A study of tailored domain wall geometries in ferromagnetic nanowires using Lorentz microscopy

This workpresentsresults on the degree of asymmetry in Transverse Domain Walls (TDWs) by means of micro-magnetic simulation and Lorentz microscopy. Here, head-to-head (H2H)-TDWs were simulated in straight permalloy(Py)-Ni80Fe20nanowires. This aimsto explore how to define symmetry and asymmetry of TDWs viaa quantitative measurement of symmetry/asymmetry as described bya correlation between magnetization components (Mx, My) and region areas (A1, A2) inside TDWs. Experimentally, curved nanowires ofdiffering widths were patterned by Focussed-Ion-Beam (FIB) irradiation. Lorentz microscopy experiments indicate that the degree of asymmetry of TDWs in the curved nanowires is strongly affected by structural effects, i.e.degree of edge roughness. Our results are a new route-map of quantitative measurement for transition between symmetric and asymmetric TDWs.The results are helpful for the determination ofTDW configurations inthe static state after the application of a magnetic field orelectrical current pulse when TDWs are driven by magnetic field or current.Indeed, our results may support a greater understanding of DW creation and propagation in magnetic nanowires which are of interest for concepts of high-density and ultrafast nonvolatile data storage devices such as racetrack memory and magnetic logic gates

Identification and characteristics of MYB4 transcription factor related to regulation of abiotic stress tolerance in peanut

Peanut (Arachis hypogaea L.), an economically valuable crop, provides protein and oil for human and animal consumption. The transcription factor MYB4 has been identified as a potential drought tolerance gene in peanut. This study aimed to isolate and characterize the MYB4 gene in the L14 peanut cultivar. The isolated AhL14_MYB4 gene was found to be 1.1 kb long, with a 663 bp coding sequence containing 3 exons and 2 introns. In silico analysis showed that AhL14_MYB4 possesses a nuclear localization signal and two DNA-binding domains characteristic of transcription factors. The findings revealed key molecular features of AhL14_MYB4 and provided insights into improving drought resistance in peanut varieties. Further research on AhL14_MYB4 may aid efforts to enhance drought tolerance in local peanut cultivars through molecular breeding or genetic engineering. Overall, this finding about preliminary characterization of the peanut MYB4 gene lays the groundwork for potential genetic improvements to this economically important crop

Dilaton in Two-Time Physics as trigger of electroweak phase transition and inflation

Within the SP(2, R) symmetry, the Two-time model (2T model) has six dimension with two time dimensions. The model has a dilaton particle that makes the symmetry breaking differently from the Standard Model. By reducing the 2T metric to the Minkowski one (1T metric), we consider the electroweak phase transition picture in the 2T model with the dilaton as the trigger. Our analysis shows that Electro-weak Phase Transition (EWPT) is a first-order phase transition at the $200$ GeV scale, its strength is about $1 - 3.08$ and the mass of dilaton is in interval $[345, 625]$ GeV. Furthermore, the metric of 2T model can be reduced to the Randall-Sundrum model, so the dilaton acts as inflaton with the slow-roll approximation. Therefore the 2T-model indirectly suggests that extra-dimension can be also a source of EWPT and inflation. The EWPT problem can be used to determine scale parameters that refer to relationships between two metrics.Comment: 25 pages, 2 figure

Some algorithms related to consistent decision table

Rough set theory is useful mathematical tool developed to deal with vagueness and uncertainty. As an important concept of rough set theory, an attribute reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of the given information table. Rough set theory is also the most popular for generating decision rules from decision table. In this paper, we propose an algorithm finding object reduct of consistent decsion table. On the other hand, we also show an algorithm to find some attribute reducts and the correctness of our algorithms is proof-theoretically. These our algorithms have polynomial time complexity. Our finding object reduct helps other algorithms of finding attribute reducts become more effectively, especially as working with huge consistent decision table

Cloning an RBD-T4-LINKER-C5a sequence encoding the SARS-CoV-2 antigen into a plant expression vector

This study aims to make a plant expression vector with an RBD-T4-Linker-C5a sequence that codes for the SARS-CoV-2 antigen and an A3Dsp signal peptide from the rice 3D amylase gene located before the RBD. Methods of molecular cloning were applied in this study. The plant expression vector pNHL22 harboring the RBD-T4-Linker-C5a sequence was successfully established and conjugated into Agrobacterium tumefaciens LBA4404 by triparental mating. Bacteria A. tumefaciens containing the RBD-T4-Linker-C5a sequence are now ready for genetic transformation into the Nicotiana benthamiana plant for future applications

Identify aerodynamic derivatives of the airplane attitude channel using a spiking neural network

The paper proposes a method for identifying aerodynamic coefficient derivatives of aircraft attitude channel using spiking neural network (SNN) and Gauss-Newton algorithm based on data obtained from actual flights. Using SNN combination with Gauss-Newton iterative calculation algorithm allows the identification of aerodynamic coefficient derivatives in a nonlinear model for aerodynamic parameters with higher accuracy and faster calculation time. The paper proposes an algorithm to train the SNN multi-layer network by Normalized Spiking Error Back Propagation (NSEBP), in which, in the forward propagation period, the time of output spikes is calculating by solving quadratic equations instead of detection by traditional methods. The phase of propagation of errors backward uses the step-by-step calculation instead of the conventional gradient calculation method. The identification results are compared with the results when using the RBN network to prove the algorithm efficienc

Symptoms, Mental Health, and Quality of Life Among Patients After COVID-19 Infection: A Cross-sectional Study in Vietnam

Objectives: This study was conducted to characterize the symptoms, mental health, quality of life (QoL), and associated factors following the coronavirus disease 2019 (COVID-19) infection. Methods: This cross-sectional study included 394 participants previously infected with COVID-19 in Ho Chi Minh City, Vietnam. Mental health was assessed using the 21-item Depression, Anxiety, and Stress Scale (DASS-21). Participants self-reported health-related QoL was measured with the EuroQol 5-Dimension 5-Level (EQ-5D-5L) scale. Results: Among the participants, 76.4% reported experiencing at least one symptom following COVID-19 infection. The most common symptoms were fatigue (42.1%), cognitive dysfunction (42.9%), and hair loss (27.9%). According to the DASS-21 results, the proportions of depression, anxiety, and stress were 28.7%, 26.4%, and 20.6%, respectively. The mean scores on the EQ-5D-5L and the EuroQol Visual Analog Scale were 0.94±0.11 and 84.20±13.11, respectively. Regarding QoL issues, the highest proportion of participants (32.7%) reported experiencing anxiety or depression, followed by pain or discomfort (25.4%). Multivariable logistic regression analysis revealed that factors associated with the presence of symptoms following COVID-19 infection included female (odds ratio [OR], 2.84; 95% confidence interval [CI], 1.65 to 4.91) and having QoL issues (OR, 3.25; 95% CI, 1.71 to 6.19). Conclusions: The study investigated the prevalence rates of various symptoms following COVID-19 infection. These findings underscore the need to prioritize comprehensive care for individuals recovering from COVID-19 and to implement strategies to mitigate the long-term impact of the disease on mental health and QoL

An Application of Analytic Network Process (ANP) to Assess Critical Risks of Bridge Projects in the Mekong Delta Region

Risk management is one of the critical factors contributing to infrastructure project success. Risk assessment enables both practitioners and decision-makers to identify and analyze potential risks and quantify risk impacts on project performance in terms of time, cost, and quality. Even though many studies attempt to investigate the risk of construction projects with the consideration of technical, organizational, and legal aspects, only a few studies deeply focus on identifying the critical risks of bridge projects with the examination of climate change impact. The current study concentrates on analyzing risks in bridge construction projects in the Mekong Delta region which has been significantly affected by climate change. An intensive review of previous publications and technical project reports from 2010 to 2021 was conducted to identify the list of potential risks and interviews and discussions with engineers and managers involved in bridge projects were carried out to identify critical risks of bridge projects. Analytic Network Process (ANP) method was introduced to evaluate the impact of such risks on the performance of bridge project implementation. The initial results of this study provide a holistic picture of risk management for bridge projects with the consideration of climate change impact. The findings can help the involved parties including owners, contractors, and project managers to assess particular risks and scheme backup plans to mitigate project delays and cost overruns

ISOLATION AND STRUCTURAL CHARACTERIZATION OF TWO SAPONINS FROM THE ROOTS OF Sansevieria trifasciata ‘Laurentii’

Sansevieria is a genus consisting of about 60 species natively distributed in tropical and subtropical areas. Many Sansevieria species are used for ornamental purposes or in traditional medicine to treat human diseases. Sansevieria trifasciata ‘Laurentii,’ commonly known as snake plant, is an evergreen perennial plant used in traditional medicine. The phytochemical investigation of the roots of S. trifasciata ‘Laurentii’ resulted in the isolation and structural characterization of two previously reported saponins: (24S,25R)-1b-[(β-D-fucopyranosyl)oxy]-3b-hydroxyspirost-5-en-24-yl β-D-glucopyranoside (1) and 26-[(β-D-glucopyranosyl)oxy]-3β,22α-dihydroxyfurosta-5,25(27)-dien-1β-yl-O-α-L-rhamnopyranosyl-(1→2)-α-L-arabinopyranoside (2) by analysis of their 1D and 2D NMR spectra and HR-ESI-MS, together with the comparison to data published in the literature