Hybrid data-driven framework for shale gas production performance analysis via game theory, machine learning, and optimization approaches

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.

Gastrointestinal tuberculosis:Diagnostic approaches for this uncommon pathology

A case report entitled“Primary gastroduodenal tuberculosis presenting as gastric outlet obstruction”recently published in the World Journal of Clinical Cases presented a rare cause of gastric outlet obstruction and highlighted the atypical manner in which gastrointestinal tuberculosis(TB)can present.The literature with regards to this rare pathology is limited to case reports and case series with the largest being published using data from between 2003 and 2013.However,since then the diagnostic tools available have significantly changed with more modern and increasingly accurate tests now available.This editorial reviews the current state of the art with regards to diagnosis in gastrointestinal TB.

Contemporary strategies and approaches for characterizing composition and enhancing biofilm penetration targeting bacterial extracellular polymeric substances

Extracellular polymeric substances(EPS)constitutes crucial elements within bacterial biofilms,facili-tating accelerated antimicrobial resistance and conferring defense against the host's immune cells.Developing precise and effective antibiofilm approaches and strategies,tailored to the specific charac-teristics of EPS composition,can offer valuable insights for the creation of novel antimicrobial drugs.This,in turn,holds the potential to mitigate the alarming issue of bacterial drug resistance.Current analysis of EPS compositions relies heavily on colorimetric approaches with a significant bias,which is likely due to the selection of a standard compound and the cross-interference of various EPS compounds.Considering the pivotal role of EPS in biofilm functionality,it is imperative for EPS research to delve deeper into the analysis of intricate compositions,moving beyond the current focus on polymeric materials.This ne-cessitates a shift from heavy reliance on colorimetric analytic methods to more comprehensive and nuanced analytical approaches.In this study,we have provided a comprehensive summary of existing analytical methods utilized in the characterization of EPS compositions.Additionally,novel strategies aimed at targeting EPS to enhance biofilm penetration were explored,with a specific focus on high-lighting the limitations associated with colorimetric methods.Furthermore,we have outlined the challenges faced in identifying additional components of EPS and propose a prospective research plan to address these challenges.This review has the potential to guide future researchers in the search for novel compounds capable of suppressing EPS,thereby inhibiting biofilm formation.This insight opens up a new avenue for exploration within this research domain.

Optimizing a Transportation System Using Metaheuristics Approaches (EGD/GA/ACO): A Forest Vehicle Routing Case Study

The large-scale optimization problem requires some optimization techniques, and the Metaheuristics approach is highly useful for solving difficult optimization problems in practice. The purpose of the research is to optimize the transportation system with the help of this approach. We selected forest vehicle routing data as the case study to minimize the total cost and the distance of the forest transportation system. Matlab software helps us find the best solution for this case by applying three algorithms of Metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Extended Great Deluge (EGD). The results show that GA, compared to ACO and EGD, provides the best solution for the cost and the length of our case study. EGD is the second preferred approach, and ACO offers the last solution.

Exploring Ester Prodrugs: A Comprehensive Review of Approaches, Applications, and Methods

The review provides an overview of the approaches, applications, and methods for ester prodrugs. Ester prodrugs are pharmacologically inactive compounds in their original form but become active drugs on biotransformation within the body, which offers advantages concerning the solubility, stability, and targeted delivery of the active drug. Several approaches of ester prodrugs have been reviewed in this review, including simple ester prodrugs, amino acid ester prodrugs, sugar ester prodrugs, lipid ester prodrugs, and polymeric ester prodrugs. This review incorporates in vitro and in vivo methods as well as the characterization of physical and chemical properties for ester prodrugs, cell culture systems, enzymatic assays, and animal models—all of these having a very important bearing on the evaluation of stability, bioavailability, and efficacy for ester prodrugs. While the benefits of using ester prodrugs are significant, there are also disadvantages like instability, poor or variable enzymatic hydrolysis, and toxicity from released promoieties or by-products. This review discusses solutions to the various limitations that include enhancing stability with ionizable promoieties and using physiologically-based pharmacokinetic modeling. The review also highlights the application of ester prodrugs in neurological disorders, such as Parkinson’s disease, and the ongoing efforts to address the critical limitations in treatment efficacy. Future prodrug strategies are poised to advance significantly by harnessing diverse transport mechanisms across the blood-brain barrier and integrating nanotechnology.

On the application of high-throughput experimentation and data-driven approaches in metallic glasses

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.

Towards Innovative Research Approaches to Investigating the Role of Emotional Variables in Promoting Language Teachers’ and Learners’ Mental Health

The adequacy of language education largely depends on the favorable and unfavorable emotions that teachers and students experience throughout the education process.Simply said,emotional factors play a key role in improving the quality of language teaching and learning.Furthermore,these emotional factors also promote the well-being of language teachers and learners and place them in a suitable mental condition.In view of the favorable impact of emotional factors on the mental health of language teachers and learners,many educational scholars around the world have studied these factors,their background,and their pedagogical consequences.Nonetheless,the majority of previous studies have employed traditional research methods in assessing these variables and their influences on language teachers’and learners’mental health.Because of the complex and dynamic quality of emotional factors,traditional research approaches often fail to evaluate these factors and their dynamic,non-linear connections with teachers’and learners’mental health and well-being.Accordingly,some novel research approaches are required to measure the dynamicity and complexity of emotional factors in language education settings.To address this call,the current state-of-the-art conceptual article seeks to provide new insights for investigating emotional factors and their potential impact on language teachers’and learners’mental states.This study also intends to offer a comprehensive list of emerging methods that take into account the complex and dynamic nature of emotional variables.Finally,the study outlines the potential implications of this review for educational researchers.

Complication rates after direct anterior vs posterior approach for hip hemiarthroplasty in elderly individuals with femoral neck fractures

BACKGROUND Dislocation rates after hemiarthroplasty reportedly vary from 1%to 17%.This serious complication is associated with increased morbidity and mortality rates.Approaches to this surgery are still debated,with no consensus regarding the superiority of any single approach.AIM To compare early postoperative complications after implementing the direct anterior and posterior approaches(PL)for hip hemiarthroplasty after femoral neck fractures.METHODS This is a comparative,retrospective,single-center cohort study conducted at a university hospital.Between March 2008 and December 2018,273 patients(a total of 280 hips)underwent bipolar hemiarthroplasties(n=280)for displaced femoral neck fractures using either the PL(n=171)or the minimally invasive direct anterior approach(DAA)(n=109).The choice of approach was related to the surgeons’practices;the implant types were similar and unrelated to the approach.Dislocation rates and other complications were reviewed after a minimum followup of 6 mo.RESULTS Both treatment groups had similarly aged patients(mean age:82 years),sex ratios,patient body mass indexes,and patient comorbidities.Surgical data(surgery delay time,operative time,and blood loss volume)did not differ significantly between the groups.The 30 d mortality rate was higher in the PL group(9.9%)than in the DAA group(3.7%),but the difference was not statistically significant(P=0.052).Among the one-month survivors,a significantly higher rate of dislocation was observed in the PL group(14/154;9.1%)than in the DAA group(0/105;0%)(P=0.002).Of the 14 patients with dislocation,8 underwent revision surgery for recurrent instability(posterior group),and one of them had 2 additional procedures due to a deep infection.The rate of other complications(e.g.,perioperative and early postoperative periprosthetic fractures and infection-related complications)did not differ significantly between the groups.CONCLUSION These findings suggest that the DAA to bipolar hemiarthroplasty for patients with femoral neck fractures is associated with a lower dislocation rate(<1%)than the PL.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

A review of data-driven whole-life state of health prediction for lithium-ion batteries:Data preprocessing,aging characteristics,algorithms,and future challenges

Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.

Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

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.

Expert Experience and Data-Driven Based Hybrid Fault Diagnosis for High-SpeedWire Rod Finishing Mills

The reliable operation of high-speed wire rod finishing mills is crucial in the steel production enterprise.As complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault location and real-time monitoring.To solve the above problems,an expert experience and data-driven-based hybrid fault diagnosis method for high-speed wire rod finishing mills is proposed in this paper.First,based on its mechanical structure,time and frequency domain analysis are improved in fault feature extraction.The approach of combining virtual value,peak value with kurtosis value index,is adopted in time domain analysis.Speed adjustment and side frequency analysis are proposed in frequency domain analysis to obtain accurate component characteristic frequency and its corresponding sideband.Then,according to time and frequency domain characteristics,fault location based on expert experience is proposed to get an accurate fault result.Finally,the proposed method is implemented in the equipment intelligent diagnosis system.By taking an equipment fault on site,for example,the effectiveness of the proposed method is illustrated in the system.

Effect of two surgical approaches on the lung function and prognosis of patients with combined esophagogastric cancer

BACKGROUND Adenocarcinoma of the esophagogastric junction has a center of origin within 5 cm of the esophagogastric junction.Surgical resection remains the main treatment.A transthoracic approach is recommended for Siewert I adenocarcinoma of the esophagogastric junction and a transabdominal approach is recommended for Siewert III adenocarcinoma of the esophagogastric junction.However,there is a need to determine the optimal surgical approach for Siewert II adenocarcinoma of the esophagogastric junction to improve lung function and the prognosis of patients.AIM To investigate and compare the surgical effects,postoperative changes in pulmonary function,and prognoses of two approaches to treating combined esophagogastric cancer.METHODS One hundred and thirty-eight patients with combined esophagogastric cancer treated by general and thoracic surgeries in our hospital were selected.They were divided into group A comprising 70 patients(transabdominal approach)and group B comprising 68 patients(transthoracic approach)based on the surgical approach.The indexes related to surgical trauma,number of removed lymph nodes,indexes of lung function before and after surgery,survival rate,and survival duration of the two groups were compared 3 years after surgery.RESULTS The duration of surgery,length of hospital stay,and postoperative drainage duration of the patients in group A were shorter than those of the patients in group B,and the volume of blood loss caused by surgery was lower for group A than for group B(P<0.05).At the one-month postoperative review,the first second,maximum ventilation volume,forceful lung volume,and lung volume values were higher for group A than for group B(P<0.05).Preoperatively,the QLQ-OES18 scale scores of the patients in group A were higher than those in group B on re-evaluation at 3 mo postoperatively(P<0.05).The surgical complication rate of the patients in group A was 10.00%,which was lower than that of patients in group B,which was 23.53%(P<0.05).CONCLUSION Transabdominal and transthoracic surgical approaches are comparable in treating combined esophagogastric cancer;however,the former results in lesser surgical trauma,milder changes in pulmonary function,and fewer complications.

Colon cancer and their targeting approaches through nanocarriers:A review

Colon cancer is the fifth most common type of cancer in the world.Colon cancer develops when healthy cells in the lining of the colon or rectum alter and grow uncontrollably to form a mass known as a tumor.Despite major medical improvements,colon cancer is still one of the leading causes of cancer-related mortality globally.One of the main issues of chemotherapy is toxicity related to conventional medicines.The targeted delivery systems are considered the safest and most effective by increasing the concentration of a therapeutic substance at the tumor site while decreasing it at other organs.Therefore,these delivery systems required lower doses for high therapeutic value with minimum side effects.The current review focuses on targeting therapeutic substances at the desired site using nanocarriers.Additionally,the diagnostic applications of nanocarriers in colorectal cancer are also discussed.

Asymmetric relationship between global and national factors and domestic food prices:evidence from Turkey with novel nonlinear approaches

This study investigates the asymmetric relationship between global and national fac-tors and domestic food prices in Turkey,considering the recent rapid and continuous increase in domestic food prices.In this context,six global and three national explana-tory variables were included,and monthly data for the period from January 2004 to June 2021 were used.In addition,novel nonlinear time-series econometric approaches,such as wavelet coherence,Granger causality in quantiles,and quantile-on-quantile regression,were applied for examination at different times,frequencies,and quan-tiles.Moreover,the Toda-Yamamoto(TY)causality test and quantile regression(QR)approach were used for robustness checks.The empirical results revealed that(i)there is a significant relationship between domestic food prices and explanatory variables at different times and frequencies;(ii)a causal relationship exists in most quantiles,excluding the lowest quantile,some middle quantiles,and the highest quantile for some variables;(iii)the power of the effect of the explanatory variables on domestic food prices varies according to the quantiles;and(iv)the results were validated by the TY causality test and QR,which show that the results were robust.Overall,the empiri-cal results reveal that global and national factors have an asymmetric relationship with domestic food prices,highlighting the effects of fluctuations in global and national variables on domestic food prices.Thus,the results imply that Turkish policymakers should consider the asymmetric effects of global and national factors on domestic food prices at different times,frequencies,and quantiles.

Data-Driven Approaches for Spatio-Temporal Analysis:A Survey of the State-of-the-Arts

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.

Organic cathode materials for rechargeable magnesium-ion batteries:Fundamentals, recent advances, and approaches to optimization

Rechargeable magnesium-ion batteries(MIBs) are favorable substitutes for conventional lithium-ion batteries(LIBs) because of abundant magnesium reserves, a high theoretical energy density, and great inherent safety. Organic electrode materials with excellent structural tunability,unique coordination reaction mechanisms, and environmental friendliness offer great potential to promote the electrochemical performance of MIBs. However, research on organic magnesium battery cathode materials is still preliminary with many significant challenges to be resolved including low electrical conductivity and unwanted but severe dissolution in useful electrolytes. Herein, we provide a detailed overview of reported organic cathode materials for MIBs. We begin with basic properties such as charge storage mechanisms(e.g., n-, p-, and bipolartype), moving to recent advances in various types of organic cathodes including carbonyl-, nitrogen-, and sulfur-based materials. To shed light on the diverse strategies targeting high-performance Mg-organic batteries, elaborate summaries of various approaches are presented.Generally, these strategies include molecular design, polymerization, mixing with carbon, nanosizing and electrolyte/separator optimization.This review provides insights on exploring high-performance organic cathodes in rechargeable MIBs.

A Comparison between Deductive and Inductive Approaches for Teaching EFL Grammar to High School Students

This study had the purpose of testingtwo methods for teaching grammar in Englishasa Foreign Language(EFL)class:the deductive and inductive approaches in terms of effectiveness and rapport.This research was conducted in a public high school in Ecuador.Seventy students enrolled in the second year of senior high school participated.One in-service teacher taught the EFL classes during the process of intervention(10 weeks),and two EFL teachers observed all of these classes and recorded the information by filling in observation sheets.The students were administered grammar pretests and post-tests in order to assess their grammar knowledge.The results of the tests showed a significant difference in the scores in favor of the inductive approach.After the statistical analysis of the data obtained from the tests and observation sheets,we concluded that the inductive approach is more effective for teaching grammar in the EFL classroom in terms of instruction and rapport.

Laparoscopic left hemihepatectomy guided by indocyanine green fluorescence: A cranial-dorsal approach

BACKGROUND Advancements in laparoscopic technology and a deeper understanding of intra-hepatic anatomy have led to the establishment of more precise laparoscopic hepatectomy(LH)techniques.The indocyanine green(ICG)fluorescence navi-gation technique has emerged as the most effective method for identifying hepatic regions,potentially overcoming the limitations of LH.While laparoscopic left hemihepatectomy(LLH)is a standardized procedure,there is a need for innova-tive strategies to enhance its outcomes.important anatomical markers,surgical skills,and ICG staining methods.METHODS Thirty-seven patients who underwent ICG fluorescence-guided LLH at Qujing Second People's Hospital between January 2019 and February 2022 were retrospectively analyzed.The cranial-dorsal approach was performed which involves dissecting the left hepatic vein cephalad,isolating the Arantius ligament,exposing the middle hepatic vein,and dissecting the parenchyma from the dorsal to the foot in order to complete the anatomical LLH.The surgical methods,as well as intra-and post-surgical data,were recorded and analyzed.Our hospital’s Medical Ethics Committee approved this study(Ethical review:2022-019-01).RESULTS Intraoperative blood loss during LLH was 335.68±99.869 mL and the rates of transfusion and conversion to laparotomy were 13.5%and 0%,respectively.The overall incidence of complications throughout the follow-up(median of 18 months;range 1-36 months)was 21.6%.No mortality or severe complications(level IV)were reported.CONCLUSION LLH has the potential to become a novel,standardized approach that can effectively,safely,and simply expose the middle hepatic vein and meet the requirements of precision surgery.

News Modeling and Retrieving Information: Data-Driven Approach

This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling.The Methodology of this study is categorized into three phases:the Text Classification Approach(TCA),the Proposed Algorithms Interpretation(PAI),andfinally,Information Retrieval Approach(IRA).The TCA reflects the text preprocessing pipeline called a clean corpus.The Global Vec-tors for Word Representation(Glove)pre-trained model,FastText,Term Frequency-Inverse Document Fre-quency(TF-IDF),and Bag-of-Words(BOW)for extracting the features have been interpreted in this research.The PAI manifests the Bidirectional Long Short-Term Memory(Bi-LSTM)and Convolutional Neural Network(CNN)to classify the COVID-19 news.Again,the IRA explains the mathematical interpretation of Latent Dirich-let Allocation(LDA),obtained for modelling the topic of Information Retrieval(IR).In this study,99%accuracy was obtained by performing K-fold cross-validation on Bi-LSTM with Glove.A comparative analysis between Deep Learning and Machine Learning based on feature extraction and computational complexity exploration has been performed in this research.Furthermore,some text analyses and the most influential aspects of each document have been explored in this study.We have utilized Bidirectional Encoder Representations from Trans-formers(BERT)as a Deep Learning mechanism in our model training,but the result has not been uncovered satisfactory.However,the proposed system can be adjustable in the real-time news classification of COVID-19.