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To assess the effect of intercropping on malting quality a field trial with spring barley (Hordeum vulgare) and legume (pea) as well as non-legume (camelina and linseed) intercrops in two additive seeding ratios as well as sole cops was established in 2017 at the organic experimental station of University of Applied Sciences Osnabrück in North-Western Germany. Two tested malting barley cultivars (cv. Marthe and cv. Odilia) showed different performance, but all variants achieved brewing quality. Results after two years indicate that linseed and camelina were able to limit protein content. For best land-use efficiency of malting barley production intercropping with linseed showed best results. Mixed intercropping can help to promote internal efficiency loops and is therefore a promising sustainable intensification strategy for more resilient future crop production under changing climate conditions.
In this paper, we evaluate the application of Bayesian Optimization (BO) to discrete event simulation (DES) models. In a first step, we create a simple model, for which we know the optimal set of parameter values in advance. We implement the model in SimPy, a framework for DES written in Python. We then interpret the simulation model as a black box function subject to optimization. We show that it is possible to find the optimal set of parameter values using the open source library GPyOpt. To enhance our evaluation, we create a second and more complex model. To better handle the complexity of the model, and to add a visual component, we build the second model in Simio, a commercial off-the-shelf simulation modeling tool. To apply BO to a model in Simio, we use the Simio API to write an extension for optimization plug-ins. This extension encapsulates the logic of the BO algorithm, which we deployed as a web service in the cloud.
The fact that simulation models are black box functions with regard to their behavior and the influence of their input parameters makes them an apparent candidate for Bayesian Optimization (BO). Simulation models are multivariable and stochastic, and their behavior is to a large extent unpredictable. In particular, we do not know for sure which input parameters to adjust to maximize (or minimize) the model’s outcome. In addition, the complex models can take a substantial amount of time to run.
Bayesian Optimization is a sequential and self-learning algorithm to optimize black box functions similar to as we find them in simulation models: they contain a set of parameters for which we want to identify the optimal set, they are expensive to evaluate, and they exhibit stochastic noise. BO has proven to efficiently optimize black box functions from varius disciplines. Among those, and most notably, it is successfully applied in machine learning algorithms to optimize hyperparameters.
Career Decisions of Indian Female Talent: Implications for Gender-sensitive Talent Management
(2020)
Purpose: Talent scarcity in emerging economies such as India poses challenges for companies,and limited labour market participation among well-educated women has been observed. The reasons that professionals decide not to pursue a further corporate career remain unclear. By investigating career decision making, this article aims to highlight (1) the contextual factors that impact those decisions, (2) individuals’ agency to handle them, and (3) the implications for talent management (TM).
Design/methodology/approach: Following a qualitative research design, computer-aided analysis was conducted on interviews with 24 internationally experienced Indian business professionals. A novel application of neo-institutionalism in the Indian context was combined with the family-relatedness of work decisions (FRWD) model.
Findings: Career decisions indicate that rebellion against Indian societal and family expectations is essential to following a career path, especially for women. TM as part of the current institutional framework serves as a legitimising façade veiling traditional practices that hinder females’ careers.
Research limitations: Interviewees adopted a retrospective perspective when describing their career decisions; therefore, different views might have existed at the moment of decision making.
Practical implications: Design and implementation of gender-sensitive TM adjusted to fit the specific Indian context can contribute to retaining female talent in companies and the labour market.
Originality/value: The importance of gender-sensitive TM can be concluded from an empirical study of the context-based career decision making of experienced business professionals from India. The synthesis of neo-institutionalism, the FRWD model and the research results provides assistance in mapping talent experiences and implications for overcoming the challenges of talent scarcity in India.
This paper presents an optimized algorithm for estimating static and dynamic gait parameters. We use a marker- and contact-less motion capture system that identifies 20 joints of a person walking along a corridor.
Based on the proposed gait cycle detection basic metrics as walking frequency, step/stride length, and support phases are estimated automatically. Applying a rigid body model, we are capable to calculate static and dynamic gait stability metrics. We conclude with initial results of a clinical study evaluating orthopaedic technical support.