国际橄榄理事会的定位和使命及相关标准（网络首发、推荐阅读）

The International Olive Council(IOC) is an international intergovernmental organization dedicated to olive oil and table olives, aiming at modernizing olive production, coordinating olive policies, improving the regulation of international trade, defending the quality of the olive sector and promoting olive oil and table olives to increase their consumption. The IOC grants recognition of laboratories and tasting panels in annual trials when they meet the conditions given in the decisions adopting the IOC certificate for laboratories for the physico–chemical testing of olive oil and laboratories for the sensory analysis of virgin olive oils. The IOC establishes analysis methods applying to olive oils and olive pomace oils for purity, quality and organoleptic assessment. The IOC elaborates guides of storage conditions for olive oils and olive pomace oils, of managing virgin olive oil tasting panels and of sensory testing laboratories. In future works, the IOC includes activities to identify analytical criteria for detecting fraud and guaranteeing the quality of olive oils and olive pomace oils.     

Techno-economic feasibility assessment of sorption enhanced gasification of municipal solid waste for hydrogen production

Waste-to-fuel coupled with carbon capture and storage is forecasted to be an effective way to mitigate the greenhouse gas emissions, reduce the waste sent to landfill and, simultaneously, reduce the dependence of fossil fuels. This study evaluated the techno-economic feasibility of sorption enhanced gasification, which involves in-situ CO₂ capture, and benchmarked it with the conventional steam gasification of municipal solid waste for H₂ production. The impact of a gate fee and tax levied on the fossil CO₂ emissions in economic feasibility was assessed. The results showed that the hydrogen production was enhanced in sorption enhanced gasification, that achieved an optimum H₂ production efficiency of 48.7% (T = 650 °C and SBR = 1.8). This was 1.0% points higher than that of the conventional steam gasification (T = 900 °C and SBR = 1.2). However, the total efficiency, which accounts for H₂ production and net power output, for sorption enhanced gasification was estimated to be 49.3% (T = 650 °C and SBR = 1.8). This was 4.4% points lower than the figure estimated for the conventional gasification (T = 900 °C and SBR = 1.2). The economic performance assessment showed that the sorption enhanced gasification will result in a significantly higher levelised cost of hydrogen (5.0 €/kg) compared to that estimated for conventional steam gasification (2.7 €/kg). The levelised cost of hydrogen can be reduced to 4.5 €/kg on an introduction of the gate fee of 40.0 €/t_MSW. The cost of CO₂ avoided was estimated to be 114.9 €/tCO₂ (no gate fee and tax levied). However, this value can be reduced to 90.1 €/tCO₂ with the introduction of an emission allowance price of 39.6 €/tCO₂. Despite better environmental performance, the capital cost of sorption enhanced gasification needs to be reduced for this technology to become competitive with mature gasification technologies.     

Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

Investigation of liquid water heterogeneities in large area proton exchange membrane fuel cells using a Darcy two-phase flow model in a multiphysics code

Proper management of the liquid water and heat produced in proton exchange membrane (PEM) fuel cells remains crucial to increase both its performance and durability. In this study, a two-phase flow and multicomponent model, called two-fluid model, is developed in the commercial COMSOL Multiphysics® software to investigate the liquid water heterogeneities in large area PEM fuel cells, considering the real flow fields in the bipolar plate. A macroscopic pseudo-3D multi-layers approach has been chosen and generalized Darcy's relation is used both in the membrane-electrode assembly (MEA) and in the channel. The model considers two-phase flow and gas convection and diffusion coupled with electrochemistry and water transport through the membrane. The numerical results are compared to one-fluid model results and liquid water measurements obtained by neutron imaging for several operating conditions. Finally, according to the good agreement between the two-fluid and experimentation results, the numerical water distribution is examined in each component of the cell, exhibiting very heterogeneous water thickness over the cell surface.     

A model of PEMFC-battery system to evaluate inner operating status and energy consumption under different energy management strategies

A hybrid system with jointed battery and PEMFC is popular and of great potential in New Energy Vehicle (NEV) application. However, reliability and efficiency remain to be improved for commercial products. To reflect the complicated physics inside the proton exchange membrane fuel cell (PEMFC), the PEMFC model consisting of inner muti-physics process and other accessories was built, then a complete hybrid system was established when a matched battery, DC/DC, regenerative braking were taken into consideration. Based on the above model, the stack state and system performance under standard cycle for heavy duty vehicle-CWTVC were obtained. According to the simulation results, fuel cell states such as pressure, water content and voltage suffers severe oscillation with external load, especially in the highway cycle. Membrane electrode assembly (MEA) suffers from pressure impact with average value of more than 24 kPa in highway cycle. In the aspect of relative humidity, the PEMFC stack is most threatened in road cycle. As for the hybrid system, its efficiency and state of charge (SOC) fluctuation perform worst in urban cycle and road cycle respectively, while its highest efficiency occurs in road test. Operating mode of fuel cell has influence on hybrid system. When 3-level mode of fuel cell output was applied, the efficiency increased to its peak value at medium level of 28 kW and then declined gradually. H₂ consumption had an opposite trend compared to efficiency. In the aspect of battery SOC, it declines in operating process and its fluctuations decreases when medium level got bigger. The 3-level mode and 4-level mode were compared using this model. It can be concluded that although 3-level mode performs slightly better in hybrid system efficiency, H₂ consumption, pressure impact, it does not have absolute advantage over 4-level mode in other indicators.     

LinkingPark: An automatic semantic table interpretation system

In this paper, we present LinkingPark, an automatic semantic annotation system for tabular data to knowledge graph matching. LinkingPark is designed as a modular framework which can handle Cell-Entity Annotation (CEA), Column-Type Annotation (CTA), and Columns-Property Annotation (CPA) altogether. It is built upon our previous SemTab 2020 system, which won the 2nd prize among 28 different teams after four rounds of evaluations. Moreover, the system is unsupervised, stand-alone, and flexible for multilingual support. Its backend offers an efficient RESTful API for programmatic access, as well as an Excel Add-in for ease of use. Users can interact with LinkingPark in near real-time, further demonstrating its efficiency.     

Efficiency evaluation of healthcare services in China based on stochastic multicriteria acceptability analysis and directional distance function

Evaluating the efficiency of healthcare services accurately can help in analyzing the rationality of inputs and outputs in such services. Considering the consistency and equity of assessment criteria, this study conducts the stochastic multicriteria acceptability analysis (SMAA-2) with a directional distance function to evaluate the efficiency of healthcare services in 31 provincial administrative regions of mainland China, as observed in 2018. We use SMAA-DDF to explore all the projection directions to the efficient frontier instead of a certain projection direction. We measure the maximum and average efficiencies for each of the 31 provincial healthcare services. Our empirical findings show that only seven provinces achieve optimal healthcare service efficiency; the eastern area performed the best, followed by the central, western, and northeast areas. Furthermore, the path along the projection directions is provided to help inefficient provinces improve their efficiency and obtain the best possible positions.     

基于K-means和SOM的水下传感器数据采集方法

随着海洋资源勘探和海洋污染物监控工作的开展，水文数据的监测和采集等已经成为重要的研究方向。其中，水下无线传感器网络在水文数据采集过程中起着举足轻重的作用。本文研究的是水下无线传感器二维监测网络模型中，传感器节点数据采集的问题，其设计方法是通过自组织映射（Self-organizing mapping，SOM）对传感器节点进行路径最优化处理，结合优化的路径图形和K-means算法找到路径内部聚合点，利用聚合点和传感器的节点得到传感器通信半径内的数据采集点，最后通过SOM得到水下机器人（Autonomous underwater vehicle，AUV）到各个数据采集点采集数据的最优路径。经过实验验证，在水下1 200 m×1 750 m范围内布置52个传感器节点的情景下，数据采集点相比于传感器节点路径规划采用相同的采集顺序得到的路径优化了6.7%；对数据采集点重新进行自组织路径规划得到的路径比传感器结点路径的最优解提高了12.2%。增加传感器节点的数量，其结果也大致相同，因此采用该方法可以提高水下机器人采集数据的效率。