Preferential Engagement and What Can We Learn from Online Chess?

Minds and Machines - An online game of chess against a human opponent appears to be indistinguishable from a game against a machine: both happen on the screen. Yet, people prefer to play chess...     

Collective reflection strategy for moderating conformity tendency and promoting reflective judgment performance

Reflective judgement is crucial for medical-related practitioners in dealing with controversial issues. However, the conformity phenomenon is likely to occur and interfere with reflective judgement learning during interactive activities. Effective strategies are required to moderate the conformity behaviour tendency (CBT) and improve reflective judgement performance (RJP). This study demonstrates two significant results: (a) Compared with the guided self-reflection learning strategy, the online collective reflection (OCR) learning strategy effectively weakened the learners' general CBT while dealing with professional controversial issues; and (b) a significantly negative correlation between the RJP achieved and the change of CBT in online environment was detected in the OCR group. The implications and potential applications in higher education were discussed. Further studies are needed to confirm the long-term effects and the extending application to other professional studies.     

Complexities of digital technology use and the teaching and learning of function

Many argue that digital technologies have the potential to enhance the teaching and learning of mathematics. However, the availability of technology is not sufficient to realise this potential. The study reported takes a detailed approach to investigate the utility of the particular offerings of the available technologies in the teaching and learning of a specific area of mathematics, functions. Sixteen affordances identified in the data are described. The complexity of the process involved in resolving a situation where particular affordances would be useful so as they are perceived and enacted is detailed. Finally, a grounded theory framework arising from the data analysis from this study that can be used to explain, predict and guide action in other digital environments is presented.     

An investigation into the cause of loss of containment from the supply of mini-bulk lubricants

An Intermediate Bulk Container (IBC) was punctured during its handling, releasing oil onto soil at an environmentally-sensitive region of Australia. The telehandler did not pierce the plastic of the IBC directly (as was expected) but rather one of the tynes had caught on the underside of the metal base plate, despite numerous controls being in place at time of spill, revealing a previously unreported mechanism for a fluid spill from handling of petroleum hydrocarbons. The diverse investigation team used a root cause analysis (RCA) technique to identify the underlying cause: the inspection process was inadequate with contributing factors of not using a spotter and design of IBC did not anticipate conditions. Engineering controls were put in place as part of the change management process to help prevent spills from occurring from piercing from telehandler tynes on the current project site.     

Excited-state absorption of meso-tetrasulfonatophenyl porphyrin: Effects of pH and micelles

The influence of the environment on the excited state transitions of meso-tetrakis(p-sulfonatophenyl) porphyrin (TPPS) is reported. TPPS was investigated in protonated and non-protonated forms, and in the presence of the cationic cetyltrimethylammonium bromide (CTAB) micelles. The singlet excited-state absorption spectra were measured by using the white-light continuum Z-scan technique and the triplet–triplet absorption spectra were acquired employing an association of laser flash photolysis and Z-scan techniques. Our results show that the perseveration of the molecular symmetry, upon excitation, depends on the state of multiplicity of the molecules, as well as on the environment and structural characteristics of the porphyrin. Additionally, it was observed that for excited molecules, the ring distortion caused by the protonation of porphyrin ring has great influence on the changes observed for the symmetry and vibronic structure. The results clearly show that the porphyrin investigated is a promising candidate for optical limiting applications for all investigated environments.     

Crystallization and spectroscopic properties in Er3+ doped oxyfluorogermanate glass ceramics containing Na

The Er³⁺ doped oxyfluorogermanate glasses, with a composition containing Na element, were synthesized by the conventional melting–quenching technique. When Na element was introduced into the composition of oxyfluorogermanate glass, the crystals behavior was investigated in details. Depending on the annealing procedure supplied, thermal annealing of precursor glasses in the system GeO₂/BaF₂/AlF₃/Na₂O/NaF/ZnO/GdF₃/ErF₃ led to the precipitation of different crystal phase nanocrystals. It was confirmed the nanocrystals in GC600 is orthorhombic NaBaAlF₆ which led to enhance obviously in the UC luminescence of Er³⁺. However, the nanocrystals in G585 led to decrease in the UC luminescence, which indicated few Er ions enter into the lattice of this nanocrystal phase. The reason of the decrease in UC emission intensity of GC585 was analyzed.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

A steelmaking-continuous casting (SCC) scheduling problem is an example of complex hybrid flow shop scheduling problem (HFSSP) with a strong industrial background. This paper investigates the SCC scheduling problem that involves controllable processing times (CPT) with multiple objectives concerning the total waiting time, earliness/tardiness and adjusting cost. The SCC scheduling problem with CPT is seldom discussed in the existing literature. This study is motivated by the practical situation of a large integrated steel company in which the just-in-time (JIT) and cost-cutting production strategy have become a significant concern. To address this complex HFSSP, the scheduling problem is decomposed into two subproblems: a parallel machine scheduling problem (PMSP) in the last stage and an HFSSP in the upstream stages. First, a hybrid differential evolution (HDE) algorithm combined with a variable neighborhood decomposition search (VNDS) is proposed for the former subproblem. Second, an iterative backward list scheduling (IBLS) algorithm is presented to solve the latter subproblem. The effectiveness of this bi-layer optimization approach is verified by computational experiments on well-designed and real-world scheduling instances. This study provides a new perspective on modeling and solving practical SCC scheduling problems.     

Framework to manage multiple goals in community-based energy sharing network in smart grid

Smart grid has opened up a new role of “prosumer” in an energy value network, transforming many conventional energy consumers into prosumers, who not only generate green energy but also share the surplus with utilities and other consumers. The concept of a goal-oriented prosumer community group (PCG) has emerged recently as an effective way to fulfill sustainable energy exchange. Such community-based energy sharing networks comprise multiple irreconcilable objectives such as demand constraints, cost constraints, and income maximization. In many cases, one goal may be achievable only at the expense of other goals. This necessitates the development of an effective framework to manage the multiple goals and reduce the gap with their achievement levels. Therefore, in this research paper, an effective framework is developed to negotiate among the multiple goals and thus to define optimal mutual goals for each PCG in a more sustainable manner using multiple-criteria goal programming techniques. Simulation results are presented to illustrate how the methods work in practical situations, where each of the objective measure is given a target value and the unwanted deviations from this set are minimized in an achievement function.