State-Dependent Approach to Entropic Measurement-Disturbance Relations

Heisenberg's intuition was that there should be a tradeoff between measuring a particle's position with greater precision and disturbing its momentum. Recent formulations of this idea have focused on the question of how well two complementary observables can be jointly measured. Here, we provide an alternative approach based on how enhancing the predictability of one observable necessarily disturbs a complementary one. Our measurement-disturbance relation refers to a clear operational scenario and is expressed by entropic quantities with clear statistical meaning. We show that our relation is perfectly tight for all measurement strengths in an existing experimental setup involving qubit measurements.Comment: 9 pages, 2 figures. v4: published versio

EC198 Revised 1957 2, 4-D for Weed Control in Field Crops

Extension Circular 198 Revised 1957 discusses 2, 4-D for weed control in field crops

EC68-178 Lawn Weeds and their Control

Extension Circular 68-178: Lawn Weeds and their control; identification and descriptions, control methods such as chemical control or mechanical control

Magnetic excitations in the spin-trimer compounds Ca3Cu3-xNix(PO4)4 (x=0,1,2)

Inelastic neutron scattering experiments were performed for the spin-trimer compounds Ca3Cu3-xNix(PO4)4 (x=0,1,2) in order to study the dynamic magnetic properties. The observed excitations can be associated with transitions between the low-lying electronic states of linear Cu-Cu-Cu, Cu-Cu-Ni, and Ni-Cu-Ni trimers which are the basic constituents of the title compounds. The exchange interactions within the trimers are well described by the Heisenberg model with dominant antiferromagnetic nearest-neighbor interactions J. For x=0 we find JCu-Cu=-4.74(2) meV which is enhanced for x=1 to JCu-Cu=-4.92(6) meV. For x=1 and x=2 we find JCu-Ni=-0.85(10) meV and an axial single-ion anisotropy parameter DNi=-0.7(1) meV. While the x=0 and x=1 compounds do not exhibit long-range magnetic ordering down to 1 K, the x=2 compound shows antiferromagnetic ordering below TN=20 K, which is compatible with the molecular-field parameter 0.63(12) meV derived by neutron spectroscopy.Comment: 22 pages (double spacing), 1 table, 9 figures, Submitted to Phys. Rev. B (2007

From Algorithmic Computing to Autonomic Computing

In algorithmic computing, the program follows a predefined set of rules – the algorithm. The analyst/designer of the program analyzes the intended tasks of the program, defines the rules for its expected behaviour and programs the implementation. The creators of algorithmic software must therefore foresee, identify and implement all possible cases for its behaviour in the future application! However, what if the problem is not fully defined? Or the environment is uncertain? What if situations are too complex to be predicted? Or the environment is changing dynamically? In many such cases algorithmic computing fails. In such situations, the software needs an additional degree of freedom: Autonomy! Autonomy allows software to adapt to partially defined problems, to uncertain or dynamically changing environments and to situations that are too complex to be predicted. As more and more applications – such as autonomous cars and planes, adaptive power grid management, survivable networks, and many more – fall into this category, a gradual switch from algorithmic computing to autonomic computing takes place. Autonomic computing has become an important software engineering discipline with a rich literature, an active research community, and a growing number of applications.:Introduction 5 1 A Process Data Based Autonomic Optimization of Energy Efficiency in Manufacturing Processes, Daniel Höschele 9 2 Eine autonome Optimierung der Stabilität von Produktionsprozessen auf Basis von Prozessdaten, Richard Horn 25 3 Assuring Safety in Autonomous Systems, Christian Rose 41 4 MAPE-K in der Praxis - Grundlage für eine mögliche automatische Ressourcenzuweisung, in der Cloud Michael Schneider 5

Impact and Challenges of Software in 2025: Collected Papers

Today (2014), software is the key ingredient of most products and services. Software generates innovation and progress in many modern industries. Software is an indispensable element of evolution, of quality of life, and of our future. Software development is (slowly) evolving from a craft to an industrial discipline. Software – and the ability to efficiently produce and evolve high-quality software – is the single most important success factor for many highly competitive industries. Software technology, development methods and tools, and applications in more and more areas are rapidly evolving. The impact of software in 2025 in nearly all areas of life, work, relationships, culture, and society is expected to be massive. The question of the future of software is therefore important. However – like all predictions – quite difficult. Some market forces, industrial developments, social needs, and technology trends are visible today. How will they develop and influence the software we will have in 2025?:Impact of Heterogeneous Processor Architectures and Adaptation Technologies on the Software of 2025 (Kay Bierzynski) 9 Facing Future Software Engineering Challenges by Means of Software Product Lines (David Gollasch) 19 Capabilities of Digital Search and Impact on Work and Life in 2025 (Christina Korger) 27 Transparent Components for Software Systems (Paul Peschel) 37 Functionality, Threats and Influence of Ubiquitous Personal Assistants with Regard to the Society (Jonas Rausch) 47 Evolution-driven Changes of Non-Functional Requirements and Their Architecture (Hendrik Schön) 5

CC125 Chemical Drying Sprays as an Aid for Seed Production

Extension Circular CC125 This circular is about using chemical drying sprays as an aid for seed production

EC62-130 Chemicals that Control Weeds

EXTENSION CIRCULAR 62-130: This circular is about different chemicals that control weeds. It is set up as a fold-out chart of multiple types of weeds and their herbicides