Induced-charge electroosmosis around conducting and Janus cylinder in microchip

The induced-charge elecetroosmosis around conducting/Janus cylinder with arbitrary Debye thickness is studied numerically, when an direct current weak electric filed is suddenly applied in a confined microchannel. It’s found that there are four large circulations around the conducting cylinder, and the total flux in the microchannel is zero; there are two smaller circulations around the Janus cylinder, and they are compressed to wall. A bulk flux, which has a parabolic relation with the applied electric field, is also predicted

Improved particle swarm optimization algorithm for multi-reservoir system operation

AbstractIn this paper, a hybrid improved particle swarm optimization (IPSO) algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization (PSO) algorithm is improved in two ways: (1) The linearly decreasing inertia weight coefficient (LDIWC) is replaced by a self-adaptive exponential inertia weight coefficient (SEIWC), which could make the PSO algorithm more balanceable and more effective in both global and local searches. (2) The crossover and mutation idea inspired by the genetic algorithm (GA) is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation (DPSA) algorithm

Establishment and simulation of dynamic model of backfilling hydraulic support with six pillars

A backfilling hydraulic support with six pillars used for working face roof support and goaf backfilling in coal mine is designed, and the structure and working principle of the backfilling hydraulic support are described. In order to improve the working stability of backfilling hydraulic support, the differential equations of motion and the state space model of backfilling hydraulic support are established based on Lagrange method and space coordinate system. According to the support structure and related parameters, the differential equation of motion of the system is solved by MATLAB. The influence law of disturbance frequency and amplitude on the top beam vertical vibration, roll and pitch vibration is obtained. The results show that the vertical vibration and roll vibration of the top beam are more severe in the low frequency range. And the degree of vibration gradually decreases as the disturbance frequency increases. As the disturbance amplitude increases, the vibration of the top beam is more severe. The vibration of the backfilling hydraulic support and the deformation distribution nephogram of the top beam are obtained by the finite element analysis, the validity of the dynamic model is verified by finite element simulation. The results provide the basis for the optimization design and the stability evaluation of backfilling hydraulic support

Overexpression of an isoform of AML1 in acute leukemia and its potential role in leukemogenesis

AML1/RUNX1 is a critical transcription factor in hematopoietic cell differentiation and proliferation. From the _AML1_ gene, at least three isoforms, _AML1a_, _AML1b_ and _AML1c_, are produced through alternative splicing. AML1a interferes with the function of AML1b/1c, which are often called AML1. In the current study, we found a higher expression level of _AML1a_ in ALL patients in comparison to the controls. Additionally, AML1a represses transcription from promotor of macrophage-colony simulating factor receptor (M-CSFR) mediated by AML1b, indicating that AML1a antagonized the effect of AML1b. In order to investigate the role of _AML1a_ in hematopoiesis and leukemogenesis _in vivo_, bone marrow mononuclear cells (BMMNCs) from mice were transduced with AML1a and transplanted into lethally irradiated mice, which develop lymphoblastic leukemia after transplantation. Taken together, these results indicate that overexpression of AML1a may be an important contributing factor to leukemogenesis

Toward Real-world Single Image Deraining: A New Benchmark and Beyond

Single image deraining (SID) in real scenarios attracts increasing attention in recent years. Due to the difficulty in obtaining real-world rainy/clean image pairs, previous real datasets suffer from low-resolution images, homogeneous rain streaks, limited background variation, and even misalignment of image pairs, resulting in incomprehensive evaluation of SID methods. To address these issues, we establish a new high-quality dataset named RealRain-1k, consisting of $1,120$ high-resolution paired clean and rainy images with low- and high-density rain streaks, respectively. Images in RealRain-1k are automatically generated from a large number of real-world rainy video clips through a simple yet effective rain density-controllable filtering method, and have good properties of high image resolution, background diversity, rain streaks variety, and strict spatial alignment. RealRain-1k also provides abundant rain streak layers as a byproduct, enabling us to build a large-scale synthetic dataset named SynRain-13k by pasting the rain streak layers on abundant natural images. Based on them and existing datasets, we benchmark more than 10 representative SID methods on three tracks: (1) fully supervised learning on RealRain-1k, (2) domain generalization to real datasets, and (3) syn-to-real transfer learning. The experimental results (1) show the difference of representative methods in image restoration performance and model complexity, (2) validate the significance of the proposed datasets for model generalization, and (3) provide useful insights on the superiority of learning from diverse domains and shed lights on the future research on real-world SID. The datasets will be released at https://github.com/hiker-lw/RealRain-1

Aqua­bis(triphenyl­phosphine-κP)copper(I) tetra­fluoridoborate

In the title compound, [Cu(C18H15P)2(H2O)]BF4, the CuI atom is coordinated by two P atoms from triphenyl­phosphine ligands and one water mol­ecule in a distorted trigonal geometry. In the BF4 − anion, three F atoms are disordered over two sites around the B—F bond, the site-occupancy ratio being 0.67 (6):0.33 (6). The Cu⋯F distance of 2.602 (5) Å between the Cu atom and the ordered F atom may suggest a weak but genuine inter­action. O—H⋯F and weak C—H⋯F hydrogen bonding is present in the crystal structure

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