Improving Neural Relation Extraction with Implicit Mutual Relations

Relation extraction (RE) aims at extracting the relation between two entities from the text corpora. It is a crucial task for Knowledge Graph (KG) construction. Most existing methods predict the relation between an entity pair by learning the relation from the training sentences, which contain the targeted entity pair. In contrast to existing distant supervision approaches that suffer from insufficient training corpora to extract relations, our proposal of mining implicit mutual relation from the massive unlabeled corpora transfers the semantic information of entity pairs into the RE model, which is more expressive and semantically plausible. After constructing an entity proximity graph based on the implicit mutual relations, we preserve the semantic relations of entity pairs via embedding each vertex of the graph into a low-dimensional space. As a result, we can easily and flexibly integrate the implicit mutual relations and other entity information, such as entity types, into the existing RE methods. Our experimental results on a New York Times and another Google Distant Supervision datasets suggest that our proposed neural RE framework provides a promising improvement for the RE task, and significantly outperforms the state-of-the-art methods. Moreover, the component for mining implicit mutual relations is so flexible that can help to improve the performance of both CNN-based and RNN-based RE models significant.Comment: 12 page

The Effect of Nonlinear Charging Function and Line Change Constraints on Electric Bus Scheduling

The recharging plans are a key component of the electric bus schedule. Since the real-world charging function of electric vehicles follows a nonlinear relationship with the charging duration, it is challenging to accurately estimate the charging time. To provide a feasible bus schedule given the nonlinear charging function, this paper proposes a mixed integer programming model with a piecewise linear charging approximation and multi-depot and multi-vehicle type scheduling. The objective of the model is to minimise the total cost of the schedule, which includes the vehicle purchasing cost and operation cost. From a practical point of view, the number of line changes of each bus is also taken as one of the constraints in the optimisation. An improved heuristic algorithm is then proposed to find high-quality solutions of the problem with an efficient computation. Finally, a real-world dataset is used for the case study. The results of using different charging functions indicate a large deviation between the linear charging function and the piecewise linear approximation, which can effectively avoid the infeasible bus schedules. Moreover, the experiments show that the proposed line change constraints can be an effective control method for transit operators

Genome-Wide Association Studies Identified Three Independent Polymorphisms Associated with α-Tocopherol Content in Maize Kernels

Tocopherols are a class of four natural compounds that can provide nutrition and function as antioxidant in both plants and animals. Maize kernels have low α-tocopherol content, the compound with the highest vitamin E activity, thus, raising the risk of vitamin E deficiency in human populations relying on maize as their primary vitamin E source. In this study, two insertion/deletions (InDels) within a gene encoding γ-tocopherol methyltransferase, Zea mays VTE4 (ZmVTE4), and a single nucleotide polymorphism (SNP) located ∼85 kb upstream of ZmVTE4 were identified to be significantly associated with α-tocopherol levels in maize kernels by conducting an association study with a panel of ∼500 diverse inbred lines. Linkage analysis in three populations that segregated at either one of these three polymorphisms but not at the other two suggested that the three polymorphisms could affect α-tocopherol content independently. Furthermore, we found that haplotypes of the two InDels could explain ∼33% of α-tocopherol variation in the association panel, suggesting ZmVTE4 is a major gene involved in natural phenotypic variation of α-tocopherol. One of the two InDels is located within the promoter region and associates with ZmVTE4 transcript level. This information can not only help in understanding the underlying mechanism of natural tocopherol variations in maize kernels, but also provide valuable markers for marker-assisted breeding of α-tocopherol content in maize kernels, which will then facilitate the improvement of maize as a better source of daily vitamin E nutrition

Study on mechanical and energy characteristics of coal samples under different unloading states

There are many types of coal seams in China, and the mining of protective layers will cause different rates of stress reduction in protected coal seams at different intervals. Therefore, experiments were conducted at different unloading rates to explore the strength, deformation, and energy characteristics of coal. Research findings: the AE (acoustic emission) signal of the coal body before unloading has a small range of changes and similar characteristics. After unloading begins, because of the different development rates of internal crack in the coal body under different unloading states, the AE signal of the coal body varies at different unloading rates. The maximum stress increases exponentially with the increase of unloading rate. It was found that the higher the unloading rate, the easier and earlier the coal sample is to be damaged. And it was discovered that the dissipated energy of the coal sample in the elastic stage is extremely low, and a large amount of total energy is converted into elastic energy and stored inside the coal sample. The dissipation energy increases during the plastic stage, while the trend of increasing elastic energy slows down. After the peak stage, the dissipated energy rapidly increases and the elastic energy decreases

Fuel consumption and exhaust emissions of diesel vehicles in worldwide harmonized light vehicles test cycles and their sensitivities to eco-driving factors

Large amounts of fossil fuels are 14 consumed by motor vehicles annually, and hazardous exhaust emissions from the motor vehicles have caused serious problems to environment and human health. Eco-driving can effectively improve the fuel economy and decrease the exhaust emissions, which makes it vital to analyze the fuel consumption and exhaust emissions at given driving cycle, and investigate their sensitivities to eco-driving factors. In this paper, the fuel consumption and exhaust emissions of a Euro-6 compliant light-duty diesel vehicle were tested in Worldwide Harmonized Light Vehicles Test Cycles on a chassis dynamometer; further, the sensitivities of the eco-driving factors that influence the fuel economy and exhaust emissions were analyzed using validated vehicle model. For the vehicle model simulation, the effect of the coolant temperature on fuel consumption and exhaust emission only considered its effect on lubricating oil viscosity. The results showed that vehicle acceleration and velocity dominates the fuel consumption rates in Worldwide Harmonized Light Vehicles Test Cycles, where more than 50% of the exhaust emissions was emitted in the first 300 seconds; also, fuel economy and exhaust emission factors showed a significant dependency on the road grade, coolant temperature, vehicle velocity and mass. For the driver-controllable factors, high vehicle velocity and low road grade (via route-choice) were recommended to achieve low fuel consumption and exhaust emissions

Review of thermal management of catalytic converters to decrease engine emissions during cold start and warm up

Catalytic converters mitigate carbon monoxide, hydrocarbon, nitrogen oxides and particulate matter emissions from internal combustion engines, and allow meeting the increasingly stringent emission regulations. However, catalytic converters experience light-off issues during cold start and warm up. This paper reviews the literature on the thermal management of catalysts, which aims to significantly reduce the light-off time and emission concentrations through appropriate heating methods. In particular, methods based on the control of engine parameters are easily implementable, as they do not require extra heating devices. They present good performance in terms of catalyst light-off time reduction, but bring high fuel penalties, caused by the heat loss and unburnt fuel. Other thermal management methods, such as those based on burners, reformers and electrically heated catalysts, involve the installation of additional devices, but allow flexibility in the location and intensity of the heat injection, which can effectively reduce the heat loss in the tailpipe. Heat storage materials decrease catalyst light-off time, emission concentrations and fuel consumption, but they are not effective if the engine remains switched off for long periods of time. The main recommendation of this survey is that integrated and more advanced thermal management control strategies should be developed to reduce light-off time without significant energy penalty