Wissenschaftlicher Artikel (Fachzeitschriften)
Filtern
Erscheinungsjahr
Dokumenttyp
- Wissenschaftlicher Artikel (Fachzeitschriften) (178) (entfernen)
Volltext vorhanden
- ja (178)
Gehört zur Bibliographie
- nein (178)
Schlagworte
- Deutschland (13)
- Nachhaltigkeit (11)
- Rückenschmerz (11)
- COVID-19 (7)
- Digitalisierung (7)
- China (6)
- Gesundheitswesen (6)
- Künstliche Intelligenz (6)
- Maschinelles Lernen (6)
- Pandemie (6)
Institut
- FB Bauen + Leben (49)
- FB Umweltplanung/-technik (UCB) (44)
- FB Informatik + Therapiewissenschaft (30)
- FB Technik (11)
- FB Umweltwirtschaft/-recht (UCB) (10)
- IfaS - Institut für angewandtes Stoffstrommanagement (10)
- InDi - Institut für Internationale und Digitale Kommunikation (6)
- LaROS - Labor für Radiotechnologie und optische Systeme (6)
- ISS - Institut für Softwaresysteme in Wirtschaft, Umwelt und Verwaltung (5)
- FB Wirtschaft (3)
Sustainable software products - Towards assessment criteria for resource and energy efficiency
(2018)
Many authors have proposed criteria to assess the “environmental friendliness” or “sustainability” of software products. However, a causal model that links observable properties of a software product to conditions of it being green or (more general) sustainable is still missing. Such a causal model is necessary because software products are intangible goods and, as such, only have indirect effects on the physical world. In particular, software products are not subject to any wear and tear, they can be copied without great effort, and generate no waste or emissions when being disposed of. Viewed in isolation, software seems to be a perfectly sustainable type of product. In real life, however, software products with the same or similar functionality can differ substantially in the burden they place on natural resources, especially if the sequence of released versions and resulting hardware obsolescence is taken into account. In this article, we present a model describing the causal chains from software products to their impacts on natural resources, including energy sources, from a life-cycle perspective. We focus on (i) the demands of software for hardware capacities (local, remote, and in the connecting network) and the resulting hardware energy demand, (ii) the expectations of users regarding such demands and how these affect hardware operating life, and (iii) the autonomy of users in managing their software use with regard to resource efficiency. We propose a hierarchical set of criteria and indicators to assess these impacts. We demonstrate the application of this set of criteria, including the definition of standard usage scenarios for chosen categories of software products. We further discuss the practicability of this type of assessment, its acceptability for several stakeholders and potential consequences for the eco-labeling of software products and sustainable software design.
The present work aimed at investigating an extraction protocol based on consecutive steps of isoelectric point (pH ~ 4.25) mediated gum swelling and deproteinisation as an alternative method to produce flaxseed gum extracts of enhanced techno-functional characteristics. The osidic and proximate composition, structure conformation, flow behaviour, dynamic rheological and thermal properties of gums isolated from brown and golden flaxseeds were assessed. Gum extraction under near-to-isoelectric point conditions did not impair the extraction yield, residual protein and ash content, whilst it resulted in minor changes in the sugar composition of the flaxseed gum extracts. The deconvolution of the GPC/SEC chromatographs revealed the presence of four major polysaccharidic populations corresponding to arabinoxylans, rhamnogalacturonan–I and two AX-RG-I composite fractions. The latter appeared to minimise the intra- and interchain polymer non-covalent interactions (hydrogen bonding) leading to a better solvation affinity in water and lyotropic solvents. Golden flaxseed gums exerted higher molecular weight (Mw = 1.34–1.15 × 106 Da) and intrinsic viscosities (6.63–5.13 dL g−1) as well as better thickening and viscoelastic performance than the brown flaxseed gum exemplars. Golden flaxseed gums exhibited a better thermal stability compared to the brown flaxseed counterparts and therefore, they are suitable for product applications involving severe heat treatments.
Deep brain stimulation (DBS) is a neurosurgical intervention where electrodes are permanently implanted into the brain in order to modulate pathologic neural activity. The post-operative reconstruction of the DBS electrodes is important for an efficient stimulation parameter tuning. A major limitation of existing approaches for electrode reconstruction from post-operative imaging that prevents the clinical routine use is that they are manual or semi-automatic, and thus both time-consuming and subjective. Moreover, the existing methods rely on a simplified model of a straight line electrode trajectory, rather than the more realistic curved trajectory. The main contribution of this paper is that for the first time we present a highly accurate and fully automated method for electrode reconstruction that considers curved trajectories. The robustness of our proposed method is demonstrated using a multi-center clinical dataset consisting of N = 44 electrodes. In all cases the electrode trajectories were successfully identified and reconstructed. In addition, the accuracy is demonstrated quantitatively using a high-accuracy phantom with known ground truth. In the phantom experiment, the method could detect individual electrode contacts with high accuracy and the trajectory reconstruction reached an error level below 100 μm (0.046 ± 0.025 mm). An implementation of the method is made publicly available such that it can directly be used by researchers or clinicians. This constitutes an important step towards future integration of lead reconstruction into standard clinical care.
Global change effects on biodiversity and human wellbeing call for improved long-term environmental data as a basis for science, policy and decision making, including increased interoperability, multifunctionality, and harmonization. Based on the example of two global initiatives, the International Long-Term Ecological Research (ILTER) network and the Group on Earth Observations Biodiversity Observation Network (GEO BON), we propose merging the frameworks behind these initiatives, namely ecosystem integrity and essential biodiversity variables, to serve as an improved guideline for future site-based long-term research and monitoring in terrestrial, freshwater and coastal ecosystems. We derive a list of specific recommendations of what and how to measure at a monitoring site and call for an integration of sites into co-located site networks across individual monitoring initiatives, and centered on ecosystems. This facilitates the generation of linked comprehensive ecosystem monitoring data, supports synergies in the use of costly infrastructures, fosters cross-initiative research and provides a template for collaboration beyond the ILTER and GEO BON communities.
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.
Optimal mental workload plays a key role in driving performance. Thus, driver-assisting systems that automatically adapt to a drivers current mental workload via brain–computer interfacing might greatly contribute to traffic safety. To design economic brain computer interfaces that do not compromise driver comfort, it is necessary to identify brain areas that are most sensitive to mental workload changes. In this study, we used functional near-infrared spectroscopy and subjective ratings to measure mental workload in two virtual driving environments with distinct demands. We found that demanding city environments induced both higher subjective workload ratings as well as higher bilateral middle frontal gyrus activation than less demanding country environments. A further analysis with higher spatial resolution revealed a center of activation in the right anterior dorsolateral prefrontal cortex. The area is highly involved in spatial working memory processing. Thus, a main component of drivers’ mental workload in complex surroundings might stem from the fact that large amounts of spatial information about the course of the road as well as other road users has to constantly be upheld, processed and updated. We propose that the right middle frontal gyrus might be a suitable region for the application of powerful small-area brain computer interfaces.
Intraspecific diet specialization, usually driven by resource availability, competition and predation, is common in natural populations. However, the role of parasites on diet specialization of their hosts has rarely been studied. Eye flukes can impair vision ability of their hosts and have been associated with alterations of fish feeding behavior. Here it was assessed whether European perch (Perca fluviatilis) alter their diet composition as a consequence of infection with eye flukes. Young-of-the-year (YOY) perch from temperate Lake Müggelsee (Berlin, Germany) were sampled in two years, eye flukes counted and fish diet was evaluated using both stomach content and stable isotope analyses. Perch diet was dominated by zooplankton and benthic macroinvertebrates. Both methods indicated that with increasing eye fluke infection intensity fish had a more selective diet, feeding mainly on the benthic macroinvertebrate Dikerogammarus villosus, while less intensively infected fish appeared to be generalist feeders showing no preference for any particular prey type. Our results show that infection with eye flukes can indirectly affect interaction of the host with lower trophic levels by altering the diet composition and highlight the underestimated role of parasites in food web studies.
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.
Driven by decreasing PV and energy storage prices, increasing electricity costs and policy supports from Thai government (self-consumption era), rooftop PV and energy storage systems are going to be deployed in the country rapidly that may disrupt existing business models structure of Thai distribution utilities due to revenue erosion and lost earnings opportunities. The retail rates that directly affect ratepayers (non-solar customers) are expected to increase. This paper focuses on a framework for evaluating impacts of PV with and without energy storage systems on Thai distribution utilities and ratepayers by using cost-benefit analysis (CBA). Prior to calculation of cost/benefit components, changes in energy sales need to be addressed. Government policies for the support of PV generation will also help in accelerating the rooftop PV installation. Benefit components include avoided costs due to transmission losses and deferring distribution capacity with appropriate PV penetration level, while cost components consist of losses in revenue, program costs, integration costs and unrecovered fixed costs. It is necessary for Thailand to compare total costs and total benefits of rooftop PV and energy storage systems in order to adopt policy supports and mitigation approaches, such as business model innovation and regulatory reform, effectively.
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.