Magnesium alloys have found widespread applications in diverse fields such as aerospace, automotive, bio-medical and electronics industries due to its relatively high strength-to-weight ratio. However, stress corrosion cracking of these alloys severely restricts their applications in several novel technologies. Hence, it will be useful to identify the corrosion mechanics of magnesium alloys under external stresses as it can provide further insights on design of these alloys for critical applications. In the present study, the corrosion mechanics of a commonly used magnesium alloy, AZ31, is studied using finite element simulation with a modified constitutive material damage model. The data obtained from the finite element modeling were further used to formulate a mathematical model using computational intelligence algorithm. Sensitivity and parametric analysis of the derived model further corroborated the mechanical response of the alloy in line with the corrosion physics. The proposed approach is anticipated to be useful for materials engineers for optimizing the design criteria for magnesium alloys catered for high temperature applications. Full article

Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT) production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T), cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA) is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multiobjective Particle Swarm Optimization (OMOPSO), and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems. Full article

Solitary pulmonary nodule (SPN or coin lesion) is a mass in the lung and can be commonly found in chest X-rays or computerized tomography (CT) scans. However, despite the advancement of imaging technologies, it is still difficult to distinguish malignant cancer from benign SPNs. Here we investigated the metabolic profiling of patients with benign and malignant pulmonary nodules. A combination of gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS) was used to profile the plasma metabolites in 17 patients with malignant SPNs, 15 patients with benign SPNs and 20 healthy controls. The metabolic profiles were assayed using OPLS-DA, and further analyzed to identify marker metabolites related to diseases. Both GC/MS- and LC/MS-derived models showed clear discriminations in metabolic profiles among three groups. It was found that 63 metabolites (12 from GC/MS, 51 from LC/MS) contributed to the differences. Of these, 48 metabolites showed same change trend in both malignant and benign SPNs as compared with healthy controls, indicating some common pathways including inflammation and oxidative injury shared by two diseases. In contrast, 14 metabolites constituted distinct profiles that differentiated malignant from benign SPNs, which might be a unique biochemical feature associated with lung cancer. Overall, our data suggested that integration of two highly sensitive and complementary metabolomics platforms could enable a comprehensive metabolic profiling and assist in discrimination malignant from benign SPNs. Full article