Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

Dialogic weaving: a favorable tension between design and craft

Focusing on a process where a designer embeds herself in craftspeople’s workshops with the intention of ‘learning from inside’ and ‘making together’; this paper proposes pathways for considering collaboration between designers and craftspeople via digital fabrication and weaving. The tension of forces that make weaving possible, act as an analogy for this collaboration as well as providing the title of the project as ‘One Over, One Under’. The project proposes a mode of working and a spectrum of outcomes where the designer has a first-hand experience of production techniques, engages in a serious and continuous dialogue through making, and develops an intervention that also brings forth her own skill set (introducing digital fabrication and structural variations), thus transforming the conventions and boundaries between established roles and manufacturing techniques. An experiment that resulted in a series of objects considering both the technologies of production and the input of the designer, this process not only increases the potentials that crafts hold for the field of design, but it also offers possibilities of collaboration and a further articulation of the design act.     

Input-to-state learning of recurrent neural networks with delay and disturbance

This article deals with the issue of input-to-state stabilization for recurrent neural networks with delay and external disturbance. The goal is to design a suitable weight-learning law to make the considered network input-to-state stable with a predefined -gain. Based on the solution of linear matrix inequalities, two schemes for the desired learning law are presented via using decay-rate-dependent and decay-rate-independent Lyapunov functionals, respectively. It is shown that, in the absence of external disturbance, the proposed learning law also guarantees the exponential stability of the network. To illustrate the applicability of the present weight-learning law, two numerical examples with simulations are given.     

Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain

Recently, the applications of Blockchain technology have begun to revolutionise different aspects of supply chain (SC) management. Among others, Blockchain is a platform to execute the smart contracts in the SC as transactions. We develop and test a new model for smart contract design in the SC with multiple logistics service providers and show that this problem can be presented as a multi-processor flexible flow shop scheduling. A distinctive feature of our approach is that the execution of physical operations is modelled inside the start and completion of cyber information services. We name this modelling concept ‘virtual operation’. The constructed model and the developed experimental environment constitute an event-driven dynamic approach to task and service composition when designing the smart contract. Our approach is also of value when considering the contract execution stage. The use of state control variables in our model allows for operations status updates in the Blockchain that in turn, feeds automated information feedbacks, disruption detection and control of contract execution. The latter launches the re-scheduling procedure, comprehensively combining planning and adaptation decisions within a unified methodological framework of dynamic control theory. The modelling complex developed can be used to design and control smart contracts in the SC.     

The analysis for morphological evolution and crystallography of degenerate pearlite in 100Mn13 steel

During approximate 773 K aging treatment of 100Mn13 steel, degenerate pearlite will occur and evolve into lamellar pearlite during growth process. The microstructures of degenerate pearlite and its evolutionary lamellar pearlite are observed by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The results show that after 748 K, 773 K and 798 K aging, degenerate pearlites occur at grain boundary. At growth front of degenerate pearlite forming at 773 K and 798 K, pearlite presents a morphology of short lamellae of carbide and ferrite, indicating a trend of developing into lamellar pearlite. The higher the temperature is, the more obvious the trend is, and even a conventional lamellar pearlite has developed. However, there is no morphological evolution for degenerate pearlite forming at 748 K aging. Besides, the constituents of degenerate pearlite is identified as M₂₃C₆ and ferrite, and Kurdjumov-Sachs orientation relationship exists between them, (01 )_α//( 1 )_M23C6, [111]_α//[110]_M23C6. This orientation relationship maintains in morphological evolution from degenerate pearlite to lamellar pearlite.     

沿对角线方向倾倒爆破拆除楼房

在楼房爆破拆除中,根据待拆楼房的围围环境,采用了沿楼房对角线方向定向倾倒拆除的方案,并取得了成功。文中介绍了爆破方案、布孔方法及具体的爆破参数。