266 research outputs found
A global meta-analysis of greenhouse gases emission and crop yield under no-tillage as compared to conventional tillage
No full textNo-tillage (NT) practice is extensively adopted with aims to improve soil physical conditions, carbon (C) sequestration and to alleviate greenhouse gases (GHGs) emissions without compromising crop yield. However, the influences of NT on GHGs emissions and crop yields remains inconsistent. A global meta-analysis was performed by using fifty peer-reviewed publications to assess the effectiveness of soil physicochemical properties, nitrogen (N) fertilization, type and duration of crop, water management and climatic zones on GHGs emissions and crop yields under NT compared to conventional tillage (CT) practices. The outcome reveals that compared to CT, NT increased CO2, N2O, and CH4 emissions by 7.1, 12.0, and 20.8%, respectively. In contrast, NT caused up to 7.6% decline in global warming potential as compared to CT. However, absence of difference in crop yield was observed both under NT and CT practices. Increasing N fertilization rates under NT improved crop yield and GHGs emission up to 23 and 58%, respectively, compared to CT. Further, NT practices caused an increase of 16.1% CO2 and 14.7% N2O emission in the rainfed areas and up to 54.0% CH4 emission under irrigated areas as compared to CT practices. This meta-analysis study provides a scientific basis for evaluating the effects of NT on GHGs emissions and crop yields, and also provides basic information to mitigate the GHGs emissions that are associated with NT practice
Phytomelatonin: An overview of the importance and mediating functions of melatonin against environmental stresses
No full textRecently, melatonin has gained significant importance in plant research. The presence of melatonin in the plant kingdom has been known since 1995. It is a molecule that is conserved in a wide array of evolutionary distant organisms. Its functions and characteristics have been found to be similar in both plants and animals. The review focuses on the role of melatonin pertaining to physiological functions in higher plants. Melatonin regulates physiological functions regarding auxin activity, root, shoot, and explant growth, activates germination of seeds, promotes rhizogenesis (growth of adventitious and lateral roots), and holds up impelled leaf senescence. Melatonin is a natural bio-stimulant that creates resistance in field crops against various abiotic stress, including heat, chemical pollutants, cold, drought, salinity, and harmful ultra-violet radiation. The full potential of melatonin in regulating physiological functions in higher plants still needs to be explored by further research
Entwicklung von Hochentropie-Karbid-Dünnschichten mittels DC-Magnetronsputtern
No full textA thin film is a layer of material having a thickness of a few nanometers up to several micrometers. In materials science, thin films are grown on materials to improve certain properties such as mechanical properties, thermal stability, oxidation and corrosion resistance. Thin films are deposited via various methods of which physical vapor deposition (PVD) is most common. PVD includes several methods in which a solid target material is evaporated by physical means. The evaporated atoms then travel to the substrate where the thin film is formed. The properties of deposited thin films can be controlled by tuning deposition parameters such as voltage, current, temperature, time, and working gas pressure.This PhD research focuses on the development of high-entropy alloy (HEA) thin films, with particular emphasis on high-entropy carbides (HEC)s. HEAs are multicomponent systems, typically composed of five or more principal elements, in near-equiatomic proportions and are characterized by four fundamental effects: high configurational entropy, sluggish atomic diffusion, severe lattice distortion, and the cocktail effect. For materials to qualify as a high-entropy systems, the configurational entropy of mixing should exceed 1.5R, where Ris the universal gas constant.High-entropy carbides are a subclass of high-entropy ceramics in which a small anionic element, carbon, occupies interstitial sites and forms strong covalent bonds with the metallic cation sublattice. Transition metals from groups IV, V, and VI of the periodic table are commonly selected for the formation of HECs due to their strong carbide-forming tendencies.In this research work, HEC thin films are grown and their compositions, crystal structures, microstructure, mechanical properties, thermal and oxidation resistance are investigated through energy dispersive spectroscopy (EDS), X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning election microscope (SEM), transmission electron microscope (TEM), nano-indentation, and thermogravimetric analyses. Some HEC thin films were developed with Si addition to study the influence of Si on structure, morphology, hardness, elastic modulus, thermal and oxidation resistance of thin films. The results show that Si addition to HEC increases the deposition rate while preserving a dense, fibrous morphology. Si coatings form a continuous, adherent oxide scale that enhances the oxidation resistance of HEC thin films. During vacuum thermal annealing, Si promotes the formation of nanoscale, coherent SiC precipitates along grain boundaries, which impede grain-boundary motion and dislocation glide, thereby maintaining high hardness and elastic modulus. The outcomes of this PhD research work will be helpful in the design and development of functional thin films
Ewings Sarcoma Mimicking a Schwannoma: MRI Findings of a Rare Case
No full textEwing’s sarcoma is among the most frequent yet highly aggressive neoplasms of the bones presenting in adolescents and children under the age of ten with a slight male predilection. It is now broadly categorized into a set of tumors recognized as Ewing’s sarcoma family based on the same histology and genotype of these tumors. This group of tumors includes Ewing’s sarcoma of bones, the 2nd most frequent bone malignancy, Askin tumors, PNET, and those rarely occurring extraosseous Ewing sarcoma (peripheral neuroepithelioma). Extra-osseous Ewing’s is a rare presentation occurring in only 5% of patients. Here we present such a case of an extra-osseous Ewing’s sarcoma in a 13-year-old female presenting to our hospital with a large mass in the sacral region. The interesting imaging findings and his histopathology are discussed
A Study of Reduced Order 4D-VAR with a Finite Element Shallow Water Model
No full textForecast models often depend on unknown parameters, such as model initial and boundary conditions, or other tunable parameters not necessarily having any physical meaning. Calibration of these parameters to minimize errors between forecasted and observed states is called data assimilation. A common approach in this context are variational methods, of which four dimensional data variation (4D-VAR) is studied in this thesis. In 4D-VAR, a cost function is defined that penalizes misfits between observations and the corresponding numerical model results, obtained by running the model with the chosen configuration. Performing optimization with regard to this cost function yields an improved initial parameter set. Associated with this type of methods, however, are difficulties in connection with programming the adjoint model, which is needed to compute the exact gradient of the cost function. Additionally, having to integrate the adjoint model backwards in time adds significantly to the computational cost of the data assimilation process. To avoid manual implementation of adjoint code and to reduce computational complexity, approximation of the gradient calculation is considered through the use of proper orthogonal decomposition (POD), a flexible data-driven order reduction method. To facilitate this, a finite element model of the shallow water equations is tested with both the full adjoint 4D-VAR method and two different POD-reduced approaches. Twin experiments are performed and comparisons are made in terms of accuracy, computational complexity and sensitivity to perturbation and number of observation points.Applied mathematicsElectrical Engineering, Mathematics and Computer Scienc
Model reduced variational data assimilation for shallow water flow models
No full textIdentifying uncertain parameters in large-scale numerical flow models can be done using the variational method. However, for implementing the variational method the adjoint model have to be available, which requires highly complex computer code and maintenance and thus hampers its applications. To ease this problem, this thesis has explored several methods for efficiently identifying uncertain parameters in a large-scale tidal model of the entire European continental shelf which does not require the implementation of these complex adjoint code. In this study, as a first step an estimation method based on model reduction is developed and investigated for the estimation of diffusion coefficient in a simple 2D-advection diffusion model. Two projection based model reduction methods were considered, namely proper orthogonal decomposition (POD) and Balanced proper orthogonal decomposition (BPOD). In the POD based estimation method an ensemble of forward model simulations is used to determine an approximation of the covariance matrix of the model variability and a small number of the leading eigenvectors of this matrix is used to define a model subspace. By projecting the original model onto this subspace an approximate linear reduced model is obtained. Once the reduced model is available its adjoint can be implemented easily and the minimization problem is solved completely in reduced space with very low computational cost. BPOD is also a model reduction method which considers both inputs and outputs of the system while determining the reduce subspace. The estimation method has been extended by including BPOD procedure into the estimation procedure. Numerical results from a simple pollution model demonstrate that the POD based estimation approach successfully estimate the diffusion coefficient for both advection dominated problems as for diffusion dominated problems. Another important message in this study, although lots of effort had been made in constructing a reduced order model by the BPOD method, the minimization results demonstrated that both the POD and the BPOD methods performed similarly. Preliminary results showed the validity of the POD based model reduction methods for parameter estimation. As a next step, the POD based estimation method is used to calibrate numerical tidal models. Results from (twin) numerical experiments showed that the POD based calibration method performed very efficiently to estimate depth values in the selected regions of the model domain. The computational costs of the POD based calibration method are dominated by the generation of an ensemble of forward model simulations where the simulation period of the ensemble is equivalent to the timescale of the original model. It has also been found in the study that it is not needed to use a full simulations of the original model for the generation of the ensemble. The POD based calibration method has also been implemented for the estimation of the water depth and space varying bottom friction coefficient values in a very large-scale DCSM model. The recently designed large-scale spherical grid based water level model for the northwest European continental shelf (around 1000000 computational grid points) has been used for this purpose. This has been the first application of the POD based calibration method to a very large-scale model and with real data. Results from numerical experiments showed that the calibration method performs very efficiently. An overall improvement of more than 50\% was observed after the calibration in comparison with the initial model. The results also demonstrated that the POD based calibration method offered a very efficient minimization technique compared to the classical adjoint method without the burden of implementation of the adjoint. As a concluding step, to estimate depth values in the model DCSM, a Simultaneous perturbation stochastic approximation (SPSA) method has been used. The method uses stochastic simultaneous perturbation of all model parameters to generate a search at each iteration. SPSA is based on a highly efficient and easily implemented simultaneous perturbation approximation to the gradient. This gradient approximation for the central difference method uses only two objective function evaluations independent of the number of parameters being optimized. The results from experiments showed that SPSA has a lower convergence rate than POD based calibration method, however the computational cost in each iteration of the SPSA method is usually far less then the POD based calibration method. The results also demonstrated that the SPSA algorithm proved to be a promising optimization algorithm for model calibration for cases where adjoint code is not available for computing the gradient of the objective function.Applied mathematicsElectrical Engineering, Mathematics and Computer Scienc
A 16-channel, 1-second latency patient-specific seizure onset and termination detection processor with dual detector architecture and digital hysteresis
No full textThis paper presents an area-power-efficient 16 channel seizure onset and termination detection processor with patient-specific machine learning techniques. This is the first work in literature to report an on-chip classification to detect both start and end of seizure event simultaneously with high accuracy. Frequency-Time Division Multiplexing (FTDM) filter architecture and Dual-Detector Architecture (D(2)A) is proposed, implemented and verified. The D(2)A incorporates two area efficient Linear Support Vector Machine (LSVM) classifiers along with digital hysteresis to achieve a high sensitivity and specificity of 95.7% and 98%, respectively, using CHB-MIT EEG database [1], with a small latency of is. The overall energy efficiency is measured as 1.85 mu J/Classification at 16-channel mode.N
A 2.45μW patient-specific non-invasive transcranial electrical stimulator with an adaptive skin-electrode impedance monitor
No full textThis paper presents a power-efficient noninvasive transcranial electrical stimulator (tES) with patient-specific impedance adaptation. The proposed tES exploits the varying skin-electrode impedance which results in different RC relaxation time, where the tES adapts the number of pulses to deliver a constant charge. This eliminates the need of an additional current source for impedance monitoring widely used in conventional stimulation techniques, thereby reducing power consumption. To ensure safety for patients, a charge balanced bi-phasic stimulation is implemented with maximum current of < 1mA. The proposed tES is implemented in 0.18 mu m CMOS with an area and power consumption of 0.035mm(2) and 2.45 mu W, respectively.N
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