A closed-form optimal tuning of mass dampers for one degree-of-freedom systems under rotating unbalance forcing

This paper is focused on the study of a vibrating system forced by a rotating unbalance and coupled to a tuned mass damper (TMD). The analysis of the dynamic response of the entire system is used to define the parameters of such device in order to achieve optimal damping properties. The inertial forcing due to the rotating unbalance depends quadratically on the forcing frequency and it leads to optimal tuning parameters that differ from classical values obtained for pure harmonic forcing. Analytical results demonstrate that frequency and damping ratios, as a function of the mass parameter, should be higher than classical optimal parameters. The analytical study is carried out for the undamped primary system, and numerically investigated for the damped primary system. We show that, for practical applications, proper TMD tuning allows to achieve a reduction in the steady-state response of about 20% with respect to the response achieved with a classically tuned damper. Copyright © 2015 by ASME.

Optimal foreign aid and tariffs

This paper investigates the optimal choice of foreign aid when trade policies are decided in a non-cooperative fashion. Three alternative scenarios, depending on the timing of the actions and on whether aid is tied, are analyzed. It is shown that, in the case where aid is decided before tariffs, untied aid can lead to the reduction of the recipient's optimal trade tax. When the donor can tie the aid to a reduction in the recipient's tariff, the optimal aid level is always positive and the world can always achieve a Pareto-efficient equilibrium.

Scattering theory and the discrete linear optimal control problem

This paper deals with the interpretation of the discrete-time optimal control problem as a scattering process in a discrete medium. We treat the discrete optimal linear regulator, constrained end-point and servo and tracking problems, providing a unified approach to these problems. This approach results in an easy derivation of the desired results as well as several new ones.

Cellular Automata Models of Traffic Behavior in Presence of Speed Breaking Structures

In this article, we study traffic flow in the presence of speed breaking structures. The speed breakers are typically used to reduce the local speed of vehicles near certain institutions such as schools and hospitals. Through a cellular automata model we study the impact of such structures on global traffic characteristics. The simulation results indicate that the presence of speed breakers could reduce the global flow under moderate global densities. However, under low and high global density traffic regime the presence of speed breakers does not have an impact on the global flow. Further the speed limit enforced by the speed breaker creates a phase distinction. For a given global density and slowdown probability, as the speed limit enforced by the speed breaker increases, the traffic moves from the reduced flow phase to maximum flow phase. This underlines the importance of proper design of these structures to avoid undesired flow restrictions.

Structure and function of GDNF receptor alpha splice variants

The growth factors of the glial cell line-derived neurotrophic factor (GDNF) family consisting of GDNF, neurturin (NRTN), artemin (ARTN) and persephin (PSPN), are involved in the development, differentiation and maintenance of many types of neurons. They also have important functions outside the nervous system in the development of kidney, testis and thyroid gland. Each of these GFLs preferentially binds to one of the glycosylphosphatidylinositol (GPI)-anchored GDNF family receptors α (GFRα). GDNF binds to GFRα1, NRTN to GFRα2, ARTN to GFRα3 and PSPN to GFRα4. The GFLs in the complex with their cognate GFRα receptors all bind to and signal through the receptor tyrosine kinase RET. Alternative splicing of the mouse GFRα4 gene yields three splice isoforms. These had been described as putative GPI-anchored, transmembrane and soluble forms. My goal was to characterise the function of the different forms of mouse GFRα4. I firstly found that the putative GPI-anchored GFRα4 (GFRα4-GPI) is glycosylated, membrane-bound, GPI-anchored and interacts with PSPN and RET. We also showed that mouse GFRα4-GPI mediates PSPN-induced phosphorylation of RET, promotes PSPN-dependent neuronal differentiation of the rat pheochromocytoma cell line PC6-3 and PSPN-dependent survival of cerebellar granule neurons (CGN). However, although this receptor can mediate PSPN-signalling and activate RET, GFRα4-GPI does not recruit RET into lipid rafts. The recruitment of RET into lipid rafts has previously been thought to be a crucial event for GDNF- and GFL-mediated signalling via RET. I secondly demonstrated that the putative transmembrane GFRα4 (GFRα4-TM) is indeed a real transmembrane GFRα4 protein. Although it has a weak binding capacity for PSPN, it can not mediate PSPN-dependent phosphorylation of RET, neuronal differentiation or survival. These data show that GFRα4-TM is inactive as a receptor for PSPN. Surprisingly, GFRα4-TM can negatively regulate PSPN-mediated signalling via GFRα4-GPI. GFRα4-TM interacts with GFRα4-GPI and blocks PSPN-induced phosphorylation of RET, neuronal differentiation as well as survival. Taken together, our data show that GFRα4-TM may act as a dominant negative inhibitor of PSPN-mediated signaling. The most exciting part of my work was the finding that the putative soluble GFRα4 (GFRα4-sol) can form homodimers and function as an agonist of the RET receptor. In the absence of PSPN, GFRα4-sol can promote the phosphorylation of RET, trigger the activation of the PI-3K/AKT pathway, induce neuronal differentiation and support the survival of CGN. Our findings are in line with a recent publication showing the GFRα4-sol might contribute to the inherited cancer syndrome multiple endocrine neoplasia type 2. Our data provide an explanation to how GFRα4-sol may cause or modify the disease. Mammalian GFRα4 receptors all lack the first Cys-rich domain which is present in other GFRα receptors. In the final part of my work I have studied the function of this particular domain. I created a truncated GFRα1 construct lacking the first Cys-rich domain. Using binding assays in both cellular and cell-free systems, phosphorylation assays with RET, as well as neurite outgrowth assays, we found that the first Cys-rich domain contributes to an optimal function of GFRα1, by stabilizing the interaction between GDNF and GFRα1.

Shape effect on optimal geometric conditions in surface aeration systems

The performance of surface aeration systems, among other key design variables, depends upon the geometric parameters of the aeration tank. Efficient performance and scale up or scale down of the experimental results of an aeration ystem requires optimal geometric conditions. Optimal conditions refer to the conditions of maximum oxygen transfer rate, which assists in scaling up or down the system for ommercial utilization. The present work investigates the effect of an aeration tank's shape (unbaffled circular, baffled circular and unbaffled square) on oxygen transfer. Present results demonstrate that there is no effect of shape on the optimal geometric conditions for rotor position and rotor dimensions. This experimentation shows that circular tanks (baffled or unbaffled) do not have optimal geometric conditions for liquid transfer, whereas the square cross-section tank shows a unique geometric shape to optimize oxygen transfer.

Speed the plough

Dancing was an important pastime in colonial society. People would dance to celebrate events such as weddings and harvests, but also as a general recreational activity. Dancing is a shared experience which forges and maintains a sense of community and cultural identity – a sense of belonging. This was significant in the new land, where many people had left their families and friends behind. Speed the Plough was a very popular English country dance which came from a play of the same name, first performed in London in 1798.

The influence of the elements of procedural justice and speed camera enforcement on young novice driver self-reported speeding

Road policing is an important tool used to modify road user behaviour. While other theories, such as deterrence theory, are significant in road policing, there may be a role for using procedural justice as a framework to improve outcomes in common police citizen interactions such as traffic law enforcement. This study, using a sample of 237 young novice drivers, considered how the four elements of procedural justice (voice, neutrality, respect and trustworthiness) were perceived in relation to two forms of speed enforcement: point-to-point (or average) speed and mobile speed cameras. Only neutrality was related to both speed camera types suggesting that it may be possible to influence behaviour by emphasising one or more elements, rather than using all components of procedural justice. This study is important as it indicates that including at least some elements of procedural justice in more automated policing encounters can encourage citizen compliance.

An optimal dynamic inversion-based neuro-adaptive approach for treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an innovative technique is presented to design an automatic drug administration strategy for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used to design the controller (medication dosage). First, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat the nominal model patients (patients who can be described by the mathematical model used here with the nominal parameter values) effectively. However, since the system parameters for a realistic model patient can be different from that of the nominal model patients, simulation studies for such patients indicate that the nominal controller is either inefficient or, worse, ineffective; i.e. the trajectory of the number of cancer cells either shows non-satisfactory transient behavior or it grows in an unstable manner. Hence, to make the drug dosage history more realistic and patient-specific, a model-following neuro-adaptive controller is augmented to the nominal controller. In this adaptive approach, a neural network trained online facilitates a new adaptive controller. The training process of the neural network is based on Lyapunov stability theory, which guarantees both stability of the cancer cell dynamics as well as boundedness of the network weights. From simulation studies, this adaptive control design approach is found to be very effective to treat the CML disease for realistic patients. Sufficient generality is retained in the mathematical developments so that the technique can be applied to other similar nonlinear control design problems as well.

An optimal weighted-average congestion based pricing scheme for enhanced QoS

Pricing is an effective tool to control congestion and achieve quality of service (QoS) provisioning for multiple differentiated levels of service. In this paper, we consider the problem of pricing for congestion control in the case of a network of nodes under a single service class and multiple queues, and present a multi-layered pricing scheme. We propose an algorithm for finding the optimal state dependent price levels for individual queues, at each node. The pricing policy used depends on a weighted average queue length at each node. This helps in reducing frequent price variations and is in the spirit of the random early detection (RED) mechanism used in TCP/IP networks. We observe in our numerical results a considerable improvement in performance using our scheme over that of a recently proposed related scheme in terms of both throughput and delay performance. In particular, our approach exhibits a throughput improvement in the range of 34 to 69 percent in all cases studied (over all routes) over the above scheme.

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies-Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

DNA looping and translocation provide an optimal cleavage mechanism for the type III restriction enzymes

EcoP15I is a type III restriction enzyme that requires two recognition sites in a defined orientation separated by up to 3.5 kbp to efficiently cleave DNA. The mechanism through which site- bound EcoP15I enzymes communicate between the two sites is unclear. Here, we use atomic force microscopy to study EcoP15I-DNA pre-cleavage complexes. From the number and size distribution of loops formed, we conclude that the loops observed do not result from translocation, but are instead formed by a contact between site- bound EcoP15I and a nonspecific region of DNA. This conclusion is confirmed by a theoretical polymer model. It is further shown that translocation must play some role, because when translocation is blocked by a Lac repressor protein, DNA cleavage is similarly blocked. On the basis of these results, we present a model for restriction by type III restriction enzymes and highlight the similarities between this and other classes of restriction enzymes.

Jointly optimal power control and routing for a single cell, dense, ad hoc wireless network

We consider a dense, ad hoc wireless network confined to a small region, such that direct communication is possible between any pair of nodes. The physical communication model is that a receiver decodes the signal from a single transmitter, while treating all other signals as interference. Data packets are sent between source-destination pairs by multihop relaying. We assume that nodes self-organise into a multihop network such that all hops are of length d meters, where d is a design parameter. There is a contention based multiaccess scheme, and it is assumed that every node always has data to send, either originated from it or a transit packet (saturation assumption). In this scenario, we seek to maximize a measure of the transport capacity of the network (measured in bit-meters per second) over power controls (in a fading environment) and over the hop distance d, subject to an average power constraint. We first argue that for a dense collection of nodes confined to a small region, single cell operation is efficient for single user decoding transceivers. Then, operating the dense ad hoc network (described above) as a single cell, we study the optimal hop length and power control that maximizes the transport capacity for a given network power constraint. More specifically, for a fading channel and for a fixed transmission time strategy (akin to the IEEE 802.11 TXOP), we find that there exists an intrinsic aggregate bit rate (Theta(opt) bits per second, depending on the contention mechanism and the channel fading characteristics) carried by the network, when operating at the optimal hop length and power control. The optimal transport capacity is of the form d(opt)((P) over bar (t)) x Theta(opt) with d(opt) scaling as (P) over bar (1/eta)(t), where (P) over bar (t) is the available time average transmit power and eta is the path loss exponent. Under certain conditions on the fading distribution, we then provide a simple characterisation of the optimal operating point.

Deploy and search strategy for multi-agent systems using Voronoi partitions

In this paper we analyze a deploy and search strategy for multi-agent systems. Mobile agents equipped with sensors carry out search operation in the search space. The lack of information about the search space is modeled as an uncertainty density distribution over the space, and is assumed to be known to the agents a priori. In each step, the agents deploy themselves in an optimal way so as to maximize per step reduction in the uncertainty density. We analyze the proposed strategy for convergence and spatial distributedness. The control law moving the agents has been analyzed for stability and convergence using LaSalle's invariance principle, and for spatial distributedness under a few realistic constraints on the control input such as constant speed, limit on maximum speed, and also sensor range limits. The simulation experiments show that the strategy successfully reduces the average uncertainty density below the required level.