bluuebunny/crossref_metadata_2025 huggingface dataset:
Myocardial infarction coincides with increased NOX2 and Nε-(carboxymethyl) lysine expression in the cerebral microvasculature
Authors: Korn Amber , Baylan Umit , Simsek Suat , Schalkwijk Casper G , Niessen Hans W M , Krijnen Paul A J
Published: November 2021

Background

Myocardial infarction (MI) is associated with mental health disorders, in which neuroinflammation and cerebral microvascular dysfunction may play a role. Previously, we have shown that the proinflammatory factors Nε-(carboxymethyl)lysine (CML) and NADPH oxidase 2 (NOX2) are increased in the human infarcted heart microvasculature. The aim of this study was to analyse the presence of CML and NOX2 in the cerebral microvasculature of patients with MI.

Methods

Brain tissue was obtained at autopsy from 24 patients with MI and nine control patients. According to their infarct age, patients with MI were divided into three groups: 3–6 hours old (phase I), 6 hours–5 days old (phase II) and 5–14 days old (phase III). CML and NOX2 in the microvasculature were studied through immunohistochemical analysis.

Results

We observed a 2.5-fold increase in cerebral microvascular CML in patients with phase II and phase III MI (phase II: 21.39±7.91, p=0.004; phase III: 24.21±10.37, p=0.0007) compared with non-MI controls (8.55±2.98). NOX2 was increased in microvessels in patients with phase II MI (p=0.002) and phase III MI (p=0.04) compared with controls. No correlation was found between CML and NOX2 (r=0.58, p=0.13).

Conclusions

MI coincides with an increased presence of CML and NOX2 in the brain microvasculature. These data point to proinflammatory alterations in the brain microvasculature that may underlie MI-associated mental health disorders.
Health-related quality of life and disability in adults with juvenile idiopathic arthritis: comparison with adult-onset rheumatic diseases
Authors: Oliveira Ramos Filipa , Rodrigues Ana , Magalhaes Martins Fernando , Melo Ana Teresa , Aguiar Francisca , Brites Luisa , Azevedo Soraia , Duarte Ana Catarina , Furtado Carolina , Mourão Ana Filipa , Sequeira Graça , Cunha Inês , Figueira Ricardo , Melo Gomes Jose Antonio , Santos Maria Jose , Fonseca Joao Eurico
Published: November 2021

Objective

To compare physical disability, mental health, fatigue and health-related quality of life (HRQoL) across juvenile idiopathic arthritis (JIA) categories in adulthood and between JIA and adult-onset rheumatic diseases.

Methods

Cross-sectional analysis nested in a cohort of adult patients with JIA registered in the Rheumatic Diseases Portuguese Register (Reuma.pt). Physical disability (Health Assessment Questionnaire—Disability Index), mental health symptoms (Hospital Anxiety and Depression Scale), fatigue (Functional Assessment of Chronic Illness Therapy—Fatigue Scale (FACIT-F)) and HRQoL (EuroQol-5D (EQ5D) and Short Form (SF-36)) were compared across JIA categories. Patients with polyarticular JIA and enthesis-related arthritis (ERA) JIA were compared respectively to patients with rheumatoid arthritis (RA) and spondyloarthritis (SpA), matched for gender and age, adjusted for disease duration and activity.

Results

585 adult patients with JIA were included. Comparison across JIA categories showed that persistent oligoarthritis and patients with ERA reported a higher score in EQ5D and SF-36 physical component when compared with other JIA categories.

Polyarticular JIA reported less disability and fatigue than patients with RA (median Health Assessment Questionnaire of 0.25 vs 0.63; p<0.001 and median FACIT-F score 42 vs 40 ; p=0.041). Polyarticular JIA had also better scores on EQ5D and all domains of SF-36, than patients with RA. Patients with ERA reported less depression and anxiety symptoms (0% vs 14.8%; p=0.003% and 9% vs 21.3%; p=0.002) and less fatigue symptoms (45 vs 41; p=0.01) than patients with SpA.

Conclusion

Persistent oligoarticular JIA and ERA are the JIA categories in adulthood with better HRQoL. Overall, adult polyarticular and patients with ERA JIA have lower functional impairment and better quality-of-life than patients with RA and SpA.
Three-month and six-month outcomes of patients with COVID-19 associated hyperinflammation treated with short-term immunosuppressive therapy: follow-up of the CHIC study
Authors: Janssen Marlou THF , Ramiro Sofia , Mostard Rémy LM , Magro-Checa Cesar , Landewé Robert BM
Published: November 2021

Objectives

To prospectively investigate differences in medium-term patient-reported outcome measures and objective functional outcome measures, between patients receiving and those not receiving intensive short-term immunosuppressive therapy for coronavirus disease 19 (COVID-19)-associated hyperinflammation.

Methods

Patients previously included in the COVID-19 High-intensity Immunosuppression in Cytokine storm syndrome (CHIC) study who received immunosuppressive treatment versus standard of care for COVID-19-associated hyperinflammation were invited for follow-up at 3 and 6 months after hospitalisation. At both visits, patients were assessed by a pulmonologist, completed quality of life (QoL) questionnaires and performed pulmonary and exercise function tests. At 3 months, patients additionally completed questionnaires on dyspnoea, anxiety, depression and trauma. Outcomes were compared between patients receiving and those not receiving intensive short-term immunosuppressive therapy for COVID-19-associated hyperinflammation.

Results

131 (66.5%) patients survived hospitalisation due to COVID-19-associated hyperinflammation and 118 (90.1%) were included. QoL questionnaires, pulmonary- and exercise function tests showed improvement between 3 and 6 months after discharge, which was similar in both groups. Assessed patients reached levels that were close to levels predicted from the normal population. In contrast, diffusing capacity of the lung for carbon monoxide was disturbed in both groups: 69.6% predicted (SD 16.2) and 73.5% predicted (SD 16.5) in control group and treated group, respectively.

Conclusions

No differences in medium-term outcomes are demonstrated in survivors of COVID-19-associated hyperinflammation treated or not treated with methylprednisolone with or without tocilizumab during the acute phase. Short-term benefits of this therapy, as showed in the baseline CHIC study analysis, are thus not hampered by medium-term adverse events.
Particle physics in cosmic rays
Authors: Góra Dariusz
Published: November 2021

The Pierre Auger Observatory is the world’s largest detector of ultra-high energy cosmic rays (UHECRs). It uses an array of fluorescence telescopes and particle detectors at the ground to obtain detailed measurements of the energy spectrum, mass composition and arrival directions of primary cosmic rays (above the energy of [Formula: see text] eV) with accuracy not attainable until now.

Observations of extensive air showers performed by the Pierre Auger Observatory can also be used to probe hadronic interactions at high energy, in a kinematic and energy region not accessible by man-made accelerators. Indeed, exploiting Auger data, we reach center-of-mass energies up to 400 TeV, i.e. more than 30 times of those attainable at the Large Hadron Collider (LHC), and explore interactions in the very forward region of phase space on targets of atomic mass of 14. In addition, a precise measurement of the muon component of air showers at the ground is more sensitive to the details of the hadronic interactions along many steps of the cascade development, such as the multiplicity of the secondaries and the fraction of electromagnetic component with respect to the total signal. On the other hand, the intrinsic muon fluctuations mostly depend on the first interaction.

In this paper, we overview the new Auger studies exploring the connection between the dynamics of the air shower and the multi-particle production, and how this knowledge can be translated into constraints of the high-energy hadronic models as well as direct measurements, complementary to, and beyond the reach of, accelerator experiments.
EXPLAINABILITY OF NEURAL NETWORK CLUSTERING IN INTERPRETING THE COVID-19 EMERGENCY DATA
Authors: YU ZHENHUA , SOHAIL AYESHA , NOFAL TAHER A. , TAVARES JOÃO MANUEL R. S.
Published: November 2021

Among other hospitalization causes and cases, the clinical emergency is a critical case and the data of the reporting patients are biased as well as poorly managed due to the chaotic situation. The world has faced chaos over the past year due to the frequent waves of COVID-19 and the resulting emergencies. The data banks, linked with the clinical emergencies require serious quantitative and qualitative analysis to drive interpretable conclusions for necessary future emergency measures and to develop explainable artificial intelligence tools. This important procedure involves the clear understanding of the data patterns and topologies, which is a great challenge for the multidimensional data sets. Mathematically, the topological mapping can resolve this problem by mapping higher-dimensional data to two-dimensional representation, based on the overall association. Proper data mining and pattern recognition can help in improving the rapid patients admission, in providing the medical resources timely and in proper patient administration. In this paper, the importance of self-organizing maps, to interpret the hospital data, particularly for the COVID-19 epidemic is discussed in detail. Important variables are identified with the aid of networks and mappings.
New material for Peleserphidae (Proctotrupoidea, Hymenoptera) in Mid-Cretaceous Burmese (Kachin) amber
Authors: Yu Yu , Hu Guifang , Zhu Yunyang , Li Yuxiao , Jiang Wenjun , Rasnitsyn Alexandr P. , Zhang Haichun , Zhang Qi
Published: November 2021

A new female wasp from Cenomanian Burmese amber was described as
Peleserphus qufuensis
sp. nov. in the proctotrupoid family Peleserphidae, which emerged in the Late Jurassic–mid-Cretaceous. It provides new data on the diversity of the family in Cenomanian Burmese amber and extends the records to five species.
Personalized, Sequential, Attentive, Metric-Aware Product Search
Authors: Pan Yaoxin , Liang Shangsong , Ren Jiaxin , Meng Zaiqiao , Zhang Qiang
Published: November 2021

The task of personalized product search aims at retrieving a ranked list of products given a user’s input query and his/her purchase history. To address this task, we propose the PSAM model, a Personalized, Sequential, Attentive and Metric-aware (PSAM) model, that learns the semantic representations of three different categories of entities, i.e., users, queries, and products, based on user sequential purchase historical data and the corresponding sequential queries. Specifically, a query-based attentive LSTM (QA-LSTM) model and an attention mechanism are designed to infer users dynamic embeddings, which is able to capture their short-term and long-term preferences. To obtain more fine-grained embeddings of the three categories of entities, a metric-aware objective is deployed in our model to force the inferred embeddings subject to the triangle inequality, which is a more realistic distance measurement for product search. Experiments conducted on four benchmark datasets show that our PSAM model significantly outperforms the state-of-the-art product search baselines in terms of effectiveness by up to 50.9% improvement under NDCG@20. Our visualization experiments further illustrate that the learned product embeddings are able to distinguish different types of products.
Significant DBSCAN+: Statistically Robust Density-based Clustering
Authors: Xie Yiqun , Jia Xiaowei , Shekhar Shashi , Bao Han , Zhou Xun
Published: November 2021

Cluster detection is important and widely used in a variety of applications, including public health, public safety, transportation, and so on. Given a collection of data points, we aim to detect density-connected spatial clusters with varying geometric shapes and densities, under the constraint that the clusters are statistically significant. The problem is challenging, because many societal applications and domain science studies have low tolerance for spurious results, and clusters may have arbitrary shapes and varying densities. As a classical topic in data mining and learning, a myriad of techniques have been developed to detect clusters with both varying shapes and densities (e.g., density-based, hierarchical, spectral, or deep clustering methods). However, the vast majority of these techniques do not consider statistical rigor and are susceptible to detecting spurious clusters formed as a result of natural randomness. On the other hand, scan statistic approaches explicitly control the rate of spurious results, but they typically assume a single “hotspot” of over-density and many rely on further assumptions such as a tessellated input space. To unite the strengths of both lines of work, we propose a statistically robust formulation of a multi-scale DBSCAN, namely Significant DBSCAN+, to identify significant clusters that are density connected. As we will show, incorporation of statistical rigor is a powerful mechanism that allows the new Significant DBSCAN+ to outperform state-of-the-art clustering techniques in various scenarios. We also propose computational enhancements to speed-up the proposed approach. Experiment results show that Significant DBSCAN+ can simultaneously improve the success rate of true cluster detection (e.g., 10–20% increases in absolute F1 scores) and substantially reduce the rate of spurious results (e.g., from thousands/hundreds of spurious detections to none or just a few across 100 datasets), and the acceleration methods can improve the efficiency for both clustered and non-clustered data.
A Comprehensive Numerical Model for One-Dimensional Pit Growth of 316L SS under a Salt Film
Authors: Wang Ke , Salasi Mobin , Iannuzzi Mariano
Published: November 2021

A comprehensive mathematical model, including electrode kinetics, hydrolysis and complexation reactions, salt film precipitation and pit interface movement, was developed to investigate pit growth of 316L stainless steel under a salt film. The new mathematical framework incorporates activity coefficients into the hydrolysis and complexation reaction calculations for the first time, using experimental results to parametrize the electrode kinetics in a saturated pit solution. The model was validated by 1D pit experiments and results documented in the literature. It can successfully estimate the transition potentials, salt film thickness, pit stability product, saturated pit concentration, and the pH at the pit base during pit propagation under the presence of a salt film. Moreover, the model can predict the Cr enrichment and Fe depletion in the saturated solution at the pit base, attributed to the higher diffusion coefficient of Fe and the lower Cr diffusivity.
Electrochemical Preparation of Fe0.5CoNiCuSnx Medium Entropy Alloys and Their Corrosion Properties
Authors: Huang Jian , Wang Peilin , Du Kaifa , Yin Huayi , Wang Dihua
Published: November 2021

The exploration of efficient preparation methods and corrosion-resistant medium entropy alloys (MEAs) has attracted significant attentions in recent years. In this paper, powdery Fe0.5CoNiCuSnx (x = 0, 0.05, 0.08, and 0.1) MEAs were prepared by the one-step electrochemical reduction of metal oxides in molten Na2CO3-K2CO3 using a Ni11Fe10Cu oxygen-evolution inert anode. The effects of Sn on the structures, morphologies, and corrosion behaviors of the prepared MEAs were systematically investigated. The electrolytic MEAs exhibited a single face-centered cubic phase at x≤0.05, and the CuSn-rich phase would be segregated in the alloys at 0.08 ≤ x ≤ 0.1. Moreover, increasing Sn reduced the particles size of MEAs, and Sn improved the corrosion resistance of MEAs in 0.5 M H2SO4, 1 M KOH, and 3.5% NaCl solutions. The electrolytic MEA(Sn0.05) exhibited the best corrosion resistance, which had the corrosion current densities of 3.7 × 10−5 A cm−2 (0.5 M H2SO4), 1.2 × 10−5 A cm−2 (1 M KOH), and 1.6 × 10−5 A cm−2 (3.5 wt% NaCl) at room temperature. Overall, this paper not only provides a green approach to preparing Sn-containing MEAs, but also offers an efficient way to control structures and morphologies, thereby improving the corrosion resistance.
Thermal and Chemical Integrity of Ru Electrode in Cu/TaOx/Ru ReRAM Memory Cell
Authors: Al-Mamun Mohammad , King Sean W. , Orlowski Marius
Published: November 2021

A good candidate for replacing the inert platinum (Pt) electrode in the well-behaved Cu/TaOx/Pt resistive RAM memory cell is ruthenium (Ru), already successfully deployed in the CMOS back end of line. We benchmark Cu/TaOx/Ru device against Cu/TaOx/Pt and investigate the impact of embedment of Cu/TaOx/Ru on two different substrates, Ti(20nm)/SiO2(730nm)/Si and Ti(20nm)/TaOx(30nm)/SiO2(730nm)/Si, on the cell's electrical performance. While the devices show similar switching performance at some operating conditions, there are notable differences at other operation regimes shedding light on the basic switching mechanisms and the role of the inert electrode. The critical switching voltages are significantly higher for Ru than for Pt devices and can be partly explained by the work function difference and different surface roughness of the inert electrode. The poorer switching properties of the Ru device are attributed to the degraded inertness properties of the Ru electrode as a stopping barrier for Cu+ions as compared to the Pt electrode. However, some of the degraded electrical properties of the Ru devices can be mitigated by an improved integration of the device on the Si wafer. This improvement is attributed to the suppression of crystallization of Ru and its silicidation reactions that take place at elevated local temperatures, present mainly during the reset operation. This hypothesis has been corroborated by extensive XRD studies of multiple layer systems annealed at temperatures between 300K and 1173K.
Behavior of Viscous Liquid Byproduct Formed in Exhaust Tube by Silicon Carbide Epitaxial Growth
Authors: Mizushima Ichiro , Habuka Hitoshi
Published: November 2021

The behavior of the by-product produced in an exhaust tube by the semiconductor silicon carbide epitaxial growth process was evaluated. The by-product layer was a dark-colored viscous liquid; it captured precursor gases and gaseous by-products, such as hydrogen, monosilane, propane, hydrogen chloride and chlorosilanes. These captured gases were spontaneously emitted in ambient nitrogen at room temperature. By an etching process using a chlorine trifluoride gas at low concentrations in ambient nitrogen, the by-product layer could be safely decomposed to gaseous fluorides of silicon and carbon. After finishing the etching, there remained a small amount of residue which included particles of carbon, silicon carbide and related compounds. Based on these results, the by-product layer formation process was discussed.
Development of Dielectric Film Based on Cellulose Loaded Nano-Silver and Carbon for Potential Energy Storage
Authors: Dacrory Sawsan , Kamel Samir , Turky Gamal
Published: November 2021

Cellulose has attracted much attention as a potential substrate for low-cost, flexible electronics. Here, new cellulose-based films embedded with nano-silver (AgNs) and carbon (C) were successfully prepared. First, cellulose was oxidized to tricarboxy cellulose (TCC) using 2,2,6,6 tetramethylpiperidine-1-oxyl followed by periodate oxidation. Then, nano-silver was prepared by polyol method and carbon was prepared via a single-step from bagasse. The structure, thermal, morphology, mechanical properties, and broad-band were characterized by infrared spectroscopy, thermogravimetric analysis, scanning electron microscopy with energy-dispersive X-ray, X-ray diffraction, and stress-strain relation. The results showed that the tensile strength and thermal stability of the films were improved. The temperature dependence of permittivity, ε′ of the TCC film, increased in two trends. However, TCC film shows non-conducting features, especially at lower temperatures; its nanocomposites films show a semiconducting behavior, and its ac-conductivity follows the empirical Jonscher law. Although the temperature dependence of dc-conductivity of the TCC/C, shows an Arrhenius behavior with low activation energy (≈3.74 kJ mol−1.), its investigated nanocomposites follow the well-known Vogel Fulcher Tamman equation according to the fragility of the prepared samples and/or the correlation between the interfacial polarization and conductivity.
Mitochondrial bioenergetics of astrocytes in Fragile X syndrome: new perspectives on culture conditions and sex effects
Authors: Vandenberg Gregory G. , Thotakura Aasritha , Scott Angela L.
Published: November 2021

Fragile X syndrome (FXS) is a genetic disorder that is characterized by a range of cognitive and behavioral deficits, including mild-moderate intellectual disability. The disease is characterized by an X-linked mutation of the Fmr1 gene, which causes silencing of the gene coding for fragile X mental retardation protein (FMRP), a translational regulator integral for neurodevelopment. Mitochondrial dysfunction has been recently associated with FXS, with reports of increases in oxidative stress markers, reactive oxygen species, and lipid peroxidation being present in the brain tissue. Astrocytes, a prominent glial cell within the central nervous system (CNS), play a large role in regulating oxidative homeostasis within the developing brain and dysregulation of astrocyte redox balance in FXS, which may contribute to oxidative stress. Astrocyte function and mitochondrial bioenergetics are significantly influenced by oxygen availability and circulating sex hormones; yet, these parameters are rarely considered during in vitro experimentation. Given that the brain normally develops in a range of hypoxic conditions and FXS is a sex-linked genetic disorder, we investigated how different oxygen levels (normoxic vs. hypoxic) and biological sex affected mitochondrial bioenergetics of astrocytes in FXS. Our results demonstrate that both mitochondrial respiration capacity and reactive oxygen species emission are altered with Fmr1 deletion in astrocytes and these changes were dependent upon both sexual dimorphism and oxygen availability.
Changes in plasma concentration of kynurenine following intake of branched-chain amino acids are not caused by alterations in muscle kynurenine metabolism
Authors: Jonsson William O. , Ponette Jonathan , Horwath Oscar , Rydenstam Tomas , Söderlund Karin , Ekblom Björn , Azzolini Michele , Ruas Jorge L. , Blomstrand Eva
Published: November 2021

Administration of branched-chain amino acids (BCAA) has been suggested to enhance mitochondrial biogenesis, including levels of PGC-1α, which may, in turn, alter kynurenine metabolism. Ten healthy subjects performed 60 min of dynamic one-leg exercise at ∼70% of Wmax on two occasions. They were in random order supplied either a mixture of BCAA or flavored water (placebo) during the experiment. Blood samples were collected during exercise and recovery, and muscle biopsies were taken from both legs before, after, and 90 and 180 min following exercise. Ingestion of BCAA doubled their concentration in both plasma and muscle while causing a 30%–40% reduction ( P < 0.05 vs. placebo) in levels of aromatic amino acids in both resting and exercising muscle during 3-h recovery period. The muscle concentration of kynurenine decreased by 25% ( P < 0.05) during recovery, similar in both resting and exercising leg and with both supplements, although plasma concentration of kynurenine during recovery was 10% lower ( P < 0.05) when BCAA were ingested. Ingestion of BCAA reduced the plasma concentration of kynurenic acid by 60% ( P < 0.01) during exercise and recovery, whereas the level remained unchanged with placebo. Exercise induced a three- to fourfold increase ( P < 0.05) in muscle content of PGC-1α1 mRNA after 90 min of recovery under both conditions, whereas levels of KAT4 mRNA and protein were unaffected by exercise or supplement. In conclusion, the reduction of plasma levels of kynurenine and kynurenic acid caused by BCAA were not associated with any changes in the level of muscle kynurenine, suggesting that kynurenine metabolism was altered in tissues other than muscle.
Dopamine D1 and D4 receptors contribute to light adaptation in ON-sustained retinal ganglion cells
Authors: Flood Michael D. , Eggers Erika D.
Published: November 2021

Dopamine by bright light conditions allows retinal neurons to reduce sensitivity to adapt to bright light conditions. It is not clear how and why dopamine receptors modulate retinal ganglion cell signaling. We found that both D1 and D4 dopamine receptors in photoreceptors and inner retinal neurons contribute significantly to the reduction in sensitivity of ganglion cells with light adaptation. However, light adaptation also requires dopamine-independent mechanisms that could reflect inherent sensitivity changes in photoreceptors.
Tumor necrosis factor-α modulates GABAergic and dopaminergic neurons in the ventrolateral periaqueductal gray of female mice
Authors: Pati Dipanwita , Kash Thomas L.
Published: November 2021

This study describes the effects of TNF-α on two distinct subpopulations of neurons in the vlPAG. We show that TNF-α alters both neuronal excitability and glutamatergic synaptic transmission on GABA and dopamine neurons within the vlPAG of female mice. This provides critical new information on the role of TNF-α in the potential modulation of pain, since activation of vlPAG GABA neurons drives nociception, whereas activation of dopamine neurons drives analgesia.
Respiratory disorders of Parkinson’s disease
Authors: Aquino Yasmin C. , Cabral Laís M. , Miranda Nicole C. , Naccarato Monique C. , Falquetto Bárbara , Moreira Thiago S. , Takakura Ana C.
Published: November 2021

Parkinson’s disease (PD) is characterized by the progressive loss of dopaminergic neurons in the substantia nigra, mainly affecting people over 60 yr of age. Patients develop both classic symptoms (tremors, muscle rigidity, bradykinesia, and postural instability) and nonclassical symptoms (orthostatic hypotension, neuropsychiatric deficiency, sleep disturbances, and respiratory disorders). Thus, patients with PD can have a significantly impaired quality of life, especially when they do not have multimodality therapeutic follow-up. The respiratory alterations associated with this syndrome are the main cause of mortality in PD. They can be classified as peripheral when caused by disorders of the upper airways or muscles involved in breathing and as central when triggered by functional deficits of important neurons located in the brainstem involved in respiratory control. Currently, there is little research describing these disorders, and therefore, there is no well-established knowledge about the subject, making the treatment of patients with respiratory symptoms difficult. In this review, the history of the pathology and data about the respiratory changes in PD obtained thus far will be addressed.
Kv1 channels regulate variations in spike patterning and temporal reliability in the avian cochlear nucleus angularis
Authors: Baldassano James F. , MacLeod Katrina M.
Published: November 2021

The dendrotoxin-sensitive voltage-gated potassium conductance typically associated with neuronal coincidence detection in the timing pathway for sound localization is demonstrated to affect spiking patterns and temporal input sensitivity in the intensity pathway in the avian auditory brain stem. The Kv1-family channels appear to be present in a subset of cochlear nucleus angularis neurons, regulate spike threshold dynamics underlying high-pass membrane filtering, and contribute to intrinsic firing diversity.
Multitarget Position-Sensitivity Security Transmission with OAM-DM Based on FDA
Authors: Zhu Changju , Song Maozhong , Dang Xiaoyu , Zhu Qiuming
Published: November 2021

The multitarget position-sensitivity security transmission scheme with orbital angular momentum (OAM) directional modulation (DM) waveform using the uniform circular frequency diverse array (FDA) is proposed. The transmitter employs FDA to generate dual-mode range-dependent OAM beam pattern, and the direction information of OAM beam is modulated into the signal. According to the modulation method, orthogonal carrier frequency can be designed to realize multitarget position-dependent OAM pattern. In the undesired position, the intensity pattern and the phase front of the radio beam vary randomly with the digital transmission sequence. Because modulation waveform is position-dependent, the technique offers security, as the signal can be purposely distorted in other positions. The composite dual-mode OAM signal makes it more difficult for eavesdroppers to demodulate correct messages. The receiver with a single antenna employs the phase compensation and helical phase factor to restore the correct digital signal in the desired position. Numerical results show that multitarget position-sensitivity OAM-DM technology based on FDA offers the security transmission scheme.
Modelling to Engineering Data Using a New Class of Continuous Models
Authors: Elbatal I. , Alotaibi Naif
Published: November 2021

In this paper, a new flexible generator of continuous lifespan models referred to as the Topp-Leone Weibull G (TLWG) family is developed and studied. Several mathematical characteristics have been investigated. The new hazard rate of the new model can be “monotonically increasing,” “monotonically decreasing,” “bathtub,” and “J shape.” The Farlie Gumbel Morgenstern (FGM) and the modified FGM (MFGM) families and Clayton Copula (CCO) are used to describe and display simple type Copula. We discuss the estimation of the model parameters by the maximum likelihood (MLL) estimations. Simulations are carried out to show the consistency and efficiency of parameter estimates, and finally, real data sets are used to demonstrate the flexibility and potential usefulness of the proposed family of algorithms by using the TLW exponential model as example of the new suggested family.
MapReduce-Based Dynamic Partition Join with Shannon Entropy for Data Skewness
Authors: Chen Donghua , Zhang Runtong
Published: November 2021

Join operations of data sets play a crucial role in obtaining the relations of massive data in real life. Joining two data sets with MapReduce requires a proper design of the Map and Reduce stages for different scenarios. The factors affecting MapReduce join efficiency include the density of the data sets and data transmission over clusters like Hadoop. This study aims to improve the efficiency of MapReduce join algorithms on Hadoop by leveraging Shannon entropy to measure the information changes of data sets being joined in different MapReduce stages. To reduce the uncertainty of data sets in joins through the network, a novel MapReduce join algorithm with dynamic partition strategies called dynamic partition join (DPJ) is proposed. Leveraging the changes of entropy in the partitions of data sets during the Map and Reduce stages revises the logical partitions by changing the original input of the reduce tasks in the MapReduce jobs. Experimental results indicate that the entropy-based measures can measure entropy changes of join operations. Moreover, the DPJ variant methods achieved lower entropy compared with the existing joins, thereby increasing the feasibility of MapReduce join operations for different scenarios on Hadoop.
Multiperson Target Dynamic Tracking Method for Athlete Training Based on Wireless Body Area Network
Authors: Du Diandian
Published: November 2021

Aiming at the problems of large tracking error and long tracking time in traditional multiperson target dynamic tracking methods, a new method based on wireless body area network for athlete training multiperson target dynamic tracking is proposed. First, the microinertial sensor in the wireless body area network is used to collect the multiperson image data of the athlete training, and the sparse representation is performed after processing, which improves the reliability of the data and reduces the tracking error. Secondly, the multiperson target dynamic tracking method based on the adaptive search box is used, combined with target isolation and occlusion detection, to judge the athlete’s training target. Finally, the nearest neighbor algorithm is used to construct an adaptive search box to achieve dynamic tracking of multiple targets. Experimental results show that this method can accurately measure the similarity of target features, with small tracking error and short tracking time. The minimum tracking error is only 0.11 frame.
Design, Control, Modeling, and Simulation of Mechanical Ventilator for Respiratory Support
Authors: Tran Anh Son , Thinh Ngo Ha Quang , Dong Van Keo , Vo Anh Huy
Published: November 2021

In the early stage of the 21st century, humankind is facing high medical risks. To the best of our knowledge, there is currently no efficient way to stop chains of infections, and hence citizens suffer significantly increasing numbers of diseases. The most important factor in this scenario is the lack of necessary equipment to cure disease and maintain our living. Once breath cannot be guaranteed, humans find themselves in a dangerous state. This study aimed to design, control, model, and simulate mechanical ventilator that is open-source structure, lightweight, and portable, which is proper for patients to cure themselves at home. In the scope of this research, the hardware platform for the mechanical design, implementation of control rules, and some trials of both simulations and experiments are presented as our methodology. The proposed design of ventilator newly features the bioinspired mechanism, finger-like actuator, and flow rate-based control. Firstly, the approximate evaluation of the lung model is presented with some physiological characteristics. Owing to this investigation, the control scheme was established to adapt to the biological body. Moreover, it is essential for the model to be integrated to determine the appropriate performance of the closed-loop system. Derived from these theoretical computations, the innovative concept of mechanical design was demonstrated using the open-source approach, and the real-world model was constructed. In order to estimate the driving torque, the hardware modeling was conducted using mathematical expressions. To validate the proposed approach, the overall system was evaluated using Matlab/Simulink, and experiments with the proposed platform were conducted in two situations: 20 lpm as a reference flow rate for 4 seconds and 45 lpm for 2.5 seconds, corresponding to normal breath and urgent breath. From the results of this study, it can be clearly observed that the system’s performance ensures that accurate airflow is provided, although the desired airflow fluctuates. Based on the test scenario in hardware, the RMS (root-mean-square) values of tracking errors in airflow for both cases were 1.542 and 1.767. The proposed design could deal with changes in airflow, and this machine could play a role as a proper, feasible, and robust solution to support human living.
Numerical Study of Duffing Nonlinearity in the Quantum Dot Embedded Nanomechanical Resonator
Authors: Wang Yue , Bary Ghulam , Ahmad Riaz , Yin Dameng , Xie Shiwei , Lu Qing , Khan Ilyas , Alshammari Nawa , Hamadneh Nawaf N. , Andualemb Mulugeta
Published: November 2021

Geometry, electrostatics, and single-electron tunneling contribute to the nonlinearity in the quantum dot embedded nanomechanical resonator, while “Duffing term” is a kind of mathematics describing the third-order nonlinearity of the system as a whole. We study theoretically the influence of a variation of a mathematical parameter Fuffing term on the actual physical effect. The position probability distribution, the average current, and the displacement fluctuation spectrum with the different Duffing parameter and electromechanical coupling are obtained through numerically calculating the Fokker Planck equation. The mechanical bistability has been described by these quantities under different electromechanical coupling and Duffing parameters. We conclude that the nonlinearities of the nanotube resonator contribute to the mechanical bistability, which induces the asymmetry of the position probability distribution, compresses the current, and softens or stiffens the mechanical resonance frequency as the same as the electromechanical coupling to use it in mechanical engineering.
An Approach for Generating Weights Using the Pairwise Comparison Matrix
Authors: Sepehrian Zaher , Khoshfetrat Sahar , Ebadi Said
Published: November 2021

Data envelopment analysis (DEA) has been used for obtaining weights for the analytic hierarchy process (AHP), an approach known as DEAHP. This method sometimes identifies more than one decision criterion or alternative as DEAHP-efficient. To overcome this problem, this paper proposes a new approach that not only generates appropriate weights for the decision criteria or alternatives, but also differentiates between DEAHP-efficient decision criteria or alternatives. To this end, we propose a DEA model with an assurance region and a cross-weight model that prioritizes decision criteria or alternatives by considering their most unfavorable weights. Two numerical examples are also provided to illustrate the advantages and potential applications of the proposed model.
A Novel Random Error Approximate Adder-Based Lightweight Medical Image Encryption Scheme for Secure Remote Monitoring of Health Data
Authors: Manikandan Nagarajan , Muthaiah Rajappa , Teekaraman Yuvaraja , Kuppusamy Ramya , Radhakrishnan Arun
Published: November 2021

In the present global scenario, social distancing is an inevitable one. The need for social distancing and advancements of technology to facilitate the patients and doctors around the world mandated the telemedicine and remote monitoring of patience details as the pivotal way to diagnose the disease. In this, it is essential to transmit the patient’s information such as X-ray and scan images of them to the doctor in the remote location. Preventing the medical data from the technological adversaries is the need of the hour. Infinitesimal attacks in medical images may cost human lives. This work proposes a lightweight, secure medical image encryption scheme for the remote monitoring of health data. The proposed encryption scheme uses computationally less complex weighted shift approximate adder (WSAA)–based encryption logic. The scheme uses a 256-bit key for the encryption process that strengthens the encryption and robust against various attacks. The proposed encryption scheme deploys the WSAA for diffusing the pixel values. A unique way of key distribution for pixel-wise encryption within the image is proposed that avoids the need for separate logic for the pixel-wise confusion. The proposed Encryption scheme is evaluated for its entropy and horizontal, vertical, diagonal correlation, histogram, key space, and sensitivity. Experimental results affirm that the proposed scheme significantly good with less computational complexity. The peak signal-to-noise ratio (PSNR) value of the decrypted image is infinity, and this matches the ideal requirement of the medical encryption scheme.
Therapeutic Effect of Biejiaxiaozheng Pills on Carbon Tetrachloride-Induced Hepatic Fibrosis in Rats through the NF-κB/Nrf2 Pathway
Authors: Wu Weibin , Li Liqiang , Yang Jian , Li Pinyu , Hu Yuying , Zhang Guifeng , Zhu Xiaozhong
Published: November 2021

Aims. To explore the effects of Biejiaxiaozheng pills on carbon tetrachloride-induced hepatic fibrosis in rats through the NF-κB/Nrf2 pathway and to explore the possible antifibrotic mechanisms of the drug. Material and Method. A rat model of hepatic fibrosis was established via CCl4 induction. Liver function and antioxidant indices were detected using commercial kits. Hematoxylin-eosin and Masson staining were used to detect pathological changes in hepatic tissues. ELISA was used to measure plasma TNF-α, IL-β, and IL-6 levels. RT-PCR was used to measure changes in TNF-α, IL-β, and IL-6 levels in hepatic tissues. Changes in p65, P-p65, Nrf2, and HO-1 protein expression were detected using western blotting. Results. In rats with hepatic fibrosis, Biejiaxiaozheng pills effectively improved liver function, alleviated fibrosis in hepatic tissues, and significantly reduced collagen accumulation. The pills significantly downregulated inflammatory cytokine expression in hepatic tissues by suppressing p65 phosphorylation and reduced plasma inflammatory cytokine levels to some extent. The pills upregulated Nrf2 and HO-1 expression in hepatic tissues, enhanced antioxidant potential, and upregulated plasma antioxidant levels. Conclusion. Biejiaxiaozheng pills improved hepatic fibrosis symptoms and lesions in rats, likely by inhibiting the NF-κB pathway and promoting the Nrf2 pathway.
Joint Modeling of User Behaviors Based on Variable‐Order Additive Markov Chain for POI Recommendation
Authors: Li RuiChang
Published: November 2021

The POI recommendation system has become an important means to help people discover attractive and interesting places. Based on our data analysis, we observe that users pay equal attention to conservatism and curiosity. In particular, adopting analysis corresponding to different time intervals, we find that users lean towards old POIs in the short term and look for new POIs with the increase of the time interval. However, existing approaches usually neglect users’ conservatism and curiosity preferences. Therefore, they are confronted with a bottleneck of depicting accurate user needs, making it difficult to improve the recommendation performance further. Besides, we further find that the number of user daily check‐ins has uneven distribution, which is not conducive to capture the accurate transition patterns of user behaviors. In light of the above, we design a single POI sequential method. On this basis, we propose a recommendation method of the variable additive Markov chain. We consider the user sequential preferences, especially liking old and pursuing new features. In addition, our model exploits the geographical tendency of user behaviors. Finally, we conduct abundant experiments on four cities in the two real datasets, i.e., Foursquare and Jiepang. The experimental results show its superiority over other competitors.
Research on Outdoor Garden Scene Reconstruction Based on PMVS Algorithm
Authors: He Wei
Published: November 2021

The three-dimensional reconstruction of outdoor landscape is of great significance for the construction of digital city. With the rapid development of big data and Internet of things technology, when using the traditional image-based 3D reconstruction method to restore the 3D information of objects in the image, there will be a large number of redundant points in the point cloud and the density of the point cloud is insufficient. Based on the analysis of the existing three-dimensional reconstruction technology, combined with the characteristics of outdoor garden scene, this paper gives the detection and extraction methods of relevant feature points and adopts feature matching and repairing the holes generated by point cloud meshing. By adopting the candidate strategy of feature points and adding the mesh subdivision processing method, an improved PMVS algorithm is proposed and the problem of sparse point cloud in 3D reconstruction is solved. Experimental results show that the proposed method not only effectively realizes the three-dimensional reconstruction of outdoor garden scene, but also improves the execution efficiency of the algorithm on the premise of ensuring the reconstruction effect.
A Massive Image Recognition Algorithm Based on Attribute Modelling and Knowledge Acquisition
Authors: Li Guohua , Liu An , Shen Huajie
Published: November 2021

In this paper, an in-depth study and analysis of attribute modelling and knowledge acquisition of massive images are conducted using image recognition. For the complexity of association relationships between attributes of incomplete data, a single-output subnetwork modelling method for incomplete data is proposed to build a neural network model with each missing attribute as output alone and other attributes as input in turn, and the network structure can deeply portray the association relationships between each attribute and other attributes. To address the problem of incomplete model inputs due to the presence of missing values, we propose to treat and describe the missing values as system-level variables and realize the alternate update of network parameters and dynamic filling of missing values through iterative learning among subnets. The method can effectively utilize the information of all the present attribute values in incomplete data, and the obtained subnetwork population model is a fit to the attribute association relationships implied by all the present attribute values in incomplete data. The strengths and weaknesses of existing image semantic modelling algorithms are analysed. To reduce the workload of manually labelling data, this paper proposes the use of a streaming learning algorithm to automatically pass image-level semantic labels to pixel regions of an image, where the algorithm does not need to rely on external detectors and a priori knowledge of the dataset. Then, an efficient deep neural network mapping algorithm is designed and implemented for the microprocessing architecture and software programming framework of this edge processor, and a layout scheme is proposed to place the input feature maps outside the kernel DDR and the reordered convolutional kernel matrices inside the kernel storage body and to design corresponding efficient vectorization algorithms for the multidimensional matrix convolution computation, multidimensional pooling computation, local linear normalization, etc., which exist in the deep convolutional neural network model. The efficient vectorized mapping scheme is designed for the multidimensional matrix convolution computation, multidimensional pooling computation, local linear normalization, etc. in the deep convolutional neural network model so that the utilization of MAC components in the core loop can reach 100%.
MiRNAs in Gestational Diabetes Mellitus: Potential Mechanisms and Clinical Applications
Authors: Liu Zhao-Nan , Jiang Ying , Liu Xuan-Qi , Yang Meng-Meng , Chen Cheng , Zhao Bai-Hui , Huang He-Feng , Luo Qiong
Published: November 2021

Gestational diabetes mellitus (GDM) is a common pregnancy complication which is normally diagnosed in the second trimester of gestation. With an increasing incidence, GDM poses a significant threat to maternal and offspring health. Therefore, we need a deeper understanding of GDM pathophysiology and novel investigation on the diagnosis and treatment for GDM. MicroRNAs (miRNAs), a class of endogenic small noncoding RNAs with a length of approximately 19-24 nucleotides, have been reported to exert their function in gene expression by binding to proteins or being enclosed in membranous vesicles, such as exosomes. Studies have investigated the roles of miRNAs in the pathophysiological mechanism of GDM and their potential as noninvasive biological candidates for the management of GDM, including diagnosis and treatment. This review is aimed at summarizing the pathophysiological significance of miRNAs in GDM development and their potential function in GDM clinical diagnosis and therapeutic approach. In this review, we summarized an integrated expressional profile and the pathophysiological significance of placental exosomes and associated miRNAs, as well as other plasma miRNAs such as exo-AT. Furthermore, we also discussed the practical application of exosomes in GDM postpartum outcomes and the potential function of several miRNAs as therapeutic target in the GDM pathological pathway, thus providing a novel clinical insight of these biological signatures into GDM therapeutic approach.