Nonlinear buckling of CNT-reinforced composite toroidal shell segment surrounded by an elastic medium and subjected to uniform external pressure

Buckling and postbuckling behaviors of Toroidal Shell Segment (TSS) reinforced by single-walled carbon nanotubes (SWCNT), surrounded by an elastic medium and subjected to uniform external pressure are investigated in this paper. Carbon nanotubes (CNTs) are reinforced into matrix phase by uniform distribution (UD) or functionally graded (FG) distribution along the thickness direction. Effective properties of carbon nanotube reinforced composite (CNTRC) are estimated by an extended rule of mixture through a micromechanical model. Governing equations for TSSs are based on the classical thin shell theory taking into account geometrical nonlinearity and surrounding elastic medium. Three-term solution of deflection and stress function are assumed to satisfy simply supported boundary condition, and Galerkin method is applied to obtain nonlinear load-deflection relation from which buckling loads and postbuckling equilibrium paths are determined. The effects of CNT volume fraction, distribution types, geometrical ratios and elastic foundation on the buckling and postbuckling behaviors of CNTRC TSSs are analyzed and discussed

Multifactorial Evolutionary Algorithm For Clustered Minimum Routing Cost Problem

Minimum Routing Cost Clustered Tree Problem (CluMRCT) is applied in various fields in both theory and application. Because the CluMRCT is NP-Hard, the approximate approaches are suitable to find the solution for this problem. Recently, Multifactorial Evolutionary Algorithm (MFEA) has emerged as one of the most efficient approximation algorithms to deal with many different kinds of problems. Therefore, this paper studies to apply MFEA for solving CluMRCT problems. In the proposed MFEA, we focus on crossover and mutation operators which create a valid solution of CluMRCT problem in two levels: first level constructs spanning trees for graphs in clusters while the second level builds a spanning tree for connecting among clusters. To reduce the consuming resources, we will also introduce a new method of calculating the cost of CluMRCT solution. The proposed algorithm is experimented on numerous types of datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, partially on large instance

An in-situ thermoelectric measurement apparatus inside a thermal-evaporator

At the ultra-thin limit below 20 nm, a film's electrical conductivity, thermal conductivity, or thermoelectricity depends heavily on its thickness. In most studies, each sample is fabricated one at a time, potentially leading to considerable uncertainty in later characterizations. We design and build an in-situ apparatus to measure thermoelectricity during their deposition inside a thermal evaporator. A temperature difference of up to 2 K is generated by a current passing through an on-chip resistor patterned using photolithography. The Seebeck voltage is measured on a Hall bar structure of a film deposited through a shadow mask. The measurement system is calibrated carefully before loading into the thermal evaporator. This in-situ thermoelectricity measurement system has been thoroughly tested on various materials, including Bi, Te, and Bi$_2$Te$_3$, at high temperatures up to 500 K

Spondylolysis-induced Multilevel Lumbar Spondylolisthesis; Challenges in Lumbar Spine Surgery

Lumbar spondylolysis and multilevel lumbar spondylolysis account for 4.4-5.8% and 0.3% of the general population, and multilevel lumbar spondylolysis resulting in spondylolisthesis is even rarer. Herein, we report two cases of three-level lumbar spondylolisthesis because of spondylolysis: A 49-year-old woman was admitted to the hospital for dull lower back pain over the past 8 months, with exacerbating symptoms when standing and walking. Spasticity at lumbar region and radiculopathy at S1 nerve root was found on examination and a 63-year-old man was admitted to the hospital because of numbness and perianal sensory disturbances with difficulty urinating 2 weeks ago, the symptoms gradually increased to the time of examination. Both patients were diagnosed with multilevel lumbar spondylolisthesis because of spondylolysis and were indicated for posterior lumbar interbody fusion (PLIF). After surgery, both patients recovered well without any significant complications. The improved treatment results suggest the application of PLIF technique to treat spondylolysis-induced multilevel lumbar spondylolisthesis

Application of multispectral UAV to estimate mangrove biomass in Vietnam: A case study in Dong Rui commune, Quang Ninh Province

Mangroves play an important role in coastal estuarine areas with different ecological functions, such as reducing the impact of waves and currents, accumulating biomass and sequestering carbon. However, estimation of terrestrial biomass in mangrove areas, especially in Vietnam, has not been fully studied. The application of unmanned aerial vehicles (UAV), mounted with multispectral cameras combined with field verification is an effective method for estimating terrestrial biomass for mangroves, as it reduces field survey time and allows for greater spatial range research. In this study, ground biomass was estimated for the mangrove area in the Dong Rui commune, based on multispectral image data obtained from UAV and survey results in 16 standard cells measuring actual biomass according to four regression models: Log-Log, Log-Lin, Lin-Log and Lin-Lin. The results of comparing the data from these four models show that the log-log model has the highest accuracy with a high correlation coefficient (R2 = 0.831). Based on the results of the analysis and selection of ground-based biomass estimation models, a biomass map was established for the UAV flying area in the Dong Rui mangrove forest with biomass values ranging from 20 Mg/ha to 150 Mg/ha. In summary, we present a biomass estimation method through four basic linear regression models for mangrove areas, based on multispectral image data obtained from ultrahigh-resolution UAV. The resulting research results can serve as a basis for managers to calculate and synchronise the payment of carbon services, thus contributing to effectively promoting the livelihoods of local people

Long short-term memory (LSTM) neural networks for short-term water level prediction in Mekong river estuaries

This study firstly adopts a state-of-the-art deep learning approach based on a Long Short-Term Memory (LSTM) neural network for predicting the hourly water level of Mekong estuaries in Vietnam. The LSTM models were developed from around 8,760 hourly data points within 2018 and were evaluated using the Nash-Sutcliffe efficiency coefficient (NSE), mean absolute error (MAE), and root mean square error (RMSE). The results showed that the NSE values for the training and testing steps were both above 0.98, which can be regarded as very good performance. Furthermore, the RMSE were between 0.09 and 0.11 m for the training and between 0.10 and 0.12 m for the testing, while MAE for the training ranged from 0.07 to 0.08 m and varied from 0.08 to 0.10 m for the testing. The LSTM networks appear to enable high precision and robustness in water level time series prediction. The outcomes of this research have crucial implications in river water level predictions, especially from the viewpoint of employing deep learning algorithms

FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients. This work introduces a novel non-IID type encountered in real-world datasets, namely cluster-skew, in which groups of clients have local data with similar distributions, causing the global model to converge to an over-fitted solution. To deal with non-IID data, particularly the cluster-skewed data, we propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client's impact factor (which will be used as the weights in the aggregation process). Extensive experiments on a suite of federated datasets confirm that the proposed FedDRL improves favorably against FedAvg and FedProx methods, e.g., up to 4.05% and 2.17% on average for the CIFAR-100 dataset, respectively.Comment: Accepted for presentation at the 51st International Conference on Parallel Processin

Mitigating effect of embankment to adjacent pipe with CDM columns

Pipelines are valuable infrastructures that covering a large area or expanding to long distance for the transporting function. This leads to the variety of loads and effects applied on such buried structures. A thread to pipeline integrity is the construction of the embankment on the soft soil which leads to the displacement of the pipe adjacent to the slope. This displacement will effect to the increase of internal force or causing failure of the near-by pipes. The use of concrete pile to improve the soil properties may be a solution; however, the cost for this is expensive. To propose an alternative solution for the problem, this study uses a system of cement deep mixing, CDM, columns as the solution for protecting the pipe. A simple 2D Finite Element, FE, model using Plaxis software has been established based on the equivalent soil approach which considering the CDM columns and their surrounding soil as an unified soil. The effectiveness of the proposed solution has been numerically investigated and proven. The lateral displacement of pipe and the maximum ring bending moment and other internal forces are significantly reduced with the appearance of the CDM columns. The selective parametric study has been implemented revealing the critical input variables are the distance of pipe to the slope and the length of the CDM column

Selective breeding of saline-tolerant striped catfish (Pangasianodon hypophthalmus) for sustainable catfish farming in climate vulnerable Mekong Delta, Vietnam

peer reviewedStriped catfish (Pangasianodon hypophthalmus), a freshwater species cultured mainly in the Mekong Delta region in Southern Vietnam, is facing a significant challenge due to salinity intrusion as a result of climatic changes. Given these evolving environmental conditions, selecting new strains with a higher salinity tolerance could make production of striped catfish economically feasible in brackish environments. In this study, we carried out a selection program aimed at developing a striped catfish strain able to survive and grow fast in a saline environment. To implement the selection program, we first collected males and females from different provinces in the Mekong delta. We next performed a factorial cross of these breeders to produce half- and full-sib families. When fish reached fry stage (47 dph), we put them in a saline environment (10 ppt) and subsequently kept 50 % of the fastest-growing fish after 143 days post hatching (dph). We repeated this mass selection procedure after 237 dph and 340 dph. We maintained in parallel a randomly selected group in saline conditions and a group of fish reared in freshwater to serve as controls. After crossing the selected individuals, we performed several tests on the next generation of fish to evaluate the effectiveness of selection after one generation in saline conditions. Average direct responses to selection were 18.0 % for growth and 11.4 % for survival rate after one generation of selection. We estimated a moderate realized heritability (0.29) for body weight. The genetic gains obtained in our study for body weight and survival rate after one generation of selection under saline conditions suggest that selection can be effective to improve ability of striped catfish to cope with saline stress. We conclude that our selection program has succeeded in developing a productive strain of striped catfish with better tolerance to salinity. © 2022 The Author