Converting detailed estimates to primary estimates with data augmentation

In general, preliminary or primary cost estimates are used to select contractors from among bidders in Japan. The primary cost estimate must be accurate, otherwise the contractor selected from the bidding process will lose profit. A general contractor in the world does not have a super-skilled engineer who can achieve the accurate primary cost estimates. The conventional primary estimate has a high error range and low reliability. An automated system converting detailed estimates to primary estimates has been highly demanded in the world. This paper presents a prototype AI converter that can accurately and automatically convert detailed cost estimates into primary estimates. Converting detailed cost estimates to primary estimates lies in a regression problem. This paper proposes a feature-elimination based data augmentation method for regression problems. The empirical experiment shows that the proposed data augmentation method is quite effective with an Extra-Trees ensemble method. The proposed method was empirically examined by using Colorado Department of Transportation (CDOT) dataset for accurately predicting constructions costs with the Extra-Trees algorithm and random forest algorithm respectively. The CDOT dataset is one and only one of the largest datasets available in public for constructions costs quotation/estimation of roads, bridges and buildings.     

The impact of recrystallisation on the freeze-thaw cycles of red seabream (Pagrus major) fillets

Recrystallisation often occurs in the freeze-thaw cycles and results in poor food quality. This study was undertaken to analyse the effects of recrystallisation on the changes of protein conformation and moisture migration within red seabream fillets after the freeze-thaw cycles. The UV second-derivative spectroscopy, intrinsic fluorescence measurement and Raman spectroscopy were employed to evaluate the protein conformational changes. The Low-field NMR spectra analysis was used to evaluate moisture migration. The results indicated that the freeze-thaw cycles altered the secondary and tertiary structure of proteins. It was noticed that interactions between water and protein molecules were decreased gradually and eventually induced the thermal instability of the myosin molecules. The immobilised water content declined and the free water content increased. There were significant destructions in fillet muscle microstructure. These observations unambiguously reflect how the recrystallisations affect the protein conformation and moisture migration patterns of red seabream fillets during freeze-thaw cycles.     

Explicit and implicit measures of emotions: Data-science might help to account for data complexity and heterogeneity

Measuring emotions is a real challenge for fundamental and applied research, especially in ecological contexts. de Wijk and Noldus propose combining two types of measures - explicit to characterize a specific food, and implicit -physiological- to capture the whole experience of a meal in real-life situations. This raises several challenges including development of new and miniaturized sensors and devices but also developing new ways of data analysis. We suggest a path to follow for future studies regarding data analysis: to include Data Science in the game. This field of research may enable developing predictive but also explicative models that link subjective experience of emotions and physiological responses in real-life contexts. We suggest that food scientists should go out of their comfort zone by collaborating with computer scientists and then be trained with the new tools of Data Science, which will undoubtedly enable them 1/ to better manage complex and heterogeneous data sets, 2/ to extract knowledge that will be essential to this field of research.     

坚硬顶板切顶卸压围岩变形规律研究

为了进一步揭示坚硬顶板超前切顶卸压轨道下山的变形规律和保安煤柱的应力分布特征,以潞宁煤矿22115工作面开采为工程背景,利用相似材料模拟和现场实验,分析了坚硬顶板切顶轨道下山变形规律。结果表明:切顶可使采空区坚硬顶板在剪应力作用下沿着预制裂隙垮落,缩短悬顶长度,切断应力传递途径,有效缓解了保安煤柱承担的载荷。现场实践效果较好,巷道顶底板移近量约为320 mm,两帮移近量约为245 mm;切顶卸压技术是高应力坚硬顶板大变形巷道围岩控制有效途径之一。     

JRF33铁路车号识别装置在矿车计数系统的应用

针对采厂矿车翻卸人工计数可靠性低、安全风险大等问题，提出一种矿车翻卸自动计数系统设计方案，以JRF33铁路车号识别装置为载体，以RS485为通讯协议，对采集数据进行统计分析。结果显示，计数系统运行良好，翻卸数据采集及时、准确、完整。系统对采矿原料运输数据统计、分析应用和生产指导有一定的指导意义。     

A Novel 1,8-Naphthyridine-2-Carboxamide Derivative Attenuates Inflammatory Responses and Cell Migration in LPS-Treated BV2 Cells via the Suppression of ROS Generation and TLR4/Myd88/NF-κB Signaling Pathway

Novel 1,8-naphthyridine-2-carboxamide derivatives with various substituents (HSR2101-HSR2113) were synthesized and evaluated for their effects on the production of pro-inflammatory mediators and cell migration in lipopolysaccharide (LPS)-treated BV2 microglial cells. Among the tested compounds, HSR2104 exhibited the most potent inhibitory effects on the LPS-stimulated production of inflammatory mediators, including nitric oxide (NO), tumor necrosis factor-α, and interleukin-6. Therefore, this compound was chosen for further investigation. We found that HSR2104 attenuated levels of inducible NO synthase and cyclooxygenase 2 in LPS-treated BV2 cells. In addition, it markedly suppressed LPS-induced cell migration as well as the generation of intracellular reactive oxygen species (ROS). Moreover, HSR2104 abated the LPS-triggered nuclear translocation of nuclear factor-κB (NF-κB) through inhibition of inhibitor kappa Bα phosphorylation. Furthermore, it reduced the expressions of Toll-like receptor 4 (TLR4) and myeloid differentiation factor 88 (MyD88) in LPS-treated BV2 cells. Similar results were observed with TAK242, a specific inhibitor of TLR4, suggesting that TLR4 is an upstream regulator of NF-κB signaling in BV2 cells. Collectively, our findings demonstrate that HSR2104 exhibits anti-inflammatory and anti-migratory activities in LPS-treated BV2 cells via the suppression of ROS and TLR4/MyD88/NF-κB signaling pathway. Based on our observations, HSR2104 may have a beneficial impact on inflammatory responses and microglial cell migration involved in the pathogenesis of various neurodegenerative disorders.     

Pre-stack elastic reverse time migration in tunnels based on cylindrical coordinates

Seismic forward-prospecting in tunnels is an important step to ensure excavation safety. Nowadays, most advanced imaging techniques in seismic exploration involve calculating the solution of elastic wave equation in a certain coordinate system. However, considering the cylindrical geometry of common tunnel body, Cartesian coordinate system seemingly has limited applicability in tunnel seismic forward-prospecting. To accurately simulate the seismic signal received in tunnels, previous imaging method using decoupled non-conversion elastic wave equation is extended from Cartesian coordinates to cylindrical coordinates. The proposed method preserves the general finite-difference time-domain (FDTD) scheme in Cartesian coordinates, except for a novel wavefield calculation strategy addressing the singularity issue inherited at the cylindrical axis. Moreover, the procedure of cylindrical elastic reverse time migration (CERTM) in tunnels is introduced based on the decoupled non-conversion elastic wavefield. Its imaging effect is further validated via numerical experiments on typical tunnel models. As indicated in the synthetic examples, both the PP- and SS-images could clearly show the geological structure in front of the tunnel face without obvious crosstalk artifacts. Migration imaging using PP-waves can present satisfactory results with higher resolution information supplemented by the SS-images. The potential of applying the proposed method in real-world cases is demonstrated in a water diversion tunnel. In the end, we share our insights regarding the singularity treatment and further improvement of the proposed method.     

A blockchain-based audit approach for encrypted data in federated learning

The development of data-driven artificial intelligence technology has given birth to a variety of big data applications. Data has become an essential factor to improve these applications. Federated learning, a privacy-preserving machine learning method, is proposed to leverage data from different data owners. It is typically used in conjunction with cryptographic methods, in which data owners train the global model by sharing encrypted model updates. However, data encryption makes it difficult to identify the quality of these model updates. Malicious data owners may launch attacks such as data poisoning and free-riding. To defend against such attacks, it is necessary to find an approach to audit encrypted model updates. In this paper, we propose a blockchain-based audit approach for encrypted gradients. It uses a behavior chain to record the encrypted gradients from data owners, and an audit chain to evaluate the gradients’ quality. Specifically, we propose a privacy-preserving homomorphic noise mechanism in which the noise of each gradient sums to zero after aggregation, ensuring the availability of aggregated gradient. In addition, we design a joint audit algorithm that can locate malicious data owners without decrypting individual gradients. Through security analysis and experimental evaluation, we demonstrate that our approach can defend against malicious gradient attacks in federated learning.     

Estimation of maximum available capacity of lithium-ion battery based on multi-view features extracted from reconstructed charging curve

A new method is developed in the paper to estimate the maximum available capacity which is an important basis for indicating the State of Health (SOH) of lithium-ion batteries. Firstly, a data reconstruction approach is proposed to pre-process the acquired data to suppress the influence of measurement noise and reduce the negative impact on estimation precision when measuring equipment adopts different sampling frequencies. Then, the variation trend of the incremental capacity curve obtained based on the reconstructed data with the battery aging is analyzed, and a health indicator (HI) including multi-view features is put forward to characterize the battery degradation more comprehensively. The multi-view features are coming from the capacity increment curve versus voltage and time, including the maximum value of the capacity increment curve, the voltage corresponding to the maximum value, other values surrounding the maximum value and so on. Finally, Support Vector Regression is used to establish a model between the extracted HI and the maximum available capacity, and two types of open source data are used to verify the performance. The experimental results show that the data reconstruction method and multi-view health indicator proposed in the paper can obtain high precision estimation results.     

A survey of outlier detection in high dimensional data streams

The rapid evolution of technology has led to the generation of high dimensional data streams in a wide range of fields, such as genomics, signal processing, and finance. The combination of the streaming scenario and high dimensionality is particularly challenging especially for the outlier detection task. This is due to the special characteristics of the data stream such as the concept drift, the limited time and space requirements, in addition to the impact of the well-known curse of dimensionality in high dimensional space. To the best of our knowledge, few studies have addressed these challenges simultaneously, and therefore detecting anomalies in this context requires a great deal of attention. The main objective of this work is to study the main approaches existing in the literature, to identify a set of comparison criteria, such as the computational cost and the interpretation of outliers, which will help us to reveal the different challenges and additional research directions associated with this problem. At the end of this study, we will draw up a summary report which summarizes the main limits identified and we will detail the different directions of research related to this issue in order to promote research for this community.