A Scatter Search Approach for Multiobjective Selective Disassembly Sequence Problem

Disassembly sequence has received much attention in recent years. This work proposes a multiobjective optimization of model for selective disassembly sequences and maximizing disassembly profit and minimizing disassembly time. An improved scatter search (ISS) is adapted to solve proposed multiobjective optimization model, which embodies diversification generation of initial solutions, crossover combination operator, the local search strategy to improve the quality of new solutions, and reference set update method. To analyze the effect on the performance of ISS, simulation experiments are conducted on different products. The validity of ISS is verified by comparing the optimization effects of ISS and nondominated sorting genetic algorithm (NSGA-II)

Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics

Feynman's path integral approach is to sum over all possible spatio-temporal paths to reproduce the quantum wave function and the corresponding time evolution, which has enormous potential to reveal quantum processes in classical view. However, the complete characterization of quantum wave function with infinite paths is a formidable challenge, which greatly limits the application potential, especially in the strong-field physics and attosecond science. Instead of brute-force tracking every path one by one, here we propose deep-learning-performed strong-field Feynman's formulation with pre-classification scheme which can predict directly the final results only with data of initial conditions, so as to attack unsurmountable tasks by existing strong-field methods and explore new physics. Our results build up a bridge between deep learning and strong-field physics through the Feynman's path integral, which would boost applications of deep learning to study the ultrafast time-dependent dynamics in strong-field physics and attosecond science, and shed a new light on the quantum-classical correspondence

Serum soluble triggering receptor levels expressed on myeloid cells2 identify early acute kidney injury in infants and young children after pediatric cardiopulmonary bypass

BackgroundAcute kidney injury (AKI) is a potential complication after cardiopulmonary bypass (CPB) of pediatric cardiac surgery and contributes to a certain amount of perioperative mortality. Serum soluble triggering receptor expressed on myeloid cells2 (sTREM2) is an inflammation-associated cytokine in circulation. Alterations of sTREM2 level have been reported in Alzheimer's disease, sepsis, and some other pathologic conditions. This study aimed to investigate the role of sTREM2 as a forecasting factor for AKI in infants and young children and other factors associated with early renal injury after pediatric CPB.MethodsA prospective cohort study with consecutive infants and young children ≤ 3 years old undergoing CPB from September 2021 to August 2022 was conducted in an affiliated university children's hospital. These patients were divided into an AKI group (n = 10) and a non-AKI group (n = 60). Children′s characteristics and clinical data were measured. Perioperative sTREM2 levels were analyzed with enzyme-linked immunosorbent assay (ELISA).ResultsIn children developing AKI, the sTREM2 levels significantly decreased at the beginning of CPB compared to the non-AKI group. Based on binary logistic regression analysis and multivariable regression analysis, risk-adjusted classification for congenital heart surgery (RACHS-1), operation time, and the s-TREM2 level at the beginning of CPB (AUC = 0.839, p = 0.001, optimal cut-off value: 716.0 pg/ml) had predictive value for post-CPB AKI. When combining the sTREM2 level at the beginning of CPB and other indicators together, the area under the ROC curve enlarged.ConclusionsOperation time, RACHS-1 score, and sTREM2 level at the beginning of CPB were independent prognosis factors of post-CPB AKI in infants and young children ≤ 3 years old. Decreased sTREM2 identified post-CPB AKI, and ultimately hampered the outcomes. Our findings indicated that sTREM2 may be a protective factor for AKI after CPB in infants and young children ≤ 3 years old

The porcine piRNA transcriptome response to Senecavirus a infection

IntroductionSenecavirus A (SVA) belongs to the genus Senecavirus in the family Picornaviridae. PIWI-interacting RNAs (piRNAs) are a class of small Ribonucleic Acids (RNAs) that have been found in mammalian cells in recent years. However, the expression profile of piRNAs in the host during SVA infection and their roles are poorly understood.MethodsHere, we found the significant differential expression of 173 piRNAs in SVA-infected porcine kidney (PK-15) cells using RNA-seq and 10 significant differentially expressed (DE) piRNAs were further verified by qRT-PCR.ResultsGO annotation analysis showed that metabolism, proliferation, and differentiation were significantly activated after SVA infection. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that significant DE piRNAs were mainly enriched in AMPK pathway, Rap1 pathway, circadian rhythm and VEGF pathway. It was suggested that piRNAs may regulated antiviral immunity, intracellular homeostasis, and tumor activities during SVA infection. In addition, we found that the expression levels of the major piRNA-generating genes BMAL1 and CRY1 were significantly downregulated after SVA infection.DiscussionThis suggests that SVA may affect circadian rhythm and promote apoptosis by inhibiting the major piRNA-generating genes BMAL1 and CRY1. The piRNA transcriptome in PK-15 cells has never been reported before, and this study will further the understanding of the piRNA regulatory mechanisms underlying SVA infections

Localization of CO2 leakage from transportation pipelines through low frequency acoustic emission detection

Carbon Capture and Storage is a technology to reduce greenhouse gas emissions. CO2 leak from high pressure CO2 transportation pipelines can pose a significant threat to the safety and health of the people living in the vicinity of the pipelines. This paper presents a technique for the efficient localization of CO2 leakage in the transportation pipelines using acoustic emission method with low frequency and narrow band sensors. Experimental tests were carried out on a lab scale test rig releasing CO2 from a stainless steel pipe. Further, the characteristics of the acoustic emission signals are analyzed in both the time and the frequency domains. The impact of using the transverse wave speed and the longitudinal wave speed on the accuracy of the leak localization is investigated. Since the acoustic signals are expected to be attenuated and dispersed when propagating along the pipe, empirical mode decomposition, signal reconstruction and a data fusion method are employed in order to extract high quality data for accurate localization of the leak source. It is demonstrated that a localization error of approximately 5% is achievable with the proposed detecting system