bluuebunny/crossref_metadata_2025 huggingface dataset:
Competition intensity varies with hardwood species identity and constrains stand-level productivity in southeastern pine–hardwood mixtures compared to loblolly pine monocultures
Authors: Willis John L. , Blazier Michael A.
Published: August 2022

We examined the performance of sweetgum ( Liquidambar styraciflua L.), cherrybark oak ( Quercus pagoda Raf.), and loblolly pine ( Pinus taeda L.) in two-species pine–hardwood mixtures (sweetgum : loblolly pine and cherrybark oak : loblolly pine) at various planting densities (1482–1976 trees per hectare (TPH)) over 23 years in northern Louisiana, USA. Species were planted in alternating rows with hardwood establishment occurring 1 year prior to loblolly pine. Mixtures were also compared to loblolly pine monocultures at a common density (1482 TPH) to assess whether mixing improved productivity. Sweetgum exerted more competitive pressure on loblolly pine than cherrybark oak. At final measurement, sweetgum survival statistically exceeded that of loblolly pine across mixture density. Moreover, sweetgum cumulative basal area and volume growth nearly tripled that of loblolly pine in balanced high-density mixtures (1976 TPH, 50 pine : 50 hardwood). In contrast, cherrybark oak basal area and volume growth did not significantly exceed loblolly pine at any density. At a common density (1482 TPH), loblolly pine monoculture cumulative basal area and volume significantly exceeded those of mixtures with sweetgum and cherrybark oak by 19% and 24%, respectively. Collectively, these results indicate that growing loblolly pine in mixture with these two species did not produce complementary interactions and negatively affected stand-level growth.
Dynamics of evolving cavity in cluster of stars
Authors: Manzoor Rubab , Mumtaz Saadia , Intizar Daoud
Published: August 2022

We examine the evolution of cavities within spherically symmetric cluster of stars in high curvature gravity. For this purpose, we usef(R) gravity through the Starobinsky model to incorporate dark matter effects in the discussion. In particular, we check the physical significance of thef(R) model by associating it with the observational data of stellar object 4U182030. For evolution of the cavity, we consider the purely areal evolutionary phase by assuming that the proper distance (in a radial direction) among neighboring stars remains constant. The analytical solutions are obtained among which a few solutions fulfill the Darmois conditions. It is found that the evolution of the cavity in a cluster of stars is highly controlled by the influence of dark matter.
Mode-Locked Fiber Ring Laser Using Graphene Nanoparticles as Saturable Absorbers
Authors: Thapa Sunil , Rahman Ashiq , Dutta Niloy K.
Published: August 2022

Mode locked fiber ring laser using graphene nanoparticles as saturable absorbers to compress the pulse width has been studied. An experimental method to demonstrate the generation of pulse train with 50 GHz repetition rate with an ultrashort pulse width has been demonstrated. This method uses a combination of rational harmonic mode-locking (RHML) and a saturable absorber in the fiber ring laser. The pulse width using saturable absorbers is shorter by about a factor of 2 compared to that without a saturable absorber. Pulse generation using fiber ring laser with a saturable absorber has been analyzed. The experimental results are in agreement with the results from the simulation.
Power Dissipation and Cell Area: Quaternary Logic CMOS Inverter vs. Four-State SWS-FET Inverter
Authors: Husawi A. , Saman B. , Almalki A. , Gudlavalleti R. , Jain F. C.
Published: August 2022

Power dissipation of spatial wave function switched (SWS) FETs four-state inverter is the focus of this paper. The current can flow through multiple channels in a (SWS)-FETs, which comprises vertically stacked quantum well/quantum dot channels. Using four voltage levels, the four-state SWS transistor inverter circuit is simulated. Cadence was used to perform SWS-FET inverter circuit simulations to evaluate power dissipation. To develop the SWS-FET model, an Analog Behavioral Model (ABM) and the Berkeley Short-Channel IGFET Model (BSIM4.6) were combined. To evaluate the inverter circuit’s transient behavior, a 0.18-μm technology node was used. The improvement in power dissipation, the frequency of operation, and reduction in cell area are all approximately is by a factor of 2-3. As a result, circuits based on SWS-FET inverters overcome the limitations of earlier implementations of 4-state logic circuits.
Novel Additive Manufacturing-Enabled RF Devices for 5G/mmWave, IoT, Smart Skins, and Wireless Sensing Applications
Authors: Soto-Valle Genaro , Hu Kexin , Holda Madeline , Cui Yepu , Tentzeris Manos
Published: August 2022

The recent developments in mmWave and Internet of Things (IoT) technologies have dramatically increased the interest and demand for radio frequency (RF) devices that can be used for applications such as smart cities, energy harvesting, and ubiquitous wireless sensor networks. Additive manufacturing technologies (AMT) plays an important role to support these applications, as they allows to significantly reduce fabrication costs and times while enabling the achievement of devices with more complex geometries and the possibility of using a wide variety of materials. This publication reviews recent developments of state-of-the-art wireless devices including reconfigurable antennas, frequency-selective surfaces and highly scalable phased arrays enabled by AMT capabilities. It also discusses the benefits of AMT in the fabrication of interconnects that are suitable for packaging of fully-integrated antennas.
Integrating QD-NVRAMs and QDC-SWS FET-Based Logic for Multi-Bit Computing
Authors: Jain F. , Gudlavalleti R. , Mays R. , Saman B. , Chandy J. , Heller E.
Published: August 2022

This paper presents FETs with vertically-stacked multiple quantum dot (QD) layers, comprising of GeOx cladded Ge quantum dots, serving as transport channel, floating gate in memory cell, and quantum dot gate that exhibit multi-state characteristics. The structures can be used as 8- and 16-state logic, a room-temperature alternative to sub-milliKelvin Si/SiGe qubits. In addition, they can be used as distributed NVRAMs with fast Write/Erase, SRAMs, and multi-state CMOS-X logic applications. Here, X symbolizes Multi-state CMOS compatible operations using SWS or QDC FETs or memories. The novelty includes: (i) coding of distinct states based on drain current changes in quantum dot channels via intra-sub band transitions and spatial location of carrier wavefunctions, (ii) integrating QDC-NVRAM cells with SRAM based cache, and multi-bit logic based computing.
Umbelliferone Inhibits Migration, Invasion and Inflammation of Rheumatoid Arthritis Fibroblast-Like Synoviocytes and Relieves Adjuvant-Induced Arthritis in Rats by Blockade of Wnt/β-Catenin Signaling Pathway
Authors: Cai Li , Zhou Meng-Yuan , Hu Shuang , Liu Fang-Yuan , Wang Meng-Qing , Wang Xiao-Hua , Jiang Fei , Feng Xiao-Wen , Liu Xue-Song , Li Rong
Published: August 2022

Umbelliferone (UMB), a natural coumarin compound, has been reported to possess anti-rheumatic effects on rheumatoid arthritis (RA) experimental models, but its potential role of UMB in regulating migration, invasion and inflammation of RA fibroblast-like synoviocytes (FLS) remain unclear. Herein, MTT assay was performed to confirm the non-cytotoxic concentrations (10, 20, and 40[Formula: see text][Formula: see text]M) and the treatment time (24[Formula: see text]h) of UMB on TNF-[Formula: see text]-stimulated RA FLS (MH7A cells) in vitro. Results of wound-healing, transwell and phalloidin staining assays revealed that UMB inhibited TNF-[Formula: see text]-induced migration, invasion and F-actin cytoskeletal reorganization in MH7A. Results of ELISA, western blot and gelatin zymography indicated that UMB decreased the productions of pro-inflammatory factors, including IL-1[Formula: see text], IL-6, IL-8, MMP-2 and MMP-9, and inhibited MMP-2 activity in TNF-[Formula: see text]-stimulated MH7A cells. In vivo, UMB (25[Formula: see text]mg/kg and 50[Formula: see text]mg/kg) relieved the joint damage and synovial inflammation in rats with adjuvant-induced arthritis (AIA). Mechanistically, UMB could suppress Wnt/[Formula: see text]-catenin signaling both in TNF-[Formula: see text]-induced MH7A cells and in AIA rat synovium, evidenced by decreasing Wnt1 protein level, activating GSK-3[Formula: see text] kinase by blocking GSK-3[Formula: see text] (Ser9) phosphorylation, and reducing the protein level and nuclear translocation of [Formula: see text]-catenin. Importantly, combined use of lithium chloride (a Wnt/[Formula: see text]-catenin signaling agonist) eliminated the inhibitory effects of UMB on migration, invasion and inflammation in vitro and the anti-arthritic effects of UMB in vivo. We concluded that UMB inhibited TNF-[Formula: see text]-induced migration, invasion and inflammation of RA FLS and attenuated the severity of rat AIA through its ability to block Wnt/[Formula: see text]-catenin signaling pathway.
A NEGATIVE BINOMIAL AUTOREGRESSION WITH A LINEAR CONDITIONAL VARIANCE-TO-MEAN FUNCTION
Authors: ALMOHAIMEED BADER S.
Published: August 2022

A general integer-valued time-series model with a conditional variance proportional to the conditional mean is proposed. Specifically, the conditional distribution is a Poisson mixture with a dependent mixing sequence, which results in a negative binomial distribution with a linear conditional variance-to-mean relationship. In addition, the conditional mean is specified as a general parametric function of past observations. We first propose stationarity, ergodicity, and finite moment conditions for the model. Furthermore, the parameters are estimated using the Poisson quasi-maximum likelihood estimate, whose asymptotic properties are studied under weak conditions. Illustrations of the proposed methodology on simulated and actual time series of counts are given.
The influence of geological structure on chalk karst aquifer function and its consequences for the protection of drinking-water supply; examples from Normandy, France
Authors: Hauchard Emmanuel , Turban Ludovic , Leclerq Emmanuel
Published: August 2022

The implementation of new groundwater tracer tests to the chalky karst plateau of the northwestern end of the Paris Basin, combined with a critical review of previous tracer test investigations, makes it possible to characterize the role of karsts in relation to many structural features. The impact of tectonic structures and lithology on the development and evolution of the karst networks is analysed. The consequences for drinking-water supplies and its protection are examined.
Volcanically-derived deposits and sequences: a unified terminological scheme for application in modern and ancient environments
Authors: Di Capua Andrea , De Rosa Rosanna , Kereszturi Gabor , Le Pera Emilia , Rosi Mauro , Watt Sebastian F. L.
Published: August 2022

The genesis of particles and their transport mechanisms are the two fundamental factors driving the accumulation of sediments associated with volcanism or derived from volcanic sources. These factors are therefore the most important criteria on which to base a useful classification of sediments in such environments. However, the relative significance of the nature of particles v. the transporting mechanism forming a deposit varies in existing terminological schemes: those schemes applied where volcanological contextual information is available tend to give precedence to the transportation process; whereas sedimentological schemes examining ancient deposits tend to focus principally on the nature of particles. Here, we provide an outline of the challenges in classifying volcanically derived sediments and put forward a scheme that aims to bridge current terminological differences and accommodate variable levels of uncertainty. This work defines three endmembers (
primary volcaniclastic
,
secondary volcaniclastic
,
volcanic epiclastic
) that correspond to (a) deposits whose particles are produced, transported and emplaced directly by volcanic mechanisms; (b) deposits whose particles are produced directly by volcanic events but transported and accumulated by non-volcanic mechanisms, either in continuum with the events or after interim storage; (c) deposits whose particles are produced by weathering/erosion of volcanic terrains and transportation of derived material by non-volcanic mechanisms. When the complex combination of genetic and transportation processes accumulating volcaniclastic sequences is not clear, but a strong relationship between an eruptive event and the studied volcaniclastic deposit can still be demonstrated, a further category (
volcanogenic
) has been introduced.
Underground hydrogen storage: a review
Authors: Miocic Johannes , Heinemann Niklas , Edlmann Katriona , Scafidi Jonathan , Molaei Fatemeh , Alcalde Juan
Published: August 2022

Large-scale underground storage of hydrogen gas is expected to play a key role in the energy transition and in near future renewable energy systems. Despite this potential, experience in underground hydrogen storage remains limited. This work critically reviews the most important elements of this crucial technology, including hydrogen properties and their significance for subsurface operations, sources for hydrogen and historical hydrogen storage operations, to set the state of the art. The cyclical nature of hydrogen storage operations will produce pressure and stress changes within the reservoir that could affect the integrity of the well, the reservoir, the caprock and the entire subsurface storage complex. To minimize geomechanical leakage risks and optimize the storage operation it is crucial to understand the pressure and stress history of the storage site, to optimize well locations to manage pressure and to identify the reservoir-specific cushion gas to working gas ratio. Finally, we outline the major scientific and operational challenges required to ensure the safe and efficient deployment of underground hydrogen storage at a large scale.
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks
Authors: Daghero Francesco , Burrello Alessio , Xie Chen , Castellano Marco , Gandolfi Luca , Calimera Andrea , Macii Enrico , Poncino Massimo , Pagliari Daniele Jahier
Published: August 2022

Human Activity Recognition (HAR) based on inertial data is an increasingly diffused task on embedded devices, from smartphones to ultra low-power sensors. Due to the high computational complexity of deep learning models, most embedded HAR systems are based on simple and not-so-accurate classic machine learning algorithms. This work bridges the gap between on-device HAR and deep learning, proposing a set of efficient one-dimensional Convolutional Neural Networks (CNNs) that can be deployed on general purpose microcontrollers (MCUs). Our CNNs are obtained combining hyper-parameters optimization with sub-byte and mixed-precision quantization, to find good trade-offs between classification results and memory occupation. Moreover, we also leverage adaptive inference as an orthogonal optimization to tune the inference complexity at runtime based on the processed input, hence producing a more flexible HAR system.

With experiments on four datasets, and targeting an ultra-low-power RISC-V MCU, we show that (i) we are able to obtain a rich set of Pareto-optimal CNNs for HAR, spanning more than 1 order of magnitude in terms of memory, latency, and energy consumption; (ii) thanks to adaptive inference, we can derive >20 runtime operating modes starting from a single CNN, differing by up to 10% in classification scores and by more than 3× in inference complexity, with a limited memory overhead; (iii) on three of the four benchmarks, we outperform all previous deep learning methods, while reducing the memory occupation by more than 100×. The few methods that obtain better performance (both shallow and deep) are not compatible with MCU deployment; (iv) all our CNNs are compatible with real-time on-device HAR, achieving an inference latency that ranges between 9 μs and 16 ms. Their memory occupation varies in 0.05–23.17 kB, and their energy consumption in 0.05 and 61.59 μJ, allowing years of continuous operation on a small battery supply.
CrowdAtlas: Estimating Crowd Distribution within the Urban Rail Transit System
Authors: E Jinlong , Li Mo , Huang Jianqiang
Published: August 2022

While urban rail transit systems are playing an increasingly important role in meeting the transportation demands of people, precise awareness of how the human crowd is distributed within such a system is highly necessary, which serves a range of important applications including emergency response, transit recommendation, and commercial valuation. Most rail transit systems are closed systems where once entered the passengers are free to move around all stations and are difficult to track. In this article, we attempt to estimate the crowd distribution based only on the tap-in and tap-out records of all the rail riders. Specifically, we study Singapore MRT (Mass Rapid Transit) as a vehicle and leverage EZ-Link transit card records to estimate the crowd distribution. Guided by a key observation that the passenger inflows and arrival flows at different MRT stations and time are spatio-temporally correlated due to behavioral consistency of MRT riders, we design and implement a machine learning-based solution, CrowdAtlas, that captures MRT riders’ transition probabilities among stations and across time, and based on that accurately estimates the crowd distribution within the MRT system. Our comprehensive performance evaluations with both trace-driven studies and real-world experiments in MRT disruption cases demonstrate the effectiveness of CrowdAtlas.
Synthesizing Game Levels for Collaborative Gameplay in a Shared Virtual Environment
Authors: Liu Huimin , Choi Minsoo , Kao Dominic , Mousas Christos
Published: August 2022

We developed a method to synthesize game levels that accounts for the degree of collaboration required by two players to finish a given game level. We first asked a game level designer to create playable game level chunks. Then, two
artificial intelligence (AI)
virtual agents driven by behavior trees played each game level chunk. We recorded the degree of collaboration required to accomplish each game level chunk by the AI virtual agents and used it to characterize each game level chunk. To synthesize a game level, we assigned to the total cost function cost terms that encode both the degree of collaboration and game level design decisions. Then, we used a Markov-chain Monte Carlo optimization method, called simulated annealing, to solve the total cost function and proposed a design for a game level. We synthesized three game levels (low, medium, and high degrees of collaboration game levels) to evaluate our implementation. We then recruited groups of participants to play the game levels to explore whether they would experience a certain degree of collaboration and validate whether the AI virtual agents provided sufficient data that described the collaborative behavior of players in each game level chunk. By collecting both in-game objective measurements and self-reported subjective ratings, we found that the three game levels indeed impacted the collaboration gameplay behavior of our participants. Moreover, by analyzing our collected data, we found moderate and strong correlations between the participants and the AI virtual agents. These results show that game developers can consider AI virtual agents as an alternative method for evaluating the degree of collaboration required to finish a game level.
Effect of Migrant Labourer Inflow on the Early Spread of Covid-19 in Odisha: A Case Study
Authors: Behera Shreetam , Dogra Debi Prosad , Satpathy Manoranjan
Published: August 2022

Odisha is a state in the eastern part of India with a population of 46 million. Annually, a large number of people migrate to financial and industrial centers in other states for their livelihood earning. Bulk of them returned to Odisha during the early stage of national lockdown (March–June 2020) due to the Covid-19 outbreak as their places of work became Covid hotspots while Odisha was much less affected. This triggered the Odisha government to take precautionary measures such as mandatory quarantine of returning migrants, setting up of containment zones, and establishing temporary medical centres (TMC). Moreover, it was necessary for the government to devise a policy that could slow down the spread of Covid-19 in Odisha due to inflow of migrants. Being part of a task-force constituted by government to understand Covid-19 spread dynamics in Odisha, we predicted the number of people who would get infected primarily due to reverse-migration. This helped the government to make timely resource mobilisation. After analyzing reasons behind the rise in infections at various districts with large migrant population, we mapped the prediction problem to Sequential Probability Ratio Test (SPRT) of Abraham Wald. Our predictions were highly accurate when compared with real data that were obtained at a later stage. Two levels of SPRT were carried out over the data provided by the government. Use of SPRT for Covid-19 spread analysis is novel, particularly to predict the number of possible infections much ahead in time due to the sudden inflow of migrants.
Role of Monocharged Anions and Cations in the Chemical Enhancement of Ag(0) Nanoparticle Surface-Enhanced Raman Scattering Systems
Authors: Redón Rocío , Ocampo-Bravo Cristina C. , Sato-Berrú Roberto , Daza-Gómez Lucy-Caterine
Published: August 2022

In the present investigation, we show the effect of the addition of different salts on the response and enhancement of the Raman signal of triazines. To do this, we have used different monocharged anions (F−, Cl−, Br−, I−) and cations of the alkali metal family (Li+, Na+, K+); as supports, we have used four colloids of silver nanoparticles that were photoreduced with white, purple, blue, and green light filters, and as test analytes, we have used two different triazines (cyanuric acid and sodium trithiocyanurate). The highest Raman enhancement signals were produced by the combination of the smallest nanoparticles obtained (photo-irradiated with blue light) together with the halide anion (I−) and the alkali metal cation (K+). Regarding the analyte, sodium trithiocyanurate registered greater Surface-Enhanced Raman Scattering (SERS) improvements than cyanuric acid under these same conditions. Our research highlights the activating role of SERS played by highly polarizable monocharged ions (I− and K+), which facilitate the electronic transfer of metal to the analyte, improving the Raman signal markedly.
Study on Tensile and Compressive Bending Deformation Sensor Based on Surface Acoustic Wave
Authors: Xu Zhangliang , Luo Yang
Published: August 2022

SAW sensors are becoming interesting in the field of strain and stress detection applications. The strain of complex structures can be divided into a combination of local surface tension and compression, hence it is pretty important that study the detection of tension and compression for the analysis of complex structures. Here, we fabricated two SAW sensors based ST-cut quartz (78.65 MHz and 157.1 MHz) through photo-lithography and wet-etching processes and focused on their comparative analysis for the bending displacement and strain testing with the performance of the sensor sensitivity studied. The SAW-IDT10 and SAW-IDT5 sensor showed the maximum bending displacement sensitivity of ∼1.9369 kHz mm−1 and ∼4.3887 kHz mm−1, respectively. Additionally, the SAW-IDT5 sensor exhibited up to 2 times higher sensitivity (1570 kHz/%) than that of SAW-IDT10 sensor (786 kHz/%) under the same bending strain conditions. And the SAW-IDT5 and the SAW-IDT10 have both 0.99 and 1 tensile and compression strain gauge factors. Thus SAW sensors with high operating frequency have a higher frequency shift response in detecting micro-strain. Meanwhile, negative frequency shift response to tension and positive frequency shift response to compression of SAW sensors can be used to classify structural deformation in a complex environment in the future.
ARDS in the time of corona: context and perspective
Authors: Voelkel Norbert F. , Bogaard Harm Jan , Kuebler Wolfgang M.
Published: August 2022

For more than 2 years, COVID-19 has been holding the world at awe with new waves of infections, novel mutants, and still limited (albeit improved) means to combat SARS-CoV-2-induced respiratory failure, the most common and fatal presentation of severe COVID-19. In the present perspective, we draw from the successes and—mostly—failures in previous acute respiratory distress syndrome (ARDS) work and the experiences from COVID-19 to define conceptual barriers that have so far hindered therapeutic breakthroughs in this deadly disease, and to open up new avenues of thinking and thus, ultimately of therapy.
A Systematic Review of Clinical Validated and Potential miRNA Markers Related to the Efficacy of Fluoropyrimidine Drugs
Authors: Sun Xiaomeng , Chen Jiani , Chen Xintao , Gao Qianmin , Chen Wei , Zou Xun , Zhang Feng , Gao Shouhong , Qiu Shi , Yue Xiaoqiang , Yao Houshan , Liu Xuan , Li Mingming
Published: August 2022

Colorectal cancer (CRC) is becoming increasingly prevalent worldwide. Fluoropyrimidine drugs are the primary chemotherapy regimens in routine clinical practice of CRC. However, the survival rate of patients on fluoropyrimidine-based chemotherapy varies significantly among individuals. Biomarkers of fluoropyrimidine drugs’' efficacy are needed to implement personalized medicine. This review summarized fluoropyrimidine drug-related microRNA (miRNA) by affecting metabolic enzymes or showing the relevance of drug efficacy. We first outlined 42 miRNAs that may affect the metabolism of fluoropyrimidine drugs. Subsequently, we filtered another 41 miRNAs related to the efficacy of fluoropyrimidine drugs based on clinical trials. Bioinformatics analysis showed that most well-established miRNA biomarkers were significantly enriched in the cancer pathways instead of the fluoropyrimidine drug metabolism pathways. The result also suggests that the miRNAs screened from metastasis patients have a more critical role in cancer development than those from non-metastasis patients. There are five miRNAs shared between these two lists. The miR-21, miR-215, and miR-218 can suppress fluoropyrimidine drugs’' catabolism. The miR-326 and miR-328 can reduce the efflux of fluoropyrimidine drugs. These five miRNAs could jointly act by increasing intracellular levels of fluoropyrimidine drugs’' cytotoxic metabolites, leading to better chemotherapy responses. In conclusion, we demonstrated that the dynamic changes in the transcriptional regulation via miRNAs might play significant roles in the efficacy and toxicity of the fluoropyrimidine drug. The reported miRNA biomarkers would help evaluate the efficacy of fluoropyrimidine drug-based chemotherapy and improve the prognosis of colorectal cancer patients.
Research on a Machine Learning-Based Method for Assessing the Safety State of Historic Buildings
Authors: Peng Xiao-Hong , Zhang Zi-Hao
Published: August 2022

Historic and protected buildings are increasingly valued due to their valuable historical and cultural value. The assessment of the safety state of historic buildings has received more attention. Emerging machine learning algorithms, with their excellent computational performance, provide new ideas and new means to solve practical problems in various fields. Therefore, this paper proposes a method for assessing the safety state of historic buildings based on machine learning techniques. Firstly, based on the analysis of the characteristics of historical buildings and common security problems, the application of wireless sensor networks to the security monitoring of historical buildings is proposed in order to improve the automation of monitoring. Then, in order to improve the accuracy of the assessment, a combination of kernel canonical correlation analysis (KCCA) and support vector machine (SVM) is used to establish the security monitoring model. The experimental results show that by choosing a suitable KCCA function, the redundant features of the data can be reduced while the comprehensiveness of the building structure identification features can be retained, thus effectively improving the prediction accuracy of the SVM. The KCCA-SVM model can accurately predict the physical quantities such as relative structural displacement of historical buildings with good reliability.
miR-214-3p Deficiency Enhances Caspase-1-Dependent Pyroptosis of Microglia in White Matter Injury
Authors: He Liufang , Wei Tingyan , Huang Yong , Zhang Xueli , Zhu Dongbo , Liu Huazhen , Wang Zhangxing
Published: August 2022

White matter injury (WMI) is the most frequent impairment of neurodevelopment in preterm infants. Here, we report that the caspase-1 inflammasome is abundantly activated in the microglia of WMI mice and results in increased pyroptosis of microglia. Pharmacology inhibition of caspase-1 cleavage alleviated the pathogenesis of WMI mice. The expression of microRNA miR-214-3p was largely reduced in the microglia of WMI mice compared to controls. Compromised expression of miR-214-3p on microglia gives rise to the inflammasome activation and microglial pyroptosis. Treatment with miR-214-3p agomir is sufficient to relieve the white matter lesion and demyelination in WMI mice. miR-214-3p is able to bind to the 3

′

region of the NLRP-3 inflammasome compartment NEK7, preventing the transcription of NEK7 mRNA. As a result, in WMI mice, the lack of miR-214-3p leads to the accumulation of NEK7 which supports NLRP 3 inflammasome activation, microglial pyroptosis, and white matter pathogenesis.
Effects of N6-Methyladenosine Regulators on LAG3 and Immune Infiltrates in Lung Adenocarcinoma
Authors: Wang Nengchao , Xu Yue , Jin Linzhi , Wang Xiaomin , Wu Shouxin , Wang Yu , Zhao Jiangman , Zhou Fuyou , Ge Hong
Published: August 2022

Background. Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer, which is one of the most commonly diagnosed tumors and the leading causes of death from cancer around the world. Since RNA methylation is a posttranscriptional modification and affects so much biological progress, it is urged to explore the role of N6-methyladenosine (m6A) methylation in LUAD. Methods. We explored the expression of 24 m6A methylation genes, as well as their correlations with LAG3 in 561 LUAD samples from TCGA. Consensus clustering was applied to m6A methylation genes, and two LUAD subgroups were identified. The expression of m6A genes was analyzed by the Wilcoxon test. KEGG and GO enrichment analyses were performed to indicate the pathway affected by differentially expressed genes in the two groups. A prognostic model based on LASSO regression using an eleven-m6A gene signature was constructed according to the expression of these genes. Receiver operating characteristic (ROC) curve was used to confirm the accuracy of the model in the TCGA cohort, as well as in the test cohort from the Gene Expression Omnibus (GEO) database. Results. Compared to cluster 1, cluster 2 showed poorer overall survival (OS) and higher LAG3 expression. In addition, KEGG and GO enrichment analyses indicated that differentially expressed genes are enriched in the immune response. We also observed that the expression of LAG3 is positively correlated with IGF2BP2, CBLL1, and HNRNPA2B1 and negatively correlated with YTHDF2, YTHDF3, and FTO. For patients in the TCGA cohort, the AUC score is 0.7, and the AUC score for the GSE50081 cohort is 0.675. Patients with lower risk scores exhibited better overall survival and lower expression of LAG3 than patients with higher risk scores. Conclusions. In brief, our results indicated the important role of m6 methylation in affecting the tumor immune microenvironment and the survival of patients with LUAD. The m6A methylation gene signatures might serve as promising therapeutic targets and help the immunotherapy of LUAD in the future.
Method Validation and Characterization of Red Pigment in Beta vulgaris Peels and Pomaces by HPLC-UV and UHPLC-MS/MS
Authors: Rotich Vincent , Wangila Phanice , Cherutoi Jackson
Published: August 2022

Color pigments from plant, animal, or mineral sources can be identified, separated, and quantified for various purposes. It is expected that pigments from Beta vulgaris L. peels and pomaces could be used to develop natural dyes that can find applications in areas such as food or textile dyeing industries. This work aimed at identifying and quantifying the pigment in the B. vulgaris L. peels and pomaces extracts as well as validating the method by high-performance liquid chromatography combined with ultraviolet spectroscopy (HPLC-UV) and ultra-high-performance liquid chromatography coupled with triple quadrupole (TSQ) mass spectrometry (UHPLC-MS/MS). Column chromatography was used to isolate compounds after methanolic solvent extraction. Identification and quantification of the pigments in the extract were achieved using reverse-phase HPLC with a UV detector (538 nm). The UHPLC-MS/MS was used for further confirmation of colored compounds in the extract. Method validation included the use of betanin standard (betanidin 5-β-D-glucopyranoside), determination of repeatability (precision), calibration curve linearity, and sensitivity (LOD and LOQ) tests. Betanin was detected in the sample at retention times of 7.699 and 7.71 minutes, respectively, which closely matched the tR (7.60 min) of the standard, according to HPLC-UV and LC-MS/MS data. The average betanin concentration was 3.81 0.31 mg/g of dry weight, according to the HPLC-UV analysis. The LC-MS/MS data revealed the existence of several compounds, including betanin (4.31 ± 2.15 mg/g), isobetanin (1.85 ± 2.20 mg/g), 2, 17-bidecarboxy-neobetanin (0.71 ± 0.02 mg/g), betanidin (0.71 ± 0.03 mg/g), 2-O-glucosyl-betanin (0.40 ± 0.10 mg/g), and isobetanidin (0.36 ± 1.26 mg/g), among other compounds whose yields were too low. In conclusion, the peels and pomaces of B. vulgaris L. can be a useful source for the extraction of a red dye for use in coloring, such as the dyeing of textile substrates.
The Application Method of Big Data of Data Mining Algorithm in College Basketball Teaching
Authors: OuYang Yu , Zhang YanHong , Li Youliang
Published: August 2022

The teaching quality monitoring and evaluation system in colleges and universities gives the scientific and objective evaluation of school teaching work and effectively optimizes and adjusts the important system of teaching work under the condition of deep excavation of the problems. However, the data on the teaching quality of colleges and universities in the state of static storage has seriously affected the value of data existence and the mining of laws. This paper mainly analyzes the application types of data mining technology in the college basketball teaching quality monitoring and evaluation system and discusses the data mining implementation method in the college basketball teaching quality and evaluation system, hoping to have a certain reference for relevant personnel.
Formal Analysis of the Security Protocol with Timestamp Using SPIN
Authors: Xiao Meihua , Song Weiwei , Yang Ke , OuYang Ri , Zhao Hanyu
Published: August 2022

The verification of security protocols is an important basis for network security. Now, some security protocols add timestamps to messages to defend against replay attacks by network intruders. Therefore, verifying the security properties of protocols with timestamps is of great significance to ensure network security. However, previous formal analysis method of such protocols often extracted timestamps into random numbers in order to simplify the model before modeling and verification, which probably cause time-dependent security properties that are ignored. To solve this problem, a method for verifying security protocols with timestamps using model checking technique is proposed in this paper. To preserve the time-dependent properties of the protocol, Promela (process meta language) is utilized to define global clock representing the protocol system time, timer representing message transmission time, and the clock function representing the passage of time; in addition, a mechanism for checking timestamps in messages is built using Promela. To mitigate state space explosion in model checking, we propose a vulnerable channel priority method of using Promela to build intruder model. We take the famous WMF protocol as an example by modeling it with Promela and verifying it with model checker SPIN (Simple Promela Interpreter), and we have successfully found two attacks in the protocol. The results of our work can make some security schemes based on WMF protocol used in the Internet of things or other fields get security alerts. The results also show that our method is effective, and it can provide a direction for the analysis of other security protocols with timestamp in many fields.
Tensile Properties of Thermal Cycled Titanium Alloy (Ti–6Al–4V)
Authors: Siva Teja Putti Venkata , S Manikandan , Kumar Ayyagari Kiran , K Suresh , Mahilraj Jenifer
Published: August 2022

The effect of thermal cycling on tensile properties and microstructural characterization of Titanium (Ti–6Al–4V) alloy which were subjected to various types of thermomechanical treatments especially stress relief annealing and solution treated aging were studied and results presented. The specimens were thermally treated at a temperature of 427°C for up to 1500 cycles. The test setup used for thermal treatment of the specimens was a specially designed experimental setup. After thermal treatment the specimens, were prepared for a tensile test to get the desired tensile properties. It was noticed that the maximum tensile strength of the specimen was observed when reached at 250 cycles and thereafter marginally increasing up to 1000 cycles. After 1000 cycles the tensile strength of the specimen decreases. Similarly, the hardness of the specimen increases up to 1000 cycles and then decreases after 1000 cycles, while elongation increases marginally when the cycle increases. Solution-treated aged specimen attains greater strength and hardness when compared to stress relief annealed specimen. With the increased number of cycles, the strength of the specimen when treated with stress relief annealing and solution-treated aging specimen increases. It was observed that the hardness and strength values decrease after 1000 cycles. With an increasing number of cycles, the tensile elongation increased. When compared with both the treatment processes the solution-treated aged specimen shows better results after thermal cycling.
Debris Bed Self-Leveling Mechanism and Characteristics for Core Disruptive Accident of Sodium-Cooled Fast Reactor: Review of Experimental and Modeling Investigations
Authors: Xu Ruicong , Cheng Songbai
Published: August 2022

Evaluations of the Core Disruptive Accident (CDA) are significantly important for safety analysis of Sodium-cooled Fast Reactor (SFR) despite the very low probability of occurrence for CDA. During the material-relocation phase in CDA of SFR, the molten materials are possibly released from the core region into subcooled sodium, subsequently forming the debris bed on the lower part of the reactor vessel after being quenched and fragmented. The accumulated high-temperature debris with decay heat can cause sodium coolant boiling, leading to the so-called “debris bed self-leveling behavior” during which the shape of the debris bed becomes flattered (leveling). It is important to investigate the debris bed self-leveling behavior due to its potential capacity to induce the transfer of debris and affect the ability of cooling and criticality of the debris bed. Thus, in recent years, valuable knowledge concerning the mechanism and characteristics of this behavior was accumulated through lots of experimental results and modeling developments. Aimed at providing a valuable guideline for future investigations on this issue, in this study, the past experimental and modeling investigations on debris bed self-leveling mechanism and characteristics are systematically summarized and reviewed, and some future remarks are also proposed to promote the progression of further research for SFR severe accident analysis.
Research of Vertical Domain Entity Linking Method Fusing Bert-Binary
Authors: Wang Hairong , Zhou Beijing , Li Bo , Xu Xi
Published: August 2022

To solve the problem of unclear entity boundaries and low recognition accuracy in Chinese text, we construct the crop dataset and propose a Bert-binary-based entity link method. Candidate entity sets are generated through entity matching in multiple data sources. The Bert-binary model is called to calculate the correct probability of the candidate entity, and the entity with the highest score is screened for linking. In comparative experiments with three models on the crop dataset, the

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value is increased by 2.5% on the best method or by 8.8% on average. The experimental results show the effectiveness of Bert-binary method in this paper.
Aging Time Prediction Model Analysis and Numerical Simulation of Random Degradation Equipment Based on Big Data Linkage Technology
Authors: Ye Chen , Peng Xuefeng
Published: August 2022

In this study, we focus on the relevance of remaining life prediction of randomly degraded equipment in the context of big data monitoring and the core issue of quantifying uncertainty in remaining life prediction. We analyze the limitations and common problems of current research. To address the limitations and common problems, a solution for predicting the remaining life of randomly degraded devices with multisource sensing monitoring in the context of big data is proposed, and the feasibility and effectiveness of the idea are verified using battery data. Finally, multiple machine learning methods, such as support vector machines, random forests, recurrent neural networks, and convolutional neural networks, are combined to predict the remaining life of batteries, and these four machine learning methods perform well in the work of battery remaining life prediction and solve the key scientific problems.
Design and Implementation of Vocational Education Management System Based on Discrete Fourier Transform
Authors: Han Zhiqiang
Published: August 2022

In some remote areas and even some vocational colleges, the information management of students is still stagnant on paper. Such traditional methods have obviously lagged behind the development and needs of society. The traditional manual management is a waste of both manpower and material resources. In today’s rapidly developing information age, it is very necessary to study an efficient vocational education management system. The vocational education management system designed in this paper includes Web server, DB server, college management terminal, and load balancing switch. In this system, CONN.ASP is a common module of all ASP programs in the whole system. It is mainly used to realize the connection between ASP program and database and the calling of common function submodules. In order to reduce the power consumption of the vocational education management system, the Discrete Fourier Transform (DFT) theory is proposed in this paper. This algorithm can effectively save the size of the storage unit and the number and frequency of reading the twiddle factor, so as to achieve the purpose of improving the operation speed. This paper randomly selected the scores of 10 classes and use the system for data processing test. A total of 50% of teachers were particularly satisfied with the tables generated by the discrete Fourier transform-based vocational education management system. They believed that the vocational education management system based on discrete Fourier transform is very fast in data processing, and the processing speed was within 0.1 s. The system designed in this paper can meet various demands in vocational education management and continuously enhance the function of educational administration management.
A Comparison on LSTM Deep Learning Method and Random Walk Model Used on Financial and Medical Applications: An Example in COVID-19 Development Prediction
Authors: Yao Yifan , Li Xinxin , Li Qing
Published: August 2022

This study aims to establish the model of the cryptocurrency price trend based on a financial theory using the Long Short-Term Memory (LSTM) networks model with multiple combinations between the window length and the predicting horizons. The Random Walk model is also applied with different parameter settings. The object of this study is the cryptocurrency and medical issues, primarily the Bitcoin and Ethereum and the COVID-19. Quantitative analysis is adopted as the method of this dissertation. The research tool is Python programming language, and the TensorFlow package is employed to model and analyze research topics. The results of this study show the limitations of the LSTM and Random Walk model for price prediction while demonstrating the different characteristics of both models with different parameter settings, providing a balance between the model’s accuracy and the model’s practicality.
Numerical Simulation of Ground Effect on Circulation Control Airfoil
Authors: Sun Quanbing , Shi Zhiwei , Zhang Weilin , Sun Zhikun , Chen Yongliang
Published: August 2022

Circulation control (CC) is used extensively to control the attitude of rudderless aircraft experiencing ground effect in take-off and landing phases. The investigation of ground effect on airfoil CC is necessary to improve flight performance and quality in proximity to the ground. The aerodynamics and flow field of a modified NACA0012 airfoil with CC in ground effect are investigated with numerical simulations. The compressible Reynolds-averaged Navier–Stokes equations with the shear-stress transport

k
−
ω

turbulence model equations are solved using the Finite Volume Method (FVM). Simulation results show that the ground effect changes the lift increment per unit jet momentum coefficient, and CC can reverse the polarity ground effect. The effective angle of attack

α

E

and the downwash space downstream airfoil are altered by the ground effect resulting in variations of airfoil surface pressure and lift. Unlike the unbounded flow field, the jet attachment distance is not only determined by the jet momentum coefficient but it can change with the ride height, which is the distance from the ground to the center of the semicircular Coanda surface, for the same jet momentum coefficient.