BACKGROUND Due to the increasing rate of thyroid nodules diagnosis,and the desire to avoid the unsightly cervical scar,remote thyroidectomies were invented and are increasingly performed.Transoral endoscopic thyroidec...BACKGROUND Due to the increasing rate of thyroid nodules diagnosis,and the desire to avoid the unsightly cervical scar,remote thyroidectomies were invented and are increasingly performed.Transoral endoscopic thyroidectomy vestibular approach and trans-areolar approaches(TAA)are the two most commonly used remote approaches.No previous meta-analysis has compared postoperative infections and swallowing difficulties among the two procedures.AIM To compared the same among patients undergoing lobectomy for unilateral thyroid carcinoma/benign thyroid nodule.METHODS We searched PubMed MEDLINE,Google Scholar,and Cochrane Library from the date of the first published article up to August 2025.The term used were transoral thyroidectomy vestibular approach,trans areolar thyroidectomy,scarless thyroidectomy,remote thyroidectomy,infections,postoperative,inflammation,dysphagia,and swallowing difficulties.We identified 130 studies,of them,30 full texts were screened and only six studies were included in the final meta-analysis.RESULTS Postoperative infections were not different between the two approaches,odd ratio=1.33,95%confidence interval:0.50-3.53,theχ2 was 1.92 and the P-value for overall effect of 0.57.Similarly,transient swallowing difficulty was not different between the two forms of surgery,with odd ratio=0.91,95%confidence interval:0.35-2.40;theχ2 was 1.32,and the P-value for overall effect of 0.85.CONCLUSION No significant statistical differences were evident between trans-oral endoscopic Mirghani H.Infections and swallowing difficulty in scarless thyroidectomy WJCC https://www.wjgnet.com 2 January 6,2026 Volume 14 Issue 1 thyroidectomy vestibular approach and trans-areolar approach regarding postoperative infection and transient swallowing difficulties.Further longer randomized trials are needed.展开更多
The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this chal...The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.展开更多
This paper focuses on the numerical solution of a tumor growth model under a data-driven approach.Based on the inherent laws of the data and reasonable assumptions,an ordinary differential equation model for tumor gro...This paper focuses on the numerical solution of a tumor growth model under a data-driven approach.Based on the inherent laws of the data and reasonable assumptions,an ordinary differential equation model for tumor growth is established.Nonlinear fitting is employed to obtain the optimal parameter estimation of the mathematical model,and the numerical solution is carried out using the Matlab software.By comparing the clinical data with the simulation results,a good agreement is achieved,which verifies the rationality and feasibility of the model.展开更多
A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis ca...A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.展开更多
With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal referen...With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal references.This huge volume of available spatio-temporal(ST)data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns,relationships,and knowledge embedded in such large ST datasets.In this survey,we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis.The focus is on outlining various state-of-the-art spatio-temporal data mining techniques,and their applications in various domains.We start with a brief overview of spatio-temporal data and various challenges in analyzing such data,and conclude by listing the current trends and future scopes of research in this multi-disciplinary area.Compared with other relevant surveys,this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives.We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.展开更多
The progressive loss of dopaminergic neurons in affected patient brains is one of the pathological features of Parkinson's disease,the second most common human neurodegenerative disease.Although the detailed patho...The progressive loss of dopaminergic neurons in affected patient brains is one of the pathological features of Parkinson's disease,the second most common human neurodegenerative disease.Although the detailed pathogenesis accounting for dopaminergic neuron degeneration in Parkinson's disease is still unclear,the advancement of stem cell approaches has shown promise for Parkinson's disease research and therapy.The induced pluripotent stem cells have been commonly used to generate dopaminergic neurons,which has provided valuable insights to improve our understanding of Parkinson's disease pathogenesis and contributed to anti-Parkinson's disease therapies.The current review discusses the practical approaches and potential applications of induced pluripotent stem cell techniques for generating and differentiating dopaminergic neurons from induced pluripotent stem cells.The benefits of induced pluripotent stem cell-based research are highlighted.Various dopaminergic neuron differentiation protocols from induced pluripotent stem cells are compared.The emerging three-dimension-based brain organoid models compared with conventional two-dimensional cell culture are evaluated.Finally,limitations,challenges,and future directions of induced pluripotent stem cell–based approaches are analyzed and proposed,which will be significant to the future application of induced pluripotent stem cell-related techniques for Parkinson's disease.展开更多
Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensem...Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions.With the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data.Ensemble deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning algorithms.Although ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble learning.Ensemble deep learning has been successfully used in several areas,such as bioinformatics,finance,and health care.In this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug discovery.We cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also demonstrated.Furthermore,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and explored.Finally,future directions and opportunities for enhancing healthcare model performance are discussed.展开更多
This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal disease...This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal diseases.The study sheds light on how illness perception,anxiety,and depression significantly influence recovery outcomes.Hou et al developed a predictive model that demonstrated high accuracy in identifying patients at risk of poor recovery.The article explores the critical role of pre-operative psychological assessment,highlighting the need for mental health support and personalized recovery plans in enhancing POR quality.A multidisciplinary approach,integrating mental health professionals with surgeons,anesthesiologists,and other specialists,is emphasized to ensure comprehensive care for patients.The study’s findings serve as a call to integrate psychological care into surgical practice to optimize outcomes for patients with perianal diseases.展开更多
Materials genome engineering(MGE)has been successfully applied in various fields,resulting in a series of novel materials with excellent performance.Significant progress has been made in high-throughput simulation,exp...Materials genome engineering(MGE)has been successfully applied in various fields,resulting in a series of novel materials with excellent performance.Significant progress has been made in high-throughput simulation,experimentation,and data-driven techniques,enabling the effective prediction,rapid synthesis,and characterization of many classes of materials.In this brief review,we introduce the achievements made in the field of metallic glasses(MGs)using MGE,in particular high-throughput experimentation and data-driven approaches.High-throughput experiments help to efficiently synthesize and characterize many materials in a short period of time,enabling the construction of high-quality material databases for data-driven methods.Paired with machine learning,potential alloys of desired properties may be revealed and predicted.Along with the progress in computational power and algorithms of machine learning,the complex composition-structure-properties relationship is hopefully established,which in turn help efficient and precise prediction of new MGs.展开更多
System identification is a data-driven modeling technique that originates from the control field.It constructs models from data to mimic the behavior of dynamic systems.However,in the network era,scenarios such as sen...System identification is a data-driven modeling technique that originates from the control field.It constructs models from data to mimic the behavior of dynamic systems.However,in the network era,scenarios such as sensor malfunctions,packet loss,cyber-attacks,and big data affect the quality,integrity,and security of the data.These data issues pose significant challenges to traditional system identification methods.This paper presents a comprehensive survey of the emergent challenges and advances in system identification in the network era.It explores cutting-edge methodologies to address data issues such as data loss,outliers,noise and nonlinear system identification for complex systems.To tackle the data loss,the methods based on imputation and likelihood-based inference(e.g.,expectation maximization)have been employed.For outliers and noise,methods like robust regression(e.g.,least median of squares,least trimmed squares)and lowrank matrix decomposition show progress in maintaining data integrity.Nonlinear system identification has advanced through kernel-based methods and neural networks,which can model complex data patterns.Finally,this paper provides valuable insights into potential directions for future research.展开更多
Metabolomics utilizes advanced analytical profiling techniques to comprehensively measure small molecules in cells,tissues,and biological fluids.Nutritional metabolomics studies in pigs have reported changes in hundre...Metabolomics utilizes advanced analytical profiling techniques to comprehensively measure small molecules in cells,tissues,and biological fluids.Nutritional metabolomics studies in pigs have reported changes in hundreds of metabolites across various sample types,including plasma,serum,urine,digesta,and feces,following dietary interventions.These findings can help identify biomarkers of gastrointestinal functionality and beyond,as well as investigate mechanistic interactions between diet,host,microbiome,and metabolites.This review aims to summarize the current literature on nutritional metabolomics in pigs and its use to investigate how different dietary approaches impact the gut health of pigs.Here,we critically assessed and categorized the impact of the main macronutrients-carbohydrates,proteins,and fats—along with feed additives such as amino acids,bile acids,and probiotics,as well as feeding strategies like creep feeding,milk replacer introduction,and time-restricted feeding,on the pig metabolome.Additionally,we discuss the potential modes of action of the key affected metabolites on pig gut health.展开更多
Pelvic fractures are rare but severe injuries that severely affect patients’quality of life.Treatment of these fractures often involves invasive approaches with high risk of injuries to nervous structures,particularl...Pelvic fractures are rare but severe injuries that severely affect patients’quality of life.Treatment of these fractures often involves invasive approaches with high risk of injuries to nervous structures,particularly lumbosacral plexus.The introduction of minimally invasive surgical approaches,such as the lateral rectus approach,not only contributes to preserving lumbar plexus integrity in operated patients but also positively impacts their psychological well-being.Patients treated by surgical reduction of pelvic fractures with lumbosacral plexus injury often experience states of anxiety and depression.The lateral rectus approach is associated with lower levels of anxiety and depression compared to more invasive surgical techniques used for similar fractures.展开更多
Approximately 5%of patients with renal cancer present with synchronous bilateral renal masses(SBRM).1,2 Bilateral renal tumors associated with hereditary syndromes often exhibit more aggressive biological behaviors co...Approximately 5%of patients with renal cancer present with synchronous bilateral renal masses(SBRM).1,2 Bilateral renal tumors associated with hereditary syndromes often exhibit more aggressive biological behaviors compared to sporadic SBRM cases.3,4 Notably,the prognosis for sporadic cases,in terms of cancerspecific and distant metastasis-free survival,is comparable to that of unilateral renal masses.展开更多
The chiral-induced spin selectivity(CISS)effect has attracted widespread interest due to its potential applications in spintronics.However,the influence of electron-vibration coupling on spin polarization has no unifi...The chiral-induced spin selectivity(CISS)effect has attracted widespread interest due to its potential applications in spintronics.However,the influence of electron-vibration coupling on spin polarization has no unified theoretical description.In this work,we revisit the widely used model proposed by 100%Jonas Fransson[Phys.Rev.B 102,235416(2020)]with two mixed quantum-classical approaches,namely mean-field(MF)dynamics and surface hopping(SH).Our results show that,in the absence of electron-vibration coupling,the transient spin polarization vanishes for short molecular chains or without spin-orbit coupling(SOC).For longer chains and stronger SOC,the polarization exhibits coherent oscillations reaching up to.When electron-vibration coupling is included,pronounced spin polarization emerges only at early femtosecond timescales,whose magnitude increases with SOC strength and chain length but decreases as the electron-vibration coupling grows.On picosecond timescales,the polarization decays and fluctuates narrowly around zero,yielding a negligible long-time average.These findings indicate that electron-vibration coupling generally suppresses spin polarization,in contrast to predictions in previous studies.We suggest that alternative or extended theoretical frameworks are required to elucidate the key role of electron-vibration coupling in the CISS effect.展开更多
The theory of new quality productive forces provides a foundational framework for cultivating pre-service English teachers.There is a high degree of consistency between the development of new quality productive forces...The theory of new quality productive forces provides a foundational framework for cultivating pre-service English teachers.There is a high degree of consistency between the development of new quality productive forces and the cultivation of pre-service English teachers.The development of new quality productive forces has put forward new requirements for the cultivation of pre-service English teachers,while the cultivation of pre-service English teachers will also promote the development of new quality productive forces and provide talent support for it.Currently,the cultivation of pre-service English teachers faces numerous challenges,which requires strengthening top-level program design,reconstructing the curriculum system,expanding cultivation fields for pre-service English teachers,improving the digital literacy of pre-service English teachers,deepening international exchanges and cooperation,and building an evidence-based evaluation system as a guarantee to achieve new breakthroughs in the cultivation of pre-service English teachers and promote the development of new quality productive forces.展开更多
With the rapid development of information technology and the advancement of educational modernization,the teaching mode of vocal music in colleges and universities is undergoing a new transformation,which complies wit...With the rapid development of information technology and the advancement of educational modernization,the teaching mode of vocal music in colleges and universities is undergoing a new transformation,which complies with the trend of digital age and brings new challenges.This paper explores the specific implementation path of artificial intelligence technology,virtual reality technology,big data technology and intelligent interaction technology in vocal music teaching in colleges and universities,aiming to inject new vitality into the traditional teaching mode and improve teaching quality and efficiency.展开更多
The rising prevalence of chronic multimorbidity poses substantial challenges to healthcare systems,necessitating the development of innovative management strategies to optimize patient care and system efficiency.The s...The rising prevalence of chronic multimorbidity poses substantial challenges to healthcare systems,necessitating the development of innovative management strategies to optimize patient care and system efficiency.The study by Fontalba-Navas et al investigates the implementation of a novel high complexity unit(HCU)specifically designed to improve the management of patients with chronic complex conditions.By adopting a multidisciplinary approach,the HCU aims to provide comprehensive,patient-centered care that enhances health outcomes and alleviates the strain on traditional hospital services.Utilizing a longitudinal analysis of data from the Basic Minimum Data Set,this study compares hospitalization metrics among the HCU,Internal Medicine,and other departments within a regional hospital throughout 2022.The findings reveal that the HCU's integrated care model significantly reduces readmission rates and boosts patient satisfaction compared to conventional care practices.The study highlights the HCU's potential as a replicable model for managing chronic multimorbidity,emphasizing its effectiveness in minimizing unnecessary hospitalizations and enhancing the overall quality of patient care.This innovative approach not only addresses the complexities associated with chronic multimorbid conditions but also offers a sustainable framework for healthcare systems confronting similar challenges.展开更多
Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands...Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.展开更多
Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subju...Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subjunctive conditionals.Motivated by the so-called OK pairs,they try to provide a convincing explanation of the intuition underlying the OK pairs.As far as we know,what they have done is to provide not only an explanation of the OK pairs,but also a way of distinguishing between indicative and subjunctive conditionals.Although we agree with their success in explaining the OK pairs within a causal modelling framework,we argue that their ways of distinguishing between indicative and subjunctive conditionals fail.Instead,we argue that their approaches can be used to distinguish between two readings of conditionals,the epistemic reading and the ontic reading.which can be applied to both indicative and subjunctive conditionals.We conclude by arguing that these two readings are related to two approaches to asking and answering causal questions:the“auses-of-effects"approach and the"effects-of-causes"approach.展开更多
Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,a...Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,and stage of diagnosis,which directly impacts clinical decision-making.Various biological entities,including genes,proteins,mRNAs,miRNAs,and metabolites,contribute to cancer development.The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers.This integrative approach supports the notion that cancer is fundamentally driven by such alterations,enabling the discovery ofmolecular signatures for precision oncology.This reviewexplores the role of AI-drivenmulti-omics analyses in cancer medicine,emphasizing their potential to identify novel biomarkers and therapeutic targets,enhance understanding of Tumor biology,and address integration challenges in clinical workflows.Network biology analyzes identified ERBB2,KRAS,and TP53 as top hub genes in lung cancer based on Maximal Clique Centrality(MCC)scores.In contrast,TP53,ERBB2,ESR1,MYC,and BRCA1 emerged as central regulators in breast cancer,linked to cell proliferation,hormonal signaling,and genomic stability.The review also discusses how specific Artificial Intelligence(AI)algorithms can streamline the integration of heterogeneous datasets,facilitate the interpretation of the tumor microenvironment,and support data-driven clinical strategies.展开更多
文摘BACKGROUND Due to the increasing rate of thyroid nodules diagnosis,and the desire to avoid the unsightly cervical scar,remote thyroidectomies were invented and are increasingly performed.Transoral endoscopic thyroidectomy vestibular approach and trans-areolar approaches(TAA)are the two most commonly used remote approaches.No previous meta-analysis has compared postoperative infections and swallowing difficulties among the two procedures.AIM To compared the same among patients undergoing lobectomy for unilateral thyroid carcinoma/benign thyroid nodule.METHODS We searched PubMed MEDLINE,Google Scholar,and Cochrane Library from the date of the first published article up to August 2025.The term used were transoral thyroidectomy vestibular approach,trans areolar thyroidectomy,scarless thyroidectomy,remote thyroidectomy,infections,postoperative,inflammation,dysphagia,and swallowing difficulties.We identified 130 studies,of them,30 full texts were screened and only six studies were included in the final meta-analysis.RESULTS Postoperative infections were not different between the two approaches,odd ratio=1.33,95%confidence interval:0.50-3.53,theχ2 was 1.92 and the P-value for overall effect of 0.57.Similarly,transient swallowing difficulty was not different between the two forms of surgery,with odd ratio=0.91,95%confidence interval:0.35-2.40;theχ2 was 1.32,and the P-value for overall effect of 0.85.CONCLUSION No significant statistical differences were evident between trans-oral endoscopic Mirghani H.Infections and swallowing difficulty in scarless thyroidectomy WJCC https://www.wjgnet.com 2 January 6,2026 Volume 14 Issue 1 thyroidectomy vestibular approach and trans-areolar approach regarding postoperative infection and transient swallowing difficulties.Further longer randomized trials are needed.
文摘The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.
基金National Natural Science Foundation of China(Project No.:12371428)Projects of the Provincial College Students’Innovation and Training Program in 2024(Project No.:S202413023106,S202413023110)。
文摘This paper focuses on the numerical solution of a tumor growth model under a data-driven approach.Based on the inherent laws of the data and reasonable assumptions,an ordinary differential equation model for tumor growth is established.Nonlinear fitting is employed to obtain the optimal parameter estimation of the mathematical model,and the numerical solution is carried out using the Matlab software.By comparing the clinical data with the simulation results,a good agreement is achieved,which verifies the rationality and feasibility of the model.
基金This work was supported by the National Natural Science Foundation of China(Grant No.42050104)the Science Foundation of SINOPEC Group(Grant No.P20030).
文摘A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.
文摘With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal references.This huge volume of available spatio-temporal(ST)data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns,relationships,and knowledge embedded in such large ST datasets.In this survey,we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis.The focus is on outlining various state-of-the-art spatio-temporal data mining techniques,and their applications in various domains.We start with a brief overview of spatio-temporal data and various challenges in analyzing such data,and conclude by listing the current trends and future scopes of research in this multi-disciplinary area.Compared with other relevant surveys,this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives.We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.
基金supported by Singapore National Medical Research Council(NMRC)grants,including CS-IRG,HLCA2022(to ZDZ),STaR,OF LCG 000207(to EKT)a Clinical Translational Research Programme in Parkinson's DiseaseDuke-Duke-NUS collaboration pilot grant(to ZDZ)。
文摘The progressive loss of dopaminergic neurons in affected patient brains is one of the pathological features of Parkinson's disease,the second most common human neurodegenerative disease.Although the detailed pathogenesis accounting for dopaminergic neuron degeneration in Parkinson's disease is still unclear,the advancement of stem cell approaches has shown promise for Parkinson's disease research and therapy.The induced pluripotent stem cells have been commonly used to generate dopaminergic neurons,which has provided valuable insights to improve our understanding of Parkinson's disease pathogenesis and contributed to anti-Parkinson's disease therapies.The current review discusses the practical approaches and potential applications of induced pluripotent stem cell techniques for generating and differentiating dopaminergic neurons from induced pluripotent stem cells.The benefits of induced pluripotent stem cell-based research are highlighted.Various dopaminergic neuron differentiation protocols from induced pluripotent stem cells are compared.The emerging three-dimension-based brain organoid models compared with conventional two-dimensional cell culture are evaluated.Finally,limitations,challenges,and future directions of induced pluripotent stem cell–based approaches are analyzed and proposed,which will be significant to the future application of induced pluripotent stem cell-related techniques for Parkinson's disease.
基金funded by Taif University,Saudi Arabia,project No.(TU-DSPP-2024-263).
文摘Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions.With the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data.Ensemble deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning algorithms.Although ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble learning.Ensemble deep learning has been successfully used in several areas,such as bioinformatics,finance,and health care.In this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug discovery.We cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also demonstrated.Furthermore,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and explored.Finally,future directions and opportunities for enhancing healthcare model performance are discussed.
基金Supported by National Research Foundation of Korea,No.NRF-2021S1A5A8062526.
文摘This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal diseases.The study sheds light on how illness perception,anxiety,and depression significantly influence recovery outcomes.Hou et al developed a predictive model that demonstrated high accuracy in identifying patients at risk of poor recovery.The article explores the critical role of pre-operative psychological assessment,highlighting the need for mental health support and personalized recovery plans in enhancing POR quality.A multidisciplinary approach,integrating mental health professionals with surgeons,anesthesiologists,and other specialists,is emphasized to ensure comprehensive care for patients.The study’s findings serve as a call to integrate psychological care into surgical practice to optimize outcomes for patients with perianal diseases.
基金support by the National Key Research and Development Program of China(grant no.2018YFA0703600)the National Natural Science Foundation of China(grant no.51825104).
文摘Materials genome engineering(MGE)has been successfully applied in various fields,resulting in a series of novel materials with excellent performance.Significant progress has been made in high-throughput simulation,experimentation,and data-driven techniques,enabling the effective prediction,rapid synthesis,and characterization of many classes of materials.In this brief review,we introduce the achievements made in the field of metallic glasses(MGs)using MGE,in particular high-throughput experimentation and data-driven approaches.High-throughput experiments help to efficiently synthesize and characterize many materials in a short period of time,enabling the construction of high-quality material databases for data-driven methods.Paired with machine learning,potential alloys of desired properties may be revealed and predicted.Along with the progress in computational power and algorithms of machine learning,the complex composition-structure-properties relationship is hopefully established,which in turn help efficient and precise prediction of new MGs.
基金supported in part by the National Natural Science Foundation of China(62373060)the BNU Talent seed fund,and the Guangdong Provincial Key Laboratory IRADS for Data Science(2022B1212010006)Recommended by Associate Editor Zhengcai Cao.(Corresponding author:Liang Zhang.)。
文摘System identification is a data-driven modeling technique that originates from the control field.It constructs models from data to mimic the behavior of dynamic systems.However,in the network era,scenarios such as sensor malfunctions,packet loss,cyber-attacks,and big data affect the quality,integrity,and security of the data.These data issues pose significant challenges to traditional system identification methods.This paper presents a comprehensive survey of the emergent challenges and advances in system identification in the network era.It explores cutting-edge methodologies to address data issues such as data loss,outliers,noise and nonlinear system identification for complex systems.To tackle the data loss,the methods based on imputation and likelihood-based inference(e.g.,expectation maximization)have been employed.For outliers and noise,methods like robust regression(e.g.,least median of squares,least trimmed squares)and lowrank matrix decomposition show progress in maintaining data integrity.Nonlinear system identification has advanced through kernel-based methods and neural networks,which can model complex data patterns.Finally,this paper provides valuable insights into potential directions for future research.
基金the PIG-PARADIGM project,funded by the Novo Nordisk Foundation(Grant No.NNFSA210073688).
文摘Metabolomics utilizes advanced analytical profiling techniques to comprehensively measure small molecules in cells,tissues,and biological fluids.Nutritional metabolomics studies in pigs have reported changes in hundreds of metabolites across various sample types,including plasma,serum,urine,digesta,and feces,following dietary interventions.These findings can help identify biomarkers of gastrointestinal functionality and beyond,as well as investigate mechanistic interactions between diet,host,microbiome,and metabolites.This review aims to summarize the current literature on nutritional metabolomics in pigs and its use to investigate how different dietary approaches impact the gut health of pigs.Here,we critically assessed and categorized the impact of the main macronutrients-carbohydrates,proteins,and fats—along with feed additives such as amino acids,bile acids,and probiotics,as well as feeding strategies like creep feeding,milk replacer introduction,and time-restricted feeding,on the pig metabolome.Additionally,we discuss the potential modes of action of the key affected metabolites on pig gut health.
文摘Pelvic fractures are rare but severe injuries that severely affect patients’quality of life.Treatment of these fractures often involves invasive approaches with high risk of injuries to nervous structures,particularly lumbosacral plexus.The introduction of minimally invasive surgical approaches,such as the lateral rectus approach,not only contributes to preserving lumbar plexus integrity in operated patients but also positively impacts their psychological well-being.Patients treated by surgical reduction of pelvic fractures with lumbosacral plexus injury often experience states of anxiety and depression.The lateral rectus approach is associated with lower levels of anxiety and depression compared to more invasive surgical techniques used for similar fractures.
文摘Approximately 5%of patients with renal cancer present with synchronous bilateral renal masses(SBRM).1,2 Bilateral renal tumors associated with hereditary syndromes often exhibit more aggressive biological behaviors compared to sporadic SBRM cases.3,4 Notably,the prognosis for sporadic cases,in terms of cancerspecific and distant metastasis-free survival,is comparable to that of unilateral renal masses.
基金the financial support from the National Natural Science Foundation of China(Grant No.22361142829)the Zhejiang Provincial Natural Science Foundation(Grant No.XHD24B0301)。
文摘The chiral-induced spin selectivity(CISS)effect has attracted widespread interest due to its potential applications in spintronics.However,the influence of electron-vibration coupling on spin polarization has no unified theoretical description.In this work,we revisit the widely used model proposed by 100%Jonas Fransson[Phys.Rev.B 102,235416(2020)]with two mixed quantum-classical approaches,namely mean-field(MF)dynamics and surface hopping(SH).Our results show that,in the absence of electron-vibration coupling,the transient spin polarization vanishes for short molecular chains or without spin-orbit coupling(SOC).For longer chains and stronger SOC,the polarization exhibits coherent oscillations reaching up to.When electron-vibration coupling is included,pronounced spin polarization emerges only at early femtosecond timescales,whose magnitude increases with SOC strength and chain length but decreases as the electron-vibration coupling grows.On picosecond timescales,the polarization decays and fluctuates narrowly around zero,yielding a negligible long-time average.These findings indicate that electron-vibration coupling generally suppresses spin polarization,in contrast to predictions in previous studies.We suggest that alternative or extended theoretical frameworks are required to elucidate the key role of electron-vibration coupling in the CISS effect.
基金supported by the National Education Sciences Planning Program of China through the National Office for Education Sciences Planning(Grant No.DIA220376).
文摘The theory of new quality productive forces provides a foundational framework for cultivating pre-service English teachers.There is a high degree of consistency between the development of new quality productive forces and the cultivation of pre-service English teachers.The development of new quality productive forces has put forward new requirements for the cultivation of pre-service English teachers,while the cultivation of pre-service English teachers will also promote the development of new quality productive forces and provide talent support for it.Currently,the cultivation of pre-service English teachers faces numerous challenges,which requires strengthening top-level program design,reconstructing the curriculum system,expanding cultivation fields for pre-service English teachers,improving the digital literacy of pre-service English teachers,deepening international exchanges and cooperation,and building an evidence-based evaluation system as a guarantee to achieve new breakthroughs in the cultivation of pre-service English teachers and promote the development of new quality productive forces.
基金Education Department of Hainan Province(Project No.:Hnjg2024-112&Hnjg2025ZC-80)。
文摘With the rapid development of information technology and the advancement of educational modernization,the teaching mode of vocal music in colleges and universities is undergoing a new transformation,which complies with the trend of digital age and brings new challenges.This paper explores the specific implementation path of artificial intelligence technology,virtual reality technology,big data technology and intelligent interaction technology in vocal music teaching in colleges and universities,aiming to inject new vitality into the traditional teaching mode and improve teaching quality and efficiency.
基金Supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,No.NRF-RS-2023-00237287.
文摘The rising prevalence of chronic multimorbidity poses substantial challenges to healthcare systems,necessitating the development of innovative management strategies to optimize patient care and system efficiency.The study by Fontalba-Navas et al investigates the implementation of a novel high complexity unit(HCU)specifically designed to improve the management of patients with chronic complex conditions.By adopting a multidisciplinary approach,the HCU aims to provide comprehensive,patient-centered care that enhances health outcomes and alleviates the strain on traditional hospital services.Utilizing a longitudinal analysis of data from the Basic Minimum Data Set,this study compares hospitalization metrics among the HCU,Internal Medicine,and other departments within a regional hospital throughout 2022.The findings reveal that the HCU's integrated care model significantly reduces readmission rates and boosts patient satisfaction compared to conventional care practices.The study highlights the HCU's potential as a replicable model for managing chronic multimorbidity,emphasizing its effectiveness in minimizing unnecessary hospitalizations and enhancing the overall quality of patient care.This innovative approach not only addresses the complexities associated with chronic multimorbid conditions but also offers a sustainable framework for healthcare systems confronting similar challenges.
文摘Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.
基金supported by China’s MOE project of Key Research Institute of Humanities and Social Sciences at Universities(22JJD720021)the project of Shandong University(11090087395308).
文摘Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subjunctive conditionals.Motivated by the so-called OK pairs,they try to provide a convincing explanation of the intuition underlying the OK pairs.As far as we know,what they have done is to provide not only an explanation of the OK pairs,but also a way of distinguishing between indicative and subjunctive conditionals.Although we agree with their success in explaining the OK pairs within a causal modelling framework,we argue that their ways of distinguishing between indicative and subjunctive conditionals fail.Instead,we argue that their approaches can be used to distinguish between two readings of conditionals,the epistemic reading and the ontic reading.which can be applied to both indicative and subjunctive conditionals.We conclude by arguing that these two readings are related to two approaches to asking and answering causal questions:the“auses-of-effects"approach and the"effects-of-causes"approach.
基金funded by KAU Endowment(WAQF)at King Abdulaziz University,Jeddah,Saudi Arabia.
文摘Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,and stage of diagnosis,which directly impacts clinical decision-making.Various biological entities,including genes,proteins,mRNAs,miRNAs,and metabolites,contribute to cancer development.The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers.This integrative approach supports the notion that cancer is fundamentally driven by such alterations,enabling the discovery ofmolecular signatures for precision oncology.This reviewexplores the role of AI-drivenmulti-omics analyses in cancer medicine,emphasizing their potential to identify novel biomarkers and therapeutic targets,enhance understanding of Tumor biology,and address integration challenges in clinical workflows.Network biology analyzes identified ERBB2,KRAS,and TP53 as top hub genes in lung cancer based on Maximal Clique Centrality(MCC)scores.In contrast,TP53,ERBB2,ESR1,MYC,and BRCA1 emerged as central regulators in breast cancer,linked to cell proliferation,hormonal signaling,and genomic stability.The review also discusses how specific Artificial Intelligence(AI)algorithms can streamline the integration of heterogeneous datasets,facilitate the interpretation of the tumor microenvironment,and support data-driven clinical strategies.
