Refine
Year of publication
Document Type
- Article (17)
- Postprint (4)
- Doctoral Thesis (2)
- Monograph/Edited Volume (1)
- Report (1)
Language
- English (25) (remove)
Is part of the Bibliography
- yes (25)
Keywords
- monitoring (25) (remove)
Institute
- Institut für Geowissenschaften (5)
- Department Sport- und Gesundheitswissenschaften (4)
- Strukturbereich Kognitionswissenschaften (4)
- Institut für Informatik und Computational Science (3)
- Humanwissenschaftliche Fakultät (2)
- Institut für Biochemie und Biologie (2)
- Institut für Ernährungswissenschaft (2)
- Fachgruppe Betriebswirtschaftslehre (1)
- Hasso-Plattner-Institut für Digital Engineering gGmbH (1)
- Institut für Physik und Astronomie (1)
Understanding the key factors influencing the water quality of large river systems forms an important basis for the assessment and protection of cross-regional ecosystems and the implementation of adapted water management concepts. However, identifying these factors requires in-depth comprehension of the unique environmental systems, which can only be achieved by detailed water quality monitoring.
Within the scope of the joint science and sports event "Elbschwimmstaffel" (swimming relay on the river Elbe) in June/July 2017 organized by the German Ministry of Education and Research, water quality data were acquired along a 550 km long stretch of the Elbe River in Germany. During the survey, eight physiochemical water quality parameters were recorded in high spatial and temporal resolution with the BIOFISH multisensor system. Multivariate statistical methods were applied to identify and delineate processes influencing the water quality.
The BIOFISH dataset revealed that phytoplankton activity has a major impact on the water quality of the Elbe River in the summer months. The results suggest that phytoplankton biomass constitutes a substantial proportion of the suspended particles and that photosynthetic activity of phytoplankton is closely related to significant temporal changes in pH and oxygen saturation.
An evaluation of the BIOFISH data based on the combination of statistical analysis with weather and discharge data shows that the hydrological and meteorological history of the sampled water body was the main driver of phytoplankton dynamics. This study demonstrates the capacity of longitudinal river surveys with the BIOFISH or similar systems for water quality assessment, the identification of pollution sources and their utilization for online in situ monitoring of rivers.
Additive manufacturing (AM) of metals and in particular laser powder bed fusion (LPBF) enables a degree of freedom in design unparalleled by conventional subtractive methods. To ensure that the designed precision is matched by the produced LPBF parts, a full understanding of the interaction between the laser and the feedstock powder is needed. It has been shown that the laser also melts subjacent layers of material underneath. This effect plays a key role when designing small cavities or overhanging structures, because, in these cases, the material underneath is feed-stock powder. In this study, we quantify the extension of the melt pool during laser illumination of powder layers and the defect spatial distribution in a cylindrical specimen. During the LPBF process, several layers were intentionally not exposed to the laser beam at various locations, while the build process was monitored by thermography and optical tomography. The cylinder was finally scanned by X-ray computed tomography (XCT). To correlate the positions of the unmolten layers in the part, a staircase was manufactured around the cylinder for easier registration. The results show that healing among layers occurs if a scan strategy is applied, where the orientation of the hatches is changed for each subsequent layer. They also show that small pores and surface roughness of solidified material below a thick layer of unmolten material (>200 mu m) serve as seeding points for larger voids. The orientation of the first two layers fully exposed after a thick layer of unmolten powder shapes the orientation of these voids, created by a lack of fusion.
Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 x 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.
Background. Despite considerable progress made in the past decade through salt iodization programs, over 2 billion people worldwide still have inadequate iodine intake, with devastating consequences for brain development and intellectual capacity. To optimize these programs with regard to salt iodine content, careful monitoring of salt iodine content is essential, but few methods are available to quantitatively measure iodine concentration in a simple, fast, and safe way.
Objective. We have validated a newly developed device that quantitatively measures the content of potassium iodate in salt in a simple, safe, and rapid way.
Methods. The linearity, determination and detection limit, and inter- and intra-assay variability of this colorimetric method were assessed and the method was compared with iodometric titration, using salt samples from several countries.
Results. Linearity of analysis ranged from 5 to 75 mg/kg iodine, with I mg/kg being the determination limit; the intra- and interassay imprecision was 0.9%, 0.5%, and 0.7% and 1.5%, 1.7%, and 2.5% for salt samples with iodine contents of 17, 30, and 55 mg/kg, respectively; the interoperator imprecision for the same samples was 1.2%, 4.9%, and 4.7%, respectively. Comparison with the iodometric method showed high agreement between the methods (R-2 = 0.978; limits of agreement, -10.5 to 10.0 mg/kg).
Conclusions. The device offers a field- and user-friendly solution to quantifying potassium iodate salt content reliably. For countries that use potassium iodide in salt iodization programs, further validation is required.
Vitamin A deficiency continues to be a global public health problem. Fortification of oil with vitamin A is considered a cost-effective, feasible strategy to prevent this problem but quality control poses a challenge to program implementation. To overcome this, we have validated a newly developed device that quantitatively measures the content of retinyl palmitate in refined palm oil, is simple to use, and yields immediate results.
Linearity of analysis rand from 2.5-30 mg retinol equivalents (RE)/kg of palm oil, with 2.5 mg RE/kg being the determination limit; inter- and intra-assay precision ranged from 1.4-7.1 To. Comparison with a high-performance Liquid chromatography method showed high agreement between the methods (R-2 = 0.92; Limits of Agreement: -1.24 mg to 2.53 mg RE/kg), and further comparisons illustrate that the new device is useful in low resource settings. This device offers a field- and user-friendly solution to quantifying the vitamin A content in refined palm oil.
Purpose:
This study aimed to examine the effects of individualized-load power training (IPT) versus traditional moderate-load power training (TPT) on strength, power, jump performance, and body composition in elite young Nordic athletes.
Methods:
In a randomized crossover design, 10 young male athletes (ski jumpers, Nordic combined athletes) age 17.5 (0.6) years (biological maturity status: +3.5 y postpeak height velocity) who competed on a national or international level performed 5 weeks of IPT (4 x 5 repetitions at 49%-72% 1-repetiton maximum [RM]) and TPT (5 x 5 repetitions at 50%-60% 1-RM) in addition to their regular training. Testing before, between, and after both training blocks comprised the assessment of muscle strength (loaded back squat 3-RM), power (maximal loaded back squat power), jump performance (eg, drop-jump height, reactive strength index), and body composition (eg, skeletal muscle mass).
Results:
Significant, large-size main effects for time were found for muscle strength (P < .01; g = 2.7), reactive strength index (P = .03; g= 1.6), and drop jump height (P = .02; g= 1.9) irrespective of the training condition (IPT, TPT). No significant time-by-condition interactions were observed. For measures of body composition, no significant main effects of condition and time or time-by-condition interactions were found.
Conclusions:
Our findings demonstrate that short-term IPT and TPT at moderate loads in addition to regular training were equally effective in improving measures of muscle strength (loaded back squat 3-RM) and vertical jump performance (reactive strength index, drop jump, and height) in young Nordic athletes.
This exploratory study aimed to monitor long-term seasonal developments in measures of anthropometry, body composition, and physical fitness in young judo athletes, and to compute associations between these measures and sporting success. Forty-four young judoka (20 females, 24 males) volunteered to participate. Tests for the assessment of anthropometry (e.g., body height/mass), body-composition (e.g., lean body mass), muscle strength (isometric handgrip strength), vertical jumping (e.g., countermovement-jump (CMJ) height), and dynamic balance (Y-balance test) were conducted at the beginning and end of a 10-month training season. Additionally, sporting success at the end of the season was recorded for each athlete. Analyses revealed significant time x sex interaction effects for lean-body-mass, isometric handgrip strength, and CMJ height (0.7 <= d <= 1.6). Post-hoc analyses showed larger gains for all measures in young males (1.9 <= d <= 6.0) compared with females (d = 2.4) across the season. Additionally, significant increases in body height and mass as well as Y-balance test scores were found from pre-to-post-test (1.2 <= d <= 4.3), irrespective of sex. Further, non-significant small-to-moderate-sized correlations were identified between changes in anthropometry/body composition/physical fitness and sporting success (p > 0.05; -0.34 <= rho <= 0.32). Regression analysis confirmed that no model significantly predicted sporting success. Ten months of judo training and/or growth/maturation contributed to significant changes in anthropometry, body composition, and physical fitness, particularly in young male judo athletes.
Merapi volcano is one of the most active and dangerous volcanoes of the earth. Located in central part of Java island (Indonesia), even a moderate eruption of Merapi poses a high risk to the highly populated area. Due to the close relationship between the volcanic unrest and the occurrence of seismic events at Mt. Merapi, the monitoring of Merapi's seismicity plays an important role for recognizing major changes in the volcanic activity. An automatic seismic event detection and classification system, which is capable to characterize the actual seismic activity in near real-time, is an important tool which allows the scientists in charge to take immediate decisions during a volcanic crisis. In order to accomplish the task of detecting and classifying volcano-seismic signals automatically in the continuous data streams, a pattern recognition approach has been used. It is based on the method of hidden Markov models (HMM), a technique, which has proven to provide high recognition rates at high confidence levels in classification tasks of similar complexity (e.g. speech recognition). Any pattern recognition system relies on the appropriate representation of the input data in order to allow a reasonable class-decision by means of a mathematical test function. Based on the experiences from seismological observatory practice, a parametrization scheme of the seismic waveform data is derived using robust seismological analysis techniques. The wavefield parameters are summarized into a real-valued feature vector per time step. The time series of this feature vector build the basis for the HMM-based classification system. In order to make use of discrete hidden Markov (DHMM) techniques, the feature vectors are further processed by applying a de-correlating and prewhitening transformation and additional vector quantization. The seismic wavefield is finally represented as a discrete symbol sequence with a finite alphabet. This sequence is subject to a maximum likelihood test against the discrete hidden Markov models, learned from a representative set of training sequences for each seismic event type of interest. A time period from July, 1st to July, 5th, 1998 of rapidly increasing seismic activity prior to the eruptive cycle between July, 10th and July, 19th, 1998 at Merapi volcano is selected for evaluating the performance of this classification approach. Three distinct types of seismic events according to the established classification scheme of the Volcanological Survey of Indonesia (VSI) have been observed during this time period. Shallow volcano-tectonic events VTB (h < 2.5 km), very shallow dome-growth related seismic events MP (h < 1 km) and seismic signals connected to rockfall activity originating from the active lava dome, termed Guguran. The special configuration of the digital seismic station network at Merapi volcano, a combination of small-aperture array deployments surrounding Merapi's summit region, allows the use of array methods to parametrize the continuously recorded seismic wavefield. The individual signal parameters are analyzed to determine their relevance for the discrimination of seismic event classes. For each of the three observed event types a set of DHMMs has been trained using a selected set of seismic events with varying signal to noise ratios and signal durations. Additionally, two sets of discrete hidden Markov models have been derived for the seismic noise, incorporating the fact, that the wavefield properties of the ambient vibrations differ considerably during working hours and night time. A total recognition accuracy of 67% is obtained. The mean false alarm (FA) rate can be given by 41 FA/class/day. However, variations in the recognition capabilities for the individual seismic event classes are significant. Shallow volcano-tectonic signals (VTB) show very distinct wavefield properties and (at least in the selected time period) a stable time pattern of wavefield attributes. The DHMM-based classification performs therefore best for VTB-type events, with almost 89% recognition accuracy and 2 FA/day. Seismic signals of the MP- and Guguran-classes are more difficult to detect and classify. Around 64% of MP-events and 74% of Guguran signals are recognized correctly. The average false alarm rate for MP-events is 87 FA/day, whereas for Guguran signals 33 FA/day are obtained. However, the majority of missed events and false alarms for both MP and Guguran events are due to confusion errors between these two event classes in the recognition process. The confusion of MP and Guguran events is interpreted as being a consequence of the selected parametrization approach for the continuous seismic data streams. The observed patterns of the analyzed wavefield attributes for MP and Guguran events show a significant amount of similarity, thus providing not sufficient discriminative information for the numerical classification. The similarity of wavefield parameters obtained for seismic events of MP and Guguran type reflect the commonly observed dominance of path effects on the seismic wave propagation in volcanic environments. The recognition rates obtained for the five-day period of increasing seismicity show, that the presented DHMM-based automatic classification system is a promising approach for the difficult task of classifying volcano-seismic signals. Compared to standard signal detection algorithms, the most significant advantage of the discussed technique is, that the entire seismogram is detected and classified in a single step.