Life cycle assessment of coal mines of diverse scales over time in China.

Coal mining has important detrimental effects on the environment and human health. By the end of 2022, China mined more than 4 billion tons of raw coal, and coal mining contributed to adverse environmental impacts. The objective of this work is to evaluate the environmental impacts emanated from coal mines in different periods (construction period, production period and closing period) and to find the relationship between coal mine scale and ecological impacts. This study uses coal mines that produce 0.45 Mt/a (considered a medium sized mine), 3 Mt/a and 8 Mt/a (both classified as large mines in this study) and a 12 Mt/a extra-large coal mine. Based on the time dimension, the mine life cycle was classified into construction, production and closing period, and the life cycle assessment method was used to conduct environmental assessment. The main influencing substances and key processes were tracked. The results indicated that mining engineering and gangue are the main factors affecting the construction and production periods of coal mines. Freshwater ecotoxicity, marine ecotoxicity, and human toxicity are the main environmental effects of coal produce, and they are mostly brought up by the release of hazardous elements like copper, chromium, zinc, nickel, and copper. Furan, formaldehyde, and chromium emissions during mine closure can be effectively reduced through environmental compensation, however coal mines' environmental compensation during mine closure is minimal. The environmental impact of coal mines producing 3 Mt and 8 Mt annually is minimal. The environmental impact of 0.45 Mt/a and 3 Mt/a coal mines is more prominent in the construction period. The pollutant discharge throughout the production phase, particularly the metal leaching discharge from gangue, needs to receive more attention from the 8 Mt/a and 12 Mt/a coal mines. Additionally, the larger the scale of coal mine production, the greater the proportion of the total environmental impact in the production stage.

A new strategy for evaluating antitumor activity in vitro with time-dimensional characteristics of RTCA technology / 中国药理学通报

Aim To analyze the anti-A549 and HI299 lung ade-nocarcinoma activities via using examples of baicalin, astragalo-side, hesperidin and cisplatin based on real time cellular analysis (RTCA) technology, and to build a new strategy for EC50 e-valuation reflecting the time-dimensional characteristic. Methods Using RTCA Software Pro for data analysis and GraphPad Prism and Origin Pro plotting, the in vitro anti-A549 and H1299 lung adenocarcinoma activities of baicalin, astragaloside, hesperidin, and cisplatin were characterized using the endpoint method and time dimension, respectively. Results (X) There were significant differences in EC50 values of A549 and H1299 cells at 24 h and 48 h endpoint methods. (2) The correlation coefficient of the curve fitted with the four-parameter equation was > 0. 9, and the dynamic change of EC50 remained relatively stable (the linear fitting of EC50 at adjacent 4 points I slope 1

An interpretable deep learning model for time-series electronic health records: Case study of delirium prediction in critical care.

Deep Learning (DL) models have received increasing attention in the clinical setting, particularly in intensive care units (ICU). In this context, the interpretability of the outcomes estimated by the DL models is an essential step towards increasing adoption of DL models in clinical practice. To address this challenge, we propose an ante-hoc, interpretable neural network model. Our proposed model, named double self-attention architecture (DSA), uses two attention-based mechanisms, including self-attention and effective attention. It can capture the importance of input variables in general, as well as changes in importance along the time dimension for the outcome of interest. We evaluated our model using two real-world clinical datasets covering 22840 patients in predicting onset of delirium 12 h and 48 h in advance. Additionally, we compare the descriptive performance of our model with three post-hoc interpretable algorithms as well as with the opinion of clinicians based on the published literature and clinical experience. We find that our model covers the majority of the top-10 variables ranked by the other three post-hoc interpretable algorithms as well as the clinical opinion, with the advantage of taking into account both, the dependencies among variables as well as dependencies between varying time-steps. Finally, our results show that our model can improve descriptive performance without sacrificing predictive performance.

Investigating PTG in Cancer Patients: The Role of Time Dimension in the Experience of Personal Growth.

This study explored the experience of growth related to being a cancer patient by implementing a thematic analysis. An online questionnaire was completed by 69 patients narrating their growth experience related to cancer. Collected narratives were analyzed by running a deductive thematic analysis, starting from the five domains of the Post-Traumatic Growth Inventory (PTGI) and searching for the presence or absence of topics. Descriptive statistics and correlational analysis were performed. The five factors of the PTGI were identified in the narratives. The thematic analysis we performed defined a further theme that we labeled the "time dimension", which saturated 37.7% of the entire sample. The presences of four sub-themes related to the "time dimension" were also found: "tracing a new temporal rhythm", "the value of deserved time", "facing the caducity of life" and "a view on the future". Each sub-theme significantly correlated with the theme of the "time dimension". This emergent theme does not correlate in our results with other domains of personal growth in cancer previously described in the scientific literature, emerging as an independent variable not significantly associated with other domains of post-traumatic growth. Our results suggest further investigation in the role of the time dimension in the practical and emotional experience of growth with regard to cancer.

A time-incorporated SOFA score-based machine learning model for predicting mortality in critically ill patients: A multicenter, real-world study.

BACKGROUND: Organ dysfunction (OD) assessment is essential in intensive care units (ICUs). However, current OD assessment scores merely describe the number and the severity of each OD, without evaluating the duration of organ injury. The objective of this study is to develop and validate a machine learning model based on the Sequential Organ Failure Assessment (SOFA) score for the prediction of mortality in critically ill patients. MATERIAL AND METHODS: Data from the eICU Collaborative Research Database and Medical Information Mart for Intensive Care (MIMIC) -III were mixed for model development. The MIMIC-IV and Nanjing Jinling Hospital Surgical ICU database were used as external test set A and set B, respectively. The outcome of interest was in-ICU mortality. A modified SOFA model incorporating time-dimension (T-SOFA) was stepwise developed to predict ICU mortality using extreme gradient boosting (XGBoost), support vector machine, random forest and logistic regression algorithms. Time-dimensional features were calculated based on six consecutive SOFA scores collected every 12 h within the first three days of admission. The predictive performance was assessed with the area under the receiver operating characteristic curves (AUROC) and calibration plot. RESULTS: A total of 82,132 patients from the real-world datasets were included in this study, and 7,494 patients (9.12%) died during their ICU stay. The T-SOFA M3 that incorporated the time-dimension features and age, using the XGBoost algorithm, significantly outperformed the original SOFA score in the validation set (AUROC 0.800 95% CI [0.787-0.813] vs. 0.693 95% CI [0.678-0.709], p < 0.01). Good discrimination and calibration were maintained in the test set A and B, with AUROC of 0.803, 95% CI [0.791-0.815] and 0.830, 95% CI [0.789-0.870], respectively. CONCLUSIONS: The time-incorporated T-SOFA model could significantly improve the prediction performance of the original SOFA score and is of potential for identifying high-risk patients in future clinical application.

[What does systemic relevance mean in times of the pandemic?Que signifie la notion d' « activité essentielle ¼ en temps de pandémie ?] / Was bedeutet Systemrelevanz in Zeiten der Pandemie?

This article explores the question of what systemic relevance means in times of the pandemic and to what extent "systemic relevance" could become a component of the socio-political semantics of the 21st century. To answer this question, empirical, theoretical, and thought-experimental considerations are being combined. The first part understands systemic relevance as an actor category and examines the term's career and shifting meanings in different discursive contexts. The second part understands systemic relevance as an analytical category and discusses three theoretical perspectives that accompany the establishment of quasi-sociological observational schemes in everyday life and politics. Finally, the third part is devoted to the conceptual challenges for sociology during and after the pandemic. Starting from a conceptual gap in the discourse of systemic relevance, it is argued that we cannot talk meaningfully about systemic relevance without at the same time considering the expected or planned duration with which certain institutions are being closed down or put into minimal operation in critical situations.

Cet article s'interroge sur le sens de la notion d' « activité essentielle ¼ en temps de pandémie et sur sa capacité à devenir, au-delà de la pandémie, un élément constitutif de la sémantique de la politique sociétale au 21e siècle. Pour répondre à cette question, l'article combine des réflexions d'ordre empirique, théorique et expérimental. La première partie conçoit la notion d' « activité essentielle ¼ comme une catégorie utilisée par les acteurs et analyse la carrière et le glissement de sens de cette notion dans divers contextes discursifs. La deuxième partie conçoit la notion d'activité essentielle comme une catégorie analytique et discute trois perspectives théoriques qui vont de pair avec l'établissement de grilles de lecture quasi-sociologiques utilisables dans la vie quotidienne et la politique. Enfin, la troisième partie est consacrée aux défis conceptuels rencontrés par la sociologie pendant et après la pandémie. À partir du constat d'un vide dans le discours sur la notion d'activité essentielle, la thèse développée ici est que nous ne pouvons pas parler d'activité essentielle de manière censée sans parler en même temps de la durée attendue ou prévue durant laquelle certaines institutions sont mises hors service ou en service minimum dans des situations de crise.

A grey spatiotemporal incidence model with application to factors causing air pollution.

The factors causing air pollution in China has caused extensive concern, but there are still many problems in the grey incidence model of identifying air pollution factors. The results produced by the existing grey incidence models are not stable when the order of objects in a given panel data is changed. In order to improve the reliability and uniformity of the grey incidence model, a new grey incidence model, called the grey spatiotemporal incidence model, abbreviated as the GSTI model, is designed in this paper. In the proposed model, the spatiotemporal data which can represent the spatial relationship among different objects rather than the three-dimensional panel data are defined. In addition, the new model includes two procedures. Firstly, the trend coefficient is used to measure the positive and negative connections between two data sequences. Secondly, the measurement coefficient is utilized to calculate the size of grey incidence degree. Subsequently, five properties of the GSTI model are discussed. To demonstrate its practicability and compatibility, the novel model is utilized to identify south Jiangsu province's main factors causing air pollution according to monthly data for 2018. The abundant comparison shows the applicability and superiority of the model in the identification of air pollution factors and the construction of grey incidence model.

A versatile method for groundwater vulnerability projections in future scenarios.

Water scarcity and associated risks are serious societal problems. A major challenge for the future will be to ensure the short-term and long-term provision of accessible and safe freshwater to meet the needs of the rapidly growing human population and changes in land cover and land use, where conservation and protection play a key role. Through a Bayesian spatial statistical method, a time-dependent approach for groundwater vulnerability assessment is developed to account for both the recent status of groundwater contamination and its evolution, as required by the European Union (Groundwater Directive, 2006/118/EC). This approach combines natural and anthropogenic factors to identify areas with a critical combination of high levels and increasing trends of nitrate concentrations, together with a quantitative evaluation of how different future scenarios would impact the quality of groundwater resources in a given area. In particular, the proposed approach can determine potential impacts on groundwater resources if policies are maintained at the status quo or if new measures are implemented for safeguarding groundwater quality, as natural factors are changing under climatic or anthropogenic stresses.

Question Insistently about Etiopathogenesis of Summer Febrile Disease:Also a Discuss on Primary Cause of "Bird Flu" / 中华中医药杂志

The characteristics of the pathogenic summer-heat and the primary important element of the season time dimension present parallelism principle on the forming mechanisms of five supremacies principal syndromes of initial summer febrile disease and four clinical characteristics of pathogenic factor summer heat. To explore the characteristics and the five successive evolutive phases corresponding to five elements phase theory of season and seasonal periods is provided with decisive guiding effect of passing through overall situation in the course of treating heat diseases caused by exogenous pathogenic factor based on syndrome differentiation in terms of the syndrome recognizing,establishing therapy principle and selecting Chinese materia medica to from a formula.It also presents effective resources and thoughts used for reference on the "bird flu" of nowadays,influenza of the human being(epidemic) that is likely to break out and how to treat these super-variations of virus(pestilential qi) based on syndrome differentiation with traditional Chinese medicine.

Elementary discussion on pharmacodynamic methodology of Chinese materia medica (Ⅰ) / 中草药

The pharmacodynamic methodology of Chinese materia medica (PMCMM) at present was discussed comprehensively in this article and the problems were found While the primary study on the PMCMM was carried out This article offers that the development of the science of Chinese traditional medicine is the premise and basis of the study of PMCMM, especially that the breakthrough of modern study on the theory of viscera state and channel theory and channel tropism theory leads dominantly the study of PMCMM; the four dimensional system theory has been investigated in PMCMM, on the two dimensional basis of the physical foundation of medicinal efficacy and the mechanism of medicinal efficacy, the time dimension and human spirit dimension are added