Finite-power spectral analytic framework for quantized sampled signals

Publication in the conference proceedings of SampTA, Bremen, Germany, 201

Optimization of Superplastic Forming Process of AA7075 Alloy for the Best Wall Thickness Distribution

This work aims to optimize the process parameters for improving the wall thickness distribution of the sheet superplastic forming process of AA7075 alloy. The considered factors include forming pressure p (MPa), deformation temperature T (°C), and forming time t (minutes), while the responses are the thinning degree of the wall thickness ε (%) and the relative height of the product h*. First, a series of experiments are conducted in conjunction with response surface method (RSM) to render the relationship between inputs and outputs. Subsequently, an analysis of variance (ANOVA) is conducted to verify the response significance and parameter effects. Finally, a numerical optimization algorithm is used to determine the best forming conditions. The results indicate that the thinning degree of 13.121% is achieved at the forming pressure of 0.7 MPa, the deformation temperature of 500°C, and the forming time of 31 minutes

Influence of biofertilizer produced using drumstick (Moringa oleifera L.) unused parts on the growth performance of two leafy vegetables

The non-edible parts of Moringa oleifera, such as stems, branches or leaf petioles, have often been discarded while the leaves are consumed as a vegetable or are used to produce organic fertilizer. This study aimed to determine the optimal conditions for producing Moringa organic fertilizer (MOF) from previously unused parts and to compare these fertilizers with cow manure and bio-organic fertilizer. Seventy kilograms of the unused Moringa parts were blended with fifty kilograms of manure, 0.2 kilogram of Trichoderma-based product and two kilograms of superphosphate. The mixture was incubated at different intervals, including 5, 7 or 9 weeks. Next, the effects of MOF on the growth, yield, ascorbic acid content and Brix of lettuce and mustard spinach were also determined and compared with other organic fertilizers (cow manure and bio-organic fertilizer). Results of the study revealed that 25 tons per ha of MOF were significantly superior to those treated with cow manure and bio-organic fertilizer in the case of vegetable yields. Further, 7 weeks of MOF incubation was found suitable to produce an optimal yield during the various incubation period. These results suggested that the Moringa non-edible parts can make organic fertilizer and enhance growth, yield, and leafy vegetable production

Selecting target market by similar measures in interval intuitionistic fuzzy set

The selection of the target market plays vital role in promoting the marketing strategies of companies. We presented is a method for target market selection. We introduce some novel similarity measures between intuitionistic fuzzy sets and the novel similarity measures between interval-valued intuitionistic fuzzy sets. They are constructed by combining exponential and other functions. Finally, we introduce a multi-criteria decision making model to select target market by using the novel similarity measure of interval intuitionistic fuzzy sets

Structural and Optoelectronic Properties of CdSe Tetrapod Nanocrystals for Bulk Heterojunction Solar Cell Applications

Semiconducting CdSe tetrapod nanoparticles were prepared, and their structural and optical properties were examined. The surface capping molecule, octylphosphonic acid, was replaced with butylamine after the particle synthesis. The exchange of surface ligands changed the physical properties of the nanocrystals, which resulted in a slight decrease in the nanoparticles size. The effects of changing surface ligands of CdSe tetrapod nanocrystals on the structural and optoelectronic properties were investigated, and it was found that the surfactant of nanoparticles could affect the device performance by enhancing the charge carrier separation at the active layer interfaces. Power conversion efficiency of the bulk heterojunction solar cells having the structure of glass/ITO/PEDOT:PSS/(CdSe + PCPDTBT)/Al was improved from 1.21% to 1.52% with the use of ligand-exchanged nanoparticles

Vietnamese version of the general medication adherence scale (Gmas):Translation, adaptation, and validation

Background: We aimed to translate, cross-culturally adapt, and validate the General Medication Adherence Scale (GMAS) into Vietnamese. Methods: We followed the guidelines of Beaton et al. during the translation and adaptation process. In Stage I, two translators translated the GMAS to Vietnamese. Stage II involved synthesizing the two translations. Stage III featured a back translation. Stage IV included an expert committee review and the creation of the pre-final version of the GMAS, and in stage V, pilot testing was conducted on 42 Vietnamese patients with type 2 diabetes. The psychometric validation process evaluated the reliability and validity of the questionnaire. The in-ternal consistency and test–retest reliability were assessed by Cronbach’s alpha and Spearman’s correlation coefficients. The construct validity was determined by an association examination between the levels of adherence and patient characteristics. The content validity was based on the opinion and assessment score by the expert committee. The Vietnamese version of the GMAS was created, in-cluding 11 items divided into three domains. There was a good equivalence between the English and the Vietnamese versions of the GMAS in all four criteria. Results: One hundred and seventy-seven patients were participating in the psychometric validation process. Cronbach’s alpha was acceptable for all questionnaire items (0.817). Spearman’s correlation coefficient of the test–retest reliability was acceptable for the GMAS (0.879). There are significant correlations between medication adherence levels and occupation, income, and the Beliefs about Medicines Questionnaire (BMQ) score regarding construct validity. Conclusions: The Vietnamese version of GMAS can be considered a reliable and valid tool for assessing medication adherence in Vietnamese patients

FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Co-Training

We introduce FedDCT, a novel distributed learning paradigm that enables the usage of large, high-performance CNNs on resource-limited edge devices. As opposed to traditional FL approaches, which require each client to train the full-size neural network independently during each training round, the proposed FedDCT allows a cluster of several clients to collaboratively train a large deep learning model by dividing it into an ensemble of several small sub-models and train them on multiple devices in parallel while maintaining privacy. In this co-training process, clients from the same cluster can also learn from each other, further improving their ensemble performance. In the aggregation stage, the server takes a weighted average of all the ensemble models trained by all the clusters. FedDCT reduces the memory requirements and allows low-end devices to participate in FL. We empirically conduct extensive experiments on standardized datasets, including CIFAR-10, CIFAR-100, and two real-world medical datasets HAM10000 and VAIPE. Experimental results show that FedDCT outperforms a set of current SOTA FL methods with interesting convergence behaviors. Furthermore, compared to other existing approaches, FedDCT achieves higher accuracy and substantially reduces the number of communication rounds (with $4-8$ times fewer memory requirements) to achieve the desired accuracy on the testing dataset without incurring any extra training cost on the server side.Comment: Under review by the IEEE Transactions on Network and Service Managemen

1M parameters are enough? A lightweight CNN-based model for medical image segmentation

Convolutional neural networks (CNNs) and Transformer-based models are being widely applied in medical image segmentation thanks to their ability to extract high-level features and capture important aspects of the image. However, there is often a trade-off between the need for high accuracy and the desire for low computational cost. A model with higher parameters can theoretically achieve better performance but also result in more computational complexity and higher memory usage, and thus is not practical to implement. In this paper, we look for a lightweight U-Net-based model which can remain the same or even achieve better performance, namely U-Lite. We design U-Lite based on the principle of Depthwise Separable Convolution so that the model can both leverage the strength of CNNs and reduce a remarkable number of computing parameters. Specifically, we propose Axial Depthwise Convolutions with kernels 7x7 in both the encoder and decoder to enlarge the model receptive field. To further improve the performance, we use several Axial Dilated Depthwise Convolutions with filters 3x3 for the bottleneck as one of our branches. Overall, U-Lite contains only 878K parameters, 35 times less than the traditional U-Net, and much more times less than other modern Transformer-based models. The proposed model cuts down a large amount of computational complexity while attaining an impressive performance on medical segmentation tasks compared to other state-of-the-art architectures. The code will be available at: https://github.com/duong-db/U-Lite.Comment: 6 pages, 7 figure