Distributed and Deep Vertical Federated Learning with Big Data

In recent years, data are typically distributed in multiple organizations while the data security is becoming increasingly important. Federated Learning (FL), which enables multiple parties to collaboratively train a model without exchanging the raw data, has attracted more and more attention. Based on the distribution of data, FL can be realized in three scenarios, i.e., horizontal, vertical, and hybrid. In this paper, we propose to combine distributed machine learning techniques with Vertical FL and propose a Distributed Vertical Federated Learning (DVFL) approach. The DVFL approach exploits a fully distributed architecture within each party in order to accelerate the training process. In addition, we exploit Homomorphic Encryption (HE) to protect the data against honest-but-curious participants. We conduct extensive experimentation in a large-scale cluster environment and a cloud environment in order to show the efficiency and scalability of our proposed approach. The experiments demonstrate the good scalability of our approach and the significant efficiency advantage (up to 6.8 times with a single server and 15.1 times with multiple servers in terms of the training time) compared with baseline frameworks.Comment: To appear in CCPE (Concurrency and Computation: Practice and Experience

Research of Interindividual Differences in Physiological Response under Hot-Dry and Warm-Wet Climates

Somatotype and habitus parameters may affect physiological control system, so the changes of physiological parameters are not the same when various people work in hot-dry and warm-wet climates. In this paper, a chamber built in Tianjin University was used to simulate comfortable, hot-dry and warm-wet climates. Sixty healthy university students were selected as subjects who were divided into four groups based on somatotype and habitus differences. The subjects were asked to exercise on a treadmill at moderate and heavy work intensities. Physiological parameters (rectal temperature and heart rate) were measured after every 10-min work in the climate chamber. For different groups, the change trends of physiological parameters were different. With the enhancement of experimental conditions, the differences among four groups were weakened. Body surface area per unit of body mass (BSA/mass), percentage of body fat (%fat), and maximum oxygen consumption per unit of body mass (VO2max/mass) were adopt to establish a revised body characteristic index (RBCI). RBCI was proved having significant correlation with physiological parameters, which means RBCI as the combined factors of somatotype and habitus parameters can be applied to evaluate the effect of individual characteristics on physiological systems

Synthesis and Optimization of Cs₂B′B″X₆ Double Perovskite for Efficient and Sustainable Solar Cells

Hybrid perovskite materials with high light absorption coefficients, long diffusion lengths, and high mobility have attracted much attention, but their commercial development has been seriously hindered by two major problems: instability and lead toxicity. This has led to lead-free halide double perovskite becoming a prominent competitor in the photovoltaic field. For lead-free double perovskites, Pb2+ can be heterovalent, substituted by non-toxic metal cations as a double perovskite structure, which promotes the flexibility of the composition. However, the four component elements and low solubility in the solvent result in synthesis difficulties and phase impurity problems. And material phase purity and film quality are closely related to the number of defects, which can limit the photoelectric performance of solar cells. Therefore, based on this point, we summarize the synthesis methods of Cs2B′B″X6 double perovskite crystals and thin films. Moreover, in the application of solar cells, the existing research mainly focuses on the formation process of thin films, band gap adjustment, and surface engineering to improve the quality of films and optimize the performance of devices. Finally, we propose that Cs2B′B″X6 lead-free perovskites offer a promising pathway toward developing highly efficient and stable perovskite solar cells

A New Decision Framework of Online Multi-Attribute Reverse Auctions for Green Supplier Selection under Mixed Uncertainty

In order to realize the “dual carbon” goal proposed for the world and to seek the low-carbon and sustainable development of the economy and society, the green supply chain management problem faced by Chinese enterprises and governments is particularly important. At the same time, how to quickly and efficiently select the suitable green supplier is the most basic and critical link in green supply chain management, as well as an important issue that Chinese government and enterprises must face in the process of green material procurement. In addition, there are various uncertainties emerging in the current market environment that hinder the green suppliers and the buyer to make the efficient decisions. Therefore, in order to find a more suitable and efficient method for green supplier selection, from the standpoint of the buyer, a new decision framework of online multi-sourcing, multi-attribute reverse auction (OMSMARA), which effectively improves the procurement efficiency and reduces procurement costs and risks, is proposed under the mixed uncertainty. Specifically, the main innovation work includes three aspects: Firstly, the trapezoidal fuzzy numbers are applied to describe the uncertain bidding attribute values by the green suppliers. Secondly, the hesitant fuzzy sets theory is introduced to characterize the buyer’s satisfaction degrees of the bidding evaluation attribute information, and the attribute weights are determined by using the hesitant fuzzy maximizing deviation method. Thirdly, a fuzzy multi-objective mixed integer programming model is proposed to solve the green supplier selection and quantity allocation question in OMSMARA. A numerical example is given to demonstrate the feasibility and effectiveness of the proposed decision framework, and the sensitivity analysis and comparison analysis further show the robustness and reliability of the proposed solution method

A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams

Quickly detecting related primitive events for multiple complex events from massive event stream usually faces with a great challenge due to their single pattern characteristic of the existing complex event detection methods. Aiming to solve the problem, a multiple pattern complex event detection scheme based on decomposition and merge sharing is proposed in this article. The achievement of this article lies that we successfully use decomposition and merge sharing technology to realize the high-efficient detection for multiple complex events from massive event streams. Specially, in our scheme, we first use decomposition sharing technology to decompose pattern expressions into multiple subexpressions, which can provide many sharing opportunities for subexpressions. We then use merge sharing technology to construct a multiple pattern complex events by merging sharing all the same prefix, suffix, or subpattern into one based on the above decomposition results. As a result, our proposed detection method in this article can effectively solve the above problem. The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.Published versio

Hepatoprotective Effects of Nicotiflorin from Nymphaea candida against Concanavalin A-Induced and D-Galactosamine-Induced Liver Injury in Mice

Nymphaea candida was used to treat hepatitis in Ugyhur medicine, and nicotiflorin (kaempferol 3-O-β-rutinoside) is the main characteristic component in this plant. In this study, The the hepatoprotective activities of nicotiflorin from N. candida were investigated by Concanavalin A (Con A, 20 mg/kg bw)- and d-Galactosamine (d-GalN, 800 mg/kg bw)-induced acute liver injury in mice. Pretreatment with nicotiflorin (25, 50, 100 mg/kg bw/day, p.o.) for ten days significantly reduced the impact of Con A toxicity (20 mg/kg bw) on the serum markers of liver injury, aspartate aminotransferase (AST), and alanine aminotransferase (ALT). The hepatic anti-oxidant parameters (malondialdehyde, MDA; superoxide dismutase, SOD; glutathione, GSH; and nitric oxide, NO) in mice with nicotiflorin treatment were significantly antagonized for the pro-oxidant effects of Con A. Moreover, pretreatment with nicotiflorin (100 mg/kg bw) significantly decreased Con A-induced elevation in the serum levels of pro-inflammatory cytokines interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and interferon-γ (IFN-γ) (p < 0.05). A protective effect was reconfirmed against d-GalN-induced chemical liver injury, elevated serum enzymatic and cytokines levels were significantly decreased by nicotiflorin, and liver homogenate antioxidant indicators were significantly restored toward normal levels. Both histopathological studies also supported the protective effects of nicotiflorin. Therefore, the presented results suggest that nicotiflorin is the potent hepatoprotective agent that could protect the liver against acute immunological and chemical injury; this ability might be attributed to its antioxidant and immunoregulation potential