Hybrid of memory andprediction strategies for dynamic multiobjective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic multiobjective optimization problems (DMOPs) are characterized by a time-variant Pareto optimal front (PF) and/or Pareto optimal set (PS). To handle DMOPs, an algorithm should be able to track the movement of the PF/PS over time efficiently. In this paper, a novel dynamic multiobjective evolutionary algorithm (DMOEA) is proposed for solving DMOPs, which includes a hybrid of memory and prediction strategies (HMPS) and the multiobjective evolutionary algorithm based on decomposition (MOEA/D). In particular, the resultant algorithm (MOEA/D-HMPS) detects environmental changes and identifies the similarity of a change to the historical changes, based on which two different response strategies are applied. If a detected change is dissimilar to any historical changes, a differential prediction based on the previous two consecutive population centers is utilized to relocate the population individuals in the new environment; otherwise, a memory-based technique devised to predict the new locations of the population members is applied. Both response mechanisms mix a portion of existing solutions with randomly generated solutions to alleviate the effect of prediction errors caused by sharp or irregular changes. MOEA/D-HMPS was tested on 14 benchmark problems and compared with state-of-the-art DMOEAs. The experimental results demonstrate the efficiency of MOEA/D-HMPS in solving various DMOPs

Building the Multi-layer Theory of Association Semantic based on the Power-law Distribution of Linking Keywords

Abstract-Web information contain plentiful, significant knowledge which is eager to be explored by users. Effective semantic layered technology not only can provide theoretical support for knowledge discovery in Web resources, but also can improve the searching efficiency of the related information system. This paper builds the multi-layer theory of association semantic based on the power-law distribution of linking keywords. First, some experiments of four types of keywords with different linking role are done to discover the possible distribution law. Experiment results show that four types of keywords are all reveal power-law distribution. Then, based on the discovered power-law distribution, the multi-layer theory of association semantic is built. The multi-layer theory of association semantic can provide a theoretical support for knowledge recommendation with different particle size on Association Link Network (ALN). Keywords-Association Link Network, power-law distribution, multi-layer theory of association semantic, knowledge discovery in Web resources

Lightweight multilayer interactive attention network for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) aims to automatically identify the sentiment polarity of specific aspect words in a given sentence or document. Existing studies have recognised the value of interactive learning in ABSA and have developed various methods to precisely model aspect words and their contexts through interactive learning. However, these methods mostly take a shallow interactive way to model aspect words and their contexts, which may lead to the lack of complex sentiment information. To solve this issue, we propose a Lightweight Multilayer Interactive Attention Network (LMIAN) for ABSA. Specifically, we first employ a pre-trained language model to initialise word embedding vectors. Second, an interactive computational layer is designed to build correlations between aspect words and their contexts. Such correlation degree is calculated by multiple computational layers with neural attention models. Third, we use a parameter-sharing strategy among the computational layers. This allows the model to learn complex sentiment features with lower memory costs. Finally, LMIAN conducts instance validation on six publicly available sentiment analysis datasets. Extensive experiments show that LMIAN performs better than other advanced methods with relatively low memory consumption

A feedback-based prediction strategy for dynamic multi-objective evolutionary optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Prediction methods are widely used to solve dynamic multi-objective optimization problems (DMOPs). The key to the success of prediction methods lies in the accurate tracking of the new location of the Pareto set (PS) or Pareto front (PF) in a new environment. To improve the prediction accuracy, this paper proposes a novel feedback-based prediction strategy (FPS), which consists of two feedback mechanisms, namely correction feedback (CF) and effectiveness feedback (EF). CF is used to correct an initial prediction model. When the environment changes, CF constructs a representative individual to reflect the characteristics of the current population. The predicted solution of this individual in the new environment is calculated based on the initial prediction model. Afterward, a step size exploration method based on variable classification is introduced to adaptively correct the prediction model. EF is applied to enhance the effectiveness of re-initialization in two stages. In the first stage, half of the individuals in the population are re-initialized based on the corrected prediction model. In the second stage, EF re-initializes the rest of the individuals in the population using two rounds of roulette method based on the re-initialization effectiveness feedback of the first stage. The proposed FPS is incorporated into a dynamic multi-objective optimization evolutionary algorithm (DMOEA) based on decomposition resulting in a new algorithm denoted as MOEA/D-FPS. MOEA/D-FPS is compared with six state-of-the-art DMOEAs on twenty-two different benchmark problems. The experimental results demonstrate the effectiveness and efficacy of MOEA/D-FPS in solving DMOPs

Measuring knowledge delivery quantity of associated knowledge flow

Associated knowledge flow (AKF) is a sequential link between associated topics, which can be applied to intelligent browsing and personalized recommendation. One key problem is how to measure the knowledge delivery quantity (KDQ) on an AKF. In this paper, a computational method of knowledge delivery quantity on an AKF is proposed. Firstly, considering the keywords and associated relations between two nodes, four key factors for knowledge delivery quantity between two nodes are investigated. Secondly, based on the four factors, an algorithm is proposed to calculate the knowledge delivery quantity between two nodes. Thirdly, the knowledge delivery quantity of a node with adjacent nodes is calculated for the measurement of local knowledge delivery on an AKF. Lastly, according to the local knowledge delivery, the average knowledge delivery quantity is proposed to measure an AKF. Experimental results show that the proposed measurement method is accurate and effective

Photosynthetic Product Allocations to the Organs of Pinus massoniana Are Not Affected by Differences in Synthesis or Temporal Variations in Translocation Rates

Photosynthesis and the allocation of photosynthetic products are the two main factors that determine plant growth. To understand the growth and productivity of Pinus massoniana Lamb., the diurnal changes in photosynthetic rate were continuously monitored. Furthermore, the translocation and allocation of the photosynthetic products synthesized in the morning and afternoon were explored using 13C pulse labeling. The results showed that: (1) on sunny days, the diurnal variation of the net photosynthetic rate showed a “double peak” curve, with an obvious “a depression” when temperatures were highest and humidity lowest. On cloudy days, it showed an irregular “jagged” curve, which was curve consistent with the variations in photosynthetically active radiation (PAR). Meanwhile, the photosynthetic rate changed with the transient changes in environmental factors such as PAR, temperature, and humidity. (2) The mean value of the net photosynthetic rate in the morning was higher than in the afternoon, and the response of the net photosynthetic rate to environmental change (PAR, temperature, humidity, and CO2 concentration) in the morning was greater than that in the afternoon. (3) The translocation of photosynthetic products synthesized in the afternoon was faster than that in the morning. Shortly after synthesis of photosynthetic products, the translocation of products synthesized in the morning tended toward upper organs (including current-year leaves and 1-year leaves), while the translocation of products synthesized in the afternoon decreased in the upper organs. However, after 15 days of 13C pulse labeling, the allocation of the photosynthetic products synthesized in the morning and afternoon tended to be the same. These results indicate that the differences in the photosynthetic products synthesized and the temporal differences in the translocation rates did not affect the final allocation of the photosynthetic products in the various organs of the P. massoniana. These results improve our knowledge of the functional phases of P. massoniana during the diurnal cycle

Engineered biochemical cues of regenerative biomaterials to enhance endogenous stem/progenitor cells (ESPCs)-mediated articular cartilage repair

As a highly specialized shock-absorbing connective tissue, articular cartilage (AC) has very limited self-repair capacity after traumatic injuries, posing a heavy socioeconomic burden. Common clinical therapies for small- to medium-size focal AC defects are well-developed endogenous repair and cell-based strategies, including microfracture, mosaicplasty, autologous chondrocyte implantation (ACI), and matrix-induced ACI (MACI). However, these treatments frequently result in mechanically inferior fibrocartilage, low cost-effectiveness, donor site morbidity, and short-term durability. It prompts an urgent need for innovative approaches to pattern a pro-regenerative microenvironment and yield hyaline-like cartilage with similar biomechanical and biochemical properties as healthy native AC. Acellular regenerative biomaterials can create a favorable local environment for AC repair without causing relevant regulatory and scientific concerns from cell-based treatments. A deeper understanding of the mechanism of endogenous cartilage healing is furthering the (bio)design and application of these scaffolds. Currently, the utilization of regenerative biomaterials to magnify the repairing effect of joint-resident endogenous stem/progenitor cells (ESPCs) presents an evolving improvement for cartilage repair. This review starts by briefly summarizing the current understanding of endogenous AC repair and the vital roles of ESPCs and chemoattractants for cartilage regeneration. Then several intrinsic hurdles for regenerative biomaterials-based AC repair are discussed. The recent advances in novel (bio)design and application regarding regenerative biomaterials with favorable biochemical cues to provide an instructive extracellular microenvironment and to guide the ESPCs (e.g. adhesion, migration, proliferation, differentiation, matrix production, and remodeling) for cartilage repair are summarized. Finally, this review outlines the future directions of engineering the next-generation regenerative biomaterials toward ultimate clinical translation.</p

The Annexin a2 Promotes Development in Arthritis through Neovascularization by Amplification Hedgehog Pathway.

The neovascularization network of pannus formation plays a crucial role in the development of rheumatoid arthritis (RA). Annexin a2 (Axna2) is an important mediating agent that induces angiogenesis in vascular diseases. The correlation between Axna2 and pannus formation has not been studied. Here, we provided evidence that compared to osteoarthritis (OA) patients and healthy people, the expression of Axna2 and Axna2 receptor (Axna2R) were up-regulated in patients with RA. Joint swelling, inflammation and neovascularization were increased significantly in mice with collagen-induced arthritis (CIA) that were exogenously added Axna2. Cell experiments showed that Axna2 promoted HUVEC proliferation by binding Axna2R, and could activate Hedgehog (HH) signaling and up-regulate the expression of Ihh and Gli. Besides, expression of Ihh, Patched (Ptc), Smoothened (Smo) and Gli and matrix metalloproteinase-2 (MMP-2), vascular endothelial growth factor (VEGF) and angiopoietin-2 (Ang-2), angiogenic growth factor of HH signaling downstream, were down-regulated after inhibition of expression Axna2R on HUVEC. Together, our research definitely observed that over-expression of Axna2 could promote the development of CIA, especially during the process of pannus formation for the first time. Meanwhile, Axna2 depended on combining Axna2R to activate and enlarge HH signaling and the expression of its downstream VEGF, Ang-2 and MMP-2 to promote HUVEC proliferation, and eventually caused to angiogenesis. Therefore, the role of Axna2 is instructive for understanding the development of RA, suppress the effect of Axna2 might provide a new potential measure for treatment of RA

Engineered biochemical cues of regenerative biomaterials to enhance endogenous stem/progenitor cells (ESPCs)-mediated articular cartilage repair

As a highly specialized shock-absorbing connective tissue, articular cartilage (AC) has very limited self-repair capacity after traumatic injuries, posing a heavy socioeconomic burden. Common clinical therapies for small- to medium-size focal AC defects are well-developed endogenous repair and cell-based strategies, including microfracture, mosaicplasty, autologous chondrocyte implantation (ACI), and matrix-induced ACI (MACI). However, these treatments frequently result in mechanically inferior fibrocartilage, low cost-effectiveness, donor site morbidity, and short-term durability. It prompts an urgent need for innovative approaches to pattern a pro-regenerative microenvironment and yield hyaline-like cartilage with similar biomechanical and biochemical properties as healthy native AC. Acellular regenerative biomaterials can create a favorable local environment for AC repair without causing relevant regulatory and scientific concerns from cell-based treatments. A deeper understanding of the mechanism of endogenous cartilage healing is furthering the (bio)design and application of these scaffolds. Currently, the utilization of regenerative biomaterials to magnify the repairing effect of joint-resident endogenous stem/progenitor cells (ESPCs) presents an evolving improvement for cartilage repair. This review starts by briefly summarizing the current understanding of endogenous AC repair and the vital roles of ESPCs and chemoattractants for cartilage regeneration. Then several intrinsic hurdles for regenerative biomaterials-based AC repair are discussed. The recent advances in novel (bio)design and application regarding regenerative biomaterials with favorable biochemical cues to provide an instructive extracellular microenvironment and to guide the ESPCs (e.g. adhesion, migration, proliferation, differentiation, matrix production, and remodeling) for cartilage repair are summarized. Finally, this review outlines the future directions of engineering the next-generation regenerative biomaterials toward ultimate clinical translation