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The increasing availability of off-the-shelf high-frequency components makes radar measurement become popular in mainstream industrial applications. We present a cooperative FM radar for strongly reflective environments, being devised for a range of up to approx. 120 m. The target is designed with an unambiguous signature method and satisfies coherence. A prototype is built with commercial semiconductor components that operates in the 24 GHz industrial, scientific and medical band. First experimental results taken in sewage pipes are presented, using the target prototype and a standard FMCW radio station. An overview on four data acquisition procedures is given.
Following a quantitative analysis of adequate feedstock, comprising 11 woody biomass species, four biochars were generated using a Kon-Tiki flame curtain kiln in the state of Aguascalientes, Mexico. Despite the high quality (certified by European Biochar Certificate), the biochars contain substantial quantities of hazardous substances, such as polycyclic aromatic hydrocarbons, polychlorinated dibenzo-p-dioxins and dibenzofurans, polychlorinated biphenyls, and heavy metals, which can induce adverse effects if wrongly applied to the environment. To assess the toxicity of biochars to non-target organisms, toxicity tests with four benthic and zooplanktonic invertebrate species, the ciliate Paramecium caudatum, the rotifer Lecane quadridentata, and the cladocerans Daphnia magna and Moina macrocopa were performed using biochar elutriates. In acute and chronic toxicity tests, no acute toxic effect to ciliates, but significant lethality to rotifers and cladocerans was detected. This lethal toxicity might be due to ingestion/digestion by enzymatic/mechanic processes of biochar by cladocerans and rotifers of toxic substances present in the biochar. No chronic toxicity was found where biochar elutriates were mixed with soil. These data indicate that it is instrumental to use toxicity tests to assess biochars’ toxicity to the environment, especially when applied close to sensitive habitats, and to stick closely to the quantitative set-point values.
Online Learning algorithms and Indoor Positioning Systems are complex applications in the environment of cyber-physical systems. These distributed systems are created by networking intelligent machines and autonomous robots on the Internet of Things using embedded systems that enable the exchange of information at any time. This information is processed by Machine Learning algorithms to make decisions about current developments in production or to influence logistics processes for optimization purposes. In this article, we present and categorize the further development of the prototype of a novel Indoor Positioning System, which constantly adapts its knowledge to the conditions of its environment with the help of Online Learning. Here, we apply Online Learning algorithms in the field of sound-based indoor localization with low-cost hardware and demonstrate the improvement of the system over its predecessor and its adaptability for different applications in an experimental case study.
Internet of Things (IoT) and Artificial Intelligence (AI) are one of the most promising and disruptive areas of current research and development. However, these areas require deep knowledge in multiple disciplines such as sensors, protocols, embedded programming, distributed systems, statistics and algorithms. This broad knowledge is not easy to acquire and the software used to design these systems is becoming increasingly complex. Small and medium-sized enterprises therefore have problems in developing new business ideas. However, node- and block-based software tools have also been released and are freely available as open source toolboxes. In this paper, we present an overview of multiple node- and block-based software tools to develop IoT- and AI-based business ideas. We arrange these tools according their capabilities and further propose extension and combinations of tools to design a useful open-source library for small and medium-sized enterprises, that is easy to use and helps with rapid prototyping, enabling new business ideas to be developed using distributed computing.
Background: Telerehabilitation can contribute to the maintenance of successful rehabilitation regardless of location and time. The aim of this study was to investigate a specific three-month interactive telerehabilitation routine regarding its effectiveness in assisting patients with physical functionality and with returning to work compared to typical aftercare.
Objective: The aim of the study was to investigate a specific three-month interactive telerehabilitation with regard to effectiveness in functioning and return to work compared to usual aftercare.
Methods: From August 2016 to December 2017, 111 patients (mean 54.9 years old; SD 6.8; 54.3% female) with hip or knee replacement were enrolled in the randomized controlled trial. At discharge from inpatient rehabilitation and after three months, their distance in the 6-minute walk test was assessed as the primary endpoint. Other functional parameters, including health related quality of life, pain, and time to return to work, were secondary endpoints.
Results: Patients in the intervention group performed telerehabilitation for an average of 55.0 minutes (SD 9.2) per week. Adherence was high, at over 75%, until the 7th week of the three-month intervention phase. Almost all the patients and therapists used the communication options. Both the intervention group (average difference 88.3 m; SD 57.7; P=.95) and the control group (average difference 79.6 m; SD 48.7; P=.95) increased their distance in the 6-minute-walk-test. Improvements in other functional parameters, as well as in quality of life and pain, were achieved in both groups. The higher proportion of working patients in the intervention group (64.6%; P=.01) versus the control group (46.2%) is of note.
Conclusions: The effect of the investigated telerehabilitation therapy in patients following knee or hip replacement was equivalent to the usual aftercare in terms of functional testing, quality of life, and pain. Since a significantly higher return-to-work rate could be achieved, this therapy might be a promising supplement to established aftercare.
Background: To facilitate access to evidence-based care for back pain, a German private medical insurance offered a health program proactively to their members. Feasibility and long-term efficacy of this approach were evaluated.
Methods: Using Zelen’s design, adult members of the health insurance with chronic back pain according to billing data were randomized to the intervention (IG) or the control group (CG). Participants allocated to the IG were invited to participate in the comprehensive health program comprising medical exercise therapy and life style coaching, and those allocated to the CG to a longitudinal back pain survey. Primary outcomes were back pain severity (Korff’s Chronic Pain Grade Questionnaire) as well as health-related quality of life (SF-12) assessed by identical online questionnaires at baseline and 2-year follow-up in both study arms. In addition to analyses of covariance, a subgroup analysis explored the heterogeneity of treatment effects among different risks of back pain chronification (STarT Back Tool).
Results: Out of 3462 persons selected, randomized and thereafter contacted, 552 agreed to participate. At the 24-month follow-up, data on 189 of 258 (73.3%) of the IG were available, in the CG on 255 of 294 (86.7%). Significant, small beneficial effects were seen in primary outcomes: Compared to the CG, the IG reported less disability (1.6 vs 2.0; p = 0.025; d = 0.24) and scored better at the SF-12 physical health scale (43.3 vs 41.0; p < 0.007; d = 0.26). No effect was seen in back pain intensity and in the SF-12 mental health scale. Persons with medium or high risk of back pain chronification at baseline responded better to the health program in all primary outcomes than the subgroup with low risk at baseline.
Conclusions: After 2 years, the proactive health program resulted in small positive long-term improvements. Using risk screening prior to inclusion in the health program might increase the percentage of participants deriving benefits from it.
Background: Stratified care is an up-to-date treatment approach suggested for patients with back pain in several guidelines. A comprehensively studied stratification instrument is the STarT Back Tool (SBT). It was developed to stratify patients with back pain into three subgroups, according to their risk of persistent disabling symptoms. The primary aim was to analyse the disability differences in patients with back pain 12 months after inclusion according to the subgroups determined at baseline using the German version of the SBT (STarT-G). Moreover, the potential to improve prognosis for disability by adding further predictor variables, an analysis for differences in pain intensity according to the STarT-Classification, and discriminative ability were investigated.
Methods: Data from the control group of a randomized controlled trial were analysed. Trial participants were members of a private medical insurance with a minimum age of 18 and indicated as having persistent back pain. Measurements were made for the risk of back pain chronification using the STarT-G, disability (as primary outcome) and back pain intensity with the Chronic Pain Grade Scale (CPGS), health-related quality of life with the SF-12, psychological distress with the Patient Health Questionnaire-4 (PHQ-4) and physical activity. Analysis of variance (ANOVA), multiple linear regression, and area under the curve (AUC) analysis were conducted.
Results: The mean age of the 294 participants was 53.5 (SD 8.7) years, and 38% were female. The ANOVA for disability and pain showed significant differences (p < 0.01) among the risk groups at 12 months. Post hoc Tukey tests revealed significant differences among all three risk groups for every comparison for both outcomes. AUC for STarT-G’s ability to discriminate reference standard ‘cases’ for chronic pain status at 12 months was 0.79. A prognostic model including the STarT-Classification, the variables global health, and disability at baseline explained 45% of the variance in disability at 12 months.
Conclusions: Disability differences in patients with back pain after a period of 12 months are in accordance with the subgroups determined using the STarT-G at baseline. Results should be confirmed in a study developed with the primary aim to investigate those differences.
This article investigates the representation of the issue of refugees travelling to the Italian coast that was reported by two major Italian newspapers between August 8th and August 19th, 2017. Using analysis tools belonging to communication theory and cognitive sciences, i.e. the concepts of frame and attitude, this article highlights two major points: firstly, the analysis reveals how the two newspapers aimed at establishing a specific relationship with their readers on this topic in the relevant period on the basis of specific interpretative models; secondly, each of these interpretative models relies on the representation of specific emotions which play a central role in the interpretation of reality according to a characteristic facet of the definition of post-truth.
We present the concrete realization of a virtual laboratory equipped with a pedagogical agent. Its functionality and media didactics takes into account the results of an usability test on a prototype system, and the students' demand on such an automated assistance as obtained from a preliminary survey. The pedagogical agent mediates between the content and the learner by activating him or her. To provide information about the learner's skills, we propose a pragmatic and simplified competence model that is based on fundamental representations in physics (experiment, figure, text and equation). Moreover, an automated feedback relates the student's self-assessment with the submitted answer to the correctness of the respective task. In consequence, the pedagogical agent enables mental reflection for a crucial review of the own learning process. Interestingly, learning pathways can be envisioned, thus, giving valuable insight into individual strengths and weaknesses.
This study compares the environmental impacts of petrol, diesel, natural gas, and electric vehicles using a process-based attributional life cycle assessment (LCA) and the ReCiPe characterization method that captures 18 impact categories and the single score endpoints. Unlike common practice, we derive the cradle-to-grave inventories from an originally combustion engine VW Caddy that was disassembled and electrified in our laboratory, and its energy consumption was measured on the road. Ecoivent 2.2 and 3.0 emission inventories were contrasted exhibiting basically insignificant impact deviations. Ecoinvent 3.0 emission inventory for the diesel car was additionally updated with recent real-world close emission values and revealed strong increases over four midpoint impact categories, when matched with the standard Ecoinvent 3.0 emission inventory. Producing batteries with photovoltaic electricity instead of Chinese coal-based electricity decreases climate impacts of battery production by 69%. Break-even mileages for the electric VW Caddy to pass the combustion engine models under various conditions in terms of climate change impact ranged from 17,000 to 310,000 km. Break-even mileages, when contrasting the VW Caddy and a mini car (SMART), which was as well electrified, did not show systematic differences. Also, CO2-eq emissions in terms of passenger kilometers travelled (54–158 g CO2-eq/PKT) are fairly similar based on 1 person travelling in the mini car and 1.57 persons in the mid-sized car (VW Caddy). Additionally, under optimized conditions (battery production and use phase utilizing renewable electricity), the two electric cars can compete well in terms of CO2-eq emissions per passenger kilometer with other traffic modes (diesel bus, coach, trains) over lifetime. Only electric buses were found to have lower life cycle carbon emissions (27–52 g CO2-eq/PKT) than the two electric passenger cars.
Background: As electric kick scooters, three-wheelers, and passenger cars enter the streets, efficiency trade-offs across vehicle types gain practical relevance for consumers and policy makers. Here, we compile a comprehensive dataset of 428 electric vehicles, including seven vehicle types and information on certified and real-world energy consumption. Regression analysis is applied to quantify trade-offs between energy consumption and other vehicle attributes.
Results: Certified and real-world energy consumption of electric vehicles increase by 60% and 40%, respectively, with each doubling of vehicle mass, but only by 5% with each doubling of rated motor power. These findings hold roughly also for passenger cars whose energy consumption tends to increase 0.6 ± 0.1 kWh/100 km with each 100 kg of vehicle mass. Battery capacity and vehicle mass are closely related. A 10 kWh increase in battery capacity increases the mass of electric cars by 15 kg, their drive range by 40–50 km, and their energy consumption by 0.7–1.0 kWh/100 km. Mass-produced state-of-the-art electric passenger cars are 2.1 ± 0.8 kWh/100 km more efficient than first-generation vehicles, produced at small scale.
Conclusion: Efficiency trade-offs in electric vehicles differ from those in conventional cars—the latter showing a strong dependency of fuel consumption on rated engine power. Mass-related efficiency trade-offs in electric vehicles are large and could be tapped by stimulating mode shift from passenger cars to light electric road vehicles. Electric passenger cars still offer potentials for further efficiency improvements. These could be exploited through a dedicated energy label with battery capacity as utility parameter.
Hydrochar derived from Argan nut shell (ANS) was synthesized and applied to remove bisphenol A (BPA) and diuron. The results indicated that the hydrochar prepared at 200 °C (HTC@ANS-200) possessed a higher specific surface area (42 m2/g) than hydrochar (HTC@ANS-180) prepared at 180 °C (17 m2/g). The hydrochars exhibited spherical particles, which are rich in functional groups. The HTC@ANS-200 exhibited high adsorption efficiency, of about 92% of the BPA removal and 95% of diuron removal. The maximum Langmuir adsorption capacities of HTC@ANS-200 at room temperature were 1162.79 mg/for Bisphenol A and 833.33 mg/g for diuron (higher than most reported adsorbents). The adsorption process was spontaneous (− ΔG°) and exothermic (− ΔH°). Excellent reusability was reclaimed after five cycles, the removal efficiency showed a weak decrease of 4% for BPA and 1% for diuron. The analysis of Fourier transforms infrared spectrometry demonstrated that the aromatic C=C and OH played major roles in the adsorption mechanisms of BPA and diuron in this study. The high adsorption capacity was attributed to the beneficial porosity (The pore size of HTC@ANS-200 bigger than the size of BPA and diuron molecule) and surface functional groups. BPA and diuron adsorption occurred also via multiple adsorption mechanisms, including pore filling, π–π interactions, and hydrogen bonding interactions on HTC@ANS-200.
Zur Optimierung von Zulaufsatzkultur-Fermentationen von methylotrophen Organismen wird eine Online-Messmethode vorgestellt, mit der die Methanol-Konzentration im Medium während einer Fermentation durch ein Spülgaspervaporations-Prinzip bestimmt werden kann. Im Gegensatz zu anderen Analysemethoden bietet die Messmethode die Möglichkeit, die Substratkonzentration bei Prozessen mit Methanol als zentralem Substrat über eine Regelung auf einem definierten Wert zu halten. Es werden Schwierigkeiten, aber auch deren Überwindung bei der Adaption der Messmethode auf Fermentationsprozesse dargestellt.
Irrigated paddy rice agriculture accounts for a major share of Asia Pacific’s total water withdrawal. Furthermore, climate change induced water scarcity in the Asia-Pacific region is projected to intensify in the near future. Therefore, methods to reduce water consumption through efficiency measures are needed to ensure the long-term (water) sustainability. The irrigation systems, subak of Karangasem, Indonesia, and the tameike of Kunisaki, Japan, are two examples of sustainable paddy rice irrigation. This research, through interviews and an extensive survey, comparatively assessed the socio-environmental sustainability of the two irrigation management systems with special reference to the intensity and nature of social capital, equity of water distribution, water demand, water footprint, and water quality, etc. The prevailing social capital paradigm of each system was also compared to its overall managerial outcomes to analyze how cooperative action contributes to sustainable irrigation management. Both systems show a comparable degree of sustainable irrigation management, ensuring an equitable use of water, and maintain relatively fair water quality due to the land-use practices adapted. However, the systems differ in water demand and water efficiency principally because of the differences in the irrigation management strategies: human and structural. These findings could help devise mechanisms for transitioning to sustainable irrigation management in the commercially-oriented paddy rice agricultural systems across the Asia-Pacific region.
Automated evaluation of contact angles in a three-phase system of selective agglomeration in liquids
(2020)
This study aims to an automated evaluation of contact angles in a three-phase system of selective agglomeration in liquids. Wetting properties, quantified by contact angles, are essential in many industries and their processes. Selective agglomeration as a three-phase system consists of a suspension liquid, a heterogeneous solid phase and an immiscible binding liquid. It offers the chance of establishing more efficient separation processes because of the shape-dependent wetting properties of fine particles (size ≤ 10 µm). In the present paper, an experimental setup for contact angle measurements of fine particles based on the Sessile Drop Method is described. Moreover, a new algorithm is discussed, which can be used to automatically compute contact angles from image data captured by a high-speed camera. The algorithm uses a marker-based watershed transform to segment the image data into regions representing the droplet, the carrier plate coated by fine particles, and the background. The main idea is a parametric modelling approach forthe time-dependent droplet’s contour by an ellipse.
The results show that the development of the dynamic contact angles towards a static contact angle can be efficiently determined based on this novel technique. These findings are useful for a detailed discrimination of wetting properties of spherical and irregularly shaped particles as well as their wetting kinetics. Also, a better understanding of selective agglomeration processes will be promoted by this user-friendly method.
Local biodiversity trends over time are likely to be decoupled from global trends, as local processes may compensate or counteract global change. We analyze 161 long-term biological time series (15–91 years) collected across Europe, using a comprehensive dataset comprising ~6,200 marine, freshwater and terrestrial taxa. We test whether (i) local long-term biodiversity trends are consistent among biogeoregions, realms and taxonomic groups, and (ii) changes in biodiversity correlate with regional climate and local conditions. Our results reveal that local trends of abundance, richness and diversity differ among biogeoregions, realms and taxonomic groups, demonstrating that biodiversity changes at local scale are often complex and cannot be easily generalized. However, we find increases in richness and abundance with increasing temperature and naturalness as well as a clear spatial pattern in changes in community composition (i.e. temporal taxonomic turnover) in most biogeoregions of Northern and Eastern Europe.
Background: Recent shoulder injury prevention programs have utilized resistance exercises combined with different forms of instability, with the goal of eliciting functional adaptations and thereby reducing the risk of injury. However, it is still unknown how an unstable weight mass (UWM) affects the muscular activity of the shoulder stabilizers. Aim of the study was to assess neuromuscular activity of dynamic shoulder stabilizers under four conditions of stable and UWM during three shoulder exercises. It was hypothesized that a combined condition of weight with UWM would elicit greater activation due to the increased stabilization demand.
Methods: Sixteen participants (7 m/9 f) were included in this cross-sectional study and prepared with an EMG-setup for the: Mm. upper/lower trapezius (U.TA/L.TA), lateral deltoid (DE), latissimus dorsi (LD), serratus anterior (SA) and pectoralis major (PE). A maximal voluntary isometric contraction test (MVIC; 5 s.) was performed on an isokinetic dynamometer. Next, internal/external rotation (In/Ex), abduction/adduction (Ab/Ad) and diagonal flexion/extension (F/E) exercises (5 reps.) were performed with four custom-made-pipes representing different exercise conditions. First, the empty-pipe (P; 0.5 kg) and then, randomly ordered, water-filled-pipe (PW; 1 kg), weight-pipe (PG; 4.5 kg) and weight + water-filled-pipe (PWG; 4.5 kg), while EMG was recorded. Raw root-mean-square values (RMS) were normalized to MVIC (%MVIC). Differences between conditions for RMS%MVIC, scapular stabilizer (SR: U.TA/L.TA; U.TA/SA) and contraction (CR: concentric/eccentric) ratios were analyzed (paired t-test; p ≤ 0.05; Bonferroni adjusted α = 0.008).
Results: PWG showed significantly greater muscle activity for all exercises and all muscles except for PE compared to P and PW. Condition PG elicited muscular activity comparable to PWG (p > 0.008) with significantly lower activation of L.TA and SA in the In/Ex rotation. The SR ratio was significantly higher in PWG compared to P and PW. No significant differences were found for the CR ratio in all exercises and for all muscles.
Conclusion: Higher weight generated greater muscle activation whereas an UWM raised the neuromuscular activity, increasing the stabilization demands. Especially in the In/Ex rotation, an UWM increased the RMS%MVIC and SR ratio. This might improve training effects in shoulder prevention and rehabilitation programs.
The objective investigation of the dynamic properties of vocal fold vibrations demands the recording and further quantitative analysis of laryngeal high-speed video (HSV). Quantification of the vocal fold vibration patterns requires as a first step the segmentation of the glottal area within each video frame from which the vibrating edges of the vocal folds are usually derived. Consequently, the outcome of any further vibration analysis depends on the quality of this initial segmentation process. In this work we propose for the first time a procedure to fully automatically segment not only the time-varying glottal area but also the vocal fold tissue directly from laryngeal high-speed video (HSV) using a deep Convolutional Neural Network (CNN) approach. Eighteen different Convolutional Neural Network (CNN) network configurations were trained and evaluated on totally 13,000 high-speed video (HSV) frames obtained from 56 healthy and 74 pathologic subjects. The segmentation quality of the best performing Convolutional Neural Network (CNN) model, which uses Long Short-Term Memory (LSTM) cells to take also the temporal context into account, was intensely investigated on 15 test video sequences comprising 100 consecutive images each. As performance measures the Dice Coefficient (DC) as well as the precisions of four anatomical landmark positions were used. Over all test data a mean Dice Coefficient (DC) of 0.85 was obtained for the glottis and 0.91 and 0.90 for the right and left vocal fold (VF) respectively. The grand average precision of the identified landmarks amounts 2.2 pixels and is in the same range as comparable manual expert segmentations which can be regarded as Gold Standard. The method proposed here requires no user interaction and overcomes the limitations of current semiautomatic or computational expensive approaches. Thus, it allows also for the analysis of long high-speed video (HSV)-sequences and holds the promise to facilitate the objective analysis of vocal fold vibrations in clinical routine. The here used dataset including the ground truth will be provided freely for all scientific groups to allow a quantitative benchmarking of segmentation approaches in future.
Electric drive systems are increasingly used in automobiles. However, the combination of comfort, dynamics and safety requirements places high demands on the torque accuracy. The complex interplay of battery, inverter and electrical machine causes a lot of system uncertainties based on parameter fluctuations and measurement errors that influence the system performance. In this paper these influences on the closed loop torque control are analyzed and quantified using a variance based sensitivity analysis. The method enables to connect the variance of the torque accuracy with the parameter uncertainties causing this variance. Moreover, it quantifies the influences of the parameters independent of the complexity of the analyzed system. In addition, two methods to ensure convergence of the estimated variance based sensitivity measures are proposed. The results of the analysis are presented for 19 static working points of an battery electric drive system.
Since operational managers often monitor large numbers of wind turbines (WTs), they depend on a toolset to provide them with highly condensed information to identify and prioritize low performing WTs or schedule preventive maintenance measures. Power curves are a frequently used tool to assess the performance of WTs. The power curve health value (HV) used in this work is supposed to detect power curve anomalies since small deviations in the power curve are not easy to identify. It evaluates deviations in the linear region of power curves by performing a principal component analysis. To calculate the HV, the standard deviation in direction of the second principal component of a reference data set is compared to the standard deviation of a combined data set consisting of the reference data and data of the evaluated period. This article examines the applicability of this HV for different purposes as well as its sensitivities and provides a modified HV approach to make it more robust and suitable for heterogeneous data sets. The modified HV was tested based on ENGIE's open data wind farm and data of on- and offshore WTs from the WInD-Pool. It proved to detect anomalies in the linear region of the power curve in a reliable and sensitive manner and was also eligible to detect long term power curve degradation. Also, about 7 % of all corrective maintenance measures were preceded by high HVs with a median alarm horizon of three days. Overall, the HV proved to be a promising tool for various applications.