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The 3-year COVID-19 epidemic has a huge impact on the survival and development of micro and small enterprises. Taking micro and small enterprises in Beijing as an example, the paper analyzes the impact of COVID-19 epidemic on micro and small enterprises, as well as the survival of micro and small enterprises in response to COVID-19 and the changes of practitioners' working style, and focuses on the change of business philosophy, change of business objectives, and analyzed the business strategies and innovations of micro and small enterprises in the post-epidemic era, focusing on five aspects: change in business philosophy, change in business objectives, change in business strategies, innovation in business methods, and innovation in business transformation.

Addressing non-convexity plays a fundamental role in solving the optimal electricity-gas flow models. In this paper, an improved spatial branch-and-bound algorithm is proposed to solve the non-convex problem, which is formulated as a mixed-integer bilinear programming, for its exact solution. The core of the algorithm is to divide the non-convex model into small and convex sub-problems by branching on some specific continuous variables, so that the problem can be equivalent to a rooted tree for exploration. The exactness of the algorithm is guaranteed by the same criterion as the classical branch-and-bound algorithm. To alleviate the computational burden, a novel two-stage spatial branching strategy is developed to improve the effectiveness and efficiency of the branching operations. The performance of the proposed algorithm is verified on two integrated electricity-gas systems with different sizes. Numerical results demonstrate that our method achieves a balance among feasibility, optimality and efficiency. The comparison with another 6 convexification-based methods, 3 state-of-the-art non-convex optimization solvers, and 2 spatial branch-and-bound algorithms with classical branching rules further shows the superiority of the proposed algorithm.

An optical parametric oscillator based on

Ion selectivity is an essential property of ion-selective membranes (ISMs). To date, all of the artificial ISMs have been reported to exhibit sole ion selectivity (SIS), either cation or anion selectivity. Here, we first demonstrate unconventional dual ion selectivity (DIS) in a bipolar channel membrane determined by the forward side toward ion flux. When the bipolar membrane meets the conditions of opposite ion selectivities and comparable resistance for both constructive layers, no matter which layer faces the ion flux, it functions as a selective layer and determines the selectivity of the whole membrane. The exploration of the unconventional DIS property inspires us to fabricate a new generation of ISMs, as well as other membranes for separation.

In the last decade, DNA-based tension sensors have made significant contributions to the study of the importance of mechanical forces in many biological systems. Albeit successful, one shortcoming of these techniques is their inability to reversibly measure receptor forces in a higher regime (that is, >20 pN), which limits our understanding of the molecular details of mechanochemical transduction in living cells. Here, we developed a reversible shearing DNA-based tension probe (RSDTP) for probing molecular piconewton-scale forces between 4 and 60 pN transmitted by cells. Using these probes, we can easily distinguish the differences in force-bearing integrins without perturbing adhesion biology and reveal that a strong force-bearing integrin cluster can serve as a ‘mechanical pivot’ to maintain focal adhesion architecture and facilitate its maturation. The benefits of the RSDTP include a high dynamic range, reversibility and single-molecule sensitivity, all of which will facilitate a better understanding of the molecular mechanisms of mechanobiology. Li et al. develop reversible shearing DNA-based tension probes to quantify molecular piconewton-scale forces, estimate the number of mechanically active receptors with single-molecule sensitivity and study mechanisms of force transduction in live cells.

Mechanosensitive ion channels play crucial roles in physiological activities, where small mechanical stimuli induce the deformation of the ion channels, and are further transformed into significant electrochemical signals. Artificial ion channels with stiff modulus have been developed to mimic mechanosensory behaviors. However, fabricating flexible mechanosensitive channels that are capable of regulating ionic current upon dramatic deformation still has remained a challenge. Here, we demonstrate bioinspired high-density elastomeric channels self-assembled by polyisoprene-b-poly4-vinylpyridine (PI-b-P4VP), which exhibit ultra-mechanosensitive ion transport. The PI-formed continuous elastic matrix can transmit external force into internal tension. While P4VP forms transmembrane ion-selective channels which undergo dramatic deformation responded to mechanical stimuli. The integrated P4VP channels present an ultra-mechanosensitive electrochemical response, with five orders of magnitude more sensitive than biological mechanosensitive channels. The results deepen the comprehension of the mechanosensory mechanism in cells and provide a promising avenue for designing flexible and responsive channel membranes.

Ion-exchange membranes (IEMs) convert osmotic energy into electricity when embedded in a reverse electrodialysis cell. IEMs with both high permselectivity and ionic conductivity are highly needed to increase the energy conversion efficiency. The ionic conductivity can be improved by increasing the content of immobile charge carriers, but it is always accompanied by undesirable permselectivity decrease due to excess swelling. Until now, breaking the permselectivity-conductivity tradeoff still has remained a challenge. Here, we demonstrate a membrane with the least ion-exchange capacity (∼10

Robust optimization (RO) has been a proficient approach for uncertainty dispatch in microgrids. In view of the strong conservativeness of traditional RO models, this article proposes a historical-correlation-driven (HCD) RO method for the MG dispatch problem. Firstly, the distinct spatiotemporal correlations in renewable energy (RE) generation are confirmed based on the historical data series of RE power. To effectively extract the correlation characteristics, the new intervals that envelop all correlation data points of similar days are formulated via the line-fitting approach, and the interval boundaries are added as liner constraints into the polyhedral uncertainty set that actively integrates the spatiotemporal correlations and avoids unreasonable scenarios. Secondly, a gradient-approximating decomposition algorithm with equilibrium constraints is put forward to address the nonlinear RO problem caused by non-independent uncertainties. The alternative optimization procedure ensures the gradient ascent of the objective, thus achieving the fast solution convergence of the Max-Min model with binary recourse variables. Besides, the equilibrium constraints realize the precise linearization of bilinear terms, as well as avoid the introduction of numerous binaries. Case tests verify the effectiveness and superiority of the proposed HCD-RO method comparing with existing models and algorithms.

Objective. In order to deeply understand the neurophysiological mechanism of the spectra decrease in mild cognitive impairment (MCI), this paper studies a new neural mass model, which can simulate various intracerebral electrophysiological activities. Approach. In this study, a thalamo-cortical coupled neural mass model (TCC-NMM) is proposed. The influences of the coupling coefficients and other key parameters on the model spectra are simulated. Then, the unscented Kalman filter (UKF) algorithm is used to reversely identify the parameters in the TCC-NMM. Furthermore, the TCC-NMM and UKF are combined to analyze the spectra reduction mechanism of electroencephalogram (EEG) signals in MCI patients. The independent sample t-test is carried out to statistical analyze the differences of the identified parameters between MCI and normal controls. The Pearson correlation analysis is used to analyze the intrinsic relationship between parameters and the scores of the comprehensive competence assessment scale. Main results. The simulation results show that the decreased cortical synaptic connectivity constants C 1 can result in spectra decrease of the TCC-NMM outputs. The real EEG analysis results show that the identified values of parameter C 1 are significant lower in the MCI group than in control group in frontal and occipital areas and the parameters C 1 are positively correlated with the Montreal Cognitive Assessment (MoCA) scores in the two areas. This consistency suggests that the cortical synaptic connectivity loss from pyramidal cells to excitatory interneurons (eIN) may be one of the neural mechanisms of EEG spectra decrease in MCI. Significance. (a) In this study, a new mathematical model TCCNMM based on anatomy and neurophysiology is proposed. (b) All key parameters in TCC-NMM are studied in detail through the forward and reverse analysis and the influence of these parameters on the output spectra of the model is pointed out. (c) The possible neural mechanism of the decreased spectra in MCI patients is pointed out by the joint analysis of simulation in forward with TCC-NMM and analysis of the actual EEG signals in reverse with UKF identification algorithm. (d) We find that the identified parameter C1 of MCI patients is significantly lower than that of the control group, which is consistent with the simulation analysis of TCC-NMM. So, we suggest that the decreased MCI alpha power spectrum is likely related to the cortical synaptic connection loss from pyramidal cells to eIN.

We report an experimental study of long-wave infrared difference frequency generation based on

To meet the diverse operational requirements of microgrids with multi-uncertainties, a tri-level two-stage mixed-integer robust optimization model is proposed to solve the optimal scheduling problem with source-load power and unit N-1 uncertainties. In the first stage, the optimal goal is to obtain robust scheduling plans that can be applied effectively in satisfying the operation and adjustment constraints, as well as to find the minimal operating cost in the basic scenario. Considering the system may concern a specific operating factor (e.g., load satisfaction rate) under a N-1 contingency, the second stage of the model minimizes the total energy of load shedding and renewable energy abandonment in the worst scenario. As the proposed robust model is a multi-level coupled mixed-integer optimization problem, which is difficult to address by either the duality method or the Karush–Kuhn–Tucker (KKT) method, a novel nested reformulation-and-decomposition (R&D) algorithm is proposed to handle this complex problem. Numerical simulations and tests have verified the effectiveness and superiority of the proposed robust model and the solution algorithm. Furthermore, the robust optimal schemes satisfy the engineering application more reasonably.

We analyse a method of high repetition rate, efficient terahertz wave difference frequency generation (DFG) by using a self-Raman Nd-doped vanadate laser to pump 4’-dimethylamino-N-methyl-4-stilbaz...

At present, Mashup development has attracted much attention in the field of software engineering. It is the focus of this article to use existing open APIs to meet the needs of Mashup developers. Therefore, how to select the most appropriate open API for a specific user requirement is a crucial problem to be solved. We propose a Hybrid Open API Selection Approach for Mashup development (HyOASAM), which consists of two basic approaches: one is a user-story-driven open API discovery approach, and the other is multidimensional-information-matrix- (MIM-) based open API recommendation approach. The open API discovery approach introduces user stories in agile development to capture Mashup requirements. First, it extracts three components from user stories, and then, it extracts three corresponding properties from open API descriptions. Next, the similarity calculation is performed on two sets of data. The open API recommendation approach first uses MIM to store open APIs, Mashups, and the invoking relationship between them. Second, it enters the matrix obtained in the previous step into a factorization machine model to calculate the association scores between the Mashups and the open APIs, and TOP-N open API lists for creating the Mashup are obtained. Finally, experimental comparison and analysis are carried out on the PWeb dataset. The experimental results show that our approach has improved significantly.

In recent years, business process re-engineering has played an important role in the development of large-scale web-based applications. To re-engineer business processes, business services are developed and coordinated by reusing a set of open APIs and services on the internet. Yet, the number of services on the internet has grown drastically, making it difficult for them to be discovered to support the changing business goals. One major challenge is therefore to search for a suitable service that matches a specific business goal from a large number of available services in an efficient and effective manner. To address this challenge, this paper proposes a deep learning approach for massive service discovery. The approach, thus called MassRAFF, employs a combination of the recurrent attention and feature fusion methods. This paper first presents the MassRAFF approach and then reports on an experiment for evaluating this approach. The experimental results show that the MassRAFF approach has performed reasonably well and has potential to be improved further in future work.

We propose a nonlinear optical frequency conversion method based on cascaded difference frequency generation. Feasible designs are presented that enable the cascaded optical waves. They are initially generated by cascaded difference frequency generation with two pump waves, then transferred to high-order Stokes waves by optimized cascaded difference frequency generation, and finally congregated at a few spectral lines. We demonstrate wide frequency tuning characteristics and high conversion efficiency characteristics of the nonlinear frequency convertor by providing a theoretical framework. The frequency tuning from 291 THz to 175 THz and energy conversion efficiency exceeding 30% are achieved by numerical calculations. The nonlinear frequency convertor exhibits physics distinctly different from conventional three-wave mixing parametric amplification or difference frequency generation. We argue that the nonlinear frequency convertor based on cascaded difference frequency generation is promising for achieving efficient and robust frequency conversion.