A two-stage optimization method is proposed for optimal distributed generation (DG) planning considering the integration of energy storage in this paper. The first stage determines the installation locations and the initial capacity of DGs using the well-known loss sensitivity factor (LSF) approach, and the second stage identifies the optimal installation capacities of DGs to maximize the investment benefits and system voltage stability and to minimize line losses. In the second stage, the multi-objective ant lion optimizer (MOALO) is first applied to obtain the Pareto-optimal solutions, and then the 'best' compromise solution is identified by calculating the priority memberships of each solution via grey relation projection (GRP) method, while finally, in order to address the uncertain outputs of DGs, energy storage devices are installed whose maximum outputs are determined with the use of chance-constrained programming. The test results on the PG&E 69-bus distribution system demonstrate that the proposed method is superior to other current state-of-the-art approaches, and that the integration of energy storage makes the DGs operate at their pre-designed rated capacities with the probability of at least 60% which is novel. Index Terms--active distribution network; distributed generation planning; two-stage optimization; energy storage; chance-constrained programming; multi-objective ant lion optimizer.

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a b s t r a c tThe member selection problem is an important aspect of the formation of cross-functional teams (CFTs). Selecting suitable members from a set of candidates will facilitate the successful task accomplishment. In the existing studies of member selection, the individual performance concerning a single candidate is mostly used, whereas the collaborative performance associating with a pair of candidates is overlooked. In this paper, as a solution to this problem, we propose a method for member selection of CFTs, where both the individual performance of candidates and the collaborative performance between candidates are considered. In order to select the desired members, firstly, a multi-objective 0-1 programming model is built using the individual and collaborative performances, which is an NP-hard problem. To solve the model, we develop an improved nondominated sorting genetic algorithm II (INSGA-II). Furthermore, a real example is employed to illustrate the suitability of the proposed method. Additionally, extensive computational experiments to compare INSGA-II with the nondominated sorting genetic algorithm II (NSGA-II) are conducted and much better performance of INSGA-II is observed.

Background: Colorectal cancer is the fourth most common cancer worldwide and the second leading cause of cancer-related death. FOLFOX is the most common regimen used in the first-line chemotherapy in advanced colorectal cancer, but only half of the patients respond to this regimen and we have almost no clue in predicting resistance in such first-line application. Methods: To explore the potential molecular biomarkers predicting the resistance of FOLFOX regimen as the first-line treatment in advanced colorectal cancer, we screened microRNAs in serum samples from drug-responsive and drug-resistant patients by microarrays. Then differential microRNA expression was further validated in an independent population by reverse transcription and quantitative realtime PCR. Results: 62 microRNAs expressing differentially with fold-change >2 were screened out by microarray analysis. Among them, 5 (miR-221, miR-222, miR-122, miR-19a, miR-144) were chosen for further validation in an independent population (N=72). Our results indicated serum miR-19a to be significantly up-regulated in resistance-phase serum (p=0.009). The ROC curve analysis showed that the sensitivity of serum miR-19a to discriminate the resistant patients from the response ones was 66.7%, and the specificity was 63.9% when the AUC was 0.679. We additionally observed serum miR-19a had a complementary value for cancer embryonic antigen (CEA). Stratified analysis further revealed that serum miR-19a predicted both intrinsic and acquired drug resistance. Conclusions: Our findings confirmed aberrant expression of serum miR-19a in FOLFOX chemotherapy resistance patients, suggesting serum miR-19a could be a potential molecular biomarker for predicting and monitoring resistance to first-line FOLFOX chemotherapy regimens in advanced colorectal cancer patients.

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