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How Practitioners Adapt to Clumsy Technology
Published in David D. Woods, Sidney Dekker, Richard Cook, Leila Johannesen, Nadine Sarter, Behind Human Error, 2017
David D. Woods, Sidney Dekker, Richard Cook, Leila Johannesen, Nadine Sarter
Studies have revealed several types of practitioner adaptation to the impact of new information technology. In system tailoring, practitioners adapt the device and context of activity to preserve existing strategies used to carry out tasks (e.g., adaptation focuses on the set-up of the device, device configuration, how the device is situated in the larger context). In task tailoring, practitioners adapt their strategies, especially cognitive and collaborative strategies, for carrying out tasks to accommodate constraints imposed by the new technology.
Bioavailability of selenium in soil-plant system and a regulatory approach
Published in Critical Reviews in Environmental Science and Technology, 2019
Quang Toan Dinh, Mengke Wang, Thi Anh Thu Tran, Fei Zhou, Dan Wang, Hui Zhai, Qin Peng, Mingyue Xue, Zekun Du, Gary S. Bañuelos, Zhi-Qing Lin, Dongli Liang
The ability to absorb Se from soil varies among plants (Terry et al., 2000). Based on the ability to accumulate Se, plant species can be classified into three groups, namely: Se non-accumulators (<100 mg·Se kg−1 DW), secondary Se accumulators (100–1000 mg Se kg−1 DW) and Se hyper-accumulators (>1000 mg Se kg−1 DW) (Schiavon & Pilon‐Smits, 2017b). When grown in the same environment, the tendency of Se accumulation in the plant shoots is in the following order: hyper-accumulators > secondary accumulators > non-accumulators (White et al., 2004; Bitterli, Bañuelos, & Schulin, 2010). Hyper-accumulators have the potential to accumulate Se far greater than the other species. Their Se concentrations are 10–1009 times higher, as well as higher Se: S ratios in tissue, which suggests that these species have a transporter with a priority for Se over S (White, Bowen, Marshall, & Broadley, 2007; Schiavon & Pilon‐Smits, 2017b). This super-accumulative mechanism may involve a long-term evolution that the species adapted to environmental conditions (White & Broadley, 2009; Schiavon & Pilon‐Smits, 2017b). In this regard, Se promotes growth and may protect hyper-accumulators against adverse conditions through enhanced antioxidant activity (Hartikainen, 2005). A higher concentration of Se may help plants protect themselves from herbivores (El Mehdawi & Pilon-Smits, 2012). Through the evolution of Se tolerance mechanisms, hyper-accumulators isolate Se in their epidermis, where excessive Se is kept away from sensitive metabolic process and helps prevent Se toxicity (Freeman, Zhang, Marcus, Fakra, & Pilon-Smits, 2006; Quinn et al., 2011).
A new predictive medical approach based on data mining and Symbiotic Organisms Search algorithm
Published in International Journal of Computers and Applications, 2022
Samia Noureddine, Baarir Zineeddine, Abida Toumi, Abir Betka, Aïcha-Nabila Benharkat
As the main objective of this work consists on proposing a new supervised clustering method for data mining using SOS metaheuristic, we present in the following some other metaheuristics which will be used for comparison. In the literature review, several specific techniques have been adopted for data mining, and during these last years the focus was on the metaheuristics, not only in the field of operational research, but especially in artificial intelligence. As an example, the Artificial Bees Colony (ABC) algorithm was proposed by Karaboga in 2005 to solve constrained optimization problems [34]. The cultural algorithm (CA) is based on the idea that the experiences of selected individuals are then added to the content of the belief space using an update function [35]. The Grey Wolf Optimization (GWO) is an intelligent approach proposed by Mirjalili et al in 2014. This approach is inspired from the hunting behavior and the social hierarchy of grey wolves [36]. The Chaotic Dynamic Weight Particle Swarm Optimization algorithm (CDWPSO) improves the efficiency of the PSO by using the idea of dynamic weight [37]. Genetic Algorithms (GA) are optimization algorithms based on the mechanisms of natural selection and genetics, and they were adapted to the optimization in [38]. Another metaheuristic called Differential Evolution algorithm (DE) is a branch of scalable programming developed by Rainer Storn and Kenneth Price for optimization problems on continuous domains [39]. Particle Swarm Optimization (PSO) is a stochastic optimization method for nonlinear functions based on the reproduction of social behavior [40]. Butterfly Optimization Algorithm (BOA) is, also, a novel metaheuristic approach presented in [41].