An improved memory prediction strategy for dynamic multiobjective optimization

The file attached to this record is the author's final peer reviewed version.In evolutionary dynamic multiobjective optimization (EDMO), the memory strategy and prediction method are considered as effective and efficient methods. To handling dynamic multiobjective problems (DMOPs), this paper studies the behavior of environment change and tries to make use of the historical information appropriately. And then, this paper proposes an improved memory prediction model that uses the memory strategy to provide valuable information to the prediction model to predict the POS of the new environment more accurately. This memory prediction model is incorporated into a multiobjective evolutionary algorithm based on decomposition (MOEA/D). In particular, the resultant algorithm (MOEA/D-MP) adopts a sensor-based method to detect the environment change and find a similar one in history to reuse the information of it in the prediction process. The proposed algorithm is compared with several state-of-the-art dynamic multiobjective evolutionary algorithms (DMOEA) on six typical benchmark problems with different dynamic characteristics. Experimental results demonstrate that the proposed algorithm can effectively tackle DMOPs

Analysis of Flavor Quality of Two Cherry Tomatoes After Refrigeration

To investigate the differences in the types and relative contents of refrigerated volatile flavor substances between 'Chitose' and 'Ji Tian No.1' cherry tomatoes, the effect of cold storage at 4 ℃ for 16 d on the flavor quality of cherry tomatoes ('Chitose' and 'Ji Tian No.1') was analyzed by using electronic nose and headspace solid-phase microextraction-gas chromatography. The results showed that electronic nose analysis results displayed, the volatile substances that undergo significant changes before and after refrigeration in two cherry tomatoes were inorganic sulfides, nitrogen oxides, and aromatic substances, a total of 93 volatile substances were detected by HS-SPME-GC-MS technique, including 27 aldehydes, 23 alcohols, 4 esters, 11 ketones, 3 furans, 9 alkanes, 12 olefins and 4 other substances. Compared to 0 d, the relative content of total volatile substances in 'Chitose' and 'Ji Tian No.1' after cold storage decreased by 7.08% and 3.68% respectively. The relative content of the main volatile compounds hexanal and trans-2-hexenal in two cherry tomatoes decreased after refrigeration, the relative content of trans 2-pentenal, heptanal, 1-pentanol, 6-methyl-5-hepten-2-one, and 1-penten-3-one increased after refrigeration. Compared with 'Chitose', 'Ji Tian No.1' could retain higher contents of main volatile substances of cherry tomato such as aldehydes, esters and ketones after cold storage. Therefore, 'Ji Tian No.1' was more suitable for post-harvest storage and transportation and low-temperature refrigeration, which would provide a theoretical basis for high-quality cherry tomato post-harvest storage and transportation technology

A VMD and LSTM based hybrid model of load forecasting for power grid security

As the basis for the static security of the power grid, power load forecasting directly affects the safety of grid operation, the rationality of grid planning, and the economy of supply-demand balance. However, various factors lead to drastic changes in short-term power consumption, making the data more complex and thus more difficult to forecast. In response to this problem, a new hybrid model based on Vari-ational mode decomposition (VMD) and Long Short-Term Memory (LSTM) with seasonal factors elimination and error correction is proposed in this paper. Comprehensive case studies on four real-world load datasets from Singapore and the United States are employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction accuracy of the proposed model is significantly higher than that of the contrast models. Index Terms-Power grid security, short-term load forecasting , seasonal factors elimination, error correction

Germline-Encoded TCR-MHC Contacts Promote TCR V Gene Bias in Umbilical Cord Blood T Cell Repertoire.

T cells recognize antigens as peptides bound to major histocompatibility complex (MHC) proteins through T cell receptors (TCRs) on their surface. To recognize a wide range of pathogens, each individual possesses a substantial number of TCRs with an extremely high degree of variability. It remains controversial whether germline-encoded TCR repertoire is shaped by MHC polymorphism and, if so, what is the preference between MHC genetic variants and TCR V gene compatibility. To investigate the "net" genetic association between MHC variations and TRBV genes, we applied quantitative trait locus (QTL) mapping to test the associations between MHC polymorphism and TCR β chain V (TRBV) genes usage using umbilical cord blood (UCB) samples of 201 Chinese newborns. We found TRBV gene and MHC loci that are predisposed to interact with one another differ from previous conclusions. The majority of MHC amino acid residues associated with the TRBV gene usage show spatial proximities in known structures of TCR-pMHC complexes. These results show for the first time that MHC variants bias TRBV gene usage in UCB of Chinese ancestry and indicate that germline-encoded contacts influence TCR-MHC interactions in intact T cell repertoires

Classification of a Complexly Mixed Magnetic Mineral Assemblage in Pacific Ocean Surface Sediment by Electron Microscopy and Supervised Magnetic Unmixing

Unambiguous magnetic mineral identification in sediments is a prerequisite for reconstructing paleomagnetic and paleoenvironmental information from environmental magnetic parameters. We studied a deep-sea surface sediment sample from the Clarion Fracture Zone region, central Pacific Ocean, by combining magnetic measurements and scanning and transmission electron microscopic analyses. Eight titanomagnetite and magnetite particle types are recognized based on comprehensive documentation of crystal morphology, size, spatial arrangements, and compositions, which are indicative of their corresponding origins. Type-1 particles are detrital titanomagnetites with micron- and submicron sizes and irregular and angular shapes. Type-2 and -3 particles are well-defined octahedral titanomagnetites with submicron and nanometer sizes, respectively, which are likely related to local hydrothermal and volcanic activity. Type-4 particles are nanometer-sized titanomagnetites hosted within silicates, while type-5 particles are typical dendrite-like titanomagnetites that likely resulted from exsolution within host silicates. Type-6 particles are single domain magnetite magnetofossils related to local magnetotactic bacterial activity. Type-7 particles are superparamagnetic magnetite aggregates, while Type-8 particles are defect-rich single crystals composed of many small regions. Electron microscopy and supervised magnetic unmixing reveal that type-1 to -5 titanomagnetite and magnetite particles are the dominant magnetic minerals. In contrast, the magnetic contribution of magnetite magnetofossils appears to be small. Our work demonstrates that incorporating electron microscopic data removes much of the ambiguity associated with magnetic mineralogical interpretations in traditional rock magnetic measurements.This study was supported financially by the National Natural Science Foundation of China (Grant Nos. 41920104009, 41890843, and 41621004), The Senior User Project of RVKEXUE2019GZ06 (Center for Ocean Mega-Science, Chinese Academy of Sciences)

Mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes with an MT-TL1 m.3243A>G point mutation: Neuroradiological features and their implications for underlying pathogenesis

ObjectiveMitochondrial encephalomyopathy with lactic acidosis and stroke−like episodes (MELAS) is one of the most common inherited mitochondrial disorders. Due to the high clinical and genetic heterogeneity of MELAS, it is still a major challenge for clinicians to accurately diagnose the disease at an early stage. Herein, we evaluated the neuroimaging findings of MELAS with an m.3243A>G mutation in MT−TL1 and analyzed the possible underlying pathogenesis of stroke-like episodes.Materials and methodsFifty-nine imaging studies in 24 patients who had a confirmed genetic diagnosis of m.3243A>G (MT-TL1; tRNALeu) associated with MELAS were reviewed in our case series. The anatomic location, morphological features, signal/intensity characteristics and temporal evolution of lesions were analyzed on magnetic resonance imaging (MRI), and computed tomography (CT) images. The supplying vessels and metabolite content of the lesions were also evaluated by using MR angiography (MRA)/CT angiography (CTA), and MR spectroscopy (MRS), respectively.ResultsThe lesions were most commonly located in the posterior brain, with 37 (37/59, 63%) in the occipital lobe, 32 (32/59, 54%) in the parietal lobe, and 30 (30/59, 51%) in the temporal lobe. The signal characteristics of the lesions varied and evolved over time. Bilateral basal ganglia calcifications were found in 6 of 9 (67%) patients who underwent CT. Cerebral and cerebellar atrophy were found in 38/59 (64%) and 40/59 (68%) patients, respectively. Lesion polymorphism was found in 37/59 (63%) studies. MRS showed elevated lactate doublet peaks in 9/10 (90%) cases. MRA or CTA revealed that the lesion-related arteries were slightly dilated compared with those of the contralateral side in 4 of 6 (67%) cases.ConclusionThe imaging features of MELAS vary depending on the disease stage. Polymorphic lesions in a single imaging examination should be considered a diagnostic clue for MELAS. Stroke-like episodes may be involved in a complex pathogenetic process, including mitochondrial angiopathy, mitochondrial cytopathy, and neuronal excitotoxicity

Novel Swine Influenza Virus Reassortants in Pigs, China

During swine influenza virus surveillance in pigs in China during 2006–2009, we isolated subtypes H1N1, H1N2, and H3N2 and found novel reassortment between contemporary swine and avian panzootic viruses. These reassortment events raise concern about generation of novel viruses in pigs, which could have pandemic potential

Bio-inspired plasmonic nanoarchitectured hybrid system towards enhanced far red-to-near infrared solar photocatalysis

Solar conversion to fuels or to electricity in semiconductors using far red-to-near infrared (NIR) light, which accounts for about 40% of solar energy, is highly significant. One main challenge is the development of novel strategies for activity promotion and new basic mechanisms for NIR response. Mother Nature has evolved to smartly capture far red-to-NIR light via their intelligent systems due to unique micro/nanoarchitectures, thus motivating us for biomimetic design. Here we report the first demonstration of a new strategy, based on adopting nature’s far red-to-NIR responsive architectures for an efficient bio-inspired photocatalytic system. The system is constructed by controlled assembly of light-harvesting plasmonic nanoantennas onto a typical photocatalytic unit with butterfly wings’ 3D micro/nanoarchitectures. Experiments and finite-difference time-domain (FDTD) simulations demonstrate the structural effects on obvious far red-to-NIR photocatalysis enhancement, which originates from (1) Enhancing far red-to-NIR (700~1200 nm) harvesting, up to 25%. (2) Enhancing electric-field amplitude of localized surface plasmon (LSPs) to more than 3.5 times than that of the non-structured one, which promotes the rate of electron-hole pair formation, thus substantially reinforcing photocatalysis. This proof-of-concept study provides a new methodology for NIR photocatalysis and would potentially guide future conceptually new NIR responsive system designs