Identification of traits and QTLs contributing to salt tolerance in barley (Hordeum vulgare L.)

Salinity is the most severe abiotic stress perceived by plants and affects about 800 million hectares of land worldwide, including 20% of the world’s highly productive irrigated land. Significant crop yield losses are observed due to salinity. Salinization is increasing because of poor irrigation management and climate change. Improving salt tolerance in crops is for these reasons an important target for plant breeding in the near future. However, salinity tolerance in plants is not easy to breed for due to its interaction with many physiological processes controlled by many genes and their interaction with the environment. Barley is a good model crop to study different mechanisms conferring salt tolerance in cereals. A traditional QTL mapping approach in combination with a new association mapping method allowed us to efficiently explore the genetics and genetic diversity of salt tolerance in barley. Improvements of the association mapping technology highly increased detection power and mapping accuracy. The traits and QTLs identified in this thesis point out both osmotic and ionic stress tolerant genes as important targets for salt tolerance breeding. This thesis provides tools to plant breeders for the application of marker-assisted introgression breeding of salt tolerance genes in their breeding programs. Some QTLs were found to be syntenic with the important QTLs/genes for salt tolerance found in wheat and rice such as Na+ and K+ transporter gene families. Other QTLs were new and suggest the presence of novel genes that play an important role in plant ion homeostasis, transportation of Cl- and Ca2+ and osmotic tolerance. We demonstrated that association mapping can be a powerful approach to dissect the complexity of salt tolerance in barley. The newly available high-density SNP map of barley and the barley genome sequence in the near future further increases the accuracy of mapping studieswith the association panel and will greatly facilitate the cloning of the genes underlying salt tolerance in barley. This thesis thus contributes to better a understanding of the physiological and genetic basis of salt tolerance and improved breeding strategies for the development of salt tolerant varieties. </p

Prevalence of trichinellosis and cysticercosis in indigenous pigs from ethnic minorities for selected communes in the Central Highlands (Dak Lak)

Traditionally applied free grazing/roaming of pigs is a known risks factor for selected zoonoses such trichinellosis and cysticercosis which have the potential to cause long lasting health problems in affected humans with sporadic complications such as fatal encephalitis. The ban of free grazing in pigs due to recent policy changes may have contributed to a decrease of both zoonoses and consequently making pork from local or wild pigs safer for the consumer. Despite of the ban some farmers might still use free roaming management at least partly for certain age classes of their pigs. Information on the presence of these zoonoses in pigs produced by ethnic groups is lacking or not updated. Therefore, a serological survey was carried out to provide base line information on the presence of cysticercosis and/or trichinellosis in native pigs in selected communes of the Central Highlands as being part of the Cross CRP project “Scoping study to evaluate the potential of integrated indigenous pig systems to improve livelihoods and safe pork consumption for poor ethnic minority smallholders in the Central Highlands of Vietnam”. While the serological sampling was implemented by WASI all laboratory analysis were carried out by NIVR, a research institute with known experience on the diagnosis for both zoonoses in Vietnam. In addition NIVR provided a training on sample collection and storage

A hybrid heuristic optimization algorithm PSOGSA coupled with a hybrid objective function using ECOMAC and frequency in damage detection

Presence of damage leads to variation in modal properties of observed structures. The majority of studies use the changes in natural frequencies for damage detection. The reason is that the frequencies are often easily measurable with high accuracy by using reasonable sensors. However, frequencies are more sensitive to environmental effects, such as temperature, in comparison with mode shapes. Besides, defects in symmetric structures can cause the same changes in frequency. In contrast, mode shapes are more sensitive to local damage because they own local information and are independent of symmetric characteristics. These make mode shapes have dominant advantages in detecting nonlinear and multiple damage. ECOMAC is an index derived from mode shapes. It is a fact that these indices are not always possible to detect faults successfully in structures. Therefore, in this paper, a hybrid optimization algorithm, particle swarm optimization – gravitational search algorithm, namely PSOGSA, is used to improve the accuracy of infect detection using a hybrid objective function combined ECOMAC and frequency based on the inverse problem. Numerical studies of a two-span continuous beam, a simply supported truss, and a free-free beam, are utilized to verify the effectiveness and reliability of the proposal. From the obtained results, the proposed approach shows high potential in damage identification for different structures

First Principles Prediction Unveils High-Tc_c Superconductivity in YSc2_2H24_{24} Cage Structures

The quest for room-temperature superconductivity has been a long-standing aspiration in the field of materials science, driving extensive research efforts. In this work, we present a novel hydride, YSc$_2$H$_{24}$, which is stable at high pressure using a crystal structure prediction approach with a fixed composition based on known structures. The discovered material is crystalline in a hexagonal unit cell with space group P6/mmm and has a fastinating structure consisting of two distinct cages: Sc@H$_{24}$ and Y@H$_{30}$. By conducting an extensive numerical investigation of lattice dynamics, electron-phonon coupling, and solving the isotropic Eliashberg equation, we have revealed a significant value of $\lambda$ = 2.96 as the underlying factor responsible for the remarkably high critical temperature (T$_c$) of 306-332 K in YSc$_2$H$_{24}$. As pressure increases, the T$_c$ remains above the ambient temperature. Our work has the potential to enhance the existing understanding of high-temperature superconductors, with implications for practical applications. The unique network of these cage-like structures holds great promise for advancing our understanding of high-temperature superconductors, potentially leading to innovative applications

A hybrid heuristic optimization algorithm PSOGSA coupled with a hybrid objective function using ECOMAC and frequency in damage detection

Presence of damage leads to variation in modal properties of observed structures. The majority of studies use the changes in natural frequencies for damage detection. The reason is that the frequencies are often easily measurable with high accuracy by using reasonable sensors. However, frequencies are more sensitive to environmental effects, such as temperature, in comparison with mode shapes. Besides, defects in symmetric structures can cause the same changes in frequency. In contrast, mode shapes are more sensitive to local damage because they own local information and are independent of symmetric characteristics. These make mode shapes have dominant advantages in detecting nonlinear and multiple damage. ECOMAC is an index derived from mode shapes. It is a fact that these indices are not always possible to detect faults successfully in structures. Therefore, in this paper, a hybrid optimization algorithm, particle swarm optimization – gravitational search algorithm, namely PSOGSA, is used to improve the accuracy of infect detection using a hybrid objective function combined ECOMAC and frequency based on the inverse problem. Numerical studies of a two-span continuous beam, a simply supported truss, and a free-free beam, are utilized to verify the effectiveness and reliability of the proposal. From the obtained results, the proposed approach shows high potential in damage identification for different structures

Revisiting LARS for Large Batch Training Generalization of Neural Networks

LARS and LAMB have emerged as prominent techniques in Large Batch Learning (LBL), ensuring the stability of AI training. One of the primary challenges in LBL is convergence stability, where the AI agent usually gets trapped into the sharp minimizer. Addressing this challenge, a relatively recent technique, known as warm-up, has been employed. However, warm-up lacks a strong theoretical foundation, leaving the door open for further exploration of more efficacious algorithms. In light of this situation, we conduct empirical experiments to analyze the behaviors of the two most popular optimizers in the LARS family: LARS and LAMB, with and without a warm-up strategy. Our analyses give us a comprehension of the novel LARS, LAMB, and the necessity of a warm-up technique in LBL. Building upon these insights, we propose a novel algorithm called Time Varying LARS (TVLARS), which facilitates robust training in the initial phase without the need for warm-up. Experimental evaluation demonstrates that TVLARS achieves competitive results with LARS and LAMB when warm-up is utilized while surpassing their performance without the warm-up technique