500 Naturwissenschaften und Mathematik
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In the last decade, the utilization of waste by-product apple pomace has been extensively researched (due to its difficult disposal) and currently finds beneficial usage in various industries; as substrate for microbial growth or recovery of pectin, xyloglucan and polyphenols. In this research apple juice was produced at pilot scale. Furthermore, apple pomace was employed as substrate for the production of pectin, biofuel (pellets) and concentrated apple pomace extract. Extensive mass and heat balances were conducted to evaluate the feasibility of this approach on industrial scale. The produced pellets had very similar characteristics to wood pellets (net calorific value of 20.3 MJ/kg). Dried apple pomace contained 11.9% of pectin. Fed-batch cultivation of baker´s yeast with apple pomace extract demonstrated a potential for partial substitution of molasses in industrial bioprocesses. This concept shows how a zero discharge biorefinery process converts waste from apple juice production into three valuable products enabling connections between different industries.
The paper focused on an analytical analysis of the main features of heat transfer in incompressible steady-state flow in a microconfusor with account for the second-order slip boundary conditions. The second-order boundary conditions serve as a closure of a system of the continuity, transport, and energy differential equations. As a result, novel solutions were obtained for the velocity and temperature profiles, as well as for the friction coefficient and the Nusselt number. These solutions demonstrated that an increase in the Knudsen number leads to a decrease in the Nusselt number. It was shown that the account for the second-order terms in the boundary conditions noticeably affects the fluid flow characteristics and does not influence on the heat transfer characteristics. It was also revealed that flow slippage effects on heat transfer weaken with an increase in the Prandtl number.
High-quality rendering of spatial sound fields in real-time is becoming increasingly important with the steadily growing interest in virtual and augmented reality technologies. Typically, a spherical microphone array (SMA) is used to capture a spatial sound field. The captured sound field can be reproduced over headphones in real-time using binaural rendering, virtually placing a single listener in the sound field. Common methods for binaural rendering first spatially encode the sound field by transforming it to the spherical harmonics domain and then decode the sound field binaurally by combining it with head-related transfer functions (HRTFs). However, these rendering methods are computationally demanding, especially for high-order SMAs, and require implementing quite sophisticated real-time signal processing. This paper presents a computationally more efficient method for real-time binaural rendering of SMA signals by linear filtering. The proposed method allows representing any common rendering chain as a set of precomputed finite impulse response filters, which are then applied to the SMA signals in real-time using fast convolution to produce the binaural signals. Results of the technical evaluation show that the presented approach is equivalent to conventional rendering methods while being computationally less demanding and easier to implement using any real-time convolution system. However, the lower computational complexity goes along with lower flexibility. On the one hand, encoding and decoding are no longer decoupled, and on the other hand, sound field transformations in the SH domain can no longer be performed. Consequently, in the proposed method, a filter set must be precomputed and stored for each possible head orientation of the listener, leading to higher memory requirements than the conventional methods. As such, the approach is particularly well suited for efficient real-time binaural rendering of SMA signals in a fixed setup where usually a limited range of head orientations is sufficient, such as live concert streaming or VR teleconferencing.
We study p-adic L-functions Lp(s, 휒) for Dirichlet characters 휒. We show that Lp(s, 휒) has a Dirichlet series expansion for each regularization parameter c that is prime to p and the conductor of 휒. The expansion is proved by transforming a known formula for p-adic L-functions and by controlling the limiting behavior. A fnite number of Euler factors can be factored of in a natural manner from the p-adic Dirichlet series. We also provide an alternative proof of the expansion using p-adic measures and give an explicit formula for the values of the regularized Bernoulli distribution. The result is particularly simple for c = 2, where we obtain a Dirichlet series expansion that is similar to the complex case.
In Latin America and the Caribbean, river restoration projects are increasing, but many lack strategic planning and monitoring. We tested the applicability of a rapid visual social–ecological stream assessment method for restoration planning, complemented by a citizen survey on perceptions and uses of blue and green infrastructure. We applied the method at three urban streams in Jarabacoa (Dominican Republic) to identify and prioritize preferred areas for nature-based solutions. The method provides spatially explicit information for strategic river restoration planning, and its efficiency makes it suitable for use in data-poor contexts. It identifies well-preserved, moderately altered, and critically impaired areas regarding their hydromorphological and socio-cultural conditions, as well as demands on green and blue infrastructure. The transferability of the method can be improved by defining reference states for assessing the hydromorphology of tropical rivers, refining socio-cultural parameters to better address river services and widespread urban challenges, and balancing trade-offs between ecological and social restoration goals.
AbstractThis paper discusses the comparison of two methods to achieve thermal comfort utilising air conditioning (AC) system in a small indoor space – adaptive control and fuzzy control. Thermal comfort indoors is performed to provide comfortability individually or for a group of people. Due to the small indoor space which usually a bit cramped, crowded and less airy, the ambience can be very uncomfortable either for doing sedentary or active work, thus the AC system can be very useful to provide thermal comfort. Both methods can be utilised depending on how thermal comfort is viewed and how the level of thermal comfort is decided. Every method has its own advantage and limitations, and will be covered in this paper as well.
Different methods have been proposed for in situ root-length density (RLD) measurement. One widely employed is the time-consuming sampling of soil cores or monoliths (MO). The profile wall (PW) method is a less precise, but faster and less laborious alternative. However, depth-differentiated functions to convert PW RLD estimates to MO RLD measurements have not yet been reported. In this study, we perform a regression analysis to relate PW results to MO results and determine whether calibration is possible for distinct crop groups (grasses, brassicas and legumes) consisting of pure and mixed stands, and whether soil depth affects this calibration. The methods were applied over two years to all crop groups and their absolute and cumulative RLD were compared using a linear (LR) and multiple linear (MLR) regression. PW RLD was found to highly underestimate MO RLD in absolute values and in highly rooted areas. However, a close agreement between both methods was found for cumulative root-length (RL) when applying MLR, highlighting the influence of soil depth. The level of agreement between methods varied strongly with depth. Therefore, the application of PW as the main RLD estimation method can provide reliable estimates of cumulative root distribution traits of cover crops.
The increase in greenhouse gas emissions, mainly due to the burning of fossil fuels and land use change, has led to changes in the global climate. Agriculture is one of the economic sectors most vulnerable to the impacts generated by climate change. For this reason, the challenge facing humanity today is to develop innovative solutions to address the complexity of agricultural sustainability.
On the other hand, sugarcane is one of the crops that emits the most pollutants into the atmosphere, mainly due to the burning of sugarcane before and after harvesting. Most of these atmospheric pollutants are precursors of climate change and have an impact on the health and quality of life of communities. Moreover, this agricultural practice causes the gradual deterioration of the soil, directly affecting sugarcane production. Consequently, several sugarcane-producing countries have established regulations or dispositions to eliminate this agricultural practice, and one option to eliminate it is the mechanization of harvesting. However, its implementation implies social, environmental, and economic impacts that must be analyzed systemically to avoid potential failures during the technological transition process. It is for this reason that this research, through the MICMAC method, focused on identifying the variables associated with the reduction of sugarcane burning in Campos dos Goytacazes and Tamasopo, to subsequently analyze their direct and indirect interrelationship, and, thus, determine the opportunities and limitations of each locality for the reduction of sugarcane burning.
Through this analysis, it became evident that although the technological transition is an imminent step for the sustainability of sugarcane cultivation, certain factors such as legislation, technological innovation, and the perception of the stakeholders regarding the consequences of sugarcane burning, is what defines in the study sites the speed and subsequent success of this process of change towards green harvesting.
Table Tennis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks
(2021)
Beginner table-tennis players require constant real-time feedback while learning the fundamental techniques. However, due to various constraints such as the mentor’s inability to be around all the time, expensive sensors and equipment for sports training, beginners are unable to get the immediate real-time feedback they need during training. Sensors have been widely used to train beginners and novices for various skills development, including psychomotor skills. Sensors enable the collection of multimodal data which can be utilised with machine learning to classify training mistakes, give feedback, and further improve the learning outcomes. In this paper, we introduce the Table Tennis Tutor (T3), a multi-sensor system consisting of a smartphone device with its built-in sensors for collecting motion data and a Microsoft Kinect for tracking body position. We focused on the forehand stroke mistake detection. We collected a dataset recording an experienced table tennis player performing 260 short forehand strokes (correct) and mimicking 250 long forehand strokes (mistake). We analysed and annotated the multimodal data for training a recurrent neural network that classifies correct and incorrect strokes. To investigate the accuracy level of the aforementioned sensors, three combinations were validated in this study: smartphone sensors only, the Kinect only, and both devices combined. The results of the study show that smartphone sensors alone perform sub-par than the Kinect, but similar with better precision together with the Kinect. To further strengthen T3’s potential for training, an expert interview session was held virtually with a table tennis coach to investigate the coach’s perception of having a real-time feedback system to assist beginners during training sessions. The outcome of the interview shows positive expectations and provided more inputs that can be beneficial for the future implementations of the T3.
Remote sensing applications of change detection are increasingly in demand for many areas of land use and urbanization, and disaster risk reduction. The Sendai Framework for Disaster Risk Reduction and the New Urban Agenda by the United Nations call for risk monitoring. This study maps and assesses the urban area changes of 23 Mexican-USA border cities with a remote sensing-based approach. A literature study on existing studies on hazard mapping and social vulnerability in those cities reveals a need for further studies on urban growth. Using a multi-modal combination of aerial, declassified (CORONA, GAMBIT, HEXAGON programs), and recent (Sentinel-2) satellite imagery, this study expands existing land cover change assessments by capturing urban growth back to the 1940s. A Geographic Information System and census data assessment results reveal that massive urban growth has occurred on both sides of the national border. On the Mexican side, population and area growth exceeds the US cities in many cases. In addition, flood hazard exposure has grown along with growing city sizes, despite structural river training. These findings indicate a need for more risk monitoring that includes remote sensing data. It has socio-economic implications, too, as the social vulnerability on Mexican and US sides differ. This study calls for the maintenance and expansion of open data repositories to enable such transboundary risk comparisons. Common vulnerability variable sets could be helpful to enable better comparisons as well as comparable flood zonation mapping techniques. To enable risk monitoring, basic data such as urban boundaries should be mapped per decade and provided on open data platforms in GIS formats and not just in map viewers.