Objective:To investigate the spatial gradient of intraoperative impedance across the cochlear electrode array in pediatric cochlear implant recipients and assess its potential as a physiological indicator for the elec...Objective:To investigate the spatial gradient of intraoperative impedance across the cochlear electrode array in pediatric cochlear implant recipients and assess its potential as a physiological indicator for the electrode-neural interface.Methods:A prospective observational study involving 56 pediatric patients underwent cochlear implantation with Cochlear Nucleus devices.Intraoperative polarized impedance and electrically evoked compound action potential(ECAP)threshold were recorded across all 1232 electrodes using AutoNRT software.Eight electrodes with open-or short-circuit were excluded,leaving 1,224 for analysis.Impedance values were categorized by cochlear region(basal,middle,apical),and electrodes with elevated impedance(10-20 kΩ)were analyzed for regional distribution and clinical relevance.Data were analyzed for spatial patterns and correlation with the ECAP threshold profiles.Results:A consistent basal-to-apical increase in impedance was observed(7.7±1.9,9.2±1.4,10.8±1.5 kΩ;p<0.001).Impedance and ECAP threshold were weakly correlated(ρ=-0.20,p<0.001;β=-1.26,p<0.001),with a positive association in the apical region(ρ=0.12,p=0.048).Electrodes with higher impedance(1020 kΩ)were less likely to show elevated or absent TNRT(OR=0.175,p=0.02).The impedance gradient persisted across age groups and was significantly correlated with ECAP threshold patterns.Conclusion:Intraoperative impedance monitoring reveals a strong and physiologically consistent gradient,with higher values in apical electrodes.This gradient reflects anatomical and tissue interface variations,which may offer a valuable physiological indicator for intraoperative electrode positioning and neural interface integrity.展开更多
To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-r...To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.展开更多
Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,espe...Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.展开更多
This work presents and analyses a geostatistical methodology for spatial modelling of Soil Lime Requirements (SLR) considering punctual samples of Cation Exchange Capacity (CEC) and Base Saturation (BS) soil propertie...This work presents and analyses a geostatistical methodology for spatial modelling of Soil Lime Requirements (SLR) considering punctual samples of Cation Exchange Capacity (CEC) and Base Saturation (BS) soil properties. Geostatistical Sequential Indicator Simulation is used to draw realizations from the joint uncertainty distributions of the CEC and the BS input variables. The joint distributions are accomplished applying the Principal Component Analyses (PCA) approach. The Monte Carlo method for handling error propagations is used to obtain realization values of the SLR model which are considered to compute and store statistics from the output uncertainty model. From these statistics, it is obtained predictions and uncertainty maps that represent the spatial variation of the output variable and the propagated uncertainty respectively. Therefore, the prediction map of the output model is qualified with uncertainty information that should be used on decision making activities related to the planning and management of environmental phenomena. The proposed methodology for SLR modelling presented in this article is illustrated using CEC and BS input sample sets obtained in a farm located in Ponta Grossa city, Paraná state, Brazil.展开更多
Normality testing is a fundamental hypothesis test in the statistical analysis of key biological indicators of diabetes.If this assumption is violated,it may cause the test results to deviate from the true value,leadi...Normality testing is a fundamental hypothesis test in the statistical analysis of key biological indicators of diabetes.If this assumption is violated,it may cause the test results to deviate from the true value,leading to incorrect inferences and conclusions,and ultimately affecting the validity and accuracy of statistical inferences.Considering this,the study designs a unified analysis scheme for different data types based on parametric statistical test methods and non-parametric test methods.The data were grouped according to sample type and divided into discrete data and continuous data.To account for differences among subgroups,the conventional chi-squared test was used for discrete data.The normal distribution is the basis of many statistical methods;if the data does not follow a normal distribution,many statistical methods will fail or produce incorrect results.Therefore,before data analysis and modeling,the data were divided into normal and non-normal groups through normality testing.For normally distributed data,parametric statistical methods were used to judge the differences between groups.For non-normal data,non-parametric tests were employed to improve the accuracy of the analysis.Statistically significant indicators were retained according to the significance index P-value of the statistical test or corresponding statistics.These indicators were then combined with relevant medical background to further explore the etiology leading to the occurrence or transformation of diabetes status.展开更多
Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
Succinylcholine(SC)is a widely used depolarizing muscle relaxant,but improper use can lead to arrhythmias and,in severe cases,pose a life-threatening risk.Additionally,some criminals exploit SC for illicit activities....Succinylcholine(SC)is a widely used depolarizing muscle relaxant,but improper use can lead to arrhythmias and,in severe cases,pose a life-threatening risk.Additionally,some criminals exploit SC for illicit activities.Therefore,rapid SC detection is paramount for clinical practice and public safety.Currently,however,limited methods are available for the rapid detection of SC.A fluorescent indicator displacement assay sensor based on molecular recognition of an amide naphthotube was developed.This sensor enabled the rapid fluorescent detection of SC through competitive binding between SC and methylene blue with the amide naphthotube.The sensor exhibited exceptional sensitivity with a detection limit as low as 1.1μmol/L and a detection range of 1.1~60μmol/L,coupled with outstanding selectivity and robust stability.Furthermore,this sensor accurately determined SC levels in biological samples such as serum.In summary,this research provides a new solution for the rapid and accurate sensing of SC in complex matrices and offers new insights for the swift identification and detection of toxins.展开更多
Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit signifi...Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.展开更多
Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experime...Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.展开更多
In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-p...In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.展开更多
Objectives:We systematically reviewed the rational use of medicines using the World Health Organization/International Network of Rational Use of Drugs(WHO/INRUD)core drug use indicators.We also assessed the impact of ...Objectives:We systematically reviewed the rational use of medicines using the World Health Organization/International Network of Rational Use of Drugs(WHO/INRUD)core drug use indicators.We also assessed the impact of the coronavirus disease 2019 pandemic and the National Drug Policy(NDP)2005 on the rational use of medicines.Methods:Searches were conducted in PubMed,Scopus,and Google Scholar databases to identify studies that met our eligibility criteria.Assessment of the quality of studies was conducted using the Joanna Briggs Institute criteria for analytical studies.We reported and compared the median values of WHO/INRUD core drug use indicators with standard thresholds.Data were presented with median,interquartile range(IQR),and percentages.MannWhitney and Kruskal-Wallis tests were conducted to assess for statistical significance(P<0.05)across variables.Results:Thirty-one studies were included in the review,comprising 50,931 patient encounters across 268 health facilities.Within prescribing indicators,average number of medicines per patient encountered[3.4(IQR:3.0to 4.0)],percentage of medicines prescribed by generic[50.4%(IQR:47.4%to 65.0%)],percentage of encounters with antibiotic prescribed[40.2%(IQR:30.5%to 52.7%)],percentage of encounters with injection prescribed[18%(IQR:3.2%to 30.0%)]and the percentage of medicines prescribed from essential medicines list[82.0%(IQR:66.4%to 89.3%)].The median percentage of encounters with antibiotics(P=0.04)and the median percentage of medicines prescribed by generics(P=0.03)increased during and after the COVID-19 pandemic.Prescribing indicators were worse in primary and secondary health facilities,with significant differences in the median percentage of encounters with antibiotics(P=0.007)and injections(P=0.0002)across primary,secondary,and tertiary health facilities.There were improvements across all prescribing indicators after the implementation of NDP 2005.Conclusions:Core drug use indicators in Nigerian health facilities deviated from the WHO/INRUD thresholds,with noticeable improvement after the implementation of NDP 2005.More efforts are needed to improve rational drug use in Nigerian hospitals.展开更多
Background: The irrational use of medicines remains a key health problem in many developing countries. The overuse of antibiotics is a key driver of antimicrobial resistance (AMR). This study surveyed antibiotic use a...Background: The irrational use of medicines remains a key health problem in many developing countries. The overuse of antibiotics is a key driver of antimicrobial resistance (AMR). This study surveyed antibiotic use and adherence to the World Health Organization (WHO) prescribing indicators at the Request Muntanga Hospital in the Kalomo District of Southern Province, Zambia. Materials and Methods: This cross-sectional study was conducted from July 2023 to September 2023 at Request Muntanga Hospital in Zambia and reviewed 600 medical record prescriptions which were issued from July 1, 2022 to June 30, 2023 using the WHO prescribing indicators. The collected data were analyzed using Statistical Package for Social Sciences version 23.0. Results: From the 600 prescriptions sampled, 1246 medicines were prescribed, with antibiotics making up 86.7% of the encounters. Additionally, the average number of drugs prescribed per encounter was 2.1 and the prevalence of polypharmacy was 61.3%. Further, 17.8% of medicines were prescribed as injectables. Furthermore, 76.7% of the drugs were prescribed from the Zambia Essential Medicines List and 38.9% by generic names. Conclusions: This study found a high use of antibiotics and deviations from the WHO/International Network of Rational Use of Drugs (INRUD) core prescribing indicators at the Request Muntanga Hospital indicating non-adherence to the prescribing indicators. There is a need to promote adherence to the WHO/INRUD core prescribing indicators to promote the rational use of antibiotics and prevent the emergence and spread of AMR.展开更多
BACKGROUND Nutritional and inflammatory indicators are crucial in assessing the nutritional health and immune function of patients with cancer,which are factors closely associated with the diagnosis and treatment of c...BACKGROUND Nutritional and inflammatory indicators are crucial in assessing the nutritional health and immune function of patients with cancer,which are factors closely associated with the diagnosis and treatment of colorectal cancer(CRC).AIM To explore the relationship between nutritional and inflammatory indicators and microsatellite stability(MSS)status in CRC.METHODS The clinical data of 56 patients who underwent surgical treatment for CRC were collected.Furthermore,the expressions of nutritional(levels of serum albumin,triglycerides,serum cholesterol,and body mass index)and inflammatory response indicators(absolute neutrophil count,absolute lymphocyte count,absolute monocyte count,neutrophil-to-lymphocyte ratio,and lymphocyte-to-monocyte ratio)as well as their correlation with microsatellite instability(MSI)status were investigated in patients with CRC.RESULTS Compared to the patients with MSS tumors,those with MSI tumors demonstrated significantly lower levels of two nutritional indicators,namely serum albumin and body mass index(P<0.05).Moreover,patients in the MSI group demonstrated significantly lower absolute lymphocyte counts and higher neutrophil-to-lymphocyte ratio than those in the MSS group(P<0.05),indicating pronounced differences in inflammatory responses and immune states between the two groups.CONCLUSION Certain nutritional and inflammatory indicators exhibit significant differences among patients with MSI and MSS CRC,highlighting their potential role in the clinical treatment and health management of this specific population.展开更多
Gastric cancer remains a major cause of cancer-related mortality worldwide,with immunotherapy emerging as a promising treatment strategy.Neoadjuvant im-mune checkpoint therapy has shown potential in enhancing antitumo...Gastric cancer remains a major cause of cancer-related mortality worldwide,with immunotherapy emerging as a promising treatment strategy.Neoadjuvant im-mune checkpoint therapy has shown potential in enhancing antitumor responses and improving surgical outcomes.However,its effects on systemic coagulation and thrombotic risk remain poorly understood.This study aims to investigate the relationship between neoadjuvant immune checkpoint therapy and coagulation dynamics in patients with gastric cancer,exploring potential mechanisms that may contribute to a hypercoagulable state.By assessing coagulation markers,thrombotic events,and inflammatory responses,this research seeks to provide insights into the interplay between immune modulation and hemostatic alte-rations.A better understanding of these interactions may help optimize patient management and guide thromboprophylaxis strategies in this clinical setting.展开更多
Colonoscopy remains the cornerstone of colorectal cancer prevention and survei-llance,but the procedure’s effectiveness is entirely dependent upon various quali-ty indicators,such as detection rates,withdrawal time,a...Colonoscopy remains the cornerstone of colorectal cancer prevention and survei-llance,but the procedure’s effectiveness is entirely dependent upon various quali-ty indicators,such as detection rates,withdrawal time,adequate bowel prepara-tion,cecal intubation rate and patient outcomes.Despite progress in endoscopic techniques,challenges persist in maintaining endoscopists’consistent perfor-mance and improving quality metrics.Artificial intelligence(AI)has emerged as a“game changer”in the gastroenterology field,offering the opportunity to signifi-cantly increase colonoscopy quality.This review highlights the role of AI-driven technologies such as deep learning,computer vision,and real-time feedback me-chanisms in optimizing key quality indicators of colonoscopy.The implementa-tion of AI in colonoscopy may reduce human error,improve endoscopist’s consis-tency in real-time decision making,ensuring higher reliability and standardiza-tion during the procedure.Furthermore,AI has the potential to reshape how en-doscopists perform and evaluate procedures,while improved lesion characte-rization may enable more precise selection for resection,reducing morbidity and the incidence of interval cancers.The review also addresses challenges and limi-tations in AI integration,including cost-effectiveness and its impact on endosco-pist training.AI holds substantial promise for advancing colonoscopy quality and elevating overall patient care,paving the way for more effective and personalized medical approaches.展开更多
This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—cove...This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—covering momentum,volatility,volume,and trend-related technical indicators—are subjected to three distinct feature selection approaches.Specifically,mutual information(MI),recursive feature elimination(RFE),and random forest importance(RFI).By extracting an optimal set of 20 predictors,the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability.These feature subsets are integrated into support vector regression(SVR),Huber regressors,and k-nearest neighbors(KNN)models to forecast the prices of three leading cryptocurrencies—Bitcoin(BTC/USDT),Ethereum(ETH/USDT),and Binance Coin(BNB/USDT)—across horizons ranging from 1 to 20 days.Model evaluation employs the coefficient of determination(R2)and the root mean squared logarithmic error(RMSLE),alongside a walk-forward validation scheme to approximate real-world trading contexts.Empirical results indicate that incorporating momentum and volatility measures substantially improves predictive accuracy,with particularly pronounced effects observed at longer forecast windows.Moreover,indicators related to volume and trend provide incremental benefits in select market conditions.Notably,an 80%–85% reduction in the original feature set frequently maintains or enhances model performance relative to the complete indicator set.These findings highlight the critical role of targeted feature selection in addressing high-dimensional financial data challenges while preserving model robustness.This research advances the field of cryptocurrency forecasting by offering a rigorous comparison of feature selection methods and their effects on multiple digital assets and prediction horizons.The outcomes highlight the importance of dimension-reduction strategies in developing more efficient and resilient forecasting algorithms.Future efforts should incorporate high-frequency data and explore alternative selection techniques to further refine predictive accuracy in this highly volatile domain.展开更多
Objective: To analyze the effectiveness of blood test indicators in the differential diagnosis of anemia. Methods: Sixty patients diagnosed with anemia (disease group) from June 2021 to June 2024 were selected. Based ...Objective: To analyze the effectiveness of blood test indicators in the differential diagnosis of anemia. Methods: Sixty patients diagnosed with anemia (disease group) from June 2021 to June 2024 were selected. Based on the type of disease, the group was subdivided into iron deficiency anemia (IDA) with 31 cases, hemolytic anemia (HA) with 11 cases, and aplastic anemia (AA) with 18 cases. Based on the severity of the disease, the group was divided into mild anemia (30 cases), moderate anemia (19 cases), and severe anemia (11 cases). Sixty healthy individuals (control group) were also included, and all underwent blood tests. Comparisons were made between the red blood cell (RBC) indicators of the disease group and the control group, the blood test indicators of different types of anemia, and the serum iron levels of varying severity of anemia. Results: Except for red cell distribution width (RDW), the RBC indicators in the disease group were lower than those in the control group (P < 0.05). Comparisons of RBC indicators among different types of anemia showed significant differences (P < 0.05). Serum iron levels varied significantly among different degrees of anemia severity (P < 0.05). Conclusion: Blood tests can detect anemia, distinguish types of anemia, and assess anemia severity, offering high diagnostic value.展开更多
This article focuses on the development of the international service trade statistics system.The 1994 General Agreement on Trade in Services(GATS)provided a institutional basis for service trade statistics.The 2002“I...This article focuses on the development of the international service trade statistics system.The 1994 General Agreement on Trade in Services(GATS)provided a institutional basis for service trade statistics.The 2002“International Service Trade Statistics Manual”(MSITS 2002)established the international balance of payments statistics paradigm.The revised MSITS 2010 in 2010 introduced the expanded balance of payments service classification(EBOPS 2010),incorporating foreign affiliate service trade statistics(FATS),and constructing a comprehensive statistics system.The update of MSITS 2010 originated from changes in the global economic environment,technological progress leading to diversified forms of service trade,and the demands of international service trade negotiations.This standard has constructed a multi-level classification system.Since the release of MSITS 2010,many countries have implemented the new statistical framework,but some developing countries face challenges.International organizations and developed countries have provided corresponding support for service trade statistics standards.展开更多
China’s urbanization has entered a mid-to-late phase,and is characterized by high-density urban engineering projects that form systems coupled to geotechnical environments.These systems exhibit significant vulnerabil...China’s urbanization has entered a mid-to-late phase,and is characterized by high-density urban engineering projects that form systems coupled to geotechnical environments.These systems exhibit significant vulnerability due to strong spatiotemporal coupling,which hampers sustainable urban development.Traditional approaches to urban engineering design,construction,and maintenance tend to focus on individual projects and lack the ability to comprehensively evaluate system-level sustainability.Thus,with current methods,it is difficult to optimize the renewal and operation of high-density urban engineering systems.In this study,the constituent elements and key features of high-density urban engineering systems are discussed,and urban engineering system sustainability evaluation indicators are comprehensively reviewed.Viewed from perspectives of resilience,low-carbon development,and ecological impact,66 performance indicators describing urban engineering systems are selected.The decision-making trial and evaluation laboratory(DEMATEL)-based analytic network process(DANP)method and the entropy weight method(EWM)are utilized to calculate these indicators’subjective and objective weights,respectively.Furthermore,the coupling relationships between evaluation indicators are explored,aiding the construction of an urban engineering sustainability evaluation index system.Finally,empirical analysis is conducted across six megacities in China(Tianjin,Hangzhou,Shanghai,Wuhan,Chongqing,and Shenzhen)to validate the effectiveness of the evaluation indicators.The findings reveal significant imbalances in the sustainability of urban engineering systems in China.Accordingly,potential strategies and indicators for targeted enhancement of these systems are discussed.展开更多
文摘Objective:To investigate the spatial gradient of intraoperative impedance across the cochlear electrode array in pediatric cochlear implant recipients and assess its potential as a physiological indicator for the electrode-neural interface.Methods:A prospective observational study involving 56 pediatric patients underwent cochlear implantation with Cochlear Nucleus devices.Intraoperative polarized impedance and electrically evoked compound action potential(ECAP)threshold were recorded across all 1232 electrodes using AutoNRT software.Eight electrodes with open-or short-circuit were excluded,leaving 1,224 for analysis.Impedance values were categorized by cochlear region(basal,middle,apical),and electrodes with elevated impedance(10-20 kΩ)were analyzed for regional distribution and clinical relevance.Data were analyzed for spatial patterns and correlation with the ECAP threshold profiles.Results:A consistent basal-to-apical increase in impedance was observed(7.7±1.9,9.2±1.4,10.8±1.5 kΩ;p<0.001).Impedance and ECAP threshold were weakly correlated(ρ=-0.20,p<0.001;β=-1.26,p<0.001),with a positive association in the apical region(ρ=0.12,p=0.048).Electrodes with higher impedance(1020 kΩ)were less likely to show elevated or absent TNRT(OR=0.175,p=0.02).The impedance gradient persisted across age groups and was significantly correlated with ECAP threshold patterns.Conclusion:Intraoperative impedance monitoring reveals a strong and physiologically consistent gradient,with higher values in apical electrodes.This gradient reflects anatomical and tissue interface variations,which may offer a valuable physiological indicator for intraoperative electrode positioning and neural interface integrity.
基金supported by the National Natural Science Foundation of China(Grant No.52339001).
文摘To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.
基金funded by Vietnam National University Ho Chi Minh City(VNU-HCM)under grant number DS.C2025-28-06.
文摘Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.
文摘This work presents and analyses a geostatistical methodology for spatial modelling of Soil Lime Requirements (SLR) considering punctual samples of Cation Exchange Capacity (CEC) and Base Saturation (BS) soil properties. Geostatistical Sequential Indicator Simulation is used to draw realizations from the joint uncertainty distributions of the CEC and the BS input variables. The joint distributions are accomplished applying the Principal Component Analyses (PCA) approach. The Monte Carlo method for handling error propagations is used to obtain realization values of the SLR model which are considered to compute and store statistics from the output uncertainty model. From these statistics, it is obtained predictions and uncertainty maps that represent the spatial variation of the output variable and the propagated uncertainty respectively. Therefore, the prediction map of the output model is qualified with uncertainty information that should be used on decision making activities related to the planning and management of environmental phenomena. The proposed methodology for SLR modelling presented in this article is illustrated using CEC and BS input sample sets obtained in a farm located in Ponta Grossa city, Paraná state, Brazil.
基金National Natural Science Foundation of China(No.12271261)Postgraduate Research and Practice Innovation Program of Jiangsu Province,China(Grant No.SJCX230368)。
文摘Normality testing is a fundamental hypothesis test in the statistical analysis of key biological indicators of diabetes.If this assumption is violated,it may cause the test results to deviate from the true value,leading to incorrect inferences and conclusions,and ultimately affecting the validity and accuracy of statistical inferences.Considering this,the study designs a unified analysis scheme for different data types based on parametric statistical test methods and non-parametric test methods.The data were grouped according to sample type and divided into discrete data and continuous data.To account for differences among subgroups,the conventional chi-squared test was used for discrete data.The normal distribution is the basis of many statistical methods;if the data does not follow a normal distribution,many statistical methods will fail or produce incorrect results.Therefore,before data analysis and modeling,the data were divided into normal and non-normal groups through normality testing.For normally distributed data,parametric statistical methods were used to judge the differences between groups.For non-normal data,non-parametric tests were employed to improve the accuracy of the analysis.Statistically significant indicators were retained according to the significance index P-value of the statistical test or corresponding statistics.These indicators were then combined with relevant medical background to further explore the etiology leading to the occurrence or transformation of diabetes status.
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
文摘Succinylcholine(SC)is a widely used depolarizing muscle relaxant,but improper use can lead to arrhythmias and,in severe cases,pose a life-threatening risk.Additionally,some criminals exploit SC for illicit activities.Therefore,rapid SC detection is paramount for clinical practice and public safety.Currently,however,limited methods are available for the rapid detection of SC.A fluorescent indicator displacement assay sensor based on molecular recognition of an amide naphthotube was developed.This sensor enabled the rapid fluorescent detection of SC through competitive binding between SC and methylene blue with the amide naphthotube.The sensor exhibited exceptional sensitivity with a detection limit as low as 1.1μmol/L and a detection range of 1.1~60μmol/L,coupled with outstanding selectivity and robust stability.Furthermore,this sensor accurately determined SC levels in biological samples such as serum.In summary,this research provides a new solution for the rapid and accurate sensing of SC in complex matrices and offers new insights for the swift identification and detection of toxins.
基金Supported by the Laoshan Laboratory(No.LSKJ 202202404)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB 42000000)+1 种基金the National Natural Science Foundation of China(NSFC)(No.42030410)the Startup Foundation for Introducing Talent of NUIST,and the Jiangsu Innovation Research Group(No.JSSCTD 202346)。
文摘Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.
基金National Key Research and Development Program of China (No.2021YFC3100800)the National Natural Science Foundation of China (Nos.42407235 and 42271026)+1 种基金the Project of Sanya Yazhou Bay Science and Technology City (No.SCKJ-JYRC-2023-54)supported by the Hefei advanced computing center
文摘Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.
文摘In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.
文摘Objectives:We systematically reviewed the rational use of medicines using the World Health Organization/International Network of Rational Use of Drugs(WHO/INRUD)core drug use indicators.We also assessed the impact of the coronavirus disease 2019 pandemic and the National Drug Policy(NDP)2005 on the rational use of medicines.Methods:Searches were conducted in PubMed,Scopus,and Google Scholar databases to identify studies that met our eligibility criteria.Assessment of the quality of studies was conducted using the Joanna Briggs Institute criteria for analytical studies.We reported and compared the median values of WHO/INRUD core drug use indicators with standard thresholds.Data were presented with median,interquartile range(IQR),and percentages.MannWhitney and Kruskal-Wallis tests were conducted to assess for statistical significance(P<0.05)across variables.Results:Thirty-one studies were included in the review,comprising 50,931 patient encounters across 268 health facilities.Within prescribing indicators,average number of medicines per patient encountered[3.4(IQR:3.0to 4.0)],percentage of medicines prescribed by generic[50.4%(IQR:47.4%to 65.0%)],percentage of encounters with antibiotic prescribed[40.2%(IQR:30.5%to 52.7%)],percentage of encounters with injection prescribed[18%(IQR:3.2%to 30.0%)]and the percentage of medicines prescribed from essential medicines list[82.0%(IQR:66.4%to 89.3%)].The median percentage of encounters with antibiotics(P=0.04)and the median percentage of medicines prescribed by generics(P=0.03)increased during and after the COVID-19 pandemic.Prescribing indicators were worse in primary and secondary health facilities,with significant differences in the median percentage of encounters with antibiotics(P=0.007)and injections(P=0.0002)across primary,secondary,and tertiary health facilities.There were improvements across all prescribing indicators after the implementation of NDP 2005.Conclusions:Core drug use indicators in Nigerian health facilities deviated from the WHO/INRUD thresholds,with noticeable improvement after the implementation of NDP 2005.More efforts are needed to improve rational drug use in Nigerian hospitals.
文摘Background: The irrational use of medicines remains a key health problem in many developing countries. The overuse of antibiotics is a key driver of antimicrobial resistance (AMR). This study surveyed antibiotic use and adherence to the World Health Organization (WHO) prescribing indicators at the Request Muntanga Hospital in the Kalomo District of Southern Province, Zambia. Materials and Methods: This cross-sectional study was conducted from July 2023 to September 2023 at Request Muntanga Hospital in Zambia and reviewed 600 medical record prescriptions which were issued from July 1, 2022 to June 30, 2023 using the WHO prescribing indicators. The collected data were analyzed using Statistical Package for Social Sciences version 23.0. Results: From the 600 prescriptions sampled, 1246 medicines were prescribed, with antibiotics making up 86.7% of the encounters. Additionally, the average number of drugs prescribed per encounter was 2.1 and the prevalence of polypharmacy was 61.3%. Further, 17.8% of medicines were prescribed as injectables. Furthermore, 76.7% of the drugs were prescribed from the Zambia Essential Medicines List and 38.9% by generic names. Conclusions: This study found a high use of antibiotics and deviations from the WHO/International Network of Rational Use of Drugs (INRUD) core prescribing indicators at the Request Muntanga Hospital indicating non-adherence to the prescribing indicators. There is a need to promote adherence to the WHO/INRUD core prescribing indicators to promote the rational use of antibiotics and prevent the emergence and spread of AMR.
基金Supported by Grants of the Suzhou Medical Key Discipline,No.SZXK202109Suzhou Clinical Key Disease Project,No.LCZX202111Suzhou Promoting Health through Science and Education Research Project,No.KJXW2021028.
文摘BACKGROUND Nutritional and inflammatory indicators are crucial in assessing the nutritional health and immune function of patients with cancer,which are factors closely associated with the diagnosis and treatment of colorectal cancer(CRC).AIM To explore the relationship between nutritional and inflammatory indicators and microsatellite stability(MSS)status in CRC.METHODS The clinical data of 56 patients who underwent surgical treatment for CRC were collected.Furthermore,the expressions of nutritional(levels of serum albumin,triglycerides,serum cholesterol,and body mass index)and inflammatory response indicators(absolute neutrophil count,absolute lymphocyte count,absolute monocyte count,neutrophil-to-lymphocyte ratio,and lymphocyte-to-monocyte ratio)as well as their correlation with microsatellite instability(MSI)status were investigated in patients with CRC.RESULTS Compared to the patients with MSS tumors,those with MSI tumors demonstrated significantly lower levels of two nutritional indicators,namely serum albumin and body mass index(P<0.05).Moreover,patients in the MSI group demonstrated significantly lower absolute lymphocyte counts and higher neutrophil-to-lymphocyte ratio than those in the MSS group(P<0.05),indicating pronounced differences in inflammatory responses and immune states between the two groups.CONCLUSION Certain nutritional and inflammatory indicators exhibit significant differences among patients with MSI and MSS CRC,highlighting their potential role in the clinical treatment and health management of this specific population.
文摘Gastric cancer remains a major cause of cancer-related mortality worldwide,with immunotherapy emerging as a promising treatment strategy.Neoadjuvant im-mune checkpoint therapy has shown potential in enhancing antitumor responses and improving surgical outcomes.However,its effects on systemic coagulation and thrombotic risk remain poorly understood.This study aims to investigate the relationship between neoadjuvant immune checkpoint therapy and coagulation dynamics in patients with gastric cancer,exploring potential mechanisms that may contribute to a hypercoagulable state.By assessing coagulation markers,thrombotic events,and inflammatory responses,this research seeks to provide insights into the interplay between immune modulation and hemostatic alte-rations.A better understanding of these interactions may help optimize patient management and guide thromboprophylaxis strategies in this clinical setting.
文摘Colonoscopy remains the cornerstone of colorectal cancer prevention and survei-llance,but the procedure’s effectiveness is entirely dependent upon various quali-ty indicators,such as detection rates,withdrawal time,adequate bowel prepara-tion,cecal intubation rate and patient outcomes.Despite progress in endoscopic techniques,challenges persist in maintaining endoscopists’consistent perfor-mance and improving quality metrics.Artificial intelligence(AI)has emerged as a“game changer”in the gastroenterology field,offering the opportunity to signifi-cantly increase colonoscopy quality.This review highlights the role of AI-driven technologies such as deep learning,computer vision,and real-time feedback me-chanisms in optimizing key quality indicators of colonoscopy.The implementa-tion of AI in colonoscopy may reduce human error,improve endoscopist’s consis-tency in real-time decision making,ensuring higher reliability and standardiza-tion during the procedure.Furthermore,AI has the potential to reshape how en-doscopists perform and evaluate procedures,while improved lesion characte-rization may enable more precise selection for resection,reducing morbidity and the incidence of interval cancers.The review also addresses challenges and limi-tations in AI integration,including cost-effectiveness and its impact on endosco-pist training.AI holds substantial promise for advancing colonoscopy quality and elevating overall patient care,paving the way for more effective and personalized medical approaches.
文摘This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—covering momentum,volatility,volume,and trend-related technical indicators—are subjected to three distinct feature selection approaches.Specifically,mutual information(MI),recursive feature elimination(RFE),and random forest importance(RFI).By extracting an optimal set of 20 predictors,the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability.These feature subsets are integrated into support vector regression(SVR),Huber regressors,and k-nearest neighbors(KNN)models to forecast the prices of three leading cryptocurrencies—Bitcoin(BTC/USDT),Ethereum(ETH/USDT),and Binance Coin(BNB/USDT)—across horizons ranging from 1 to 20 days.Model evaluation employs the coefficient of determination(R2)and the root mean squared logarithmic error(RMSLE),alongside a walk-forward validation scheme to approximate real-world trading contexts.Empirical results indicate that incorporating momentum and volatility measures substantially improves predictive accuracy,with particularly pronounced effects observed at longer forecast windows.Moreover,indicators related to volume and trend provide incremental benefits in select market conditions.Notably,an 80%–85% reduction in the original feature set frequently maintains or enhances model performance relative to the complete indicator set.These findings highlight the critical role of targeted feature selection in addressing high-dimensional financial data challenges while preserving model robustness.This research advances the field of cryptocurrency forecasting by offering a rigorous comparison of feature selection methods and their effects on multiple digital assets and prediction horizons.The outcomes highlight the importance of dimension-reduction strategies in developing more efficient and resilient forecasting algorithms.Future efforts should incorporate high-frequency data and explore alternative selection techniques to further refine predictive accuracy in this highly volatile domain.
文摘Objective: To analyze the effectiveness of blood test indicators in the differential diagnosis of anemia. Methods: Sixty patients diagnosed with anemia (disease group) from June 2021 to June 2024 were selected. Based on the type of disease, the group was subdivided into iron deficiency anemia (IDA) with 31 cases, hemolytic anemia (HA) with 11 cases, and aplastic anemia (AA) with 18 cases. Based on the severity of the disease, the group was divided into mild anemia (30 cases), moderate anemia (19 cases), and severe anemia (11 cases). Sixty healthy individuals (control group) were also included, and all underwent blood tests. Comparisons were made between the red blood cell (RBC) indicators of the disease group and the control group, the blood test indicators of different types of anemia, and the serum iron levels of varying severity of anemia. Results: Except for red cell distribution width (RDW), the RBC indicators in the disease group were lower than those in the control group (P < 0.05). Comparisons of RBC indicators among different types of anemia showed significant differences (P < 0.05). Serum iron levels varied significantly among different degrees of anemia severity (P < 0.05). Conclusion: Blood tests can detect anemia, distinguish types of anemia, and assess anemia severity, offering high diagnostic value.
基金The interim results of the postdoctoral research project“Research on the Evolution Process of Shenzhen’s High-tech Industry Policies(1980-2022)”(Project Number:6023271023S)at the end of the postdoctoral period of Shenzhen Polytechnic University.Institute of Economic and Social Development(Phase III)Project:6025310002Q.
文摘This article focuses on the development of the international service trade statistics system.The 1994 General Agreement on Trade in Services(GATS)provided a institutional basis for service trade statistics.The 2002“International Service Trade Statistics Manual”(MSITS 2002)established the international balance of payments statistics paradigm.The revised MSITS 2010 in 2010 introduced the expanded balance of payments service classification(EBOPS 2010),incorporating foreign affiliate service trade statistics(FATS),and constructing a comprehensive statistics system.The update of MSITS 2010 originated from changes in the global economic environment,technological progress leading to diversified forms of service trade,and the demands of international service trade negotiations.This standard has constructed a multi-level classification system.Since the release of MSITS 2010,many countries have implemented the new statistical framework,but some developing countries face challenges.International organizations and developed countries have provided corresponding support for service trade statistics standards.
基金supported by the Fundamental Research Funds for the Central Universities(No.226-2024-00242)the Excellent Research Groups Project(No.52588202)the National Science Fund for Distinguished Young Scholars of China(No.52125803).
文摘China’s urbanization has entered a mid-to-late phase,and is characterized by high-density urban engineering projects that form systems coupled to geotechnical environments.These systems exhibit significant vulnerability due to strong spatiotemporal coupling,which hampers sustainable urban development.Traditional approaches to urban engineering design,construction,and maintenance tend to focus on individual projects and lack the ability to comprehensively evaluate system-level sustainability.Thus,with current methods,it is difficult to optimize the renewal and operation of high-density urban engineering systems.In this study,the constituent elements and key features of high-density urban engineering systems are discussed,and urban engineering system sustainability evaluation indicators are comprehensively reviewed.Viewed from perspectives of resilience,low-carbon development,and ecological impact,66 performance indicators describing urban engineering systems are selected.The decision-making trial and evaluation laboratory(DEMATEL)-based analytic network process(DANP)method and the entropy weight method(EWM)are utilized to calculate these indicators’subjective and objective weights,respectively.Furthermore,the coupling relationships between evaluation indicators are explored,aiding the construction of an urban engineering sustainability evaluation index system.Finally,empirical analysis is conducted across six megacities in China(Tianjin,Hangzhou,Shanghai,Wuhan,Chongqing,and Shenzhen)to validate the effectiveness of the evaluation indicators.The findings reveal significant imbalances in the sustainability of urban engineering systems in China.Accordingly,potential strategies and indicators for targeted enhancement of these systems are discussed.
