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Groundwater chemical evolution is the key to ensuring the sustainability of local society and economy development. In this study, four river sections and 59 groundwater wells are investigated in the Longgang River (L.R.) basin in South China. Comprehensive hydrochemical analysis methods are adopted to determine the dominant factors controlling the chemical evolution of the local phreatic groundwater and the potential impact of human activities on groundwater quality. The results indicate that the ionic composition of the local phreatic groundwater is dominated by Ca2+ (0.9–93.8 mg/L), HCO3− (4.4–280.0 mg/L), and SO42− (1.0–91.0 mg/L). Ca–Mg–HCO3, Ca–Na–HCO3, and Na–Ca–HCO3 are the major groundwater hydrochemical facies. Water–rock interactions, such as the dissolution of calcite and dolomite, are the primary source of the major ions in the local groundwater. Cation-exchange reaction has its effects on the contents of Ca2+, Mg2+, and Na+. Ammonia concentration of the sampling sections in the L.R. increases from 0.03 to 2.01 mg/L along the flow direction. Groundwater nitrate in the regions of the farmland is attributed to the lowest level of the groundwater quality standards of China, while the same test results are obtained for heavy metals in the industrial park and landfill, suggesting a negative impact of the anthropogenic activities on the local phreatic groundwater quality.

Manufacturing resources are shared and centrally managed on the cloud platform in cloud manufacturing, which is a new model of modern manufacturing. The production data are collected, which can be used to predict the manufacturing events. Based on those, disruption problems of scheduling should be researched from a new point of view. In this paper, new job arrivals were considered as the disruption event. The time of the occurrence of disruption was predictable in contrast to uncertainty. Alternative subcontractors chosen from the cloud platform were available for outsourcing with different processing prices and transporting distances. The objective of the original scheduling, the deviation between the new schedule and the old one, and the outsourcing cost were all considered. To express the problem, mathematical models and a three-field notation model were constructed. To solve the problem, a hybrid quantum-inspired chaotic group leader optimization algorithm was proposed, in which a hybrid encoding way was applied. To verify the algorithm, experiments were carried out. The results showed that the proposed algorithm performs well.

A large number of changeovers exist in multi-item and small lot size production mode, which lead to a long setup time and significantly influence productivity. However, setup time has received significantly less attention in previous studies regarding the single machine scheduling (SMS) problem. This paper investigates the SMS problem with sequence-dependent setup time (SDST). First, a model of SMS-SDST is developed to minimise the makespan. An earliest due date (EDD), SDST, and ant colony optimisation (ACO) heuristic algorithm is presented. In order to evaluate the performance of the proposed algorithm, Taguchi’s robust design method is employed to define the best parameter values. Related results are analysed by statistical tools. The computational experiments are compared with an ACO algorithm and a genetic algorithm. The experimental results and statistical analyses show that the proposed EDD-SDST-ACO is effective for the problem.

As the structure and technologies of modern equipment become more and more complicated and it is hard to improve equipment's after-sales maintenance service under traditional mode, in this text, network-based collaborative maintenance mode for after-sales equipment was proposed. Specifically, under the leading of equipment manufacturer under guaranty or equipment user outside guaranty, manufacturer, user, designers, maintainers, spare parts suppliers and maintenance experts were integrated to establish dynamic alliance to carry out maintenance operations together based on network. In addition, the business process of network-based collaborative maintenance was analyzed and an information economics model of network-based collaborative maintenance was established to demonstrate the effectiveness of this maintenance mode.