Light management in PCPDTBT:PC70BM solar cells: A comparison of standard and inverted device structures

We compare standard and inverted bulk heterojunction solar cells composed of PCPDTBT:PC₇₀BM blends. Inverted devices comprising 100 nm thick active layers exhibited short circuit currents of 15 mA/cm², 10% larger than in corresponding standard devices. Modeling of the optical field distribution in the different device stacks proved that this enhancement originates from an increased absorption of incident light in the active layer. Internal quantum efficiencies (IQEs) were obtained from the direct comparison of experimentally derived and modeled currents for different layer thicknesses, yielding IQEs of ∼70% for a layer thickness of 100 nm. Simulations predict a significant increase of the light harvesting efficiency upon increasing the layer thickness to 270 nm. However, a continuous deterioration of the photovoltaic properties with layer thickness was measured for both device architectures, attributed to incomplete charge extraction. On the other hand, our optical modeling suggests that inverted devices based on PCPDTBT should be able to deliver high power conversion efficiencies (PCEs) of more than 7% provided that recombination losses can be reduced.     

Performance and lifetime of vacuum deposited organic light-emitting diodes: Influence of residual gases present during device fabrication

Understanding the influence of residual gases present during vacuum deposition of organic light-emitting diodes (OLEDs) and their effect on the device lifetime and the electrical characteristics of OLEDs is crucial for advancing industrial fabrication. In order to gain a more in-depth understanding, the influence of residual nitrogen, oxygen, and water vapor on lifetime and electrical characteristics is investigated. This is achieved by introducing the residual gases into the evaporation chamber through a needle valve while monitoring the partial pressures with the help of a mass spectrometer. We find that water vapor introduces a series resistance to the OLED while the other gases do not influence the electric characteristics. The presence of oxygen or nitrogen impacts the lifetime of the OLEDs by the same amount. Water vapor introduces an additional, even faster degradation process within the first hours of OLED operation. The electrically stressed OLEDs are analyzed by laser desorption/ionization time-of-flight mass spectroscopy. We identify the dimerisation of BPhen as well as the complexation reaction of α$\alpha$-NPD with an Ir(piq)₂ fragment as sources of device degradation.     

Iron Disilicide as High-Temperature Reference Material for Traceable Measurements of Seebeck Coefficient Between 300 K and 800 K

Thermoelectric generators (TEGs) convert heat to electrical energy by means of the Seebeck effect. The Seebeck coefficient is a central thermoelectric material property, measuring the magnitude of the thermovoltage generated in response to a temperature difference across a thermoelectric material. Precise determination of the Seebeck coefficient provides the basis for reliable performance assessment in materials development in the field of thermoelectrics. For several reasons, measurement uncertainties of up to 14% can often be observed in interlaboratory comparisons of temperature-dependent Seebeck coefficient or in error analyses on currently employed instruments. This is still too high for an industrial benchmark and insufficient for many scientific investigations and technological developments. The TESt (thermoelectric standardization) project was launched in 2011, funded by the German Federal Ministry of Education and Research (BMBF), to reduce measurement uncertainties, engineer traceable and precise thermoelectric measurement techniques for materials and TEGs, and develop reference materials (RMs) for temperature-dependent determination of the Seebeck coefficient. We report herein the successful development and qualification of cobalt-doped β-iron disilicide (β-Fe_0.95Co_0.05Si₂) as a RM for high-temperature thermoelectric metrology. A brief survey on technological processes for manufacturing and machining of samples is presented. Focus is placed on metrological qualification of the iron disilicide, results of an international round-robin test, and final certification as a reference material in accordance with ISO-Guide 35 and the “Guide to the expression of uncertainty in measurement” by the Physikalisch-Technische Bundesanstalt, the national metrology institute of Germany.     

Comparison of four different mobile devices for measuring heart rate and ECG with respect to aspects of usability and acceptance by older people

In the area of product design and usability, most products are developed for the mass-market by technically oriented designers and developers for use by persons who themselves are also technically adept by today's standards. The demands of older people are commonly not given sufficient consideration within the early developmental process. In the present study, the usability and acceptability of four different devices meant to be worn for the measurement of heart rate or ECG were analyzed on the basis of qualitative subjective user ratings and structured interviews of twelve older participants. The data suggest that there was a relatively high acceptance concerning these belts by older adults but none of the four harnesses was completely usable. Especially problematic to the point of limiting satisfaction among older subjects were problems encountered while adjusting the length of the belt and/or closing the locking mechanism. The two devices intended for dedicated heart rate recording yielded the highest user ratings for design, and were clearly preferred for extended wearing time. Yet for all the devices participants identified several important deficiencies in their design, as well as suggestions for improvement. We conclude that the creation of an acceptable monitoring device for older persons requires designers and developers to consider the special demands and abilities of the target group.     

Configuring use case models in product families

In many domains such as automotive and avionics, the size and complexity of software systems is quickly increasing. At the same time, many stakeholders tend to be involved in the development of such systems, which typically must also be configured for multiple customers with varying needs. Product Line Engineering (PLE) is therefore an inevitable practice for such systems. Furthermore, because in many areas requirements must be explicit and traceability to them is required by standards, use cases and domain models are common practice for requirements elicitation and analysis. In this paper, based on the above observations, we aim at supporting PLE in the context of use case-centric development. Therefore, we propose, apply, and assess a use case-driven configuration approach which interactively receives configuration decisions from the analysts to generate product-specific (PS) use case and domain models. Our approach provides the following: (1) a use case-centric product line modeling method (PUM), (2) automated, interactive configuration support based on PUM, and (3) an automatic generation of PS use case and domain models from Product Line (PL) models and configuration decisions. The approach is supported by a tool relying on Natural Language Processing (NLP) and integrated with an industrial requirements management tool, i.e., IBM DOORS. We successfully applied and evaluated our approach to an industrial case study in the automotive domain, thus showing evidence that the approach is practical and beneficial to capture variability at the appropriate level of granularity and to configure PS use case and domain models in industrial settings.     

NFAT5 Is Involved in GRP-Enhanced Secretion of GLP-1 by Sodium

Gastrin, secreted by G-cells, and glucagon-like peptide-1 (GLP-1), secreted by L-cells, may participate in the regulation of sodium balance. We studied the effect of sodium in mice in vivo and mouse ileum and human L-cells, on GLP-1 secretion, and the role of NFAT5 and gastrin-releasing peptide receptor (GRPR) in this process. A high-sodium diet increases serum GLP-1 levels in mice. Increasing sodium concentration stimulates GLP-1 secretion from mouse ileum and L-cells. GRP enhances the high sodium-induced increase in GLP-1 secretion. High sodium increases cellular GLP-1 expression, while low and high sodium concentrations increase NFAT5 and GRPR expression. Silencing NFAT5 in L-cells abrogates the stimulatory effect of GRP on the high sodium-induced GLP-1 secretion and protein expression, and the sodium-induced increase in GRPR expression. GLP-1 and gastrin decrease the expression of Na⁺-K⁺/ATPase and increase the phosphorylation of sodium/hydrogen exchanger type 3 (NHE3) in human renal proximal tubule cells (hRPTCs). This study gives a new perspective on the mechanisms of GLP-1 secretion, especially that engendered by ingested sodium, and the ability of GLP-1, with gastrin, to decrease Na⁺-K⁺/ATPase expression and NHE3 function in hRPTCs. These results may contribute to the better utilization of current and future GLP-1-based drugs in the treatment of hypertension.     

Towards a computer aided diagnosis (CAD) for brain MRI glioblastomas tumor exploration based on a deep convolutional neuronal networks (D-CNN) architectures

Manuel brain glioblastomas tumor exploration through MRI modalities is time-consuming. It is considered as a harmful and critical task due to highly inhomogeneous tumor regions composition. For this reason, clinicians recommend the Computer-Aided Diagnosis (CAD) tools to ensure a more accurate diagnostic. Based on convolutional Deep-Learning algorithms, this paper investigates a fully automatic CAD for brain Glioblastomas tumors exploration including three steps: pre-processing, segmentation, and finally classification. A denoising and an automatic contrast enhancement method have been applied to preprocess the MRI scans. A Multi-Modal Cascaded U-net architecture, based on Fully Convolutional deep Network (FCN), has been adopted for the Region of Interest (ROI) extraction and finally, Deep Convolutional Neural Network (D-CNN) architecture has been used to classify brain glioblastomas tumor into High-Grade (HG) and Low-Grade (LG). Experiments were performed on the Multimodal Brain Tumor Segmentation Challenge BraTS-2018 datasets benchmark. Several validations metric have been adopted to assess the CAD’s performances. The Dice Metric (DM) parameter has been calculated between the obtained segmentation results and the available ground truth data. The accuracy parameter has been computed for classification performance evaluation. The higher DM and accuracy values could attest the performance and the efficiency of the proposed CAD tool.

     

The Use of System Dynamics Simulation in Water Resources Management

In this paper we discuss the use of system dynamics as a methodology with which to address dynamically complex problems in water resources management. Problems in regional planning and river basin management, urban water management, flooding and irrigation exhibit important short-term and long-term effects, and are often contentious issues with high potential for conflict. We argue that system dynamics combined with stakeholder involvement provides an appropriate methodology to address these issues effectively. We trace the theoretical and practical evolution of system dynamics in these areas over the past 50 years. From this review of the literature and selected case studies we identify and discuss a number of best practices and common pitfalls in applications of system dynamics simulation.     

Nanoparticle layer deposition for plasmonic tuning of microstructured optical fibers

Plasmonic nanoparticles with spectral properties in the UV-to-near-IR range have a large potential for the development of innovative optical devices. Similarly, microstructured optical fibers (MOFs) represent a promising platform technology for fully integrated, next-generation plasmonic devices; therefore, the combination of MOFs and plasmonic nanoparticles would open the way for novel applications, especially in sensing applications. In this Full Paper, a cost-effective, innovative nanoparticle layer deposition (NLD) technique is demonstrated for the preparation of well-defined plasmonic layers of selected particles inside the channels of MOFs. This dynamic chemical deposition method utilizes a combination of microfluidics and self-assembled monolayer (SAM) techniques, leading to a longitudinal homogeneous particle density as long as several meters. By using particles with predefined plasmonic properties, such as the resonance wavelength, fibers with particle-adequate spectral characteristics can be prepared. The application of such fibers for refractive-index sensing yields a sensitivity of about 78 nm per refractive index unit (RIU). These novel, plasmonically tuned optical fibers with freely selected, application-tailored optical properties present extensive possibilities for applications in localized surface plasmon resonance (LSPR) sensing.