bluuebunny/crossref_metadata_2025 huggingface dataset:
Season of birth and vulnerability to the pathology of Alzheimer's disease: an in vivo positron emission tomography study
Authors: Yasuno Fumihiko , Minami Hiroyuki ,   Alzheimer's Disease Neuroimaging Initiative
Published: April 2022

Background

This study used positron emission tomography to examine whether the seasonal birth effect as an exogenic indicator of early life environmental factors influenced vulnerability to Alzheimer's disease (AD) pathology in the elderly.

Methods

We analysed datasets from the Alzheimer's Disease Neuroimaging Initiative, which included the data for 234 cognitively normal (CN) individuals and patients with mild cognitive impairment (MCI) (n = 114) and AD dementia (n = 38). As an index of amyloid β (Aβ)/tau accumulation, the 18F‐AV‐45‐ and 18F‐AV‐1451‐standardized uptake value ratios (SUVRs) were compared between groups of spring‐to‐summer births and fall‐to‐winter births by analysis of covariance. In addition, a multiple linear regression analysis was performed to determine whether the season of birth was a predictor of 18F‐AV‐45 and/or 18F‐AV‐1451 SUVRs, for which a difference was observed.

Results

Seasonal birth difference was a good predictor of 18F‐AV‐1451 SUVR. We found that participants with a fall‐to‐winter birth showed lower 18F‐AV‐1451 SUVRs than those with a spring‐to‐summer birth in both the CN and MCI/AD groups, after correcting for the effect of age, sex, years of education, and Alzheimer's Disease Assessment Scale cognitive subscale score, that could possibly affect tau accumulation.

Conclusions

Participants with a fall‐to‐winter birth showed less tau accumulation than those with a spring‐to‐summer birth after accounting for the factors that could affect tau accumulation. Our findings showed a vulnerability to tau pathology in participants with a fall‐to‐winter birth, which may be caused by perinatal or postnatal brain damage due to the risk factors associated with the cold season.
Interactive effects of allelopathy and arbuscular mycorrhizal fungi on the competition between the invasive species Bidens alba and its native congener Bidens biternata
Authors: Cheng Jun‐Kang , Cao Meng‐Yan , Yang Heng‐Rong , Yue Mao‐Feng , Xin Guo‐Rong , Chen Bao‐Ming
Published: April 2022

Allelopathy is an invasion driver, and arbuscular mycorrhizal fungi (AMF) also play an important role in plant invasion. However, little is known about their interactive effects on plant invasion mechanisms. In this study, we have investigated the interactive effects of allelopathy and AMF on competition between the exotic invasive species, Bidens alba, and its native congener, Bidens biternata. A two‐factor (allelopathy, AMF) common garden experiment was conducted using sterilised soil with or without AMF inoculation and with or without activated carbon (AC) addition. The results showed that both AC and AMF had significant effects on the relative yield (RY) of invasive species and competitive aggressivity (AG) between invasive and native species. Both AC and AMF exerted interactive effects on the AG. Activated carbon significantly reduced AG (0.86 vs 0.15%) without AMF, while it had no significant effects on AG with AMF. Moreover, AC significantly reduced the mycorrhizal dependency of the native species (108.06% vs 23.62%) but did not affect the invasive species. The AMF increased the variety of organic compounds in the aqueous extracts of the rhizosphere soil of invasive species, regardless of the competition. Overall, allelopathy significantly increased the mycorrhizal dependency of the native species, while it promoted the competitive ability of the invader regardless of AMF inoculation. Conversely, AMF reduced the facilitation of allelopathy in the AG of the invasive species. These results suggest that AMF play a critical role in improving the resistance of the native species to the invader by mediating allelopathy.
Generalized Formulation for the Behavior of Geometrically Curved and Twisted Three-Dimensional Timoshenko Beams and Its Isogeometric Analysis Implementation
Authors: Yin Hao , Lale Erol , Cusatis Gianluca
Published: April 2022

This article presents a novel derivation for the governing equations of geometrically curved and twisted three-dimensional Timoshenko beams. The kinematic model of the beam was derived rigorously by adopting a parametric description of the axis of the beam, using the local Frenet–Serret reference system, and introducing the constraint of the beam cross ection planarity into the classical, first-order strain versus displacement relations for Cauchy’s continua. The resulting beam kinematic model includes a multiplicative term consisting of the inverse of the Jacobian of the beam axis curve. This term is not included in classical beam formulations available in the literature; its contribution vanishes exactly for straight beams and is negligible only for curved and twisted beams with slender geometry. Furthermore, to simplify the description of complex beam geometries, the governing equations were derived with reference to a generic position of the beam axis within the beam cross section. Finally, this study pursued the numerical implementation of the curved beam formulation within the conceptual framework of isogeometric analysis, which allows the exact description of the beam geometry. This avoids stress locking issues and the corresponding convergence problems encountered when classical straight beam finite elements are used to discretize the geometry of curved and twisted beams. Finally, this article presents the solution of several numerical examples to demonstrate the accuracy and effectiveness of the proposed theoretical formulation and numerical implementation.
Immunospecific analysis of in vitro and ex vivo surface-immobilized protein complex
Authors: Sask Kyla N. , Thong Bruce , Goodarzynejad Negar , Berry Leslie R. , Chan Anthony K. C.
Published: April 2022

Biomaterials used for blood contacting devices are inherently thrombogenic. Antithrombotic agents can be used as surface modifiers on biomaterials to reduce thrombus formation on the surface and to maintain device efficacy. For quality control and to assess the effectiveness of immobilization strategies, it is necessary to quantify the surface-immobilized antithrombotic agent directly. There are limited methods that allow direct quantification on device surfaces such as catheters. In this study, an enzyme immunoassay (EIA) has been developed to measure the density of a synthetic antithrombin-heparin (ATH) covalent complex immobilized on a catheter surface. The distribution of the immobilized ATH was further characterized by an immunohistochemical assay. This analyte-specific EIA is relatively simple and has high throughput, thus providing a tool for quantitative analysis of biomaterial surface modifications. These methods may be further modified to evaluate plasma proteins adsorbed and immobilized on various biomaterial surfaces of complex shapes, with a range of bioactive functionalities, as well as to assess conformational changes of proteins using specific antibodies.
Robust shallow water reverberation reduction methods based on low-rank and sparsity decomposition
Authors: Zhu Yunchao , Duan Rui , Yang Kunde
Published: April 2022

Using the characteristics of low rank for reverberation and sparsity for the target echo in multi-ping detection, the low-rank and sparsity decomposition method can effectively reduce reverberation. However, in the case of highly sparse reverberation or a stationary target, the distinctions in the characteristics between the reverberation and target echo become ambiguous. As a result, the reverberation reduction performance is degraded. To guarantee a meaningful decomposition based on the random orthogonal model and random sparsity model, the identifiability condition (IC) for the decomposition was derived from the perspective of the low-rank matrix and sparse matrix, respectively. According to the IC, sparsity compensation for the low-rank matrix was proposed to address the false alarm probability inflation (FAPI) induced by highly sparse reverberation. In addition, increasing the dimension of the sparse matrix was also proposed to manage the detection probability shrinkage caused by a stationary target. The robust reverberation reduction performance was validated via simulations and field experiments. It is demonstrated that FAPI can be eliminated by increasing the sparse coefficient of the low-rank matrix to 0.30 and a stationary target could be detected with a large ping number, i.e., a high dimension, of the sparse matrix.
Cell surface–tethered IL-12 repolarizes the tumor immune microenvironment to enhance the efficacy of adoptive T cell therapy
Authors: Jones Douglas S. , Nardozzi Jonathan D. , Sackton Katharine L. , Ahmad Gulzar , Christensen Esben , Ringgaard Lars , Chang De-Kuan , Jaehger Ditte E. , Konakondla Jacob V. , Wiinberg Martin , Stokes Kate L. , Pratama Alvin , Sauer Karsten , Andresen Thomas L.
Published: April 2022

Immune-activating cytokines such as interleukin-12 (IL-12) hold strong potential for cancer immunotherapy but have been limited by high systemic toxicities. We describe here an approach to safely harness cytokine biology for adoptive cell therapy through uniform and dose-controlled tethering onto the surface of the adoptively transferred cells. Tumor-specific T cells tethered with IL-12 showed superior antitumor efficacy across multiple cell therapy models compared to conventional systemic IL-12 coadministration. Mechanistically, the IL-12–tethered T cells supported a strong safety profile by driving interferon-γ production and adoptively transferred T cell activity preferentially in the tumor. Immune profiling revealed that the tethered IL-12 reshaped the suppressive tumor immune microenvironment, including triggering a pronounced repolarization of monocytic myeloid-derived suppressor cells into activated, inflammatory effector cells that further supported antitumor activity. This tethering approach thus holds strong promise for harnessing and directing potent immunomodulatory cytokines for cell therapies while limiting systemic toxicities.
Escalating carbon emissions from North American boreal forest wildfires and the climate mitigation potential of fire management
Authors: Phillips Carly A. , Rogers Brendan M. , Elder Molly , Cooperdock Sol , Moubarak Michael , Randerson James T. , Frumhoff Peter C.
Published: April 2022

Wildfires in boreal forests release large quantities of greenhouse gases to the atmosphere, exacerbating climate change. Here, we characterize the magnitude of recent and projected gross and net boreal North American wildfire carbon dioxide emissions, evaluate fire management as an emissions reduction strategy, and quantify the associated costs. Our results show that wildfires in boreal North America could, by mid-century, contribute to a cumulative net source of nearly 12 gigatonnes of carbon dioxide, about 3% of remaining global carbon dioxide emissions associated with keeping temperatures within the Paris Agreement’s 1.5°C limit. With observations from Alaska, we show that current fire management practices limit the burned area. Further, the costs of avoiding carbon dioxide emissions by means of increasing investment in fire management are comparable to or lower than those of other mitigation strategies. Together, our findings highlight the climate risk that boreal wildfires pose and point to fire management as a cost-effective way to limit emissions.
COVID-19 vaccine hesitancy in eight European countries: Prevalence, determinants, and heterogeneity
Authors: Steinert Janina I. , Sternberg Henrike , Prince Hannah , Fasolo Barbara , Galizzi Matteo M. , Büthe Tim , Veltri Giuseppe A.
Published: April 2022

We examine heterogeneity in COVID-19 vaccine hesitancy across eight European countries. We reveal striking differences across countries, ranging from 6.4% of adults in Spain to 61.8% in Bulgaria reporting being hesitant. We experimentally assess the effectiveness of different messages designed to reduce COVID-19 vaccine hesitancy. Receiving messages emphasizing either the medical benefits or the hedonistic benefits of vaccination significantly increases COVID-19 vaccination willingness in Germany, whereas highlighting privileges contingent on holding a vaccination certificate increases vaccination willingness in both Germany and the United Kingdom. No message has significant positive effects in any other country. Machine learning–based heterogeneity analyses reveal that treatment effects are smaller or even negative in settings marked by high conspiracy beliefs and low health literacy. In contrast, trust in government increases treatment effects in some groups. The heterogeneity in vaccine hesitancy and responses to different messages suggests that health authorities should avoid one-size-fits-all vaccination campaigns.
The Use of Disinfectant in Barn Cleaning Alters Microbial Composition and Increases Carriage of
Campylobacter jejuni
in Broiler Chickens
Authors: Fan Yi , Forgie Andrew J. , Ju Tingting , Marcolla Camila , Inglis Tom , McMullen Lynn M. , Willing Benjamin P. , Korver Douglas R.
Published: April 2022

We compared the effects of barn FD and WW methods on gut microbial community structures and pathogen prevalence of broiler chickens in a nonchallenging commercial production setting. The results revealed that barn cleaning methods had little impact on the 30-day body weight and mortality rate of broiler chickens.
Involvement of a Phage-Encoded Wzy Protein in the Polymerization of K127 Units To Form the Capsular Polysaccharide of Acinetobacter baumannii Isolate 36-1454
Authors: Arbatsky Nikolay P. , Kasimova Anastasiya A. , Shashkov Alexander S. , Shneider Mikhail M. , Popova Anastasiya V. , Shagin Dmitry A. , Shelenkov Andrey A. , Mikhailova Yuliya V. , Yanushevich Yurii G. , Hall Ruth M. , Knirel Yuriy A. , Kenyon Johanna J.
Published: April 2022

Bacteriophage therapy is an attractive innovative treatment for infections caused by extensively drug resistant
Acinetobacter baumannii
, for which there are few effective antibiotic treatments remaining. Capsular polysaccharide (CPS) is a primary receptor for many lytic bacteriophages, and thus knowledge of the chemical structures of CPS produced by the species will underpin the identification of suitable phages for therapeutic cocktails.
Supplemental Material: Triple oxygen isotope evidence for a hot Archean ocean
Authors: McGunnigle Jesse , Sharp Zachary , al. et
Published: April 2022

Sample descriptions, analytical methods, definitions, isotope data, and a description of the mass balance model. 
Trophic Relations between Native Salvelinus malma Walb. and Introduced Oncorhynchus nerka Walb. in the Landlocked Lake Sevo, Kamchatka
Authors: Busarova O. Yu. , Esin E. V. , Markevich G. N.
Published: April 2022

Fish introductions are known to be dangerous due to the unexpected spread of diseases and disorganization of the food web. In 1986, the resident sockeye salmon Oncorhynchus nerka from Kronotskoye Lake was introduced into an isolated lake in Kamchatka inhabited by the native lacustrine Dolly Varden charr Salvelinus malma. Thirty years later, we analyzed the stomach content, stable isotope ratio and lipid fractions in muscles, as well as the parasite fauna of fish to assess the trophic relationships between the two species. It has been found that the sockeye salmon has occupied the planktivorous niche; no diversification into trophic forms depending on the number of gill rakers, like in the ancestral population, was detected. The indigenous benthivorous S. malma is represented by three trophic groups. Mature dwarf individuals, as well as the juveniles, mainly consume insect larvae on the lake slope; large fish feed on amphipods in the same habitats; and 10% of the large Dolly Varden switches to facultative piscivory. Predators die as a result of Diphyllobothrium sp. hyper invasion, which infects fish when feeding on the sockeye salmon. The diversity of parasite fauna is 3/4 poorer in the introduced sockeye in comparison with the donor population; part of the parasite species is obtained by the sockeye from the Dolly Varden.
Tissue-resident memory T cells in gastrointestinal cancer immunology and immunotherapy: ready for prime time?
Authors: Abdeljaoued Syrine , Arfa Sara , Kroemer Marie , Ben Khelil Myriam , Vienot Angélique , Heyd Bruno , Loyon Romain , Doussot Alexandre , Borg Christophe
Published: April 2022

Tissue-resident memory T (TRM) cells have emerged as immune sentinels that patrol the tissue microenvironment and orchestrate localized antitumor immunity in various solid cancers. Recent studies have revealed that TRM cells are key players in cancer immunosurveillance, and their involvement has been linked to favorable responses to immunotherapy as well as general better clinical outcome in cancer patients. In this review, we provide an overview of the major advances and recent findings regarding TRM cells phenotype, transcriptional and epigenetic regulation in cancer with a special focus on gastrointestinal tumors. Finally, we highlight the exciting clinical implication of TRM cells in these types of tumors.
A correction for serial nonindependence in mountain pine beetle aerial survey data to reduce overestimation of cumulative damage
Authors: Goodsman Devin W. , Weber Jim
Published: April 2022

At the beginning of this century, an unprecedented mountain pine beetle ( Dendroctonus ponderosae Hopkins) outbreak expanded across western North America. Methods are needed to quantify its impact on forests. Although aerial survey data of infestation are readily available, their translation to tree mortality estimates is challenging because the polygons sketched by aerial surveyors to delimit infested regions often overlap from one year to the next, which produces a lack of independence in yearly estimates of infested landscape. This problem manifests when annual proportions of infested land are summed over years because the sum often exceeds one. Cumulative proportions of infested land larger than one implies overestimation of tree mortality if initial densities of available host trees are multiplied by annual proportions of infested landscape. To address this problem, we developed a probabilistic method for correcting for nonindependence in proportions of infested land recorded using overlapping polygons. We demonstrate our approach in Jasper National Park where the probabilistic correction reduces overestimation bias in regions that were infested multiple years when predictions are compared to validation data. The approach and the spatial raster data sets we generated will be useful to forest managers seeking to quantify the impact of mountain pine beetles.
Reliability and interpretation of the microstructural parameters determined by X-ray line profile analysis for nanostructured materials
Authors: Gubicza Jenő
Published: April 2022

In this overview, the applicability of X-ray diffraction line profile analysis (XLPA) for the characterization of the microstructure in nanostructured materials is overviewed. The dislocation densities obtained by whole pattern fitting and from the breadth of the diffraction peaks are compared. Both theoretical considerations and experimental evidences prove that the evaluation of the peak breadth solely is not suitable for the determination of the dislocation density. In addition, the microstructural parameters determined by XLPA were compared to the values obtained directly by microscopic methods. It was found that the ratio of the grain size obtained by microscopy and the crystallite size determined by XLPA decreases with the reduction of the grain size in nanomaterials, and below $$\sim $$
∼
20 nm, the two values agree within the experimental error. In addition, correlation between the microstructural parameters (e.g., crystallite size and dislocation density) determined by XLPA was not found. It was revealed that bottom–up processing methods can produce similarly high defect density in nanostructured materials as severe plastic deformation (SPD). The influence of stacking fault energy, melting point, and degree of alloying on the microstructure of nanomaterials is discussed in detail.
Clinical Efficacy and Safety of Chinese Patent Medicine Combined with Oseltamivir for the Treatment of Adult Influenza: A Systematic Review and Meta-Analysis
Authors: Li Xiaona , Liu Bin , Duan Naifan , Xiong Yibai , Zhang Yan , Zhang Chi , Lu Cheng , Li Li
Published: April 2022

Influenza is a sudden and serious viral breathing and lung-related infectious disease that causes significant deadliness and death worldwide. Now, the international treatment is oseltamivir. Chinese patent medicine (CPM) as a kind of different therapy is used in the treatment of influenza in China. The aim of this study was to interpret the clinical efficacy and safety of CPM combined with oseltamivir in the treatment of adult influenza by reviewing all relevant randomized controlled trials, and to provide new ideas and methods for the treatment of influenza. PubMed, Embase, Cochrane Library, SinoMed, CNKI, and Wanfang Database were searched from the date of beginning until 1 June 2021, for the references on treatment of influenza with CPM. According to standard information extraction tables, two people worked to find and aggregate information independently. Review Manager 5.2 was used to study data carefully and evaluate risk of bias. A total of nine trials of 906 patients were included. Based on the meta-analysis, compared to oseltamivir, CPM combined with oseltamivir had better effect in the time of defervescence [MD = –17.68, 95% CI (–25.93, –9.44), P < 0.0001], the time of symptom improvement [MD = –22.28, 95% CI (–26.77, –17.80), P < 0.00001], and the time of hospitalization [MD = –2.04, 95% CI (–3.45, –0.63), P = 0.005]. Related to safety [RR = 0.69, 95% CI (0.38, 1.23), P = 0.21], the experimental group had fewer adverse reactions than the control group, but there is no statistical significance. The findings show that CPM combined with oseltamivir in adult influenza has a better efficacy in shortening the time of defervescence and symptom improvement, reducing the time of hospitalization. However, publication bias is inevitable due to the low methodological quality check of the clinical research about diagnostic criteria, definition of adult influenza, and small number of articles, and further large sample sizes and multi-center clinical trials are needed to give better proof for its efficacy and safety.
ASE4Games 2021 Workshop Summary
Authors: Cooper Kendra M. L. , Petrillo Fabio , Guéhéneuc Yann-Gaël , Politowski Cristiano
Published: April 2022

The first edition of the workshop on Automated Software Engineering For Games (ASE4Games 2021) was held virtually on November 14th, 2021, co-located with the 36v IEEE/ACM International Conference on Automated Software Engineering (ASE 2021). Five papers from all over the world were submitted, two full-papers and two short-papers were accepted. The program also featured a keynote by Mathieu Nayrolles, researcher at Ubisoft, entitled Automated Software Engineering for AAA Games.
NLP-SEA 2021 Workshop Summary
Authors: Anwar Sajid , Rauf Abdul , Ramzan Muhammad , Razzak Imran , Saadatmad Mehrdad
Published: April 2022

Inherent strengths and capabilities in Natural Language Processing (NLP), such as topics modeling, content categorization, context extraction, sentiment analysis, and text-speech conversion, have helped in rapid growth. At the same time, this rapid growth has led to numerous challenges for software development engineers both on a technical and managerial level. NLP has changed the way we interact with computers, and it'll continue doing so in the future. These technologies, mainly based on artificial intelligence (AI) and Machine Learning (ML), would be the underlying force for transformation from data-driven to intelligence-driven endeavors, as they shape and improve communication technology in the years to come. The literature and research communities are observing the same technological advancements in the domain of software engineering introduced by the use of NLP techniques in conjunction with Artificial Intelligence. Thus, the NLP-SEA (Software Engineering Automation: A Natural Language Perspective) series of workshops is a means to contribute to the advancement and furthering of knowledge in this direction. The focus of this workshop is to provide an opportunity for the researchers and practitioners in academia and industry to provide a diverse, but significant body of research contributions on every aspect related to the application of NLP in the automated software and system engineering domain.
The Philosopher's Corner
Authors: Monod Emmanuel , Boland Richard , Santoro Flavia , Joyce Elisabeth
Published: April 2022

Heidegger's philosophy has long been one of the core references for information systems (IS) researchers, often drawing upon one of two distinct perspectives he employed in his writings. The first of these perspectives is found in Heidegger's work on presence, and the second in his work on relations. We refer to these two as his presence perspective and his relation perspective. The presence perspective builds on ideas developed in early sections of his major work, Being and Time (1927:1996), contrasting the presence of being-in-the-world with that of readiness-at-hand. The relation perspective builds on ideas developed in later sections of Being and Time, elaborating on the concept of being-with [Mitsein]. We discuss the ways these two key perspectives in his philosophy are intertwined and reinforce each other when we consider them together, and how they amplify and extend each other's scope of effect in human situated practice. We argue that Heidegger's philosophical perspective on human existence is a powerful theoretical lens for understanding both the subjective and the objective poles of our experience of being in the world. It provides an integrated framework for research on both poles that is useful across the IS field.
Performance Projection of a High-Temperature CO2 Transport Membrane Reactor for Combined CO2 Capture and Methane-to-Ethylene Conversion
Authors: Li Xin , Huang Kevin , Van Dam Noah , Jin Xinfang
Published: April 2022

Direct conversion of methane into ethylene through the oxidative coupling of methane (OCM) is a technically important reaction. However, conventional co-fed fixed-bed OCM reactors still face serious challenges in conversion and selectivity. In this paper, we apply a finite element model to simulate OCM reaction in a plug-flow CO2/O2 transport membrane (CTM) reactor with a directly captured CO2 and O2 mixture as a soft oxidizer. The CTM is made of three phases: molten carbonate, 20% Sm-doped CeO2, and LiNiO2. The membrane parameters are first validated by CO2/O2 flux data obtained from CTM experiments. The OCM reaction is then simulated along the length of tubular plug-flow reactors filled with a La2O3-CaO-modified CeO2 catalyst bed, while a mixture of CO2/O2 is gradually added through the wall of the tubular membrane. A 12-step OCM kinetic mechanism is considered in the model for the catalyst bed and validated by data obtained from a co-fed fixed-bed reactor. The modeled results indicate a much-improved OCM performance by membrane reactor in terms of C2-yield and CH4 conversion rate over the state-of-the-art, co-fed, fixed-bed reactor. The model further reveals that improved performance is fundamentally rooted in the gradual methane conversion with CO2/O2 offered by the plug-flow membrane reactor.
The Effect of LiFePO4 Particle Size and Surface Area on the Performance of LiFePO4/Graphite Cells
Authors: Logan E. R. , Eldesoky A. , Liu Y. , Lei Min , Yang Xinhe , Hebecker H. , Luscombe A. , Johnson Michel B. , Dahn J. R.
Published: April 2022

In an effort to better understand capacity loss mechanisms in LiFePO4 (LFP)/graphite cells, this work considers carbon-coated LFP materials with different surface area and particle size. Cycling tests at room temperature (20 °C) and elevated temperatures show more severe capacity fade in cells with lower surface area LFP material. Measurements of Fe deposition on the negative electrode using micro X-ray fluorescence (μXRF) spectroscopy reveal more Fe on the graphite electrode from cells with low surface area. Measurements of parasitic heat flow using isothermal microcalorimetry show marginally higher parasitic heat flow in cells with low surface area. Cross-sectional SEM images of aged LFP electrodes show micro-fracture generation in large LFP particles, which are more prevalent in the low surface area material. Further, studies on the impact of vacuum drying procedures show that while Fe deposition can be inhibited by removing excess water contamination, the direct impact of Fe deposition on capacity fade is small. Despite the observed particle cracking, differential voltage analysis on aged cells suggested active material loss was not significant, leading to the conclusion that LFP particle fracture instead increases parasitic reaction rates leading to Li inventory loss.
Investigation of the Electrochemical Reduction of Porous CaTiO3 Pellets in CaCl2%–0.5%CaO Molten Salt
Authors: Zhao Kun , Liu Kejia
Published: April 2022

The formation of CaTiO3 during the electro-reduction of TiO2 pellets in CaCl2-based molten salts is unavoidable. So in present paper, the electro-deoxidation of porous CaTiO3 pellets was investigated. Cyclic voltammetry and constant potential electrolysis experiments were conducted to gain insight into the electro-deoxidation mechanism. From the results, two new reduction pathways of the porous CaTiO3 pellets were determined. The main factor influencing the reduction pathways is the porosity. When the porosity is low, CaTiO3 is directly reduced into TiO. With the porosity increases, some CaTiO3 will firstly be reduced into CaTi2O4 and then the CaTi2O4 is further reduced into TiO. The effect of the potential applied on the cathode on the possible reduction limit of the sample was discussed. The results indicated that the more negative the potential is, the faster the deoxidation rate is and the lower the deoxidation limit is. Only when the applied potential is more negative than −1.6 V vs Ag+/Ag, low-oxygen titanium (the oxygen content is lower than 1%) can be obtained in our experimental conditions. In addition, the microstructural kinetics of phase transformations during reduction of the CaTiO3 pellets were also discussed.
k
Nearest Neighbor Similarity Join Algorithm on High-Dimensional Data Using Novel Partitioning Strategy
Authors: Ma Youzhong , Hua Qiaozhi , Wen Zheng , Zhang Ruiling , Zhang Yongxin , Li Haipeng
Published: April 2022

k

nearest neighbor similarity join on high-dimensional data has broad applications in many fields; several key challenges still exist for this task such as “curse of dimensionality” and large scale of the dataset. A new dimensionality reduction scheme is proposed by using random projection technique, then we design two novel partition strategies, including equal width partition strategy and distance split tree-based partition strategy, and finally, we propose

k

nearest neighbor join algorithm on high-dimensional data based on the above partition strategies. We conduct comprehensive experiments to test the performance of the proposed approaches, and the experimental results show that the proposed methods have good effectiveness and performance.
Classification of 3D CAD Models considering the Knowledge Recognition Algorithm of Convolutional Neural Network
Authors: Wang Weiwei , Sun Dandan
Published: April 2022

In order to improve the classification effect of the 3D CAD model, this paper combines the knowledge recognition algorithm of convolutional neural network to construct the 3D CAD model classification model. Moreover, this paper analyzes the application of the convolutional neural network algorithm in the classification of 3D CAD models, improves the knowledge recognition algorithm, and builds the basic structure of the convolutional neural network. In addition, this paper builds an intelligent recognition model with the support of the improved algorithm, designs experiments to verify the effect of this paper, and draws a visual display statistical graph based on the statistical test results. Through experimental research, it can be known that the 3D CAD classification model based on the knowledge recognition algorithm of convolutional neural network proposed in this paper can effectively improve the classification effect of the CAD model.
Application of Multimodal NLP Instruction Combined with Speech Recognition in Oral English Practice
Authors: Xu Jin , Li Tong
Published: April 2022

In order to study the application of multimodal NLP instruction combined with speech recognition based on hybrid deep learning in oral English practice, firstly, the basic principle of speech recognition technology is introduced. The concept of hidden Markov model and three key algorithms are explained, and its simulation and implementation in speech recognition application are realized. The architecture and key technologies of the system are introduced. Then, it introduces the specific application of deep learning in NLP. Finally, Chinese teachers with oral English teaching experience participate in the recording. The effective reading time of each person is 65 minutes, and the reading sentences are 3100 sentences. The total number of people is 80 (40 men and 40 women). The sentences cover 1595 spoken English words. Conduct oral English training. The experimental results show that the recognition accuracy decreases by about 2%, but the recognition speed increases by 10 times. In addition, the scoring accuracy is equivalent to that of the platform system. The accuracy of this method in instruction classification is increased, which verifies the feasibility and effectiveness of this method. In the future, attention mechanism will be used to expand this method.
Roadside LiDAR Vehicle Detection and Tracking Using Range and Intensity Background Subtraction
Authors: Zhang Tianya , Jin Peter J.
Published: April 2022

In this study, we developed the solution of roadside LiDAR object detection using a combination of two unsupervised learning algorithms. The 3D point clouds are firstly converted into spherical coordinates and filled into the elevation-azimuth matrix using a hash function. After that, the raw LiDAR data were rearranged into the new data structure to store the information of range, azimuth, and intensity. Then, the dynamic mode decomposition method is applied to decompose the LiDAR data into low-rank backgrounds and sparse foregrounds based on intensity channel pattern recognition. The coarse-fine triangle algorithm (CFTA) automatically finds the dividing value to separate the moving targets from static background according to range information. After intensity and range background subtraction, the foreground moving objects will be detected using a density-based detector and encoded into the state-space model for tracking. The output of the proposed solution includes vehicle trajectories that can enable many mobility and safety applications. The method was validated at both path and point levels and outperformed the state of the art. In contrast to the previous methods that process directly on the scattered and discrete point clouds, the dynamic classification method can establish the less sophisticated linear relationship of the 3D measurement data, which captures the spatial-temporal structure that we often desire.
Research Progress of Machine Learning and Deep Learning in Intelligent Diagnosis of the Coronary Atherosclerotic Heart Disease
Authors: Lu Haoxuan , Yao Yudong , Wang Li , Yan Jianing , Tu Shuangshuang , Xie Yanqing , He Wenming
Published: April 2022

The coronary atherosclerotic heart disease is a common cardiovascular disease with high morbidity, disability, and societal burden. Early, precise, and comprehensive diagnosis of the coronary atherosclerotic heart disease is of great significance. The rise of artificial intelligence technologies, represented by machine learning and deep learning, provides new methods to address the above issues. In recent years, artificial intelligence has achieved an extraordinary progress in multiple aspects of coronary atherosclerotic heart disease diagnosis, including the construction of intelligent diagnostic models based on artificial intelligence algorithms, applications of artificial intelligence algorithms in coronary angiography, coronary CT angiography, intravascular imaging, cardiac magnetic resonance, and functional parameters. This paper presents a comprehensive review of the technical background and current state of research on the application of artificial intelligence in the diagnosis of the coronary atherosclerotic heart disease and analyzes recent challenges and perspectives in this field.
An Advanced Deep Attention Collaborative Mechanism for Secure Educational Email Services
Authors: Chen Yanfang , Yang Yongzhao
Published: April 2022

The COVID-19 crisis has once again highlighted the vulnerabilities of some critical areas in cyberspace, especially in the field of education, as distance learning and social distance have increased their dependence on digital technologies and connectivity. Many recent cyberattacks on e-learning systems, educational content services, and trainee management systems have created severe demands for specialized technological solutions to protect the security of modern training methods. Email is one of the most critical technologies of educational organizations that are attacked daily by spam, phishing campaigns, and all kinds of malicious programs. Considering the efforts made by the global research community to ensure educational processes, this study presents an advanced deep attention collaborative filter for secure academic email services. It is a specialized application of intelligent techniques that, for the first time, examines and models the problem of spam as a system of graphs where collaborative referral systems undertake the processing and analysis of direct and indirect social information to detect and categorize spam emails. In this study, nonnegative matrix factorization (NMF) is applied to the social graph adjacent table to place users in one (or more) overlapping communities. Also, using a deep attention mechanism, it becomes personalized for each user. At the same time, with the introduction of exponential random graph models (ERGMs) in the process of factorization, local dependencies are significantly mitigated to achieve the revelation of malicious communities. This methodology is being tested successfully in implementing mail protection systems for educational organizations. According to the findings, the proposed algorithm outperforms all other compared algorithms in every metric tested.
Effect of Nano Ground Granulated Blast Furnace Slag (GGBS) Volume % on Mechanical Behaviour of High‐Performance Sustainable Concrete
Authors: Srikanth Seelam , Krishna Chunchu Bala Rama , Srikanth T. , Sai Nitesh K. J. N. , Nadh V. Swamy , Kumar Sanjeev , Thanappan Subash
Published: April 2022

Utilization of various mineral admixtures in producing mortar decreases the porosity and capillarity, hence improves the durability in opposition to water and competitive solutions. In this research work, Ground Granulated Blast Furnace Slag is used to replace 30 percent, 60 percent, and 70% of ordinary Portland cement (OPC) (GGBFS). Mechanical property (compressive strength) and durability properties (permeability, porosity, and sorptivity) of high‐performance concrete (HPC) are tested. Water permeability of M85 is measured using three cell permeability apparatus. Compressive strength, porosity, and sorptivity of the same mixes are also found. According to the test results of HPC, 30% replacement level of GGBFS gives higher compressive strength than 60% and 70% replacement levels of GGBFS. An equation is developed for permeability of HPC based on mechanical strength and porosity. It is found that coefficient of permeability of water for HPC mixes ranges from 5.1 × 10‐11 cm/sec to 7.8 × 10‐11 cm/sec. It is concluded that 30% GGBFS used in HPC produces less porosity, less permeability, and less sorptivity than compared to other replacement levels.
Dynamics of a Stochastic Epidemic Model with Vaccination and Multiple Time‐Delays for COVID‐19 in the UAE
Authors: Alsakaji H. J. , Rihan F. A. , Hashish A.
Published: April 2022

In this paper, we study the dynamics of COVID‐19 in the UAE with an extended SEIR epidemic model with vaccination, time‐delays, and random noise. The stationary ergodic distribution of positive solutions is examined, in which the solution fluctuates around the equilibrium of the deterministic case, causing the disease to persist stochastically. It is possible to attain infection‐free status (extinction) in some situations, in which diseases die out exponentially and with a probability of one. The numerical simulations and fit to real observations prove the effectiveness of the theoretical results. Combining stochastic perturbations with time‐delays enhances the dynamics of the model, and white noise intensity is an important part of the treatment of infectious diseases.
Hierarchical and On-Demand Attack Defence Framework for IoT Devices
Authors: Sudhakaran Pradeep , Kaliyaperumal Manikannan , Senthilkumar T. , Jeya R. , Sowmiya B.
Published: April 2022

Internet of Things (IoT) devices are lightweight such that they generally possess low battery power. Hence, the chances of battery exhaustion and flooding attacks are more. In order to perform attack response actions against various attacks, this paper proposes Hierarchical and On-Demand Attack Defence Framework (HOAD) for IoT security. In this framework, primary (PC) and secondary controller (SC) nodes are deployed in the network along with the IoT devices. The SC scan be moved on-demand by the PC. The response agent at PC will first establish a new route via the SCs by excluding the intruders and the suspected nodes. Then, it will resend the stored packets to their destination via the newly established route. The proposed HOAD framework is implemented in NS2 and compared with the MECshield framework. Simulation results show that the HOAD has reduced end-to-end delay, increased packet delivery ratio, and increased residual energy.
Domain Transferred Image Recognition via Generative Adversarial Network
Authors: Hu Haoqi , Li Sheng , Qian Zhenxing , Zhang Xinpeng
Published: April 2022

Recent studies have demonstrated that neural networks exhibit excellent performance in information hiding and image domain transfer. Considering the tremendous progress that deep learning has made in image recognition, we explore whether neural networks can recognize the imperceptible image in the transferred domain. Our target is to transfer natural images into images that belong to a different domain, while at the same time, the attribute of natural images can be recognized on domain transferred images directly. To address this issue, we proposed domain transferred image recognition to achieve image recognition directly on the transferred images without the original images. In our proposed system, a generator is designed for the domain transfer and a recognizer is responsible for image recognition. To be flexible for the natural image restoration in some cases, we also incorporate an additional generator in our method. In addition, a discriminator will play an indispensable role in the image domain transfer. Finally, we demonstrate that our method can successfully identify the natural images on transferred images without access to original images.
Automatic Gray Image Coloring Method Based on Convolutional Network
Authors: Fan Jiayi , Xie Wentao , Ge Tiantian
Published: April 2022

Image coloring is a time-consuming and laborious work. For a work, color collocation is an important factor to determine its quality. Therefore, automatic image coloring is a topic with great research significance and application value. With the development of computer hardware, deep learning technology has achieved satisfactory results in the field of automatic coloring. According to the source of color information, this paper can divide automatic coloring methods into three types: image coloring based on prior knowledge, image coloring based on reference pictures, and interactive coloring. The coloring method can meet the needs of most users, but there are disadvantages such as users cannot get the multiple objects in a picture of different reference graph coloring. Aiming at this problem, based on the instance of color image segmentation and image fusion technology, the use of deep learning is proposed to implement regional mixed color more and master the method. It can be divided into foreground color based on reference picture and background color based on prior knowledge. In order to identify multiple objects and background areas in the image and fuse the final coloring results together, a method of image coloring based on CNN is proposed in this paper. Firstly, CNN is used to extract their semantic information, respectively. According to the extractive semantic information, the color of the designated area of the reference image is transferred to the designated area of the grayscale image. During the transformation, images combined with semantic information are input into CNN model to obtain the content feature map of grayscale image and the style feature map of reference image. Then, a random noise map is iterated to make the noise map approach the content feature map as a whole and the specific target region approach the designated area of the style feature map. Experimental results show that the proposed method has good effect on image coloring and has great advantages in network volume and coloring effect.
Synthesis and Characterization of New Magnetofluorescent Silicon Dot for Theranostic Application
Authors: Yousefieh Nourin , Khalatbari Farinaz , Ghoreishi Seyedeh Masoumeh , Seyedhamzeh Mohammad , Omoomi Farnoor Davachi , Saffari Mostafa , Ashrafi Sepehr , Ebrahimi Seyed Esmaeil Sadat , Mirzaei Mehdi , Ardestani Mehdi Shafiee
Published: April 2022

Introduction. Breast cancer is the leading cause of mortality for women, and effort is being made to provide an agent which has both imaging and therapeutic effects (theranostics) which can improve the survival, economic burden, and outcomes. Methods. In this research, for the first time, we introduce a new magnetophotoluminescent quantum dot (Mn‐Si‐QD) capable of DOX loading. Mn‐Si‐QD was synthesized and evaluated by FT‐IR, UV‐Vis, fluorescence, SEM, and TEM. The impacts of various factors such as reaction time and reaction temperature were investigated to reach the optimum fluorescence properties. Toxicity on normal and cancerous cells was evaluated with an MTT assay. Results. The characterization analysis confirmed successful synthesis, and the MTT assay showed no cytotoxicity on normal cells and showed toxicity on cancerous cells. Magnetopotential showed no difference when compared using OmniScan. In vivo fluorescence imaging was considered and indicated the acceptable (25% ID/g) uptake on the tumor region. The biodistribution study also confirmed the accumulation of the magnetophotoluminescent quantum dot in the tumor site. Conclusion. Our research indicated that DOX‐loaded Mn‐Si‐QD is both noninvasive and an agent that can be hopefully used in theranostics.