深部松软煤层钻孔孔周煤体变形产渣特征研究

针对煤矿井下高瓦斯软煤顺层长钻孔排渣困难、成孔率低、施工困难等问题,通过数值模拟实验研究了井下深部软煤体变形破坏特征,分析了顺层长钻孔孔周松软煤体变形特征及应力变化,以揭示顺层长钻孔孔周松软煤体变形产渣规律。研究表明:深部高瓦斯软煤顺层钻孔孔周煤体的应力平衡临界条件破坏后将发生大体积突然垮落;钻孔水平最大变形位移为1.22mm,垂直方向最大变形位移为10.7mm;径向孔周煤体垂向变形呈现逐渐减小趋势,且垂向变形明显大于钻孔水平变形。在水平方向上,钻孔孔周煤体应力分布呈现先增大再逐渐减小的变化规律,径向距离对水平应力分布的影响逐渐减小;随着径向距离的增加,钻孔孔周煤体应力分布逐渐降低,钻孔孔壁处煤体的应力出现最大值,且垂直方向处应力值最大。     

Effect of temperature on raw whole milk density and its potential impact on milk payment in the dairy industry

The objective of this study was to determine the effect of temperature on whole milk density measured at four different temperatures: 5, 10, 15, and 20 °C. A total of ninety-three individual milk samples were collected from morning milking of thirty-two Holstein Friesian dairy cows, of national average genetic merit, once every two weeks over a period of 4 weeks and were assessed by Fourier transform infrared spectroscopy for milk composition analysis. Density of the milk was evaluated using two different analytical methods: a portable density meter DMA35 and a standard desktop model DMA4500M (Anton Paar GmbH, UK). Milk density was analysed with a linear mixed model with the fixed effects of sampling period, temperature and analysis method; triple interaction of sampling period x analysis method x temperature; and the random effect of cow to account for repeated measures. The effect of temperature on milk density (ρ) was also evaluated including temperature (t) as covariate with linear and quadratic effects within each analytic method. The regression equation describing the curvature and density–temperature relationship for the DMA35 instrument was ρ = 1.0338−0.00017T−0.0000122T² (R² = 0.64), while it was ρ = 1.0334 + 0.000057T−0.00001T² (R² = 0.61) for DMA4500 instrument. The mean density determined with DMA4500 at 5 °C was 1.0334 g cm⁻³, with corresponding figures of 1.0330, 1.0320 and 1.0305 g cm⁻³ at 10, 15 and 20 °C, respectively. The milk density values obtained in this study at specific temperatures will help to address any bias in weight–volume calculations and thus may also improve the financial and operational control for the dairy processors in Ireland and internationally.     

Transverse magnetoresistance peculiarities of thermoelectric Lu-doped Bi2Te3 compound due to strong electrical disorder

The n-type thermoelectric Bi_1.9Lu_0.1Te₃ was prepared by microwave-solvothermal method and spark plasma sintering. The magnetic field and temperature dependences of transverse magnetoresistance measured within temperature 2–200 K interval allow finding the peculiarities characteristic for strongly disordered and inhomogeneous semiconductors. The first peculiarity is due to appearance of linear-in-magnetic field contribution to the total magnetoresistance reflected in a crossover from quadratic magnetoresistance at low magnetic fields to linear magnetoresistance at high magnetic fields. The linear magnetoresistance can result from the Hall resistance picked up from macroscopically distorted current paths due to local variations in stoichiometry of the compound studied. The second peculiarity is that both linear magnetoresistance magnitude and crossover field are functions of carrier mobility which is in agreement with the Parish and Littlewood model developed for disordered and inhomogeneous semiconductors. An increase in the mobility due to a decrease in temperature is accompanied by an increase in the magnetoresistance magnitude and a decrease in the crossover field. Finally, the third peculiarity is related to the remarkable deviation of the total magnetoresistance measured at various temperatures from the Kohler's rule. Presence of strong inhomogeneity and disorder in the Bi_1.9Lu_0.1Te₃ structure concluded from the magnetoresistance peculiarities can be responsible for the remarkable reduction in the total thermal conductivity of this compound.     

Industry 4.0: a supply chain innovation perspective

The Fourth Industrial Revolution – also known as Industry 4.0 (i4.0) – comprises the digitalisation of the industrial sector. This paper uses the theoretical lens of supply chain innovation (SCI) to investigate the implications of i4.0 on supply chain management. For these purposes, the method of structured content analysis is applied to more than 200 use cases of i4.0-enabled SCI introduced by both established and startup companies. i4.0-enabled SCI manifests along three dimensions: process, technology, and business architecture. The key findings of this study can be summarised as follows: first, i4.0-enabled SCI extends the initial focus on productivity improvements in SC processes towards scalability and flexibility. Second, extant i4.0 solutions rely mostly on analytics and smart things while omitting smart people technology and the human-centric approach associated with the i4.0 paradigm. Third, established companies adopt i4.0 merely to sustain their existing business architectures while startup companies radically change their operating models, relying heavily on data analytics and the platform economy. Consequently, established companies pursue a problem-driven, engineering-based approach to SCI while startup companies follow an ‘asset-light’, business-driven approach. Lastly, there are two distinct approaches to digitalising operational SC processes: platform-based crowdsourcing of standard processes and on-demand provision of customised services.     

大规模多状态计算系统的容错性能分析

针对异构计算节点组成的大规模多状态计算系统的容错性能分析问题，提出了一种计算系统容错性能的评估方法。该方法采用自定义的两级容错性能形式化描述框架进行系统描述，通过构造多值决策图（Multi-value Decision Diagram，MDD）模型对系统进行容错性能建模，并基于构造的模型高效地计算出部件故障的条件下计算系统在特定性能水平上运行的概率，减少了计算的冗余性。实验结果表明，该方法在模型的大小和构建时间上均优于传统方法。该方法的提出将对系统操作员或程序设计者具有重要意义，使其确保系统适合预期应用。     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Exploring design trade-offs for achieving social inclusion in multi-tiered design problems

ABSTRACT

The digital age of the future is ‘not out there to be discovered’, but it needs to be ‘designed’. The design challenge has to address questions about how we want to live, work, and learn (as individuals and as communities) and what we value and appreciate, e.g.: reflecting on quality of life and creating inclusive societies. An overriding design trade-off for the digital age is whether new developments will increase the digital divide or will create more inclusive societies. Sustaining inclusive societies means allowing people of all ages and all abilities to exploit information technologies for personally meaningful activities. Meta-design fosters the design of socio-technical environments that end-user developers can modify and evolve at use time to improve their quality of life and favour their inclusion in the society. This paper describes three case studies in the domain of assistive technologies in which end users themselves cannot act as end-user developers, but someone else (e.g.: a caregiver or a clinician) must accept this role requiring multi-tiered architectures. The design trade-offs and requirements for meta-design identified in the context of the case studies and other researchers’ projects are described to inform the development of future socio-technical environments focused on social inclusion.     

Off‐grid DoA estimation method of wideband signals

In this paper, an off‐grid direction of arrival (DoA) estimation method is proposed for wideband signals. This method is based on the sparse representation (SR) of the array covariance matrix. Similar to the time domain DoA estimation methods, the correlation function of the sources was assumed to be the same and known. A new measurement vector is obtained using the lower‐left triangular elements of the covariance matrix. The DoAs are estimated by quantizing the entire range of continuous angle space into discrete grid points. However, the exact DoAs may be located between two grid points; therefore, this estimation has errors. The accuracy of DoA estimation is improved by the minimization of the difference between the new measurement vector and its estimated values. Simulation results revealed that the proposed method can enhance the DoA estimation accuracy of wideband signals.     

基于物联网的智慧林业可持续发展策略研究

随着我国科技的不断进步,在“互联网+”大数据的背景下,林业的智慧化发展是必经之路。虽然我国智慧林业的发展已经取得了一定的突破,但是仍然存在着许多问题。基于此,文章在物联网技术的基础上,对智慧林业的可持续发展策略进行探讨。