Suggestions to the article: medical professionalism research characteristics and hotspots

Scientometrics - The article published on 5 July 2021 is well-written and of interest. However, some improvements could be made, such as ten Tables/Figures can be shortened to highlight the focused...     

北斗星历解析及实时卫星位置解算实现

利用 C 语言编程进行北斗卫星星历数据的实时读取，更新结构体的卫星轨道参数， 从而实现北斗卫星位置的解算，并与武汉大学数据中心提供的北斗精密星历作比较，对解算的卫星位置结果进行评估，最大误差不超过 3.5 米，满足实际应用需要。     

Hydrogeochemical Processes and Inverse Modeling for a Multilayer Aquifer System in the Yuaner Coal Mine,Huaibei Coalfield,China

Coal mining can dramatically change hydrogeological conditions and induce serious environmental problems. Fifty groundwater samples were collected from the main aquifers in the Yuaner coal mine (Anhui Province, China). The results show that the main hydrogeochemical processes in the mine include dissolution, precipitation, pyrite oxidation, desulfurization, and cation exchange. The Neogene porous aquifer is affected by groundwater flow conditions; its main hydrogeochemical processes are dissolution of carbonate minerals and gypsum, and cation exchange. The Permian coal measure’s fractured sandstone aquifer was confirmed to be controlled by the region’s geological structure; its main hydrogeochemical processes are desulfurization and cation exchange. The Carboniferous Taiyuan limestone aquifer was determined by both groundwater flow conditions and regional geological structure; its main hydrogeochemical processes are dissolution of carbonate minerals and gypsum, pyrite oxidation, and cation exchange. Additionally, hydrogeochemical inverse modeling of the groundwater flow path confirm the hydrochemistry results and principal component analysis.

     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Microwave drying performance of lignite with the assistance of biomass-derived char

Microwave lignite drying with assistance of biomass-derived char was addressed and effect of bio-char on drying rate and energy consumption was investigated in this work. Effective diffusion coefficient and activation energy for the drying process were also analyzed. The results indicated the drying process was largely dependent on the variation of sample temperature. Bio-char originated from pine wood was most favorable for lignite drying, considering its better promoting effect and advanced security. There existed an optimal bio-char addition ratio for drying process at different power. The corresponding optimal ratio was 10% at 231?W and 15% at 385?W, at which the biggest drying rate and the least energy consumption were reached. It was compared lignite drying initiated at 385?W was better for energy conservation. Effective diffusivity was improved and activation energy was simultaneously reduced, with the addition of bio-char. The minimum activation energy was 15.54?W?·?g^?1, which was gained at bio-char addition ratio of 10%. The results revealed the effect of bio-char on depressing activation energy could rival that of metal-based additives. The drying process with assistance of microwave and bio-char could present technical and economical benefits on lignite upgrading.     

Headspace fingerprinting approach to identify the major pathway influencing volatile patterns of vinasse-cured duck processed by high pressure,as well as its impact on physicochemical and sensory attributes

As a decisive attribute, flavour could be influenced by HP treatments through multiple physical and chemical pathways within the high pressure (HP)-assisted meat curing process. This investigation aimed to identify the major pathway influencing volatile flavour patterns of two representative vinasse-cured duck (VCD) products with HP treatments (150–300 MPa/15 min), including wet and dry types, by employing headspace fingerprinting as an untargeted approach. Results suggested that HP treatments greatly lowered moisture contents and increased Warner-Bratzler shear force and thiobarbituric acid reactive substances of the cured samples. According to multivariate models, the volatile flavour patterns of the HP-processed VCD could be clearly separated from the unprocessed samples, but the VCD pressurised at different intensities represented similar volatile fingerprinting, which was validated by e-nose analysis. The discriminant analysis (OPLS-DA) model outlined vinasse-derived ethanol, acetic acid, 3-methyl-1-butanol, 2-methyl-1-butanol, phenethyl alcohol and 2-methyl-3-octanone as the major discriminant aromas across the unpressurised and pressurised samples.     

四川盆地上三叠统划分对比研究

四川盆地上三叠统对比长期存在混乱，给盆地整体评价带来困难。通过岩石地层、地震地层、事件地层及高分辨率旋回地层等多种方法讨论了须家河组与香溪群各岩性段的对比关系，得出了全盆地上三叠统的对比关系；指出须四段底可近似看成等时界面；须家河组下部地层自西向东、向南逐层超覆在雷口坡组之上，上部地层在盆地西北部自东南向西北方向剥蚀层位渐次变老。文章建议四川盆地上三叠统废除“香溪群”，统一使用“须家河组”，以利于对全盆地上三叠统进行油气资源整体评价与深化研究。