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1. Recent reports on insect decline have highlighted the need for long-term data on insect communities towards identifying their trends and drivers.
2. With the launch of many new insect monitoring schemes to investigate insect communities over large spatial and temporal scales, Malaise traps have become one of the most important tools due to the broad spectrum of species collected and reduced capture bias through passive sampling of insects day and night. However, Malaise traps can vary in size, shape, and colour, and it is unknown how these differences affect biomass, species richness, and composition of trap catch, making it difficult to compare results between studies.
3. We compared five Malaise trap types (three variations of the Townes and two variations of the Bartak Malaise trap) to determine their effects on biomass and species richness as identified by metabarcoding.
4. Insect biomass varied by 20%–55%, not strictly following trap size but varying with trap type. Total species richness was 20%–38% higher in the three Townes trap models compared to the Bartak traps. Bartak traps captured lower richness of highly mobile taxa but increased richness of ground-dwelling taxa. The white roofed Townes trap captured a higher richness of pollinators.
5. We find that biomass, total richness, and taxa group specific richness are all sensitive to Malaise trap type. Trap type should be carefully considered and aligned to match monitoring and research questions. Additionally, our estimates of trap type effects can be used to adjust results to facilitate comparisons across studies.
Background: High numbers of consumable medical materials (eg, sterile needles and swabs) are used during the daily routine of intensive care units (ICUs) worldwide. Although medical consumables largely contribute to total ICU hospital expenditure, many hospitals do not track the individual use of materials. Current tracking solutions meeting the specific requirements of the medical environment, like barcodes or radio frequency identification, require specialized material preparation and high infrastructure investment. This impedes the accurate prediction of consumption, leads to high storage maintenance costs caused by large inventories, and hinders scientific work due to inaccurate documentation. Thus, new cost-effective and contactless methods for object detection are urgently needed.
Objective: The goal of this work was to develop and evaluate a contactless visual recognition system for tracking medical consumable materials in ICUs using a deep learning approach on a distributed client-server architecture.
Methods: We developed Consumabot, a novel client-server optical recognition system for medical consumables, based on the convolutional neural network model MobileNet implemented in Tensorflow. The software was designed to run on single-board computer platforms as a detection unit. The system was trained to recognize 20 different materials in the ICU, while 100 sample images of each consumable material were provided. We assessed the top-1 recognition rates in the context of different real-world ICU settings: materials presented to the system without visual obstruction, 50% covered materials, and scenarios of multiple items. We further performed an analysis of variance with repeated measures to quantify the effect of adverse real-world circumstances.
Results: Consumabot reached a >99% reliability of recognition after about 60 steps of training and 150 steps of validation. A desirable low cross entropy of <0.03 was reached for the training set after about 100 iteration steps and after 170 steps for the validation set. The system showed a high top-1 mean recognition accuracy in a real-world scenario of 0.85 (SD 0.11) for objects presented to the system without visual obstruction. Recognition accuracy was lower, but still acceptable, in scenarios where the objects were 50% covered (P<.001; mean recognition accuracy 0.71; SD 0.13) or multiple objects of the target group were present (P=.01; mean recognition accuracy 0.78; SD 0.11), compared to a nonobstructed view. The approach met the criteria of absence of explicit labeling (eg, barcodes, radio frequency labeling) while maintaining a high standard for quality and hygiene with minimal consumption of resources (eg, cost, time, training, and computational power).
Conclusions: Using a convolutional neural network architecture, Consumabot consistently achieved good results in the classification of consumables and thus is a feasible way to recognize and register medical consumables directly to a hospital’s electronic health record. The system shows limitations when the materials are partially covered, therefore identifying characteristics of the consumables are not presented to the system. Further development of the assessment in different medical circumstances is needed.
Purpose: The well-to-wheel (WTW) methodology is widely used for policy support in road transport. It can be seen as a simplified life cycle assessment (LCA) that focuses on the energy consumption and CO2 emissions only for the fuel being consumed, ignoring other stages of a vehicle’s life cycle. WTW results are therefore different from LCA results. In order to close this gap, the authors propose a hybrid WTW+LCA methodology useful to assess the greenhouse gas (GHG) profiles of road vehicles.
Methods: The proposed method (hybrid WTW+LCA) keeps the main hypotheses of the WTW methodology, but integrates them with LCA data restricted to the global warming potential (GWP) occurring during the manufacturing of the battery pack. WTW data are used for the GHG intensity of the EU electric mix, after a consistency check with the main life cycle impact (LCI) sources available in literature.
Results and discussion: A numerical example is provided, comparing GHG emissions due to the use of a battery electric vehicle (BEV) with emissions from an internal combustion engine vehicle. This comparison is done both according to the WTW approach (namely the JEC WTW version 4) and the proposed hybrid WTW+LCA method. The GHG savings due to the use of BEVs calculated with the WTW-4 range between 44 and 56 %, while according to the hybrid method the savings are lower (31–46 %). This difference is due to the GWP which arises as a result of the manufacturing of the battery pack for the electric vehicles.
Conclusions: The WTW methodology used in policy support to quantify energy content and GHG emissions of fuels and powertrains can produce results closer to the LCA methodology by adopting a hybrid WTW+LCA approach. While evaluating GHG savings due to the use of BEVs, it is important that this method considers the GWP due to the manufacturing of the battery pack.
Numerous research methods have been developed to detect anomalies in the areas of security and risk analysis. In healthcare, there are numerous use cases where anomaly detection is relevant. For example, early detection of sepsis is one such use case. Early treatment of sepsis is cost effective and reduces the number of hospital days of patients in the ICU. There is no single procedure that is sufficient for sepsis diagnosis, and combinations of approaches are needed. Detecting anomalies in patient time series data could help speed the development of some decisions. However, our algorithm must be viewed as complementary to other approaches based on laboratory values and physician judgments. The focus of this work is to develop a hybrid method for detecting anomalies that occur, for example, in multidimensional medical signals, sensor signals, or other time series in business and nature. The novelty of our approach lies in the extension and combination of existing approaches: Statistics, Self Organizing Maps and Linear Discriminant Analysis in a unique and unprecedented way with the goal of identifying different types of anomalies in real-time measurement data and defining the point where the anomaly occurs. The proposed algorithm not only has the full potential to detect anomalies, but also to find real points where an anomaly starts.
Concerns over climate change, air pollution, and oil supply have stimulated the market for battery electric vehicles (BEVs). The environmental impacts of BEVs are typically evaluated through a standardized life-cycle assessment (LCA) methodology. Here, the LCA literature was surveyed with the objective to sketch the major trends and challenges in the impact assessment of BEVs. It was found that BEVs tend to be more energy efficient and less polluting than conventional cars. BEVs decrease exposure to air pollution as their impacts largely result from vehicle production and electricity generation outside of urban areas. The carbon footprint of BEVs, being highly sensitive to the carbon intensity of the electricity mix, may decrease in the nearby future through a shift to renewable energies and technology improvements in general. A minority of LCAs covers impact categories other than carbon footprint, revealing a mixed picture. Up to date little attention is paid so far in LCA to the efficiency advantage of BEVs in urban traffic, the gap between on-road and certified energy consumption, the local exposure to air pollutants and noise and the aging of emissions control technologies in conventional cars. Improvements of BEV components, directed charging, second-life reuse of vehicle batteries, as well as vehicle-to-home and vehicle-to-grid applications will significantly reduce the environmental impacts of BEVs in the future.
Purification of mRNA with oligo(dT)-functionalized magnetic particles involves a series of magnetic separations for buffer exchange and washing. Magnetic particles interact and agglomerate with each other when a magnetic field is applied, which can result in a decreased total surface area and thus a decreased yield of mRNA. In addition, agglomeration may also be caused by mRNA loading on the magnetic particles. Therefore, it is of interest how the individual steps of magnetic separation and subsequent redispersion in the buffers used affect the particle size distribution. The lysis/binding buffer is the most important buffer for the separation of mRNA from the multicomponent suspension of cell lysate. Therefore, monodisperse magnetic particles loaded with mRNA were dispersed in the lysis/binding buffer and in the reference system deionized water, and the particle size distributions were measured. A concentration-dependent agglomeration tendency was observed in deionized water. In contrast, no significant agglomeration was detected in the lysis/binding buffer. With regard to magnetic particle recycling, the influence of different storage and drying processes on particle size distribution was investigated. Agglomeration occurred in all process alternatives. For de-agglomeration, ultrasonic treatment was examined. It represents a suitable method for reproducible restoration of the original particle size distribution.
One key for successful and fluent human-robot-collaboration in disassembly processes is equipping the robot system with higher autonomy and intelligence. In this paper, we present an informed software agent that controls the robot behavior to form an intelligent robot assistant for disassembly purposes. While the disassembly process first depends on the product structure, we inform the agent using a generic approach through product models. The product model is then transformed to a directed graph and used to build, share and define a coarse disassembly plan. To refine the workflow, we formulate "the problem of loosening a connection and the distribution of the work" as a search problem. The created detailed plan consists of a sequence of actions that are used to call, parametrize and execute robot programs for the fulfillment of the assistance. The aim of this research is to equip robot systems with knowledge and skills to allow them to be autonomous in the performance of their assistance to finally improve the ergonomics of disassembly workstations.
The implementation of single-use technologies offers several major advantages, e.g. prevention of cross-contamination, especially when spore-forming microorganisms are present. This study investigated the application of a single-use bioreactor in batch fermentation of filamentous fungus Penicillium sp. (IBWF 040-09) from the Institute of Biotechnology and Drug Research (IBWF), which is capable of intracellular production of a protease inhibitor against parasitic proteases as a secondary metabolite. Several modifications to the SU bioreactor were suggested in this study to allow the fermentation in which the fungus forms pellets. Simultaneously, fermentations in conventional glass bioreactor were also conducted as reference. Although there are significant differences in the construction material and gassing system, the similarity of the two types of bioreactors in terms of fungal metabolic activity and the reproducibility of fermentations could be demonstrated using statistic methods. Under the selected cultivation conditions, growth rate, yield coefficient, substrate uptake rate, and formation of intracellular protease-inhibiting substance in the single-use bioreactor were similar to those in the glass bioreactor.
Artificial light at night (ALAN) is a widespread alteration of the natural environment that can affect the functioning of ecosystems. ALAN can change the movement patterns of freshwater animals that move into the adjacent riparian and terrestrial ecosystems, but the implications for local riparian consumers that rely on these subsidies are still unexplored. We conducted a 2-year field experiment to quantify changes of freshwater-terrestrial linkages by installing streetlights in a previously light-naïve riparian area adjacent to an agricultural drainage ditch. We compared the abundance and community composition of emerging aquatic insects, flying insects, and ground-dwelling arthropods with an unlit control site. Comparisons were made within and between years using two-way generalized least squares (GLS) model and a BACI design (Before-After Control-Impact). Aquatic insect emergence, the proportion of flying insects that were aquatic in origin, and the total abundance of flying insects all increased in the ALAN-illuminated area. The abundance of several night-active ground-dwelling predators (Pachygnatha clercki, Trochosa sp., Opiliones) increased under ALAN and their activity was extended into the day. Conversely, the abundance of nocturnal ground beetles (Carabidae) decreased under ALAN. The changes in composition of riparian predator and scavenger communities suggest that the increase in aquatic-to-terrestrial subsidy flux may cascade through the riparian food web. The work is among the first studies to experimentally manipulate ALAN using a large-scale field experiment, and provides evidence that ALAN can affect processes that link adjacent ecosystems. Given the large number of streetlights that are installed along shorelines of freshwater bodies throughout the globe, the effects could be widespread and represent an underestimated source of impairment for both aquatic and riparian systems.
Universities, as innovation drivers in science and technology worldwide, should attempt to become carbon-neutral institutions and should lead this transformation. Many universities have picked up the challenge and quantified their carbon footprints; however, up-to-date quantification is limited to use-phase emissions. So far, data on embodied impacts of university campus infrastructure are missing, which prevents us from evaluating their life cycle costs. In this paper, we quantify the embodied impacts of two university campuses of very different sizes and climate zones: the Umwelt-Campus Birkenfeld (UCB), Germany, and the Nanyang Technological University (NTU), Singapore. We also quantify the effects of switching to full renewable energy supply on the carbon footprint of a university campus based on the example of UCB. The embodied impacts amount to 13.7 (UCB) and 26.2 (NTU) kg CO2e/m2•y, respectively, equivalent to 59.2% (UCB), and 29.8% (NTU), respectively, of the building lifecycle impacts. As a consequence, embodied impacts can be dominating; thus, they should be quantified and reported. When adding additional use-phase impacts caused by the universities on top of the building lifecycle impacts (e.g., mobility impacts), both institutions happen to exhibit very similar emissions with 124.5–126.3 kg CO2e/m2•y despite their different sizes, structures, and locations. Embodied impacts comprise 11.0–20.8% of the total impacts at the two universities. In conclusion, efficient reduction in university carbon footprints requires a holistic approach, considering all impacts caused on and by a campus including upstream effects.
Carbon footprinting of universities worldwide: Part I — objective comparison by standardized metrics
(2021)
Background: Universities, as innovation drivers in science and technology worldwide, should be leading the Great Transformation towards a carbon–neutral society and many have indeed picked up the challenge. However, only a small number of universities worldwide are collecting and publishing their carbon footprints, and some of them have defined zero emission targets. Unfortunately, there is limited consistency between the reported carbon footprints (CFs) because of different analysis methods, different impact measures, and different target definitions by the respective universities.
Results: Comprehensive CF data of 20 universities from around the globe were collected and analysed. Essential factors contributing to the university CF were identified. For the first time, CF data from universities were not only compared. The CF data were also evaluated, partly corrected, and augmented by missing contributions, to improve the consistency and comparability. The CF performance of each university in the respective year is thus homogenized, and measured by means of two metrics: CO2e emissions per capita and per m2 of constructed area. Both metrics vary by one order of magnitude across the different universities in this study. However, we identified ten universities reaching a per capita carbon footprint of lower than or close to 1.0 Mt (metric tons) CO2e/person and year (normalized by the number of people associated with the university), independent from the university’s size. In addition to the aforementioned two metrics, we suggested a new metric expressing the economic efficiency in terms of the CF per $ expenditures and year. We next aggregated the results for all three impact measures, arriving at an overall carbon performance for the respective universities, which we found to be independent of geographical latitude. Instead the per capita measure correlates with the national per capita CFs, and it reaches on average 23% of the national impacts per capita. The three top performing universities are located in Switzerland, Chile, and Germany.
Conclusion: The usual reporting of CO2 emissions is categorized into Scopes 1–3 following the GHG Protocol Corporate Accounting Standard which makes comparison across universities challenging. In this study, we attempted to standardize the CF metrics, allowing us to objectively compare the CF at several universities. From this study, we observed that, almost 30 years after the Earth Summit in Rio de Janeiro (1992), the results are still limited. Only one zero emission university was identified, and hence, the transformation should speed up globally.
A comprehensive overview is provided evaluating direct real-world CO2 emissions of both diesel and petrol cars newly registered in Europe between 1995 and 2015. Before 2011, European diesel cars emitted less CO2 per kilometre than petrol cars, but since then there is no appreciable difference in per-km CO2 emissions between diesel and petrol cars. Real-world CO2 emissions of diesel cars have not declined appreciably since 2001, while the CO2 emissions of petrol cars have been stagnant since 2012. When adding black carbon related CO2-equivalents, such as from diesel cars without particulate filters, diesel cars were discovered to have had much higher climate relevant emissions until the year 2001 when compared to petrol cars. From 2001 to 2015 CO2-equivalent emissions from new diesel cars and petrol cars were hardly distinguishable. Lifetime use phase CO2-equivalent emissions of all European passenger vehicles were modelled for 1995–2015 based on three scenarios: the historic case, another scenario freezing percentages of diesel cars at the low levels from the early 1990s (thus avoiding the observed “boom” in new diesel registrations), and an advanced mitigation scenario based on high proportions of petrol hybrid cars and cars burning gaseous fuels. The difference in CO2-equivalent emissions between the historical case and the scenario avoiding the diesel car boom is only 0.4%. The advanced mitigation scenario would have been able to achieve a 3.4% reduction in total CO2-equivalent emissions over the same time frame. The European diesel car boom appears to have been ineffective at reducing climate-warming emissions from the European transport sector.
The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient “data fingerprint” of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2) and ideal body weight-adjusted tidal volume (Vt). The observed outcome was in-hospital or 90-day mortality. VentAI reached a significantly increased estimated performance return of 83.3 (primary dataset) and 84.1 (secondary dataset) compared to physicians’ standard clinical care (51.1). The number of recommended action changes per mechanically ventilated patient constantly exceeded those of the clinicians. VentAI chose 202.9% more frequently ventilation regimes with lower Vt (5–7.5 mL/kg), but 50.8% less for regimes with higher Vt (7.5–10 mL/kg). VentAI recommended 29.3% more frequently PEEP levels of 5–7 cm H2O and 53.6% more frequently PEEP levels of 7–9 cmH2O. VentAI avoided high (>55%) FiO2 values (59.8% decrease), while preferring the range of 50–55% (140.3% increase). In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients.
Laboratory protocols using magnetic beads have gained importance in the purification of mRNA for vaccines. Here, the produced mRNA hybridizes specifically to oligo(dT)-functionalized magnetic beads after cell lysis. The mRNA-loaded magnetic beads can be selectively separated using a magnet. Subsequently, impurities are removed by washing steps and the mRNA is eluted. Magnetic separation is utilized in each step, using different buffers such as the lysis/binding buffer. To reduce the time required for purification of larger amounts of mRNA vaccine for clinical trials, high-gradient magnetic separation (HGMS) is suitable. Thereby, magnetic beads are selectively retained in a flow-through separation chamber. To meet the requirements of biopharmaceutical production, a disposable HGMS separation chamber with a certified material (United States Pharmacopeia Class VI) was developed which can be manufactured using 3D printing. Due to the special design, the filter matrix itself is not in contact with the product. The separation chamber was tested with suspensions of oligo(dT)-functionalized Dynabeads MyOne loaded with synthetic mRNA. At a concentration of cB = 1.6–2.1 g·L–1 in lysis/binding buffer, these 1 μm magnetic particles are retained to more than 99.39% at volumetric flows of up to 150 mL·min–1 with the developed SU-HGMS separation chamber. When using the separation chamber with volumetric flow rates below 50 mL·min–1, the retained particle mass is even more than 99.99%.
Background: Problem drinking, particularly risky single-occasion drinking is widespread among adolescents and young adults in most Western countries. Mobile phone text messaging allows a proactive and cost-effective delivery of short messages at any time and place and allows the delivery of individualised information at times when young people typically drink alcohol. The main objective of the planned study is to test the efficacy of a combined web- and text messaging-based intervention to reduce problem drinking in young people with heterogeneous educational level.
Methods/Design: A two-arm cluster-randomised controlled trial with one follow-up assessment after 6 months will be conducted to test the efficacy of the intervention in comparison to assessment only. The fully-automated intervention program will provide an online feedback based on the social norms approach as well as individually tailored mobile phone text messages to stimulate (1) positive outcome expectations to drink within low-risk limits, (2) self-efficacy to resist alcohol and (3) planning processes to translate intentions to resist alcohol into action. Program participants will receive up to two weekly text messages over a time period of 3 months. Study participants will be 934 students from approximately 93 upper secondary and vocational schools in Switzerland. Main outcome criterion will be risky single-occasion drinking in the past 30 days preceding the follow-up assessment.
Discussion: This is the first study testing the efficacy of a combined web- and text messaging-based intervention to reduce problem drinking in young people. Given that this intervention approach proves to be effective, it could be easily implemented in various settings, and it could reach large numbers of young people in a cost-effective way.
Background: Tobacco smoking prevalence continues to be high, particularly among adolescents and young adults with lower educational levels, and is therefore a serious public health problem. Tobacco smoking and problem drinking often co-occur and relapses after successful smoking cessation are often associated with alcohol use. This study aims at testing the efficacy of an integrated smoking cessation and alcohol intervention by comparing it to a smoking cessation only intervention for young people, delivered via the Internet and mobile phone.
Methods/Design: A two-arm cluster-randomised controlled trial with one follow-up assessment after 6 months will be conducted. Participants in the integrated intervention group will: (1) receive individually tailored web-based feedback on their drinking behaviour based on age and gender norms, (2) receive individually tailored mobile phone text messages to promote drinking within low-risk limits over a 3-month period, (3) receive individually tailored mobile phone text messages to support smoking cessation for 3 months, and (4) be offered the option of registering for a more intensive program that provides strategies for smoking cessation centred around a self-defined quit date. Participants in the smoking cessation only intervention group will only receive components (3) and (4). Study participants will be 1350 students who smoke tobacco daily/occasionally, from vocational schools in Switzerland. Main outcome criteria are 7-day point prevalence smoking abstinence and cigarette consumption assessed at the 6-month follow up.
Discussion: This is the first study testing a fully automated intervention for smoking cessation that simultaneously addresses alcohol use and interrelations between tobacco and alcohol use. The integrated intervention can be easily implemented in various settings and could be used with large groups of young people in a cost-effective way.
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.
Background: Electric vehicles have been identified as being a key technology in reducing future emissions and energy consumption in the mobility sector. The focus of this article is to review and assess the energy efficiency and the environmental impact of battery electric cars (BEV), which is the only technical alternative on the market available today to vehicles with internal combustion engine (ICEV). Electricity onboard a car can be provided either by a battery or a fuel cell (FCV). The technical structure of BEV is described, clarifying that it is relatively simple compared to ICEV. Following that, ICEV can be ‘e-converted’ by experienced personnel. Such an e-conversion project generated reality-close data reported here.
Results: Practicability of today's BEV is discussed, revealing that particularly small-size BEVs are useful. This article reports on an e-conversion of a used Smart. Measurements on this car, prior and after conversion, confirmed a fourfold energy efficiency advantage of BEV over ICEV, as supposed in literature. Preliminary energy efficiency data of FCV are reviewed being only slightly lower compared to BEV. However, well-to-wheel efficiency suffers from 47% to 63% energy loss during hydrogen production. With respect to energy efficiency, BEVs are found to represent the only alternative to ICEV. This, however, is only true if the electricity is provided by very efficient power plants or better by renewable energy production. Literature data on energy consumption and greenhouse gas (GHG) emission by ICEV compared to BEV suffer from a 25% underestimation of ICEV-standardized driving cycle numbers in relation to street conditions so far. Literature data available for BEV, on the other hand, were mostly modeled and based on relatively heavy BEV as well as driving conditions, which do not represent the most useful field of BEV operation. Literature data have been compared with measurements based on the converted Smart, revealing a distinct GHG emissions advantage due to the German electricity net conditions, which can be considerably extended by charging electricity from renewable sources. Life cycle carbon footprint of BEV is reviewed based on literature data with emphasis on lithium-ion batteries. Battery life cycle assessment (LCA) data available in literature, so far, vary significantly by a factor of up to 5.6 depending on LCA methodology approach, but also with respect to the battery chemistry. Carbon footprint over 100,000 km calculated for the converted 10-year-old Smart exhibits a possible reduction of over 80% in comparison to the Smart with internal combustion engine.
Conclusion: Findings of the article confirm that the electric car can serve as a suitable instrument towards a much more sustainable future in mobility. This is particularly true for small-size BEV, which is underrepresented in LCA literature data so far. While CO2-LCA of BEV seems to be relatively well known apart from the battery, life cycle impact of BEV in categories other than the global warming potential reveals a complex and still incomplete picture. Since technology of the electric car is of limited complexity with the exception of the battery, used cars can also be converted from combustion to electric. This way, it seems possible to reduce CO2-equivalent emissions by 80% (factor 5 efficiency improvement).
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.
Modeling and executing knowledge-intensive processes (KiPs) are challenging with state-of-the-art approaches, and the specific demands of KiPs are the subject of ongoing research. In this context, little attention has been paid to the ontology-driven combination of data-centric and semantic business process modeling, which finds additional motivation by enabling the division of labor between humans and artificial intelligence. Such approaches have characteristics that could allow support for KiPs based on the inferencing capabilities of reasoners. We confirm this as we show that reasoners can infer the executability of tasks based on a currently researched ontology- and data-driven business process model (ODD-BP model). Further support for KiPs by the proposed inference mechanism results from its ability to infer the relevance of tasks, depending on the extent to which their execution would contribute to process progress. Besides these contributions along with the execution perspective (start-to-end direction), we will also show how our approach can help to reach specific process goals by inferring the relevance of process elements regarding their support to achieve such goals (end-to-start direction). The elements with the most valuable process progress can be identified in the intersection of both, the execution and goal perspective. This paper will introduce this new approach and verifies its practicability with an evaluation of a KiP in the field of emergency call centers.