For ecological restoration and reconstruction of the degraded area, it is an important premise to correctly understand the degradation factors of the ecosystem in the arid-hot valleys. The factors including vegetation...For ecological restoration and reconstruction of the degraded area, it is an important premise to correctly understand the degradation factors of the ecosystem in the arid-hot valleys. The factors including vegetation degradation, land degradation, arid climate, policy failure, forest fire, rapid population growth, excessive deforestation, overgrazing, steep slope reclamation, economic poverty, engineering construction, lithology, slope, low cultural level, geological hazards, biological disaster, soil properties etc, were selected to study the Yuanmou arid-hot valleys. Based on the interpretative structural model （ISM）, it has found out that the degradation factors of the Yuanmou arid-hot valleys were not at the same level but in a multilevel hierarchical system with internal relations, which pointed out that the degradation mode of the arid-hot valleys was ＂straight （appearance）-penetrating-background＂. Such researches have important directive significance for the restoration and reconstruction of the arid-hot valleys ecosystem.展开更多
Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-...Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model(ISM) and HOS is proposed:(1) the adjacency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method; and(4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.展开更多
Objective: This study aimed to explore the experiences of women in the process of formula feeding their infants. The World Health Organization has emphasized the importance of breastfeeding for infant health. After de...Objective: This study aimed to explore the experiences of women in the process of formula feeding their infants. The World Health Organization has emphasized the importance of breastfeeding for infant health. After decades of breastfeeding promotions,breastfeeding rates in Hong Kong have been rising consistently; however, the low continuation rate is alarming. This study explores women's experiences with formula feeding their infants, including factors affecting their decision to do so.Methods: A qualitative approach using an interpretative phenomenological analysis(IPA) was adopted as the study design. Data were collected from 2014 to 2015 through individual in-depth unstructured interviews with 16 women, conducted between 3 and 12 months after the birth of their infant. Data were analyzed using IPA.Results: Three main themes emerged as follows:(1) self-struggle, with the subthemes of feeling like a milk cow and feeling trapped;(2) family conflict, with the subtheme of sharing the spotlight; and(3) interpersonal tensions, with the subthemes of embarrassment,staring, and innocence. Many mothers suffered various stressors and frustrations during breastfeeding. These findings suggest a number of pertinent areas that need to be considered in preparing an infant feeding campaign.Conclusions: The findings of this study reinforce our knowledge of women's struggles with multiple sources of pressure, such as career demands, childcare demands, and family life after giving birth. All mothers should be given assistance in making informed decisions about the optimal approach to feeding their babies given their individual situation and be provided with support to pursue their chosen feeding method.展开更多
Background: Based on the experience of hospital nurses, the aim of this study is to explore the phenomenon of how work-engaged nurses stay healthy in relationally demanding jobs involving very sick and/or dying patien...Background: Based on the experience of hospital nurses, the aim of this study is to explore the phenomenon of how work-engaged nurses stay healthy in relationally demanding jobs involving very sick and/or dying patients. Method: In-depth interviews were conducted with ten work-engaged nurses employed at the main hospital in one region in Norway. The interviews were interpreted using the Interpretative Phenomenological Analysis method (IPA). Results: The results indicate the importance of using the personal resources: authenticity and a sense of humour for staying healthy. The nurses’ authenticity, in the sense of having a strong sense of ownership towards their personal life experiences, and a sense of having a meaningful life in line with their own values and interests, was an important element when they considered their own health to be good in spite of repetitive strain injuries and perceived stress. These personal resources seem to be positively related to their well-being and work engagement, which serves as an argument for including them among other personal resources, often conceptualized in terms of Psychological Capital (PsyCap). The results also showed that the nurses worked actively and intentionally with conditions that could contribute to safeguarding their own health. Conclusion: The results indicated the importance of stimulating the nurses’ area of knowledge about caring for themselves in order to enable them to maintain good physical and mental health. A focus on self-care should be part of the agenda as early as during nursing education.展开更多
Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical str...Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.展开更多
In recent years,significant advances have been achieved in liver cancer management with the development of artificial intelligence(AI).AI-based pathological analysis can extract crucial information from whole slide im...In recent years,significant advances have been achieved in liver cancer management with the development of artificial intelligence(AI).AI-based pathological analysis can extract crucial information from whole slide images to assist clinicians in all aspects from diagnosis to prognosis and molecular profiling.However,AI techniques have a“black box”nature,which means that interpretability is of utmost importance because it is key to ensuring the reliability of the methods and building trust among clinicians for actual clinical implementation.In this paper,we provide an overview of current technical advancements in the AI-based pathological analysis of liver cancer,and delve into the strategies used in recent studies to unravel the“black box”of AI's decision-making process.展开更多
This article aims to argue that interpreting liangzhi 良知 as innate, original, or cognitive knowledge is likely to fall into ＂interpretative obfuscation regarding knowledge.＂ First, for Wang, what is inherent in ma...This article aims to argue that interpreting liangzhi 良知 as innate, original, or cognitive knowledge is likely to fall into ＂interpretative obfuscation regarding knowledge.＂ First, for Wang, what is inherent in mankind is moral agency rather than innate or original knowledge. Therefore, the focus ofzhizhi 致知 and gewu 格物 is instead on moral practice and actualization of virtue rather than on either ＂the extension of knowledge＂ or ＂the investigation of things.＂ Apart from that, drawing support from cognitive knowledge to explicate liangzhi also leads to three related but distinct misconceptions： liangzhi as perfect knowledge, the identity of knowledge and action, and liangzhi as recognition or acknowledgement. By clarifying the above misinterpretations, the meaning and implication of liangzhi will, in turn, also become clearer.展开更多
BACKGROUND To investigate the preoperative factors influencing textbook outcomes(TO)in Intrahepatic cholangiocarcinoma(ICC)patients and evaluate the feasibility of an interpretable machine learning model for preoperat...BACKGROUND To investigate the preoperative factors influencing textbook outcomes(TO)in Intrahepatic cholangiocarcinoma(ICC)patients and evaluate the feasibility of an interpretable machine learning model for preoperative prediction of TO,we developed a machine learning model for preoperative prediction of TO and used the SHapley Additive exPlanations(SHAP)technique to illustrate the prediction process.AIM To analyze the factors influencing textbook outcomes before surgery and to establish interpretable machine learning models for preoperative prediction.METHODS A total of 376 patients diagnosed with ICC were retrospectively collected from four major medical institutions in China,covering the period from 2011 to 2017.Logistic regression analysis was conducted to identify preoperative variables associated with achieving TO.Based on these variables,an EXtreme Gradient Boosting(XGBoost)machine learning prediction model was constructed using the XGBoost package.The SHAP(package:Shapviz)algorithm was employed to visualize each variable's contribution to the model's predictions.Kaplan-Meier survival analysis was performed to compare the prognostic differences between the TO-achieving and non-TO-achieving groups.RESULTS Among 376 patients,287 were included in the training group and 89 in the validation group.Logistic regression identified the following preoperative variables influencing TO:Child-Pugh classification,Eastern Cooperative Oncology Group(ECOG)score,hepatitis B,and tumor size.The XGBoost prediction model demonstrated high accuracy in internal validation(AUC=0.8825)and external validation(AUC=0.8346).Survival analysis revealed that the disease-free survival rates for patients achieving TO at 1,2,and 3 years were 64.2%,56.8%,and 43.4%,respectively.CONCLUSION Child-Pugh classification,ECOG score,hepatitis B,and tumor size are preoperative predictors of TO.In both the training group and the validation group,the machine learning model had certain effectiveness in predicting TO before surgery.The SHAP algorithm provided intuitive visualization of the machine learning prediction process,enhancing its interpretability.展开更多
In the digital age,where traditional culture is in danger of gradually disappearing,one iconic figure has taken it upon herself to reverse the trend by reviving traditional skills.A prominent Chinese vlogger,Li Ziqi,w...In the digital age,where traditional culture is in danger of gradually disappearing,one iconic figure has taken it upon herself to reverse the trend by reviving traditional skills.A prominent Chinese vlogger,Li Ziqi,whose birth name is Li Jiajia,has amassed a vast online following by reinterpreting traditions that date back thousands of years.Following a threeyear absence from social media,she made a highly anticipated return with the release of three new videos in mid-November 2024.These videos represent the culmination of several months of behind-the-scenes work.展开更多
“As a director,I hope that the audience will see a movie in cinemas instead of watching threeminute commentaries,about which I’m really speechless,”internationally renowned film director Zhang Yimou said in a recen...“As a director,I hope that the audience will see a movie in cinemas instead of watching threeminute commentaries,about which I’m really speechless,”internationally renowned film director Zhang Yimou said in a recent interview.Zhang said he believes the ritual and immersive feeling brought about by watching movies in the cinema cannot be replaced by watching movie commentaries online.He called on audiences to go to the cinema and experience in person the audio-visual feast delivered by the big screen.Zhang’s views on the so-called“threeminute movies”have become a trending topic on social media.The new genre,in which content creators on short-video platforms create summarized versions of films,also includes the creator’s own observations.The rise of these platforms has lowered barriers to entry for aspiring movie content creators,giving rise to not only high volume and great diversity but also large discrepancies in quality.Moreover,this content sometimes infringes the copyrights of the movies it features,while also misinterpreting their plots and themes.展开更多
Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural ...Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural and relational information inherent in molecular graphs. Despite their effectiveness, the “black-box” nature of GNNs remains a significant obstacle to their widespread adoption in chemistry, as it hinders interpretability and trust. In this context, several explanation methods based on factual reasoning have emerged. These methods aim to interpret the predictions made by GNNs by analyzing the key features contributing to the prediction. However, these approaches fail to answer critical questions: “How to ensure that the structure-property mapping learned by GNNs is consistent with established domain knowledge”. In this paper, we propose MMGCF, a novel counterfactual explanation framework designed specifically for the prediction of GNN-based molecular properties. MMGCF constructs a hierarchical tree structure on molecular motifs, enabling the systematic generation of counterfactuals through motif perturbations. This framework identifies causally significant motifs and elucidates their impact on model predictions, offering insights into the relationship between structural modifications and predicted properties. Our method demonstrates its effectiveness through comprehensive quantitative and qualitative evaluations of four real-world molecular datasets.展开更多
The Interpretation of Nursing Guidelines for Intravenous Thrombolysis in Acute Ischemic Stroke offers comprehensive recommendations across five key domains:hospital organizational management,patient condition monitori...The Interpretation of Nursing Guidelines for Intravenous Thrombolysis in Acute Ischemic Stroke offers comprehensive recommendations across five key domains:hospital organizational management,patient condition monitoring,complication observation and management,positioning and mobility away from the bed,and quality assurance.These Guidelines encompass all the phases of intravenous thrombolysis care for patients experiencing acute ischemic stroke.This article aims to elucidate the Guidelines by discussing their developmental background,the designation process,usage recommendations,and the interpretation of evolving perspectives,thereby providing valuable insights for clinical practice.展开更多
Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are a...Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.展开更多
The aim of the paper is to explore the main paradigms and methodology of social research,framing them in historical path and highlighting the epistemological foundations.It moves from reflection on research methodolog...The aim of the paper is to explore the main paradigms and methodology of social research,framing them in historical path and highlighting the epistemological foundations.It moves from reflection on research methodology as a‘discourse of method’to focus on the paradigmatic dimension of the social sciences,according to Kuhn’s meaning for which paradigm indicates a shared and recognized theoretical perspective within the scientific community.The paper highlights the role of paradigms in shaping theoretical and empirical inquiry.It further examines the positivist and neo-positivist paradigms,which emphasize observation and empirical verifiability,quantification,formulation of laws,and cause-and-effect relationships,arguing for the uniqueness of the scientific method.Lazarsfeld brings to the social sciences the language‘of variables’,borrowed from mathematics and statistics.The distinction introduced by Windelband between‘nomothetic’and‘idiographic’sciences is followed by Weber’s elaboration of the concept of‘Verstehen’,which shifts the focus to the understanding of social reality through the meanings that individuals attribute to their actions.The interpretive paradigm paves the way for qualitative research methods.Finally,the paper delves into the complexity paradigm,which challenges the reductionist and deterministic models of classical science and outlines an epistemological shift in the key notions of science,introducing concepts such as‘emergence’,‘auto-eco-organization’and‘recursive processes’.The complexity of social reality calls for a rethinking of sociological methods,favoring multidimensional and event-based analysis over statistical regularities,privileging observation,intervention and the‘in vivo method’on the level of empirical research.Complexity pushes sociology to redefine itself along with its object traditionally understood as‘society’.展开更多
Food is one of the biggest industries in developed and underdeveloped countries. Supply chain sustainability is essential in established and emerging economies because of the rising acceptance of cost-based outsourcin...Food is one of the biggest industries in developed and underdeveloped countries. Supply chain sustainability is essential in established and emerging economies because of the rising acceptance of cost-based outsourcing and the growing technological, social, and environmental concerns. The food business faces serious sustainability and growth challenges in developing countries. A comprehensive analysis of the critical success factors (CSFs) influencing the performance outcome and the sustainable supply chain management (SSCM) process. A theoretical framework is established to explain how they are used to examine the organizational aspect of the food supply chain life cycle analysis. This study examined the CSFs and revealed the relationships between them using a methodology that included a review of literature, interpretative structural modeling (ISM), and cross-impact matrix multiplication applied in classification (MICMAC) tool analysis of soil liquefaction factors. The findings of this research demonstrate that the quality and safety of food are important factors and have a direct effect on other factors. To make sustainable food supply chain management more adequate, legislators, managers, and experts need to pay attention to this factor. In this work. It also shows that companies aiming to create a sustainable business model must make sustainability a fundamental tenet of their organization. Practitioners and managers may devise effective long-term plans for establishing a sustainable food supply chain utilizing the recommended methodology.展开更多
In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken a...In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.展开更多
Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of...Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.展开更多
With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural network...With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.展开更多
During injection treatments, bottomhole pressure measurements may significantly mismatch modeling results. We devise a computationally effective technique for interpretation of fluid injection in a wellbore interval w...During injection treatments, bottomhole pressure measurements may significantly mismatch modeling results. We devise a computationally effective technique for interpretation of fluid injection in a wellbore interval with multiple geological layers based on the bottomhole pressure measurements. The permeability, porosity and compressibility in each layer are initially setup, while the skin factor and partitioning of injected fluids among the zones during the injection are found as a solution of the problem. The problem takes into account Darcy flow and chemical interactions between the injected acids, diverter fluids and reservoir rock typical in modern matrix acidizing treatments. Using the synchronously recorded injection rate and bottomhole pressure, we evaluate skin factor changes in each layer and actual fluid placement into the reservoir during different pumping jobs: matrix acidizing, water control, sand control, scale squeezes and water flooding. The model is validated by comparison with a simulator used in industry. It gives opportunity to estimate efficiency of a matrix treatment job, role of every injection stage, and control fluid delivery to each layer in real time. The presented interpretation technique significantly improves accuracy of matrix treatments analysis by coupling the hydrodynamic model with records of pressure and injection rate during the treatment.展开更多
Distinguishing between web traffic generated by bots and humans is an important task in the evaluation of online marketing campaigns.One of the main challenges is related to only partial availability of the performanc...Distinguishing between web traffic generated by bots and humans is an important task in the evaluation of online marketing campaigns.One of the main challenges is related to only partial availability of the performance metrics:although some users can be unambiguously classified as bots,the correct label is uncertain in many cases.This calls for the use of classifiers capable of explaining their decisions.This paper demonstrates two such mechanisms based on features carefully engineered from web logs.The first is a man-made rule-based system.The second is a hierarchical model that first performs clustering and next classification using human-centred,interpretable methods.The stability of the proposed methods is analyzed and a minimal set of features that convey the classdiscriminating information is selected.The proposed data processing and analysis methodology are successfully applied to real-world data sets from online publishers.展开更多
基金the National Basic Research Program of China (973 Program) ( 2007CB407206)the National Key Technologies Research and Develop-ment Program in the Eleventh Five-Year Plan of China (2006BAC01A11)
文摘For ecological restoration and reconstruction of the degraded area, it is an important premise to correctly understand the degradation factors of the ecosystem in the arid-hot valleys. The factors including vegetation degradation, land degradation, arid climate, policy failure, forest fire, rapid population growth, excessive deforestation, overgrazing, steep slope reclamation, economic poverty, engineering construction, lithology, slope, low cultural level, geological hazards, biological disaster, soil properties etc, were selected to study the Yuanmou arid-hot valleys. Based on the interpretative structural model （ISM）, it has found out that the degradation factors of the Yuanmou arid-hot valleys were not at the same level but in a multilevel hierarchical system with internal relations, which pointed out that the degradation mode of the arid-hot valleys was ＂straight （appearance）-penetrating-background＂. Such researches have important directive significance for the restoration and reconstruction of the arid-hot valleys ecosystem.
基金Supported by the National Natural Science Foundation of China(61374166)the Doctoral Fund of Ministry of Education of China(20120010110010)the Natural Science Fund of Ningbo(2012A610001)
文摘Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model(ISM) and HOS is proposed:(1) the adjacency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method; and(4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.
文摘Objective: This study aimed to explore the experiences of women in the process of formula feeding their infants. The World Health Organization has emphasized the importance of breastfeeding for infant health. After decades of breastfeeding promotions,breastfeeding rates in Hong Kong have been rising consistently; however, the low continuation rate is alarming. This study explores women's experiences with formula feeding their infants, including factors affecting their decision to do so.Methods: A qualitative approach using an interpretative phenomenological analysis(IPA) was adopted as the study design. Data were collected from 2014 to 2015 through individual in-depth unstructured interviews with 16 women, conducted between 3 and 12 months after the birth of their infant. Data were analyzed using IPA.Results: Three main themes emerged as follows:(1) self-struggle, with the subthemes of feeling like a milk cow and feeling trapped;(2) family conflict, with the subtheme of sharing the spotlight; and(3) interpersonal tensions, with the subthemes of embarrassment,staring, and innocence. Many mothers suffered various stressors and frustrations during breastfeeding. These findings suggest a number of pertinent areas that need to be considered in preparing an infant feeding campaign.Conclusions: The findings of this study reinforce our knowledge of women's struggles with multiple sources of pressure, such as career demands, childcare demands, and family life after giving birth. All mothers should be given assistance in making informed decisions about the optimal approach to feeding their babies given their individual situation and be provided with support to pursue their chosen feeding method.
文摘Background: Based on the experience of hospital nurses, the aim of this study is to explore the phenomenon of how work-engaged nurses stay healthy in relationally demanding jobs involving very sick and/or dying patients. Method: In-depth interviews were conducted with ten work-engaged nurses employed at the main hospital in one region in Norway. The interviews were interpreted using the Interpretative Phenomenological Analysis method (IPA). Results: The results indicate the importance of using the personal resources: authenticity and a sense of humour for staying healthy. The nurses’ authenticity, in the sense of having a strong sense of ownership towards their personal life experiences, and a sense of having a meaningful life in line with their own values and interests, was an important element when they considered their own health to be good in spite of repetitive strain injuries and perceived stress. These personal resources seem to be positively related to their well-being and work engagement, which serves as an argument for including them among other personal resources, often conceptualized in terms of Psychological Capital (PsyCap). The results also showed that the nurses worked actively and intentionally with conditions that could contribute to safeguarding their own health. Conclusion: The results indicated the importance of stimulating the nurses’ area of knowledge about caring for themselves in order to enable them to maintain good physical and mental health. A focus on self-care should be part of the agenda as early as during nursing education.
基金Supported by the National Natural Science Foundation of China(61374166,6153303)the Doctoral Fund of Ministry of Education of China(20120010110010)the Fundamental Research Funds for the Central Universities(YS1404,JD1413,ZY1502)
文摘Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.
基金supported by the National Natural Science Foundation of China(Nos 81961128025 and 82273187)the Research Projects from the Science and Technology Commission of Shanghai Municipality(Nos 21JC1401200 and 20JC1418900)the Natural Science Foundation of Fujian Province(No.2023J05292).
文摘In recent years,significant advances have been achieved in liver cancer management with the development of artificial intelligence(AI).AI-based pathological analysis can extract crucial information from whole slide images to assist clinicians in all aspects from diagnosis to prognosis and molecular profiling.However,AI techniques have a“black box”nature,which means that interpretability is of utmost importance because it is key to ensuring the reliability of the methods and building trust among clinicians for actual clinical implementation.In this paper,we provide an overview of current technical advancements in the AI-based pathological analysis of liver cancer,and delve into the strategies used in recent studies to unravel the“black box”of AI's decision-making process.
文摘This article aims to argue that interpreting liangzhi 良知 as innate, original, or cognitive knowledge is likely to fall into ＂interpretative obfuscation regarding knowledge.＂ First, for Wang, what is inherent in mankind is moral agency rather than innate or original knowledge. Therefore, the focus ofzhizhi 致知 and gewu 格物 is instead on moral practice and actualization of virtue rather than on either ＂the extension of knowledge＂ or ＂the investigation of things.＂ Apart from that, drawing support from cognitive knowledge to explicate liangzhi also leads to three related but distinct misconceptions： liangzhi as perfect knowledge, the identity of knowledge and action, and liangzhi as recognition or acknowledgement. By clarifying the above misinterpretations, the meaning and implication of liangzhi will, in turn, also become clearer.
基金Supported by National Key Research and Development Program,No.2022YFC2407304Major Research Project for Middle-Aged and Young Scientists of Fujian Provincial Health Commission,No.2021ZQNZD013+2 种基金The National Natural Science Foundation of China,No.62275050Fujian Province Science and Technology Innovation Joint Fund Project,No.2019Y9108Major Science and Technology Projects of Fujian Province,No.2021YZ036017.
文摘BACKGROUND To investigate the preoperative factors influencing textbook outcomes(TO)in Intrahepatic cholangiocarcinoma(ICC)patients and evaluate the feasibility of an interpretable machine learning model for preoperative prediction of TO,we developed a machine learning model for preoperative prediction of TO and used the SHapley Additive exPlanations(SHAP)technique to illustrate the prediction process.AIM To analyze the factors influencing textbook outcomes before surgery and to establish interpretable machine learning models for preoperative prediction.METHODS A total of 376 patients diagnosed with ICC were retrospectively collected from four major medical institutions in China,covering the period from 2011 to 2017.Logistic regression analysis was conducted to identify preoperative variables associated with achieving TO.Based on these variables,an EXtreme Gradient Boosting(XGBoost)machine learning prediction model was constructed using the XGBoost package.The SHAP(package:Shapviz)algorithm was employed to visualize each variable's contribution to the model's predictions.Kaplan-Meier survival analysis was performed to compare the prognostic differences between the TO-achieving and non-TO-achieving groups.RESULTS Among 376 patients,287 were included in the training group and 89 in the validation group.Logistic regression identified the following preoperative variables influencing TO:Child-Pugh classification,Eastern Cooperative Oncology Group(ECOG)score,hepatitis B,and tumor size.The XGBoost prediction model demonstrated high accuracy in internal validation(AUC=0.8825)and external validation(AUC=0.8346).Survival analysis revealed that the disease-free survival rates for patients achieving TO at 1,2,and 3 years were 64.2%,56.8%,and 43.4%,respectively.CONCLUSION Child-Pugh classification,ECOG score,hepatitis B,and tumor size are preoperative predictors of TO.In both the training group and the validation group,the machine learning model had certain effectiveness in predicting TO before surgery.The SHAP algorithm provided intuitive visualization of the machine learning prediction process,enhancing its interpretability.
文摘In the digital age,where traditional culture is in danger of gradually disappearing,one iconic figure has taken it upon herself to reverse the trend by reviving traditional skills.A prominent Chinese vlogger,Li Ziqi,whose birth name is Li Jiajia,has amassed a vast online following by reinterpreting traditions that date back thousands of years.Following a threeyear absence from social media,she made a highly anticipated return with the release of three new videos in mid-November 2024.These videos represent the culmination of several months of behind-the-scenes work.
文摘“As a director,I hope that the audience will see a movie in cinemas instead of watching threeminute commentaries,about which I’m really speechless,”internationally renowned film director Zhang Yimou said in a recent interview.Zhang said he believes the ritual and immersive feeling brought about by watching movies in the cinema cannot be replaced by watching movie commentaries online.He called on audiences to go to the cinema and experience in person the audio-visual feast delivered by the big screen.Zhang’s views on the so-called“threeminute movies”have become a trending topic on social media.The new genre,in which content creators on short-video platforms create summarized versions of films,also includes the creator’s own observations.The rise of these platforms has lowered barriers to entry for aspiring movie content creators,giving rise to not only high volume and great diversity but also large discrepancies in quality.Moreover,this content sometimes infringes the copyrights of the movies it features,while also misinterpreting their plots and themes.
文摘Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural and relational information inherent in molecular graphs. Despite their effectiveness, the “black-box” nature of GNNs remains a significant obstacle to their widespread adoption in chemistry, as it hinders interpretability and trust. In this context, several explanation methods based on factual reasoning have emerged. These methods aim to interpret the predictions made by GNNs by analyzing the key features contributing to the prediction. However, these approaches fail to answer critical questions: “How to ensure that the structure-property mapping learned by GNNs is consistent with established domain knowledge”. In this paper, we propose MMGCF, a novel counterfactual explanation framework designed specifically for the prediction of GNN-based molecular properties. MMGCF constructs a hierarchical tree structure on molecular motifs, enabling the systematic generation of counterfactuals through motif perturbations. This framework identifies causally significant motifs and elucidates their impact on model predictions, offering insights into the relationship between structural modifications and predicted properties. Our method demonstrates its effectiveness through comprehensive quantitative and qualitative evaluations of four real-world molecular datasets.
文摘The Interpretation of Nursing Guidelines for Intravenous Thrombolysis in Acute Ischemic Stroke offers comprehensive recommendations across five key domains:hospital organizational management,patient condition monitoring,complication observation and management,positioning and mobility away from the bed,and quality assurance.These Guidelines encompass all the phases of intravenous thrombolysis care for patients experiencing acute ischemic stroke.This article aims to elucidate the Guidelines by discussing their developmental background,the designation process,usage recommendations,and the interpretation of evolving perspectives,thereby providing valuable insights for clinical practice.
文摘Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.
文摘The aim of the paper is to explore the main paradigms and methodology of social research,framing them in historical path and highlighting the epistemological foundations.It moves from reflection on research methodology as a‘discourse of method’to focus on the paradigmatic dimension of the social sciences,according to Kuhn’s meaning for which paradigm indicates a shared and recognized theoretical perspective within the scientific community.The paper highlights the role of paradigms in shaping theoretical and empirical inquiry.It further examines the positivist and neo-positivist paradigms,which emphasize observation and empirical verifiability,quantification,formulation of laws,and cause-and-effect relationships,arguing for the uniqueness of the scientific method.Lazarsfeld brings to the social sciences the language‘of variables’,borrowed from mathematics and statistics.The distinction introduced by Windelband between‘nomothetic’and‘idiographic’sciences is followed by Weber’s elaboration of the concept of‘Verstehen’,which shifts the focus to the understanding of social reality through the meanings that individuals attribute to their actions.The interpretive paradigm paves the way for qualitative research methods.Finally,the paper delves into the complexity paradigm,which challenges the reductionist and deterministic models of classical science and outlines an epistemological shift in the key notions of science,introducing concepts such as‘emergence’,‘auto-eco-organization’and‘recursive processes’.The complexity of social reality calls for a rethinking of sociological methods,favoring multidimensional and event-based analysis over statistical regularities,privileging observation,intervention and the‘in vivo method’on the level of empirical research.Complexity pushes sociology to redefine itself along with its object traditionally understood as‘society’.
文摘Food is one of the biggest industries in developed and underdeveloped countries. Supply chain sustainability is essential in established and emerging economies because of the rising acceptance of cost-based outsourcing and the growing technological, social, and environmental concerns. The food business faces serious sustainability and growth challenges in developing countries. A comprehensive analysis of the critical success factors (CSFs) influencing the performance outcome and the sustainable supply chain management (SSCM) process. A theoretical framework is established to explain how they are used to examine the organizational aspect of the food supply chain life cycle analysis. This study examined the CSFs and revealed the relationships between them using a methodology that included a review of literature, interpretative structural modeling (ISM), and cross-impact matrix multiplication applied in classification (MICMAC) tool analysis of soil liquefaction factors. The findings of this research demonstrate that the quality and safety of food are important factors and have a direct effect on other factors. To make sustainable food supply chain management more adequate, legislators, managers, and experts need to pay attention to this factor. In this work. It also shows that companies aiming to create a sustainable business model must make sustainability a fundamental tenet of their organization. Practitioners and managers may devise effective long-term plans for establishing a sustainable food supply chain utilizing the recommended methodology.
基金This work is funded by the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the National Science Fund for Distinguished Young Scholars of China(Grant No.52222905).
文摘In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.
基金supported in part by the National Natural Science Foundation of China(82072019)the Shenzhen Basic Research Program(JCYJ20210324130209023)+5 种基金the Shenzhen-Hong Kong-Macao S&T Program(Category C)(SGDX20201103095002019)the Mainland-Hong Kong Joint Funding Scheme(MHKJFS)(MHP/005/20),the Project of Strategic Importance Fund(P0035421)the Projects of RISA(P0043001)from the Hong Kong Polytechnic University,the Natural Science Foundation of Jiangsu Province(BK20201441)the Provincial and Ministry Co-constructed Project of Henan Province Medical Science and Technology Research(SBGJ202103038,SBGJ202102056)the Henan Province Key R&D and Promotion Project(Science and Technology Research)(222102310015)the Natural Science Foundation of Henan Province(222300420575),and the Henan Province Science and Technology Research(222102310322).
文摘Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.
基金funded by the Fundamental Research Project of CNPC Geophysical Key Lab(2022DQ0604-4)the Strategic Cooperation Technology Projects of China National Petroleum Corporation and China University of Petroleum-Beijing(ZLZX 202003)。
文摘With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.
文摘During injection treatments, bottomhole pressure measurements may significantly mismatch modeling results. We devise a computationally effective technique for interpretation of fluid injection in a wellbore interval with multiple geological layers based on the bottomhole pressure measurements. The permeability, porosity and compressibility in each layer are initially setup, while the skin factor and partitioning of injected fluids among the zones during the injection are found as a solution of the problem. The problem takes into account Darcy flow and chemical interactions between the injected acids, diverter fluids and reservoir rock typical in modern matrix acidizing treatments. Using the synchronously recorded injection rate and bottomhole pressure, we evaluate skin factor changes in each layer and actual fluid placement into the reservoir during different pumping jobs: matrix acidizing, water control, sand control, scale squeezes and water flooding. The model is validated by comparison with a simulator used in industry. It gives opportunity to estimate efficiency of a matrix treatment job, role of every injection stage, and control fluid delivery to each layer in real time. The presented interpretation technique significantly improves accuracy of matrix treatments analysis by coupling the hydrodynamic model with records of pressure and injection rate during the treatment.
基金supported by the ABT SHIELD(Anti-Bot and Trolls Shield)project at the Systems Research Institute,Polish Academy of Sciences,in cooperation with EDGE NPDRPMA.01.02.00-14-B448/18-00 funded by the Regional Development Fund for the development of Mazovia.
文摘Distinguishing between web traffic generated by bots and humans is an important task in the evaluation of online marketing campaigns.One of the main challenges is related to only partial availability of the performance metrics:although some users can be unambiguously classified as bots,the correct label is uncertain in many cases.This calls for the use of classifiers capable of explaining their decisions.This paper demonstrates two such mechanisms based on features carefully engineered from web logs.The first is a man-made rule-based system.The second is a hierarchical model that first performs clustering and next classification using human-centred,interpretable methods.The stability of the proposed methods is analyzed and a minimal set of features that convey the classdiscriminating information is selected.The proposed data processing and analysis methodology are successfully applied to real-world data sets from online publishers.
