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.     

Scalable parallel implementation of migrating birds optimization for the multi-objective task allocation problem

The Journal of Supercomputing - As the distributed computing systems have been widely used in many research and industrial areas, the problem of allocating tasks to available processors in the...     

Investigation of the relationship between intergranular corrosion and retrogression and reaging in the AA6063

AA6063 was heat treated with different retrogression temperatures and durations, and the effect of heat treatment conditions on the microstructure, hardness, electrical conductivity, intergranular corrosion (IGC) and electrochemical corrosion behaviours of the AA6063 was determined compared with the T6 condition. The IGC test was applied according to the BS EN ISO 11846: 2008 standard. Moreover, potentiodynamic polarization tests were applied to determine the electrochemical corrosion behaviour of the heat‐treated samples. Electrochemical corrosion tests were carried out by using a Ivium Compactstat potentiostat in 3.5 wt.%. NaCl solution at 24°C with a scanning rate of 0.5 mV/s. The corrosion test cell consisted of the reference electrode (Ag/AgCl), working electrode (test sample) and a reference electrode (platinum). The effect of IGC on the microstructure of AA6063 and corrosion depth values was investigated by using a stereo optical microscope and a light metal microscope, respectively. Corrosion depth examinations were performed on microstructures taken from the cross‐sections of the samples. The chemistry of the precipitates formed at grain boundaries and distribution of the precipitates in the microstructure were investigated by scanning electron microscope, energy dispersive X‐ray and transmission electron microscope analyses. The results showed that retrogression and reaging heat treatment improves both the corrosion resistance and the mechanical properties of AA6063. After 50°C/15 min RRA heat treatment, the highest corrosion resistance and a higher hardness value than the T6 level were obtained.     

The Water-Soluble Peripheral Substituted Phthalocyanines as Corrosion Inhibitors for Copper in 0.1 N HCl: Gravimetric,Electrochemical, SEM-EDS,and Quantum Chemical Calculations

Protection of Metals and Physical Chemistry of Surfaces - Corrosion inhibition effects of water-soluble peripheral substituted cobalt, copper and zinc metallophthalocyanines (CoPc (1), CuPc (2) and...     

Dreissenid colonization during the initial invasion of the quagga mussel in the largest Central European shallow lake,Lake Balaton,Hungary

The colonization progress of the invasive bivalve dreissenids, the formerly dominant Dreissena polymorpha and the recently (2008) introduced Dreissena rostriformis bugensis was studied between 2009 and 2013 in the largest Central European shallow lake, Lake Balaton, Hungary. The density of dreissenid planktonic veligers, new settlers (post-veligers and early juveniles), and the population structure (density, length frequency, relative abundance) of the two species were monitored on experimentally introduced natural stone substrata, on different time scales. Dreissenids started dynamic settling following a sudden veliger bloom. As substratum saturation progressed, competition between species for places was suggested, which, after two years, led to an increased number of large individuals (> 20 mm) and also recruits of D. r. bugensis. By contrast, the population of D. polymorpha was confined to middle size (11–18 mm) individuals of the first settler generation. On local substrata, where the benthic community was already established, the replacement of D. polymorpha by D. r. bugensis took longer, but it happened in a similar way. The invasion speed of D. r. bugensis in Lake Balaton resembled the speed obtained in other European water bodies where D. r. bugensis, similar to Lake Balaton, was introduced much later than D. polymorpha. However, a longer replacement process was found in North America, where both species invaded new habitats at the same time. This suggests that the speed, and probably the success, of D. r. bugensis invasion depends on new surface availability, and whether the two dreissenid species are introduced together or at different times.     

Ensemble cluster pruning via convex-concave programming

Ensemble learning is the process of aggregating the decisions of different learners/models. Fundamentally, the performance of the ensemble relies on the degree of accuracy in individual learner predictions and the degree of diversity among the learners. The trade-off between accuracy and diversity within the ensemble needs to be optimized to provide the best grouping of learners as it relates to their performance. In this optimization theory article, we propose a novel ensemble selection algorithm which, focusing specifically on clustering problems, selects the optimal subset of the ensemble that has both accurate and diverse models. Those ensemble selection algorithms work for a given number of the best learners within the subset prior to their selection. The cardinality of a subset of the ensemble changes the prediction accuracy. The proposed algorithm in this study determines both the number of best learners and also the best ones. We compared our prediction results to recent ensemble clustering selection algorithms by the number of cardinalities and best predictions, finding better and approximated results to the optimum solutions.