Pattern Matching of Industrial Alarm Floods Using Word Embedding and Dynamic Time Warping

Dear Editor,This letter proposes a new pattern matching method based on word embedding and dynamic time warping(DTW)to identify groups of similar alarm floods.First,alarm messages are transformed into numeric values that represent alarms and also reflect the relationships between alarm occurrences.Then,similarities between numerically encoded alarm flood sequences are calculated by DTW and groups of similar floods are identified via clustering.The effectiveness of the proposed method is demonstrated by a case study with alarm&event data obtained from a public industrial simulation model.

Rheological behavior of paste in metal mines

Cemented paste backfill(CPB)has been one of the best practical approaches for tailings management and underground goaf treatment.Paste rheology is a science to study the flow and deformation behaviors of paste or filling body under the effects of stress,strain,temperature,and time during the CPB process.The goal of studying paste rheology is to solve the engineering problems existing in four key processes;that is,paste rheology should meet the engineering demands of thickening,mixing,transportation,and backfilling.However,paste rheology is extremely complicated due to its high concentration,materials complexity,and engineering characteristics of non-stratification,nonsegregation,and non-bleeding.The rheological behavior of full tailings in deep thickening,rheological behavior of paste in mixing and pipeline transportation,and rheological behavior of filling body are introduced and discussed:(1)gel point,compressive yield stress,and the hindered settling function are adopted to characterize the rheological properties of full tailings in deep thickening.Combination of Coe-Clevenger theory and Buscall-White theory can also analyze the thickening performance in the whole area of deep cone thickener;(2)yield stress and viscosity are consistent with the evolution trend of the relative structure coefficient of paste in mixing;(3)coupling effect of wall slip and time-temperature dependency has a significant influence on the rheological properties and pipeline transportation;(4)damage variable is introduced to the Burgers model to describe the creep damage of the filling body.However,in-depth and systematic studies were still needed to establish a complete theoretical system of paste rheology in metal mines.

Cooperative extended rough attribute reduction algorithm based on improved PSO

Particle swarm optimization （PSO） is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been proven that computing minimal reduc- tion of decision tables is a non-derterministic polynomial （NP）-hard problem. A new cooperative extended attribute reduction algorithm named Co-PSAR based on improved PSO is proposed, in which the cooperative evolutionary strategy with suitable fitness func- tions is involved to learn a good hypothesis for accelerating the optimization of searching minimal attribute reduction. Experiments on Benchmark functions and University of California, Irvine （UCI） data sets, compared with other algorithms, verify the superiority of the Co-PSAR algorithm in terms of the convergence speed, efficiency and accuracy for the attribute reduction.

Enhanced minimum attribute reduction based on quantum-inspired shuffled frog leaping algorithm

Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial （NP）-hard problem. Therefore, it is necessary to investigate some fast and effective approximate algorithms. A novel and enhanced quantum-inspired shuffled frog leaping based minimum attribute reduction algorithm （QSFLAR） is proposed. Evolutionary frogs are represented by multi-state quantum bits, and both quantum rotation gate and quantum mutation operators are used to exploit the mechanisms of frog population diversity and convergence to the global optimum. The decomposed attribute subsets are co-evolved by the elitist frogs with a quantum-inspired shuffled frog leaping algorithm. The experimental results validate the better feasibility and effectiveness of QSFLAR, comparing with some representa- tive algorithms. Therefore, QSFLAR can be considered as a more competitive algorithm on the efficiency and accuracy for minimum attribute reduction.

Ensemble feature selection integrating elitist roles and quantum game model

To accelerate the selection process of feature subsets in the rough set theory （RST）, an ensemble elitist roles based quantum game （EERQG） algorithm is proposed for feature selec- tion. Firstly, the multilevel elitist roles based dynamics equilibrium strategy is established, and both immigration and emigration of elitists are able to be self-adaptive to balance between exploration and exploitation for feature selection. Secondly, the utility matrix of trust margins is introduced to the model of multilevel elitist roles to enhance various elitist roles＇ performance of searching the optimal feature subsets, and the win-win utility solutions for feature selec- tion can be attained. Meanwhile, a novel ensemble quantum game strategy is designed as an intriguing exhibiting structure to perfect the dynamics equilibrium of multilevel elitist roles. Finally, the en- semble manner of multilevel elitist roles is employed to achieve the global minimal feature subset, which will greatly improve the fea- sibility and effectiveness. Experiment results show the proposed EERQG algorithm has superiority compared to the existing feature selection algorithms.

Unsupervised feature selection based on Markov blanket and particle swarm optimization

Feature selection plays an important role in data mining and recognition, especially in the large scale text, image and biological data. Specifically, the class label information is unavailable to guide the selection of minimal feature subset in unsupervised feature selection, which is challenging and interesting. An unsupervised feature selection based on Markov blanket and particle swarm optimization is proposed named as UFSMB-PSO. The proposed method seeks to find the high-quality feature subset through multi-particles' cooperation of particle swarm optimization without using any learning algorithms. Moreover, the features' relevance will be computed based on an information metric of relevance gain, which provides an information theoretical foundation for finding the minimization of the redundancy between features. Our results on several benchmark datasets demonstrate that UFSMB-PSO can achieve significant improvement over state of the art unsupervised methods. © 1990-2011 Beijing Institute of Aerospace Information.

Association of a complete breast cancer pathologic response with axillary lymph node metastasis via neoadjuvant chemotherapy:Results from the CSBrS-012 study

To the Editor:In the last decade,neoadjuvant chemotherapy(NAC)has become a well-accepted treatment option for breast cancer,although few detailed description of NAC in China has yet been reported.[1]A previous study found that among patients with clinically node-negative(cN0)breast cancer,97.7%(432/442)with breast pathologic complete response(bpCR)had ypN0(absence of metastases in the axillary lymph nodes);and 71.6%(882/1232)without bpCR achieved ypN0(P<0.001).As for human epidermal growth factor receptor 2(HER2)positive or triple-negative breast cancer(TNBC)that achieved bpCR.

Expert consensus on the diagnosis and treatment of solid tumors with BRAF mutations

The BRAF gene is an important signaling molecule in human cells that is involved in the regulation of cell growth,differentiation,and survival.When the BRAF gene mutates,it can lead to abnormal activation of the signaling pathway,which promotes cell proliferation,inhibits cell apoptosis,and ultimately contributes to the occurrence and development of cancer.BRAF mutations are widely present in various cancers,including malignant melanoma,thyroid cancer,colorectal cancer,non-small cell lung cancer,and hairy cell leukemia,among others.BRAF is an important target for the treatment of various solid tumors,and targeted combination therapies,represented by BRAF inhibitors,have become one of the main treatment modalities for a variety of BRAF-mutation-positive solid tumors.

Evolutionary under-sampling based bagging ensemble method for imbalanced data classification

In the class imbalanced learning scenario, traditional machine learning algorithms focusing on optimizing the overall accuracy tend to achieve poor classification performance especially for the minority class in which we are most interested. To solve this problem, many effective approaches have been proposed. Among them, the bagging ensemble methods with integration of the under-sampling techniques have demonstrated better performance than some other ones including the bagging ensemble methods integrated with the over-sampling techniques, the cost-sensitive methods, etc. Although these under-sampling techniques promote the diversity among the generated base classifiers with the help of random partition or sampling for the majority class, they do not take any measure to ensure the individual classification performance, consequently affecting the achievability of better ensemble performance. On the other hand, evolutionary under-sampling EUS as a novel under- sampling technique has been successfully applied in searching for the best majority class subset for training a good- performance nearest neighbor classifier. Inspired by EUS, in this paper, we try to introduce it into the under-sampling bagging framework and propose an EUS based bagging ensemble method EUS-Bag by designing a new fitness function considering three factors to make EUS better suited to the framework. With our fitness function, EUS-Bag could generate a set of accurate and diverse base classifiers. To verify the effectiveness of EUS-Bag, we conduct a series of comparison experiments on 22 two-class imbalanced classification problems. Experimental results measured using recall, geometric mean and AUC all demonstrate its superior performance.

Development of an integrated policy making tool for assessing air quality and human health benefits of air pollution control

Efficient air quality management is critical to protect public health from the adverse impacts of air pollution. To evaluate the effectiveness of air pollution control strategies, the US Environmental Protection Agency （US EPA） has developed the Software for Model Attainment Test-Community Edition （SMAT-CE） to assess the air quality attainment of emission reductions, and the Environmental Benefits Mapping and Analysis Program- Community Edition （BenMAP-CE） to evaluate the health and economic benefits of air quality improvement respectively. Since scientific decision-making requires timely and coherent information, developing the linkage between SMAT-CE and BenMAP-CE into an integrated assessment platform is desirable. To address this need, a new module linking SMAT-CE to BenMAP-CE has been developed and tested. The new module streamlines the assessment of air quality and human health benefits for a proposed air pollution control strategy. It also implements an optimized data gridding algorithm which significantly enhances the computational efficiency without compro- mising accuracy. The performance of the integrated software package is demonstrated through a case study that evaluates the air quality and associated economic benefits of a national-level control strategy of PM2.5. The results of the case study show that the proposed emission reduction reduces the number of nonattainment sites from 379 to 25 based on the US National Ambient Air Quality Standards, leading to more than USS334billion ofeconomic benefits annually from improved public health. The integration of the science-based software tools in this study enhances the efficiency of developing effective and optimized emission control strategies for policy makers.

Spatial and temporal variation of haze in China from 1961 to 2012

The purpose of this study is to analyze the climatic characteristics and long-term spatial and temporal variations of haze occurrence in China. The impact factors of haze trends are also discussed. Meteorological data from 1961 to 2012 and daily PM10 concentrations from2003 to 2012 were employed in this study. The results indicate that the annual-average hazy days at all stations have been increasing rapidly from 4 days in 1961 to 18 days in 2012. The maximum number of haze days occur in winter（41.1%） while the minimum occur in summer（10.4%）. During 1961-2012, the high occurrence areas of haze shifted from central to south and east regions of China. The Beijing-Tianjin-Hebei（Jing-Jin-Ji） region, Shanxi,Shaanxi, and Henan Province are the high occurrence areas for haze, while the Yangtze River Delta（YRD） and the Pearl River Delta（PRD） have become regions with high haze occurrences in the last 25 years. Temperature and pressure are positively correlated with the number of haze days. However, wind, relative humidity, precipitation, and sunshine duration are negatively correlated with the number of haze days. The key meteorological factors affecting the formation and dissipation of haze vary for high and low altitudes, and are closely related to anthropogenic activities. In recent years, anthropogenic activities have played a more important role in haze occurrences compared with meteorological factors.

Effects of straw mulching on microclimate characteristics and evapotranspiration of drip-irrigated winter wheat in North China Plain

Field experiments were conducted for two consecutive years(2013-2014 and 2014-2015)to study the effects of straw mulching on microclimate characteristics,energy flux,soil evaporation(Es)and evapotranspiration of winter wheat(Triticum aestivum L.)under adequate drip irrigation in North China Plain(NCP).The results revealed that straw mulching affected air temperature(T_(a))and dew point temperature(T_(d))near the soil surface but had little effect on relative humidity(RH)compared with non-mulched fields.Straw mulching increased the turbulent exchange coefficient(K),and K within the canopy was higher than that above the canopy.Straw mulching clearly increased the sensible heat flux(H)distribution in April-June,and part of the energy needed for evapotranspiration was provided by transfer from the warmer air aloft.There was a linear functional relationship between H and T_(a)measured above and within the canopy.The straw mulching decreased Es but increased crop transpiration(T).Mulched treatment(TM)can decrease the total irrigation amount by about 7%-15%compared with the non-mulched treatment(TN).There were no significant differences(p>0.05)in cumulative crop evapotranspiration(ETc)between TM and TN treatments under adequate drip irrigation,and the use of mulch may need to be combined with an optimal deficit drip irrigation schedule for managing the declining water table in NCP.

How aerosol direct effects influence the source contributions to PM2.5 concentrations over Southern Hebei, China in severe winter haze episodes

Beijing-Tianjin-Hebei area is the most air polluted region in China and the three neighborhood southern Hebei cities, Shijiazhuang, Xingtai, and Handan, are listed in the top ten polluted cities with severe PM2.5 pollution. The objective of this paper is to evaluate the impacts of aerosol direct effects on air aualitv over the southern Hebei cities, as well as the im0acts when considering those effects on source apportionment using three dimensional air quality models. The WRF/Chem model was applied over the East Asia and northern China at 36 and 12 km horizontal grid resolutions, respectively, for the period of January 2013, with two sets of simulations with or without aerosol-meteorology feedbacks. The source contributions of power plants, industrial, domestic, transportation, and agriculture are evaluated using the Brute-Force Method （BFM） under the two simulation configurations. Our results indicate that, although the increases in PM2.5 concentrations due to those effects over the three southern Hebei cities are only 3%-9% on monthly average they are much more significant under high PM2.5 Ioadmgs （-50 gg.m - when PM25 concentrations are higher than 400μg.m^-3）. When considering the aerosol feedbacks, the contributions of industrial and domestic sources assessed using the BFM will obviously increase （e.g., from 30% 34% to 32%-37% for industrial）, especiallY3under high PM2.5 loadings （e.g., from 36%-44% to 43%-47% for domestic when PM2.5〉400μg·m^-3）. Our results imply that the aerosol direct effects should not be ignored during severe pollution episodes, especially in short-term source apportionment using the BFM.

Towards seamless environmental prediction-development of Pan-Eurasian EXperiment(PEEX)modelling platform

The Pan-Eurasian Experiment Modelling Platform(PEEX-MP)is one of the key blocks of the PEEX Research Programme.The PEEX MP has more than 30 models and is directed towards seamless envir-onmental prediction.The main focus area is the Arctic-boreal regions and China.The models used in PEEX-MP cover several main components of the Earth’s system,such as the atmosphere,hydrosphere,pedosphere and biosphere,and resolve the physicalchemicalbiological processes at different spatial and temporal scales and resolutions.This paper introduces and discusses PEEX MP multi-scale modelling concept for the Earth system,online integrated,forward/inverse,and socioeconomical modelling,and other approaches with a particular focus on applications in the PEEX geographical domain.The employed high-performance com-puting facilities,capabilities,and PEEX dataflow for modelling results are described.Several virtual research platforms(PEEXView,Virtual Research Environment,Web-based Atlas)for handling PEEX modelling and observational results are introduced.The over-all approach allows us to understand better physical-chemicalbiological processes,Earth’s system interactions and feedbacks and to provide valuable information for assessment studies on evaluating risks,impact,consequences,etc.for population,envir-onment and climate in the PEEX domain.This work was also one of the last projects of Prof.Sergej Zilitinkevich,who passed away on 15 February 2021.Since the finalization took time,the paper was actually submitted in 2023 and we could not argue that the final paper text was agreed with him.