bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research of text visualization in 3D environment based on parameter study
Authors: Jing Yinggang , Zhang Fan , Fang Huajian , He Shuan 
Published: April 2022

Abstract
The text visualization in 3D environment is quite complex due to the characteristics of 3D scene. In this paper we propose to study a group of 3D text information visualization based on parameter study. The primary text information visualization position is set as an initial point. Then, several different level-of-details for the texts are designed as the Event LoDs. Firstly, the parameter study for different EventLoDs is conducted, followed by the parameter study for the different size value. Based on the two groups of parameter study result, a use case is applied to test the effect. Finally, the summary and future work is given.
An improved Criminisi method for image inpainting
Authors: Li C , Chen H , Han X , Pan X , Niu D 
Published: April 2022

Abstract
The Criminisi method is a classical image inpainting method, which has flaw of unreasonable priority calculation. To overcome this flaw, we propose an improved Criminisi method. To improve the accuracy of a repaired block’s priority, we propose a novel method by considering the continuity of structure and color information between the repaired block and its adjacent blocks. When searching the matching block for a repaired block, we compute the similar between two blocks as the sum of squared differences (SSD). Since different blocks within an image may contain different levels of information, we set block to different sizes to determine the best matching block. Experimental results demonstrate the effectiveness of the proposed method.
The correlation algorithm based on jittering structured light with multiple reflection imaging
Authors: Zheng Weixin , Huang Ming , Xu Xiao 
Published: April 2022

Abstract
As a three-dimensional reconstruction method, the structured light formed by multiple reflection of neutral beam splitter mirror can effectively solve the problem caused by high reflectivity. In order to obtain more stripes, the jittering mechanism is used in the system. In addition, the linear algorithm can not make full use of the information between different structured light, which will produce a large error and inaccurate result. To solve this problem, we propose a correlation algorithm. In this algorithm, we use two structured light as the independent variables to obtain spatial information, and a related items is calculated to take full advantage of the information between different structured light. Experimental results show that the overall measurement error is reduced by 20-30% and the repetition accuracy of the system reaches 0.02 mm.
Compact Scattering Features for Glaucoma Detection
Authors: Hussain Snawar , Guo Fan , Shi Xiangyu , Li Weiqing , Shen Ziqi 
Published: April 2022

Abstract
In the field of medical image diagnostics, automatic glaucoma classification is a daunting task that requires carefully crafted features (Machine Learning) or tuning hyperparameters over a huge search space (Deep Learning). In this paper, we proposed a wavelet scattering based approach that extracts features from fundus images at different sub-stages, reduces the feature space using principal component analysis (PCA) and represents the classifying features in terms of projection loss along the subspace for each class. This simple yet elegant approach yields fast-paced results with great interpretability, that deep learning-based approaches lack, and excels in most performance metrics when compared to other machine learning based methods.
Study on warpage of the AlSiC substrate in reflow soldering process of the IGBT module
Authors: Wang Wei , Kang Renke , Gao Shang , Wang Rongliang 
Published: April 2022

Abstract
Using the pre-warped substrate to pack IGBT modules can increase the contact area between the substrate and the radiator after the reflow soldering. There are many factors affecting the shape of the substrate’s back after soldering, and determining the effect of each factor by experiments would cost significantly. In this paper, a FEA model of the IGBT module’s reflow soldering process is established by ANSYS to predict the back surface of the pre-warped AlSiC substrate after soldering. Initially, in order to improve the accuracy of the FEA model, the material properties of AlSiC were determined at different temperatures. Subsequently, based on the result of DSC, the solder’s phase transition was simulated in the model. The calculated results showed that the proposed FEA model could predict the shape feature of the AlSiC pre-warped substrate’s back surface after the reflow soldering, and the prediction error of substrate’s residual warpage was 46.05%. The model provides a reliable reference for the design of pre-warped substrate in IGBT module, reducing the test cost and shortening the design period substantially.
Design of Data Acquisition Software for 128-channel μSR Spectrometer Prototype
Authors: Li X.J , Liang H 
Published: April 2022

Abstract
The muon is one of the 12 elementary particles in the standard model, it is a typical particle probes that can extract features such as microscopic magnetic or electronic environments in materials. The technique of injecting spin-polarized muons into a material as a probe and then detecting their spin interaction can be called the μSR technique. A high-intensity muon source experimental device is a prerequisite for the research of μSR technology, with the complettion of China Spallation Neutron Scoure, μSR research has become possible. The data acquisition software introduced in this paper is an experimental 128-channel μSR spectrometer prototype applied to the China Spallation Neutron Source, it is written in C++ based on Qt software, and realizes data acquisition and electronic system control in the form of underlying packet capture by using Winpcap. DAQ software includes data acquisition, command issuing, data format conversion and rough cumulative time spectrum display functions, Each feature will be shown accordingly in the experiment.
Cross-Temporal Snapshot Alignment for Dynamic Multi-Relational Networks
Authors: Chen Lvjia , Liang Shangsong 
Published: April 2022

AbstractA dynamic network is often represented as a sequence of snapshots evolving over time. In certain real-world scenarios, the identities of nodes in snapshots of a dynamic network are unknown and need to be figured out. To deal with such a challenge, recently, the task of cross-temporal snapshot alignment for dynamic networks is proposed, which aims to match equivalent nodes across temporal snapshots of a dynamic network given a small set of identified nodes. However, in many dynamic multi-relational networks like temporal knowledge graphs, the relation type information of edges, which can be useful for the alignment task, is neglected by existing methods. In this paper, we focus on a similar task but pay special attention to dynamic multi-relational networks. We propose a Relation-Aware Cross-Temporal Snapshot Alignment model (RCTSA) that incorporates time-dependent topological structure information into temporal node embeddings and temporal relation embeddings. To disentangle time-dependent information and time-independent information of the dynamic multi-relational network, RCTSA maintains a time embedding for each snapshot to preserve the temporal information which is incorporated into entity embeddings and relation embeddings to get the temporal embeddings. After training, the learned entity embeddings of unidentified nodes together with time embeddings can be used for the alignment task. Experimental results on real-world dynamic multi-relational networks demonstrate the effectiveness of our model.
A novel concept for a fully digital particle detector
Authors: Casse G. , Massari N. , Franks M. , Parmesan L. 
Published: April 2022

Abstract
Silicon sensors are the most diffuse position sensitive
device in particle physics experiments and in countless applications
in science and technology. They had a spectacular progress in
performance over almost 40 years since their first introduction, but
their evolution is now slowing down. The position resolution for
single particle hits is larger than a few microns in the most
advanced sensors. This value was reached already over 30 years
ago [1]. The minimum ionising path length a sensor can detect
is several tens of microns. There are fundamental reasons why these
limits will not be substantially improved by further refinements of
the current technology. This makes silicon sensors unsuitable to
applications where the physics signature is the short path of a
recoiling atom and constrains the layout of physics experiments
where they represent by far the best option like high energy physics
collider experiments. In perspective, the availability of sensors
with sub-micron spatial resolution, in the order of a few tens of
nanometres, would be a disruptive change for the sensor technology
with a foreseeable huge impact on experiment layout and various
applications of these devices. For providing such a leap in
resolution, we propose a novel design based on a purely digital
circuit. This disruptive concept potentially enables pixel sizes
much smaller than 1 μm2 and a number of advantages in
terms of power consumption, readout speed and reduced thickness (for
low mass sensors).
Any colour you like: fish interacting with bioinspired robots unravel mechanisms promoting mixed phenotype aggregations
Authors: Romano Donato , Stefanini Cesare 
Published: April 2022

Abstract
Collective behaviours in homogeneous shoals provide several benefits to conspecifics, although mixed-species aggregations have been reported to often occur. Mixed aggregations may confer several beneficial effects such as antipredator and foraging advantages. However, the mechanisms promoting phenotypically heterogeneous fish aggregations have been poorly explored so far. Herein, the neon tetra Paracheirodon innesi was selected as the ideal model organism to test the role of visible phenotypic traits in promoting fish shoaling. Robotic fish replicas of different colours, but with a morphology inspired by P. innesi, were developed to test the affiliation behaviour of neon tetra individuals towards fish replicas with different phenotypic traits. P. innesi individuals showed a decreasing preference in shoaling with the biomimetic, the blue, the red, and the grey replicas. This could be due to the greater visibility of the blue colour even in dark conditions. Furthermore, an increased reddening of the livery is often caused by physiological processes related to a nonoptimal behavioural status. The time spent in shoaling with each fish replica was strongly influenced by different ecological contexts. The longest shoaling duration was observed when a biomimetic predator was present, while the shortest shoaling duration was recorded in the presence of food. This confirms the hypothesis that heterogeneous shoals are promoted by the antipredator benefits, and reduced by competition. This study allowed us to understand basic features of the behavioural ecology favouring heterogeneous aggregations in shoaling fish, and provided a novel paradigm for biohybrid robotics.
Widening inequities in clean cooking fuel use and food security: compounding effects of COVID-19 restrictions and VAT on LPG in a Kenyan informal urban settlement
Authors: Shupler Matthew , Menya Diana , Sang Edna , Anderson de Cuevas Rachel , Mang’eni Judith , Lorenzetti Federico , Saligari Serena , Nix Emily , Mwitari James , Gohole Arthur , Pope Daniel , Puzzolo Elisa 
Published: April 2022

Abstract
Universal access to cleaner cooking fuels (including liquefied petroleum gas (LPG)) is a key target of Sustainable Development Goal 7. Currently, approximately 40 million Kenyans rely on polluting cooking fuels (e.g. charcoal, wood). While the Kenyan government aims to rapidly scale up use of LPG for cooking by 2030, COVID-19 restrictions and a 16% value added tax (VAT) re-introduced on LPG in 2021 have likely hampered progress in LPG uptake. We aimed to quantify the effect of these economic shocks on food and energy security in Langas informal urban settlement in western Kenya. We further evaluated whether households most adversely affected by COVID-19 restrictions were more likely to be socioeconomically impacted by the VAT re-imposition. A cross-sectional survey (n = 1542) assessed changes in cooking fuel patterns, food security and livelihoods of primary cooks due to these two economic shocks. While under COVID-19 restrictions, 75% (n = 1147) of participants reported income declines and 18% (n = 164) of participants using LPG (n = 922) switched their primary cooking fuel to charcoal, wood or kerosene. Households reporting lower income while under COVID-19 restrictions had 5.3 times (95% CI:[3.8,7.4]) the odds of experiencing food insecurity as those with no change in income. Unemployment and food insecurity under COVID-19 restrictions were substantially higher among informal sector workers (70% and 60%, respectively) compared with business/government employees (45% and 37%, respectively). Following the VAT re-introduction, 44% (n = 356) of households using LPG consumed less, and 34% (n = 276) cooked more frequently with polluting fuels. Individuals switching away from LPG under COVID-19 restrictions had 3.0 times (95% CI:[2.1,4.3]) the odds of reducing their LPG consumption due to the VAT re-introduction as those maintaining use of LPG. COVID-19 restrictions and the VAT re-introduction disproportionately negatively affected informal sector workers’ livelihoods. A zero-rating of VAT on LPG can help alleviate deepened inequities in LPG access in Kenya.
Social cost of carbon under a carbon-neutral pathway
Authors: Liu Aobo , Chen Yating , Cheng Xiao 
Published: April 2022

Abstract
Climate change is the challenge of the century, and achieving the goals of the Paris Agreement will require worldwide cooperation and mutual effort. Over 120 countries have made their net-zero commitments, and quantifying the social cost of carbon (SCC), i.e. the climate damage caused by an additional ton of CO2 emissions, under a carbon-neutral pathway would provide a carbon price benchmark for policymakers. Here, we set in detail the emission trajectories of different jurisdictions under a carbon-neutral pathway based on the submitted nationally determined contributions. We then assess global and regional warming, climate change damages, and the SCC with the Policy Analysis of Greenhouse Effect integrated assessment model. We find a peak warming of about 2.1 °C relatives to pre-industrial levels in this century under our carbon-neutral emission pathway. And even if all countries meet their carbon-neutral commitments, this would not be sufficient to limit global warming to 1.5 °C relative to pre-industrial levels. We compare the SCC using fixed discount rates, dynamic discounting, and an equity weighting approach. Notably, the introduction of equity weights would increase the estimated SCC from 79 (11–186) to 291 (83–592) US$ per tCO2. Climate change damages will be borne primarily by warmer and poorer countries, and this profound inequality would likely undermine efforts to eradicate extreme poverty. Statistics on current carbon taxes and carbon trading prices show that they are notably lower than global or even regional SCCs, suggesting that the current system does not adequately reflect the global externalities of CO2 emissions. More studies are needed to assess the equity aspects of climate change impacts, to help refine mechanisms to align domestic interests with global interests, and to facilitate the implementation of national carbon-neutral commitments in place.
Mitigation of air pollutant impacts on rice yields in China by sector
Authors: Xu Jiayu , Dong Xin , Zhang Tianyi , Liu Junfeng , Tao Shu 
Published: April 2022

Abstract
Air quality management is a complex project involving the coordination of multiple sectors and has been identified as an important strategy for food security. However, little is known about how pollutant emissions from each sector affect agriculture. Each sector emits different precursors, resulting in varying impacts of their pollutants and aerosol-climate on rice yield. Therefore, we provide an assessment of air pollution impacts, including direct ozone damage and aerosol-climate effects, on rice yield in four sector (industrial, energy, domestic, and transportation) emission scenarios in China. Our results suggest that without anthropogenic emissions, a reduced surface ozone concentration leads to a 9.8% increase in rice yield; lower aerosol concentrations induce an additional 2.3% increase, leading to a combined 9.7% greater rice yield. Of the four sectors, industrial sector emission control is projected to be the most effective, with 4.4% rice yield gain from ozone reductions and 0.6% from aerosol reductions. The impacts of the energy and transportation sectors are relatively low, resulting in 2.1% (energy sector) and 0.7% (transportation) yield increases on average. The domestic sector has few emissions of ozone precursors; therefore, aerosol-climate effects are more dominant, resulting in a yield increase of 0.9%. These results indicate the importance of reduced emissions in the industrial sector for future agricultural food production while also providing the relative benefits of regulating each sector for food security.
Dynamical approximations for composite quantum systems: assessment of error estimates for a separable ansatz
Authors: Burghardt Irene , Carles Rémi , Kammerer Clotilde Fermanian , Lasorne Benjamin , Lasser Caroline 
Published: April 2022

Abstract
Numerical studies are presented to assess error estimates for a separable (Hartree) approximation for dynamically evolving composite quantum systems which exhibit distinct scales defined by their mass and frequency ratios. The relevant error estimates were formally described in our previous work Burghardt et al (2021 J. Phys. A: Math. Theor.
54 414002). Specifically, we consider a representative two-dimensional tunneling system where a double well and a harmonic coordinate are cubically coupled. The time-dependent Hartree approximation is compared with a fully correlated solution, for different parameter regimes. The impact of the coupling and the resulting correlations are quantitatively assessed in terms of a time-dependent reaction probability along the tunneling coordinate. We show that the numerical error is correctly predicted on moderate time scales by a theoretically derived error estimate.
The elastic and directed percolation backbone
Authors: Deng Youjin , Ziff Robert M 
Published: April 2022

Abstract
We argue that the elastic backbone (EB) (union of shortest paths) on a cylindrical system, studied by Sampaio Filho et al [2018 Phys. Rev. Lett.
120 175701], is in fact the backbone of two-dimensional directed percolation (DP). We simulate the EB on the same system as considered by these authors, and also study the DP backbone directly using an algorithm that allows backbones to be generated in a completely periodic manner. We find that both the EB in the bulk and the DP backbone have a fractal dimension of d
b = d

B,DP = 1.681 02(15) at the identical critical point p
c,DP ≈ 0.705 485 22. We also measure the fractal dimension at the edge of the EB system and for the full DP clusters, and find d
e = d
DP = 1.840 54(4). We argue that those two fractal dimensions follow from the DP exponents as d

B,DP = 2 − 2β/ν
∥ = 1.681 072(12) and d
DP = 2 − β/ν
∥ = 1.840 536(6). Our fractal dimensions differ from the value 1.750(3) found by Sampaio Filho et al.
Wind Farm Layout: Modeling and Optimization Using Genetic Algorithm
Authors: Asfour R , Brahimi T , El-Amin MF 
Published: April 2022

Abstract
Wind Farm Layout Optimization (WFLO) is a complex multidisciplinary topic that requires a lot of expertise and is becoming an essential part of today’s wind farm planning. Yet, selecting optimum wind farm locations is complex, time-consuming, and influenced by environmental factors and upstream turbines inflow wind. The present study attempts to develop an optimization approach based on the Genetic Approach (GA) to determine the most suitable wind turbine locations that maximize the net energy production while minimizing the Cost of Energy (COE) ($/kWh). The WFLO for the optimized objective function was performed for 500, 1000, and 1500 iterations. The best output was obtained for 1500 iterations with the lowest value for the objective function.
Energy Audit of a Classroom in the Hot and Humid Region: Effect of Mechanical Dehumidification on Energy Consumption
Authors: Dezfouli M.M.S. , Kadir Kushsairy , Sopian K. , Dehghani-Sanij A.R. 
Published: April 2022

Abstract
Air conditioning (AC) systems are the most energy-consuming systems in buildings in hot and humid climates. This study investigates the effect of mechanical dehumidification on the energy apportioning of the conventional cooling system in hot and humid regions. An energy audit was carried out in a classroom with a fan coil unit as a case study. Air characteristics, cooling load, and cooling capacity of the case study were measured. According to the energy audit results, for a room with 51% latent load and 49% sensible load, the mechanical dehumidification consumed 56% of cooling capacity, while 44% of cooling capacity was used for sensible cooling. It has been shown that mechanical dehumidification consumes the majority of the energy used by AC systems in hot and humid areas.
Photovoltaic Performance Using Modify Back Propagation Neural Network Based On 3 Different Solar Panels
Authors: Liyana P N , Mekhilef S , Shah N M 
Published: April 2022

Abstract
Depletion of natural resources and increasing demand of power, sustainable energy sources such as photovoltaics (PV) are becoming increasingly more reliable and important. However, the production PV output power is unable to meet in terms of supply and demand due to irregular of solar irradiance and temperature daily. Therefore, a proper method/tool of prediction is very crucial to manage photovoltaic performance system and optimize the corresponding energy management system. In this paper, the modified Back Propagation Neural Network (BPNN) architecture is investigated. The investigating was conducted to provide more accurate prediction and error for short-term period prediction. The networkcompromises of 2 inputs parameter; the module temperature and solar irradiance and 1 output parameter; AC Power. Early research was conducted based on forecast time period on May 2021 starting from 7am to 8pm (14 hour) daily from 3 different panels; monocrystalline, polycrystalline and thin film, which are located on the rooftop of PEARL lab building. The result obtained shows that polycrystalline panel performed the best compare to monocrystalline and thin film panels.
Evaluation of Stochastic and Artificial Neural Network Models for Multi-step Lead Forecasting of NDVI
Authors: Omar Mwana Said , Kawamukai Hajime 
Published: April 2022

Abstract
Vegetation degradation is associated with human activities and climate change leading to ecosystem changes and biodiversity losses. To reduce the impacts of vegetation degradation, forecasting of vegetation condition is vital in formulating measures to prevent and reduce the losses. Vegetation indices (VI) obtained from remote sensing data, such as the normalized difference vegetation index (NDVI) are widely used to monitor and forecast vegetation condition. In the present study, a stochastic and artificial neural network (ANN) models were compared in modeling and multi-step lead forecasting of NDVI in the Middle Tana River Basin (MTRB), Kenya. Pixel-wise NDVI data for the period 2000 - 2019 was extracted from the MOD13Q1 product of the Moderate Resolution Imaging Spectroradiometer (MODIS). Time lags of NDVI was used as inputs for the models. The results showed that the ANN model outperforms the stochastic model, with a predicting accuracy of RMSE of 0.07207, MSE of 0.00589 and MAE of 0.06417. The multi-step lead forecasting produced satisfactory results indicating the suitability of the models as tools in forecasting NDVI.