mlsa-iai-msu-lab/ru_sci_bench_zho_cite_reranking huggingface dataset:
Visualization of perforin/gasdermin/complement-formed pores in real cell membranes using atomic force microscopy

Different types of pores ubiquitously form in cell membranes, leading to various types of cell death that profoundly influence the fate of inflammation and the disease status. However, these pores have never truly been visualized to date. Atomic force microscopy (AFM), which is emerging as a powerful tool to analyze the mechanical properties of biomolecules and cells, is actually an excellent imaging platform that allows biological samples to be visualized by probing surface roughness at the level of atomic resolution. Here, membrane pore structures were clearly visualized using AFM. This visualization not only describes the aperture and depth of the pore complexes but also highlights differences among the pores formed by perforin and gasdermins in tumor cell membranes and by complement in immune cell membranes. Additionally, this type of visualization also reveals the dynamic process of pore formation, fusion, and repair.
Remote Monitoring of Electric Vehicle Charging Stations in Smart Campus Parking Lot

Smart parking lots are smart places capable of supporting both parking and charging services for electric vehicles (EVs). In order to manage EV charging, the parking lot local controller (PLLC) requires data exchange with EV charging stations (EVCSs) through communication infrastructures. However, data losses and communication delays are unavoidable and may significantly degrade the system performance. This work aims to investigate the underlying communication networks for remote monitoring of EVCSs in a smart campus parking lot. The communication network consists of two subnetworks: parking area network (PAN) and campus area network (CAN). PAN covers communication among EVs, charging stations and PLLC, while CAN enables dedicated communication between PLLCs and a global controller of the university. As one of the major obstacles in EV system is the lack of unified communication architecture to integrate EVCS in the power grid, we develop communication models for the in-vehicle system and EVCSs based on logical node concept of IEC 61850 standard. Furthermore, we implement network models for EVCSs using OPNET modeler. Different communication technologies and configurations are considered in modeling and simulations, and end-toend delay is evaluated and discussed.
Marginal Bottleneck Identification in Power System Considering Correlated Wind Power Prediction Errors

This letter investigates how to identify the marginal bottleneck, which is defined as the constraint most likely to be violated with the increasing wind generation uncertainty of power system in real-time dispatch. The presented method takes the correlation of wind power prediction error (WPPE) into account, leading to an ellipsoidal formulation of wind power generation region (WGR). Based on constructed WGR, the identification procedure is formulated as a max-max-min problem, which is solved by the algorithm based on iteration linear program with the proposed method to select appropriate initial points of WPPE. Finally, two cases are tested, demonstrating the efficacy and efficiency of the procedure to identify marginal bottleneck.
Voltage Characteristic Analysis of Ultra-high Voltage Half-wavelength Transmission System Based on Wave Process Method

The voltage characteristics of a half-wavelength transmission (HWLT) system lack mechanism explanation and systematic summary. In this paper, both the steady-state and fault voltage characteristics are analyzed and explained based on the wave process method. An analysis model is established for HWLT lines and wave propagation equations with different terminal conditions are deduced. For steady-state conditions, voltage profiles along HWLT lines are depicted and partial overvoltage occurs when there is an overload or low-power factor. In addition, fault voltage characteristics are analyzed under load-rejection or short-circuit conditions. The series resonance overvoltage will happen when short-circuit faults occur at certain positions and its peak value is influenced by grid strength or fault type. Furthermore, a refined transmission line model is presented to consider non-ideal factors and simulation results demonstrate that geomorphic conditions and electromagnetic environments influence line voltage characteristics to a certain extent.
Optimal Estimation and Tracking Control for Variable-speed Wind Turbine with PMSG

Designing an optimal tracking controller and system observer is a nonlinear problem in the variable-speed wind energy conversion system (WECS). In this paper, an adjusted feedforward and feedback optimal controller with extended Kalman filter (EKF) is introduced to estimate and control the permanent magnet synchronous generator (PMSG) for the maximum power point tracking (MPPT) with several disturbances of the wind speed. An augmented model of wind turbine and PMSG is expanded, and then the parameters of the optimal controller and estimator are obtained. The dynamic stability of the closed-loop system with feedback-feedforward controller (FFC), EKF and speed controller are analyzed. To compare the dynamic performance of EKF and FFC with the conventional controllers, numerical results are demonstrated with the disturbances of the wind speed and faults in power grid.
Forecasted Scenarios of Regional Wind Farms Based on Regular Vine Copulas

Owing to the uncertainty and volatility of wind energy, forecasted wind power scenarios with proper spatio-temporal correlations are needed in various decision-making problems involving power systems. In this study, forecasted scenarios are generated from an estimated multi-variate distribution of multiple regional wind farms. According to the theory of copulas, marginal distributions and the dependence structure of multi-variate distribution are modeled through the proposed distance-weighted kernel density estimation method and the regular vine (R-vine) copula, respectively. Owing to the flexibility of decomposing correlations of high dimensions into different types of pair-copulas, the R-vine copula provides more accurate results in describing the complicated dependence of wind power. In the case of 26 wind farms located in East China, highquality forecasted scenarios as well as the corresponding probabilistic forecasting and point forecasting results are obtained using the proposed method, and the results are evaluated using a comprehensive verification framework.
Analysis of Dynamic Voltage Fluctuation Mechanism in Interconnected Power Grid with Stochastic Power Disturbances

With the integration of large-scale renewable energy and various new types of loads, the stochastic fluctuation characteristic of the power has seriously affected the secure and stable operation of interconnected power grids. In addition to active power fluctuations of AC tie-lines that have been studied in the past, the problem of dynamic fluctuation of bus voltage is also increasingly prominent. Firstly, the typical power fluctuations of electric railway traction load, smelting load, and wind power generation in modern power systems are presented and the accompanying voltage fluctuations are analyzed. Secondly, the dynamic fluctuation mechanism and distribution characteristic of the system voltage under special forced oscillation and generalized forced oscillation are studied. It is found that when the periodic disturbance induces a special forced oscillation, the voltage fluctuation has the same mode information compared to the power angle fluctuation. And it also has obvious peak characteristics near the natural frequency. When the stochastic disturbance induces generalized forced oscillation, the voltage fluctuations can be decomposed into proportional and resonant components according to its frequency domain characteristic. Finally, the above conclusions are verified by the simulation results of an IEEE 4-generator 2-area system.
Review of Service Restoration for Distribution Networks

With the rapid deployment of the advanced metering infrastructure (AMI) and distribution automation (DA), selfhealing has become a key factor to enhance the resilience of distribution networks. Following a permanent fault occurrence, the distribution network operator (DNO) implements the selfhealing scheme to locate and isolate the fault and to restore power supply to out-of-service portions. As an essential component of self-healing, service restoration has attracted considerable attention. This paper mainly reviews the service restoration approaches of distribution networks, which requires communication systems. The service restoration approaches can be classified as centralized, distributed, and hierarchical approaches according to the communication architecture. In these approaches, different techniques are used to obtain service restoration solutions, including heuristic rules, expert systems, metaheuristic algorithms, graph theory, mathematical programming, and multi-agent systems. Moreover, future research areas of service restoration for distribution networks are discussed.
Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

In a permanent magnet synchronous generator (PMSG) system, conversion systems are major points of failure that create expensive and time-consuming problems. Fault detection is usually used to achieve a steady system. This paper presents a full analysis of a PMSG system for wind turbines (WT) and proposes a fault detection method using correlation features. The proposed method is motivated by the balance among the three-phase currents both before and after an opencircuit fault occurs in a converter of the PMSG system. It is unnecessary to analyze the output waveforms of a converter during fault detection. In this study, two correlation features of stator currents, the mean and covariation, are extracted to train an artificial neural network (ANN), thereby enhancing the performance of the proposed method under different wind speed conditions. Moreover, additional sensors and the collection of a massive amount of data are not required. Model simulations of an ideal inverter and a PMSG system are conducted using PSCAD software. The simulation results show that the proposed method can detect the locations of faulty switches with a diagnostic rate greater than 99.4% for the ideal inverter, and the PMSG drives settings at different wind speeds.
Improved Deep Belief Network and Model Interpretation Method for Power System Transient Stability Assessment

The real-time transient stability assessment (TSA) and emergency control are effective measures to suppress accident expansion, prevent system instability, and avoid large-scale power outages in the event of power system failure. However, real-time assessment is extremely demanding on computing speed, and the traditional method is not competent. In this paper, an improved deep belief network (DBN) is proposed for the fast assessment of transient stability, which considers the structural characteristics of power system in the construction of loss function. Deep learning has been effective in many fields, but usually is considered as a black-box model. From the perspective of machine learning interpretation, this paper proposes a local linear interpreter (LLI) model, and tries to give a reasonable interpretation of the relationship between the system features and the assessment result, and illustrates the conversion process from the input feature space to the high-dimension representation space. The proposed method is tested on an IEEE new England test system and demonstrated on a regional power system in China. The result demonstrates that the proposed method has rapidity, high accuracy and good interpretability in transient stability assessment.
Hybritus: a password strength checker by ensemble learning from the query feedbacks of websites

Password authentication is vulnerable to dictionary attacks. Password strength measurement helps users to choose hard-to-guess passwords and enhance the security of systems based on password authentication. Although there are many password strength metrics and tools, none of them produces an objective measurement with inconsistent policies and different dictionaries. In this work, we analyzed the password policies and checkers of top 100 popular websites that are selected from Alexa rankings. The checkers are inconsistent and thus they may label the same password as different strength labels, because each checker is sensitive to its configuration, e.g., the algorithm used and the training data. Attackers are empowered to exploit the above vulnerabilities to crack the protected systems more easily. As such, single metrics or local training data are not enough to build a robust and secure password checker. Based on these observations, we proposed Hybritus that integrates different websites' strategies and views into a global and robust model of the attackers with multiple layer perceptron (MLP) neural networks. Our data set is comprised of more than 3.3 million passwords taken from the leaked, transformed and randomly generated dictionaries. The data set were sent to 10 website checkers to get the feedbacks on the strength of passwords labeled as strong, medium and weak. Then we used the features of passwords generated by term frequency–inverse document frequency to train and test Hybritus. The experimental results show that the accuracy of passwords strength checking can be as high as 97.7% and over 94% even if it was trained with only ten thousand passwords. User study shows that Hybritus is usable as well as secure.
A correlative denoising autoencoder to model social influence for top-N recommender system

In recent years, there are numerous works been proposed to leverage the techniques of deep learning to improve social-aware recommendation performance. In most cases, it requires a larger number of data to train a robust deep learning model, which contains a lot of parameters to fit training data. However, both data of user ratings and social networks are facing critical sparse problem, which makes it not easy to train a robust deep neural networkmodel. Towards this problem, we propose a novel correlative denoising autoencoder (CoDAE) method by taking correlations between users with multiple roles into account to learn robust representations from sparse inputs of ratings and social networks for recommendation. We develop the CoDAE model by utilizing three separated autoencoders to learn user featureswith roles of rater, truster and trustee, respectively. Especially, on account of that each input unit of user vectors with roles of truster and trustee is corresponding to a particular user, we propose to utilize shared parameters to learn common information of the units that corresponding to same users. Moreover, we propose a related regularization term to learn correlations between user features that learnt by the three subnetworks of CoDAE model. We further conduct a series of experiments to evaluate the proposed method on two public datasets for Top-N recommendation task. The experimental results demonstrate that the proposed model outperforms state-of-the-art algorithms on rank-sensitive metrics of MAP and NDCG.
Optimized high order product quantization for approximate nearest neighbors search

Product quantization is now considered as an effective approach to solve the approximate nearest neighbor (ANN) search. A collection of derivative algorithms have been developed. However, the current techniques ignore the intrinsic high order structures of data, which usually contain helpful information for improving the computational precision. In this paper, aiming at the complex structure of high order data, we design an optimized technique, called optimized high order product quantization (O-HOPQ) for ANN search. In O-HOPQ, we incorporate the high order structures of the data into the process of designing a more effective subspace decomposition way. As a result, spatial adjacent elements in the high order data space are grouped into the same subspace. Then, O-HOPQ generates its spatial structured codebook, by optimizing the quantization distortion. Starting from the structured codebook, the global optimum quantizers can be obtained effectively and efficiently. Experimental results show that appropriate utilization of the potential information that exists in the complex structure of high order data will result in significant improvements to the performance of the product quantizers. Besides, the high order structure based approaches are effective to the scenario where the data have intrinsic complex structures.
NEXT: a neural network framework for next POI recommendation

The task of next POI recommendations has been studied extensively in recent years. However, developing a unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging, because of the heterogeneity nature of these information. Further, effective mechanisms to smoothly handle cold-start cases are also a difficult topic. Inspired by the recent success of neural networks in many areas, in this paper, we propose a simple yet effective neural network framework, named NEXT, for next POI recommendations. NEXT is a unified framework to learn the hidden intent regarding user's next move, by incorporating different factors in a unified manner. Specifically, in NEXT, we incorporatemeta-data information, e.g., user friendship and textual descriptions of POIs, and two kinds of temporal contexts (i.e., time interval and visit time). To leverage sequential relations and geographical influence, we propose to adopt DeepWalk, a network representation learning technique, to encode such knowledge. We evaluate the effectiveness of NEXT against other state-of-the-art alternatives and neural networks based solutions. Experimental results on three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI recommendations. Further experiments show inherent ability of NEXT in handling cold-start.
Enjoy the most beautiful scene now: a memetic algorithm to solve two-fold time-dependent arc orienteering problem

Traditional route planners commonly focus on finding the shortest path between two points in terms of travel distance or time over road networks. However, in real cases, especially in the era of smart cities where many kinds of transportation-related data become easily available, recent years have witnessed an increasing demand of route planners that need to optimize for multiple criteria, e.g., finding the route with the highest accumulated scenic score along (utility) while not exceeding the given travel time budget (cost). Such problem can be viewed as a variant of arc orienteering problem (AOP), which is well-known as an NP-hard problem. In this paper, targeting a more realistic AOP, we allow both scenic score (utility) and travel time (cost) values on each arc of the road network are time-dependent (2TD-AOP), and propose a memetic algorithm to solve it. To be more specific, within the given travel time budget, in the phase of initiation, for each population, we iteratively add suitable arcs with high scenic score and build a path fromthe origin to the destination via a complicate procedure consisting of search region narrowing, chromosome encoding and decoding. In the phase of the local search, each path is improved via chromosome selection, local-improvement-based mutation and crossover operations. Finally, we evaluate the proposed memetic algorithm in both synthetic and real-life datasets extensively, and the experimental results demonstrate that it outperforms the baselines.
One-step ball milling-prepared nano Fe_2O_3 and nitrogendoped graphene with high oxygen reduction activity and its application in microbial fuel cells

Developing high activity, low-cost and long durability catalysts for oxygen reduction reaction is of great significance for the practical application of microbial fuel cells. The full exposure of active sites in catalysts can enhance catalytic activity dramatically. Here, novel Fe-N-doped graphene is successfully synthesized via a one-step in situ ball milling method. Pristine graphite, ball milling graphene, N-doped graphene and Fe-N-doped graphene are applied in air cathodes, and enhanced performance is observed in microbial fuel cells with graphene-based catalysts. Particularly, Fe-Ndoped graphene achieves the highest oxygen reduction reaction activity, with a maximum power density of 1380±20 mW/m~2 in microbial fuel cells and a current density of 23.8 A/m~2 at –0.16 V in electrochemical tests, which are comparable to commercial Pt and 390% and 640% of those of pristine graphite. An investigation of the material characteristics reveals that the superior performance of Fe-Ndoped graphene results from the full exposure of Fe_2O_3 nanoparticles, pyrrolic N, pyridinic N and excellent Fe-N-G active sites on the graphene matrix. This work not only suggests the strategy of maximally exposing active sites to optimize the potential of catalysts but also provides promising catalysts for the use of microbial fuel cells in sustainable energy generation.
A boronic acid-based fluorescent hydrogel for monosaccharide detection

A boronic acid-based anthracene fluorescent probe was functionalised with an acrylamide unit to incorporate into a hydrogel system for monosaccharide detection. In solution, the fluorescent probe displayed a strong fluorescence turn-on response upon exposure to fructose, and an expected trend in apparent binding constants, as judged by a fluorescence response where D-fructose>D-galactose>D-mannose>D-glucose. The hydrogel incorporating the boronic acid monomer demonstrated the ability to detect monosaccharides by fluorescence with the same overall trend as the monomer in solution with the addition of D-fructose resulting in a 10- fold enhancement (≤0.25 mol/L).
Highly selective detection of copper(II) by a “ligand-free” conjugated copolymer in nucleophilic solvents

The synthesis of N-cyclohexyl carbamateattached fluorene-alt-phenylene copolymer (PFPNCC) and the use of PFPNCC as a “ligand-free” fluorescent chemosensor for Cu(II) are described. Addition of Cu(II) can efficiently quench the fluorescence of PFPNCC in nucleophilic solvents such as DMF and DMSO, but not in low nucleophilic solvents such as 1,4-dioxane and THF. Ultraviolet-visible spectra of the mixture of the conjugated polymer and Cu(II) indicate the presence of a reduced Cu (I) ion in the solution. Furthermore, fluorescence recovery of PFPNCC observed at low temperature suggests that the quenching and reducing mechanism is most probably due to a photo-induced electron transfer from excited PFPNCC to Cu(II). Our findings provide a novel strategy for highly selective conjugated polymer-based chemosensors for various target analytes, albeit “ligand-free”.