FruitGrowth - Gasburning in orchards - Environment friendly weed control

Gas burning makes treatment of weed organic. The new ENVO-DAN burner saves 40% gas and treats 1/2 meter in width.It can be mounted on a standard lawn tractor, orchard tractor or a mobile robot. The modular burner system for burning weeds in orchards can be configurated to your needs

Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data

Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), and the results point towards the conclusion that FC exhibits dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a non-parametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted in Bayesian statistical modeling we use the predictive likelihood to investigate if the model can discriminate between a motor task and rest both within and across subjects. We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest. We find that the number of states extracted are driven by subject variability and preprocessing differences while the individual states are almost purely defined by either task or rest. This questions how we in general interpret dynamic FC and points to the need for more research on what drives dynamic FC.Comment: 8 pages, 1 figure. Presented at the Machine Learning and Interpretation in Neuroimaging Workshop (MLINI-2015), 2015 (arXiv:1605.04435

Implementing collaborative improvement, top-down, bottom-up, or both?

The research presented in this paper was aimed at increasing the current understanding of the process of developing collaborative improvement in Extended Manufacturing Enterprises (EME). Based on action research and action learning of three EMEs involving a total of thirteen companies from five European countries, the present study identifies three different approaches to collaborative improvement (CoI), that is, inter-organisational continuous improvement. One approach to CoI focuses on learning at a practical level, developing this knowledge into strategic and theoretical knowledge. We call this the bottom-up learning-bydoing approach. Another approach focuses on goal alignment and assessment to provide a foundation for improvement before actually improving. We call this the top-down directive approach. Yet another approach focuses on shared goals/vision and meeting on equal terms, and joint work in a non-directive matter. This is the laissez-faire approach. The different approaches influence the collaborative improvement results achieved, and how and why they do so is the question addressed this article

How Precisely Can Easily Accessible Variables Predict Achilles and Patellar Tendon Forces during Running?

Patellar and Achilles tendinopathy commonly affect runners. Developing algorithms to predict cumulative force in these structures may help prevent these injuries. Importantly, such algorithms should be fueled with data that are easily accessible while completing a running session outside a biomechanical laboratory. Therefore, the main objective of this study was to investigate whether algorithms can be developed for predicting patellar and Achilles tendon force and impulse during running using measures that can be easily collected by runners using commercially available devices. A secondary objective was to evaluate the predictive performance of the algorithms against the commonly used running distance. Trials of 24 recreational runners were collected with an Xsens suit and a Garmin Forerunner 735XT at three different intended running speeds. Data were analyzed using a mixed-effects multiple regression model, which was used to model the association between the estimated forces in anatomical structures and the training load variables during the fixed running speeds. This provides twelve algorithms for predicting patellar or Achilles tendon peak force and impulse per stride. The algorithms developed in the current study were always superior to the running distance algorithm

Bio-crude production through aqueous phase recycling of hydrothermal liquefaction of sewage sludge

Hydrothermal liquefaction (HTL) is a promising technology for the production of bio-crude. However, some unresolved issues still exist within HTL, which need to be resolved before its promotion on a commercial scale. The management of the aqueous phase is one of the leading challenges related to HTL. In this study, the sewage sludge has been liquefied at 350 °C with and without catalyst (K2CO3). Subsequently, aqueous phase recycling was applied to investigate the effect of recycling on bio-crude properties. Obtained results showed that the energy recovery in the form of bio-crude increased by 50% via aqueous phase recirculation, whereas nitrogen content in the bio-crude was approximately doubled after eight rounds of recycling. GCMS characterization of the aqueous phase indicated acetic acid as a major water-soluble compound, which employed as a catalyst (0.56 M), and resulted in a negligible increase in bio-crude yield. ICP-AES highlighted that the majority of the inorganics were transferred to the solid phase, while the higher accumulation of potassium and sodium was found in the aqueous phase via successive rounds of recycling

Comparing superconducting and permanent magnets for magnetic refrigeration

We compare the cost of a high temperature superconducting (SC) tape-based solenoid with a permanent magnet (PM) Halbach cylinder for magnetic refrigeration. Assuming a five liter active magnetic regenerator volume, the price of each type of magnet is determined as a function of aspect ratio of the regenerator and desired internal magnetic field. It is shown that to produce a 1 T internal field in the regenerator a permanent magnet of hundreds of kilograms is needed or an area of superconducting tape of tens of square meters. The cost of cooling the SC solenoid is shown to be a small fraction of the cost of the SC tape. Assuming a cost of the SC tape of 6000 $/m2 and a price of the permanent magnet of 100$/kg, the superconducting solenoid is shown to be a factor of 0.3-3 times more expensive than the permanent magnet, for a desired field from 0.5-1.75 T and the geometrical aspect ratio of the regenerator. This factor decreases for increasing field strength, indicating that the superconducting solenoid could be suitable for high field, large cooling power applications

Flexible Stamp for Nanoimprint Lithography

The design, fabrication and performance of a flexible silicon stamp for homogenous large area nanoimprint lithography (NIL) are presented. The flexible stamp is fabricated by bulk semiconductor micro machining of a 4-inch silicon wafer and consists of thick anchor like imprint areas connected by membranes. The bending stiffness difference between the imprint areas and the membranes ensures that the deformation of the stamp during the imprint process mainly takes place in the membranes, leaving the imprint structures unaffected. By this design the strong demand to the parallelism between stamp and substrate in the imprint situation is decoupled from the pressing tool and the wafer quality. The stamp consist of 1562 imprint areas (1 mm × 1 mm) containing the patterns to be replicated. The imprinted patterns are characterized with respect to the imprint depth and the polymer residual layer thickness. It is found that within a 50 mm diameter the polymer residual layer thickness is 18.8 nm with a standard deviation of 6.6 nm

Laser-Annealing and Solid-Phase Epitaxy of Selenium Thin-Film Solar Cells

Selenium has resurged as a promising photovoltaic material in solar cell research due to its wide direct bandgap of 1.95 eV, making it a suitable candidate for a top cell in tandem photovoltaic devices. However, the optoelectronic quality of selenium thin-films has been identified as a key bottleneck for realizing high-efficiency selenium solar cells. In this study, we present a novel approach for crystallizing selenium thin-films using laser-annealing as an alternative to the conventionally used thermal annealing strategy. By laser-annealing through a semitransparent substrate, a buried layer of high-quality selenium crystallites is formed and used as a growth template for solid-phase epitaxy. The resulting selenium thin-films feature larger and more preferentially oriented grains with a negligible surface roughness in comparison to thermally annealed selenium thin-films. We fabricate photovoltaic devices using this strategy, and demonstrate a record ideality factor of n=1.37, a record fill factor of FF=63.7%, and a power conversion efficiency of PCE=5.0%. The presented laser-annealing strategy is universally applicable and is a promising approach for crystallizing a wide range of photovoltaic materials where high temperatures are needed while maintaining a low substrate temperature