science Foundation of Anhui Province(No.228085ME142)Comprehensive Research Facility for Fusion Technology(No.20180000527301001228)。
文摘This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-term memory(LSTM)neural network model is proposed to monitor the operational state of the converter and accurately detect faults as they occur.By sampling and processing a large number of thyristor converter operation data,the LSTM model is trained to identify and detect abnormal state,and the power supply health status is monitored.Compared with traditional methods,LSTM model shows higher accuracy and abnormal state detection ability.The experimental results show that this method can effectively improve the reliability and safety of the thyristor converter,and provide a strong guarantee for the stable operation of the nuclear fusion reactor.
基金supported by the National Research Foundation of Korea(NRF),funded by the Ministry of Science and ICT(MSIT),Republic of Korea(grant numbers:RS-2022-NR070489 and RS-2023-00210847)the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health and Welfare,Republic of Korea(grant number HR21C1003).
文摘Hepatocellular carcinoma(HCC)remains one of the most prevalent and lethal malignancies worldwide.Long non-coding RNAs(lncRNAs)have emerged as crucial regulators of gene expression and cancer progression,yet the functional diversity of RP11-derived lncRNAs—originally mapped to bacterial artificial chromosome(BAC)clones from the Roswell Park Cancer Institute—has only recently begun to be appreciated.This mini-review aims to systematically synthesize current findings on RP11-derived lncRNAs in HCC,outlining their genomic origins,molecular mechanisms,and biological significance.We highlight their roles in metabolic reprogramming,microRNA network modulation,and tumor progression,as well as their diagnostic and prognostic value in tissue and serum-based analyses.Finally,we discuss therapeutic opportunities and propose future directions to translate RP11-derived lncRNAs into clinically actionable biomarkers and targets for precision liver cancer therapy.
基金supported by the Ministry of Trade,Industry & Energy(MOTIE,Korea) under Industrial Technology Innovation Program (No.10063424,'development of distant speech recognition and multi-task dialog processing technologies for in-door conversational robots')
文摘A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.
文摘Since 2000, the French National Radioactive Waste Management Agency （ANDRA） has been constructing an Underground Research Laboratory （URL） at Bure （east of the Paris Basin） to perform experiments in order to obtain in situ data necessary to demonstrate the feasibility of geological repository in the Callovo- Oxfordian claystone. An important experimental program is planned to characterize the response of the rock to different drift construction methods, Before 2008, at the main level of the laboratory, most of the drifts were excavated using pneumatic hammer and supported with rock bolts, sliding steel arches and fiber shotcrete. Other techniques, such as road header techniques, stiff and flexible supports, have also been used to characterize their impacts. The drift network is developed following the in situ major stresses. The parallel drifts are separated enough so as they can be considered independently when their hydromechanical （HM） behaviors are compared. Mine-by experiments have been performed to measure the HM response of the rock and the mechanical loading applied to the support system due to the digging and after excavation. Drifts exhibit extensional （mode I） and shear fractures （modes II and III） induced by excavation works. The extent of the induced fracture networks depends on the drift orientation versus the in situ stress field. This paper describes the drift convergence and deformation in the surrounding rock walls as function of time and the impact of different support methods on the rock mass behavior. An observation based method is finally applied to distinguish the instantaneous and time-dependent parts of the rock mass deformation around the drifts.
基金Supported by the National Natural Science Foun-dation of China(60301009)
文摘A new method was presented to discuss the respective roles of short-and long-range interactions in protein folding.It's based on an off-lattice model,which is also being called as toy model.Simulated annealing algorithm was used to search its native conformation.When it is applied to analysis proteins 1agt and 1aho,we find that helical segment cannot fold into native conformation without the influence of long-range interactions.That's to say that long-range interactions are the main determinants in protein folding.
