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Three Stochastic Dynamic Programming (SDP) implementations are developed for control of a diesel-electric wheel loader transmission. The implementations each use a stochastic description of the load, with the probabilities either independent of the states, dependent on previous power or on distance driven. Both the cycles used for the controller development and for the evaluation are derived from a measured sequence of cycles.

Information about wheel loader usage can be used in several ways to optimise customer adaption. First, optimising the configuration and component sizing of a wheel loader to customer needs can lead to a significant improvement in, e.g., fuel efficiency and cost. Second, relevant driving cycles to be used in the development of wheel loaders can be extracted from usage data. Third, online usage identification opens up the possibility of implementing advanced look-ahead control strategies for wheel loader operation. The main objective of this paper is to develop an online algorithm that can automatically, using production sensors only, extract information about the usage of a machine. Two main challenges are that sensors are not located with respect to this task and that significant usage disturbances typically occur during operation. The proposed method is based on a combination of several individually simple techniques using signal processing, state automaton techniques, and parameter estimation algorithms. The approach is found to be robust when evaluated on measured data of wheel loaders loading gravel and shot rock.

The article examines investor analyst calls and price signaling under European Union (EU) law. It cites two enforcement actions in the U.S. relating to information disclosures made during analyst calls. It discusses the need for U.S. companies to understand the European Commission's approach to information disclosures to help them reduce unnecessary risk as they balance the interests of investors with the demands of EU competition law.

A complete theory for paramagnetic relaxation enhancement (PRE) and its dependence on the magnetic field is developed for systems with electron spin S = 1, 3/2, 2, 5/2, 3 and 7/2, characterised by the presence of zero-field splitting (ZFS). The electron spin interacts through dipole–dipole coupling with the nuclear spin residing in the paramagnetic complex (the inner-sphere case) as well as outside of it (the outer-sphere case). The earlier theory for S = 1 and inner-sphere interaction only is included as a special case of the present, more general approach. The theory assumes a slow reorientation of the paramagnetic complex and a lack of correlation between the rotation and translation of the complex and the electron spin dynamics. The electron spin energy level structure is determined by a combination of the Zeeman interaction and the static ZFS, and depends thus on the orientation of the complex in the magnetic field. The electron spin relaxation is described by a Redfield formulation, using the pseudorotation model for the modulation of the transient zero-field splitting. Illustrative calculations are presented, showing that the typical field dependences of the inner- and outer-sphere relaxation enhancements are in general different. The static ZFS is demonstrated to influence the magnetic field dependences by affecting the frequencies occurring in the expressions for spectral densities. The model is applied to interpret the PRE of a slowly-rotating gadolinium(iii) complex, a potential magnetic resonance imaging (MRI) contrast agent.