Effect of Compressive Load on Uplift Capacity of Model Piles

Thirty six tests on model tubular steel piles embedded in sand were carried out in the laboratory to assess the effects of compressive load on uplift capacity of piles considering various parameters. The model piles were of 25 mm outside diameter and 2 mm wall thickness. The soil–pile friction angles were 21 and 29° in loose and dense conditions of sand. The piles were embedded in sand for embedment length/diameter ratios of 8,16, and 24 inside a model tank. They were subjected to a static compressive load of 0, 25, 50, 75, and 100% of their ultimate capacity in compression and subjected to pull out loading tests. The experimental results indicated that the presence of the compressive load on the pile decreases the net uplift capacity of a pile and the decrease depends on the magnitude of the compressive load. A logical approach, based on the experimental results, has been suggested to predict the net uplift capacity of a pile considering the presence of compressive load.     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.     

Functionalized Reduced Graphene Oxide as a Versatile Tool for Cancer Therapy

Cancer is one of the deadliest diseases in human history with extremely poor prognosis. Although many traditional therapeutic modalities—such as surgery, chemotherapy, and radiation therapy—have proved to be successful in inhibiting the growth of tumor cells, their side effects may vastly limited the actual benefits and patient acceptance. In this context, a nanomedicine approach for cancer therapy using functionalized nanomaterial has been gaining ground recently. Considering the ability to carry various anticancer drugs and to act as a photothermal agent, the use of carbon-based nanomaterials for cancer therapy has advanced rapidly. Within those nanomaterials, reduced graphene oxide (rGO), a graphene family 2D carbon nanomaterial, emerged as a good candidate for cancer photothermal therapy due to its excellent photothermal conversion in the near infrared range, large specific surface area for drug loading, as well as functional groups for functionalization with molecules such as photosensitizers, siRNA, ligands, etc. By unique design, multifunctional nanosystems could be designed based on rGO, which are endowed with promising temperature/pH-dependent drug/gene delivery abilities for multimodal cancer therapy. This could be further augmented by additional advantages offered by functionalized rGO, such as high biocompatibility, targeted delivery, and enhanced photothermal effects. Herewith, we first provide an overview of the most effective reducing agents for rGO synthesis via chemical reduction. This was followed by in-depth review of application of functionalized rGO in different cancer treatment modalities such as chemotherapy, photothermal therapy and/or photodynamic therapy, gene therapy, chemotherapy/phototherapy, and photothermal/immunotherapy.     

Regional Climate Simulations on EU-INDIA Grid Infrastructures: Methodologies and Performance

In this paper, we present a possible approach to use different Grid infrastructures across Europe and India for regional climate simulations and discuss in details the advantages and limitations in using them. The application taken into consideration is the Regional Climate Model RegCM4, which has been recently re-written for making it more efficient and easier to be fully exploited on any kind of computational infrastructure. We describe here the methods applied to port this package on the Grid infrastructures made available by the EU-IndiaGrid project. We also discuss different approaches, the way to run the model on both European and Indian infrastructures and our promising approach to deal with data management issues. Use of RegCM on Grid infrastructure has further been compared with that on HPC resources. The domain worked upon for these tests is the South-Asia CORDEX domain, which is of great importance for the Indian Summer Monsoon. Our final conclusion is that, for certain class of experiments, RegCM4 model can be efficiently and easily integrated on Grid infrastructures, by means of the procedures described in this paper.     

Formation of Nanocrystalline Calcia by the Decomposition of Calcite

Formation of nanocrystalline calcia from calcite has been studied in situ via transmission electron microscopy. The crystallographic transformation occurred via two mechanisms: the first is by distortion of the cleaved rhombohedron of calcite, formed by {104} planes in hexagonal coordinates, into a cube. This produced a microstructure of oriented, elongated nanocrystals of calcia with planar boundaries. In the second mechanism, the micrometer-sized parent calcite particles broke up into nano-sized grains as the decomposition began, leading to irregularly shaped, randomly oriented nanocrystals of calcia.     

FSE2R: An Improved Collision-Avoidance-based Energy Efficient Route Selection Protocol in USN

The 3D Underwater Sensor Network (USNs) has become the most optimistic medium for tracking and monitoring underwater environment. Energy and collision are two most critical factors in USNs for both sparse and dense regions. Due to harsh ocean environment, it is a challenge to design a reliable energy efficient with collision free protocol. Diversity in link qualities may cause collision and frequent communication lead to energy loss; that effects the network performance. To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing (FSE2R) is proposed. Our proposal’s key idea is based on computation of node distance from the sink, Residual Energy (RE) of each node and Signal to Interference Noise Ratio (SINR). The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision. The novel proposal compares with existing protocols like H2AB, DEEP, and E2LR to achieve Quality of Service (QoS) in terms of throughput, packet delivery ratio and energy consumption. The comparative analysis shows that FSE2R gives on an average 30% less energy consumption, 24.62% better PDR and 48.31% less end-to-end delay compared to other protocols.     

Genetic algorithm-based partial charging schedule of rechargeable sensor networks

The use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MC) are used for charging sensors using wireless energy transfer (WET) technology. In on-demand charging, a sensor transmits a charging request to the service station, and the MC visits the sensor to transfer energy. The key disadvantages of utilizing MC-based WET are its high energy expenditure rate due to mobility, long service time, and slow charging rate. Because of these reasons, sensors deplete their energy and become dead before the MC reaches the requesting nodes to recharge. We have adapted a genetic algorithm-based partial charging scheme to serve the charging requests. Our objective is to improve the survival ratio of the network. Using comprehensive simulations, we analyze the performance of our proposed method and compare it to two other existing approaches. The simulation results demonstrate that our proposed algorithm improves the survival ratio by up to 20 % by developing a dynamic energy threshold function for transmitting charging requests from the sensors and a partial charging schedule using a genetic algorithm.     

A simple decision tree-based disturbance monitoring system for VSC-based HVDC transmission link integrating a DFIG wind farm

Fault detection and classification is a key challenge for the protection of High Voltage DC (HVDC) transmission lines. In this paper, the Teager–Kaiser Energy Operator (TKEO) algorithm associated with a decision tree-based fault classi f ier is proposed to detect and classify various DC faults. The Change Identification Filter is applied to the average and differential current components, to detect the first instant of fault occurrence (above threshold) and register a Change Identified Point (CIP). Further, if a CIP is registered for a positive or negative line, only three samples of currents (i.e., CIP and each side of CIP) are sent to the proposed TKEO algorithm, which produces their respective 8 indices through which the, fault can be detected along with its classification. The new approach enables quicker detection allowing utility grids to be restored as soon as possible. This novel approach also reduces computing complexity and the time required to identify faults with classification. The importance and accuracy of the proposed scheme are also thor oughly tested and compared with other methods for various faults on HVDC transmission lines.