Behavior of Some Earth Dams on Liquefiable Soil

The 1977 March 4 Vrancea earthquake emphasized several zones with liquefiable materials on Romanian territory. Some earth dams of such zones, placed to over 200 km from the earthquake epicenter were damaged. Important hydropower works are at present in different design or construction stages in such area, comprising long earth dams. The Seismic analysis procedure applied to their design was based on the finite element method. Some characteristic cross - sections of the earth dams in different versions have been studied. The analysed sections had different shapes (with and without stabilizing benches downstream) and included different zoning of the materials (sand fine sand and free draining materials). The analyses pointed out the importance of the drainage blanket at the base of the dam for the increase of the liquefaction strength capacity of the soil - structure system. Some improvement works in certain zones of the foundation soil resulted as being necessary

The Sustainability Issues of Major Crop Yield in "Lower Danubian" Region (by the Example of Cahul and Reni Regions)

In this work we give the indicators of the yield levels and the state of major crops sustainability in the „Low Danubian” region by the example of Cahul (Republic of Moldova) and Reni  (Odessa area) regions for 2005 to 2012 planted in the unsustainable farming.  It is shown the significance of increasing the number of cultivated crops on the rise of medium yield sustainability. The recommendations are given on the growth of cultivated crops sustainability

Diagnostic and prognostic value of neopterin and RNA-ase in patients with STEMI and NSTEMI

Background Neopterin and RNA-ase are markers of inflammation with low disclosed role in diagnosis and prognosis of either STEMI or NSTEMI, although inflammation is well documented as a leader pathogenic mechanism in these pathologies. Aim Evaluation of serum admission levels of neopterin and ARN-ase in pts with STEMI and NSTEMI and their prediction value concerning the risk of MACE in 1 year of follow up period. Material and methods The admission serum concentration of neopterin and ARN-ase was determined by ELISA in 94 pts with STEMI and 92 pts with NSTEMI which was compared with normal markers appreciated in 32 healthy persons. Likewise, the rate of MACE in both groups was estimated during 1 year of post-infarction period. Diagnostic worth and MACE prediction power of markers have been established using respectively ROC curve and odds ratio. Results In patients with STEMI the serum level of neopterin was significantly increased compared with normal index by 3,5 times (11,6±3,4 vs 3,3±1,4 nM/L), but RNA-ase was significantly decreased by 43,4% (24,1±3,2 vs 42,6±5,2 nM/ml). In pts with NSTEMI neopterin level was lesser than STEMI, but significantly elevated by 39% (4,6±2,5 vs 3,3±1,4 nM/L) vs normal marker. RNA-ase level didn't significantly differ from normal level. However, adjusted to diabetes mellitus established in 19 pts, RNA-ase significantly diminished (36,4±3,9 vs 42,6±5,2 nM/ml), and its diagnostic value of NSTEMI according to ROC was 69,6% (RNA-ase level indicates inversely inflammation response, such as it breaks down extracellular RNA which has proinflammatory ability). Both markers in pts with NSTEMI and diabetes mellitus demonstrated a diagnostic value of 77,6%. In pts with STEMI highest tertile level of neopterin and lowest tertile level of ARN-ase had 2,8fold (adds ratio=2,8; CI=1,98–4,62; p<0,05) and 2,3fold (adds ratio=2,3; CI=1,71–3,89; p<0,05) higher risk of MACE development. In pts with NSTEMI the combination of these markers (highest and lowest quartile levels) also had a significant prediction regarding MACE risk (adds ratio=2,1; CI=1,86–3,77; p=0,029). Conclusions 1. In STEMI both neopterin and RNA-ase could be as diagnostic markers, due to their significant change. In NSTEMI neopterin significantly elevated, but RNA-ase didn't shift from normal. In diabetic pts with NSTEMI, however, their combination demonstrated in ROC estimation a diagnostic value of 77,6%. 2. Prediction value of markers combination regarding MACE risk in pts with NSTEMI is significant and close to each marker in partly prediction of MACE for pts with STEMI. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Research Institute of Cardiology, Moldova Republic o

Wireless sensor node design for heterogeneous networks

Two complementary wireless sensor nodes for building two-tiered heterogeneous networks are presented. A larger node with a 25 mm by 25 mm size acts as the backbone of the network, and can handle complex data processing. A smaller, cheaper node with a 10 mm by 10 mm size can perform simpler sensor-interfacing tasks. The 25mm node is based on previous work that has been done in the Tyndall National Institute that created a modular wireless sensor node. In this work, a new 25mm module is developed operating in the 433/868 MHz frequency bands, with a range of 3.8 km. The 10mm node is highly miniaturised, while retaining a high level of modularity. It has been designed to support very energy efficient operation for applications with low duty cycles, with a sleep current of 3.3 μA. Both nodes use commercially available components and have low manufacturing costs to allow the construction of large networks. In addition, interface boards for communicating with nodes have been developed for both the 25mm and 10mm nodes. These interface boards provide a USB connection, and support recharging of a Li-ion battery from the USB power supply. This paper discusses the design goals, the design methods, and the resulting implementation

Face Class Modeling in Eigenfaces Space

We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Another issue addressed is how to select the number of eigenfaces to achieve a good classification rate. As the experimental evidence show, generally one needs less eigenfaces than usually considered. We will present different strategies and discuss their effectiveness in the case of face-class modeling

Higher Order Autocorrelations for Pattern Classification

The use of higher-order local autocorrelations as features forpattern recognition has been acknowledge since many years, but their applicability was restricted to relatively low orders (2 or 3) and small local neighborhoods, due to combinatorial increase in computational costs. In this paper a new method for using these features is presented, which allows the use of autocorrelations of any order and of larger neighborhoods. The method is closely related to the classifier used, a Support Vector Machine

PCA in Autocorrelation Space

The use of higher order autocorrelations as features for pattern classification has been usually restricted to second or third orders due to high computational costs. Since the autocorrelation space is a high dimensional space we are interested in reducing the dimensionality of feature vectors for the benefit of the pattern classification task. An established technique is Principal Component Analysis (PCA) which, however, cannot be applied directly in the autocorrelation space. In this paper we develop a new method for performing PCA in autocorrelation space, without explicitly computing the autocorrelations. The connections with the nonlinear PCA and possible extensions are also discussed

Pattern Recognition using Higer-Order Local Autocorrelation Coefficients

The autocorrelations have been previously used as features fo

Face Detection using SVM Trained in Eigenfaces Space

The central problem in the case of face detectors is to build a face class model. We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Two main issues are addressed: what is the required number of eigenfaces to achieve a good classification rate and how to train the SVM for a good generalization. As the experimental evidence show, generally one needs less eigenfaces than usually considered. We will present different strategies for choosing the dimensionality of the PCA space and discuss their effectiveness in the case of face-class modeling