MOEA/D with Adaptive Weight Adjustment

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover, an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discontinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, [Formula: see text]-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.</jats:p

The evaluation of ammonia tolerance in introduced and local Pacific white shrimp, Litopenaeus vannamei, populations in China

The white shrimp, Litopenaeus vannamei, is one of the most valuable commodities in the global seafood trade. Affected by high-density farming environments, ammonia accumulates in shrimp cultures and has a strong toxic effect, resulting in poor shrimp survival and poor immune function and metabolism. We selected six different populations of L. vannamei from Xing Hai No.1 (A and B), CHAI, Sy Aqua, PRIMO, and a second-generation Sy Aqua-PRIMO hybrid population (SP). The shrimps (3.24 ± 0.71 cm body length) were exposed to ammonia (24 h, 48 h), followed by recovery (R48 h, R96 h) to assess the tolerance of different populations. The survival rate (SR), immune-related enzymes (superoxide dismutase SOD, catalase CAT, and Glutathione peroxidase GSH-PX), Malondialdehyde (MDA), and metabolism (glutamate dehydrogenase GDH, glutamine synthetase GS, and aspartic acid transaminase GOT) and were measured at different populations under acute ammonia stress. Multiple comparisons of the ammonia resistance index from six populations showed that the expression of these indicators varied among the populations. The degree of lipid peroxidation in the Sy Aqua and PRIMO was significantly higher than in the other populations (P < 0.05), and the ammonia metabolism index was poor. The GDH and GOT genes for the Xing Hai No.1 (A) were higher than for the other populations. Mortality and physiological indicators recovered to varying degrees for all experimental populations following 96 h of ammonia relief, whereas the Sy Aqua and PRIMO showed a noticeable lag. These results indicated that the immunity and metabolic capacity of Xing Hai No.1 (A) might be higher than those of Sy Aqua and PRIMO. These data could have value in developing future scientific breeding schemes and in the sustainability of shrimp farming

Thermal characteristics analysis of the slide carriage system of the X axis based on the thermal contact resistance and the environment temperature change

In the electrical discharge machine (EDM), the slide carriage system of the X axis connects the lathe bed and the ram of the Y axis, its thermal-deformation has a directly effect on machining precision. Based on Solid-works and ANSYS Workbench software to build the finite element model (FEM) of the slide carriage system, the heat generation of the motor on the Y axis, and the frictional heat of the bearing and guide as the main thermal source, there are two cases: applied and no contact thermal resistance (TCR) as the thermal-structure coupling deformation analysis. Established a model of the natural convection heat transfer coefficient with the temperature-change according to the actual measured the temperature curve of workshop and mathematical logarithm principle. The analysis told us that: in the thermal analysis of precision mechanical equipment, heat source comes from the external environment temperature and motor heat production, at the same time, the contact thermal resistance and the natural convection heat transfer coefficient with the temperature-change for the thermal characteristics of the equipment will make an important influence

Learning Interpretable Rules for Scalable Data Representation and Classification

Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability. To obtain both good scalability and interpretability, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretable non-fuzzy rules for data representation and classification. To train the non-differentiable RRL effectively, we project it to a continuous space and propose a novel training method, called Gradient Grafting, that can directly optimize the discrete model using gradient descent. A novel design of logical activation functions is also devised to increase the scalability of RRL and enable it to discretize the continuous features end-to-end. Exhaustive experiments on ten small and four large data sets show that RRL outperforms the competitive interpretable approaches and can be easily adjusted to obtain a trade-off between classification accuracy and model complexity for different scenarios. Our code is available at: https://github.com/12wang3/rrl.Comment: Accepted by IEEE TPAMI in October 2023; Interpretable ML; Neuro-Symbolic AI; Preliminary conference version (NeurIPS 2021) available at arXiv:2109.1510

High CRLF2 expression associates with IKZF1 dysfunction in adult acute lymphoblastic leukemia without CRLF2 rearrangement.

Overexpression of cytokine receptor-like factor 2 (CRLF2) due to chromosomal rearrangement has been observed in acute lymphoblastic leukemia (ALL) and reported to contribute to oncogenesis and unfavorable outcome in ALL. We studied B-ALL and T-ALL patients without CRLF2 rearrangement and observed that CRLF2 is significantly increased in a subset of these patients. Our study shows that high CRLF2expression correlates with high-risk ALL markers, as well as poor survival. We found that the IKZF1-encoded protein, Ikaros, directly binds to the CRLF2 promoter and regulates CRLF2 expression in leukemia cells. CK2 inhibitor, which can increase Ikaros activity, significantly increases Ikaros binding in ALL cells and suppresses CRLF2 expression in an Ikaros-dependent manner. CRLF2 expression is significantly higher in patients with IKZF1 deletion as compared to patients without IKZF1 deletion. Treatment with CK2 inhibitor also results in an increase in IKZF1 binding to the CRLF2 promoter and suppression of CRLF2 expression in primary ALL cells. We further observed that CK2 inhibitor induces increased H3K9me3 histone modifications in the CRLF2 promoter in ALL cell lines and primary cells. Taken together, our results demonstrate that high expression of CRLF2 correlates with high-risk ALL and short survival in patients without CRLF2 rearrangement. Our results are the first to demonstrate that the IKZF1-encoded Ikaros protein directly suppresses CRLF2 expression through enrichment of H3K9me3 in its promoter region. Our data also suggest that high CRLF2 expression works with the IKZF1 deletion to drive oncogenesis of ALL and has significance in an integrated prognostic model for adult high-risk ALL

Unveiling the Roles of Binder in the Mechanical Integrity of Electrodes for Lithium-Ion Batteries

In lithium-ion secondary batteries research, binders have received the least attention, although the electrochemical performance of Li-ion batteries such as specific capacity and cycle life cannot be achieved if the adhesion strengths between electrode particles and between electrode films and current collectors are insufficient to endure charge-discharge cycling. In this paper, the roles of binders in the mechanical integrity of electrodes for lithium-ion batteries were studied by coupled microscratch and digital image correlation (DIC) techniques. A microscratch based composite model was developed to decouple the carbon particle/particle cohesion strength from the electrode-film/copper-current-collector adhesion strength. The dependences of microscratch coefficient of friction and the critical delamination load on the PVDF binder content suggest that the strength of different interfaces is ranked as follows: Cu/PVDF \u3c carbon-particle/PVDF \u3c PVDF/PVDF. The particle/particle cohesion strength increases while electrode-film/current-collector adhesion strength decreases with increasing PVDF binder content (up to 20% of binder). The electrolyte soaking-and-drying process leads to an increase in particle/particle cohesion but a decrease in electrode-film/copper-current-collector adhesion. Finally, the methodology developed here can provide new guidelines for binder selection and electrode design and lay a constitutive foundation for modeling the mechanical properties and performance of the porous electrodes in lithium-ion batteries