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Humic substances (HS), as important environmental components, are essential to soil health and agricultural sustainability. The usage of low-rank coal (LRC) for energy generation has declined considerably due to the growing popularity of renewable energy sources and gas. However, their potential as soil amendment aimed to maintain soil quality and productivity deserves more recognition. LRC, a highly heterogeneous material in nature, contains large quantities of HS and may effectively help to restore the physicochemical, biological, and ecological functionality of soil. Multiple emerging studies support the view that LRC and its derivatives can positively impact the soil microclimate, nutrient status, and organic matter turnover. Moreover, the phytotoxic effects of some pollutants can be reduced by subsequent LRC application. Broad geographical availability, relatively low cost, and good technical applicability of LRC offer the advantage of easy fulfilling soil amendment and conditioner requirements worldwide. This review analyzes and emphasizes the potential of LRC and its numerous forms/combinations for soil amelioration and crop production. A great benefit would be a systematic investment strategy implicating safe utilization and long-term application of LRC for sustainable agricultural production.
In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.
Magnetic nanoparticle relaxation in biomedical application: focus on simulating nanoparticle heating
(2021)
The compliant nature of distal limb muscle-tendon units is traditionally considered suboptimal in explosive movements when positive joint work is required. However, during accelerative running, ankle joint net mechanical work is positive. Therefore, this study aims to investigate how plantar flexor muscle-tendon behavior is modulated during fast accelerations. Eleven female sprinters performed maximum sprint accelerations from starting blocks, while gastrocnemius muscle fascicle lengths were estimated using ultrasonography. We combined motion analysis and ground reaction force measurements to assess lower limb joint kinematics and kinetics, and to estimate gastrocnemius muscle-tendon unit length during the first two acceleration steps. Outcome variables were resampled to the stance phase and averaged across three to five trials. Relevant scalars were extracted and analyzed using one-sample and two-sample t-tests, and vector trajectories were compared using statistical parametric mapping. We found that an uncoupling of muscle fascicle behavior from muscle-tendon unit behavior is effectively used to produce net positive mechanical work at the joint during maximum sprint acceleration. Muscle fascicles shortened throughout the first and second steps, while shortening occurred earlier during the first step, where negative joint work was lower compared with the second step. Elastic strain energy may be stored during dorsiflexion after touchdown since fascicles did not lengthen at the same time to dissipate energy. Thus, net positive work generation is accommodated by the reuse of elastic strain energy along with positive gastrocnemius fascicle work. Our results show a mechanism of how muscles with high in-series compliance can contribute to net positive joint work.
Urban farming is an innovative and sustainable way of food production and is becoming more and more important in smart city and quarter concepts. It also enables the production of certain foods in places where they usually dare not produced, such as production of fish or shrimps in large cities far away from the coast. Unfortunately, it is not always possible to show students such concepts and systems in real life as part of courses: visits of such industry plants are sometimes not possible because of distance or are permitted by the operator for hygienic reasons. In order to give the students the opportunity of getting into contact with such an urban farming system and its complex operation, an industrial urban farming plant was set up on a significantly smaller scale. Therefore, all needed technical components like water aeriation, biological and mechanical filtration or water circulation have been replaced either by aquarium components or by self-designed parts also using a 3D-printer. Students from different courses like mechanical engineering, smart building engineering, biology, electrical engineering, automation technology and civil engineering were involved in this project. This “miniature industrial plant” was also able to start operation and has now been running for two years successfully. Due to Corona pandemic, home office and remote online lectures, the automation of this miniature plant should be brought to a higher level in future for providing a good control over the system and water quality remotely. The aim of giving the student a chance to get to know the operation of an urban farming plant was very well achieved and the students had lots of fun in “playing” and learning with it in a realistic way.
Phase change materials offer a way of storing excess heat and releasing it when it is needed. They can be utilized as a method to control thermal behavior without the need for additional energy. This work focuses on exploring the potential of using phase change materials to passively control the thermal behavior of a star tracker by infusing it with a fitting phase change material. Based on the numerical model of the star trackers thermal behavior using ESATAN-TMS without implemented phase change material, a fitting phase change material for selected orbits is chosen and implemented in the thermal model. The altered thermal behavior of the numerical model after the implementation is analyzed for different amounts of the chosen phase change materials using an ESATAN-based subroutine developed by the FH Aachen. The PCM-modelling-subroutine is explained in the paper ICES-2021-110. The results show that an increasing amount of phase change material increasingly damps temperature oscillations. Using an integral part structure some of the mass increase can be compensated.
Infused Thermal Solutions (ITS) introduces a method for passive thermal control to stabilize structural components thermally without active heating and cooling systems, but with phase change material (PCM) for thermal energy storage (TES), in combination with lattice - both embedded in additive manufactured functional structures. In this ITS follow-on paper a thermal model approach and associated predictions are presented, related on the ITS functional breadboards developed at FH Aachen. Predictive TES by PCM is provided by a specially developed ITS PCM subroutine, which is applicable in ESATAN. The subroutine is based on the latent heat storage (LHS) method to numerically embed thermo-physical PCM behavior. Furthermore, a modeling approach is introduced to numerically consider the virtual PCM/lattice nodes within the macro-encapsulated PCM voids of the double wall ITS design. Related on these virtual nodes, in-plane and out-of-plane conductive links are defined. The recent additive manufactured ITS breadboard series are thermally cycled in the thermal vacuum chamber, both with and without embedded PCM. Related on breadboard hardware tests, measurement results are compared with predictions and are subsequently correlated. The results of specific simulations and measurements are presented. Recent predictive results of star tracker analyses are also presented in ICES-2021-106, based on this ITS PCM subroutine.
For now, the Planetary Defense Conference Exercise 2021's incoming fictitious(!), asteroid, 2021 PDC, seems headed for impact on October 20th, 2021, exactly 6 months after its discovery. Today (April 26th, 2021), the impact probability is 5%, in a steep rise from 1 in 2500 upon discovery six days ago. We all know how these things end. Or do we? Unless somebody kicked off another headline-grabbing media scare or wants to keep civil defense very idle very soon, chances are that it will hit (note: this is an exercise!). Taking stock, it is barely 6 months to impact, a steadily rising likelihood that it will actually happen, and a huge uncertainty of possible impact energies: First estimates range from 1.2 MtTNT to 13 GtTNT, and this is not even the worst-worst case: a 700 m diameter massive NiFe asteroid (covered by a thin veneer of Ryugu-black rubble to match size and brightness), would come in at 70 GtTNT. In down to Earth terms, this could be all between smashing fireworks over some remote area of the globe and a 7.5 km crater downtown somewhere. Considering the deliberate and sedate ways of development of interplanetary missions it seems we can only stand and stare until we know well enough where to tell people to pack up all that can be moved at all and save themselves. But then, it could just as well be a smaller bright rock. The best estimate is 120 m diameter from optical observation alone, by 13% standard albedo. NASA's upcoming DART mission to binary asteroid (65803) Didymos is designed to hit such a small target, its moonlet Dimorphos. The Deep Impact mission's impactor in 2005 successfully guided itself to the brightest spot on comet 9P/Tempel 1, a relatively small feature on the 6 km nucleus. And 'space' has changed: By the end of this decade, one satellite communication network plans to have launched over 11000 satellites at a pace of 60 per launch every other week. This level of series production is comparable in numbers to the most prolific commercial airliners. Launch vehicle production has not simply increased correspondingly – they can be reused, although in a trade for performance. Optical and radio astronomy as well as planetary radar have made great strides in the past decade, and so has the design and production capability for everyday 'high-tech' products. 60 years ago, spaceflight was invented from scratch within two years, and there are recent examples of fast-paced space projects as well as a drive towards 'responsive space'. It seems it is not quite yet time to abandon all hope. We present what could be done and what is too close to call once thinking is shoved out of the box by a clear and present danger, to show where a little more preparedness or routine would come in handy – or become decisive. And if we fail, let's stand and stare safely and well instrumented anywhere on Earth together in the greatest adventure of science.
The presented paper gives an overview of the most important and most common theories and concepts from the economic field of organisational change and is also enriched with quantitative publication data, which underlines the relevance of the topic. In particular, the topic presented is interwoven in an interdisciplinary way with economic psychological models, which are underpinned within the models with content from leading scholars in the field. The pace of change in companies is accelerating, as is technological change in our society. Adaptations of the corporate structure, but also of management techniques and tasks, are therefore indispensable. This includes not only the right approaches to employee motivation, but also the correct use of intrinsic and extrinsic motivational factors. Based on the hypothesis put forward by the scientist and researcher Rollinson in his book “Organisational behaviour and analysis” that managers believe motivational resources are available at all times, socio-economic and economic psychological theories are contrasted here in order to critically examine this statement. In addition, a fictitious company was created as a model for this work in order to illustrate the effects of motivational deficits in practice. In this context, the theories presented are applied to concrete problems within the model and conclusions are drawn about their influence and applicability. This led to the conclusion that motivation is a very individual challenge for each employee, which requires adapted and personalised approaches. On the other hand, the recommendations for action for supervisors in the case of motivation deficits also cannot be answered in a blanket manner, but can only be solved with the help of professional, expert-supported processing due to the economic-psychological realities of motivation. Identifying, analysing and remedying individual employee motivation deficits is, according to the authors, a problem and a challenge of great importance, especially in the context of rapidly changing ecosystems in modern companies, as motivation also influences other factors such as individual productivity. The authors therefore conclude that good motivation through the individual and customised promotion and further training of employees is an important point for achieving important corporate goals in order to remain competitive on the one hand and to create a productive and pleasant working environment on the other.
Multi-attribute relation extraction (MARE): simplifying the application of relation extraction
(2021)
Natural language understanding’s relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations.