Thermal decomposition study of hydroxylamine nitrate during storage and handling

Hydroxylamine nitrate (HAN), an important agent for the nuclear industry and the U.S. Army, has been involved in several costly incidents. To prevent similar incidents, the study of HAN safe storage and handling boundary has become extremely important for industries. However, HAN decomposition involves complicated reaction pathways due to its autocatalytic behavior and therefore presents a challenge for definition of safe boundaries of HAN storage and handling. This research focused on HAN decomposition behavior under various conditions and proposed isothermal aging testing and kinetic-based simulation to determine safety boundaries for HAN storage and handling. Specifically, HAN decomposition in the presence of glass, titanium, stainless steel with titanium, or stainless steel was examined in an Automatic Pressure Tracking Adiabatic Calorimeter (APTAC). n-th order kinetics was used for initial reaction rate estimation. Because stainless steel is a commonly used material for HAN containers, isothermal aging tests were conducted in a stainless steel cell to determine the maximum safe storage time of HAN. Moreover, by changing thermal inertia, data for HAN decomposition in the stainless steel cell were examined and the experimental results were simulated by the Thermal Safety Software package. This work offers useful guidance for industries that manufacture, handle, and store HAN. The experimental data acquired not only can help with aspects of process safety design, including emergency relief systems, process control, and process equipment selection, but also is a useful reference for the associated theoretical study of autocatalytic decomposition behavior

Thermal decomposition study of hydroxylamine nitrate during storage and handling

Hydroxylamine nitrate (HAN), an important agent for the nuclear industry and the U.S. Army, has been involved in several costly incidents. To prevent similar incidents, the study of HAN safe storage and handling boundary has become extremely important for industries. However, HAN decomposition involves complicated reaction pathways due to its autocatalytic behavior and therefore presents a challenge for definition of safe boundaries of HAN storage and handling. This research focused on HAN decomposition behavior under various conditions and proposed isothermal aging testing and kinetic-based simulation to determine safety boundaries for HAN storage and handling. Specifically, HAN decomposition in the presence of glass, titanium, stainless steel with titanium, or stainless steel was examined in an Automatic Pressure Tracking Adiabatic Calorimeter (APTAC). n-th order kinetics was used for initial reaction rate estimation. Because stainless steel is a commonly used material for HAN containers, isothermal aging tests were conducted in a stainless steel cell to determine the maximum safe storage time of HAN. Moreover, by changing thermal inertia, data for HAN decomposition in the stainless steel cell were examined and the experimental results were simulated by the Thermal Safety Software package. This work offers useful guidance for industries that manufacture, handle, and store HAN. The experimental data acquired not only can help with aspects of process safety design, including emergency relief systems, process control, and process equipment selection, but also is a useful reference for the associated theoretical study of autocatalytic decomposition behavior

Optimization of network resource allocation for software-defined data center networks

As cloud computing and data center network flourishes, the network that was once designed to support traditional networking scenario must now satisfy new requirements to suit for the cloud environment and increasing demands. The Software-Defined Networking (SDN) paradigm, with the control plane separated from the data plane, is widely regarded as the next generation networking technique. The objective of this thesis is to optimize network resources allocation in the software-defined data center networks (DCN). The SDN resources considered here are the SDN switch to controller link bandwidth and the switch flow table size. First, a queueing model is developed to provision the SDN switches with an appropriate number of switch-to-controller connections. Second, a controller-level admission control mechanism is proposed to determine if a new flow should be admitted to the network when the flow table is congested. Third, we study the fair and high-satisfaction resources allocation problem with the routing path optimized in software-defined DCN. The delay guarantees for delay-sensitive flows are also provided. Finally, some practical issues are considered for the resources allocation algorithms. The provided theoretical analysis and simulation results in this dissertation improve the efficiency of resource allocation in software-defined DCN.Ph.D

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

We propose a novel classification framework to precisely identify individuals with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) from normal controls (NC). The proposed method combines three different features from structural MR images: gray-matter volume, gray-level cooccurrence matrix, and Gabor feature. These features can obtain both the 2D and 3D information of brains, and the experimental results show that a better performance can be achieved through the multifeature fusion. We also analyze the multifeatures combination correlation technologies and improve the SVM-RFE algorithm through the covariance method. The results of comparison experiments on public Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method. Besides, it also indicates that multifeatures combination is better than the single-feature method. The proposed features selection algorithm could effectively extract the optimal features subset in order to improve the classification performance

A Knowledge-Based Cooperative Co-Evolutionary Algorithm for Non-Contact Voltage Measurement

Li H, Ma C, Zhang C, Chen Q, He C, Jin Y. A Knowledge-Based Cooperative Co-Evolutionary Algorithm for Non-Contact Voltage Measurement. IEEE Transactions on Emerging Topics in Computational Intelligence. 2023:1-14.Non-contact three-phase instantaneous voltage measurement is an emerging and challenging topic in modern smart grids. Existing measurement methods can hardly obtain accurate or instantaneous results attributed to the coupled three-phase information. Even though some advanced optimization algorithms have been developed, their performance should be further promoted. In this study, we first transform the measurement task into a single-objective optimization problem to address the deficiencies of existing methods. Then six problems with scalable numbers of decision variables and complexity of objectives are gathered to form a test suite for global optimization. Moreover, a knowledge-based cooperative co-evolutionary algorithm is proposed for solving the formulated problem. The main idea is to incorporate the physical properties and rules of the system into the design of an effective and efficient algorithm. By proposing the knowledge-based grouping and local search strategies, the proposed algorithm follows an iterated manner for balancing diversity maintenance and convergence enhancement during the cooperative co-evolution. Numerical studies comparing the proposed algorithm with 14 popular optimization algorithms demonstrate its effectiveness and efficiency. The practicability of the proposed modelling and optimization approach is validated on a hardware platform

Synovium is a sensitive tissue for mapping the negative effects of systemic iron overload in osteoarthritis: identification and validation of two potential targets

Abstract Background The prevention and treatment of osteoarthritis (OA) pose a major challenge in its research. The synovium is a critical tissue in the systematic treatment of OA. The present study aimed to investigate potential target genes and their correlation with iron overload in OA patients. Methods The internal datasets for analysis included the microarray datasets GSE46750, GSE55457, and GSE56409, while the external datasets for validation included GSE12021 and GSE55235. The GSE176308 dataset was used to generate single-cell RNA sequencing profiles. To investigate the expression of the target genes in synovial samples, quantitative reverse transcription-PCR, western blotting, and immunohistochemical assay were conducted. ELISA was used to detect the levels of ferritin and Fe2+ in both serum and synovium. Results JUN and ZFP36 were screened from the differentially expressed genes, and their mRNA were significantly reduced in the OA synovium compared to that in normal synovium. Subsequently, complex and dynamically evolving cellular components were observed in the OA synovium. The mRNA level of JUN and ZFP36 differed across various cell clusters of OA synovium and correlated with immune cell infiltration. Moreover, ferritin and Fe2+ were significantly increased in the serum and synovium of OA patients. Further, we found that JUN elevated and ZFP36 decreased at protein level. Conclusions The synovium is a sensitive tissue for mapping the adverse effects of systemic iron overload in OA. JUN and ZFP36 represent potential target genes for attenuating iron overload during OA treatment. Some discrepancies between the transcription and protein levels of JUN suggest that post-transcriptional modifications may be implicated. Future studies should also focus on the roles of JUN and ZFP36 in inducing changes in cellular components in the synovium during OA pathogenesis

SNP- and Haplotype-Based GWAS of Flowering-Related Traits in <i>Brassica napus</i>

Traits related to flowering time are the most promising agronomic traits that directly impact the seed yield and oil quality of rapeseed (Brassica napus L.). Developing early flowering and maturity rapeseed varieties is an important breeding objective in B. napus. Many studies have reported on days to flowering, but few have reported on budding, bolting, and the interval between bolting and DTF. Therefore, elucidating the genetic architecture of QTLs and genes regulating flowering time, we presented an integrated investigation on SNP and haplotype-based genome-wide association study of 373 diverse B. napus germplasm, which were genotyped by the 60K SNP array and were phenotyped in the four environments. The results showed that a total of 15 and 37 QTLs were detected from SNP and haplotype-based GWAS, respectively. Among them, seven QTL clusters were identified by haplotype-based GWAS. Moreover, three and eight environmentally stable QTLs were detected by SNP-GWAS and haplotype-based GWAS, respectively. By integrating the above two approaches and by co-localizing the four traits, ten (10) genomic regions were under selection on chromosomes A03, A07, A08, A10, C06, C07, and C08. Interestingly, the genomic regions FT.A07.1, FT.A08, FT.C06, and FT.C07 were identified as novel. In these ten regions, a total of 197 genes controlling FT were detected, of which 14 highly expressed DEGs were orthologous to 13 Arabidopsis thaliana genes after integration with transcriptome results. In a nutshell, the above results uncovered the genetic architecture of important agronomic traits related to flowering time and provided a basis for multiple molecular marker-trait associations in B. napus

SNP- and Haplotype-Based GWAS of Flowering-Related Traits in Brassica napus

Traits related to flowering time are the most promising agronomic traits that directly impact the seed yield and oil quality of rapeseed (Brassica napus L.). Developing early flowering and maturity rapeseed varieties is an important breeding objective in B. napus. Many studies have reported on days to flowering, but few have reported on budding, bolting, and the interval between bolting and DTF. Therefore, elucidating the genetic architecture of QTLs and genes regulating flowering time, we presented an integrated investigation on SNP and haplotype-based genome-wide association study of 373 diverse B. napus germplasm, which were genotyped by the 60K SNP array and were phenotyped in the four environments. The results showed that a total of 15 and 37 QTLs were detected from SNP and haplotype-based GWAS, respectively. Among them, seven QTL clusters were identified by haplotype-based GWAS. Moreover, three and eight environmentally stable QTLs were detected by SNP-GWAS and haplotype-based GWAS, respectively. By integrating the above two approaches and by co-localizing the four traits, ten (10) genomic regions were under selection on chromosomes A03, A07, A08, A10, C06, C07, and C08. Interestingly, the genomic regions FT.A07.1, FT.A08, FT.C06, and FT.C07 were identified as novel. In these ten regions, a total of 197 genes controlling FT were detected, of which 14 highly expressed DEGs were orthologous to 13 Arabidopsis thaliana genes after integration with transcriptome results. In a nutshell, the above results uncovered the genetic architecture of important agronomic traits related to flowering time and provided a basis for multiple molecular marker-trait associations in B. napus