Durability of rammed earth walls exposed for 20 years to natural weathering

This paper presents a study on the durability of different types of stabilised and unstabilised rammed earth walls. These rammed earth walls were constructed and exposed for 20 years to natural weathering, in a wet continental climate. None of these walls have shown complete collapse to date. A method to measure the rammed earth walls erosion by stereo-photogrammetry has been developed. The result shows that the mean erosion depth of the studied walls is about 2 mm (0.5% wall thickness) in the case of rammed earth wall stabilised with 5% by dry weight of hydraulic lime and about 6.4 mm (1.6% wall thickness) in the case of unstabilised rammed earth walls. The stabilisation enables to not use any plaster to protect the walls. In the case of the unstabilised rammed earth walls, an extrapolated lifetime longer than 60 years can be assessed. This shows a potential for the use of unstabilised rammed earth in the similar climatic conditions with this study. The method of stereo-photogrammetry used to measure the erosion of rammed earth walls on site may also help to calibrate and develop more pertinent laboratory test to assess the durability of rammed earth wall.     

High-power InP MISFETs

InP MISFETs with SiO2 as the gate insulator and a deep channel recess have been fabricated. At 9 GHz the highest power output with 4 dB gain was 3.5 W/mm gate width with 33% power added efficiency. This power is more than twice that of the best GaAs MESFET.     

Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data

ABSTRACT

Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R² = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha^?1 (average = 87.67 Mg ha^?1). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.     

Improved fracture toughness and fatigue life of carbon fiber reinforced epoxy composite due to incorporation of rubber nanoparticles

In this study, core–shell rubber (CSR) nanoparticles with approximate particle size of 35 nm were used as a modifier for the epoxy polymer. The effects of various CSR contents in the epoxy matrix on mode I interlaminar fracture toughness, tensile strength, and fatigue life of the carbon fabric reinforced epoxy (CF/EP) composites were investigated. The experimental results showed that the mode I interlaminar fracture toughness at crack initiation and propagation significantly improved by 71.21 and 58.47 %, respectively, when 8.0 wt% CSR was dispersed in the epoxy matrix. The fatigue life of the modified CF/EP composites at all of CSR contents dramatically increased 75–100 times longer than that of the unmodified CF/EP composites at high cycle fatigue while tensile strength slightly increased by about 10 %. Field emission scanning electron microcopy (FESEM) observations of the fracture surfaces were conducted to explain failure mechanisms of CSR addition to the CF/EP composites. The evidences of the rubber nanoparticle debonding, plastic void growth, and microshear banding were credited for delaying the onset of matrix crack, and reducing the crack growth rate, as a result, attributed to increase in the mechanical properties of the CF/EP composites.     

Efficient duration and hierarchical modeling for human activity recognition

A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit both their typical duration patterns and inherent hierarchical structures. We exploit efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and apply it to the problem of learning and recognizing ADLs with complex temporal dependencies. The Coxian duration model has several advantages over existing duration parameterization using multinomial or exponential family distributions, including its denseness in the space of nonnegative distributions, low number of parameters, computational efficiency and the existence of closed-form estimation solutions. Further we combine both hierarchical and duration extensions of the hidden Markov model (HMM) to form the novel switching hidden semi-Markov model (SHSMM), and empirically compare its performance with existing models. The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection. Experimental results show that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. We also achieve a recognition accuracy competitive to the current state-of-the-art multinomial duration model, while gaining a significant reduction in computation. Furthermore, cross-validation model selection on the number of phases K in the Coxian indicates that only a small K is required to achieve the optimal performance. Finally, our models are further tested in a more challenging setting in which the tracking is often lost and the activities considerably overlap. With a small amount of labels supplied during training in a partially supervised learning mode, our models are again able to deliver reliable performance, again with a small number of phases, making our proposed framework an attractive choice for activity modeling.     

Plasma deposition of GaAs

The deposition of an amorphous film of GaAs with a low frequency and low power glow discharge is described. The structure and composition of the film were determined by IR, secondary ion mass spectrometry, electron paramagnetic resonance and electron diffraction measurements. First results for the dark conductivity and the optical absorption are reported.     

Chitosan effects on glass matrices evaluated by biomaterial. MAS-NMR and biological investigations

Bioactive glass 46S6 and biodegradable therapeutic polymer (Chitosan: CH) have been elaborated to form 46S6-CH composite by freeze-drying process. The kinetics of chemical reactivity and bioactivity at the surface were investigated by using physicochemical techniques, particularly solid-state MAS-NMR. Immortalized cell line used to construct multicellular spheroids was employed as three-dimensional (3D) cell cultures for in vitro studies. Obtained results showed a novel structure of the composite; the chemical treatment (ultrasound, magnetic stirring, freeze drying process and lyophilization) led the bioactive glass particles to be loaded in the chitosan-based materials. ²⁹Si and ³¹P MAS-NMR results showed the emergence of two new species, Q _Si ³ (OH) and Q _Si ⁴ , which are characteristic of the vitreous network dissolution in simulated body fluid (SBF). MAS-NMR also confirmed the formation of amorphous calcium phosphate (ACP) at the surface of the initial 46S6-CH. Three-dimensional (3D) cell cultures highlighted the effect of chitosan, where the cell viability reached up to 78% in 46S6-CH composite and up to 67% in 46S6. The association of (CH) and bioactive glass (BG) matrix promotes a highly significant bioactivity, demonstrating surface bone formation and satisfactory behavior in biological environment.