CRIME AND PUNISHMENT WITH HABIT FORMATION

Moral concepts affect crime supply. This idea is modelled assuming that illegal activities is habit forming. We introduce habits in a intertemporal general equilibrium framework to illegal activities and compare its outcomes with a model without habit formation. The findings are that habit (i) reduces the crime level; (ii) reduces the marginal effect of illegal activities return on crime; (iii) reduces the efficacy of punishment.

PUBLIC INVESTMENT IN BASIC EDUCATION AND ECONOMIC GROWTH

The main objective of this paper was to visualize the relation between government spending on basic education and the human capital accumulation process, observing the impacts of this spending on individual investments in higher education, and on economic growth. From the results obtained, we may reach the central conclusion that basic education affects agents' decisions over their lifetime, and that the significance of the relation between public spending on education and economic growth is altered by changes in the composition of government spending with regard to basic and higher education, and this relation may be insignificant when higher education is not promoted.

1014-94 Activated Endothelial and Interstitial Cells in Chronic Myocarditis – Expression of Endothelial Adhesion Molecules

A chronic immunological process — based on an initial event of myocarditis, probably triggered by a viral infection — is considered as a possible pathogenetic mechanism for dilated cardiomyopathy (DCM). The cytokine-respondent induction of adhesion molecules on endothelial cells (EC) enables the adhesion of immunocompetent cells to activated EC and the consecutive transmigration. We studied the expression pattern of adhesion molecules (immunoglobulin-superfamily and β1-integrins) in biopsies from patients with clinically suspected DCM (n=134). Immunohistologically negative specimens (n=61: poor lymphocytic infiltration) presented a missing or weak immunoreactivity of adhesion molecules on EC. An enhanced intensity of expression was noticed in the percentage of positive biopsies (n=73: pathologically increased lymphocytic infiltration >2.0 CD 3-lymphocytes per high power field/HPF, x400-fold magnification) depicted at the following table:Negative (n=61)Positive (n=73)AntigenEndothelInterstitiumEndothelInterstitiumHLA class I15%13%63%68%HLA DR20%18%55%64%ICAM-l/CD 5425%11%84%77%VCAM-123%–88%–VLA-β/CD 2926%26%89%70%VLA-β/CDw49d15%13%66%37%LFA-3/CD 5818%16%66%36%ConclusionsPathologically increased lymphocytic infiltrates in chronic myocarditis are associated with an endothelial and interstitial inflammatory activation. This phenomenon is independent of focally concentrated infiltrates. Thus, the implication of adhesion molecules in the immunohistological diagnosis of myocarditis could provide further information apart from the sole criterion “lymphocytic infiltration” and minimize the “sampling-error-effect”

Change prediction for low complexity combined beamforming and acoustic echo cancellation

Time-variant beamforming (BF) and acoustic echo cancellation (AEC) are two techniques that are frequently employed for improving the quality of hands-free speech communication. However, the combined application of both is quite challenging as it either introduces high computational complexity or insufficient tracking. We propose a new method to improve the performance of the low-complexity beamformer first (BF-first) structure, which we call change prediction(ChaP). ChaP gathers information on several BF changes to predict the effective impulse response seen by the AEC after the next BF change. To account for uncertain data and convergence states in the predictions, reliability measures are introduced to improve ChaP in realistic scenarios

Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems

With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making. However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. The paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany. Following the design science research paradigm, two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. As a basis for both components, preliminary design requirements are derived from literature and the application field. Drawing on justificatory knowledge from deep learning as well as decision support systems, tentative design principles are derived. Based on these design principles, a prototype of the artifact is implemented that allows for rigorous evaluation of the design knowledge in multiple evaluation episodes, covering different angles. Through a technical experiment the technical novelty of the artifact’s capability to capture selected faults (regarding insulators and safety pins) in unmanned aerial vehicle (UAV)-captured image data (model component) is validated. Subsequent interviews, surveys, and workshops in a natural environment confirm the usefulness of the model as well as the user interface component. The evaluation provides evidence that (1) the image processing approach manages to address the gap of power line component inspection and (2) that the proposed holistic design knowledge for image-based decision support systems enables more informed decision-making. The paper therefore contributes to research and practice in three ways. First, the technical feasibility to detect certain maintenance-intensive parts of power lines with the help of unique UAV image data is shown. Second, the distribution system operators’ specific problem is solved by supporting decisions in maintenance with the proposed image-based decision support system. Third, precise design knowledge for image-based decision support systems is formulated that can inform future system designs of a similar nature

Systems biology and ecology of microbial mat communities

Microbial mat communities consist of dense populations of microorganisms embedded in exopolymers and/or biomineralized solid phases, and are often found in mm-cm thick assemblages, which can be stratified due to environmental gradients such as light, oxygen or sulfide. Microbial mat communities are commonly observed under extreme environmental conditions, deriving energy primarily from light and/or reduced chemicals to drive autotrophic fixation of carbon dioxide. Microbial mat ecosystems are regarded as living analogues of primordial systems on Earth, and they often form perennial structures with conspicuous stratifications of microbial populations that can be studied in situ under stable conditions for many years. Consequently, microbial mat communities are ideal natural laboratories and represent excellent model systems for studying microbial community structure and function, microbial dynamics and interactions, and discovery of new microorganisms with novel metabolic pathways potentially useful in future industrial and/or medical applications. Due to their relative simplicity and organization, microbial mat communities are often excellent testing grounds for new technologies in microbiology including micro-sensor analysis, stable isotope methodology and modern genomics. Integrative studies of microbial mat communities that combine modern biogeochemical and molecular biological methods with traditional microbiology, macro-ecological approaches, and community network modeling will provide new and detailed insights regarding the systems biology of microbial mats and the complex interplay among individual populations and their physicochemical environment. These processes ultimately control the biogeochemical cycling of energy and/or nutrients in microbial systems. Similarities in microbial community function across different types of communities from highly disparate environments may provide a deeper basis for understanding microbial community dynamics and the ecological role of specific microbial populations. Approaches and concepts developed in highly-constrained, relatively stable natural communities may also provide insights useful for studying and understanding more complex microbial communities