An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility

During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, however, the performance of an MOEA can strongly depend on the Pareto front shape of the problem to be solved, whereas most existing MOEAs show poor versatility on problems with different shapes of Pareto fronts. To address this issue, we propose an MOEA based on an enhanced inverted generational distance indicator, in which an adaptation method is suggested to adjust a set of reference points based on the indicator contributions of candidate solutions in an external archive. Our experimental results demonstrate that the proposed algorithm is versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multi-objective and many-objective optimization

Design and Assembly Considerations in the Engineering of Vascular Tissue

Native vascular tissue functions are highly dependent on structural organization at the super-cellular, cellular, and sub-cellular spatial scales. We hypothesized that the structure-function relationship of vascular tissues in vivo can be leveraged to engineer vascular tissues in vitro by prescribing the shape of constituent cells and their assembly into organized three-dimensional structures. To this end, we first asked if vascular smooth muscle cell shape influences cellular contractility. We engineered human vascular smooth muscle cells to assume similar shapes to those in elastic and muscular arteries and then measured their contraction while stimulating with endothelin-1. We found that vascular smooth muscle cells with elongated shapes exhibited lower contractile strength but a greater percentage increase in contraction after endothelin-1 stimulation, suggesting that elongated vascular smooth muscle cell shape endows the muscular artery with greater dynamic contractile range. Next, we sought to assemble cells into tissues by employing a three-dimensional cellular patterning strategy based on the folding of porous, thin polymer films. We assembled different three-dimensional endothelial and vascular smooth muscle organizations by patterning two-dimensional poly(lactic-co-glycolic) acid and collagen thin films with cell suspensions at prescribed locations. The films were subsequently folded following Miura-ori geometry guidelines and the matrices were embedded subcutaneously in immunodeficient mice in order to assess the vascularization of the implanted constructs. We found that spatial organization that allowed endothelial and vascular smooth muscle cells to interact adjacent to each other laterally in the same folding plane created the densest vascularized network, suggesting that three-dimensional structural organization of vascular cells can influence the formation of vascularized networks. Taken together, our result shows that functional vascular tissues in vitro can be engineered by encoding structure cues in their design and assembly.Engineering and Applied Science

A Novel Adaptive Spectrum Noise Cancellation Approach for Enhancing Heartbeat Rate Monitoring in a Wearable Device

This paper presents a novel approach, Adaptive Spectrum Noise Cancellation (ASNC), for motion artifacts removal in Photoplethysmography (PPG) signals measured by an optical biosensor to obtain clean PPG waveforms for heartbeat rate calculation. One challenge faced by this optical sensing method is the inevitable noise induced by movement when the user is in motion, especially when the motion frequency is very close to the target heartbeat rate. The proposed ASNC utilizes the onboard accelerometer and gyroscope sensors to detect and remove the artifacts adaptively, thus obtaining accurate heartbeat rate measurement while in motion. The ASNC algorithm makes use of a commonly accepted spectrum analysis approaches in medical digital signal processing, discrete cosine transform, to carry out frequency domain analysis. Results obtained by the proposed ASNC have been compared to the classic algorithms, the adaptive threshold peak detection and adaptive noise cancellation. The mean (standard deviation) absolute error and mean relative error of heartbeat rate calculated by ASNC is 0.33 (0.57) beats·min-1 and 0.65%, by adaptive threshold peak detection algorithm is 2.29 (2.21) beats·min-1 and 8.38%, by adaptive noise cancellation algorithm is 1.70 (1.50) beats·min-1 and 2.02%. While all algorithms performed well with both simulated PPG data and clean PPG data collected from our Verity device in situations free of motion artifacts, ASNC provided better accuracy when motion artifacts increase, especially when motion frequency is very close to the heartbeat rate

Cryo-EM Studies of TMEM16F Calcium-Activated Ion Channel Suggest Features Important for Lipid Scrambling.

As a Ca2+-activated lipid scramblase and ion channel that mediates Ca2+ influx, TMEM16F relies on both functions to facilitate extracellular vesicle generation, blood coagulation, and bone formation. How a bona fide ion channel scrambles lipids remains elusive. Our structural analyses revealed the coexistence of an intact channel pore and PIP2-dependent protein conformation changes leading to membrane distortion. Correlated to the extent of membrane distortion, many tightly bound lipids are slanted. Structure-based mutagenesis studies further reveal that neutralization of some lipid-binding residues or those near membrane distortion specifically alters the onset of lipid scrambling, but not Ca2+ influx, thus identifying features outside of channel pore that are important for lipid scrambling. Together, our studies demonstrate that membrane distortion does not require open hydrophilic grooves facing the membrane interior and provide further evidence to suggest separate pathways for lipid scrambling and ion permeation

How are typical urban sewage treatment technologies going in China: from the perspective of life cycle environmental and economic coupled assessment

Sewage treatment is an important public service, but it consumes a lot of energy and chemicals in the process of removing wastewater pollutants, which may cause the risk of pollution transfer. To find the corresponding hot issues, this paper took the lead in integrating life cycle assessment (LCA) with life cycle costing (LCC) to evaluate four most typical sewage treatment technologies with more than 85% share in China. It is found that anaerobic/anoxic/oxic (AAO) was the optimal treatment scheme with relatively small potential environmental impact and economic load. The normalized results show that the trends of the four technologies on eleven environmental impact categories were basically the same. Marine aquatic ecotoxicity potential accounted for more than 70% of the overall environmental impact. Contribution analysis indicates that electricity and flocculant consumption were the main processes responsible for the environmental and economic burden. Overall, electricity consumption was the biggest hot spot. Sensitivity analysis verifies that a 10% reduction in electricity could bring high benefits to both the economy and the environment. These findings are expected to provide effective feedback on the operation and improvement of sewage treatment

Efficiency assessment of rural domestic sewage treatment facilities by a slacked-based DEA model

In the context of sustainable development, a number of rural domestic sewage treatment facilities had been built in China to solve the problem of rural domestic sewage pollution. The comprehensive, quantitative and objective efficiency assessment of facilities is urgent. This study used a non-radial slacked-based data envelopment analysis model combined with cluster analysis to construct an index system covering multiple aspects, including three inputs and four outputs to assess 681 facilities. These samples selected from the biggest demonstration area are the most representative for and exceed 2/5 of the running facilities all over the country. The average efficiency score of samples was 0.496 meaning the improvement potential was about 50.4%. Only 27 samples were relatively effective, scoring 1. The remaining 654 facilities had different levels of input excesses or output shortfalls, which should be the key objects to improve overall performance. In addition, there was evidence that output indicators had more room for improvement than input indicators. The analysis of sensitivity on inputs and outputs confirmed that the idleness and poor treatment effects of rural sewage treatment facilities should be concerned. Finally, Kruskal–Wallis non-parametric test verified that technology and load rate of facilities have significant impacts on efficiency. The performance evaluation results could not only provide guidance for the local government to strengthen the supervision and operation of facilities, but also potentially provide reference for the construction, operation and management of rural sewage treatment facilities in China

Environment Geophysics on Environmental protection in China

The environmental problem conexists with the birth and development of human being. When people entered on the Industrial Revolution, especially the twentieth century, with the rapid improvement of the productivity level, the natural resources has been exploited and used at the unprecedented level. When people are creating the material wealth, they are also producing more and more pollution. The environmental problem has been more and more serious. This problem has already done harm to the human existence directly. In recent 20 years, the environmental problem has been one of the most important problems that people are concerned with. In China, there are also many environmental problems such as air pollution, water pollution, refuse treatment, desertification, sand calamity, soil erosion, drought, flood, biodiversity damage,and so on. Some of these problems have already affected the development of national economy and the living of people. So using the modern technology, uniting different subjects, studying these problems roundly and systematacially, and harnessing the pollution are important to the sustainable development of the society and economy. Key words: Geophysics; Environmental protection; pollutio

Experimental investigation of the non-Markovian dynamics of classical and quantum correlations

We experimentally investigate the dynamics of classical and quantum correlations of a Bell diagonal state in a non-Markovian dephasing environment. The sudden transition from classical to quantum decoherence regime is observed during the dynamics of such kind of Bell diagonal state. Due to the refocusing effect of the overall relative phase, the quantum correlation revives from near zero and then decays again in the subsequent evolution. However, the non-Markovian effect is too weak to revive the classical correlation, which remains constant in the same evolution range. With the implementation of an optical $\sigma_{x}$ operation, the sudden transition from quantum to classical revival regime is obtained and correlation echoes are formed. Our method can be used to control the revival time of correlations, which would be important in quantum memory.Comment: extended revision, accepted for publication in Physical Review

Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search

Cognitive diagnosis plays a vital role in modern intelligent education platforms to reveal students' proficiency in knowledge concepts for subsequent adaptive tasks. However, due to the requirement of high model interpretability, existing manually designed cognitive diagnosis models hold too simple architectures to meet the demand of current intelligent education systems, where the bias of human design also limits the emergence of effective cognitive diagnosis models. In this paper, we propose to automatically design novel cognitive diagnosis models by evolutionary multi-objective neural architecture search (NAS). Specifically, we observe existing models can be represented by a general model handling three given types of inputs and thus first design an expressive search space for the NAS task in cognitive diagnosis. Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task's search space by maximizing model performance and interpretability. In the MOGP design, each architecture is transformed into a tree architecture and encoded by a tree for easy optimization, and a tailored genetic operation based on four sub-genetic operations is devised to generate offspring effectively. Besides, an initialization strategy is also suggested to accelerate the convergence by evolving half of the population from existing models' variants. Experiments on two real-world datasets demonstrate that the cognitive diagnosis models searched by the proposed approach exhibit significantly better performance than existing models and also hold as good interpretability as human-designed models.Comment: 15 pages, 12 figures, 5 table