The N-K Problem in Power Grids: New Models, Formulations and Numerical Experiments (extended version)

Given a power grid modeled by a network together with equations describing the power flows, power generation and consumption, and the laws of physics, the so-called N-k problem asks whether there exists a set of k or fewer arcs whose removal will cause the system to fail. The case where k is small is of practical interest. We present theoretical and computational results involving a mixed-integer model and a continuous nonlinear model related to this question.Comment: 40 pages 3 figure

Development and application of a free energy force field for all atom protein folding

Proteins are the workhorses of all cellular life. They constitute the building blocks and the machinery of all cells and typically function in specific three-dimensional conformations into which each protein folds. Currently over one million protein sequences are known, compared to about 40,000 structures deposited in the Protein Data Bank (the world-wide database of protein structures). Reliable theoretical methods for protein structure prediction could help to reduce the gap between sequence and structural databases and elucidate the biological information in structurally unresolved sequences. In this thesis we explore an approach for protein structure prediction and folding that is based on the Anfinsen’s hypothesis that most proteins in their native state are in thermodynamic equilibrium with their environment. We have developed a free energy forcefield (PFF02) that locates the native conformation of many proteins from all structural classes at the global minimum of the free-energy model. We have validated the forcefield against a large decoy set (Rosetta). The average root mean square deviation (RMSD) for the lowest energy structure for the 32 proteins of the decoy set was only 2.14 from the experimental conformation. We have successfully implemented and used stochastic optimization methods, such as the basin hopping technique and evolutionary algorithms for all atom protein structure prediction. The evolutionary algorithm performs exceptionally well on large supercomputational architectures, such as BlueGene and MareNostrum. Using the PFF02 forcefield, we were able to fold 13 proteins (12-56 amino acids), which include helix, sheet and mixed secondary structure. On average the predicted structure of these proteins deviated from their experimental conformation by only 2.89 RMSD.Proteine sind die nano-skaligen Maschinen der Zelle. Sie sind Bausteine und Funktionseinheiten aller Zellen und funktionieren typischerweise in spezifischen dreidimensionalen Konformationen, die sie als Endpunkt eines komplexen Faltungsprozesses annehmen. Gegenwärtig sind über eine Million Proteinsequenzen bekannt, es konnten jedoch nur etwa 40.000 Strukturen von Proteinen aufgelöst und in der Proteindatenbank hinterlegt werden. Verlässliche theoretische Methoden zu Proteinstrukturvorhersage könnten helfen, diese Lücke zwischen den Sequenz- und den strukturellen Datenbanken zu schließen und die biologische Information in den bislang strukturell unbekannten Proteinen zu entschlüsseln. In dieser Dissertation untersuchten wir einen Ansatz zur Proteinstrukturvorhersage und -faltung, der auf Anfinsons thermodynamischer Hypothese aufbaut, nach der sich Proteine in ihrem nativen Zustand im Gleichgewicht mit ihrer Umgebung befinden. Wir entwickelten daher ein Kraftfeld für die freie Energie von Proteinen (PFF02), das die nativen Konformationen vieler Proteine aller bekannten Strukturklassen als das globale Minimum des Modells der freien Energie beschreibt. Wir haben dieses Kraftfeld gegen die Strukturen des Rosetta Testdatensatzes getestet und fanden, dass die Strukturen mit der jeweils niedrigsten Energie für 32 Proteine dieses Datensatzes im Mittel nur 2,14 Å von der assoziierten experimentellen Konformation abwichen. Wir haben darüber hinaus stochastische Optimierungsverfahren, unter anderem die Basin-Hopping Methode und evolutionären Algorithmen, für die Proteinstrukturvorhersage und - faltung mit atomarer Auflösung entwickelt. Insbesondere der evolutionäre Algorithmus lieferte auf großen Supercomputern, wie zum Beispiel den BlueGene oder MareMonstrum Supercomputer- Clustern, hervorragende Ergebnisse. Mit dem PFF02 Kraftfeld waren wir in der Lage, 13 Proteine mit 12-56 Aminosäuren Länge mit helikaler, Faltblatt- oder gemischter Sekundärstruktur zu falten. Im Mittel wichen dabei die vorhergesagten Strukturen von den jeweiligen experimentell bekannten Strukturen dieser Proteine um nur 2,89 Å RMSD ab

A study on patterns of needle - stick injuries among gynaecologic and general surgeons in open surgery

Background: An estimated 3,84,000 percutaneous injuries are reported by HCW in hospitals in the United States each year, placing them at risk of exposure to HIV, HBV, or HCV. Suture needles have been identified as the most frequent cause of injury. They are involved in as many as 44% of such injuries. This study is designed to note the NSI in major gynaecological procedures and surgical procedures using conventional method (CM) versus (VS) use of HK.Methods: Study was conducted over a period of 12 months from January 2017 to December 2017. 60 patients were included in this study and were divided into 2 groups A and B with 30 patients in each group. Group A was major surgery performed by conventional method; Group B was major surgery performed by using harmonic knife. NSI in two groups were studied and analyzed.Results: Most of the operated patients were between 41-50 years age group. 16.6% procedures were emergency and 83.3% were elective. NSI in conventional surgery was 63.3% in the surgeon and 33.3% with harmonic knife. There were 13.3% NSI in first assistant in conventional surgery and 23.3% in harmonic scalpel group. No such injuries were reported by second assistant in either group. Injuries were more in non-dominant hand in either groups in the surgeon and first assistant.Conclusions: It is concluded that NSI are common in surgeons and first assistant. Such injuries are more in non-dominant hand and in procedures where there is little exposure like vaginal hysterectomy. Use of innovative technologies like harmonic scalpel may be useful

NUMERICAL INVESTIGATION OF CAPILLARY TUBE BY REPLACING THE INSIDE REFRIGERANT AND DIAMETER

The capillary tube used in the mostly in the refrigerant flow control devices. Hence performance of the capillary tube is best for good refrigerant flow. The many researchers had been concluding performance using experimentally, theoretically and analysis based. In this present work analyze the flow analysis of the refrigerant inside a capillary tube for adiabatic flow conditions. The proposed model can predict flow characteristics in adiabatic capillary tubes for a given mass flow rate. In the present work R-22 is replaced by Ammonia refrigerant has been used as a working fluid inside the capillary tube and the capillary tube design is changed straight to coiled capillary, which taken from good literature. The analysis is done in ANSYS CFX 16.2 software. It is observed from the results dryness fraction by using the helical capillary tube (Ammonia refrigerant flow) is better than straight and existing helical capillary tube (R22 refrigerant flow). The best suitable helical coiled design is suggested

CHARACTERIZATION OF ϕ\phi-SYMMETRIC LORENTZIAN PARA-KENMOTSU MANIFOLDS

The purpose of the present paper is to explore the characteristics of the Lorentzian $\phi$-symmetric para-Kenmotsu manifold as an Einstein manifold. In this paper, we also study the parallel 2-form on the LP-Kenmotsu manifold (LP-Kenmotsu manifold is used in lieu of Lorentzian para-Kenmotsu manifold throughout the present research article). We explain that the conformally flat LP-Kenmotsu manifold is locally $\phi$-symmetric iff, it has constant scalar curvature

Control Regularization for Reduced Variance Reinforcement Learning

Dealing with high variance is a significant challenge in model-free reinforcement learning (RL). Existing methods are unreliable, exhibiting high variance in performance from run to run using different initializations/seeds. Focusing on problems arising in continuous control, we propose a functional regularization approach to augmenting model-free RL. In particular, we regularize the behavior of the deep policy to be similar to a policy prior, i.e., we regularize in function space. We show that functional regularization yields a bias-variance trade-off, and propose an adaptive tuning strategy to optimize this trade-off. When the policy prior has control-theoretic stability guarantees, we further show that this regularization approximately preserves those stability guarantees throughout learning. We validate our approach empirically on a range of settings, and demonstrate significantly reduced variance, guaranteed dynamic stability, and more efficient learning than deep RL alone.Comment: Appearing in ICML 201