Magnetoelectric Properties of Epitaxial Spinel Oxide Thin Films

Non-collinear spinel multiferroics, which have an AB2O4 structure, have attracted a great deal of attention in the past few years. Amongst them, the one that has drawn great attention is spinel vanadate, FeV2O4 (FVO), which has transition metal elements at both A and B sites. The interplay among the spin, orbital and lattice degrees of freedom makes FVO a very complex system which exhibits interesting properties. This thesis focuses on the growth and functional characterization of high-quality FVO epitaxial thin films on perovskite SrTiO3 substrates. The magnetic and electrical properties of FVO films are presented as well as the characterization of magnetoelectric coupling of the material. Secondly, another unique spinel, Cobalt Chromite, CoCr2O4 (CCO), which has a canted conical magnetic ordering and shows spontaneous polarization, is also studied in this thesis. The polarization and resistivity-switching behaviour of CCO/LSMO (CoCr2O4/La6.67Sr3.33MnO3) heterostructure is also investigated. The thesis is organized into 7 chapters to give a systematic discussion. In Chapter 1, the structure and physical properties of spinel FVO and CCO are introduced. The background theory and experimental methods, which are used in the following chapters, are presented in Chapter 2. The general theories of ferromagnetic, ferroelectric and multiferroics are introduced in this chapter, followed by physical models for dielectric constant and resistivity. The basics of the methods employed are also discussed. In Chapter 3, the growth method and the structural characterization of epitaxial FVO films on STO substrates are presented. The effects of pulsed laser deposition (PLD) growth conditions on the FVO films are discussed and structural characterization by X-ray diffraction (XRD), atomic force microscopy (AFM), transmission electron microscopy (TEM), etc. are presented. In Chapter 4, the electron and spin states of FVO films, studied by X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism (XMCD) are presented, followed by the magnetic properties as measured by superconducting quantum interference device (SQUID) and magnetic force microscopy (MFM). In Chapter 5, the dielectric properties of FVO films are investigated by measuring the capacitance of a Pd/FVO/Nb:STO junction, and the magnetocapacitance effect of the material is also presented. The fabrication of CCO/LSMO heterostructure is described in Chapter 6 and its polarization and resistivity-switching behaviour are discussed. The conclusions of this thesis, and the directions of possible future related work, are provided in Chapter 7

Theory of functional principal components analysis for discretely observed data

For discretely observed functional data, estimating eigenfunctions with diverging index is essential in nearly all methods based on functional principal components analysis. In this paper, we propose a new approach to handle each term appeared in the perturbation series and overcome the summability issue caused by the estimation bias. We obtain the moment bounds for eigenfunctions and eigenvalues for a wide range of the sampling rate. We show that under some mild assumptions, the moment bound for the eigenfunctions with diverging indices is optimal in the minimax sense. This is the first attempt at obtaining an optimal rate for eigenfunctions with diverging index for discretely observed functional data. Our results fill the gap in theory between the ideal estimation from fully observed functional data and the reality that observations are taken at discrete time points with noise, which has its own merits in models involving inverse problem and deserves further investigation

Hybrid control and model-based assertions for autonomous intersection management system as a cyber-physical system

A cyber-physical system (CPS) consists of multiple physical components that collab- orate through a network during real-time operation according to system-level commands. A hybrid control, which generates discrete system-level commands and handles the low-level physical dynamics for each component, is of singular importance to a CPS. For a CPS, live- ness and safety require that the system is always eventually doing what is desirable without any undesirable behavior, and have to be carefully addressed. The advance in information technology and autonomous driving make it possible to establish an autonomous intersection management system (AIMS) for ground autonomous vehicles, which can potentially improve traffic efficiency and reduce intersection car accidents. This work presents a hybrid control and introduces model-based assertions for such an AIMS. System-level traffic requirements are expressed in the form of linear temporal logic (LTL) specifications in the generalized reactivity formulas with rank 1 (GR(1)) and a two-player game is solved to synthesize a discrete traffic network controller. DaNI, a motor-driven laboratory robot vehicle, is mod- eled using a bond-graph approach, and a nonlinear vehicle trajectory sliding-mode controller (SMC) is mathematically and numerically described. The discrete traffic controller instructs each robot vehicle to pass through the intersection of interest at a certain time and the vehicle’s trajectory within the intersection is controlled by the SMC. Using simulation, it is shown that the synthesized discrete controller is able to determine proper system actions in response to traffic data and environmental actions while maintaining liveness and safety specifications. A laboratory study on a simple AIMS is demonstrated to depict the basic design structure of a AIMS as a CPS. The results suggest that model-based assertions, which monitor and validate a CPS by assertions based on physical models, can be helpful in de- tecting physics-related abnormalities during operation of a CPS that may not be captured by a software-level analysis. Using simulations, it is shown how the accuracy of a continuous vehicle trajectory controller, such as a SMC, can provide informative guidance on the design of model-based assertions.Mechanical Engineerin

Study on the Preparation and Anisotropic Distribution of Mechanical Properties of Well-Aligned PMIA Nanofiber Mats Reinforced Composites

Well-aligned PMIA nanofiber mats were fabricated by electrospinning and then hot-pressing was used to produce PMIA nanofiber mats reinforced PLA matrix by layer-by-layer with the interlayer angles of 0, 45, and 90°. Orthogonal experimental design was employed to fix the effect of the hot-pressing parameters on the tensile strength of nanocomposites, and SEM was used to characterize the broken sections of the nanocomposites after tensile test. The optimized process parameters were achieved of pressure as 1000 Pa, temperature as 180°C, and time as 30 min. The SEM images of broken sections showed that the different laminate forms and the state of bearing load of nanofibers resulted in the different morphologies of broken sections. The break strength of PMIA/PLA nanocomposites with any of interlayer angles at different tensile testing directions was revealed as follows: axial > oblique > transverse, and the initial modulus also showed the same except the angle of 90° with the approach initial modulus at the axial and transverse directions. The maximum tensile strength and modulus of the nanocomposites were 17.12 MPa and 1642.17 MPa, respectively, of the axial tensile testing directions of the interlayer angle of 0°

GROUND FAULT DIAGNOSTICS FOR AUTOMOTIVE ELECTRONIC CONTROL UNITS

An electronic control unit (ECU) with a floating ground is not able to receive or transmit messages or participate in controller area network (CAN) communication. The absence of any ECU, either temporarily or permanently, negatively impacts vehicle functionalities. The offset ground, which by itself won’t affect bus functionalities if the grounding resistance is small, however, may evolve into a floating ground or behave similarly if the resistance is large. In this work, the correlation among ground faults, either offset or floating, and CAN bus voltage or messages are analyzed based on the equivalent circuit models and the bus protocol. A voltage-based solution to detect ground faults is proposed. With the help of bus messages, both faults can be isolated at the ECU level. Considering the inherent system delay between the message fetching and voltage measurement, a normalized voltage-message correlation approach with the bus load estimation is developed as well. All proposed approaches are implemented to an Arduino-based embedded system and validated on a vehicle frame

Identification and genomic analyses of a novel endophytic actinobacterium Streptomyces endophytica sp. nov. with potential for biocontrol of yam anthracnose

Anthracnose disease caused by Colletotrichum gloeosporioides is one of the devastating diseases of yams (Dioscorea sp.) worldwide. In this study, we aimed to isolate endophytic actinobacteria from yam plants and to evaluate their potential for the control of yam anthracnose based on bioassays and genomic analyses. A total of 116 endophytic actinomycete strains were isolated from the surface-sterilized yam tissues from a yam orchard in Hainan Province, China. In total, 23 isolates showed antagonistic activity against C. gloeosporioides. An endophytic actinomycete, designated HNM0140T, which exhibited strong antifungal activities, multiple biocontrol, and plant growth-promoting (PGP) traits was subsequently selected to colonize in the tissue-cultured seedlings of yam and was tested for its in vivo biocontrol potential on yam anthracnose. The results showed that treatment with strain HNM0140T markedly reduced the severity and incidence of yam anthracnose under greenhouse conditions. Morphological and chemotaxonomic analyses showed that strain HNM0140T was assigned to the genus Streptomyces. Phylogenetic analysis based on the 16S rRNA gene sequences indicated that strain HNM0140T formed a separate cluster together with Streptomyces lydicus ATCC 25470T (99.45%), Streptomyces chattanoogensis NRRL ISP-5002T (99.45%), and Streptomyces kronopolitis NEAU-ML8T (98.97%). The phylogenomic tree also showed that strain HNM0140T stably clustered with Streptomyces lydicus ATCC 25470T. The ANI and dDDH between strain HNM0140T and its closest related-type species were well below the recommended thresholds for species demarcation. Hence, based on the phylogenetic, genomic, and phenotypic analyses, strain HNM0140T should represent a new streptomycete species named Streptomyces endophytica sp. nov. Genomic analysis revealed that strain HNM0140T harbored 18 putative BGCs for secondary metabolites, some PGP-related genes, and several genes coding for antifungal enzymes. The presented results indicated that strain HNM0140T was a promising biocontrol agent for yam anthracnose

Biased Bowl-Direction of Monofluorosumanene in the Solid State

Yakiyama Y., Li M., Zhou D., et al. Biased Bowl-Direction of Monofluorosumanene in the Solid State. Journal of the American Chemical Society 146, 5224 (2024); https://doi.org/10.1021/jacs.3c11311.A new curved π-conjugated molecule 1-fluorosumanene (1) was designed and synthesized that possesses one fluorine atom on the benzylic carbon of sumanene. This compound can exhibit bowl inversion in solution, leading to the formation of two diastereomers, 1endo and 1exo, with different dipole moments. Experimental and theoretical investigation revealed an energetical relationship among 1exo, 1endo, and solvent to realize the various endo:exo ratios in the single crystals of 1 depending on the crystallization solvent. Significantly, the molecular dynamics (MD) simulations revealed that 1exo positively worked for the elongation of the stacking structure and the final endo:exo ratio was affected by the relative stability difference between 1endo and 1exo derived by solvation. Such an arrangeable endo:exo ratio of 1 realized the preparation of unique materials showing a different dielectric response from the same molecule 1 just by changing the crystallization solvent

Streptomyces tirandamycinicus sp. nov., a Novel Marine Sponge-Derived Actinobacterium With Antibacterial Potential Against Streptococcus agalactiae

A novel actinobacterium, strain HNM0039T, was isolated from a marine sponge sample collected at the coast of Wenchang, Hainan, China and its polyphasic taxonomy was studied. The isolate had morphological and chemical characteristics consistent with the genus Streptomyces. Based on the 16S rRNA gene sequence analysis, strain HNM0039T was closely related to Streptomyces wuyuanensis CGMCC 4.7042T (99.38%) and Streptomyces spongiicola HNM0071T (99.05%). The organism formed a well-delineated subclade with S. wuyuanensis CGMCC 4.7042T and S. spongiicola HNM0071T in the Streptomyces 16S rRNA gene tree. Multi-locus sequence analysis (MLSA) based on five house-keeping gene alleles (atpD, gyrB, rpoB, recA, trpB) further confirmed their relationship. DNA–DNA relatedness between strain HNM0039T and its closest type strains, namely S. wuyuanensis CGMCC 4.7042T and S. spongiicola HNM0071T, were 46.5 and 45.1%, respectively. The average nucleotide identity (ANI) between strain HNM0039T and its two neighbor strains were 89.65 and 91.44%, respectively. The complete genome size of strain HNM0039T was 7.2 Mbp, comprising 6226 predicted genes with DNA G+C content of 72.46 mol%. Thirty-one putative secondary metabolite biosynthetic gene clusters were also predicted in the genome of strain HNM0039T. Among them, the tirandamycin biosynthetic gene cluster has been characterized completely. The crude extract of strain HNM0039T exhibited potent antibacterial activity against Streptococcus agalactiae in Nile tilapia. And tirandamycins A and B were further identified as the active components with MIC values of 2.52 and 2.55 μg/ml, respectively. Based on genotypic and phenotypic characteristics, it is concluded that strain HNM0039T represents a novel species of the genus Streptomyces whose name was proposed as Streptomyces tirandamycinicus sp. nov. The type strain is HNM0039T (= CCTCC AA 2018045T = KCTC 49236T)

A comprehensive review of Tripterygium wilfordii hook. f. in the treatment of rheumatic and autoimmune diseases: Bioactive compounds, mechanisms of action, and future directions

Rheumatic and autoimmune diseases are a group of immune system-related disorders wherein the immune system mistakenly attacks and damages the body’s tissues and organs. This excessive immune response leads to inflammation, tissue damage, and functional impairment. Therapeutic approaches typically involve medications that regulate immune responses, reduce inflammation, alleviate symptoms, and target specific damaged organs. Tripterygium wilfordii Hook. f., a traditional Chinese medicinal plant, has been widely studied in recent years for its application in the treatment of autoimmune diseases, including rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis. Numerous studies have shown that preparations of Tripterygium wilfordii have anti-inflammatory, immunomodulatory, and immunosuppressive effects, which effectively improve the symptoms and quality of life of patients with autoimmune diseases, whereas the active metabolites of T. wilfordii have been demonstrated to inhibit immune cell activation, regulate the production of inflammatory factors, and modulate the immune system. However, although these effects contribute to reductions in inflammatory responses and the suppression of autoimmune reactions, as well as minimize tissue and organ damage, the underlying mechanisms of action require further investigation. Moreover, despite the efficacy of T. wilfordii in the treatment of autoimmune diseases, its toxicity and side effects, including its potential hepatotoxicity and nephrotoxicity, warrant a thorough assessment. Furthermore, to maximize the therapeutic benefits of this plant in the treatment of autoimmune diseases and enable more patients to utilize these benefits, efforts should be made to strengthen the regulation and standardized use of T. wilfordii

Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management

Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA