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Background: Despite major advance in surgical techniques from open surgery to robot-assisted surgery, acute kidney injury (AKI) is still major postoperative complication in rectal surgery. The purpose of this study is to compare the incidence of postoperative AKI according to different surgical techniques and also the risk factors, outcomes of AKI in patients undergoing rectal cancer surgery. Methods: A retrospective medical chart review was done in a total of 288 patients who received proctectomy because of rectal cancer from 2011 to 2013. Results: The mean patient age was 62 ± 12 years, and male was 64.2%. Preoperative creatinine was 0.91 ± 0.18 mg/dL. Open surgery was performed in 9%, and laparoscopy assisted surgery or robot assisted surgery were performed in 54.8% or 36.1% of patients, respectively. AKI developed in 11 patients (3.82%), 2 (18%) of them received acute hemodialysis. Incidence of AKI was not different according to the surgical technique, however, the presence of diabetes, intraoperative shock, and postoperative ileus was associated with the development of AKI. In addition, AKI patients showed significantly longer hospital stay and higher mortality than non-AKI patients. Conclusion: Our study demonstrated that despite advances in surgical techniques, incidence of postoperative AKI remains unchanged and also that postoperative AKI is associated with poor outcome. We also found that presence of diabetes, intraoperative shock and postoperative ileus are strongly associated with the development of AKI. More careful attention should be paid on high risk patients for the development of postoperative AKI regardless of surgical techniques.

The U.S. power grid is transforming to become “smarter,” cleaner, and more efficient. This is leading to the addition of significant distributed variable renewable generation. Due to the variable nature of renewable generation, the short- and long-term supply-demand imbalances are less predictable, and conventional approaches to mitigating the imbalance will not be efficient or cost-effective. To address this challenge, transactive control technologies have been proposed. The transactive control approach requires individual end-use loads to express flexibility as a function of price. To model flexibility while maintaining robustness to any non-linear behavior exhibited by end-use loads, machine learning approaches for load forecasting are being explored. However, certain aspects, such as how much training data is required and how deep models for load forecasting should be structured and trained are not well understood. This work explores how to apply sequence-to-sequence recurrent neural networks to short-term electrical load forecasting with a case study of four commercial office buildings. We find that it is best to start the training in the middle of a heating or cooling season with at least six months of data. We further show that models perform best when predictions are conditioned on three to 12 h of prior data, with a decrease in performance for shorter contexts. We identify recommended ranges for common hyperparameters that could be used by practitioners applying similar models to their own tasks. Finally, we find that transferability of models across buildings is highly dependent on the building pairs, but in the best case, models are highly transferable.

Instead of the genetic operators such as crossover and mutation, compact Genetic Algorithms (cGAs) use a probability vector (PV) for the current population to reproduce offsprings of the next generation. Therefore, the original cGA can be easily implemented with no parameter tuning of the genetic operators and with reducing memory requirements. Many researchers have suggested their own schemes to improve the performance of the cGA, such as quality of solutions and convergence speed. However, these researches mainly have given fast convergence to the original cGA. They still have the premature convergence problem resulting in the low quality of solutions. Besides, the additional control parameters such as @h of ne-cGA are even required for several cGAs. We propose two new schemes, called cGABV (an acronym for cGA using belief vectors) and cGABVE (an acronym for cGABV with elitism), in order to improve the performance of conventional cGAs by maintaining the diversity of individuals. For this purpose, the proposed algorithms use a belief vector (BV) instead of a PV. Each element of the BV has a probability distribution with a mean and a variance, whereas each element of a PV has a singular probability value. Accordingly, the proposed BV enables to affect the performances by controlling the genetic diversity of each generation. In addition, we propose two variants of the proposed cGABV and cGABVE, Var1 and Var2, employing the entropy-driven parameter control scheme in order to avoid the difficulty of designing the control parameter (@l). Experimental results show that the proposed variants of cGAs outperform the conventional cGAs. For investigating the diversity of each cGA, the entropy is employed and calculated at each generation. Finally, we discuss the effect of @l related to the variances of the BV through the additional experiment.

Film-growth precursor for microcrystalline silicon (μc-Si:H) thin films was studied by growing the films in the presence of an electric field by using plasma-enhanced chemical vapor deposition. μc-Si:H films were prepared using either hydrogen- or argon-diluted silane, which usually result in μc-Si:H films with a crystalline volume fraction of more than 75%. It was observed that for both the films the crystalline phase is markedly suppressed in the presence of an electric field. In particular, this suppression is greater for the films grown near the anode side. For the films grown near the anode side, little or no crystalline phase was observed. A possible precursor responsible for the formation of μc-Si:H will be discussed.

The low-energy photoluminescence (PL) of microcrystalline silicon films grown by plasma-enhanced chemical vapor deposition using argon (Ar) diluted silane has been investigated. For the samples with high crystalline volume fraction ( X c ) of more than 80%, it is observed that the luminescence peak appears near 1.0 eV, which is much higher than the value of 0.9 eV observed for the samples with a similar X c grown from hydrogen dilution. Also, the low-energy PL peak shifts to the higher energy with decreasing deposition power, but it is nearly independent of Ar dilution ratio. The low-energy PL band is suggested to basically arise from a superposition of at least three subbands. From the results it is suggested that the PL band centered at 1.0 eV originates from radiative band tail-to-tail transitions in the crystallites.