nils-herrmann/clean_scirepeval huggingface dataset:
Cloning and sequence analysis of ribosomal protein L11 gene (rpL11) from the Ailuropoda melanoleuca

Ribosomal protein L11 (rpL11) which is a component of the 60s large ribosomal subunit plays an important role in ribosome. To explore the structure characteristic of rpL11 gene of the Ailuropoda melanoleuca, we have designed primers based on the known nucleotide sequence of rpL11 gene. The RT-PCR and PCR strategy were employed to isolate cDNA and genome of rpL11 from the Ailuropoda melanoleuca respectively, sequenced, and analyzed preliminarily. The result indicated that the length of cDNA fragment cloned was 577bp; containing an open reading frame of 537bp. Deduced protein was composed of 178 amino acids with an estimated molecular weight of 20.25kD and PI of 9.97. The length of the genomic sequence is 3701bp, containing five exons and four introns. Alignment analysis indicates that RPL11 is highly similarity with the reported species both at the level of DNA and protein. Topology prediction shows that six different patterns were found in the RPL11 protein of the Ailuropoda melanoleuca. The rpL11 gene of the Ailuropoda melanoleuca was studied in this paper, which will enrich the rpL11 gene database.
RMPC-based security problem for polytopic uncertain system subject to deception attacks and persistent disturbances

This study investigates the security problem for a class of polytopic uncertain systems by virtue of robust model predictive control (RMPC) approach. In consideration of the difficulties in accessing the system states, a novel dynamic-output-feedback RMPC strategy is presented to guarantee the security of the discussed system subject to randomly occurring deception attacks and persistent bounded disturbances. Such the randomly occurring attack is characterised by using a Bernoulli binary distributed white sequence with a given successful probability. Under this condition, a novel concept of system security in mean-square sense is presented based on RMPC technique, in which the system is required not only to satisfy the specified performance index but also to keep all the states in an invariant set by designing a time-varying terminal constraint set. Moreover, due to the presence of the persistent bounded disturbances and model uncertainties, the worst-case cost function is considered. By using a Lyapunov function approach dependent of the input-to-state stability (ISS), the dynamic-output-feedback RMPC scheme is designed by solving an optimisation problem including some inequality constraints, and the upper bound of the investigated cost function is obtained. In addition, the ISS performance is ensured for the polytopic uncertain system with both the randomly occurring deception attacks and persistent disturbances. Finally, a numerical simulation example is developed to show the effectiveness of the proposed dynamic-output-feedback RMPC strategy.
A novel networked traffic parameter forecasting method based on Markov chain model

This paper introduces a novel networked traffic parameter forecasting method. Based on the detailed analysis of the literature, the paper describes the fundamental ideas. Then we select a typical traffic network in Beijing City. In order to simplify the problem, we classify the links using clustering analysis and find the representative links in each group. Furthermore, we introduce the Markov chain model to predict the traffic parameter. EM algorithm is applied to estimate the parameters of mixed Gaussian distributions, i.e., means, covariances and mixing coefficients. According to the regression equations between the representative links and the other links in the same group, we can obtain all the predicted traffic parameters of all the link in the road network. The case studies using real data from UTC-SCOOT system in Beijing have proved the effectiveness and applicability of the proposed method.
A recombinant single-chain trispecific antibody against three toxins and activity in the synchronization detection

The recombinant single-chain trispecific antibody against three toxins including Okadaic acid (OA), saxitoxin (STX) and tetrodotoxin (TTX) was developed. Three single-chain variable fragment (ScFv) genes were linked via linkers (Gly4-Ser)3 to construct fusion ScFv gene OST-ScFv through splicing by overlapping extension. Fusion gene OST-ScFv was 2 256 bp, encoding 752 amino acids, and was inserted into expression vector pET-22b. Recombinant plasmid OST-ScFv-22b was expressed at 37°C and at 25°C by induction with β-D-1-thiogalactopyranoside. The expressed proteins were identified by SDS-PAGE and purified by urea dissolving, gradient dialyzing and ultrafiltrating. Molecular weight of expressed protein was about 79.000 kDa. Competitive indirect enzyme linked immunosorbent assay indicated that the single-chain trispecific antibody which had different combining activity with three toxins was obtained. This work confirmed that multi-specific ScFv antibody could be obtained via linking expression of ScFvs and might also have an application in synchronization detection of toxins.
A novel module of tracking vehicles with occlusion

Video surveillance technology has been widely used in intelligent transportation system (ITS) to measure traffic flow parameters and detect accidents. Occlusion of moving objects by stationary or other moving foreground objects always causes tracking errors. This paper presents a novel module for vehicle tracking under the condition of occlusion, which can be conveniently added to the existing video surveillance systems as a complementary part. The appearance information of the object (shape and texture) is combined with the spatial-temporal Markov random field (MRF) based tracking model through a kind of special structuring elements in mathematical morphology (MM) theory. As a result, the sites in MRF are reduced, which saves computational cost greatly, and the assumption on the vehicle shape model in prior is not needed. Experiments on various kinds of image sequences show that the proposed module can effectively track vehicles with severe occlusion.
Ge n+/p shallow junctions for light emission and detection applications

Si-based light sources and photodetectors are the key active components for single chip integrated optoelectronic circuits. Ge and GeSn alloys can serve as channel material for high carrier mobility metal-oxide-semiconductor field effect transistors (MOSFET) and also be considered as the promising materials for efficient light emission and detection due to their quasi-direct band structure and compatibility with Si technology.  To improve light emitting efficiency of Ge, tensile strain and n-type heavy doping were applied for reduction of difference between direct and indirect band gap and improvement of electron occupation in Γ valley. However, it is difficult to achieve high activation n-type doping in Ge because of fast diffusion of n-type dopants and low dopant solid solubility in Ge.[2] In this work, Ge n+/p shallow junctions are fabricated with high n-type doping concentration by ion implantation and excimer laser annealing techniques, which can serve as source and drain in scaled Ge nMOSFET. Strong electroluminescence and high photoresponsivity from the Ge n+/p shallow junctions are demonstrated.
Speech reconstruction using a deep partially supervised neural network

Statistical speech reconstruction for larynx-related dysphonia has achieved good performance using Gaussian mixture models and, more recently, restricted Boltzmann machine arrays; however, deep neural network (DNN)-based systems have been hampered by the limited amount of training data available from individual voice-loss patients. The authors propose a novel DNN structure that allows a partially supervised training approach on spectral features from smaller data sets, yielding very good results compared with the current state-of-the-art.
The step-down-stress accelerated storage testing evaluation methods of small sample electronic products based on Arrhenius model

Accelerated life testing (ALT) is used to obtain failure data quickly under high stress levels in order to predict product life performance under design stress conditions. Most of the previous work on designing ALT plans is focused on the application of step-stress. According to the advantages of step-down-stress accelerated life testing about its higher failure rate, less sample size and simpler test equipment, this paper develops a method of accelerated storage life evaluation method based on step-down-stress Type-II censored accelerated life testing data in engineering practice. Firstly, the failure data under accelerated stresses are converted to normal stress employing acceleration factors of Arrhenius model based on the theory of Nelson. This paper establishes the maximum likelihood function of the whole samples under normal stress after converting failure time. Then Arrhenius model parameters and average life of product under normal stress are obtained by parameters partial derivation of the maximum likelihood estimation. Subsequently, the confidence interval and confidence lower limit of average life under a certain confidence level are obtained by the Fisher matrix. Lastly taking the step-down-stress Type-Π censored accelerated life test of a small sample electronic products as an example for analysis, this paper compares and analyzes the result of step-down-stress accelerated life evaluation method based on the Arrhenius model with the average life expectancy, failure rate and reliability function of field data. This paper verifies the efficiency and accuracy of step-down-stress accelerated storage life evaluation method based on Arrhenius model specific to small sample.
A Two-Step Structural Damage Detection Approach Based on Wavelet Packet Analysis and Neural Network

In this paper, a two-step structural damage detection approach based on wavelet packet analysis is presented. Firstly, the location of the damage member is determined by probabilistic neural network according to the wavelet packet node energy change. Secondly, the damage extent of the damaged member is determined by back-propagation network according to the wavelet packet node energy. The results of the numerical simulation demonstrate that the approach can detect the structural damage with good robustness
Functional coefficient autoregressive time series modeling based on fuzzy inference method

Functional coefficient autoregressive model is a class of useful nonlinear time series models. A new approach based on fuzzy inference modeling method for the estimation of the coefficient functions is proposed in this paper. A corresponding algorithm which is easy to be realized is designed. Besides, two typical simulation examples are studied, the estimated errors of which are minor than other models results. And the performance of forecasts on the proposed models has high degree of accuracy, which illustrates the validity of the method further.
Cyber-physical security assessment and simulation based on graph database

With the development of information technology, the operation of power grid increasingly depends upon the support of strong communication network. The power system consisting of grids infrastructure and communication equipment is a typical cyber-physical system(CPS). In recent years, power accidents that information-side failure impacts on physical operation happen at times. In order to simulate the impacts of information failure on grid operation, we develop a cyber-physical simulation tool for the secondary-voltage control of power system to implement the reasonable analysis and evaluation of the influence of different types of information failure on voltage control effect. In this paper, the graph database is introduced to construct the basic cyber-physical data model as an important part of the simulation tool, whose graphical modeling and management is friendly for users. In addition, the importance of different types of information is analyzed with the tool. It can be seen that the voltage information is more fragile and critical than the non-independent generator reactive information.
Towards GIS-T information fusion

The bottleneck of the GIS-T data gathering is the heterogeneity between the information sources. The main purpose of this paper is to make clear the idea of semantic matching and to present a prototype implementation framework. After a short presentation of GIS-T characteristics and approaches of information interoperability, we focus an the establishment of the semantic matching, one of the most critical issue in the fusion. The matching problem is addressed in detail at three levels: the classes, the roles and the entities. Then we will go on to describe a proof-of-concept framework system that we are developing.
Exploiting language cluster information for language pair identification

Recently, significant progress has been witnessed in automatic language identification. Perhaps the most important development is the application of discriminative training. However, efficient and effective discriminative training for language pair identification is still a challenging problem. Specifically, with the increase of the number of languages to be recognized, the number of the language pairs will grow quadratically, which may make the discriminative training complex. Furthermore, it is difficult to collect data for certain languages. The data imbalance also biases the discriminative training and degrades the performance. To address these issues, we propose to exploit language clustering information for effective language pair identification. It is mainly motivated from the perspective of the linguistics that all languages can be divided into several families. In our proposed method, the language clusters are constructed based on the linguistic knowledge. The language pairs are categorized into “intra-cluster” and “inter-cluster”, and different weighting schemes are proposed for identification. To evaluate the effectiveness of our proposed scheme, we conduct extensive experiment on NIST LRE 2011 and a small subset of languages selected from it. The experimental results show that by exploiting the language clustering information, the resulted identification performance has been improved significantly, compared with baseline system.
A New Intelligent Technique for Sound Quality Evaluation of Nonstationary Vehicle Noises

A new intelligent technique for sound quality evaluation, the so-called wavelet pre-processing neural network (WT-NN) model, is investigated in this paper. Based on pass-by vehicle noises, the WT-NN sound quality evaluation model was developed by combining the techniques of wavelet analysis and neural network (NN) classification. A wavelet-based, 21-point model for vehicle noise feature extraction was established. Verification shows that the trained WT-NN models are accurate and effective for sound quality evaluation of nonstationary vehicle noises. Due to its outstanding time-frequency characteristics and intelligent functions, the WT-NN model is proved more advanced than the in-situ sound quality evaluation models in common use. The proposed WT-NN model can be applied to both stationary and nonstationary signals and even to transient ones. The WT-NN technique is suggested not only for the prediction, classification, and comparison of the sound quality of pass-by vehicle noises, but also for applications in other sound-related engineering fields, in place of conventional psychoacoustical models.
A Study on Sound Roughness Evaluation Based on an Auditory Synchronization Index Model

A new auditory synchronization index model, the so- called auditory-SIM model, for sound roughness evaluation (SRE) is presented in this paper. Sample interior and exterior vehicle noises are measured and denoised by using the binaural recording and wavelet denoising techniques. The auditory-SIM model is developed by combining the auditory peripheral models of the human hearing apparatus and the SIM model of the human brain. Using the auditory-SIM model, both the specific and total sound roughnesses of the interior and exterior vehicle noises are estimated. The conclusions are drawn that the newly proposed auditory-SIM model is accurate and feasible for SRE of stationary, nonstationary, and even transient vehicle noises, and may be extended to deal with any other sound signals in engineering.
Side-scan sonar image registration based on modified phase correlation for AUV navigation

In this paper, we propose a novel approach for SSS registration based on modified phase correlation, which is capable of handling low resolution and noise. The modified phase correlation combines threshold segmentation with phase correlation to carry out side-scan sonar (SSS) image registration. Image registration aims to transform images obtained from different views to the same coordinate. Underwater environment has poor visibility and the registration for optical underwater images faces various challenges. The registration method for SSS image is different from that for optical image. Region-based registration is more robust than feature-based registration for side-scan image because there are featureless areas in underwater environment and region-based registration can handle this. When an autonomous underwater vehicle (AUV) equipped with the SSS is navigating an unknown underwater environment, the ability to realize the registration between the current sonar image and the previous ones accurately and fast does matter. When compared to a state-of-art region-based and feature-based technique, our approach shows superior performance in accuracy and time-consuming.
Application of Fuzzy Theory in Contractor Risk Assessment under EPC Model

To meet the requirements for being geared to the international standards and to meet the demand for selfdevelopment of an enterprise, it becomes more and more common for an enterprise to execute EPC projects. The EPC model compares with the traditional contract model, the risk of general contactor is the biggest undoubtedly. Through the analysis of the EPC model to determine the risk source and loss of general contractor and then use the fuzzy comprehensive evaluation method to realize the quantization of the possibility of risk occurrence and risk loss. By the quantified possibility of risk occurrence and loss consequences to calculate the risk gene and get the risk quantized valve ultimately. The example shows that fuzzy comprehensive risk evaluation method can realize quantitative treatment of fuzzy and uncertain information and can practically and effectively evaluate the EPC general contractor's risk. KeywordsEPC; fuzzy evaluation; risk gene; risk occurrence possibility; risk loss consequence
Generic Carrier-Based Core Model for Four-Terminal Double-Gate MOSFET Valid for Symmetric, Asymmetric, SOI, and Independent Gate Operation Modes

A generic carrier-based core model for undoped four-terminal double-gate (DG) MOSFET valid for symmetric, asymmetric, SOI, and independent gate operation modes is presented in this paper. Based on the exact solution of the 1-D Poisson's equation of a general DG-MOSFET configure, a generic drain current model is derived from Pao-Sah's double integral in terms of the carrier concentration. The model is verified by extensive comparisons with 2-D numerical simulations under different bias conditions to all four terminals. The concise mathematic formulation allows the unification various double-gate models into a carrier-based core model for compact DG-MOSFET model development.
Topic classification based on distributed document representation and latent topic information

The classical bag-of-words and probabilistic topic models are widely used on topic classification tasks. Recently, neural networks have achieved remarkable performance and formed the mainstream, due to their ability to encode distributed semantic features of documents based on word embeddings. To demonstrate the superiority of neural networks, this paper compares Latent Dirichlet Allocation (LDA) with Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Recurrent Convolutional Neural Network (RCNN), which are the mainstream neural network architectures. Beyond this, we combine the latent topic information inferred by LDA and distributed semantic information learned by neural networks to generate a better document representation for topic classification. The experimental results show that the proposed representation outperforms individual systems and can achieve excellent performance on topic classification tasks.
Community detection in power grids based on Louvain heuristic algorithm

The statistical tools of complex network analysis and the development of complex network theory offer a new approach into the exploration of complex power grids. As the capacity of power grid increases, grid structure becomes more and more complicated and to some extent that will impair our perception of power grids. Hence in this paper we apply Louvain heuristic algorithm to detecting community structure of power grids. We find that IEEE 118-bus system is of significant strong community structure, with the modularity reaching 0.8. Besides, when we use reciprocal of reactance as the weight of edge, the modularity of the network is larger than that of homogenous weight of edge. This implies that reactance can help reveal the community structure. Community detection may shed light on our understanding on the configuration of the power grids.
Predicting flow velocity affected by seaweed resistance using SVM regression

Sea water exchange is an important source of nutrient salts. Culture living beings such as kelp in a sea-farming region will bring resistance to the exchange of water flows and therefore affect nutrient supplement. Study of hydrodynamic environment in marine culture zones is important for revealing water exchange conditions and guiding reasonable layout of mariculture regions. In recent years, statistical learning theories represented by Support Vector Machines (SVM) have been well developed. However, no publications are available regarding using SVM to predict marine environment elements related to hydrodynamic and combining these predicted elements with ocean models. In this paper, we use SVM regression to predict water flow velocity based on an improved hydrodynamic models with the resistance by cultivation breeding such as kelp. In particular, we use SVM regression to predict the velocity of following time points at a location with the coordinate in north-south and east-west directions. The experimental results are promising.
Finite-time stabilization-based trajectory tracking under disturbances for entry vehicles

For the entry guidance based on a nominal trajectory, trajectory tracking is an important part, by which the nominal trajectory is followed and several constraints can be observed. In order to reject disturbance and achieve good tracking performance in trajectory tracking, a finite-time trajectory tracking method is presented. Utilizing the differential flatness theory, a linear tracking error system can be derived from the nonlinear point-mass dynamics of an entry vehicle. A disturbance compensator is designed based on linear extended state observers. The compensator observes the disturbances in the tracking error system, and then compensates the disturbances in the following tracking law. To track the nominal trajectory, a finite-time tracking law with disturbance compensation is derived using a linear finite-time stabilization method. This method ensures that the tracking error is finite-time stable and the settling time of the tracking error is within a specified range. Numerical simulation demonstrates the proposed trajectory tracking law. Under various disturbances, good tracking performance is achieved and all constraints are observed. The proposed trajectory tracking method is effective.