Quantum Dots Emitting in the Third Biological Window as Bimodal Contrast Agents for Cardiovascular Imaging

Physicians are demanding innovative technologies for multimodal imaging of the cardiovascular system that would lead to the appearance of advanced diagnosis and therapy procedures. This implies the simultaneous development of new imaging techniques and contrast agents whose synergy would make it possible. Optical coherence tomography (OCT) has recently emerged as a versatile and high‐resolution clinical technique for cardiovascular imaging. Unfortunately, the lack of adequate contrast agents impedes the use of OCT for intracoronary multimodal imaging. In this work, the hitherto unexplored capability of semiconductor quantum dots (IR‐QDs) emitting in the third infrared biological window (1.55–1.87 µm) to act as multimodal agents for intracoronary imaging is demonstrated. Under single line laser excitation at 1.3 µm, IR‐QDs are capable of providing simultaneous backscattering contrast and efficient luminescence at 1.6 µm. In this work, backscattered radiation is successfully employed to construct OCT images in both fluids and tissues whereas the infrared luminescence of the IR‐QDs provides the possibility for simultaneous acquisition of high penetrating fluorescence images. The first multimodal (fluorescence + OCT) imaging of an artery using IR‐QDs as contrast agents is provided herein demonstrating their outstanding potential for future clinical applications.     

Development of a unified creep‐fatigue equation including heat treatment

Background

Creep and fatigue damages in metals are known to interact and then lead to aggregated damage. While models exist for fatigue, creep and creep‐fatigue, no models cover all 3 load regimes. Also, a heat treatment–related parameter is not well included in most creep‐fatigue models.

Need

There is a need to develop a creep‐fatigue equation, which covers the full loading regime from pure fatigue to pure creep, and creep‐fatigue. Also needed is inclusion of a heat treatment–related parameter.

Approach

The unified creep‐fatigue equation was started from the Coffin‐Manson equation and integrated with the Manson‐Haferd parameter. This equation was validated on Inconel 718.

Outcomes

The method of deriving the coefficients and the formula of the creep function are demonstrated, and the resulting equation shows a good ability to describe the grain‐size effect and the fully integrated characteristics.

Originality

Original contributions of this work are the development of a new formulation to represent creep, fatigue and creep‐fatigue in metals. Also the inclusion of grain size—which is a proxy for heat treatment—in the formulation of this equation and in a proposed modified Manson‐Haferd parameter.     

Scalable Green Synthesis and Full‐Scale Test of the Metal–Organic Framework CAU‐10‐H for Use in Adsorption‐Driven Chillers

The demand for cooling devices has increased during the last years and this trend will continue. Adsorption‐driven chillers (ADCs) using water as the working fluid and low temperature waste energy for regeneration are an environmentally friendly alternative to currently employed cooling devices and can concurrently help to dramatically decrease energy consumption. Due to the ideal water sorption behavior and proven lifetime stability of [Al(OH)(m‐BDC)] ? x H₂O (m‐BDC^2? = 1,3‐benzenedicarboxylate), also denoted CAU‐10‐H, a green very robust synthesis process under reflux, with high yields up to 95% is developed and scaled up to kg‐scale. Shaping of the adsorbent is demonstrated, which is important for an application. Thus monoliths and coatings of CAU‐10‐H are produced using a water‐based binder. The composites are thoroughly characterized toward their mechanical stability and water sorption behavior. Finally a full‐scale heat exchanger is coated and tested under ADC working conditions. Fast adsorption dynamic leads to a high power output and a good power density. A low regeneration temperature of only 70 °C is demonstrated, allowing the use of low temperature sources like waste heat and solar thermal collectors.     

A Unifying View on SMT-Based Software Verification

After many years of successful development of new approaches for software verification, there is a need to consolidate the knowledge about the different abstract domains and algorithms. The goal of this paper is to provide a compact and accessible presentation of four SMT-based verification approaches in order to study them in theory and in practice. We present and compare the following different “schools of thought” of software verification: bounded model checking, k-induction, predicate abstraction, and lazy abstraction with interpolants. Those approaches are well-known and successful in software verification and have in common that they are based on SMT solving as the back-end technology. We reformulate all four approaches in the unifying theoretical framework of configurable program analysis and implement them in the verification framework CPAchecker. Based on this, we can present an evaluation that thoroughly compares the different approaches, where the core differences are expressed in configuration parameters and all other variables are kept constant (such as parser front end, SMT solver, used theory in SMT formulas). We evaluate the effectiveness and the efficiency of the approaches on a large set of verification tasks and discuss the conclusions.     

A sequential sampling strategy for adaptive classification of computationally expensive data

Many real-world problems in engineering can be represented and solved as a data-driven classification problem, where the goal is to build a classifier that maps a given set of input parameters onto a corresponding class or label. In some cases, the collection of data samples can be computationally expensive. It is therefore crucial to solve the problem using as little data as possible. To this end, a novel sequential sampling algorithm is proposed that begins with a very small training set and supplements it in each iteration by a small batch of additional (expensive) data points. The outcome is a representative set of data samples that focuses the sampling on those locations in the input space where the class labels are changing more rapidly, while making sure that no class regions are missed.     

Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir

Hydropower is the most important source of electricity in Brazil. It is subject to the natural variability of water yield. One building block of the proper management of hydropower assets is the short-term forecast of reservoir inflows as input for an online, event-based optimization of its release strategy. While deterministic forecasts and optimization schemes are the established techniques for short-term reservoir management, the use of probabilistic ensemble forecasts and multi-stage stochastic optimization techniques is receiving growing attention. The present work introduces a novel, mass conservative scenario tree reduction in combination with a detailed hindcasting and closed-loop control experiments for a multi-purpose hydropower reservoir in a tropical region in Brazil. The case study is the hydropower project Três Marias, which is operated with two main objectives: (i) hydroelectricity generation and (ii) flood control downstream. In the experiments, precipitation forecasts based on observed data, deterministic and probabilistic forecasts are used to generate streamflow forecasts in a hydrological model over a period of 2 years. Results for a perfect forecast show the potential benefit of the online optimization and indicate a desired forecast lead time of 30 days. In comparison, the use of actual forecasts of up to 15 days shows the practical benefit of operational forecasts, where stochastic optimization (15 days lead time) outperforms the deterministic version (10 days lead time) significantly. The range of the energy production rate between the different approaches is relatively small, between 78% and 80%, suggesting that the use of stochastic optimization combined with ensemble forecasts leads to a significantly higher level of flood protection without compromising the energy production.     

Short Term Optimal Operation of Water Supply Reservoir under Flood Control Stress using Model Predictive Control

Reservoir operations require enhanced operating procedures for water systems under stress attributed to growing water demand and consequences of changing hydro-climatic conditions. This study focuses on the management of the Yuvacik Dam Reservoir for water supply and flood mitigation in the Marmara Region of Turkey. We present an improved operating technique for fulfilling the conflicting water supply and flood mitigation objectives. This is accomplished by incorporating the long term water supply objectives into a Guide Curve (GC) whereas the extreme floods are attenuated by means of short-term optimization based on Model Predictive Control (MPC). The reference case implements operating rules with a constant GC at maximum forebay elevation targeting the fulfillment of the water supply objective. We compare the reference with a new time-dependent GC, derived using an Implicit Stochastic Optimization (ISO) approach. This new curve shows nearly the same performance regarding the water supply objectives, but significantly reduces the flooding risk downstream of the dam. Possible flood events observed at the end of the wet season, when the reservoir is at the maximum level to enable water supply for the dry season, can be eliminated by the application of an additional short-term optimization by MPC. The robustness of the approach is demonstrated via hindcasting experiments.