bluuebunny/crossref_metadata_2025_split huggingface dataset:
BeCaked: An Explainable Artificial Intelligence Model for COVID-19 Forecasting
Authors: Nguyen Duc Q. , Vo Nghia Q. , Nguyen Thinh T. , Nguyen-An Khuong , Nguyen Quang H. , Tran Dang N. , Quan Tho T. 
Published: May 2022

AbstractFrom the end of 2019, one of the most serious and largest spread pandemics occurred in Wuhan (China) named Coronavirus (COVID-19). As reported by the World Health Organization, there are currently more than 100 million infectious cases with an average mortality rate of about five percent all over the world. To avoid serious consequences on people’s lives and the economy, policies and actions need to be suitably made in time. To do that, the authorities need to know the future trend in the development process of this pandemic. This is the reason why forecasting models play an important role in controlling the pandemic situation. However, the behavior of this pandemic is extremely complicated and difficult to be analyzed, so that an effective model is not only considered on accurate forecasting results but also the explainable capability for human experts to take action pro-actively. With the recent advancement of Artificial Intelligence (AI) techniques, the emerging Deep Learning (DL) models have been proving highly effective when forecasting this pandemic future from the huge historical data. However, the main weakness of DL models is lacking the explanation capabilities. To overcome this limitation, we introduce a novel combination of the Susceptible-Infectious-Recovered-Deceased (SIRD) compartmental model and Variational Autoencoder (VAE) neural network known as BeCaked. With pandemic data provided by the Johns Hopkins University Center for Systems Science and Engineering, our model achieves 0.98 $$R^2$$

R
2

and 0.012 MAPE at world level with 31-step forecast and up to 0.99 $$R^2$$

R
2

and 0.0026 MAPE at country level with 15-step forecast on predicting daily infectious cases. Not only enjoying high accuracy, but BeCaked also offers useful justifications for its results based on the parameters of the SIRD model. Therefore, BeCaked can be used as a reference for authorities or medical experts to make on time right decisions.
Entomopathogenic fungus disrupts the phloem-probing behavior of Diaphorina citri and may be an important biological control tool in citrus
Authors: Maluta Nathalie , Castro Thiago , Lopes João Roberto Spotti 
Published: May 2022

AbstractCitrus is among the most important fruit crops worldwide; however, numerous pests and diseases affect the orchards, increasing production costs. The psyllid Diaphorina citri, is a vector of the phloem-limited bacteria ‘Candidatus Liberibacter spp.’, the causal agent of Huanglongbing (HLB) disease. The lack of a cure for HLB requires management of the vector, mainly by intensive use of chemical insecticides, leading to the selection of resistant populations. Our study determined the effects of the entomopathogenic fungus Cordyceps fumosorosea on the probing behavior of D. citri at different time points after the fungus was applied by spraying. The electrical penetration graph technique was used to monitor the stylet activities of D. citri after application of the microbiological product. The effects were more pronounced between 30 and 96 h after the insects were sprayed, with significant disruption of the stylet activities related to the phloem and directly associated with the transmission of HLB. Our study indicated that the microbiological product Challenger®, with the active ingredient C. fumosorosea fungus, can significantly change the probing behavior of D. citri, may be helpful in more-sustainable management of the vector, and can be used to reduce the spread of HLB.
Quasiparticle dynamics by effective $$\pi $$-field distortion
Authors: Cassiano Tiago de Sousa Araújo , de Oliveira Neto Pedro Henrique , e Silva Geraldo Magela 
Published: May 2022

AbstractModeling dynamical processes of quasiparticles in low dimensional $$\pi $$
π
-conjugated systems is challenging due to electron-phonon coupling. We show that this interaction leads to linear potential energy terms in the lattice Lagrangian similar to a local “gravitational” field. The presence of quasiparticles deforms this field in a way analogous to a low-dimension solution of general relativity. Our calculations with analytical expressions for effective $$\pi $$
π
-fields yield the correct band structure and deliver proper time evolution of the quasiparticle’s properties. Furthermore, we report a sharp reduction in the dynamics computational time up to two orders of magnitude, a result that has major simulation implications.
Influences of dielectric constant and scan rate on hysteresis effect in perovskite solar cell with simulation and experimental analyses
Authors: Huang Jun-Yu , Yang You-Wei , Hsu Wei-Hsuan , Chang En-Wen , Chen Mei-Hsin , Wu Yuh-Renn 
Published: May 2022

AbstractIn this work, perovskite solar cells (PSCs) with different transport layers were fabricated to understand the hysteresis phenomenon under a series of scan rates. The experimental results show that the hysteresis phenomenon would be affected by the dielectric constant of transport layers and scan rate significantly. To explain this, a modified Poisson and drift-diffusion solver coupled with a fully time-dependent ion migration model is developed to analyze how the ion migration affects the performance and hysteresis of PSCs. The modeling results show that the most crucial factor in the hysteresis behavior is the built-in electric field of the perovskite. The non-linear hysteresis curves are demonstrated under different scan rates, and the mechanism of the hysteresis behavior is explained. Additionally, other factors contributing to the degree of hysteresis are determined to be the degree of degradation in the perovskite material, the quality of the perovskite crystal, and the materials of the transport layer, which corresponds to the total ion density, carrier lifetime of perovskite, and the dielectric constant of the transport layer, respectively. Finally, it was found that the dielectric constant of the transport layer is a key factor affecting hysteresis in perovskite solar cells.
Geochemical evidence for the internal migration of gas condensate in a major unconventional tight petroleum system
Authors: Wood James M. , Cesar Jaime , Ardakani Omid H. , Rudra Arka , Sanei Hamed 
Published: May 2022

AbstractUnconventional petroleum systems go through multiple episodes of internal hydrocarbon migration in response to evolving temperature and pressure conditions during burial and uplift. Migrated fluid signatures can be recognized using stable carbon isotope and PVT compositional data from produced samples representative of in-situ petroleum fluids. Such samples, however, are seldom collected due to operational complexity and high cost. Here, we use carbon isotope and PVT data from co-produced hydrocarbon gas and liquid to provide evidence for widespread migration of gas-condensate in the Montney unconventional petroleum system of western Canada. Extended C1–C33isotopic profiles exhibit convex upward signatures with C4–C5maxima at low molecular weight, and increasing or nearly uniform signatures at high molecular weight. Additionally, recombination PVT compositional data show C6–C15condensate concentrations are higher than expected for unmodified oils. The combined convex upward and increasing or uniform isotopic signatures are interpreted as mixing profiles formed by the introduction of high-maturity gas-condensate (C1–C15) to shallower zones with in-situ hydrocarbon fluids of lower thermal maturity. The recognition of widespread gas-condensate migration adds to the complex history of internal hydrocarbon migration within the Montney tight-petroleum system including previously identified migration episodes of early oil and late-stage methane-rich gas.
Development of a textile based protein sensor for monitoring the healing progress of a wound
Authors: ElSaboni Yomna , Hunt John A. , Stanley Jessica , Moffatt Christine , Wei Yang 
Published: May 2022

AbstractThis article focuses on the design and fabrication of flexible textile-based protein sensors to be embedded in wound dressings. Chronic wounds require continuous monitoring to prevent further complications and to determine the best course of treatment in the case of infection. As proteins are essential for the progression of wound healing, they can be used as an indicator of wound status. Through measuring protein concentrations, the sensor can assess and monitor the wound condition continuously as a function of time. The protein sensor consists of electrodes that are directly screen printed using both silver and carbon composite inks on polyester nonwoven fabric which was deliberately selected as this is one of the common backing fabric types currently used in wound dressings. These sensors were experimentally evaluated and compared to each other by using albumin protein solution of pH 7. A comprehensive set of cyclic voltammetry measurements was used to determine the optimal sensor design the measurement of protein in solution. As a result, the best sensor design is comprised of silver conductive tracks but a carbon layer as the working and counter electrodes at the interface zone. This design prevents the formation of silver dioxide and protects the sensor from rapid decay, which allows for the recording of consecutive measurements using the same sensor. The chosen printed protein sensor was able to detect bovine serum albumin at concentrations ranging from 30 to 0.3 mg/mL with a sensitivity of $$0.0026 \mu $$

0.0026
μ

A/M. Further testing was performed to assess the sensor’s ability to identify BSA from other interferential substances usually present in wound fluids and the results show that it can be distinguishable.
Modified precordial lead ECG SafOne on electrocardiography recordings
Authors: Dewi Wan Nishfa , Safri Safri , Rosma Iswadi Hasyim 
Published: May 2022

AbstractAdaptability in precordial lead placement is one of the sources of electrocardiography inaccuracy. The present experimental study aimed to investigate the modified precordial lead ECG SafOne on electrocardiography recordings. Fourteen subjects were selected using purposive sampling. All the artefacts that emerged in the ECG recording results from the subjects using both the modified precordial lead ECG SafOne and precordial lead standard ECG were measured and identified. Data were analysed using a t test to examine the difference in the artefacts from all ECG recordings. The electrocardiography recordings of males aged 21–25 years using modified precordial lead ECG SafOne showed that out of 168 precordial leads from 14 subjects, two indicated artefact images in lead II (1.19%) and three in lead III (1.79%). The statistical test showed no significant difference in terms of artefacts that emerged in the electrocardiography recording results from both standard ECG and modified precordial lead ECG SafOne (p = 0.096). The modified precordial lead ECG SafOne showed no significant effect on ECG recordings related to artefacts. Additionally, the precordial lead ECG SafOne had no substantial difference in the presence of artefacts compared to the standard ECG. Therefore, ECG SafOne was usable as an ECG precordial lead for electrocardiography recording.
Spatial distribution and source identification for heavy metals in surface sediments of East Dongting Lake, China
Authors: Yuan Yi , Liu Baolin , Liu Hao 
Published: May 2022

AbstractDongting Lake is one of the most important inland freshwater lakes in China. To investigate the spatial distribution and seasonal variation characteristics of heavy metals (Cr, Co, Cu, Zn, Cd, and Pb) in the lake, 53 surface sediment samples were collected in the East Dongting Lake (ED Lake) in the wet and dry seasons. Results show Cr, Co, Cu, Zn, Cd, and Pb contents were 1.7 (1.9), 1.8 (2.0), 2.9 (3.0), 1.9 (1.9), 11.7 (13.1), and 2.0 (2.2)-fold of their geochemical soil background values of Hunan province (China) in the wet (dry) season. Spatial and seasonal heterogeneity could be found in the distribution of Cr, Co, Cu, Zn, and Pb in the surface sediments. The enrichment factor (EF) suggested that Cd has reached severe enrichment in the sediment. The result of the geo-accumulation index ($${I}_{geo}$$

I

geo

) indicated that Cr, Co, Cu, Zn, and Pb were at levels corresponding to low contamination, and moderately to highly polluted with Cd. Multivariate statistical analysis including pearson correlation analysis and principal component analysis was used for the identification of potential sources of the heavy metals in the sediments. The results showed that Cu, Zn, and Pb from the sediments of the East Dongting Lake in the wet and dry seasons were possibly anthropogenic sources, such as emissions from mining and smelting while Al, Fe, and Cr are attributed for natural sources. Cd enrichment in the sediments is influenced by both natural factors, and human activities in local areas.
Heat transfer through hydrogenated graphene superlattice nanoribbons: a computational study
Authors: Dehaghani Maryam Zarghami , Habibzadeh Sajjad , Farzadian Omid , Kostas Konstantinos V. , Saeb Mohammad Reza , Spitas Christos , Mashhadzadeh Amin Hamed 
Published: May 2022

AbstractOptimization of thermal conductivity of nanomaterials enables the fabrication of tailor-made nanodevices for thermoelectric applications. Superlattice nanostructures are correspondingly introduced to minimize the thermal conductivity of nanomaterials. Herein we computationally estimate the effect of total length and superlattice period ($$l_{p}$$

l
p

) on the thermal conductivity of graphene/graphane superlattice nanoribbons using molecular dynamics simulation. The intrinsic thermal conductivity ($$\kappa_{\infty }$$

κ
∞

) is demonstrated to be dependent on $$l_{p}$$

l
p

. The $$\kappa_{\infty }$$

κ
∞

of the superlattice, nanoribbons decreased by approximately 96% and 88% compared to that of pristine graphene and graphane, respectively. By modifying the overall length of the developed structure, we identified the ballistic-diffusive transition regime at 120 nm. Further study of the superlattice periods yielded a minimal thermal conductivity value of 144 W m−1 k−1 at $$l_{p}$$

l
p

= 3.4 nm. This superlattice characteristic is connected to the phonon coherent length, specifically, the length of the turning point at which the wave-like behavior of phonons starts to dominate the particle-like behavior. Our results highlight a roadmap for thermal conductivity value control via appropriate adjustments of the superlattice period.
Influence of caffeine on the maximal isometric and concentric force produced by skinned fibers
Authors: Fukutani Atsuki , Kunimatsu Shiho , Isaka Tadao 
Published: May 2022

AbstractCaffeine is one of the most famous and widely used ergogenic drugs, especially by athletes to improve sports performance. Caffeine is known to enhance muscle contraction by facilitating Ca2+ release from the sarcoplasmic reticulum. While the effect of caffeine on the cross-bridge dynamics has also investigated, the results is controversial. Therefore, the purpose of this study was to examine the influence of caffeine on cross-bridge dynamics using skinned fiber preparations from rabbit soleus (N = 19 in total). We performed isometric contractions at an average sarcomere length of 2.4 μm; thereafter, skinned fibers were shortened by 20% of the fiber length at a velocity of 0.1 mm/s (slow shortening) or 0.5 mm/s (fast shortening). The contractions were performed under both normal and caffeine-containing activating solution conditions to compare the isometric, slow concentric, and fast concentric forces between conditions. The isometric force did not differ between normal and caffeine-containing activating solution conditions. Similarly, the concentric forces obtained during the slow and fast shortening trials did not differ between conditions. We also measured the stiffness and the rate of force redevelopment (kTR) during the isometric contraction phase and found that these values were not different between normal and caffeine conditions. Based on these results, we conclude that the influence of caffeine on cross-bridge dynamics is negligible, and the ergogenic effect of caffeine, from the view of muscle contractility, is by facilitating Ca2+ release, as suggested in previous studies, and not by modulating the cross-bridge dynamics.
Adaptive resource management for cognitive power line communication system
Authors: Fu Meiming , Wang Kang , Xu Bo , Xu Shuming , Wu Weihua 
Published: May 2022

AbstractThe authors investigate the adaptive resource allocation problem for cognitive power line communication system. The dynamic optimisation model is employed to maximise the energy‐aware network utility, which is defined as the difference of the weighted network throughput and the power consumption. Then, the authors decompose the stochastic optimisation into a traffic management subproblem and a joint subchannel and power allocation subproblem. Since the joint subchannel and power allocation subproblem is a mixed integer non‐linear optimisation, a continuity relaxation and Lagrange dual method is proposed to obtain the optimal solution of the mixed integer non‐linear optimisation subproblem. After that, the authors develop an adaptive power line communication resource allocation algorithm to cope with the power line communication network dynamics only according to the current network state information. Finally, we derive a trade‐off mechanism between network utility and network delay, in which the network delay is proportional to the control parameter V and the network utility is proportional to the parameter . Simulation results not only show the effectiveness but also verify the theoretical analysis of our proposed algorithm.
Optimal attitude consensus for multi rigid bodies network considered on the lie group SO(3) with connectivity preserving property
Authors: Mansourinasab Sara , Sojoodi Mahdi , Moghadasi S. Reza 
Published: May 2022

AbstractThis paper proposes distributed optimal attitude consensus control for single‐integrator multi rigid bodies with undirected network evolving on Special Orthogonal Group SO(3) while simultaneously guarantees the connectivity preservation property for agents using descent gradient algorithm. Since by Use of the Euclidean distance on Lie group as a measure of the energy of the state does not define and preserve the topology of SO(3); besides, solving the Hamilton–Jacobi–Bellman equation in optimal control problems shows difficulty implementing Euclidean distances and limits the results for SO(3) configuration state spaces. As a result, in this paper, the generic distance on SO(3) associated to the natural Riemannian metric structure is used. Using this structure, Firstly, a distributed potential function based consensus control law is applied to the system exploiting Riemannian distance on SO(3). Then, for relaxing some restrictive conditions, finite‐time convergence, and increasing the speed of convergence the kinematic optimal control on SO(3) is considered. Referring to the proposed potential function designed in the previous section, an inverse optimal attitude consensus control problem is considered, which is solved by an inverse optimal control method. Finally, the designed method validates via two simulation examples.
Data‐driven spatio‐temporal analysis of wildfire risk to power systems operation
Authors: Umunnakwe Amarachi , Parvania Masood , Nguyen Hieu , Horel John D. , Davis Katherine R. 
Published: May 2022

AbstractWildfires are natural or man‐made disasters that continuously threaten portions of the transmission and distribution grid, and thus the stability of the electric grid. This paper presents a two‐stage framework for assessing power system‐wildfire risk using a data‐driven wildfire prediction model. The first stage of the framework estimates the spatio‐temporal probability of potential wildfire ignition and propagation using a deep neural network in combination with the wildfire physical spread model. Analysis reveals similar spatial and temporal patterns between the model‐predicted wildfire ignition potential and actual wildfire ignition. Motivated by these observations, the second stage assesses the wildfire risk in the power grid operation in terms of potential loss of load by de‐energisation, through combining geospatial information system data of the power grid topology and the stochastic spatio‐temporal wildfire model developed in the first stage. The electric power utility applications introduced by the proposed framework are twofold: 1) a spatio‐temporal risk model for proactive de‐energisation against potential power system failure‐induced wildfire, and 2) a spatio‐temporal spreading model for optimal grid operations against exogenous wildfire. The proposed model, based on real‐world dataset, is demonstrated on the IEEE 24‐bus test system mapped to a study area in Northern California, while the results illustrate the proposed model can achieve the best performance in potential wildfire ignition detection (AUC of 0.995) compared to other baselines, as well as demonstrates the risk‐aware operation of the power system enabled by the proposed framework.
Graph convolutional network‐based image matting algorithm for computer vision applications
Authors: Dong Li , Liang Zheng , Wang Yue 
Published: May 2022

AbstractImage matting plays a vital role in a variety of computer vision tasks including video editing and image fusion. Previously presented image matting algorithms might fail in producing favorable results since most of them concentrate on the similarity between the neighboring pixels while neglecting the corresponding spatial relationship. To address this issue, an end‐to‐end image matting framework through leveraging deep learning mechanism and graph theory is proposed. The proposed pipeline is a concatenation of one deep feature extraction component and a Graph Convolutional Network (GCN). The former part takes an image and its corresponding trimap as inputs and can generate the pixel‐wise features, which are then exploited as the input of the GCN locating at the latter part of the proposed framework. The GCN would refine the features for every pixel and predict the alpha matte outcome of the image. The approach outperforms a group of state‐of‐the‐art matting techniques as shown by the theoretical analysis and experimental results in terms of both accuracy and visual effects.
Research on integrated control strategy of doubly‐fed induction generator‐based wind farms on traction power supply system
Authors: Liao Jiasi , Mastoi Muhammad Shahid , Wang Delin , Sheng Shixian , Zhou Xin , Haris Malik 
Published: May 2022

AbstractWith the large‐scale construction of high‐speed railway and wind farms, the traction power supply system (TPSS) and wind farms are connected through the power system. The three‐phase unbalance of grid voltage caused by the TPSS will seriously affect traditional wind farms’ operation control. Considering the TPSS and wind farms are connected to the power system's public point of common coupling (PCC), the wind farm's current control and voltage support strategy are studied. Firstly, aiming to suppress the influence of harmonics and the unbalanced power grid caused by the TPSS, the repetitive controller (RC) is introduced to the current control of the doubly‐fed induction generator (DFIG). The vector control strategy based on the combination of proportion integral (PI) and RC is adopted, where the PI controller is used to adjust the average value of DFIG output power and the RC regulator controls DFIG output sinusoidal stator current. Secondly, the wind farm's voltage support strategy is designed to reduce the voltage fluctuation caused by the TPSS's loads. By coordinating three measures of putting in reactive power compensation devices, DFIG working in voltage control mode, and limiting DFIG output active power, the voltage of wind farm and traction station is effectively supported. Finally, a simulation model with the TPSS and wind farm is built to verify the proposed control strategy's effectiveness.