bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance, Feeding Behavior and Immune Response in Nellore and Angus × Nellore Steers Fed Whole Shelled Corn Diets with or without Fiber
Authors: Rodrigues Aline Castro , Teixeira Priscilla Dutra , Casagrande Daniel Rume , Peconick Ana Paula , Coelho Tamara Cristina , Paulino Pedro Veiga Rodrigues , Ladeira Márcio Machado
Published: October 2022

This study aimed to evaluate performance, to evaluate performance, carcass traits, feeding behavior, and humoral immune response in Nellore and Angus × Nellore steers fed whole shelled corn diets. Sixteen Nellore and 16 Angus × Nellore steers with 353 ± 25.3 kg were randomly assigned into 2 feeding groups, whole shelled corn without forage (WSC) or whole shelled corn and sugarcane bagasse (WSCB). The data were analyzed using the MIXED procedure of SAS (SAS Inst. Inc., Cary, NC, USA). Angus × Nellore steers had greater final BW, DMI, ADG, and subcutaneous fat thickness than Nellore. Moreover, Nellore steers had lower neutral detergent fiber (NDF) intake but spent more time ruminating and had a greater rumination rate of NDF. Regarding the diets, steers fed the WSCB had greater DMI and spent more time chewing and ruminating. Nellore × Angus steers had a greater haptoglobin concentration. The concentration of D-Lactate tended to be greater in the Nellore steers regardless of diet. In conclusion, the use of sugarcane bagasse in WSC diets increases DMI and ADG without affecting feed efficiency or the carcass traits of the steers. Although Angus × Nellore steers have increased haptoglobin concentration, this effect was not enough to reduce the performance of these animals.
High-Accuracy Clothing and Style Classification via Multi-Feature Fusion
Authors: Chen Xiaoling , Deng Yun , Di Cheng , Li Huiyin , Tang Guangyu , Cai Hao
Published: October 2022

In recent years, the online selection of virtual clothing styles has been used to explore and expand diversified personal aesthetics, and it is also an overall reform and challenge to the clothing industry. Under the condition of the existing clothing style categories, this paper puts forward a style classification method combining fine-grained and coarse-grained techniques. Furthermore, a new deep neural network is proposed, which can improve the robustness of recognition and avoid the interference of image background through the pan learning and the background learning of image features. In order to study the relationship between the fine-grained attributes of clothing and the whole style, firstly, the clothing types are learned to realize the pre-training of model parameters. Secondly, through the transfer learning of the first stage of the pre-training model parameters, the model parameters are fine-tuned to make them more suitable for identifying the coarse-grained style types. Finally, a network structure based on the dual attention mechanism is proposed to improve the accuracy of final identification by adding different attention mechanisms at different stages of the network to enhance the performance of network features. In the experiment, we collected 50,000 images of 10 clothing styles to train and evaluate the models. The results show that the proposed classification method can effectively distinguish clothing styles and types.
Long Text Truncation Algorithm Based on Label Embedding in Text Classification
Authors: Chen Jingang , Lv Shu
Published: October 2022

The long text classification task has become a hot research topic in the field of text classification due to its long length and redundant information. At present, the common processing methods for long text data, such as the truncation method and pooling method, are prone to the problem of too many sentences or loss of contextual semantic information. To deal with these issues, we present LTTA-LE (Long Text Truncation Algorithm Based on Label Embedding in Text Classification), which consists of three key steps. Firstly, we build a pretraining prefix template and a label word mapping prefix template to obtain the label word embedding, and we realize the joint training of long text and label words. Secondly, we calculate the cosine similarity between the label word embedding and the long text embedding, and we filter the redundant information of the long text to reduce the text length. Finally, a three-stage model training architecture is introduced to effectively improve the classification performance and generalization ability of the model. We conduct comparative experiments on three public long text datasets, and the results show that LTTA-LE has an average F1 improvement of 1.0518% over other algorithms, which proves that our method can achieve satisfactory performance.
Comparison of Canopy Cover and Leaf Area Index Estimation from Airborne LiDAR and Digital Aerial Photogrammetry in Tropical Forests
Authors: Li Chenyun , Zheng Yanfeng , Zhang Xinjie , Wu Fayun , Li Linyuan , Jiang Jingyi
Published: October 2022

Digital aerial photogrammetry (DAP) has emerged as an alternative to airborne laser scanning (ALS) for forest inventory applications, as it offers a low-cost and flexible three-dimensional (3D) point cloud. Unlike the forest inventory attributes (e.g., tree height and diameter at breast height), the relative ability of DAP and ALS in predicting canopy structural variables (i.e., canopy cover and leaf area index (LAI)) has not been sufficiently investigated by previous studies. In this study, we comprehensively compared the canopy cover and LAI estimates using DAP- and ALS-based methods over 166 selected tropical forest sample plots with seven different tree species and forest types. We also explored the relationship between field-measured aboveground biomass (AGB) and the LAI estimates. The airborne LAI estimates were subsequently compared with the Sentinel-2-based LAI values that were retrieved using a one-dimensional radiative transfer model. The results demonstrated that the DAP-based method generally overestimated the two canopy variables compared to ALS-based methods but with relatively high correlations regardless of forest type and species (R2 of 0.80 for canopy cover and R2 of 0.76 for LAI). Under different forest types and species, the R2 of canopy cover and LAI range from 0.64 to 0.89 and from 0.54 to 0.87, respectively. Apparently, different correlations between AGB and LAI were found for different forest types and species where the mixed coniferous and broad-leaved forest shows the best correlation with R2 larger than 0.70 for both methods. The comparison with satellite retrievals verified that the ALS-based estimates are more consistent with Sentinel-2-based estimates than DAP-based estimates. We concluded that DAP data failed to provide analogous results to ALS data for canopy variable estimation in tropical forests.
Application of Deep Learning to Spectroscopic Features of the Balmer-Alpha Line for Hydrogen Isotopic Ratio Determination in Tokamaks
Authors: Koubiti Mohammed , Kerebel Malo
Published: October 2022

We propose in this paper the use of artificial intelligence, especially deep learning algorithms, for the isotopic ratio determination for hydrogen–deuterium mixtures. Our approach is based on the Balmer-α line emitted by hydrogen and deuterium, but unlike the standard method, it does not consist of fitting the Hα/Dα line spectra. Instead, only some basic spectroscopic features such as the line peak-to-dip wavelength separation, peak-to-peak and dip-to-peak intensity ratios of the Zeeman–Doppler-broadened Hα/Dα line spectra are used by the regression algorithm for training. We demonstrate the proof-of-principle of our approach by applying deep learning from the open-access machine-learning platform TensorFlow to Hα/Dα line profiles, which we have synthetized with pre-determined parameters such as neutral temperatures, the magnetic field strength and the H/(H+D) isotopic ratio. The used regression algorithm allowed us to retrieve with a good accuracy the isotopic ratios used for the synthetized line profiles.
Delirium after Spinal Surgery: A Pilot Study of Electroencephalography Signals from a Wearable Device
Authors: Lee Soo-Bin , Kwon Ji-Won , Sung Sahyun , Moon Seong-Hwan , Lee Byung Ho
Published: October 2022

Postoperative delirium after spinal surgery in elderly patients has been a recent concern. However, there has not been a study of delirium after spinal surgery based on electroencephalography (EEG) signals from a compact wearable device. We aimed to analyze differences in EEG signals from a wearable device in patients with and without delirium after spinal surgery. Thirty-seven patients who underwent cervical or lumbar decompression and instrumented fusion for degenerative spinal disease were included. EEG waves were collected from a compact wearable device, and percentage changes from baseline to within 1 week and 3 months after surgery were compared between patients with and without delirium. In patients with delirium, the anxiety- and stress-related EEG waves—including the H-beta (19.3%; p = 0.003) and gamma (18.8%; p = 0.006) waves—and the tension index (7.8%; p = 0.011) increased, and the relaxation-related theta waves (−23.2%; p = 0.016) decreased within 1 week after surgery compared to the non-delirium group. These results will contribute to understanding of the EEG patterns of postoperative delirium and can be applied for the early detection and prompt treatment of postoperative delirium after spinal surgery.
Undamped Free Vibration Analysis of Functionally Graded Beams: A Dynamic Finite Element Approach
Authors: Gee Aaron , Hashemi Seyed M.
Published: October 2022

A Dynamic Finite Element (DFE) method for coupled axial–flexural undamped free vibration analysis of functionally graded beams is developed and subsequently used to investigate the system’s natural frequencies and mode shapes. The formulation is based on the Euler–Bernoulli beam theory and material grading is assumed to follow a power law variation through the thickness direction. Using the closed-form solutions to the uncoupled segments of the system’s governing differential equations as the basis functions of approximation space, the dynamic, frequency-dependent, trigonometric interpolation functions are developed. The interpolation functions are used with the weighted residual method to develop the DFE of the system. The resulting nonlinear eigenvalue problem is then solved to determine the coupled natural frequencies. Example elements using DFE, Finite Element Method (FEM) and the Dynamic Stiffness Method (DSM) are implemented in MATLAB for testing, verification, and validation. Good agreement was observed and the DFE formulation exhibited superior convergence performance compared to the FEM.
Combined Biological and Chemical/Physicochemical Pretreatment Methods of Lignocellulosic Biomass for Bioethanol and Biomethane Energy Production—A Review
Authors: Meenakshisundaram Shruthi , Fayeulle Antoine , Léonard Estelle , Ceballos Claire , Liu Xiaojun , Pauss André
Published: October 2022

Lignocellulosic biomass is a low-cost and environmentally-friendly resource that can be used to produce biofuels such as bioethanol and biogas, which are the leading candidates for the partial substitution of fossil fuels. However, the main challenge of using lignocellulosic materials for biofuel production is the low accessibility to cellulose for hydrolysis of enzymes and microorganisms, which can be overcome by pretreatment. Biological and chemical pretreatments have their own disadvantages, which could be reduced by combining the two methods. In this article, we review biological–chemical combined pretreatment strategies for biogas and bioethanol production. The synergy of fungal/enzyme–NaOH pretreatment is the only biological–chemical combination studied for biogas production and has proven to be effective. The use of enzyme, which is relatively expensive, has the advantage of hydrolysis efficiency compared to fungi. Nonetheless, there is vast scope for research and development of other chemical–biological combinations for biogas production. With respect to ethanol production, fungal–organosolv combination is widely studied and can achieve a maximum of 82% theoretical yield. Order of pretreatment is also important, as fungi may reduce the accessibility of cellulose made available by prior chemical strategies and suppress lignin degradation. The biofuel yield of similarly pretreated biomass can vary depending on the downstream process. Therefore, new strategies, such as bioaugmentation and genetically engineered strains, could help to further intensify biofuel yields.
Machine Learning Based Surrogate Models for the Thermal Behavior of Multi-Plate Clutches
Authors: Schneider Thomas , Bedrikow Alexandre Beiderwellen , Dietsch Maximilian , Voelkel Katharina , Pflaum Hermann , Stahl Karsten
Published: October 2022

Multi-plate clutches play safety-critical roles in many applications. For this reason, correct functioning and safe operation are essential. Spontaneous damages are particularly critical because the failure of the clutch can lead to a failure of the system. Such damage mainly occurs due to very high loads and ultimately very high temperatures. Finite Element Analysis (FEA) enables simulation and prediction of these temperatures, but it is very time-consuming and costly. In order to reduce this computational effort, surrogate models can be created using machine learning (ML) methods, which reproduce the input and output behavior. In this study, various ML methods (polynomial regression, decision tree, support vector regressor, Gaussian process and neural networks) are evaluated with respect to their ability to predict the maximum clutch temperature based on the loads of a slip cycle. The models are examined based on two use cases. In the first use case, the axial force and the speed are varied. In the second use case, the lining thickness is additionally modified. We show that ML approaches fundamentally achieve good results for both use cases. Furthermore, we show that Gaussian process and backpropagation neural network provide the best results in both cases and that the requirement to generate predictions during operation is fulfilled.
The Effect of a Hybrid Pretreatment Device for CEMS on the Simultaneous Removal of PM2.5 and Water Vapor
Authors: Choi In-Young , Dinh Trieu-Vuong , Kim Dong-Eun , Jun Bong-Hyun , Lee Seung-Ae , Park Young-Min , Kim Jo-Chun
Published: October 2022

Stationary emission sources still account for a significant portion of total air pollution emissions. Continuous emission monitoring systems (CEMS) have been used to estimate the emissions of stack pollutants. A large amount of moisture and other interfering factors in the sample discharged from a stack result in the loss of target gases due to artifact formation or gas absorption, thereby reducing measurement accuracy. Therefore, a pretreatment process is essential. Among various pretreatment technologies available, a cyclone with a rapid cooling unit is a special one that can be applied to remove particles and water vapor at the same time in CEMS. This study aimed at the simultaneous removal of water vapor and particles by operating a hybrid pretreatment device at low temperatures such as −5, −15, and −25 °C. When using the hybrid cyclone under the conditions of high temperature (180 °C), humidity (150 g/m3), PM2.5 (1 mg/m3), and SO2 (105.2 ppm) concentrations, the reduction rates of water vapor and PM2.5 concentration and the recovery rates of SO2 concentration were 82.2, 80.2, and 96.4%, respectively. These data suggested that the hybrid cyclone could be used as a pretreatment device for CEMS.
Climate Change Mitigation in Forestry: Paying for Carbon Stock or for Sequestration?
Authors: Enríquez-de-Salamanca Álvaro
Published: October 2022

Climate change requires mitigation actions, mainly preventive, by reducing greenhouse gas emissions; however, carbon sequestration is a complementary measure. Although short-term carbon sequestration can be somewhat effective, it is really interesting when it is permanent. Sequestration calculates the carbon removed from the atmosphere over a period, while the stock expresses the cumulative carbon of a forest. Sequestration and stock are closely related, but ecosystem service valuation often focuses on the former, which can discourage forest maintenance. This study analyzes carbon sequestration and storage in four pine forests located in central Spain, comparing its valuation for different equivalence times, a period considered sufficient to compensate for the emission of one ton of CO2 into the atmosphere. Equilibrium equivalence times were calculated for each forest, defined as the period in which carbon sequestration and stock payments are equal; values ranged from 33 to 101 years, with significant correlations with commercial volume and carbon stock. Equivalence times of 30–50 years are reasonable in Mediterranean forest stands with moderate growth and density, while in dense mature stands this time should increase to 50–100 years. Valuing carbon stocks and paying for them in a sustained manner over time promotes sustainable forest management, while the sale of sequestration credits may generate a speculative “greenwashing” market. In addition, payments for stocks can be applied to any forest stand and not only to new plantations. Carbon stock valuation is a win–win strategy for climate change mitigation, sustainable forest management, and rural development.
On the Spatio-Temporal Dependence of Anomalies in the Atmospheric Electric Field Just around the Time of Earthquakes
Authors: Hobara Yasuhide , Watanabe Mako , Miyajima Risa , Kikuchi Hiroshi , Tsuda Takuo , Hayakawa Masashi
Published: October 2022

In this study, we report atmospheric electric field (AEF) anomalies observed around the time of earthquakes (EQs) in Japan. Using a newly developed AEF observation network with three spatially separated stations in Japan (Chofu, Kakioka, and Iwaki), we conducted a study for two EQs that occurred within a few 100 km from the EQ epicenter under relatively good local weather conditions as shown by a local all-sky camera and weather information. Time series and wavelet analyses of the AEF indicate that fluctuation anomalies in the AEF with periods of 10–60 min and larger than 70 min were observed from a few hours before up to a few hours after the occurrence of the EQs. The lag in the onset time increased with increasing distance from the EQ epicenter to the field site. The above-mentioned characteristics of these AEF fluctuation anomalies were similar among the three stations, and therefore the observed AEF anomalies were considered to be an imminent precursor of EQs. The observed AEF anomalies were likely to be caused by internal gravity waves (IGWs) generated around the EQ epicenter a few hours before the EQ, passing over the field site while changing the AEF by changing the space charge density in the surface layer of the atmosphere.
A Review of Atmospheric Aerosols in Antarctica: From Characterization to Data Processing
Authors: Anzano Jesús , Abás Elisa , Marina-Montes César , del Valle Javier , Galán-Madruga David , Laguna Mariano , Cabredo Susana , Pérez-Arribas Luis-Vicente , Cáceres Jorge , Anwar Jamil
Published: October 2022

One of the major problems of the present era is air pollution, not only for its impact on climate change but also for the diseases provoked by this scourge. Among the most concerning air pollutants is particulate matter, since it can travel long distances and affect the entire globe. Antarctica is extremely sensitive to climate change and essential for regulating temperature and permitting life on Earth. Therefore, air quality studies in this region are extremely important. The aim of this review is to present the work conducted on the identification and detection of aerosols and particulate matter in the Antarctic region in the last 20 years. These studies revealed a large number of organic and inorganic species. Organochlorine pesticides or polychlorinated biphenyls represent almost 50% of the organic fraction detected in Antarctica. Furthermore, heavy metals such as Hg and Pb were also found in the region related to anthropogenic activities. To summarize, this work detailed different analytical techniques and data processing to help characterize Antarctic aerosols and their potential sources.
Impacts of Nano SiO2 Addition on the Formation of Ultrafine Particulate Matter during Coal Combustion
Authors: Wang Huakun , Xu Yishu , Zhang Kai , Zhang Baohua , Min Shanshan , Liu Yimin , Zhu Jingji , Ma Jingjing
Published: October 2022

Clay minerals composed of Si and Al could help reduce ultrafine particulate matter (PM) formation as an additive during coal combustion while currently unacceptable high adding dosages (normally 3–5 wt.%) are required due to their inadequate capture efficiency. To find additives that could effectively reduce the formation of ultrafine PM, coal combustion with a novel nano SiO2 additive (<100 nm) was performed to evaluate its effects on reducing ultrafine PM. The generated PM10 was sampled to characterize their particle size distribution, mass yield, size-resolved composition and micromorphology. The results showed that adding a small dosage (0.6%) of nano SiO2 reduced the mass yield of ultrafine PM by 30.70%, showing a much higher ultrafine PM capture efficiency than an existing micron-sized natural clay mineral. However, its performance on different coals varied due to disparities in ash content and composition in coal. A composition analysis revealed that the Na content in the ultrafine PM was decreased after adding nano SiO2, indicating that nano SiO2 inhibited the migration of volatile alkali metals such as Na into ultrafine PM because the Na-containing mineral vapor reacted with the nano SiO2 additive particles with a large specific surface area at a high temperature and inhibited their transformation into ultrafine PM via homogenous nucleation. Changes in the element size distributions and micromorphology showed that the majority of the added nano SiO2 particles reacted or coalesced with each other and/or the minerals embedded in coal, finally growing into a larger PM.
Negative Storm Surges in the Elbe Estuary—Large-Scale Meteorological Conditions and Future Climate Change
Authors: Jensen Corinna , Mahavadi Tara , Schade Nils H. , Hache Ingo , Kruschke Tim
Published: October 2022

Negative storm surges in the Elbe estuary can affect shipping as well as shoreline infrastructure. The significant reduction of water level caused by strong offshore winds can lead to extreme low water events, which endanger waterfront structures. The current study analyses the large-scale meteorological conditions inducing such situations and possible future changes due to climate change. The analysis is based on tide gauge data from Cuxhaven, atmospheric reanalysis data and an objective weather classification approach. It is found that south-easterly wind directions in combination with strong gales favour extreme low water events at Cuxhaven. Furthermore, the analysis of a single model large ensemble of climate projections shows a significant decrease in the frequency of such conditions for the far future (2071–2100). Regarding future global mean sea level rise the simulation results of a sensitivity study indicate that water levels during such extreme events approximately follow the development of the mean sea level rise. Therefore, our study suggests that both meteorological conditions and mean sea levels in a warmer future climate will be less favourable for the occurrence of extreme low water events in the Elbe estuary.
Screening Constant per Unit Nuclear Charge Studies of Rydberg Resonances Due to the 4p → nd and ns Transitions in Se+ Ions
Authors: Sakho Ibrahima
Published: October 2022

Calculations of high-lying energy resonances of 12 Rydberg series due to the 4p → nd and 4p → ns transitions from the 4s24p3 3S°3/2 ground-state and the 4s24p3 2P°3/2,1/2 and 4s24p3 2D°5/2,3/2 metastable states of the Se+ ion are reported. The calculations believed to be the first theoretical ones are performed using the Screening Constant per Unit Nuclear Charge (SCUNC) method up to n = 40. Analysis of the present results is achieved in the framework of the standard quantum-defect theory and of the SCUNC-procedure by calculating the effective nuclear charge. For many resonances, the SCUNC-method reproduces the ALS measurements excellently [Esteves et al., Phys. Rev. A, 84 013406 (2011)] up to n = 25. However, for some resonances belonging to the 4s24p2 (1D2)nd (2D) and 4s24p2 (1D2)ns (2D) and to the 4s24p2 (1D2)nd (2S) and 4s24p2 (1D2)nd (2P1/2) Rydberg series, the ALS data overlap at high n values. In addition, negative quantum-defect values were determined where positive values are allowable. In the present work, positive quantum defect values almost constants are obtained along all the series investigated up to n = 40, and the narrow resonances are clearly separated. Overall, the present theoretical study provides confidence in the ALS data on Se+ ions for astrophysical interest as far as the understanding of the chemical evolution of Se in the universe is concerned.
Fractional Nonlinearity for the Wave Equation with Friction and Viscoelastic Damping
Authors: Mohammed Djaouti Abdelhamid , Latif Muhammad Amer
Published: October 2022

In this paper we consider a fractional nonlinearity for the wave equation with friction and viscoelastic damping. Using Fixed point theorem a global in time existence of small data solutions to the Cauchy problem is investigated in this research. Our main interest is to show the influence of the fractional nonlinearity parameter to the admissible range of exponent ς comparing with power nonlinearity and also the generating of loss of decay.
Permutation Groups Generated by γ-Cycles
Authors: Diaconescu Răzvan
Published: October 2022

A γ-cycle is a cycle of the form (i+1,i+2,…,i+m) in the symmetric group Sn. We study the subgroups of Sn generated by several sets of γ-cycles. Our mathematical development is strongly supported by computational experiments and proofs based on do-it-yourself programming with the logic-based language Maude.
Fuzzy Differential Subordination for Meromorphic Function
Authors: El-Deeb Sheza , Khan Neelam , Arif Muhammad , Alburaikan Alhanouf
Published: October 2022

This paper is related to notions adapted from fuzzy set theory to the field of complex analysis, namely fuzzy differential subordinations. This work aims to present new fuzzy differential subordinations for which the fuzzy best dominant and fuzzy best subordinate are given, respectively. The original theorems proved in the paper generate interesting corollaries for particular choices of functions acting as fuzzy best dominant. Here, in this article, fuzzy differential subordination results are obtained using a new integral operator introduced in this paper for meromorphic function, such that the newly-defined integral operator is starlike and convex, respectively.
Early Prediction of the Health Conditions for Battery Cathodes Assisted by the Fusion of Feature Signal Analysis and Deep-Learning Techniques
Authors: Wang Wentao , Zhang Lisheng , Yu Hanqing , Yang Xianbin , Zhang Teng , Chen Siyan , Liang Fengwei , Wang Huizhi , Lu Xuekun , Yang Shichun , Liu Xinhua
Published: October 2022

With rapid development of clean energy vehicles, the health diagnosis and prognosis of lithium batteries remain challenging for practical applications. Accurate state-of-health (SOH) and remaining useful life (RUL) estimation provides crucial information for improving the safety, reliability and longevity of batteries. In this paper, a fusion of deep-learning model and feature signal analysis methods are proposed to realize accurate and fast estimation of the health conditions for battery cathodes. Specifically, the long short-term memory (LSTM) network and differential thermal voltammetry (DTV) are utilized to verify our fusion method. Firstly, the DTV feature signal analysis is executed based on battery charging and discharging data, based on which useful feature variables are extracted with Pearson correlation analysis. Next, the deep-learning model is constructed and trained with the LSTM as the core based on timeseries datasets constructed with features. Finally, the validation and error analysis of proposed model are provided, showing a max mean absolute error of 0.6%. The proposed method enables highly accurate models for SOH and RUL estimation that can be potentially deployed on cloud-end for offline battery degradation tracking.
One-Step Hydrothermal Reaction Induced Nitrogen-Doped MoS2/MXene Composites with Superior Lithium-Ion Storage
Authors: Gong Cheng , Long Mengqi , Xiao Jun , Li Jiayi , Chen Jun , Xiao Yang , Zhang Guilai , Gao Hong , Liu Hao
Published: October 2022

MoS2, a typical transition metal dichalcogenide (TMDs), inheriting high theoretical capacity, open framework and unique electrochemical properties, is regarded as a promising electrode material. However, the low electronic conductivity and slow chemical kinetics of two-dimensional (2D) MoS2 lamellars, along with the large volume expansion during cycling hinder their application in Li-ion batteries. MXene inherits the strengths of excellent metallic conductivity, a low lithium-ion diffusion potential barrier and superior mechanical stability; however, its low reversible capacity and self-stacking problems as anode still need to be solved. Herein, the MXene Ti3C2Tx compound with MoS2 through a simple one-step hydrothermal reaction is introduced. The introduction of nitrogen-doped Ti3C2Tx can effectively restrain the volume change of MoS2 and ameliorate the electronic conductivity of the whole electrode, while MoS2 can alleviate the self-stacking of Ti3C2Tx during cycling. The as-prepared MoS2/MXene electrode delivers an initial discharge capacity of 1087 mA h g−1 with an initial Coulombic efficiency (ICE) of 81.6% at 100 mA g−1, and a specific discharge capacity of 731 mA h g−1 can be retained after 100 cycles. The excellent electrochemical performance demonstrates that nitrogen-doped MoS2/MXene can be a potential electrode material for Li-ion batteries.
Effect of Ga2O3 Addition on the Properties of Garnet-Type Ta-Doped Li7La3Zr2O12 Solid Electrolyte
Authors: Yamazaki Yusuke , Miyake Shotaro , Akimoto Keigo , Inada Ryoji
Published: October 2022

Garnet-type Ta-doped Li7La3Zr2O12 (LLZO) ceramic solid electrolytes with Ga2O3 additive were synthesized using a conventional solid-state reaction process. When the amounts of Ga2O3 additive were below 2 mol %, the sintered sample has a dense structure composed of grains with an average size of 5 to 10 μm, whereas 3 mol % or more Ga2O3 addition causes a significant increase in grain size above several 10 to 100 μm, due to high-temperature sintering with a large amount of liquid Li-Ga-O phase. At room temperature, the highest total (bulk + grain-boundary) ionic conductivity of 1.1 mS cm−1 was obtained in the sample with 5 mol % Ga2O3 addition. However, this sample was shorted by Li dendrite growth into solid electrolyte at a current density below 0.2 mA cm−2 in galvanostatic testing of the symmetric cell with Li metal electrodes. The tolerance for Li dendrite growth is maximized in the sample sintered with 2 mol % Ga2O3 addition, which was shorted at 0.8 mA cm−2 in the symmetric cell. Since the interfacial resistance between Li metal and solid electrolyte was nearly identical among all samples, the difference in tolerance for Li dendrite growth is primarily attributed to the difference in microstructure of sintered samples depending on the amounts of Ga2O3.
Comparative Study of Onion-like Carbons Prepared from Different Synthesis Routes towards Li-Ion Capacitor Application
Authors: Permana Antonius Dimas Chandra , Ding Ling , Gonzalez-Martinez Ignacio Guillermo , Hantusch Martin , Nielsch Kornelius , Mikhailova Daria , Omar Ahmad
Published: October 2022

Li-ion capacitors (LIC) have emerged as a promising hybrid energy storage system in response to increasing energy demands. However, to achieve excellent LIC performance at high rates, along with cycling stability, an alternative anode to graphite is needed. Porous high-surface-area carbons, such as onion-like carbons (OLCs), have been recently found to hold high potential as high-rate-capable LIC anodes. However, a systematic understanding of their synthesis route and morphology is lacking. In this study, OLCs prepared from self-made metal organic frameworks (MOFs) Fe-BTC and Fe-MIL100 by a simple pyrolysis method were compared to OLCs obtained via high-temperature annealing of nanodiamonds. The LICs with OLCs produced from Fe-BTC achieved a maximum energy density of 243 Wh kg−1 and a power density of 20,149 W kg−1. Furthermore, excellent capacitance retention of 78% after 10,000 cycles was demonstrated. LICs with MOF-derived OLCs surpassed the energy and power density of LICs with nanodiamond-derived OLCs. We determined the impact of the MOF precursor structure and morphology on the resulting OLC properties, as well as on the electrochemical performance. Thus, MOF-derived OLCs offer significant potential toward high-performance anode material for LICs, enabling control over structure and morphology, as well as easy scalability for industrial implementation.
Melamine-Sacrificed Pyrolytic Synthesis of Spiderweb-like Nanocages Encapsulated with Catalytic Co Atoms as Cathode for Advanced Li-S Batteries
Authors: Wang Han , Jamil Sidra , Tang Wenwen , Zhao Jing , Liu Hui , Bao Shujuan , Liu Yijun , Xu Maowen
Published: October 2022

Due to the high theoretical capacity of 1675 mAh g−1 of sulfur, lithium-sulfur (Li-S) batteries can reach a high energy density of 2600 Wh kg−1, which has shown fascinating potential in recent decades. Herein, we report the spiderweb-like nanocage (Co/Mel) as a novel sulfur host with a melamine-sacrificed pyrolysis method. The incorporation of embedded cobalt nanoparticles (Co NPs) in the tips of carbon nanotubes (CNTs) can catalyze polysulfide transformation kinetics. In addition, the nanocages form a conductive three-dimensional spiderweb-like network that facilitates electrolytic penetration and electronic/ionic transportation. Moreover, the porous internal nano-cavities not only improve sulfur loading levels but also provide buffer space for volume expansion during charging and discharging. As a result, the hollow Co/Mel polyhedra with a high content of sulfur (75.5 wt%) displays outstanding electrochemical performance with an initial discharge-specific capacity of 1425.2 mAh g−1 at 0.1 C and a low decay rate of only 0.028% after 1000 cycles at 1 C.
Electric Vehicle Battery Health Expected at End of Life in the Upcoming Years Based on UK Data
Authors: Canals Casals Lluc , Etxandi-Santolaya Maite , Bibiloni-Mulet Pere Antoni , Corchero Cristina , Trilla Lluis
Published: October 2022

Second-life businesses from Electric Vehicle (EV) batteries are gaining attention considering that these batteries are deemed as inappropriate for transport purposes once they reach 80 or 70% of State of Health (SoH). However, the limited number of retired batteries and the trend in battery capacity increase hinder a realistic evaluation of second-life applications. To analyze battery reuse, a closer look at the End of Life (EoL) conditions of these batteries must be taken. This study presents a battery ageing model to estimate the SoH of EV batteries according to their age and mileage. The model is applied to the current retirement characteristics of combustion vehicles to statistically determine the expected SoH at the vehicle EoL. Results indicate that most EVs will reach EoL for reasons other than under-performance. Once retired, most EV batteries will have a SoH higher than 75% within the next 20 years, opening an interesting market for second-life businesses. However, battery reuse is an option that, considering the growing EV market, will rapidly saturate the stationary energy storage demand. Before 2040, most EV batteries will follow streams towards the circular economy, although at some point, they will have to be sent directly to recycling after the vehicular use.
Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics
Authors: Ramaswami Gomathy , Susnjak Teo , Mathrani Anuradha
Published: October 2022

Learning Analytics (LA) refers to the use of students’ interaction data within educational environments for enhancing teaching and learning environments. To date, the major focus in LA has been on descriptive and predictive analytics. Nevertheless, prescriptive analytics is now seen as a future area of development. Prescriptive analytics is the next step towards increasing LA maturity, leading to proactive decision-making for improving students’ performance. This aims to provide data-driven suggestions to students who are at risk of non-completions or other sub-optimal outcomes. These suggestions are based on what-if modeling, which leverages machine learning to model what the minimal changes to the students’ behavioral and performance patterns would be required to realize a more desirable outcome. The results of the what-if modeling lead to precise suggestions that can be converted into evidence-based advice to students. All existing studies in the educational domain have, until now, predicted students’ performance and have not undertaken further steps that either explain the predictive decisions or explore the generation of prescriptive modeling. Our proposed method extends much of the work performed in this field to date. Firstly, we demonstrate the use of model explainability using anchors to provide reasons and reasoning behind predictive models to enable the transparency of predictive models. Secondly, we show how prescriptive analytics based on what-if counterfactuals can be used to automate student feedback through prescriptive analytics.
Automatic Medical Face Mask Detection Based on Cross-Stage Partial Network to Combat COVID-19
Authors: Dewi Christine , Chen Rung-Ching
Published: October 2022

According to the World Health Organization (WHO), the COVID-19 coronavirus pandemic has resulted in a worldwide public health crisis. One effective method of protection is to use a mask in public places. Recent advances in object detection, which are based on deep learning models, have yielded promising results in terms of finding objects in images. Annotating and finding medical face mask objects in real-life images is the aim of this paper. While in public places, people can be protected from the transmission of COVID-19 between themselves by wearing medical masks made of medical materials. Our works employ Yolo V4 CSP SPP to identify the medical mask. Our experiment combined the Face Mask Dataset (FMD) and Medical Mask Dataset (MMD) into one dataset to investigate through this study. The proposed model improves the detection performance of the previous research study with FMD and MMD datasets from 81% to 99.26%. We have shown that our proposed Yolo V4 CSP SPP model scheme is an accurate mechanism for identifying medically masked faces. Each algorithm conducts a comprehensive analysis of, and provides a detailed description of, the benefits that come with using Cross Stage Partial (CSP) and Spatial Pyramid Pooling (SPP). Furthermore, after the study, a comparison between the findings and those of similar works has been provided. In terms of accuracy and precision, the suggested detector surpassed earlier works.
Question Answer System: A State-of-Art Representation of Quantitative and Qualitative Analysis
Authors: Zope Bhushan , Mishra Sashikala , Shaw Kailash , Vora Deepali Rahul , Kotecha Ketan , Bidwe Ranjeet Vasant
Published: October 2022

Question Answer System (QAS) automatically answers the question asked in natural language. Due to the varying dimensions and approaches that are available, QAS has a very diverse solution space, and a proper bibliometric study is required to paint the entire domain space. This work presents a bibliometric and literature analysis of QAS. Scopus and Web of Science are two well-known research databases used for the study. A systematic analytical study comprising performance analysis and science mapping is performed. Recent research trends, seminal work, and influential authors are identified in performance analysis using statistical tools on research constituents. On the other hand, science mapping is performed using network analysis on a citation and co-citation network graph. Through this analysis, the domain’s conceptual evolution and intellectual structure are shown. We have divided the literature into four important architecture types and have provided the literature analysis of Knowledge Base (KB)-based and GNN-based approaches for QAS.
Why Oxidation Should Be Still More Feared in NABLABs: Fate of Polyphenols and Bitter Compounds
Authors: Simon Margaux , Collin Sonia
Published: October 2022

Nowadays, non-alcoholic (NAB) and low-alcoholic beers (LAB) still significantly suffer from staling defects when fresh, partially due to absence of ethanol as antioxidant. In the current work, the fate of flavan-3-ols (monomers, dimers, and trimers) and bitter compounds (isohumulones, humulinones, etc.) of 11 commercial NABLABs available on the Belgian market was monitored through one year of aging at 20 °C in the dark. Fresh NABLABs contained variable flavan-3-ols and bitter compounds levels (between 3.0–10.0 mg/L and 8.0–39.0 mg/L, respectively), depending on different technological processes used. Chill haze and color were also investigated as potential oxidation markers of fresh and aged beers. Surprisingly, contrary to conventional beers, the oligomers’ concentration (dimer and trimer procyanidins) exhibited a strong correlation (R2 = 0.95) with chill haze before aging, suggesting prematured oxidation of the samples. After a year of storage, significant degradation occurred as for regular dry hopped beers (process very sensitive to oxidation), only 27% remaining for flavan-3-ol dimers and an average 16% for trans-isohumulones. Oxidation risk appears here as the main weakness of NABLABs, which could be probably improved by spiking very efficient antioxidants.
Innovative “Soft” Maceration Techniques in Red Grape Fermentation
Authors: Pettinelli Stefano , Pardini Luca , De Angeli Giorgio , Bianchi Alessandro , Najar Basma , Cerreta Raffaele , Bellincontro Andrea , Floridia Giuseppe , Mencarelli Fabio
Published: October 2022

Two innovative soft maceration techniques of vinification based on red grape Cabernet Sauvignon were compared in 2020 and 2021 vintages with the most used system of maceration (pump-over and delestage) in today’s wine sector in order to verify the efficiency in polyphenol extraction and fermentation rate. Fermentation kinetics and final wine characteristics were evaluated as the main parameters for comparing the systems. The AIR MIX (AIRMIXING M.I.™) technique is based on the use of a fixed sequential small injections of compressed air (3 jets) from the bottom of the tank, aimed at creating waves (resonance waves) able to prevent the cap formation. The ADCF (NECTAR-ADCF™) technique uses the overpressure produced by carbon dioxide in the wine tank during alcoholic fermentation to keep the cap submerged and to favor its disruption by the CO2 outside release through a valve. As a reference, the control vinification consisted of the use of “delestage” and pump-over to facilitate the extraction and good management of the cap. ADCF, at the end, extracted a greater quantity of polyphenols and anthocyanins. AIR MIX speeded up the fermentation, which ended 4–7 days before the control and, initially, provoked a greater extraction of phenols and anthocyanins as ADCF. By the end, the concentration of polyphenols and anthocyanins was the highest in ADCF, followed by control and AIR MIX approximately at the same amount. The control wine had a slightly higher volatile acidity. AIR MIX consumed more than 60% less energy because the nonuse of pump-over and delestage, and also, no personnel was requested.
Stress Distribution on Spinal Cord According to Type of Laminectomy for Large Focal Cervical Ossification of Posterior Longitudinal Ligament Based on Finite Element Method
Authors: Sim On , Ryu Dongman , Lee Junghwan , Lee Chiseung
Published: October 2022

Most studies on the ossification of the posterior longitudinal ligament (OPLL) using the finite element method were conducted in the neutral state, and the resulting decompression was judged to be good. As these studies do not reflect the actual behavior of the cervical spine, this study conducted an analysis in the neutral state and a biomechanical analysis during flexion and extension behaviors. After validation via the construction of an intact cervical spine model, the focal OPLL model was inserted into the C4–C5 segment and a simulation was performed. The neutral state was shown by applying a fixed condition to the lower part of the T1 and Y-axis fixed condition of the spinal cord and simulating spinal cord compression with OPLL. For flexion and extension simulation, a ±30-degree displacement was additionally applied to the top of the C2 dens. Accordingly, it was confirmed that spinal cord decompression did not work well during the flexion and extension behaviors, but rather increased. Thus, if patients with focal OPLL inevitably need to undergo posterior decompression, additional surgery using an anterior approach should be considered.
Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks
Authors: Esmaeili Fatemeh , Cassie Erica , Nguyen Hong Phan T. , Plank Natalie O. V. , Unsworth Charles P. , Wang Alan
Published: October 2022

Nanomaterial-based aptasensors are useful devices capable of detecting small biological species. Determining suitable signal processing methods can improve the identification and quantification of target analytes detected by the biosensor and consequently improve the biosensor’s performance. In this work, we propose a data augmentation method to overcome the insufficient amount of available original data and long short-term memory (LSTM) to automatically predict the analyte concentration from part of a signal registered by three electrochemical aptasensors, with differences in bioreceptors, analytes, and the signals’ lengths for specific concentrations. To find the optimal network, we altered the following variables: the LSTM layer structure (unidirectional LSTM (LSTM) and bidirectional LSTM (BLSTM)), optimizers (Adam, RMSPROP, SGDM), number of hidden units, and amount of augmented data. Then, the evaluation of the networks revealed that the highest original data accuracy increased from 50% to 92% by exploiting the data augmentation method. In addition, the SGDM optimizer showed a lower performance prediction than that of the ADAM and RMSPROP algorithms, and the number of hidden units was ineffective in improving the networks’ performances. Moreover, the BLSTM nets showed more accurate predictions than those of the ULSTM nets on lengthier signals. These results demonstrate that this method can automatically detect the analyte concentration from the sensor signals.