Search

The performance of traditional penalty boundary intersection (PBI) decomposition-based evolutionary algorithm is totally determined by the penalty factor. The fixed penalty factor causes the imbalance between the convergence and the diversity when solving many-objective problems. So, an adaptive decomposition evolutionary algorithm based on environmental information (MaOEA/ADEI) is proposed to solve the imbalance. The penalty factor of PBI decomposition is determined by the environmental information (include distribution information of weight vectors and population). Furthermore, the parent individual selection strategy is introduced to select promising individuals for variation and the weight vectors adaption strategy is used to handle problems with scaled objectives. Comparisons with 4 algorithms on 24 benchmark instances are used to test the property of MaOEA/ADEI. The experimental results show MaOEA/ADEI performs best on 14 test instances. • A convergence (CW) function is used to construct a mating pool in order to get offspring with good convergence performance. • The adaptive decomposition method based on environmental information is proposed to dynamically adjust the penalty factor of PBI. • The weight vectors adaptation strategy is used to produce uniformly distributed individuals specially the disparately scaled objectives. [ABSTRACT FROM AUTHOR]

High-speed cold tandem rolling process control system consists of complex mechanical and electrical equipments. The coupling association of these equipments makes multi-objective rolling process complicated to be predicted and controlled. In order to achieve higher prediction precision, a multi-parameter depth perception model is established based on a deep belief network. To get higher control precision in real time, a multi-objective rolling optimization method is introduced, which is supported by many-objective evolutionary algorithm. Five objectives are selected as rolling schedule optimization objective: equal relative power margin, slippage prevent, good flatness, total energy consumption and energy consumption per ton. Simulation results show that many-objective evolutionary algorithm based on decomposition and Gaussian mixture model achieves a set of balance solutions on these objectives. The proposed method could not only predict rolling force and rolling power in real time, but also give the solutions for many-objective reduction schedule. • The relationships between different control variables and optimization objectives are analyzed. • The model combined deep learning network and mechanism model is used to improve the prediction of rolling parameter. • Tension and speed, which effect rolling power and quality, are considered for rolling schedule. • Many-objective evolutionary algorithm is employed to optimize rolling schedule. [ABSTRACT FROM AUTHOR]

With continued exposure to CuO nanoparticles (NPs) which were toxic to organisms, the performance of wastewater treatment facility might be affected. In present study, the feasibility of constructed wetlands (CWs) for wastewater treatment containing CuO NPs and common pollutants was comprehensively explored. It was found that CWs removed 98.80–99.84% CuO NPs and 90.91–91.83% COD within 300 days. However, N and P removals were affected to varying degrees by CuO NPs. N removal was inhibited only by 0.5 mg/L CuO NPs with 19.75% decreases on the mean from day 200–300. P removal was reduced by 3.80–50.75% and 1.92–7.19% under exposure of 0.5 and 5 mg/L CuO NPs throughout the experiment. Moreover, CuO NPs changed the adsorption potential of P and ammonium-N on sand-biofilm. Cu concentrations in spatial distribution decreased, while they in temporal distribution increased from 36.94 to 97.78 μg/g and from 70.92 to 282.66 μg/g at middle sand layer exposed to 0.5 and 5 mg/L CuO NPs. Mass balance model showed that substrate-biofilm was main pollutant sink for CuO NPs, N, and P. The minor Cu was absorbed by plants exposed to 0.5 and 5 mg/L CuO NPs, which decreased N by 53.40% and 18.51%，and P by 52.35% and 21.62%. Sequencing analysis indicated that CuO NPs also altered spatial microbial community. N-degrading bacteria (Rhodanobacter , Thauera , Nitrospira) changed differently, while phosphate accumulation organisms (A cinetobacter , Pseudomonas , Microlunatus) reduced. Overall, the negative effects of CuO NPs on N and P removal should be noted when CWs as ecological technologies are used to treat CuO NPs-containing wastewater. [Display omitted] • CWs were highly effective for CuO NPs and COD removal. • CuO NPs at 0.5 mg/L caused more inhibitions on N and P removals than at 5 mg/L. • Substrate-biofilm was main pollutant sink, especially for CuO NPs. • Compared to minor Cu, N and P were abundant in plant and decreased by CuO NPs. • N and P functional genera showed different response to CuO NPs. [ABSTRACT FROM AUTHOR]

Objective Multi-sequence magnetic resonance (MR) imaging is a frequently used method for characterising and quantifying white matter (WM) lesions in the human brain. The number and size of lesions are commonly determined to assess the diseases in clinical settings. Accurate WM lesion segmentation is very important for disease diagnosis and progression surveillance. The goal of this paper is to present an approach for improving WM lesion segmentation accuracy. Methods In this paper, we propose a novel method integrating the multi-sequence and spatial information in a Bayesian framework for WM lesion detection from multi-sequence human brain magnetic resonance images (MRIs). The entire framework is based on a three-step approach: First, a multinomial logistic regression (MLR) algorithm is used to assess the conditional probability distributions of intensities in WM lesions and brain tissues from training data. Second, the spatial information previously given by a Markov random field (MRF) prior is integrated with multimodal information in the Bayesian framework to strengthen the spatial constraint. This step is especially effective when WM lesions have intensity values similar to those of other brain tissues. Finally, a post-processing step based on biological knowledge is used to remove some false positives. Results Our method is validated using two datasets. The experimental results show that our algorithm agrees well with manual expert labelling and indicate that our multimodal spatial-based method offers a significant advantage over other approaches. Conclusions A three-step approach for combining multimodal and spatial information is proposed for WM lesion segmentation. The advantages of this approach are discussed, and a practical application to two datasets is presented. [ABSTRACT FROM AUTHOR]

Mn 2 O 3 is a conventional catalyst that is applied in photothermal catalytic oxidation of toluene while regular anions like SO 4 2-, Cl-, and NO 3 - from different Mn precursors are left in Mn 2 O 3. When applied in toluene degradation, the declining activity of Mn 2 O 3 is always observable because the residual anions block the Mn4+/Mn3+ active sites, shield the oxygen vacancies, and cube the production of active oxygen species (O 2 -, O-). Herein, it is found that the residual SO 4 2-, Cl-, and NO 3 - make Mn 2 O 3 incapacitated in deeply oxidizing the intermediates and cutting off the reaction process, and their influence ranks in the order of SO 4 2->Cl->NO 3 -. Compared with pristine Mn 2 O 3 , the drops in CO 2 conversion efficiency account for 11 %, 60 %, and 73 %, respectively. Nevertheless, rinsing with water is an effective method to remove the residual anions, and the more the rinsing, the better the toluene mineralization degree. This work provides a profound cognition of the deactivation mechanism of SO 4 2-, Cl-, and NO 3 - and points out an effective way to abate their negative effect for the first time. [Display omitted] • NO 3 - poses a neglectable impact on Mn 2 O 3 's catalytic activity. • SO 4 2- and Cl- anions cover the OVs and active sites, causing reduced catalytic properties. • Multiple rinsing washes the anions away from MnC 2 O 4 to endow high activity of Mn 2 O 3. [ABSTRACT FROM AUTHOR]

Completely removing chromium (Cr) from contaminated water with simultaneous reduction of Cr(VI) and sequestration of Cr(III) is an efficient strategy to restrain its migration in natural water. Herein, sulfonated carbon hemisphere (Labeled as CHS, Inner diameter: 150 nm) worked as a supporter to hold mesoporous TiO 2 (MT) nanoparticles (Diameter: 10 nm), constructing a bifunctional catalyst with photocatalytic activity and adsorption capacity. During a typical Cr removal process, efficient photocatalytic reduction Cr(VI) performed alongside the utter adsorption of the generated Cr(III) on the sulfonated CS by ion exchange for the grafted -SO 3 H+. The reaction rate constant (k) for removing Cr(VI) by TiO 2 -C-SO 3 H is 0.04998 min−1, which is 3.72 times higher than that (0.01342 min−1) unmodified TiO 2 -C, and 23.68 times and that of MT (0.00223 min−1). No residual Cr was determined in the treated water. The highest removal efficiency was achieved when the mass ratio of carbon and TiO 2 hemispheres is 0.62%. Two factors contributing to the exceptional reduction efficiency include improved electron-hole separation efficiency endowed by the CHS conductive framework, sensitive light absorption. Ultimately, the -SO 3 H+ grafted on CHS can immobilize Cr(III) by ion exchange, performing like a resin, which realizes the total removal of Cr(VI) or Cr(III) and restrains the possible hazardous influence. [Display omitted] • An all-in-one bifunctional TiO 2 -C-SO 3 H was prepared. • The strong contact between TiO 2 and CHS constructs an electron channel. • Numerous de-protonation of grafted -SO 3 H+ to anchor Cr3+ cations. • Sulfonated C-TiO 2 has the prospects of actual contaminated water treatment. • TiO 2 -C-SO 3 H photocatalyst has long-term durability. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we developed an iteratively approximated gradient projection algorithm for -minimization problems arising from sparse signal reconstruction in compressive sensing. By introducing a relaxed variable, the noisy problem can be transformed into the problem with equality constraints. The nonsmooth term was tackled by variable-splitting techniques. Thus the problem was transformed into a quadratic programming problem. All linear variables in the objective function were imposed on regularization. Based on ideas of quasi-Lagrangian functions and partial duality, a reduced quadratic programming problem can be obtained iteratively. At each iteration, we applied gradient projection methods with approximated gradients to get the next iterates. The computational experiments show the proposed method is very effective. [Copyright &y& Elsevier]

Oxygen vacancy/Ti3+ engineered TiO 2 nanotube arrays were prepared through electrochemical strategy at high potential (−6 V). The electrons surging from DC power initiate the valance decline of Ti4+, and countless H 2 bubbles released from TiO 2 surface lead to the in-situ exfoliation of some TiO 2 tiny debris, generating point defects containing Ti3+ inside the TiO 2 and oxygen vacancy (OV) embedded on TiO 2. The generated Ti3+/ OV help TiO 2 to increase the harvest toward visible light and serve as active sites for activating molecular oxygen to form superoxide radical (·O 2 -). Besides, as improved separation of electron-hole pairs endowed by the defects, the yield of hydroxyl radicals (·OH) is enhanced compared with the pristine TiO 2. Chloramphenicol (CAP, an antibiotic) molecules can be efficiently degraded with the reduced TiO 2 under the visible light with its degradation and evolution pathway expounded in detail. Fukui index is employed to anticipate the probable sites that are subjected to oxidation by·OH and the MS spectra evidence that the·OH governs the decomposition routes of CAP molecules. ECOSAR simulation evaluates the toxicity of CAP and the intermediates, demonstrating that the final products from the photocatalysis do not post any hazardous risk on ecosystem. [Display omitted] • Oxygen vacancy/Ti3+ engineered TiO 2 nanotube arrays were constructed by in-situ exfoliation. • Oxygen vacancy and Ti3+ serve as intrinsic active sites. • Hydroxyl radicals govern the degradation pathway of CAP molecule. • Final products from the photocatalysis do not pose any hazardous risk on ecosystem. [ABSTRACT FROM AUTHOR]

Spiking Neural Network (SNN) has been recognized as the third generation of neural networks. Conventionally, a SNN can be converted from a pre-trained Artificial Neural Network (ANN) with less computation and memory than training from scratch. But, these converted SNNs are vulnerable to adversarial attacks. Numerical experiments demonstrate that the SNN trained by optimizing the loss function will be more adversarial robust, but the theoretical analysis for the mechanism of robustness is lacking. In this paper, we provide a theoretical explanation by analyzing the expected risk function. Starting by modeling the stochastic process introduced by the Poisson encoder, we prove that there is a positive semidefinite regularizer. Perhaps surprisingly, this regularizer can make the gradients of the output with respect to input closer to zero, thus resulting in inherent robustness against adversarial attacks. Extensive experiments on the CIFAR10 and CIFAR100 datasets support our point of view. For example, we find that the sum of squares of the gradients of the converted SNNs is 13 ∼ 160 times that of the trained SNNs. And, the smaller the sum of the squares of the gradients, the smaller the degradation of accuracy under adversarial attack. • The adversarial robustness of SNNs based on rate encoder is explained mathematically. • A regularizer distinguishes the directly trained SNN from the converted SNN. • The coding length can also change the gradient of the input to affect the robustness. • We compare more types of the trained SNN with the converted SNN. [ABSTRACT FROM AUTHOR]

Dynamic stochastic resonance (DSR) is a distinctive technique for enhancement of dark and low-contrast image. Noise is necessary for DSR based image enhancement and the level of noise will be enlarged simultaneously with brightness, which reduces the perceptual quality of the enhanced image greatly and also increases the difficulty of subsequent denoising because removing high level of noise often leads to serious loss of image details. In this paper, instead of removing noise after the enhancement process is complete, we propose to suppress noise gradually and simultaneously in the process of enhancement. We rewrite the traditional partial differential equation (PDE) based DSR model in variational framework firstly, and then propose a novel total variation regularized (TV) DSR method for image enhancement. The existence and uniqueness of solution of the TV regularized DSR model is proved theoretically. Moreover, we generalize the TV regularized DSR model in variational framework and in PDE framework, respectively, and therefore we can incorporate more existing denoising methods into our approach. Numerical comparisons demonstrate that the proposed technique gives significant performance in terms of contrast and brightness enhancement as well as noise suppression, and therefore can obtain enhanced image with good perceptual quality. [ABSTRACT FROM AUTHOR]

Photoelectrocatalysis (PEC) is an effective approach to eliminate carcinogen hexavalent chromium (Cr(VI)) in wastewater, in which high-performance catalysts are crucial. Herein, controlled growth of thin molybdenum disulphide (MoS 2) nanosheets on self-supported tungsten trioxide (WO 3) created an all-solid-state MoS 2 /WO 3 heterojunction serving as electrode and catalyst simultaneously for removing Cr(VI). Countless small and thin MoS 2 nanoflakes build in a huge and porous interface for harvesting lights and adsorbing chromium species. And the highly conductive WO 3 substrate facilitates the transfer of those photoexcited-electron and therefore suppresses the recombination between electrons and holes. Furthermore, assisted by bias potential, electron streams from external circuit render an electron-rich interface at the MoS 2 /WO 3 cathode, accelerating the Cr(VI) reduction by PEC. At −1.2 V, the PEC reduction efficiency of Cr(VI) reaches 100% within 30 min, surpassing the pristine WO 3 by 2.7 times. The generated Cr(III) ions can be immobilized on the porous MoS 2 /WO 3 cathode through electrostatic attraction, enabling removal of total chromium. More importantly, the Cr(III) anchored to the catalyst can be effortlessly recovered by eluting with clean water, which also refreshes the MoS 2 /WO 3 cathode. This study provides a new approach to fabricating photoelectrodes for sustainable PEC reduction and treatment of Cr(VI) containing wastewater. [ABSTRACT FROM AUTHOR]

• We define a new inner interaction potential function based on the Skellam distribution. • The inner interaction potential function reflects the relationship of pixels in local neighborhood. • Our plug-and play scheme exploits both local and non-local priors of the image. • Our plug-and-play based algorithm can solve the new model efficiently. • Our method performs well for Poisson image denoising in photon-limited case. Photon-limited Poisson image denoising is urgent demand in many application fields, but is particularly challenging because the image structures are often damaged seriously. The effective using of the image prior is very important for improving the quality of the denoised image. In this paper, we exploit the prior of inner statistical relationships among pixels in local spatial neighborhood of the Poisson noisy image and introduce a new Skellam distribution based inner interaction potential function as a distance measurement between the pixels. In the framework of conditional random field (CRF) modeling, we propose a novel Poisson-Skellam distribution based regularization CRF model for photon-limited Poisson noise removal. Using the alternating direction method of multipliers technique, we extend our method to a flexible plug-and-play scheme in which we can combine powerful Gaussian denoising method to improve the denoising performance better. [ABSTRACT FROM AUTHOR]

Fine-grained visual classification (FGVC) is a difficult task due to the challenges of discriminative feature learning. Most existing methods directly use the final output of the network which always contains the global feature with high-level semantic information. However, the differences between fine-grained images are reflected in subtle local regions which often appear in the front of the network. When the texture of the background and object are similar or the proportion of the background is too large, the prediction will be greatly affected. In order to solve the above problems, this paper proposes multi-granularity feature fusion module (MGFF) and two-stage classification based on Vision-Transformer (ViT). The former comprehensively represents images by fusing features of different granularities, thus avoiding the limitations of single-scale features. The latter leverages the ViT model to separate the object from the background at a very small cost, thereby improving the accuracy of the prediction. We conduct comprehensive experiments and achieves the best performance in two fine-grained tasks on CUB-200-2011 and NA-Birds. • A multi-granularity feature fusion module is proposed to solve the limitations of single-scale features. • A two-stage classification based on Vision-Transformer is proposed to reduce background interference on predictions. By leveraging the ViT model, the object can be separated from the background and the details can be enlarged. • Extensive experiments prove the superiority of our model. The visualization results illustrate that our two-stage classification can accurately localize objects and facilitate correct predictions. [ABSTRACT FROM AUTHOR]

Study Objective: Nonsteroidal anti-inflammatory drugs (NSAIDs) play a role in pain relief, especially in postoperative pain caused by inflammation. They have demonstrated significant opioid dose-sparing effects, which help in reducing postoperative effects and opioid side effects. The objective of this meta-analysis was to explore the role of NSAIDs in reducing postoperative pain at different time intervals and provide reference for medication after lumbar spine surgery by a meta-analysis of randomized controlled trials (RCT).Design: A meta-analysis study of randomized controlled trials.Setting: Postoperative recovery area.Patients: Adult patients who have undergone lumbar spine surgery.Intervention: Patients received NSAIDs for pain control after lumbar spine surgery.Measurements: Standardized mean difference (SMD) and 95%CI were used to evaluate the visual analog scale of postoperative pain.Main Results: Four hundred and eight participants from eight studies were included in this study. The difference between the NSAIDs group and placebo is significant in 0-6, 12, and 24h groups (overall: SMD=-0.72, 95%CI -0.98 to -0.45; 0-6h: SMD=0.50, 95%CI -0.81 to -0.19; 12h: SMD=-1.07, 95%CI -1.45 to -0.70; 24h: SMD=-1.16, 95%CI -1.87 to -0.45). Heterogeneity and publication bias were observed in the 0-6 and 24h groups.Conclusion: NSAIDs are effective in postoperative analgesia after lumbar spine surgery. The study type, NSAID dose, different surgery types, and analgesic type might influence the efficacy of NSAIDs. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose an improved variational level set approach to correct the bias and to segment the magnetic resonance (MR) images with inhomogeneous intensity. First, we use a Gaussian distribution with bias field as a local region descriptor in two-phase level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. By using the information of the local variance in this descriptor, our method is able to obtain accurate segmentation results. Furthermore, we extend this method to three-phase level set formulation for brain MR image segmentation and bias field correction. By using this three-phase level set function to replace the four-phase level set function, we can reduce the number of convolution operations in each iteration and improve the efficiency. Compared with other approaches, this algorithm demonstrates a superior performance. [Copyright &y& Elsevier]

Abstract: Image restoration is one of the most classical problems in image processing. The main issues of image restoration are deblurring, denoising and preserving fine details. In order to obtain good restored images, we propose a new image restoration method based on a compound regularization model associated with the weighted anisotropic total variation (WATV) and the tetrolets-based sparsity. The WATV recovers sharp edges by embedding two directional gradient operators into the original anisotropic total variation (ATV), and the tetrolet transform adapts its basis to the local image structures. Thus, our model can preserve details such as textures and edges in the processing of image restoration by combining the WATV with the tetrolets-based sparsity. We present an alternate iterative scheme which consists of the variable splitting method and the operator splitting method to solve the proposed minimization problem. Experimental results demonstrate the efficiency of our image restoration method for preserving the structure details and the sharp edges of image. [Copyright &y& Elsevier]