bluuebunny/crossref_metadata_2025 huggingface dataset:
Silicon rubber/expandable microsphere based triboelectric nanogenerator for harvesting biomechanical energy
Authors: Lv Shasha , Huang Tao , Yu Hao
Published: November 2021

Triboelectric nanogenerator (TENG) is favorable for harvesting adaptable and complex biomechanical energy in our daily life. Here, silicon rubber/expandable microsphere TENG was achieved by spin-coating a mixture of expandable microspheres and silicon rubber on a flat plate with conductive fabric. Furthermore, self-made flexible TPU/MWCNTs electrodes replaced commercial conductive fabric to make TENG more adapt to skin of human body. Finally, the optimized TENG in this work demonstrates energy harvesting capabilities and can be applied in self-powered sensor systems and provides new dimensions for biomechanical energy harvesters and wearable self-powered electronics.
Fracture Failure Analysis of the Connecting Bolt of Circuit Breaker in 500kV Converter Station
Authors: Wang Jun , Cao Xianhui , Huang Rong , Hu Yujing , Liu Weike , Chen Junjun , Feng Chao
Published: November 2021

Through a macroscopic inspection, a stereomicroscopic inspection, a material analysis, a hardness inspection, a metallographic analysis, scanning electron microscope and energy spectrum analysis, the reasons for the breakage of the connecting bolts of the circuit breaker in a 500kV converter station are studied. The results show that the bolt fracture is caused by the combined effect of a hydrogen embrittlement and a high-stress low-cycle fatigue. The corresponding recommended measures are proposed to improve the reliability of the bolts and ensure the safe operation of the circuit breaker.
Research on application of mine transient electromagnetic method in detecting collapse column
Authors: Du Hailong , Li Wen , An Jinsong , Zhang Lin , Zhao Weiye , Liu Qinghua , Dou Wenwu , Lian Yuguang , Cui Jinliang , Wang Guoku , Chen Jianqiang , Ma Zhicha
Published: November 2021

The transient electromagnetic detection has been widely applied to mine roadway excavation engineering. In order to figure out the front geological conditions of roadway excavation, the application of mine transient electromagnetic method (TEM) in detecting collapse column was conducted in this paper by taking the TEM-based advanced detection of 53152 working face in a mine located in Jincheng, Shanxi Province as an example. The results showed that if TEM is applied, geological structures like water-free collapse column could be largely delineated. Besides, by combining the interference source of detection environment and related geological data nearby the detection site, ‘false abnormalities’ could be effectively eliminated, thus providing the reference basis for the water prevention and control work of mines.
The Design of Parameter Modeling Software Applicable for Turbine Control Systems of Power Units Operated at Deep Shaving States
Authors: Zhu Longfei , Si Paiyou , Liu Shuangbai , Xie ChangYa , Zhang Teng , Hu Yuou , Qiu Xiaozhi
Published: November 2021

Against the backdrop of achieving the “dual carbon” reduction goals, the proportion of renewable energy consumption in China is gradually increasing, and more and more thermal power units are gradually being operated at the deep peak shaving states, which puts forward higher requirements for stable operation of the power grid system. In view of the current situation, this paper designs a new type of Python-based parameter modeling software for turbine control systems, including the load selection module, data pre-processing module, parameter identification module, and result output module. To solve the problem that the primary frequency regulation parameters of the units operated at the deep peak shaving states change with the load, the software is designed to set key model parameters of different load segments of the units. The actual measurement results of a unit operated at the deep peak shaving state show that this developed software is convenient and friendly to operate, creates accurate parameter identification results, and has a wider range of applicability.
Experimental analysis of zero-natural-frequency damper for transmission line large cross project based on NES
Authors: Zhao Bin , Si Jiajun , Zhang Zhao , Han Jingshan
Published: November 2021

The operation experience of transmission line large cross project shows that the traditional anti-aeolian-vibration devices had poor adaptability to complex terrain and micro meteorological conditions, and they were difficult to meet the application requirements of super large cross project at this stage. A zero-natural-frequency damper was designed by introducing nonlinear energy sink in this paper, and the solid prototypes were processed. According to the actual design parameters of conductor using in a large cross project, the anti-vibration effect was tested by using the indoor simulation test span. The test results showed that the anti-vibration effect of the scheme based on the zero-natural-frequency damper could meet the needs of practical engineering. The installation of this damper could improve conductors’ and fittings’ adaptability to complex terrain and enhance the wind and anti-vibration ability of transmission line.
Inside-wind-farm/Wind-farm-grid sub-synchronous oscillation characteristics analysis in grid-connected system of multiple DFIGs
Authors: Sun Mengxue , Gao Ruixin , Ji Yuhui , Long Xin , Yao Changzhi , Wu Fan
Published: November 2021

Aiming at the sub-synchronous oscillation (SSO) problem of the grid-connected system of multiple DFIGs, most of the existing theoretical studies take the entire wind farm as a single-machine model, the stand-alone model cannot reflect the inside-wind-farm oscillation mode produced by the interactions among DFIGs in the wind farm. Therefore, this paper takes the equivalent value of DFIG-based wind farm to three DFIGs, establishes a mathematical model of the grid-connected system of three DFIGs, and studies the sub-synchronous oscillation modes existing in the system through eigenvalue analysis and participation factor analysis. The results show: When the length of transmission line increases, the oscillation frequency of the inside-wind-farm/wind-farm-grid sub-synchronous oscillation mode increases, the damping decreases and the stability weakens; when the number of grid-connected DFIGs increases, the oscillation frequency of the inside-wind-farm/wind-farm-grid sub-synchronous oscillation mode decreases, the damping increases and the stability enhances. Finally, a time-domain simulation model of the grid-connected system of multiple DFIGs was built in PSCAD/EMTDC to verify the correctness of the theoretical analysis results.
Design of Maximum Power Tracking System for Photovoltaic Power Generation
Authors: Liu Yuewen
Published: November 2021

Solar power generation is related to climatic conditions, and its high cost and low power generation efficiency have become the main factors restricting its development. Realizing the maximum power tracking of solar photovoltaic power generation through power electronic technology and control technology is an effective measure to increase the power generation of photovoltaic power generation systems, reduce power generation costs, improve solar energy conversion efficiency, and widely promote photovoltaic power generation technology. According to the illumination characteristics of solar cells, using a single-chip microcomputer to control the DC-DC converter and conductance increment method as the control algorithm, a solar maximum power tracking system is designed.
Preparation and electrochemical properties of MXene based conductive composites for food contamination detection
Authors: Zhang Xiang , Gu Yu
Published: November 2021

MXene has unique intercalation structure, excellent interlayer spacing and excellent biocompatibility. It is considered as an excellent nano material carrier and can be used as a good adsorbent in the detection of food pollutants. In this study, a large number of experimental materials and reagents were selected to prepare Co3O4 / MXene composites by in-situ growth of Co3O4 nanoparticles on MXene support by impregnation pyrolysis. The composite retained the original accordion like intercalation structure. The size and number of Co3O4 particles on the surface of MXene and in the intercalation increased with the increase of loading, showing regular order. The prepared composite has abundant lithium intercalation active sites, fast electron and ion transport channels, good rate performance and energy density at high current density, showing excellent electrochemical performance.
Degradation Effect of UV Synergistic Biomass Activated Carbon Materials on Phenol Pollutants in Water
Authors: Zhang Qi
Published: November 2021

The purpose of the study is to purify the water containing phenol pollutants. The degradation effect of phenol pollutants in water is studied through the combined action of UV and biomass-activated carbon. First, the phenol solution is prepared in the laboratory to simulate the polluted water. Second, the phenol adsorption effects of UV synergistic biomass activated carbon, biomass activated carbon and ordinary industrial activated carbon under different influencing factors are compared by experiments. Finally, the results are analyzed and the conclusions are drawn. The results show that the UV synergistic biomass activated carbon has the strongest degradation ability for phenol, and the highest removal rate is 66.5% when the shaking time is 65 minutes. The adsorption ability of the industrial activated carbon for phenol is the worst. When the initial concentration of phenol is 25mg/L, the maximum phenol removal rate is 96.8%. The maximum phenol removal rate of biomass activa ted carbon appears in the initial concentration of phenol and the phenol removal rate is 60 mg/L. The reaction temperature has little effect on the phenol removal rate of UV synergistic biomass activated carbon and biomass activated carbon. The phenol removal ability of UV synergistic biomass activated carbon and biomass activated carbon reaches the highest when the dosage of activated carbon is 2.0 g, and the rates are 96.4% and 91.1%, respectively. When the pH of the solution is 7, the removal rate of UV synergistic biomass activated carbon reaches a maximum of 97%. When the pH of the solution is 6, the removal rate of biomass-activated carbon reaches the maximum. When the pH of the ordinary industrial activated carbon is 7, the removal rate is the maximum. Due to different influencing factors, UV synergistic biomass activated carbon has the strongest phenol degradation ability. This study provides a reference for the purification of polluted water.
Ultrasonic enhanced water washing method for extracting sodium fluoride from spent pot lining
Authors: Qi Yongrong , Gong Yuan , Wang Haibin , Peng Li , Fan Liting , Li Chunlei
Published: November 2021

A large amount of spent pot lining (SPL) is produced by cryolite-alumina melting electrolysis process, and the fluoride content in the leaching solution is up to 6000 mg/L, which belongs to hazardous waste. If SPL is not handled effectively, it will cause great harm to the environment. Because the NaF rich in SPL is an important raw material for the synthesis of cryolite by carbonation, this paper uses SPL as the raw material to extract NaF. On the basis of exploring the process conditions of water washing leaching NaF, ultrasonic wave was introduced to enhance mass transfer and the effects of ultrasonic cavitation on water washing process was compared. The results show that ultrasonic waves can effectively shorten the time for water washing to reach equilibrium and further improve the efficiency of NaF leaching. Under the optimal process conditions determined by the experiment, when the ultrasonic power is 400 W, the time for washing to reach equilibrium is shortened from 50 min to 40 min, and the NaF leaching efficiency is increased from 67.25 % to 70.42 %. While improving the leaching efficiency, the water consumption is effectively reduced, and the purity of the recovered product NaF is 96.82 %. This research provides a technical reference for the harmless and low cost leaching of NaF from SPL in the aluminium electrolysis industry.
Design of ultrafine silicon structure for lithium battery and research progress of silicon-carbon composite negative electrode materials
Authors: Zhang Baoguo , Tong Ling , Wu Lin , Yang Xiaoyu , Liao Zhiyuan , Chen Ao , Zhou Yilai , Liu Ying , Hu Ya
Published: November 2021

As demand for high-performance electric vehicles, portable electronic equipment, and energy storage devices increases rapidly, the development of lithium-ion batteries with higher specific capacity and rate performance has become more and more urgent. As the main body of lithium storage, negative electrode materials have become the key to improving the performance of lithium batteries. The high specific capacity and low lithium insertion potential of silicon materials make them the best choice to replace traditional graphite negative electrodes. Pure silicon negative electrodes have huge volume expansion effects and SEI membranes (solid electrolyte interface) are easily damaged. Therefore, researchers have improved the performance of negative electrode materials through silicon-carbon composites. This article introduces the current design ideas of ultra-fine silicon structure for lithium batteries and the method of compounding with carbon materials, and reviews the research progress of the performance of silicon-carbon composite negative electrode materials. Ultra-fine silicon materials include disorderly dispersed ultra-fine silicon particles such as porous structures, hollow structures, and core-shell structures; and ordered ultra-fine silicon, such as silicon nanowire arrays, silicon nanotube arrays, and interconnected silicon nano-films. The article analyzes and compares the composite method of ultrafine silicon and carbon materials with different structural designs, and the effect of composite negative electrode materials on the specific capacity and cycle performance of the battery. Finally, the research direction of silicon-carbon composite negative electrode materials is prospected.
Estimation of health status of lithium-ion battery based on PF-SA
Authors: Li Jianlin , Wang Zhe , Ma Suliang
Published: November 2021

The aging of lithium-ion batteries is a gradual process, which can usually be expressed by the state of health (SOH). Effective estimation of its SOH is helpful to reduce the probability of fire, fire and other dangerous accidents. In this paper, particle filter (PF) is used to predict the health of the battery, and then the simulated annealing algorithm (SA) is used to optimize the PF to realize the prediction of battery SOH.
Construction of dibutyl phthalate molecularly imprinted electrochemical sensor based on multi-walled carbon nanotubes-modified boron-doped diamond electrode
Authors: Wu Xiaoqiong , Jia Fan , Fu Zhixian
Published: November 2021

In order to construct a dibutyl phthalate (DBP) molecularly imprinted electrochemical sensor, the DBP was used as the template molecule, due to the characteristics of multi-walled carbon nanotubes (MWCNTs) loaded with nano-gold to enhance the electrical conductivity of the composite material, a molecularly imprinted film was prepared on the surface of boron-doped diamond electrode (BDD) by potential deposition. The morphology of the composite material MIP/AuNPs/MWCNTs/BDD was analyzed and characterized by scanning electron microscopy (SEM), and the performance of the electrochemical sensor was characterized by cyclic voltammetry (CV), and differential pulse voltammetry (DPV) was used to detect DBP. The electrochemical sensor had a linear range of 1×10-8 ~ 1×10-5 mol/L and a detection limit of 3.3×10 -9 mol/L. The sensor was applied to the detection of DBP in water samples.
In-situ preparation of hierarchical Beta zeolite by steam- assisted crystallization method using meso-macroporous silica
Authors: Kong Delong , Li Hansheng , Liu XiaoBo , Xu Chunxiao , Ding Chen , Wang Wei , Shi Jiawei
Published: November 2021

In this paper, a series of meso-macroporous silica (MMS) were prepared by phase separation, then mic-meso-macroporous Beta zeolite was prepared by steam-assisted crystallization (SAC) method using the meso-macroporous silica as substrate. The factors such as the amount of water, crystallization time, aluminum source, amount of template agent during the SAC method were investigated. Various characterization methods such as BET, XRD, SEM were used to investigate the physical and chemical properties of the prepared materials. The results showed that the meso-macroporous silica could be transformed into Beta zeolite through the SAC method while the macroporous structure was still maintained.
Research on Hyperspectral Inversion of Soil Alkaline Hydrolysis Nitrogen Content and pH Value Based on DWD
Authors: Liu Sijia , Xiao Zhiyun
Published: November 2021

Aiming at the shortcomings of traditional methods for detecting the content of Alkaline Hydrolysis Nitrogen (AHN) and pH value in soil, such as time-consuming and labor-consuming, this paper proposes a rapid quantitative inversion method based on hyperspectral analysis of AHN content and pH value. This method uses db4 discrete wavelet denoising (DWD) and wavelet denoising normalization (DWD-N) to carry out Pearson correlation analysis, and two methods, Ridge regression and Partial Least Squares Regression (PLSR), were used to compare the accuracy of hyperspectral inversion of soil AHN content and pH value. Experiments have demonstrated that in the inversion of the AHN content prediction model, Ridge regression has a good modeling effect under the DWD-N model, where R2=0.647, RMSE=7.067mg/kg. PLSR has good prediction effect under DWD-N, where R2 is the highest of 0.792, RMSE is 3.438mg/kg; in the model inversion of pH prediction, the full-band PLSR modeling effect of pH value under DWD pretreatment is the best, which modeling set and the prediction set of R2 is 0.826 and 0.875, the RMSE is 0.217 mg/kg and 0.191 mg/kg respectively.
Underwater Acoustic Covert Communication Based on Compressed Sensing
Authors: Li Qiyun , Zhou Chunyan , Cao Hao
Published: November 2021

Compressed sensing technology is currently a relatively mature channel estimation technology, and the sparsity of the underwater acoustic channel provides a basis for the application of compressed sensing technology to the underwater acoustic channel. Channel estimation is one of the most commonly used signal reconstruction methods. The signal is reconstructed based on compressed sensing technology to improve the reliability of signal transmission. The key to compressed sensing technology is the SL0 algorithm. Based on the SL0 algorithm, a new objective function is proposed to better approximate the L0 norm. In a very short time, the original signal can be reconstructed accurately and the signal transmission is improved. Finally a simulation experiment is used to compare the proposed objective function with the previously proposed objective function, which proves that the performance of the proposed objective function is better.
Hybrid feature selection model based on machine learning and knowledge graph
Authors: Peng Xiaoqing , Shuai Yong , Gan Yaxi , Chen Yaokai
Published: November 2021

Aiming at the problem that the current feature selection algorithm can not adapt to both supervised learning data and unsupervised learning data, and had poor feature interpretability, this paper proposed a hybrid feature selection model based on machine learning and knowledge graph. By the idea of hybridization, this model used supervised learning algorithms, unsupervised learning algorithms and knowledge graph technology to model from the perspective of data features and text features. Firstly, the data-based feature weights were obtained through the machine learning model, and then the text-based weights were obtained by using the knowledge graph technology, and the weight sets are combined to obtain a feature matrix with good explanatory properties that meets both the data and text features. Finally, the case analysis proves that the method proposed in this paper has good effects and interpretability.
Research on Neural Style Transfer
Authors: Liu Xueyuan
Published: November 2021

The process of using CNN (Convolutional Neural Network) to blend the contents of a picture with different styles is called neural style transfer (NST). The purpose of this paper is to introduce current progress of NST, and introduce in detail the classification of the main NST algorithms based on deep learning, and make qualitative and quantitative comparisons of different algorithms, and then analyze the application prospects of image style migration in related fields, and finally summarize the existing problems and future research directions of NST.
Application of 3D vision intelligent calibration and imaging technology for industrial robots
Authors: Yang Rui , Mo Qinglong , Li Yuhong , Liu Yizhi , Hu Ruihan
Published: November 2021

During modern flexible lean manufacturing, flexible operation of irregular and complex workpieces with different specifications and arbitrary placement is an essential ability of industrial robots, while it cannot be met by traditional clamping methods. Vision technology brings flexibility and convenience to industrial robots, but the common two-dimensional technology only involves three degrees of freedom (plane displacement and rotation), which hinders the positioning of arbitrarily placing workpieces (often six degrees of freedom) and disorderly sorting. In addition, for typical visual tasks in industrial environments like defect detection, accurate distinguishing of such defects as pits and scratches is challenging under two-dimensional plane imaging. The introduction of three-dimensional information provides an effective solution to this problem. Thus, in the face of increasingly complex, flexible, intelligent and personalized manufacturing needs, the acquisition and processing of 3D visual information are of much importance.
Breast cancer X-ray image staging: based on efficient net with multi-scale fusion and cbam attention
Authors: Li Runyi , Wang Sen , Wang Zizhou , Zhang Lei
Published: November 2021

With ever-progressing development period, image classification algorithms based on deep learning have shown good performance on some large datasets. In the development of classification algorithms, many proposals related to attention mechanism have greatly improved the accuracy of the model, and at the same time increased the interpretability of the network structure. However, on medical image data, the performance of the classification algorithm is not as expected, and the reason is that the fine-grained image data differs little among all classes, resulting that the knowledge domain is also hard to learn for models. We (1) proposed the Efficientnet model based on the cbam attention mechanism, and added a multi-scale fusion method; (2) applied the model to the breast cancer medical image data set, and completed the breast cancer classification task with high accuracy (Phase I, Phase II, Phase III, etc.); (3) Compared with other existing image classification algorithms, our method has the highest accuracy, thus the researchers conclude that EfficientNet with CBAM and multi-scale fusion will improve the classification performance. This result is helpful for deeper research on medical image processing and breast cancer staging.
Hypergraph Neural Networks with Attention Mechanism for Session-based Recommendation
Authors: Ding Meirong , Lin Xiaokang , Zeng Biqing , Chai Yuan
Published: November 2021

The session-based recommendation task is designed to predict the behavior of the current session at the next moment based on multiple anonymous sessions. Due to the lack of user information in the session, the traditional recommendation model cannot be used directly to model the interest of specific users. In this paper, a session recommendation model based on hypergraph neural networks and attention mechanism (HGNNA) is proposed. Firstly, the features of items are learned by constructing hypergraph neural networks, then the conversation information is aggregated by self-attention mechanism, and finally the information among similar sessions is aggregated by graph attention networks. The hypergraph neural networks can capture the correlation between items, the self-attention mechanism can show the interest of the current session, and the graph attention networks can find the interest pattern between similar sessions, so that the representation vector of the session includes the information of the items in the session, other items outside the session and other sessions. In the experiments on two datasets, Yoochoose1/4 and Diginetica, the recommendation effect of HGNNA is higher than that of other relevant methods, especially in the P@20, which is improved by 0.69 and 1.40 respectively.
Dangerous goods identification based on multi-channel neural network
Authors: Wei XiaoTian , Hao ZiQiang , Du Bo
Published: November 2021

In the current society, there is an increasing demand for dangerous goods identification technology in X-ray images, but at the current stage, most of the identification of dangerous goods in X-ray images still relies on artificial eye recognition. In order to solve this problem, this paper proposes A method for automatically and intelligently identifying dangerous goods in X-ray images based on the transformation of the convolutional neural network. By adding multi-channel convolution and normalization to the convolutional neural network, the target features are extracted to achieve automatic detection of dangerous goods. The purpose of better identification. In the simulation experiment, using the public data set and self-built data set in the X-ray security inspection field, the accuracy of the identification of dangerous goods in the X-ray image was obtained more satisfactory results than the traditional dangerous goods identification. The improved Alex Net network’ s testing accuracy on contraband knives and guns is 8.53% and 11.6% higher than the training accuracy of the original Alex Net network.
Design of stepper motor driver based on STM32
Authors: Liu Haowen , Li Ripeng
Published: November 2021

In the field of industrial control, stepping has been widely used as an actuator. For this article, based on STM32F103 as the control core, the functions of start and stop, forward rotation, reverse rotation and speed regulation of the stepping motor are realized through four buttons. The system uses L293D to drive a stepper motor, LED0 and LED1 two status lights display the motor’s forward and reverse rotation status, and the digital tube displays the current stepper motor speed. The whole system includes L293D module, button module, status light module and speed display module. The whole system is modeled, theoretically analyzed and simulated based on the drive control principle of the stepper motor, and the precise and stable control performance of the system under different occasions is further verified through experiments.
Path Aggregation Model for Attributed Network Embedding with a Smart Random Walk
Authors: Xiao Xiang , Zhang Kang , Qiu Shuang , Liu Wei
Published: November 2021

Network embedding has attracted a surge of attention recently. In this field, how to preserve high-order proximity has long been a difficult task. Graph convolutional network (GCN) and random walk-based approaches can preserve high-order proximity to a certain extent. However, they partially concentrate on the aggregation process and sampling process respectively. Path aggregation methods combine the merits of GCN and random walk, and thus can preserve more high-order information and achieve better performance. However, path aggregation framework has not been applied in attributed network embedding yet. In this paper, we propose a path aggregation model for attributed network embedding, with two main contributions. First, we claim that there always exists implicit edge weight in networks, and design a tweaked random walk algorithm to sample paths accordingly. Second, we propose a path aggregation framework dealing with both nodes and attributes. Extensive experimental results show that our proposal outperforms the cutting-edge baselines on downstream tasks, such as node clustering, node classification, and link prediction.
Effects of temperature anneal cycling on a cryogenically proton irradiated CCD
Authors: Parsons S. , Buggey T. , Holland A. , Sembay S. , Randall G. , Hetherington O. , Yeoman D. , Hall D. , Verhoeve P. , Soman M.
Published: November 2021

Throughout a typical Earth orbit a satellite is constantly bombarded by radiation with trapped and solar protons being of particular concern as they gradually damage the focal plane devices throughout the mission and degrade their performance. To understand the impact the damage has on CCDs and how it varies with their thermal history a proton radiation campaign has been carried out using a CCD280. The CCD is irradiated at 153 K and gradually warmed to 188 K in 5 K increments with Fe55 X-ray, dark current and trap pumping images taken at 153 K after each anneal step.

The results show that despite the trap landscape changing throughout the anneal it has little impact on parallel charge transfer inefficiency. This is thought to be because most traps are unaffected and a lot of those that do anneal only move from the continuum between distinct trap species and into a nearby divacancy trap “peak” whose emission time constant is similar enough to still impact the CTI.

In terms of using a CCD280 or similar devices in a mission the CTI being unaffected by thermal annealing up to 188 K means that any CTI correction needed as the radiation damage builds up does not have to take into account the thermal history of the focal plane. However, it is possible that a significant amount of annealing will occur at temperatures greater than 188 K and care should be taken when a mission is operating in this range to gather accurate pre-flight data.
Using precipitation sensitivity to temperature to adjust projected global runoff
Authors: Chai Yuanfang , Berghuijs Wouter R , Naudts Kim , Janssen Thomas A J , Yao Yue , Dolman Han
Published: November 2021

Climate change affects the water cycle. Despite the improved accuracy of simulations of historical temperature, precipitation and runoff in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), the uncertainty of the future sensitivity of global runoff to temperature remains large. Here, we identify a statistical relationship at the global scale between the sensitivity of precipitation to temperature change (1979–2014) and the sensitivity of runoff to temperature change (2015–2100). We use this relation to constrain future runoff sensitivity estimates. Our statistical relationship only slightly reduces the uncertainty range of future runoff sensitivities (order 10% reduction). However, more importantly, it raises the expected global runoff sensitivity to background global warming by 36%–104% compared to estimates taken directly from the CMIP6 model ensemble. The constrained sensitivities also indicate a shift towards globally more wet conditions and less dry conditions.
Carbon and ecohydrological priorities in managing woody encroachment: UAV perspective 63 years after a control treatment
Authors: Sankey Temuulen Ts , Leonard Jackson , Moore Margaret M , Sankey Joel B , Belmonte Adam
Published: November 2021

Woody encroachment, including both woody species expansion and density increase, is a globally observed phenomenon that deteriorates arid and semi-arid rangeland health, biodiversity, and ecosystem services. Mechanical and chemical control treatments are commonly performed to reduce woody cover and restore ecohydrologic function. While the immediate impacts of woody control treatments are well documented in short-term studies, treatment impacts at decadal scales are not commonly studied. Using a controlled herbicide treatment from 1954 in the Sierra Ancha Experimental Forest in central Arizona, USA, we quantify woody encroachment and associated aboveground carbon accumulation in treated and untreated watersheds. Woody encroachment and aboveground carbon are estimated using high resolution multispectral images and photogrammetric data from a fixed-wing unmanned aerial vehicle (UAV). We then combine the contemporary UAV image-derived estimates with historical records from immediately before and after the treatment to consider long-term trends in woody vegetation cover, aboveground carbon, water yield, and sedimentation. Our results indicate that the treatment has had a lasting impact. More than six decades later, woody cover in two treated watersheds are still significantly lower compared to two control watersheds, even though woody cover increased in all four drainages. Aboveground woody carbon in the treated watersheds is approximately one half that accumulated in the control watersheds. The historical records indicate that herbicide treatment also increased water yield and reduced annual sedimentation. Given the sustained reduction in woody cover and aboveground woody biomass in treated watersheds, we infer that the herbicide treatment has had similarly long lasting impacts on ecohydrological function. Land managers can consider legacy impacts from control treatments to better balance carbon and ecohydrological consequences of woody encroachment and treatment activities.
Trade-offs between efficiency, equality and equity in restoration for flood protection
Authors: Villarreal-Rosas Jaramar , Vogl Adrian L , Sonter Laura J , Possingham Hugh P , Rhodes Jonathan R
Published: November 2021

Conservation decision-makers and practitioners increasingly strive for efficient and equitable outcomes for people and nature. However, environmental management programs commonly benefit some groups of people more than others, and very little is known about how efforts to promote equality (i.e. even distributions) and equity (i.e. proportional distributions) trade-off against efficiency (i.e. total net outcome per dollar spent). Based on a case study in the Brigalow Belt Bioregion, Australia, we quantified trade-offs between equality, equity, and efficiency in planning for flood protection. We considered optimal restoration strategies that allocate a fixed budget (a) evenly among beneficiary sectors (i.e. seeking equality among urban residents, rural communities, and the food sector), (b) evenly among local government areas (LGAs) within the Brigalow Belt (i.e. seeking spatial equality), and (c) preferentially to areas of highest socioeconomic disadvantage (i.e. seeking equity). We assessed equality using the Gini coefficient, and equity using an index of socioeconomic disadvantage. At an AUD10M budget, evenly distributing the budget among beneficiary sectors was 80% less efficient than ignoring beneficiary groups, and did not improve equality in the distribution of flood protection among beneficiary sectors. Evenly distributing the budget among LGAs ensured restoration in four areas that were otherwise ignored, with a modest reduction in efficiency (12%–25%). Directing flood protection to areas of highest socioeconomic disadvantage did not result in additional reductions in efficiency, and captured areas of high disadvantage for the rural and urban sectors that were missed otherwise. We show here that different ways of targeting equity and equality lead to quite different trade-offs with efficiency. Our approach can be used to guide transparent negotiations between beneficiaries and other stakeholders involved in a planning process.
Unsupervised learning of anomalous diffusion data: an anomaly detection approach
Authors: Muñoz-Gil Gorka , Guigo i Corominas Guillem , Lewenstein Maciej
Published: November 2021

The characterization of diffusion processes is a keystone in our understanding of a variety of physical phenomena. Many of these deviate from Brownian motion, giving rise to anomalous diffusion. Various theoretical models exists nowadays to describe such processes, but their application to experimental setups is often challenging, due to the stochastic nature of the phenomena and the difficulty to harness reliable data. The latter often consists on short and noisy trajectories, which are hard to characterize with usual statistical approaches. In recent years, we have witnessed an impressive effort to bridge theory and experiments by means of supervised machine learning techniques, with astonishing results. In this work, we explore the use of unsupervised methods in anomalous diffusion data. We show that the main diffusion characteristics can be learnt without the need of any labelling of the data. We use such method to discriminate between anomalous diffusion models and extract their physical parameters. Moreover, we explore the feasibility of finding novel types of diffusion, in this case represented by compositions of existing diffusion models. At last, we showcase the use of the method in experimental data and demonstrate its advantages for cases where supervised learning is not applicable.
Tunable coupling of terahertz Dirac plasmons and phonons in transition-metal dichalcogenide-based van der Waals heterostructures
Authors: Lavor I R , Chaves Andrey , Peeters F M , Van Duppen B
Published: November 2021

Dirac plasmons in graphene hybridize with phonons of transition metal dichalcogenides (TMDs) when the materials are combined in so-called van der Waals heterostructures (vdWh), thus forming surface plasmon-phonon polaritons (SPPPs). The extend to which these modes are coupled depends on the TMD composition and structure, but also on the plasmons’ properties. By performing realistic simulations that account for the contribution of each layer of the vdWh separately, we calculate how the strength of plasmon-phonon coupling depends on the number and composition of TMD layers, on the graphene Fermi energy and the specific phonon mode. From this, we present a semiclassical theory that is capable of capturing all relevant characteristics of the SPPPs. We find that it is possible to realize both strong and ultra-strong coupling regimes by tuning graphene’s Fermi energy and changing TMD layer number.
Photoelectric and magnetic properties of boron nitride nanosheets with turbostratic structure and oxygen doping
Authors: Cao Chaochao , Yang Jingwen , Yan Song , Bai Wenjuan , Ma Yuanhui , Xue Yanming , Tang Chengchun
Published: November 2021

Turbostratic and oxygen doping (3.7 atom %) hexagonal boron nitride nanosheets (TO-BNNSs) with abundant defect sites, were synthesized by pyrolyzing the mixture of melamine cyanurate and boric acid. Systematic analyses reveal a highly disordered structures and covalent oxygen-doping in the TO-BNNSs. These features endow the product with increased unpaired electrons, localized charge asymmetry and spin polarization. While compared with bulk h-BN, the optical bandgap of TO-BNNSs drop down to ∼5.2 from ∼5.7 eV, dielectric constant raised from ∼2.1 to ∼2.4, the saturation magnetic moment increased from ∼0.011 to ∼0.033 emu g−1, and the coercivity enlarged from ∼73.56 to ∼367.39 Oe. These results suggest that h-BN materials with turbostratic structure and heteroatom-doping have extensive application prospect in the fields of nanoscale optics, electronics and magnetics.
High carrier mobility in single-crystal PtSe2 grown by molecular beam epitaxy on ZnO(0001)
Authors: Bonell Frédéric , Marty Alain , Vergnaud Céline , Consonni Vincent , Okuno Hanako , Ouerghi Abdelkarim , Boukari Hervé , Jamet Matthieu
Published: November 2021

PtSe2 is attracting considerable attention as a high mobility two-dimensional material with envisioned applications in microelectronics, photodetection and spintronics. The growth of high quality PtSe2 on insulating substrates with wafer-scale uniformity is a prerequisite for electronic transport investigations and practical use in devices. Here, we report the growth of highly oriented few-layers PtSe2 on ZnO(0001) by molecular beam epitaxy. The crystalline structure of the films is characterized with electron and x-ray diffraction, atomic force microscopy and transmission electron microscopy. The comparison with PtSe2 layers grown on graphene, sapphire, mica, SiO2 and Pt(111) shows that among insulating substrates, ZnO(0001) yields films of superior structural quality. Hall measurements performed on epitaxial ZnO/PtSe2 with five monolayers of PtSe2 show a clear semiconducting behaviour and a high mobility in excess of 200 cm2 V−1 s−1 at room temperature and up to 447 cm2 V−1 s−1 at low temperature.
Constitutive modelling with a novel two-step optimization for an Al-Zn-Mg-Cu alloy and its application in FEA
Authors: Wu Daoxiang , Long Shuai , Wang Shuyan , Li Shi-Shan , Zhou Yu-Ting
Published: November 2021

The modified Johnson-Cook constitutive model was developed for describing the flow behavior of Al-7.8Zn-1.65Mg-2.0Cu (wt%) alloy based on the flow curves in the temperature range of 300 °C∼450 °C and strain rate range of 0.01 s−1 ∼ 10 s−1 which were obtained by isothermal compression tests conducted on a Gleeble-3500 isothermal simulator. A two-step optimization method was proposed to optimize the prediction precision according to the evaluation of average absolute relative error (AARE). By using a traversal procedure for calculating the model under different reference conditions, this evaluator was found varying in the range of 4.1837% ∼ 11.105%, revealing the great influence of reference condition on the precision, then the reference condition optimization (RCO) was conducted. Genetic algorithm (GA) was introduced as the second step of the two-step optimization (TSO) to optimize the material constants of the model, which furtherly improved the precision by reducing the AARE-value to 3.801%. The models before and after optimization were written into subroutines for the software DEFORM and the compression tests were investigated through finite element analysis (FEA). The simulated results (forming load and temperature rise) revealed that the model after TSO has the highest agreement with the experimental.
Photocatalytic application of graphene oxide–ZnO nanocomposite for the reduction of methylene blue dye
Authors: Banu K Bansura , Ferdila Sr Udith , Jennifer M D Mercy
Published: November 2021

The graphene oxide (GO) and GO–zinc oxide (GO–ZnO) nanocomposite were prepared using simplified techniques with modified Hummer’s and solvothermal methods for photocatalytic application. In a comparative study, the optimized geometries, binding energies, electronic properties, non-linear optical properties and density of states (DOS) of GO–ZnO were calculated using density functional theory calculations with the B3LYP method and 6-31G (d,p) and LanL2DZ basis sets to examine the binding site of a methylene blue (MB) dye systematically. The result of natural bond orbital analysis revealed the effective charge transfer and also explained the mechanism and efficiency of the photocatalytic activity of GO–ZnO. DOS supported the strong interaction of MB with GO–ZnO leading to the degradation of the MB dye. The theoretical results obtained depict the existence of n → σ*, n→ n* and σ → σ* interactions, improved charge transfer, and reduced band gap which establish the use of GO–ZnO in visible light photocatalysis. Characterization methods such as x-ray diffraction (XRD), Fourier transform infrared (FTIR) and ultraviolet (UV) analysis were carried out to support our theoretical results. The XRD results confirmed the particle size of 21 nm with inter-layer spacing of 0.87 nm. FTIR spectroscopy indicated the characteristic bands related to the elements in GO–ZnO. The higher electrical conductivity was studied using UV–Vis spectral analysis. The calculated results show good agreement with experimental observations which reveal that the GO–ZnO nanocomposite has good photocatalytic behavior.
Naturality and Mapping Class Groups in Heegaard Floer Homology
Authors: Juhász András , Thurston Dylan , Zemke Ian
Published: November 2021

We show that all versions of Heegaard Floer homology, link Floer homology, and sutured Floer homology are natural. That is, they assign concrete groups to each based 3-manifold, based link, and balanced sutured manifold, respectively. Furthermore, we functorially assign isomorphisms to (based) diffeomorphisms, and show that this assignment is isotopy invariant.

The proof relies on finding a simple generating set for the fundamental group of the “space of Heegaard diagrams,” and then showing that Heegaard Floer homology has no monodromy around these generators. In fact, this allows us to give sufficient conditions for an arbitrary invariant of multi-pointed Heegaard diagrams to descend to a natural invariant of 3-manifolds, links, or sutured manifolds.
Application of integrative physiological approach to evaluate human physiological responses to the inhalation of essential oils of Japanese citrus fruits iyokan (Citrus iyo) and yuzu (Citrus junos)
Authors: Ohata Motoko , Zhou Lanxi , Ando Shiori , Kaneko Shu , Osada Kazumi , Yada Yukihiro
Published: November 2021

This study investigated the effects of essential oil odors from Japanese citrus fruits, iyokan (Citrus iyo) and yuzu (Citrus junos), on human psychology and both the autonomic and central nervous systems. The inhalation of both essential oils significantly increased miosis rate and fingertip temperature and could induce parasympathetic dominance by suppressing sympathetic nerve activity. Oxyhemoglobin concentration in the prefrontal cortex increased after the inhalation of yuzu essential oil and decreased after the inhalation of iyokan essential oil. Subjectively, the inhalation of both essential oils reduced the feelings of fatigue and improved the feelings of refreshment, suggesting that the effect of autonomic nervous activity might involve in these psychological changes directly. Moreover, we observed that task performance improved after inhaling yuzu essential oil, which may be due to the increase in oxyhemoglobin concentration in the prefrontal cortex.
Predicting drug–drug interactions by graph convolutional network with multi-kernel
Authors: Wang Fei , Lei Xiujuan , Liao Bo , Wu Fang-Xiang
Published: November 2021

Drug repositioning is proposed to find novel usages for existing drugs. Among many types of drug repositioning approaches, predicting drug–drug interactions (DDIs) helps explore the pharmacological functions of drugs and achieves potential drugs for novel treatments. A number of models have been applied to predict DDIs. The DDI network, which is constructed from the known DDIs, is a common part in many of the existing methods. However, the functions of DDIs are different, and thus integrating them in a single DDI graph may overlook some useful information. We propose a graph convolutional network with multi-kernel (GCNMK) to predict potential DDIs. GCNMK adopts two DDI graph kernels for the graph convolutional layers, namely, increased DDI graph consisting of ‘increase’-related DDIs and decreased DDI graph consisting of ‘decrease’-related DDIs. The learned drug features are fed into a block with three fully connected layers for the DDI prediction. We compare various types of drug features, whereas the target feature of drugs outperforms all other types of features and their concatenated features. In comparison with three different DDI prediction methods, our proposed GCNMK achieves the best performance in terms of area under receiver operating characteristic curve and area under precision-recall curve. In case studies, we identify the top 20 potential DDIs from all unknown DDIs, and the top 10 potential DDIs from the unknown DDIs among breast, colorectal and lung neoplasms-related drugs. Most of them have evidence to support the existence of their interactions. fangxiang.wu@usask.ca