A comparative study between the augmented Lagrangian method and the complementarity approach for modeling the contact problem

The increasing demand for real-time high-fidelity multibody dynamics simulations in several modern fields such as robotics and computer game industries has motivated many researches to propose novel approaches to model multibody systems with several contacts. The possibility of different contact conditions in a system with several contacts yields a combinatorial problem of potentially large size. Rigid contact model which is the most common model used for real-time simulations yields a non-smooth dynamic formulation. The solution of such a system can be governed using different methods. In this paper a comparison between the complementarity approaches and the augmented Lagrangian based formulations to deal with non-smooth contact models is presented via numerical examples, and the advantages and shortcomings of each method are discussed.     

L-overlay: A layered data management scheme for peer-to-peer computing

Efficient storage and handling of data stored in a peer-to-peer (P2P) network, proves vital for various applications such as query processing and data mining. This paper presents a distributed, scalable and robust layered overlay (L-overlay) to index and manage multidimensional data in a dynamic P2P network. The proposed method distinguishes between the data and peer layers, with efficient mapping between the two. The data is organized such that semantically similar data objects are accessed hastily. Grid and tree structures are proposed for the peer layer. As application examples of L-overlay in query processing and data mining, k-nearest neighbors query processing and distributed Naïve Bayes classification algorithms, are proposed. We show the effectiveness of our scheme in static and dynamic environments using simulation. L-overlay is shown to be more efficient than SSW, an available semantic overly, in terms of maintenance and query processing costs.     

Pulmonary surfactant suppressed phenanthrene adsorption on carbon nanotubes through solubilization and competition as examined by passive dosing technique

Adsorption of phenanthrene on carbon nanotubes (CNTs) was examined in the presence of pulmonary surfactant (Curosurf) and its main components, dipalmitoyl phosphatidylcholine (DPPC) and bovine serum albumin (BSA). A passive-dosing method based on equilibrium partitioning from a preloaded polymer was successfully employed to measure phenanthrene binding and speciation at controlled freely dissolved concentrations while avoiding phase separation steps. Curosurf, DPPC, and BSA could all linearly solubilize phenanthrene, and phenanthrene solubilization by Curosurf was 4 times higher than individual components (DPPC or BSA). In the presence of Curosurf, DPPC or BSA, adsorption of phenanthrene by multiwalled CNTs (MWCNTs) was suppressed, showing competitive adsorption between pulmonary surfactant (or DPPC, BSA) and phenanthrene. Competitive adsorption between Curosurf and phenanthrene was the strongest. Therefore, when phenanthrene-adsorbed CNTs enter the respiratory tract, phenanthrene can be desorbed due to both solubilization and competition. The bioaccessibility of phenanthrene adsorbed on three MWCNTs in the respiratory tract would be positively related to the size of their outer diameters. Moreover, the contribution of solubilization and competition to desorption of phenanthrene from MWCNTs was successfully separated for the first time. These findings demonstrate the two mechanisms on how pulmonary surfactants can enhance desorption and thus possibly biological absorption of phenanthrene adsorbed on CNTs.     

Mechanistic Investigations of Metallo-β-lactamase Inhibitors: Strong Zinc Binding Is Not Required for Potent Enzyme Inhibition**

Metallo-β-lactamases (MBLs) are zinc-dependent bacterial enzymes that inactivate essentially all classes of β-lactam antibiotics including last-resort carbapenems. At present there are no clinically approved MBL inhibitors, and in order to develop such agents it is essential to understand their inhibitory mechanisms. Herein, we describe a comprehensive mechanistic study of a panel of structurally distinct MBL inhibitors reported in both the scientific and patent literature. Specifically, we determined the half-maximal inhibitory concentration (IC₅₀) for each inhibitor against MBLs belonging to the NDM and IMP families. In addition, the binding affinities of the inhibitors for Zn²⁺, Ca²⁺ and Mg²⁺ were assessed by using isothermal titration calorimetry (ITC). We also compared the ability of the different inhibitors to resensitize a highly resistant MBL-expressing Escherichia coli strain to meropenem. These investigations reveal clear differences between the MBL inhibitors studied in terms of their IC₅₀ value, metal binding ability, and capacity to synergize with meropenem. Notably, our studies demonstrate that potent MBL inhibition and synergy with meropenem are not explicitly dependent on the capacity of an inhibitor to strongly chelate zinc.     

A self‐learning stream classifier for flow‐based botnet detection

Botnets have been recently recognized as one of the most formidable threats on the Internet. Different approaches have been designed to detect these types of attacks. However, as botnets evolve their behavior to mislead the signature‐based detection systems, learning‐based methods may be deployed to provide a generalization capacity in identifying unknown botnets. Developing an adaptable botnet detection system, which incrementally evolves with the incoming flow stream, remains as a challenge. In this paper, a self‐learning botnet detection system is proposed, which uses an adaptable classification model. The system uses an ensemble classifier and, in order to enhance its generalization capacity, updates its model continuously on receiving new unlabeled traffic flows. The system is evaluated with a comprehensive data set, which contains a wide variety of botnets. The experiments demonstrate that the proposed system can successfully adapt in a dynamic environment where new botnet types are observed during the system operation. We also compare the system performance with other methods.     

Improved passivity criterion in haptic rendering: influence of Coulomb and viscous friction

In this paper, a new criterion for passivity of haptic devices is obtained. This criterion creates a relationship between Coulomb friction coefficient, viscous friction coefficient, sampling rate, and the maximum simulated stiffness. The process of derivation of the passivity criterion is described in detail. This criterion is improved compared with other existing criteria and predicts passivity in haptic rendering more accurately. In particular, for speeds of less than 5?cm/s, the new passivity criterion should replace the previous criteria to avoid unwanted vibrations of stiff virtual walls. Analytical and numerical investigations are presented to validate the new criterion. A specific trajectory is designed and the movement of the haptic robot is investigated on this trajectory to validate and compare this passivity criterion with the previous criteria.     

Accelerating Distributed Graphical Fluid Simulations with Micro-partitioning

Graphical fluid simulations are CPU-bound. Parallelizing simulations on hundreds of cores in the computing cloud would make them faster, but requires evenly balancing load across nodes. Good load balancing depends on manual decisions from experts, which are time-consuming and error prone, or dynamic approaches that estimate and react to future load, which are non-deterministic and hard to debug. This paper proposes Birdshot scheduling, an automatic and purely static load balancing algorithm whose performance is close to expert decisions and reactive algorithms without their difficulty or complexity. Birdshot scheduling's key insight is to leverage the high-latency, high-throughput, full bisection bandwidth of cloud computing nodes. Birdshot scheduling splits the simulation domain into many micro-partitions and statically assigns them to nodes randomly. Analytical results show that randomly assigned micro-partitions balance load with high probability. The high-throughput network easily handles the increased data transfers from micro-partitions, and full bisection bandwidth allows random placement with no performance penalty. Overlapping the communications and computations of different micro-partitions masks latency. Experiments with particle-level set, SPH, FLIP and explicit Eulerian methods show that Birdshot scheduling speeds up simulations by a factor of 2-3, and can out-perform reactive scheduling algorithms. Birdshot scheduling performs within 21% of state-of-the-art dynamic methods that require running a second, parallel simulation. Unlike speculative algorithms, Birdshot scheduling is purely static: it requires no controller, runtime data collection, partition migration or support for these operations from the programmer.     

A novel mixed polymeric micelle for co-delivery of paclitaxel and retinoic acid and overcoming multidrug resistance: synthesis,characterization, cytotoxicity,and pharmacokinetic evaluation

In the current study, retinoic acid (RA) was conjugated to Pluronic F127 (PF127) through an esterification process. Mixed micelles were formed with tocopheryl polyethylene glycol 1000 (TPGS) for co-delivery of paclitaxel (PTX) and RA to the cancer cells. Mixed micelles of RA-PF127 and TPGS in different weight ratios (10:0, 7:3, 5:5, 3:7, 0:10 w/w) were prepared and physicochemical properties including, particle size, zeta potential, critical micelle concentration (CMC), drug loading content, entrapment efficiency, drug release, cellular uptake and in vitro cytotoxicity, were investigated in details. Furthermore, the pharmacokinetics of PTX-loaded optimized mixed micelles were evaluated in Sprague-Dawley rats and compared with Stragen^® (PTX in Cremophor EL^®). Particle sizes and zeta potentials of the drug-loaded micelles were in the range of 102.6–223.5?nm and ?5.3 to ?9.6?mV, respectively. The 7:3 and 5:5 micellar combinations had lower CMC values (0.034–0.042?mg/mL) than 0:10 (0.124?mg/mL). The entrapment efficiencies of 10:0, 7:3, and 5:5 were 53.4?±?9.3%, 61.3?±?0.5%, and 78.7?±?1.66%, respectively. The release rates of PTX from 7:3 and 5:5 mixed micelles were significantly slower than other formulations. Cytotoxicity assay demonstrated increased cytotoxic activity of PTX-loaded mixed micelles compared to free PTX. The V_d and t_1/2ß of PTX-loaded RA-PF127/TPGS (7:3) were increased by 2.61- and 1.27-fold, respectively, while the plasma area under the curve (AUC) of the micelles was 2.03-fold lower than those of Stragen^®. Therefore, these novel mixed micelles could be effectively used for delivery of PTX and RA to the cancer cells. Moreover, TPGS as part of micelle composition could enhance the therapeutic effect of PTX and reduce side effects.     

Design and manufacturing a novel screw-in-pipe inspection robot with steering capability

In this article, a novel in-pipe inspection robot is designed and manufactured in Kharazmi University KharazmPipeBot. This robot is able to move through any pipeline with a predefined diameter range with a variable pitch rate and report any desired data within the pipe with the aid of the installed camera. To achieve the highest stability of the robot through the pipe, the robot's movement is based on the screw locomotion protocol provided by the aid of its rotor and stator. A simple suspension is designed for three legs of the robot by installing a passive prismatic joint equipped with a spring for each leg to provide a smoother movement for the robot chassis. The main novelty of the robot is adding an extra controlling actuator for the robot which is the steer of the front wheels. This input can control the pitch rate of the robot movement and consequently the spiral track of the wheels can be actively managed. This importance lets us to bypass the probable obstacles attached to the inner wall of the pipes. A brief presentation of the robot model is delivered. Afterward, to verify the claimed novelties of the system, a prototype of the robot is manufactured in Kharazmi University and the efficiency of the robot is demonstrated by conducting some initial experimental tests. It is shown that the robot can move with a variable pitch rate through the wall and pass a detected obstacle accordingly.     

GoSCAN: Decentralized scalable data clustering

Identifying clusters is an important aspect of analyzing large datasets. Clustering algorithms classically require access to the complete dataset. However, as huge amounts of data are increasingly originating from multiple, dispersed sources in distributed systems, alternative solutions are required. Furthermore, data and network dynamicity in a distributed setting demand adaptable clustering solutions that offer accurate clustering models at a reasonable pace. In this paper, we propose GoScan, a fully decentralized density-based clustering algorithm which is capable of clustering dynamic and distributed datasets without requiring central control or message flooding. We identify two major tasks: finding the core data points, and forming the actual clusters, which we execute in parallel employing gossip-based communication. This approach is very efficient, as it offers each peer enough authority to discover the clusters it is interested in. Our algorithm poses no extra burden of overlay formation in the network, while providing high levels of scalability. We also offer several optimizations to the basic clustering algorithm for improving communication overhead and processing costs. Coping with dynamic data is made possible by introducing an age factor, which gradually detects data-set changes and enables clustering updates. In our experimental evaluation, we will show that GoSCAN can discover the clusters efficiently with scalable transmission cost.