nils-herrmann/clean_scirepeval huggingface dataset:
An efficient branch-and-bound algorithm for the assignment of protein backbone NMR peaks

NMR resonance assignment is one of the key steps in solving an NMR protein structure. The assignment process links resonance peaks to individual residues of the target protein sequence, providing the prerequisite for establishing intra- and inter-residue spatial relationships between atoms. The assignment process is tedious and time-consuming, which could take many weeks. Though there exist a number of computer programs to assist the assignment process, many NMR labs are still doing the assignments manually to ensure quality. This paper presents a new computational method based on our recent work towards automating the assignment process, particularly the process of backbone resonance peak assignment. We formulate the assignment problem as a constrained weighted bipartite matching problem. While the problem, in the most general situation, is NP-hard, we present an efficient solution based on a branch-and-bound algorithm with effective bounding techniques and a greedy filtering algorithm for reducing the search space. Our experimental results on 70 instances of (pseudo) real NMR data derived from 14 proteins demonstrate that the new solution runs much faster than a recently introduced (exhaustive) two-layer algorithm and recovers more correct peak assignments than the two-layer algorithm.
Computational prediction of ubiquitination protein using evolutionary profiles and functional domains

Ubiquitination, as a post-translational modification, is a crucial biological process presented in cell signaling, death and localization. Identification of ubiquitination protein is of fundamental importance for understanding molecular mechanisms in biological systems and diseases. Although high-throughput experimental studies using mass spectrometry have identified many ubiquitination proteins and ubiquitination sites, the vast majority of ubiquitination proteins remain undiscovered, even in well studied model organisms. To reduce experimental costs, computational (in silico) methods have been introduced to predict ubiquitination sites. If we can predict whether a query protein can be ubiquitinated or not, it is meaningful by itself and helpful for predicting ubiquitination sites. However, all the computational methods so far only predict ubiquitination sites, with unsatisfactory accuracy. In this study, we developed the first computational method for predicting ubiquitination proteins without relying on ubiquitination site prediction. The method extracts features from sequence conservation information via a grey system model, as well as functional domain annotation and subcellular localization. Together with the detailed feature analysis and application of the Relief feature selection algorithm, the results of 5-fold cross-validation on three datasets achieved a high accuracy of 0.8981, with the Matthew's correlation coefficient 0.7963. Our study may guide the related experimental design and provide useful insights for studying the mechanisms and modulation of ubiquitination pathways.
Improved KICA alogrithm and its application on face recognition

In order to reduce the original algorithm's dependence on the primary matrix and raise the classification accuracy, we combine the five order Newton method and the steepest descent method. We also introduce punishing factors into algorithm, and apply them to the core of the iterative process of the Fast ICA algorithm, an improved kernel independent component analysis algorithm (KICA) is proposed in this paper. The improved algorithm is applied to face feature extraction. The traditional KICA algorithm and the improved algorithm are respectively used to extract features. Through the experiment of the ORL face database. We prove that the improved KICA algorithm is good for the separation of the independent component, and its classification accuracy is improved.
A clinical omics database integrating epidemiology, clinical, and omics data for colorectal cancer translational research

To provide an open translational research platform to narrow the gap between clinical practice and laboratory research of colorectal cancer, we construct a more comprehensive clinical omics database integrating not only clinical and omics data, but also epidemiology data from more than 150 patients. Based on this integrated database, we provide query, integrated view, and data analysis for clinical physicians, molecular biologists, and other researchers related with colorectal cancer translational research, facilitating the investigation of genotype-phenotype correlations in colorectal cancer and the application of basic research results in clinical practice. This paper presents our present work on the clinical omics database. Specifically, we apply EAV-ER model in the concept model design of this integrated database to accommodate the dynamic and sparse nature of biomedical data.
Experimental research on electrical breakdown strength of long-gap vacuum-insulated coaxial line under microsecond pulses

Experiments to test the breakdown voltage (Ub) of centimetre-scale vacuum gap using the coaxial electrodes under microsecond pulses are carried out. The vacuum gaps are set as 5.75 cm, 7.67 cm and 10.2 cm, respectively. The ratio of the outer diameter to the inner diameter of the coaxial electrodes for each gap is set as constant to produce a fixed field enhancement factor. The experimental results show that Ub increases while the electric breakdown field (Eb) decreases as the vacuum gap increases. In addition, the increasing and the decreasing tendencies are nonlinear. Since both the gap distance (g) and the electrode area (A) change for each set of experiments, both the two factors are taken into account together to obtain a unified formula to describe the Eb dependencies on g and A. The fitting results show that Eb is linearly dependent on g−α and A−β, where α is about 0.3 and β is about 1/5.6. Both the gap effect and the area effect are consistent with those of the plane-plate electrodes. The mechanism responsible for the coaxial long-gap vacuum breakdown under microsecond pulses is also discussed.
Mobile robot navigation in unknown corridors using line and dense features of point clouds

This paper addresses the problem of mobile robot navigation in unknown corridors using RGB-Depth cameras. Instead of building a full and global 3D map of the environment, the approach exploits line and dense features extracted from RGB-D sensors. Wall-floor boundary lines are extracted from pre-processed point clouds, which ensure reliable line segmentation results compared with monocular based methods. A strategy is then proposed to compute the reference tracking points along the corridor for a wall-following behaviour. Meanwhile, dense 3D point clouds with ground-plane removed are projected which provide occupancy information, so that existing obstacle avoidance algorithms can be reused. A goal-directed navigation function is also developed by constructing a Nearness Diagram based obstacle avoidance behaviour guided by a wall-following behaviour. Experiment results validate the practicability and effectiveness of the approach.
McCTRP: A cross-layer tree routing protocol for multichannel wireless sensor networks

Single channel wireless sensor networks may be unreliable when the channel of choice is either unstable or interfered. Previous work has shown that multi-channel solutions could effectively improve network reliability and deliver higher throughput as well. While a number of multi-channel medium access control schemes have been proposed, there are not much work on routing protocol design addressing the unique challenges multi-channel schemes are facing. In this paper we present a multi-channel cross-layer tree routing protocol. Our contribution includes: Adaptive beaconing corresponding to network topology; Beacon-driven, send-data and receive-data driven link quality estimations; and synchronized MAC and routing table management. Our protocol has been implemented on a testbed consisting of 53 nodes deployed in a 5-story building. Experimental results show that our approach provides fast convergence rate, stable topology and over 99.8% data transmission reliability.
A new method for automatic construction of fault propagation digraph based on OO models

This paper proposes a new method for automatic construction of fault tree of control systems. The design of the knowledge base for the control systems are explained in details. An algorithm based on the OO models is presented for automatic generation of the digraph which describes the normal, failed and conditional propagation relations among the events in systems. This algorithm can also find the negative feedback and feedforward loops of control system automatically during the digraph construction. Simulation results show that the fault tree can then be simply constructed from the digraph by using a number of methods in the literatures.
Framework for developing a hierarchical model of reward focusing on the nucleus accumbens

Computational modeling using GENESIS platform has led to advances in fabricating a model to test the influence of molecular/proteomic adaptations on behavior due to reward. The nucleus accumbens is an area of the brain that processes information from other parts of the brain and is an integral element of the 'reward pathway' in the brain. A simplified model of the accumbens using one neuron is developed as part of a larger effort to study reward and chemical dependency with a focus on cocaine addiction. A preliminary model of a biologically realistic neuron was developed with inhibitory and excitatory afferents as well as intrasynapse dynamics. The neuron displayed characteristic behavior of a neuron found in the nucleus accumbens including bistability. The neuron has afferents from other neurons via dendrites which carry the inputs relating to behavioral aspects and to learning. To add behavioral aspects to the model, a methodology is developed to model contexts and their reinforcing effects on behavior, similar to cocaine addiction. Results using both the biological and behavioral modeling are encouraging for this preliminary model.
A cooperative caching algorithm for multi-cell data broadcasting

Broadcasting is an effective technique to reduce network traffic, and is inherently supported by wireless networks. It thus has been advocated by numerous on-demand data access protocols. In a wireless cellular network, broadcasting can be implemented within a single cell. However, the data owned by different cells could be different. If a client requests a data item available only in a remote cell, an inter-cell data transmission over some wired link is needed, which introduces additional access delay. In this paper, we demonstrate that such delay can be minimized through the use of remote caching. Specifically, we propose a novel cooperative caching scheme, in which each cell dynamically allocates the cache spaces for data from different remote cells. It makes replacement decisions according to several important factors: data item access frequency, cell traffic, and retrieval delay. Simulation results show that the proposed scheme can significantly reduce the response time over the non-cooperative caching scheme under various system configurations.
A New Real-Time Reliability Prediction Method for Dynamic Systems Based on On-Line Fault Prediction

While a specific system is in use, its reliability will decrease gradually after the infant mortality period because of the components' degradation, or external attacks. Thus, reliability is a natural characteristic of a system's health, and can be used for condition monitoring & predictive maintenance. This paper introduces a new real-time reliability prediction method for dynamic systems which incorporates an on-line fault prediction algorithm. The factors that may reduce a system's reliability are modeled as an additive fault input to the system, and the fault is assumed to be varying linearly with time, approximately. The time-varying fault is roughly estimated based on a modified particle filtering algorithm at first. Then, as a time series, the fault estimate sequence is smoothed, and predicted by an exponential smoothing method. Mathematical analysis shows that the effects of the system, and measurement noises on the fault estimates are greatly reduced by exponential smoothing, which indicates that the comparatively high accuracy of the fault estimates & predictions is guaranteed. Based on the particle filtering & fault prediction results, the whole system's predictive reliability is computed through a Monte Carlo simulation strategy. The effectiveness of the proposed real-time reliability prediction method is validated by a computer simulation of a three-vessel water tank system.
A framework for earthquake disaster mitigation system

China is an earthquake-prone country, and it is also one of the most serious earthquake disaster countries in the world. Establishment and improvement of earthquake disaster mitigation systems, integrating existing earthquake information and avoiding islands of information, has great significance to reduce the adverse impact on the country and the people. A more complete architecture of the earthquake disaster mitigation system based on computer and system engineering is put forward in this paper, considering the data flow in the work of earthquake disaster reduction. The architecture includes four levels: data collection level, data processing level, data application level and data presentation level. At the same time this paper describes the system design principles, organizational framework, functions. Finally the paper summarizes some problems which need pay more attention and focus on in the system building process.
A SAW-based commutation signaling modem for broadband indoor wireless communication

Commutation signaling is a bandwidth expanding modulation scheme that is robust to multipath induced intersymbol interference making it suitable for wireless digital communications. By using multipath diversity combining, commutation signaling exploits the time diversity that is inherent in a multipath propagation environment. This paper considers a surface acoustic wave (SAW) implementation of a commutation signaling modem for broadband indoor wireless communication. The modem employs differential encoding and a form of direct sequence spread spectrum modulation with the following specifications: data rate 40 Mb/sec, chip rate 200 MHz, and IF frequency 1 GHz. The differential coherent detector is a key element of a low cost, low complexity commutation signaling modem. A commutation signaling differential coherent detector has been implemented using SAW and RF integrated circuit (RF IC) technologies. The SAW devices have been fabricated on 1280-rotated Y-cut, X-propagating lithium niobate using approximately 1 micron line widths. RF IC technology Is used to implement the high-speed bilinear multipliers needed for differential coherent detection as well as the low-impedance buffers used to drive these multipliers.
Global output-feedback stabilisation of switched stochastic non-linear time-delay systems under arbitrary switchings

This study considers the global output-feedback control for a class of switched stochastic non-linear time-delay systems under arbitrary switchings. By using the backstepping technique and choosing an appropriate Lyapunov–Krasoviskii functional, an output-feedback controller independent of switching signals is constructed to render the closed-loop system globally asymptotically stable in probability and the output can be regulated to the origin almost surely. Two simulation examples are provided to show the effectiveness of the designed controller.
Improving Depth Compression in HEVC by Pre/Post Processing

Depth images have different characteristics from that of color images. They usually have gradual changes within objects while steep changes happen around object boundaries. Compression standards such as H.264/AVC and High Efficiency Video Coding (HEVC) are efficient in dealing with the gradual change regions but usually result in poor performance at edge regions. To facilitate the reuse of the current video coding design and to further improve the depth compression performance, we propose a pre/post processing based compression strategy. By modifying the edge blocks in the depth image to flat blocks, the pre-processed image can be efficiently compressed using existing compression schemes. Meanwhile, those edge blocks are compressed by an edge preserving codec separately. At the decoder, the decoded modified image and the edge blocks will be merged together to form the final reconstructed image. In our simulations, we implement this strategy to HEVC to evaluate the coding performance. Experimental results show the proposed scheme can achieve about 30% - 40% bit savings for Ballet and Break dancers sequences and 60% - 70% bit savings for Kinect captured depth sequences in comparison with HEVC.
Adaptive protection of short circuits in transmission lines

This paper proposes an adaptive protection scheme which has been developed through the analysis of short circuits in transmission lines. The method assumes a different protection pattern depending upon the distance between the fault position and the location of any protection devices. An adaptive threshold dependent upon the load on the system is used for initiation of the over-current protection routines. The adaptive quick-break current protection adopts a different threshold current according to the system mode and the type of short circuit. It is shown that the selectivity and reliability of the power system can be ensured, and sensitivity improved.
Feedforward current reference control for DFIG during grid faults

For doubly fed induction generation (DFIG) based wind turbine, large electromotive force will be induced in the rotor circuit under severe grid faults, and it usually far exceeds the DC-bus voltage. In that case, it is necessary to inject transient and negative-sequence components into rotor current reference to fulfill controllable low-voltage ride through (LVRT). On the synchronous reference frame of vector control system, such current references are 50 Hz and 100 Hz AC items, respectively. Thus, the classical proportional-integral (PI) controller cannot accurately track this AC reference. To solve this problem, a feedforward current reference (FFCR) control scheme is proposed to improve the tracking capability of AC reference. Hence, it unearths the characteristics of such reference. The proposed FFCR control scheme will not alter the stability of original current loop. Furthermore, it has strong robustness for frequency fluctuations of grid voltage and DFIG parameters drift. Finally, simulation and experimental results are presented to demonstrate the effectiveness of the proposed control scheme.
A Critical Salt Concentration of Illite Clay for Particle Release in Aquifer Media

Particle release is the main reason for permeability decrease in the coastal aquifers with the existence of illite clay. In this paper, an attempt was made to determine the CSC with both batch and column experiments. Stepwise decreasing concentrations of NaCl solution were applied until fine particles were released from the sediments and the CSC was determined. The critical oscillation frequency of batch experiment was determined at 150 rpm. The critical salt concentration was 0.054 mol/l determined by column experiments and 0.06 ±± 0.005 mol/l by batch experiments, respectively. CSC from batch experiments were compared to those obtained from column experiments. It was found that both of results were nearly consistent with each other. The flow velocity had little impact on the critical salt concentration in those experiments, while ion valence had a significant impact on the critical salt concentration.
AVSS: An Adaptable Virtual Storage System

This paper presents the design and implementation of the Adaptable Virtual Storage System (AVSS) and introduces the capacity virtualization and performance virtualization for storage systems. AVSS has the following characteristics: 1) adoption of extended YFQ algorithm to control the allocation of bandwidth resources, realization of the performance isolation and guarantees of virtual disks; 2) adoption of hierarchy structure and dynamic mapping mechanism to manage heterogeneous storage resources flexibly and effectively, which lays a foundation for allocating storage resources on demand; 3) application of data-access frequency statistics and dynamic behavior analysis to supervise storage layout reorganization. The experimental results proved the correctness of our design. AVSS can isolate different applications and avoid performance interference. It can adjust storage layout according to the behavior of applications and improve the utilization of storage resources while improving the performance of the storage system.
Improving a Consensus Approach for Protein Structure Selection by Removing Redundancy

In protein tertiary structure prediction, a crucial step is to select near-native structures from a large number of predicted structural models. Over the years, extensive research has been conducted for the protein structure selection problem with most approaches focusing on developing more accurate energy or scoring functions. Despite significant advances in this area, the discerning power of current approaches is still unsatisfactory. In this paper, we propose a novel consensus-based algorithm for the selection of predicted protein structures. Given a set of predicted models, our method first removes redundant structures to derive a subset of reference models. Then, a structure is ranked based on its average pairwise similarity to the reference models. Using the CASP8 data set containing a large collection of predicted models for 122 targets, we compared our method with the best CASP8 quality assessment (QA) servers, which are all consensus based, and showed that our QA scores correlate better with the GDT-TSs than those of the CASP8 QA servers. We also compared our method with the state-of-the-art scoring functions and showed its improved performance for near-native model selection. The GDT-TSs of the top models picked by our method are on average more than 8 percent better than the ones selected by the best performing scoring function.
A Grid Service Model Framework Based on Object-Z and Statechart

In order to manage the dynamic characteristics and provide theoretical foundation for modeling the grid services and services collaboration, by combining Object-Z with formal Statechart based on CREs (Concurrent Regular Expressions), this paper proposes an extension version of Object-Z language, GS-Z. As well, GS-Z extends Object-Z to enable the lifecycle management of grid service by introducing a concept of channel and a concept of life-thread. Furthermore, on the basis of GS-Z, this paper presents a framework for modeling grid services following WSRF specification. The model framework is illustrated through a case study.
High-Power CW and Q-Switched Tm:CaYAlO4 Lasers at $1.94~\mu \text{m}$ for Shallow Water Absorption

We report on 790-nm diode-pumped continuous-wave and passively Q-switched Tm:CYA lasers. For continuous-wave laser operation, a maximum output power of 7.62 W is achieved with slope efficiency of 50.9%. Passive Q-switching of the Tm:CYA crystal is also obtained with a maximum average output power of 1.32 W using a Cr:ZnSe saturable absorber. The shortest pulse width is about 42.6 ns with a pulse energy of 60.2 <inline-formula> <tex-math notation="LaTeX">$\mu \text{J}$ </tex-math></inline-formula> and a pulse peak power of 1.41 kW. These results represent the best laser performances of continuous-wave and passively Q-switched Tm:CYA lasers, to date. The 1.94-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> Tm:CYA lasers provide better laser sources for shallow absorption of biological tissue.