Energy conservation is one of the main problems in a wireless sensor network (WSN). Compared with a single cluster head (CH), a dual CH optimization was proposed for less energy consumption by the WSN and an acquisition delay by the mobile sink (MS). Firstly, a fuzzy c-means clustering algorithm and a multi-objective particle swarm optimization were utilized for the determinations of the first and second CHs. Following that, the ideal trajectory of MS was assessed using the improved ant colony algorithm. Finally, the lifetimes, the death rounds of the first node and the 50% node, and the number of packets received at the base station were compared among the proposed approach. Moreover, five algorithms were compared to validate the optimization, and the improved trajectory was compared with the original one as well. It was found that, for 100 nodes, the number of dead rounds from the proposal increased by 7.9%, 22.9%, 25.1%, 61%, and 74.4% for the first node, and that of the 50% nodes increased by 27.8%, 34.2%, 98.3%, 213.1%, and 211.2%, respectively. The base station packet reception increased by about 19.3%, 53.5%, 27%, 86.8%, and 181.2%, respectively. The trajectory of MS could also decrease by about 10%.

Fin-and-flat tube heat exchangers are widely applied to construction vehicles due to native advantages. An elementary unit out of such one was numerically compared with the corresponding experimental validation to secure accuracy, then, saw-tooth vortex generators were introduced to the rear of the tube, and further analyzed under the same configuration. JF factors from two models were utilized to confirm the initial expectation for a higher comprehensive performance, which also encouraged the following L18(3⁶) orthogonal test on wing width ( w_w), wing height ( s_w), blade height ( s_b), flow attack angle ( G_f), installation angle ( G_i), and saw-tooth number ( N_s) for signal-to-noise ratio and contribution rate (CR). The results stated that the numerical implementation could be capable of the following analyses with the maximum errors of 5.00% for heat transfer coefficient and 5.33% for pressure loss, and also corroborate performance enhancement with a JF increment of 29.9% in the following comparison. The CRs of G_i, N_s, s_w, w_w, G_f, and s_b are 30.39%, 19.61%, 15.69%, 13.73%, 10.78%, and 9.80%, respectively.

This paper works on the dynamic characteristics of implement mechanism of mining excavator. The dynamic model of the working implement is established based on the application of Lagrange's equation. Digging resistant force, crowd force and ore gravity were discussed in detail. A full working cycle is simulated by using Matlab/Simulink. The results show the validity of the established dynamic model and the importance of the dynamic analysis for the mining excavator.

The work numerically predicted a Fin-and-flat tube heat exchanger with an experimental validation. The unit was firstly redefined as five basic modules. Based on L9(3⁴) orthogonal tests involving vertical fin spacing, horizontal tube spacing, fin length, and fin thickness, the formed overdetermined equations were solved and discussed by regression and F significance test. After a derivation, the general correlation was compared with published, fitted, and numerical results. Finally, its applicable range was numerically clarified. It is found that the maximum 3.62% error of heat transfer coefficient and 4.51% error of pressure loss, respectively. The fitted correlation can predict over 88.9% of j under 3.10% average error and 96.30% of f under 2.33%. 2.6-4.0 mm vertical spacing, 16-100 mm fin length, 5-8 mm horizontal spacing, 0.03-0.09 mm fin thickness, 3-4.4 mm hydraulic diameter, 1-5 tube rows, and Reynolds number of 550-3290 might work for the efficient deployment of the general correlation.

A New Probability Prediction method is proposed for the key subsystems of Machining Center (MC). Firstly, the Failure Mode Effects and Criticality Analysis (FMECA) method is used for finding out the key subsystems which affect the Machining Center reliability most seriously. Then, the empirical models of key subsystems and the Machining Center are built based on the fault time. Under the assumption that the MC is a Series System, the posterior probability of the key subsystems is obtained by Bayesian th eory. The case example shows that the fault probability of key subsystems is changing as the MC fault probability changes. At a given time t, the fault probability of key subsystems can be calculated by the empirical models of key subsystems and the MC.