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Recently, due to the growth in machine learning and data mining, for scheduling applications in China's industrial intelligence, we are quite fortunate to witness a paradigm of evolutionary scheduling via learning, which includes a new tool of evolutionary transfer optimization (ETO). As a new subset in ETO, single-objective to multi-objective/many-objective optimization (SMO) acts as a powerful, abstract and general framework with wide industrial applications like shop scheduling and vehicle routing. In this paper, we focus on the general mechanism of selection that selects or gathers elite and high potential solutions towards gathering/transferring strength from single-objective problems, or gathering/transferring storms of knowledge from solved tasks. Extensive studies in vehicle routing problems with time windows (VRPTW) on well-studied benchmarks validate the great universality of the SMO framework. Our investigations (1) contribute to a deep understanding of SMO, (2) enrich the classical and fundamental theory of building blocks for genetic algorithms and memetic algorithms, and (3) provide a completive and potential solution for VRPTW. [ABSTRACT FROM AUTHOR]

Background: Robotic colorectal cancer surgery is widely accepted and applied. However, there is still no objective and comprehensive assessment on the data of nationwide multicenter series. Method: A total of 28 medical centers in Mainland China participated in this nationwide retrospective observational study. From the first case performed in each center to the last until December 2017, patients with robotic resection for primary tumor and pathologically confirmed colorectal adenocarcinoma were consecutively enrolled. Clinical, pathological and follow-up data were collected and analyzed. Results: A total of 5389 eligible patients were finally enrolled in this study, composing 72.2% of the total robotic colorectal surgery volume of Mainland China in the same period. For resections of one bowel segment of primary tumor, the postoperative mortality rate was 0.08% (4/5063 cases), and the postoperative complication rate (Clavien–Dindo grade II or higher) was 8.6% (434/5063 cases). For multiple resections, the postoperative mortality rate was 0.6% (2/326 cases), and the postoperative complication rate was 16.3% (53/326 cases). Out of 2956 patients receiving sphincter-preserving surgery in only primary resection, 130 (4.4%) patients had anastomotic leakage. Traditional low anterior resection (tumor at middle rectum) (OR 2.384, P < 0.001), traditional low anterior resection (tumor at low rectum) (OR 1.968, P = 0.017) and intersphincteric resection (OR 5.468, P = 0.006) were significant independent risk factors for anastomotic leakage. Female gender (OR 0.557, P = 0.005), age ≥ 60 years (OR 0.684, P = 0.040), and preventive stoma (OR 0.496, P = 0.043) were significant independent protective factors. Body mass index, preoperative chemotherapy/radiotherapy, tumor size, and TNM stage did not independently affect the occurrence of anastomotic leakage. Conclusion: Robotic colorectal cancer surgery was safe and reliable and might have advantages in patients at high risk of anastomotic leakage. [ABSTRACT FROM AUTHOR]

In recent years, diverse energy has been integrated into the power system, which constitutes a regional integrated energy system (IES). However, the coupling and complementation of multiple energy sources make load forecasting more difficult. For the time‐sequence and non‐linear characteristics of electric load and the complementarity of different energy in IES, this paper proposed an attention‐based convolutional neural network (CNN) combined with long short‐term memory (LSTM) and bidirectional long short‐term memory (BiLSTM) model for short‐term load forecasting in IES. The historical load, temperature, cooling load, and gas consumption of the past 5 days are used as the input features. CNN integrated with attention block is utilized to extract effective features of the load impact factors. Then the load of the next hour is forecasted by the LSTM combined with BiLSTM layers. Finally, the model is verified by the data from an integrated energy park in North China. The results show that the proposed method has better forecasting performance than CNN‐BiLSTM, CNN‐LSTM, BiLSTM, LSTM, backpropagation neural network (BPNN), random forest regression (RFR), and support vector machine regression (SVR). [ABSTRACT FROM AUTHOR]

Background: Threatened premature labor (TPL) is a severe obstetric complication which affects the mental and physical health of both the mother and fetus. Family resilience may have protective role against psychological distress in women experiencing these pregnancy complications. There may be resilience related risk factors in TPL women, and interplays may exist among psychological variables and within couples. This study aims to examine psychological outcomes influenced by different levels of resilience, and explore psychological interactions in TPL women, spouses, and between women and spouses.Methods: Six validated questionnaires were used to measure the psychological outcomes (Connor-Davidson resilience scale CD-RISC, Edinburgh postnatal depression scale EPDS, positive and negative affect scale PANAS, pregnancy pressure scale PPS, simplified coping style questionnaire SCSQ, social support rating scale SSRS) in 126 TPL women hospitalized in three tertiary hospitals and 104 spouses in Southwest China.Results: Low resilient women had significantly more complicated placenta praevia, longer pediatric observation, more pressure than high resilient women. They also had significantly less active coping and positive affect, more negative affect and depression compared to high resilient women and their spouses. Although the socio-demographic characteristics of both TPL women and spouses and psychometric parameters of spouses had no significant differences, the prevalence rates of depression in spouses were notable. Compared with spouses, TPL women had a more complex interaction among these psychometric factors, with women's resilience negatively associated with their partners' negative affect, and their pressure positively correlated with pressure and negative affect of spouses.Conclusions: Pregnancy complicated with placenta praevia and pediatric observation may be risk factors for resilience of women with TPL. Maternal resilience has an important impact on the psychological outcomes in TPL women. A screening for resilience, depression and other psychological outcomes in couples with TPL and early psychological intervention of low resilient couples may be appropriate to promote resilience and well-being of these families. [ABSTRACT FROM AUTHOR]