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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.
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.
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.
Large areas in the humid tropics are currently undergoing land-use change. The decrease of tropical rainforest, which is felled for land clearing and timber production, is countered by increasing areas of tree plantations and secondary forests. These changes are known to affect the regional water cycle as a result of plant-specific water demand and by influencing key soil properties which determine hydrological flow paths. One of these key properties sensitive to land-use change is the saturated hydraulic conductivity (Ks) as it governs vertical percolation of water within the soil profile. Low values of Ks in a certain soil depth can form an impeding layer and lead to perched water tables and the development of predominantly lateral flow paths such as overland flow. These processes can induce nutrient redistribution, erosion and soil degradation and thus affect ecosystem services and human livelihoods. Due to its sensitivity to land-use change, Ks is commonly used to assess the associated changes in hydrological flow paths. The objective of this dissertation was to assess the effect of land-use change on hydrological flow paths by analysing Ks as indicator variable. Sources of Ks variability, their implications for Ks monitoring and the relationship between Ks and near-surface hydrological flow paths in the context of land-use change were studied. The research area was located in central Panama, a country widely experiencing the abovementioned changes in land use. Ks is dependent on both static, soil-inherent properties such as particle size and clay mineralogy and dynamic, land use-dependent properties such as organic carbon content. By conducting a pair of studies with one of these influences held constant in each, the importance of static and dynamic properties for Ks was assessed. Applying a space-for-time approach to sample Ks under secondary forests of different age classes on comparable soils, a recovery of Ks from the former pasture use was shown to require more than eight years. The process was limited to the 0−6 cm sampling depth and showed large variability among replicates. A wavelet analysis of a Ks transect crossing different soil map units under comparable land cover, old-growth tropical rainforest, showed large small-scale variability, which was attributed to biotic influences, as well as a possible but non-conclusive influence of soil types. The two results highlight the importance of dynamic, land use-dependent influences on Ks. Monitoring studies can help to quantify land use-induced change of Ks, but there is a variety of sampling designs which differ in efficiency of estimating mean Ks. A comparative study of four designs and their suitability for Ks monitoring is used to give recommendations about designing a Ks monitoring scheme. Quantifying changes in spatial means of Ks for small catchments with a rotational stratified sampling design did not prove to be more efficient than Simple Random Sampling. The lack of large-scale spatial structure prevented benefits of stratification, and large small-scale variability resulting from local biotic processes and artificial effects of destructive sampling caused a lack of temporal consistency in the re-sampling of locations, which is part of the rotational design. The relationship between Ks and near-surface hydrological flow paths is of critical importance when assessing the consequences of land-use change in the humid tropics. The last part of this dissertation aimed at disclosing spatial relationships between Ks and overland flow as influenced by different land cover types. The effects of Ks on overland-flow generation were spatially variable, different between planar plots and incised flowlines and strongly influenced by land-cover characteristics. A simple comparison of Ks values and rainfall intensities was insufficient to describe the observed pattern of overland flow. Likewise, event flow in the stream was apparently not directly related to overland flow response patterns within the catchments. The study emphasises the importance of combining pedological, hydrological, meteorological and botanical measurements to comprehensively understand the land use-driven change in hydrological flow paths. In summary, Ks proved to be a suitable parameter for assessing the influence of land-use change on soils and hydrological processes. The results illustrated the importance of land cover and spatial variability of Ks for decisions on sampling designs and for interpreting overland-flow generation. As relationships between Ks and overland flow were shown to be complex and dependent on land cover, an interdisciplinary approach is required to comprehensively understand the effects of land-use change on soils and near-surface hydrological flow paths in the humid tropics.
The 933 km(2) Bengue catchment in northeastern Brazil is characterized by distinct rainy and dry seasons. Precipitation is stored in variously sized reservoirs, which is essential for the local population. In this study, we used TerraSAR-X SM(HH) data for an one-year monitoring of seasonal changes in the reservoir areas from July 2011 to July 2012. The monitoring was based on acquisitions in the ascending pass direction, complemented by occasional descending-pass images. To detect water surface areas, a histogram analysis followed by a global threshold classification was performed, and the results were validated using in situ GPS data. Distinguishing between small reservoirs and similar looking dark areas was difficult. Therefore, we tested several approaches for identifying misclassified areas. An analysis of the surface area dynamics of the reservoirs indicated high spatial and temporal heterogeneities and a large decrease in the total water surface area of the reservoirs in the catchment by approximately 30% within one year.
Traditionally, business process management systems only execute and monitor business process instances based on events that originate from the process engine itself or from connected client applications. However, environmental events may also influence business process execution. Recent research shows how the technological improvements in both areas, business process management and complex event processing, can be combined and harmonized. The series of technical reports included in this collection provides insights in that combination with respect to technical feasibility and improvements based on real-world use cases originating from the EU-funded GET Service project – a project targeting transport optimization and green-house gas reduction in the logistics domain. Each report is complemented by a working prototype.
This collection introduces six use cases from the logistics domain. Multiple transports – each being a single process instance – may be affected by the same events at the same point in time because of (partly) using the same transportation route, transportation vehicle or transportation mode (e.g. containers from multiple process instances on the same ship) such that these instances can be (partly) treated as batch. Thus, the first use case shows the influence of events to process instances processed in a batch. The case of sharing the entire route may be, for instance, due to origin from the same business process (e.g. transport three containers, where each is treated as single process instance because of being transported on three trucks) resulting in multi-instance process executions. The second use case shows how to handle monitoring and progress calculation in this context. Crucial to transportation processes are frequent changes of deadlines. The third use case shows how to deal with such frequent process changes in terms of propagating the changes along and beyond the process scope to identify probable deadline violations. While monitoring transport processes, disruptions may be detected which introduce some delay. Use case four shows how to propagate such delay in a non-linear fashion along the process instance to predict the end time of the instance. Non-linearity is crucial in logistics because of buffer times and missed connection on intermodal transports (a one-hour delay may result in a missed ship which is not going every hour). Finally, use cases five and six show the utilization of location-based process monitoring. Use case five enriches transport processes with real-time route and traffic event information to improve monitoring and planning capabilities. Use case six shows the inclusion of spatio-temporal events on the example of unexpected weather events.
There is controversy in the literature in regards of the link between training load and injury rate. Thus, the aims of this non-interventional study were to evaluate relationships between pre-season training load with biochemical markers, injury incidence and performance during the first month of the competitive period in professional soccer players.
The aim of this study is to monitor short-term seasonal development of young Olympic weightlifters’ anthropometry, body composition, physical fitness, and sport-specific performance. Fifteen male weightlifters aged 13.2 ± 1.3 years participated in this study. Tests for the assessment of anthropometry (e.g., body-height, body-mass), body-composition (e.g., lean-body-mass, relative fat-mass), muscle strength (grip-strength), jump performance (drop-jump (DJ) height, countermovement-jump (CMJ) height, DJ contact time, DJ reactive-strength-index (RSI)), dynamic balance (Y-balance-test), and sport-specific performance (i.e., snatch and clean-and-jerk) were conducted at different time-points (i.e., T1 (baseline), T2 (9 weeks), T3 (20 weeks)). Strength tests (i.e., grip strength, clean-and-jerk and snatch) and training volume were normalized to body mass. Results showed small-to-large increases in body-height, body-mass, lean-body-mass, and lower-limbs lean-mass from T1-to-T2 and T2-to-T3 (∆0.7–6.7%; 0.1 ≤ d ≤ 1.2). For fat-mass, a significant small-sized decrease was found from T1-to-T2 (∆13.1%; d = 0.4) and a significant increase from T2-to-T3 (∆9.1%; d = 0.3). A significant main effect of time was observed for DJ contact time (d = 1.3) with a trend toward a significant decrease from T1-to-T2 (∆–15.3%; d = 0.66; p = 0.06). For RSI, significant small increases from T1-to-T2 (∆9.9%, d = 0.5) were noted. Additionally, a significant main effect of time was found for snatch (d = 2.7) and clean-and-jerk (d = 3.1) with significant small-to-moderate increases for both tests from T1-to-T2 and T2-to-T3 (∆4.6–11.3%, d = 0.33 to 0.64). The other tests did not change significantly over time (0.1 ≤ d ≤ 0.8). Results showed significantly higher training volume for sport-specific training during the second period compared with the first period (d = 2.2). Five months of Olympic weightlifting contributed to significant changes in anthropometry, body-composition, and sport-specific performance. However, hardly any significant gains were observed for measures of physical fitness. Coaches are advised to design training programs that target a variety of fitness components to lay an appropriate foundation for later performance as an elite athlete.
There is controversy in the literature in regards of the link between training load and injury rate. Thus, the aims of this non-interventional study were to evaluate relationships between pre-season training load with biochemical markers, injury incidence and performance during the first month of the competitive period in professional soccer players.