DANH LỤC CÁC LOÀI THUỘC HỌ LAUXANIIDAE (DIPTERA, LAUXANIOIDEA) TẠI VIỆT NAM, CHỦ YẾU THUỘC PHÂN HỌ LAUXANIINAE

I reviewed the lauxaniid literature as many as possible and listed any lauxaniid species with Vietnamese records. In this paper, I provided a taxonomic checklist of 41 nominal species under two subfamilies Homoneurinae and Lauxaniinae. In total, the number of lauxaniid species in Vietnam became 101 species belong to 21 genera. The Homoneurinae consists of 6 genera and 63 species. The Lauxaniinae includes 15 genera and 38 species. These two checklists will be the fundamental base for comprehensive taxonomic research of lauxaniid fauna in Vietnam.Dựa trên các tài liệu, danh lục các loài thuộc họ Lauxaniidae ở Việt Nam đã được thống kê, bài báo này cung cấp danh lục bao gồm 41 loài đã được định danh thuộc hai phân họ là Homoneurinae và Lauxaniinae. Tổng cộng, số lượng loài thuộc họ Lauxaniidae ở Việt Nam gồm 101 loài thuộc 21 giống. Trong đó, phân họ Homoneurinae gồm 63 loài thuộc 6 giống và phân họ Lauxaniinae gồm 38 loài thuộc 15 giống. Hai danh lục này là cơ sở cho những nghiên cứu sâu hơn về khu hệ họ Lauxaniidae ở Việt Nam

Effect of ferromagnetic contacts on spin accumulation in an all-metallic lateral spin-valve system: Semiclassical spin drift-diffusion equations

We study the effect of the ferromagnetic (FM) contacts on the spin accumulation in the lateral spin valve system for the collinear magnetization configurations. When an additional FM electrode is introduced in the all-metallic lateral spin-valve system, we find that the transresistance can be fractionally suppressed or very weakly influenced depending on the position of the additional FM electrode, and relative magnitudes of contact resistance and the bulk resistance defined over the spin diffusion length. Nonlocal spin signals such as nonlocal voltage drop and leakage spin currents are independent of the magnetization orientation of the additional FM electrode. Even when the additional contact is nonmagnetic, nonlocal spin signals can be changed by the spin current leaking into the nonmagnetic electrode.Comment: 13 pages, 1 figure, revised versio

Feasibility study using remote sensing technologies to improve zonal vineyard management

The primary purpose of this research was to examine the feasibility of using remote sensing data to improve efficiency of zonal vineyard management. To achieve this goal, correlation analysis between the significant vineyard management variables and different remote sensing data analysis tools were undertaken. The variables included leaf water potential, soil moisture, canopy size, vine health, vineyard yield, and fruit composition, which further impacts wine quality. The remote sensing data analysis tools included normalized difference vegetation index (NDVI), and other indices extracted from electromagnetic reflectance data of grapevine leaves and canopies. In each site, sentinel vines (i.e., 72-81) were identified in a grid form. GPS-based geolocation was carried out for six Cabernet Franc vineyards in Ontario's Niagara wine country. Even though remote sensing data analysis tools were not associated with several other important variables for quality grape production, this research still confirmed that remote sensing data analysis has significant potential to differentiate specific zones of canopy size, water stress, yield, some superior fruit compositions, and the resulting wine sensory attributes within a single vineyard site. This study also confirmed that the mechanism of plant defense systems against biotic stress could have impacts on the spectral behaviour of grapevine leaves and hyperspectral remote sensing technologies could be applied as a tool to identify the spectral behaviour changes due to stress. Overall, this study verified the feasibility of remote sensing technologies to enhance the efficiency of vineyard management in the correlation of data from various remote sensing data-analysis techniques and viticulturally important variables for plant health and growth, and fruit and wine quality. As a first step to develop a site-specific crop management (SSCM) model for vineyard management, it also proposes future research opportunities to test and develop an efficient vineyard management decision making model

Analysis on Convergent Factors related to the Hopelessness of Health College Students

This study investigates the convergence relationship of factors related to hopelessness among some health college students to identify the advantage of data analysis in the field of health care. The questionnaire was conducted using an unregistered self-administered questionnaire for 214 students from a college located in J area from October 1, 2018 to October 31, 2018. The hierarchical multiple regression analysis shows the following results. The hopelessness of respondents turned out to be significantly higher in following groups: a group in which academic burnout is higher, a group in which anxiety is higher, and a group in which psychosocial stress is higher. The results show explanatory power of 43.3%. And this indicates that the efforts to decrease academic burnout, anxiety, and psychosocial stress are required to decrease hopelessness among health college students. These results can be used to guide college life counseling to lower hopelessness among health college students. It is also expected to be used for efficient data analysis of health problems. Further research requires the development of sophisticated data analysis techniques and procedures that can be used more efficiently in health care

The Hβ index as an age indicator of old stellar systems: The effects of horizontal-branch stars

The strength of the Hβ index is computed for the integrated spectra of model globular clusters from the evolutionary population synthesis. For the first time, these models take into account the detailed systematic variation of horizontal-branch (HB) morphology with age and metallicity. Our models show that the Hβ index is significantly affected by the presence of blue HB stars. Because of the contribution from blue HB stars, the Hβ does not monotonically decrease as metallicity increases at a given age. Instead, it reaches a maximum strength when the distribution of HB stars is centered around 9500 K, the temperature at which the Hβ index becomes strongest. Our models indicate that the strength of the Hβ index increases as much as 0.75 Å because of the presence of blue HB stars. The comparison of the recent Keck observations of the globular cluster system in the Milky Way with those in the giant elliptical galaxies NGC 1399 and M87 shows a systematic shift in the Hβ-metallicity plane. Our models suggest that this systematic difference is explained if the mean age of globular cluster systems in giant elliptical galaxies is several billion years older than the Galactic counterpart. Further observations of globular cluster systems in the external galaxies from the large ground-based telescopes and space UV facilities will enable us to clarify whether this difference is indeed due to the age difference or whether other explanations are also possible

Self-Improving Interference Management Based on Deep Learning With Uncertainty Quantification

This paper presents a groundbreaking self-improving interference management framework tailored for wireless communications, integrating deep learning with uncertainty quantification to enhance overall system performance. Our approach addresses the computational challenges inherent in traditional optimization-based algorithms by harnessing deep learning models to predict optimal interference management solutions. A significant breakthrough of our framework is its acknowledgment of the limitations inherent in data-driven models, particularly in scenarios not adequately represented by the training dataset. To overcome these challenges, we propose a method for uncertainty quantification, accompanied by a qualifying criterion, to assess the trustworthiness of model predictions. This framework strategically alternates between model-generated solutions and traditional algorithms, guided by a criterion that assesses the prediction credibility based on quantified uncertainties. Experimental results validate the framework's efficacy, demonstrating its superiority over traditional deep learning models, notably in scenarios underrepresented in the training dataset. This work marks a pioneering endeavor in harnessing self-improving deep learning for interference management, through the lens of uncertainty quantification