Applying a systems thinking framework to assess knowledge assets dynamics for business performance improvement

Knowledge assets represent strategic resources and sources of organizational value creation. Their effective development and deployment is at the basis of organizational value creation capacity. However there is still a lack of applied approaches and tools explaining how knowledge assets dynamics take place in organizational value creation mechanisms. In particular, there is a managerial need to define decision-support frameworks that can enable managers to understand how knowledge assets interact each other and with organizational performance in order to support the achievement of company’s strategic objectives. A better understanding of why and how knowledge assets management initiatives can be turned into value creation mechanisms with positive impacts on business performance is fundamental to avoid misallocation of resources and to support management decisions.This paper proposes a systems thinking-based framework, the Knowledge Assets Dynamics Value Map (KAVDM), to explicate the working mechanisms by means knowledge assets can evolve on the basis of knowledge management initiatives and affect business performance improvements. The KAVDM offers a holistic view of the mechanisms at the basis of how knowledge assets are translated into organizational value. It supports the explanation and monitoring of how knowledge assets are interpedently and dependently linked, and how the management of one knowledge asset activates flow dynamics, that influence both other knowledge assets and business performance. Using the KAVDM managers can reflect upon the knowledge components grounding a company’s value creation and assess their mental models and views of the reality. Finally, an application of the KAVDM within a construction company is presented and its main managerial benefits addressed.     

A virtual sensor for backlash in robotic manipulators

Journal of Intelligent Manufacturing - Gear backlash is a quite serious problem in industrial robots, it causes vibrations and impairs the robot positioning accuracy. Backlash estimation allows...     

Unknown-state, unknown-input reconstruction in discrete-time nonminimum-phase systems: Geometric methods

The complete solution to the unknown-state, unknown-input reconstruction problem in systems with invariant zeros is inherently conditioned by the fact that, for any invariant zero, at least one initial state exists, such that the output is not affected when the mode of the invariant zero is properly injected into the system. Despite this intrinsic limitation, the problem of reconstructing the initial state and the inaccessible inputs from the available measurements has recently attracted remarkable interest, owing to its impact on the synthesis of enhanced-reliability control systems. This contribution consists of a geometric method which solves the unknown-state, unknown-input reconstruction problem in discrete-time systems with invariant zeros anywhere in the complex plane, except the unit circumference. The case of systems with the invariant zeros in the open set outside the unit disc is regarded as the basic one. The difficulties related to the presence of those invariant zeros are overcome by accepting a reconstruction delay commensurate to the invariant zero time constants and the accuracy required for reconstruction. The solution devised for that case also applies to systems without invariant zeros. However, in this case, reconstruction is exact and the delay depends on the number of iterations needed for a certain conditioned invariant algorithm to converge. Finally, the more general case of systems with invariant zeros lying anywhere in the complex plane, with the sole exception of the unit circumference, is reduced to the fundamental one through the synthesis of an appropriate filter.     

Multi-scale simulations of plasma with iPIC3D

The implicit Particle-in-Cell method for the computer simulation of plasma, and its implementation in a three-dimensional parallel code, called iPIC3D, are presented. The implicit integration in time of the Vlasov–Maxwell system, removes the numerical stability constraints and it enables kinetic plasma simulations at magnetohydrodynamics time scales. Simulations of magnetic reconnection in plasma are presented to show the effectiveness of the algorithm.     

Narrow-band filtering by means of triangular meta-material resonators based on RF MEMS cantilevers in CPW configuration

Microsystem Technologies - Triangular resonators, designed by using Radio Frequency Micro Electro Mechanical Systems (RF MEMS) cantilevers in coplanar waveguide (CPW) configuration, have been...     

A High Order Multi-Dimensional Characteristic Tracing Strategy for the Vlasov–Poisson System

In this paper, we consider a finite difference grid-based semi-Lagrangian approach for solving the Vlasov–Poisson (VP) system. Many of existing methods are based on dimensional splitting, which decouples the problem into solving linear advection problems, see Cheng and Knorr (J Comput Phys 22:330–351, 1976). However, such splitting is subject to the splitting error. If we consider multi-dimensional problems without splitting, difficulty arises in tracing characteristics with high order accuracy. Specifically, the evolution of characteristics is subject to the electric field which is determined globally from the distribution of particle density via Poisson’s equation. In this paper, we propose a novel strategy of tracing characteristics high order in time via a two-stage multi-derivative prediction–correction approach and by using moment equations of the VP system. With the foot of characteristics being accurately located, we propose to use weighted essentially non-oscillatory interpolation to recover function values between grid points, therefore to update the solution at the next time level. The proposed method does not have time step restriction as the Eulerian approach and enjoys high order spatial and temporal accuracy. The performance of the proposed schemes are numerically demonstrated via classical test problems such as Landau damping and two stream instabilities.     

Computer-aided engineering for sheet metal forming: definition of a springback quality function

Computer-aided engineering methods are extensively applied to sheet metal forming integrated design. The adoption of a new class of materials, the advanced high strength steels, has increased the occurrence of springback, and consequently the request for tools oriented to springback reduction and optimization. This paper presents an approximated formulation to compute the springback field after stamping through the finite element analysis of the process. This can be found assuming that the residual field of nodal forces after stamping produces a springback shape referable to a linear combination of n modes of vibration of the nominal shape of the component. The aim of this formulation is not that of substituting the finite element analysis of the springback but rather to make use of the coefficients of the linear combination, so to define a global quality function for springback. In this way, Robust Design methods or other current optimization procedures to improve the stamping process as for structural defects (such wrinkling, necking and flatness) can be applied also for the reduction of springback. The meaning of these coefficients will be shown through three test cases and the consistency of the formulation will be discussed according to the number of modes of vibration included in the computation.     

Advances in remote sensing of hydraulic roughness

Riparian vegetation plays a crucial role in affecting the floodplain hydraulic roughness, which in turn significantly influences the dynamics of flood waves. Systematic detection, identification and assessment of flow resistance factors using conventional field sampling is often unfeasible as these techniques are time-consuming and expensive. As in many other environmental monitoring problems, remote sensing may provide unprecedented mapping capabilities. In this article we present an overview focusing on the different methods that can be used to remotely derive floodplain hydraulic roughness. The overview is based on an extensive literature review on recent estimation techniques of riparian roughness using remote sensing data from different platforms. The outlined methods of floodplain roughness parameterization include: (1) classification-derived hydraulic roughness maps and (2) estimation of vegetation hydrodynamic properties. Possible directions for a multiscale analysis of riparian flow resistance are also described in a short section by focusing on the potential of data assimilation methods for the estimation of floodplain roughness. The literature reveals that many valuable remote-sensing techniques have been developed for riparian corridor parameterization. Methodologies based on the fusion of multispectral/temporal imagery with data of different origin, such as light detection and ranging (LiDAR) and radar/microwave, appear to be powerful tools for characterizing riparian ecosystems for hydraulic purposes.     

ISWEC: A gyroscopic mechanism for wave power exploitation

In the past four decades, hundreds of Wave Energy Converters (WECs) have been proposed and studied, but so far a final architecture to harvest wave power has not been identified. Many engineering problems are still to be solved, like survivability, durability and effective power capture in a variable wave climate. ISWEC (Inertial Sea Wave Energy Converter) is a system using the gyroscopic reactions provided from a spinning flywheel to extract power. The flywheel works inside a sealed floating body in order to be protected from the outer environment and grant a reliable and durable operation. The article summarizes the design procedure of a 1:45 scaled ISWEC device with rated power 2.2 W and the tank tests performed with a simplified plain float to verify the actual prototype power capabilities. The article then focuses on the implementation of a non-linear coupled model (mechanics + hydrodynamics) to improve the float shape in order to maximize the power absorption. The final result is a float shape capable to absorb a power almost three times bigger (5.96 W) than the initial float shape.     

Learning noisy linear classifiers via adaptive and selective sampling

We introduce efficient margin-based algorithms for selective sampling and filtering in binary classification tasks. Experiments on real-world textual data reveal that our algorithms perform significantly better than popular and similarly efficient competitors. Using the so-called Mammen-Tsybakov low noise condition to parametrize the instance distribution, and assuming linear label noise, we show bounds on the convergence rate to the Bayes risk of a weaker adaptive variant of our selective sampler. Our analysis reveals that, excluding logarithmic factors, the average risk of this adaptive sampler converges to the Bayes risk at rate N ^{−(1+α)(2+α)/2(3+α)} where N denotes the number of queried labels, and α>0 is the exponent in the low noise condition. For all $\alpha>\sqrt{3}-1\approx0.73$\alpha>\sqrt{3}-1\approx0.73 this convergence rate is asymptotically faster than the rate N ^{−(1+α)/(2+α)} achieved by the fully supervised version of the base selective sampler, which queries all labels. Moreover, for α→∞ (hard margin condition) the gap between the semi- and fully-supervised rates becomes exponential.