bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-scale residual network-based image restoration
Authors: Yang Liu , You Fucheng 
Published: February 2022

Abstract
All Image restoration technology has undergone extensive research. For image degradation, inverse filtering was proposed in the last century. Then Helstrom proposed a Wiener filtering algorithm. In recent years, the convolutional neural network has promoted the further development of image restoration technology. The coarse-to-fine multi-scale residual network Multiscale Deblur Net (MDN) used in this paper is stable in operation, simple in structure, and easy to train. It has a good deblurring ability for motion-blurred images by testing in the GO_PRO dataset.
Underwater Image Saliency Detection Via Attention-based Mechanism
Authors: Liu Lidan , Yu Weiwei 
Published: February 2022

Abstract
Due to complex lighting circumstances and other factors, visual saliency recognition of underwater images is not as good as visual saliency detection models that function well on land when applied to underwater environments. The dearth of large-scale annotated underwater picture datasets has further hindered the development of visual saliency detection in underwater images. we propose an attention-based approach for underwater image saliency detection to address this issue. The backbone network is pre-trained using both land and underwater image datasets, which effectively solves the problem of fewer datasets. After that, a residual refinement module incorporating a channel-space attention mechanism is merged, the model as a whole is trained end-to-end using underwater image datasets, and the model accuracy is experimentally tested. The experimental results in three underwater image datasets show that our method is better than several other methods in the mainstream evaluation indicators of the saliency detection.
Research on intelligent peak-cutting and valley-filling charging and swapping mode based on potential game theory
Authors: Hu Lanxin , Yue Feiyang 
Published: February 2022

Abstract
With the continuous increase in the proportion of renewable energy represented by wind and light in the power grid in recent years, on the one hand, the energy structure of the existing power grid has been directly optimized, and on the other hand, the full utilization of renewable clean energy can also reduce traditional power generation. The pollution caused by the industry to the environment. As an indispensable infrastructure for electric vehicles, charging and swapping stations, after being connected to a distributed micro-grid, can play a role in reducing peaks and valleys, promoting the consumption of new energy, and saving costs through reasonable configuration. The analysis of calculation examples shows that the intelligent charging and swapping system model based on the potential game theory proposed in this paper can effectively reduce the operating cost and user cost of the swap station, and effectively improve the level of wind and solar consumption, and achieve the level of smoothing the grid curve and peak-shaving and valley-filling. Purpose has important practical significance and engineering value.
Short-Term Wind Speed Interval Prediction using LUBE based Quasi-Recurrent Neural Network
Authors: Saeed Adnan , Li Chaoshun , Gan Zhenhao 
Published: February 2022

Abstract
Accurate wind speed forecasting is an extremely difficult task and therefore less accurate forecasts may sometimes make the power systems vulnerable. Interval predictions on the other hand may help in maximizing the usage of integrated wind energy, as well as to reduce the adverse effects of the uncertainties introduced by the random fluctuations of wind, to the power systems. In this paper, we propose a novel wind speed interval prediction model by integrating lower upper bound estimation method (LUBE) into a quasi-recurrent neural network (QRNN). LUBE is a non-differentiable loss function requiring usage of meta-heuristic optimization techniques for training the model. By incorporating two objective functions, we proposed a new form of LUBE, using which the network can be trained with conventional stochastic gradient descent. The proposed model is evaluated for eight different datasets distributed among two wind fields. Results obtained from the computational experiments reveals that the proposed method generated narrow intervals with high coverage which helps it in achieving an improvement of 33% in coverage width criterion over the traditional models.
Monthly-scale runoff forecast model based on PSO-SVR
Authors: Qiao Guangchao , Yang Mingxiang , Zeng Xiaoling 
Published: February 2022

Abstract
The current methods used in the Lubbog reservoir runoff forecast generally have shortcomings such as low forecast accuracy and low stability. Aiming at these problems, this paper constructs a PSO-SVR mid-and-long term forecast model, and it uses the particle swarm optimization algorithm (PSO) to find the penalty coefficient C, the insensitivity coefficient ε and the gamma parameter of the Gaussian radial basis kernel function of the support vector regression machine (SVR). The results demonstrates that the average relative errors of the PSO-SVR forecast model is relatively small, which are all within a reasonable range; the qualification rates for most monthly forecasts are above 80%. Experimental results indicate that compared with multiple regression analysis, the PSO-SVR model has a higher forecast accuracy, a stronger stability, and a higher credibility. It has a certain practical value and provides a reference for related research.
Short-term load forecasting based on improved manta ray algorithm to optimize neural network
Authors: Hao Gang , Xianyu Jianchuan 
Published: February 2022

Abstract
Accurate short-term load forecasting can reduce the shutdown reserve and rotating reserve of generator units, which directly affects the safety, stability and economic benefits of power grid. Aiming at the shortcomings of sensitive initial value and easy to fall into local optimization of BP neural network, a load forecasting method based on improved manta ray algorithm to optimize BP neural network is proposed. The location update formula of manta ray foraging is improved through Levy flight strategy, the local and global search performance of the algorithm is balanced, its ability to jump out of the local optimization is enhanced, the weight and threshold of BP network are optimized, and the optimization model for short-term load forecasting is established. The results show that the prediction accuracy and convergence speed of the improved prediction model have been greatly improved. It has engineering practical value.
A new algorithm for precise location of power cable fault based on zero phase filter
Authors: Zhang Xiaojun , Zhang Bo , Wu Suzhou , Li Chao , Lei Zeyang 
Published: February 2022

Abstract
In order to improve the fault location accuracy of power cables, the two-side method is used for fault location. The main factors affecting the cable fault accuracy of the double-ended fault location method are the wave speed of traveling waves transmitted in the cable and the time difference between the traveling waves arriving at the monitoring equipment. In fact, traveling wave signals with different frequencies propagate at different wave speeds, and the use of fixed wave speeds for positioning calculations will inevitably cause positioning errors. First, the center frequency spectrum method is used to calculate the wave speed, and different wave speeds are used for different types of cables, so as to reduce the positioning error caused by the wave speed. Then, the devices on both sides use high-precision GPS to synchronize the time to reduce the natural error. For the time difference between the detected traveling waves reaching the devices on both sides, the triangle algorithm can be used to find the starting point for time difference calculation, thereby constructing a new cable fault location algorithm. Finally, a large number of PSCAD simulations and field discharge tests of cable grounding were carried out. The simulation results and test results verified the feasibility and superiority of this algorithm.
An anti-outlier robust clock compensated algorithm for wireless network
Authors: Chen Kai , Chen Xin , Gao Xiang 
Published: February 2022

Abstract
Clock synchronization technology plays an important role in wireless networks, and the improvement of clock synchronization precision has always been the goal pursued by research. This paper introduces a clock compensation technology in wireless networks, which solves two main problems influencing clock accuracy in current clock synchronization technology: time-varying clock frequency deviation and interference from outliers. In this paper, the linear regression algorithm with local weighting and least square sum estimation is used to solve the above problems. Simulation results show that the algorithm has higher clock synchronization accuracy than other algorithms, and the clock synchronization error is significantly lower than the non-robust regression algorithm when there are outliers, proving that this method can effectively resist the influence of outliers on clock synchronization accuracy.
High-Resolution Representation Learning for Human Pose Estimation based on Transformer
Authors: Fu Dengyu , Wu Wei 
Published: February 2022

Abstract
Human pose estimation requires accurate coordinate values for the prediction of human joints, which requires a high-resolution representation to effectively improve accuracy. For some difficult joint prediction tasks, it is not only necessary to look at the characteristics of the joint points themselves, but also to make judgments in combination with the context of the whole image. Generally, the resolution will be reduced when the context information is obtained. In this process, it will inevitably lose some spatial information and make the prediction inaccurate. In this paper, we propose a high-resolution human pose estimation network based on Transformer to reduce the impact of spatial information loss on keypoints estimation. In detail, we use low-level convolution neural network to extract low-level semantics from the image, and then the Transformer is used to capture the image context to further predict the key points of the human body, obtain the high-resolution representation. The experiments show that our network can accurately predict the positions of keypoints, we achieve state-of-art results on the COCO keypoint detection dataset.
Research on the Methods to Improve RFSS Accuracy
Authors: Yuan Qingze 
Published: February 2022

Abstract
RFSS is a common test method in the research and development of radar guidance systems, which overcomes the problem of the large cost of the field test to a certain extent. With the development of radar technology, the evaluation of hardware in the loop RF simulation error needs to be more accurate. This paper summarizes the current research, mainly focusing on simulation error analysis, near-field error and near-field correction, expansion of simulation object and suppression of simulation error. At present, most studies focus on azimuth error and pitch error respectively, but do not take into account the correlation of the errors in these two different angle domains. Or only the root mean square of the amplitude of the two-dimensional angle error is considered, without considering the directional distribution of the angle error.
Aptamer-functionalized targeted siRNA delivery system for tumor immunotherapy
Authors: Lv Haiyin , Wang Tengfei , Ma Fanshu , Zhang Kunchi , Gao Tian , Pei Renjun , Zhang Ye 
Published: February 2022

Abstract
Programmed death ligand 1 (PD-L1) overexpressed on the surface of tumor cells is one of the reasons for tumor immune escape. Reducing PD-L1 expression has been proved to be an effective strategy to facilitate immune system activation and inhibit tumor progression. RNA interference (RNAi) is a promising technology for gene regulation in tumor therapy. In this study, we constructed a targeted siRNA delivery system NPs@apt to transfect PD-L1 siRNA into human non-small-cell lung carcinoma cell line (A549) for inhibiting tumor immune evasion. NPs@apt was prepared by compressing PD-L1 siRNA with cationic Lipofectamine 2000, fusing with erythrocyte membrane-derived nanovesicles, and further modifying with targeting AS1411 aptamer. The introduction of erythrocyte membrane endowed the siRNA delivery system with lower cytotoxicity and the ability to escape from the phagocytosis of macrophages. The stability of NPs@apt and the protection to loaded siRNA were confirmed. In vitro studies after NPs@apt treatment demonstrated that PD-L1 siRNA was selectively delivered into A549 cells, and further resulted in PD-L1 gene knockdown, T cell activation and tumor cell growth inhibition. This study offered an alternative strategy for specific siRNA transfection for improving anti-tumor immunity.
Projected risks associated with heat stress in the UK Climate Projections (UKCP18)
Authors: Kennedy-Asser Alan T , Owen Gwilym , Griffith Gareth J , Andrews Oliver , Lo Y T Eunice , Mitchell Dann M , Jenkins Katie , Warren Rachel F 
Published: February 2022

Abstract
Summer heat extremes in the UK pose a risk to health (amongst other sectors) and this is exacerbated by localised socio-economic factors that contribute to vulnerability. Here, regional climate model simulations from the UK Climate Projections are used to assess how different elements of extreme heat will vary across the UK in the future under global mean surface temperature warming levels of +1.5 °C, +2.0 °C and +3.0 °C above pre-industrial. Heat stress metrics incorporating daily maximum and minimum temperature, temperature variability and vapour pressure are included. These show qualitatively similar spatial patterns for the recent past, with the most pronounced heat hazards found in south-eastern regions of the UK. Projected heat hazard changes across the UK are not homogeneous, with southern regions (e.g. Greater London, South East) showing greater increases in maximum temperatures and northern regions (e.g. Scotland and Northern Ireland) showing greater increases in humidity. With +3.0 °C warming, the relative change in combined heat hazards is found to be greatest in the south-western UK, however, in absolute terms, south-eastern regions will still experience the greatest hazards. When combined with socio-economic factors, hotspots of high heat stress risk emerge in parts of London, the Midlands and eastern England along with southern and eastern coastal regions. Weighting of different heat risk factors is subjective and to this end we have developed and made available an interactive app which allows users to assess sensitivities and uncertainties in the projected UK heat risk.
Single step method for two-frame phase-shifting interferometry with unknown phase shift
Authors: López-Álvarez Yadira F , Muñoz-Maciel Jesús , Peña-Lecona Francisco G , Duran-Rámirez Víctor M 
Published: February 2022

Abstract
A procedure to recover the phase from two interferograms with unknown phase step is proposed. A least-square approach is used to solve simultaneously for the illumination background and the introduced phase shift in a local window. This allows to calculate the interferogram phase of the pixel at the center of the window with the standard arc-tangent formula. This process is carried out sequentially across the interferogram field until all pixels are recovered. The proposed scheme is fast and robust, and unlike others methods eliminates the need to implement prior steps of background suppression or interferogram normalization. The developed procedure may be used in problematic environments where vibrations or other disturbances difficult to acquire a full set of phase shifted interferograms with constants phase shifts. The developed procedure is validated in real and simulated data with good results.
Graphdiyne based GDY/CuI/NiO parallel double S-scheme heterojunction for efficient photocatalytic hydrogen evolution
Authors: Jin Zhiliang , Wang Xiangyi , Hao Xuqiang , Wang Guorong , Guo Xin , Wang Kai 
Published: February 2022

Abstract
As a new kind of two-dimensional (2D) layered carbon allotrope, graphdiyne (GDY) is rarely studied in the application field of photocatalytic hydrogen production. In addition, the efficient construction of photocatalyst heterostructure is a promising strategy to improve the yield of hydrogen production from photocatalytic split of water. Therefore, it is an excellent method to construct heterostructure photocatalytic system by introducing GDY into semiconductor photocatalytic materials. Herein, it is an excellent method to construct heterostructure photocatalytic system by introducing the cuprous iodide based 2D layered carbon allotrope (GDY) into metallic oxide semiconductor (NiO). Thus, a ternary hybrid photocatalyst (GDY/CuI/NiO) was prepared by in situ ultrasonic agitation method. X-ray diffraction, SEM, transmission electron microscope and x-ray photoelectron spectroscopy results showed that NiO nanosheets were successfully adsorbed by GDY/CuI. In addition, the composite catalyst (GDY/CuI/NiO) showed excellent photocatalytic performance, which performed a high hydrogen production activity of 5955 μmol g−1 and good stability in the 20 h hydrogen production experiment. Amorphous GDY provides more active sites for the process of hydrogen evolution in this photocatalytic system. Most importantly, the construction of S-scheme heterojunction promotes electron transfer and plays an important role in enhancing the hydrogen production activity. These findings provide new ideas for realizing efficient solar hydrogen production system.
Vaccine efficacy at a point in time
Authors: Follmann Dean A , Fay Michael P 
Published: February 2022

SummaryVaccine trials are generally designed to assess efficacy on clinical disease. The vaccine effect on infection, while important both as a proxy for transmission and to describe a vaccine’s entire effects, requires frequent (e.g., twice a week) longitudinal sampling to capture all infections. Such sampling may not always be feasible. A logistically easy approach is to collect a sample to test for infection at a regularly scheduled visit. Such point or cross-sectional sampling does not permit estimation of classic vaccine efficacy on infection, as long duration infections are sampled with higher probability. Building on work by Rinta-Kokko and others (2009) and Lipsitch and Kahn (2021), we evaluate proxies of the vaccine effect on transmission at a point in time; the vaccine efficacy on prevalent infection and on prevalent viral load, VE$_{\rm PI}$ and VE$_{\rm PVL}$, respectively. Longer infections with higher viral loads should have more transmission potential and prevalent vaccine efficacy naturally captures this aspect. We demonstrate how these parameters obtain from an underlying proportional hazards model for infection and allow for waning efficacy on infection, duration, and viral load. We estimate these parameters based on regression models with either repeated cross-sectional sampling or frequent longitudinal sampling. We evaluate the methods by simulation and analyze a phase III vaccine trial with polymerase chain reaction (PCR) cross-sectional sampling for subclinical infection.
Diffusion microstructure imaging in progressive supranuclear palsy: reduced axonal volumes in the superior cerebellar peduncles, dentato-rubro-thalamic tracts, ventromedial thalami, and frontomesial white matter
Authors: Rau Alexander , Jost Wolfgang H , Demerath Theo , Kellner Elias , Reisert Marco , Urbach Horst 
Published: February 2022

Abstract
Differentiating between Parkinson’s disease (PD) and atypical Parkinson syndromes such as progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and corticobasal degeneration is challenging. Diffusion microstructure imaging (DMI) was analyzed in patients with clinically suspected atypical Parkinson syndromes and healthy controls. In an exploration cohort, the spatial distribution of PSP-related changes of DMI parameters were evaluated in a voxel-wise analysis and a region-of-interest (ROI)-based approach was established. The diagnostic performance was subsequently tested in an independent validation cohort. In the exploration cohort, 53 PSP patients were compared to a pooled comparison group of 19 patients with PD, 26 patients with MSA, 7 patients with corticobasal syndrome, and 25 healthy controls. PSP patients showed widespread axonal loss in the superior cerebellar peduncles, the dentato-rubro-thalamic tracts, the thalami and the frontal white matter (each P &lt; 0.001). In the validation cohort consisting of 12 patients with PSP vs. 13 patients with other movement disorders, the accuracy of this ROI-based approach for identifying the PSP was highest in the thalamus and the frontal white matter (accuracy 0.96 each). This DMI approach can identify PSP patients on an individual level in a collective with suspected atypical Parkinson syndromes and allows further insight on microstructural alterations in vivo.
Altered prefrontal signaling during inhibitory control in a salient drug context in cocaine use disorder
Authors: Ceceli Ahmet O , Parvaz Muhammad A , King Sarah , Schafer Matthew , Malaker Pias , Sharma Akarsh , Alia-Klein Nelly , Goldstein Rita Z 
Published: February 2022

Abstract

Introduction
Drug addiction is characterized by impaired response inhibition and salience attribution (iRISA), where the salience of drug cues is postulated to overpower that of other reinforcers with a concomitant decrease in self-control. However, the neural underpinnings of the interaction between the salience of drug cues and inhibitory control in drug addiction remain unclear.

Methods
We developed a novel stop-signal functional magnetic resonance imaging task where the stop-signal reaction time (SSRT–a classical inhibitory control measure) was tested under different salience conditions (modulated by drug, food, threat, or neutral words) in individuals with cocaine use disorder (CUD; n = 26) versus demographically matched healthy control participants (n = 26).

Results
Despite similarities in drug cue-related SSRT and valence and arousal word ratings between groups, dorsolateral prefrontal cortex (dlPFC) activity was diminished during the successful inhibition of drug versus food cues in CUD and was correlated with lower frequency of recent use, lower craving, and longer abstinence (Z &gt; 3.1, P &lt; 0.05 corrected).

Discussion
Results suggest altered involvement of cognitive control regions (e.g. dlPFC) during inhibitory control under a drug context, relative to an alternative reinforcer, in CUD. Supporting the iRISA model, these results elucidate the direct impact of drug-related cue reactivity on the neural signature of inhibitory control in drug addiction.