Dissolved carbon and CDOM in lake ice and underlying waters along a salinity gradient in shallow lakes of Northeast China

The variations of DOC and DIC concentrations in lake ice and underlying waters were examined in 40 shallow lakes across the Songnen Plain, Northeast China. The lakes, frozen annually during winter, included freshwater and brackish systems (EC > 1000 μS cm−1; range: 171–12607 μS cm−1 in underlying water). Results showed that lake ice contained lower DOC (7.2 mg L−1) and DIC (6.7 mg L−1) concentration compared to the underlying waters (58.2 and 142.4 mg L−1, respectively). Large differences in DOC and DIC concentrations of underlying waters were also observed between freshwater (mean ± SD: 22.3 ± 11.5 mg L−1, 50.7 ± 20.6 mg L−1) and brackish lakes (83.3 ± 138.0 mg L−1, 247.0 ± 410.5 mg L−1). A mass balance model was developed to describe the relative distribution of solutes and chemical attributes between ice and the underlying waters. Results showed that water depth and ice thickness were the key factors regulating the spatial distribution of solutes in the frozen lakes. Chromophoric dissolved organic matter (CDOM) absorption coefficient at 320 nm, aCDOM(320) and specific UV absorbance (SUVA254) were used to characterize CDOM composition and quality. Compared to the underlying waters, CDOM present in ice largely included low aromaticity organic substances, an outcome perhaps facilitated by ice formation and photo-degradation. In ice and underlying freshwaters, CDOM predominantly included organic C fractions of high aromaticity, while low aromaticity organic substances were observed for brackish lakes. Results of this study suggest that, if water salinity increases due to climate change and anthropogenic activities, significant changes can occur in the dissolved carbon and fate of CDOM in these shallow lakes

Self-supporting structure design in additive manufacturing through explicit topology optimization

One of the challenging issues in additive manufacturing (AM) oriented topology optimization is how to design structures that are self-supportive in a manufacture process without introducing additional supporting materials. In the present contribution, it is intended to resolve this problem under an explicit topology optimization framework where optimal structural topology can be found by optimizing a set of explicit geometry parameters. Two solution approaches established based on the Moving Morphable Components (MMC) and Moving Morphable Voids (MMV) frameworks, respectively, are proposed and some theoretical issues associated with AM oriented topology optimization are also analyzed. Numerical examples provided demonstrate the effectiveness of the proposed methods.Comment: 81 pages, 45 figure

Strategic Vertical Pricing in the U.S. Butter Market

This article develops a methodology for empirically analyzing vertically strategic interactions in a multi-level supply channel. The model is used to analyze the vertical channel for U.S. butter manufacturing and retailing. Aggregating products to the firm level and using a nonlinear AIDS demand system under alternative strategic pricing assumptions is estimated using full information maximum likelihood (FIML) for seven geographic markets from 1998-2002. The market demand for butter was found to very price elastic. Furthermore, cross price elasticities between private labels and the two large national brands were also very elastic. The selected market structure was one indicating category profit maximization of national brands (separate from private label) at the retail level, Vertical Nash competition in the vertical channel, and Bretrand competition at the manufacturing level. Our results strongly suggest that the retail market for food products is impacted by the underlying vertical structure. The study provides useful measures of imperfect competition in the retail manufacturing sector.Vertical interaction, market structure, strategic pricing, market power, AIDS model, butter., Agribusiness, Agricultural and Food Policy, Consumer/Household Economics, Demand and Price Analysis, Industrial Organization, L13, L22, L66,

Spectrum Structure of Fermion on Bloch Branes with Two Scalar-fermion Couplings

It is known that the Bloch brane is generated by an odd scalar field $\phi$ and an even one $\chi$. In order to localize a bulk fermion on the Bloch brane, the coupling between the fermion and scalars should be introduced. { There are two localization mechanisms in the literature, the Yukawa coupling $-\eta \bar{\Psi} F_1(\phi,\chi) \Psi$ and non-Yukawa coupling $\lambda \bar \Psi \Gamma^M \partial_M F_2(\phi,\chi) \gamma^5 \Psi$. The Yukawa coupling has been considered.} In this paper, we consider { both couplings between the fermion and the scalars with $F_1=\chi^m\phi^{2p+1}$ and $F_2=\chi^n\phi^{2q}$}, and investigate the localization and spectrum structure of the fermion on the Bloch brane. { It is found that the} left-handed fermion zero mode can be localized on the Bloch brane under some conditions, and the effective potentials have { rich} structure and may be volcano-like, finite square well-like, and infinite potentials. As a result, the spectrum { consists of} a series of resonant Kaluza-Klein fermions, finite or infinite numbers of bound Kaluza-Klein fermions. { Especially, we find a new feature of the introduction of both couplings: the spectrum for the case of finite square well-like potentials contains discrete quasi-localized and localized massive KK modes simultaneously.Comment: 22 pages, 13 figure

MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data

Knowledge Graph (KG) contains entities and the relations between entities. Due to its representation ability, KG has been successfully applied to support many medical/healthcare tasks. However, in the medical domain, knowledge holds under certain conditions. For example, symptom \emph{runny nose} highly indicates the existence of disease \emph{whooping cough} when the patient is a baby rather than the people at other ages. Such conditions for medical knowledge are crucial for decision-making in various medical applications, which is missing in existing medical KGs. In this paper, we aim to discovery medical knowledge conditions from texts to enrich KGs. Electronic Medical Records (EMRs) are systematized collection of clinical data and contain detailed information about patients, thus EMRs can be a good resource to discover medical knowledge conditions. Unfortunately, the amount of available EMRs is limited due to reasons such as regularization. Meanwhile, a large amount of medical question answering (QA) data is available, which can greatly help the studied task. However, the quality of medical QA data is quite diverse, which may degrade the quality of the discovered medical knowledge conditions. In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation. We conduct series of experiments on real-world medical datasets to demonstrate that the proposed method can discover meaningful and accurate conditions for medical knowledge by leveraging both EMR and QA data. Further, the proposed method is tested on synthetic datasets to validate its effectiveness under various scenarios.Comment: Accepted as CIKM2019 long pape

PUEPro : A Computational Pipeline for Prediction of Urine Excretory Proteins

This work is supported by the National Natural Science Foundation of China (Grant Nos. 81320108025, 61402194, 61572227), Development Project of Jilin Province of China (20140101180JC) and China Postdoctoral Science Foundation (2014T70291).Postprin