Non Reciprocal Passive Components on LTCC Ferrite Substrate

This thesis investigates passive components (mainly circulators) based on ferrite LTCC substrate. The external magnets used in conventional circulators must be strong to overcome the ferrite's demagnetization factor. The novel circulator presented herein uses an embedded winding within the ferrite to magnetize the material from the inside, thereby significantly reducing the demagnetization effects. Because of the controllability of the bias field, the resulting device is also multifunctional: when the windings are energized by a current, the device operates as a dynamic circulator in which the circulation direction can be changed by switching the direction of the current. If an external magnet is placed on the circulator, its operating frequency can be changed by adjusting the bias current. Unlike other LTCC circulators with external magnets, the proposed device can even operate as a power splitter by removing the bias current. A circulator prototype has been characterized in three states: unbiased, biased by windings and biased by windings and external magnets. When no current is applied, the transmission of each port is about -5 dB with return loss better than 20 dB at 14.8 GHz. When a current of 300 mA is injected into the windings, the measured insertion loss and isolation of the circulator are approximately 3 dB and 8 dB, respectively, whereas the return loss is better than 20 dB at 14.2 GHz. When external magnets are added in addition to the current of 200 mA, the insertion loss and isolation improve to 1.6 dB and 23 dB, respectively at 14.2 GHz. The variation of the circulator's working frequency is 0.6 GHz. This is achieved firstly by the change of internal magnetization M when current is less than 120 mA, then the heat due to the winding increases the ferrite's μeff leading to more frequency shifting. The total size (L*W*H) is 8mm*8mm*1.1mm

Strategies for Searching Video Content with Text Queries or Video Examples

The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search. However, metadata is often lacking for user-generated videos, thus these videos are unsearchable by current search engines. Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity problem by directly analyzing the visual and audio streams of each video. CBVR encompasses multiple research topics, including low-level feature design, feature fusion, semantic detector training and video search/reranking. We present novel strategies in these topics to enhance CBVR in both accuracy and speed under different query inputs, including pure textual queries and query by video examples. Our proposed strategies have been incorporated into our submission for the TRECVID 2014 Multimedia Event Detection evaluation, where our system outperformed other submissions in both text queries and video example queries, thus demonstrating the effectiveness of our proposed approaches

Treatment of Livestock Odor and Pathogens with Ultraviolet Light

Livestock production systems are associated with aerial emissions of odor, volatile organic compounds (VOCs), other gases, and particular matter including airborne pathogens. Control of those emissions is needed to assure compliance with environmental regulations and long-term viability of the industry. The focus of this research is a novel approach to abatement of livestock odor and pathogens utilizing photocatalysis, i.e., UV irradiation in presence of TiO2 as a catalyst. A standard gas generation system was built and tested to generate ten odorous VOCs commonly defining livestock odors. These VOCs included methylmercaptan, ethylmercaptan, dimethylsulfide, butylmercaptan, acetic, propanoic, butyric, and isovaleric acid, p-cresol, and H2S. Our previous research established a reduction of VOCs with UV light only of 60~98% for sulfur VOCs and 91% for p-cresol, but only 20 to 45% removal for volatile fatty acids (VFAs). Titanium dioxide was used in the current research to catalyze UV reactions in the same gas mixtures of VOCs held in a small photoreactor. The reactor was designed to conduct controlled tests with UV light under dynamic (with airflows) conditions that facilitate experiments simulating exhaust from mechanically-ventilated barns. Six 10W lamps with characteristic bands at (185), 254, 312, 365 nm, respectively, and principle output at 254 nm were used as UV source in dynamic system. Solid phase microextraction (SPME) fibers were used to sample VOCs before and after UV treatment and for transfer of samples to a gas chromatography and mass spectrometry olfactometry (GC-MS-O) system. Odor analysis was completed by a forced-choice dynamic-dilution olfactometer in the Olfactometry lab at ISU. Effectiveness of four different treatment options, i.e., UV254, UV185+254, UV254+TiO2, and UV185+254+TiO2 was assessed. Effect of light energy, catalyst presence and light wavelength was evaluated. More than 50% in chemical reduction was found for all VOCs tested with a treatment time of 18.5 second. A linearly positive correlation was found between the percent conversion of tested VOCs and light energy dose. TiO2 showed to greatly improve the treatment effectiveness on VOCs, VFAs in particular, no matter deep UV was used or not. However, when TiO2 was used, deep UV showed very little improvement in degrading VOCs tested, while significant improvement was observed when no TiO2 was used. Total odor reduction of 70% by certain energy level indicated the feasibility of odor mitigation by UV light. Continued work includes simultaneous inactivation of airborne pathogens with UV light

Astragaloside IV inhibits pathological functions of gastric cancer-associated fibroblasts through regulation of HOXA6/ZBTB12 axis

[email protected], [email protected] Cancer-associated fibroblasts (CAFs) play critical roles in the tumor microenvironment and exert tumor-promoting or tumor-retarding effects on cancer development. Astragaloside IV has been suggested to rescue the pathological impact of CAFs in gastric cancer. This study aimed to investigate the potential mechanism of astragaloside IV in the regulation of CAF pathological functions in gastric cancer development. Homeobox A6 (HOXA6), and Zinc Finger and BTB Domain Containing 12 (ZBTB12) are highly expressed in gastric CAFs compared with normal fibroblasts (NFs) based on the GSE62740 dataset. We found that astragaloside IV-stimulated CAFs suppressed cell growth, migration, and invasiveness of gastric cancer cells. HOXA6 and ZBTB12 were downregulated after astragaloside IV treatment in CAFs. Further analysis revealed that HOXA6 or ZBTB12 knockdown in CAFs also exerted inhibitory effects on the malignant phenotypes of gastric cells. Additionally, HOXA6 or ZBTB12 overexpression in CAFs enhanced gastric cancer cell malignancy, which was reversed after astragaloside IV treatment. Moreover, based on the hTFtarget database, ZBTB12 is a target gene that may be transcriptionally regulated by HOXA6. The binding between HOXA6 and ZBTB12 promoter in 293T cells and CAFs was further confirmed. HOXA6 silencing also induced the downregulation of ZBTB12 mRNA and protein in CAFs. Astragaloside IV was demonstrated to regulate the expression of ZBTB12 by mediating the transcriptional activity of HOXA6. Our findings shed light on the therapeutic value of astragaloside IV for gastric cancer

A 3D decoupling Alzheimer’s disease prediction network based on structural MRI

Purpose This paper aims to develop a three-dimensional (3D) Alzheimer’s disease (AD) prediction method, thereby bettering current predictive methods, which struggle to fully harness the potential of structural magnetic resonance imaging (sMRI) data. Methods Traditional convolutional neural networks encounter pressing difficulties in accurately focusing on the AD lesion structure. To address this issue, a 3D decoupling, self-attention network for AD prediction is proposed. Firstly, a multi-scale decoupling block is designed to enhance the network’s ability to extract fine-grained features by segregating convolutional channels. Subsequently, a self-attention block is constructed to extract and adaptively fuse features from three directions (sagittal, coronal and axial), so that more attention is geared towards brain lesion areas. Finally, a clustering loss function is introduced and combined with the cross-entropy loss to form a joint loss function for enhancing the network’s ability to discriminate between different sample types. Results The accuracy of our model is 0.985 for the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and 0.963 for the Australian Imaging, Biomarker & Lifestyle (AIBL) dataset, both of which are higher than the classification accuracy of similar tasks in this category. This demonstrates that our model can accurately distinguish between normal control (NC) and Alzheimer’s Disease (AD), as well as between stable mild cognitive impairment (sMCI) and progressive mild cognitive impairment (pMCI). Conclusion The proposed AD prediction network exhibits competitive performance when compared with state-of-the-art methods. The proposed model successfully addresses the challenges of dealing with 3D sMRI image data and the limitations stemming from inadequate information in 2D sections, advancing the utility of predictive methods for AD diagnosis and treatment

Channel-Spatial Support-Query Cross-Attention for Fine-Grained Few-Shot Image Classification

Few-shot fine-grained image classification aims to use only few labelled samples to successfully recognize subtle sub-classes within the same parent class. This task is extremely challenging, due to the co-occurrence of large inter-class similarity, low intra-class similarity, and only few labelled samples. In this paper, to address these challenges, we propose a new Channel-Spatial Cross-Attention Module (CSCAM), which can effectively drive a model to extract discriminative fine-grained feature representations with only few shots. CSCAM collaboratively integrates a channel cross-attention module and a spatial cross-attention module, for the attentions across support and query samples. In addition, to fit for the characteristics of fine-grained images, a support averaging method is proposed in CSCAM to reduce the intra-class distance and increase the inter-class distance. Extensive experiments on four few-shot fine-grained classification datasets validate the effectiveness of CSCAM. Furthermore, CSCAM is a plug-and-play module, conveniently enabling effective improvement of state-of-the-art methods for few-shot fine-grained image classification

Screening and Optimization of Microalgae Biomass and Plastic Material Coprocessing by Hydrothermal Liquefaction

In the past decade, microalgae biomass has been attracting considerable interest in valuable biocomponents and biofuel production. Meanwhile, plastic waste handling has become one of the most pressing global environmental concerns. Coprocessing of plastic waste and biomass has previously been reported to produce good quality fuel oil and high-value chemicals. In this study, we examined a coliquefaction process (co-HTL) of 2 microalgae, Chlorella vulgaris (Cv) and Nannochloropsis gaditana (Ng), with nine types of common plastics. In a first step, the co-HTL process was conducted in microautoclave reactors with a fixed algae/plastic mass ratio (50:50) at a temperature of 350 °C and a pressure of 16 MPa for a holding time of 15 min. Among the different types of plastics, positive synergistic effects between polycarbonate (PC), polystyrene (PS), and microalgae have been observed: (1) Plastics showed greater decomposition. (2) HTL crude oil yields were increased. Ng algae exhibits a higher interaction ability with plastics. Then, PC and PS were coprocessed with Ng algae using the response surface methodology to optimize the effects of temperature (300–400 °C), algae/plastic mass ratio (20:80–80:20), and holding time (5–45 min) on HTL crude oil yield. Software-based data analysis of the co-HTL experiments were conducted, and the optimal parameters were proposed, which were verified by the experiment results; Ng+PC (20:80 wt %) exhibits the highest crude oil yield of 67.2% at 300 °C with a 5 min holding time, while Ng+PS (80:20 wt %) generates 51.4 wt % crude oil yield at 400 °C and a 25 min holding time. Finally, the analytical results of elemental analysis, FTIR, 1H NMR, GPC, GC-MS, and TGA on the crude oil produced from pure microalgae HTL and co-HTL were compared, indicating that Ng+PC crude oil is more suitable for aromatic chemicals production and Ng+PS crude oil could be more favorable for biofuel applications