Development of an Expert System for Selection of Dam Type on Alluvium Foundations

. A knowledge-based expert system was developed to aid in the selection of the type of dam. The dam type selector expert system (DTSA ES) was designed to determine the type of dam on the alluvium foundations. Detailed expert knowledge is required to estimate the type of dam and to develop an expert system. The DTSA ES utilizes rules of thumb used by an expert for determining the selection of the type of dam. The DTSA ES was developed using a shell program. The expert system was tested on several dam sites in order to validate the decision obtained. The use of this expert system, containing knowledge about the selection of dam type, can be helpful to students, potential owners or contractors in selecting dam types. The current prototype always needs additional parameters for more detailed analyses of new developments. However, the current DTSA ES is designed to include existing information about dam types.     

Deep learning frameworks to learn prediction and simulation focused control system models

Deep learning (DL) methods have brought world-shattering breakthroughs, especially in computer vision and classification problems. Yet, the design and deployment of DL methods in time series prediction and nonlinear system identification applications still need more progress. In this paper, we present DL frameworks that are developed to provide novel approaches as solutions to the aforementioned engineering problems. The proposed DL frameworks leverage the advantages of autoencoders and long-short term memory network, which are known being data compression and recurrent structures, respectively, to design Deep Neural Networks (DNN) for modeling time series and nonlinear systems with high performance. We provide recommendations on how deep AEs and LSTMs should be utilized to end up with efficient Prediction-focused (Pf) and Simulation-focused (Sf) DNNs for time series and system identification problems. We present systematic learning methods for the DL frameworks that allow straightforward learning of Pf-DNN and Sf-DNN models in detail. To demonstrate the efficiency of the developed DNNs, we present various comparative results conducted on the benchmark and real-world datasets in comparison with their conventional, shallow, and deep neural network counterparts. The results clearly show that the deployment of the proposed DL frameworks results with DNNs that have high accuracy, even with a low dimensional feature vector.

     

Boosting learning and inference in Markov logic through metaheuristics

Markov Logic (ML) combines Markov networks (MNs) and first-order logic by attaching weights to first-order formulas and using these as templates for features of MNs. State-of-the-art structure learning algorithms in ML maximize the likelihood of a database by performing a greedy search in the space of structures. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for structure learning in ML, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a biased sampling of the set of local optima. We show through real-world experiments that the algorithm improves accuracy and learning time over the state-of-the-art algorithms. On the other side MAP and conditional inference for ML are hard computational tasks. This paper presents two algorithms for these tasks based on the Iterated Robust Tabu Search (IRoTS) metaheuristic. The first algorithm performs MAP inference and we show through extensive experiments that it improves over the state-of-the-art algorithm in terms of solution quality and inference time. The second algorithm combines IRoTS steps with simulated annealing steps for conditional inference and we show through experiments that it is faster than the current state-of-the-art algorithm maintaining the same inference quality.     

Synchronization analysis of coupled Lienard-type oscillators by averaging

Sufficient conditions for the synchronization of coupled Lienard-type oscillators are investigated via averaging technique. The coupling considered here is fixed, nonsymmetric, and nonlinear. Under the assumption that the interconnection topology defines a connected graph, it is shown that the solutions of oscillators converge arbitrarily close to each other, starting from initial conditions arbitrarily far apart, provided that the frequency of oscillations is large enough and the initial phases of oscillators all lie in an open semicircle. It is also shown that the nearly-synchronized oscillations always take place around some fixed magnitude independent of the initial conditions and the coupling functions.     

Ecological efficiency and thermoeconomic analysis of a cogeneration system at a hospital

This work aims with an approach for cogeneration plants evaluation based on thermoeconomic functional diagram analysis. The second law of thermodynamics is used to develop a methodology to analyse cogeneration systems, based on exergoeconomics evaluation. The thermoeconomic optimisation method developed is applied to allow a better configuration of the cogeneration plant associated to a university hospital. Also ecological efficiency is evaluated. The method was efficient and contributes for thermoeconomics modelling and analysis and can be applied to any sort of thermal system, especially those with combined heat and power in thermal parity.     

The Antimicrobial Peptide Temporin G: Anti-Biofilm,Anti-Persister Activities,and Potentiator Effect of Tobramycin Efficacy Against Staphylococcus aureus

Bacterial biofilms are a serious threat for human health, and the Gram-positive bacterium Staphylococcus aureus is one of the microorganisms that can easily switch from a planktonic to a sessile lifestyle, providing protection from a large variety of adverse environmental conditions. Dormant non-dividing cells with low metabolic activity, named persisters, are tolerant to antibiotic treatment and are the principal cause of recalcitrant and resistant infections, including skin infections. Antimicrobial peptides (AMPs) hold promise as new anti-infective agents to treat such infections. Here for the first time, we investigated the activity of the frog-skin AMP temporin G (TG) against preformed S. aureus biofilm including persisters, as well as its efficacy in combination with tobramycin, in inhibiting S. aureus growth. TG was found to provoke ~50 to 100% reduction of biofilm viability in the concentration range from 12.5 to 100 µM vs ATCC and clinical isolates and to be active against persister cells (about 70–80% killing at 50–100 µM). Notably, sub-inhibitory concentrations of TG in combination with tobramycin were able to significantly reduce S. aureus growth, potentiating the antibiotic power. No critical cytotoxicity was detected when TG was tested in vitro up to 100 µM against human keratinocytes, confirming its safety profile for the development of a new potential anti-infective drug, especially for treatment of bacterial skin infections.     

Statistical Design of the Four-MOSFET Structure

The statistical design of the four-MOSFET structure is presented in this paper. The quantitative measure of the effect of mismatch between the four transistors on nonlinearity and offset current is provided through contours. Statistical optimization of the transistor and values is demonstrated. The four-MOSFET structure was fabricated through the MOSIS 2 m process using MOS transistor Level-3 model parameters. Experimental results are included in the paper.     

Synthesis and characterization of two-armed poly(ɛ-caprolactone) polymers initiated by the Schiff's base complexes of copper(II) and nickel(II)

Two-armed poly(?-caprolactone) (TAPCL) polymers were successfully synthesized via the ring opening polymerization (ROP) of ?-caprolactone (?-CL) using the Schiff's base complexes [Cu(SAEE)₂] (1) and [Ni(SAEE)₂] (2), which have two hydroxyl functional groups, as the two-site initiators and tin(II) 2-ethylhexanoate (Sn(Oct)₂) as the catalyst in bulk at 115 °C. The Schiff's base complexes (1 and 2) were synthesized by utilizing the concentrated template synthesis method starting from salicyl aldehyde, 2-(2-aminoethoxy) ethanol and related metal acetate salts. The synthesized TAPCL polymers were characterized by GPC, FTIR, UV–vis, and electron paramagnetic resonance (EPR). The molecular weights of TAPCL polymers linearly increased with increasing molar ratio of the monomer to the initiator. The results obtained from FTIR, UV–vis, and EPR studies indicated that TAPCL polymers had the Schiff's base complexes at the junction point of PCL arms. The crystallization behavior of TAPCL was studied by using differential scanning calorimetry (DSC) and polarizing optical microscopy (POM). Thermal behavior of TAPCL was also investigated by thermogravimetrical analysis (TGA).     

Statistical Design of Low Power Square-Law CMOS Cells for High Yield

A robust design of low voltage low power square law CMOS composite cells using statistical VLSI design techniques is presented. Since random device/process variations do not scale down with feature size or supply voltage, the statistical design of low voltage circuits is essential in order to keep functional yields of low voltage circuits at levels that are competitive and cost effective. The Response Surface Methodology and Design of Experiment techniques were used as statistical techniques. This article shows that statistical techniques will result in area/layout optimization which will enhance functional yield of low voltage analog ICs.