J-modulated 19 F- and 1 H-detected dual-optimized inverted 1 JCC 1,n-ADEQUATE: A universal ADEQUATE experiment.

The recently reported 19 F-detected dual-optimized inverted 1 JCC 1,n-ADEQUATE experiment and the previously reported 1 H-detected version have been modified to incorporate J-modulation, making it feasible to acquire all 1,1- and 1,n-ADEQUATE correlations as well as 1 JCC and n JCC homonuclear scalar couplings in a single experiment. The experiments are demonstrated using N,N-dimethylamino-2,5,6-trifluoro-3,4-phthalonitrile and N,N-dimethylamino-3,4-phthalonitrile.

Model-Based Condition Monitoring of the Sensors and Actuators of an Electric and Automated Vehicle.

Constant monitoring of driving conditions and observation of the surrounding area are essential for achieving reliable, high-quality autonomous driving. This requires more reliable sensors and actuators, as there is always the potential that sensors and actuators will fail under real-world conditions. The sensitive condition-monitoring methods of sensors and actuators should be used to improve the reliability of the sensors and actuators. They should be able to detect and isolate the abnormal situations of faulty sensors and actuators. In this paper, a developed model-based method for condition monitoring of the sensors and actuators in an electric vehicle is presented that can determine whether a sensor has a fault and further reconfigure the sensor signal, as well as detect the abnormal behavior of the actuators with the reconfigured sensor signals. Through the simulation data obtained by the vehicle model in complex road conditions, it is proved that the method is effective for the state detection of sensors and actuators.

19 F-detected dual-optimized inverted 1 JCC 1,n-ADEQUATE.

Modification of the recently reported 19 F-detected 1,1-ADEQUATE experiment that incorporates dual-optimization to selectively invert a wide range of 1 JCC correlations in a 1,n-ADEQUATE experiment is reported. Parameters for the dual-optimization segment of the pulse sequence were modified to accommodate the increased size of 1 JCC homonuclear coupling constants of poly- and perfluorinated molecules relative to protonated molecules to allow broadband inversion of the 1 JCC correlations. The observation and utility of isotope shifts are reported for the first time for 1,1- and 1,n-ADEQUATE correlations.

Neoadjuvant Chemotherapy in Pancreatic Cancer: An Appraisal of the Current High-Level Evidence.

At the time of diagnosis, only about 20% of patients with pancreatic ductal adenocarcinoma (PDAC) have resectable disease. PDAC treatment necessitates a multidisciplinary approach, and adjuvant chemotherapy after upfront resection is an established means of preventing recurrence. Neoadjuvant chemotherapy (NAT), originally introduced to downstage tumor size, is nowadays more frequently used for selection of patients with favorable tumor biology and to control potential micrometastases. While NAT is routinely applied in locally advanced (LA) PDAC, there is increasing evidence demonstrating benefits of NAT in borderline resectable (BR) PDAC. The concept of NAT has recently been tested in resectable PDAC, but to date NAT has been restricted to clinical trials, as the data are limited and no clear benefits have yet been shown in this patient group. This review summarizes the current evidence for NAT in resectable, BR, and LA PDAC, with a focus on high-level evidence and randomized controlled trials.

Generative Adversarial Network Performance in Low-Dimensional Settings.

A generative adversarial network (GAN) is an artifcial neural network with a distinctive training architecture, designed to create examples that faithfully reproduce a target distribution. GANs have recently had particular success in applications involving high-dimensional distributions in areas such as image processing. Little work has been reported for low dimensions, where properties of GANs may be better identifed and understood. We studied GAN performance in simulated low-dimensional settings, allowing us to transparently assess effects of target distribution complexity and training data sample size on GAN performance in a simple experiment. This experiment revealed two important forms of GAN error, tail underflling and bridge bias, where the latter is analogous to the tunneling observed in high-dimensional GANs.

Development of 19 F-detected 1,1-ADEQUATE for the characterization of polyfluorinated and perfluorinated compounds.

Polyfluorinated and perfluorinated compounds in the environment are a growing health concern. 19 F-detected variants of commonly employed heteronuclear shift correlation experiments such as heteronuclear single quantum correlation (HSQC) and heteronuclear multiple bond correlation (HMBC) are available; 19 F-detected experiments that employ carbon-carbon homonuclear coupling, in contrast, have never been reported. Herein, we report the measurement of the 1 JCC and n JCC coupling constants of a simple perfluorinated phthalonitrile and the first demonstration of a 19 F-detected 1,1-ADEQUATE experiment.

Modular Approach for Odometry Localization Method for Vehicles with Increased Maneuverability.

Localization and navigation not only serve to provide positioning and route guidance information for users, but also are important inputs for vehicle control. This paper investigates the possibility of using odometry to estimate the position and orientation of a vehicle with a wheel individual steering system in omnidirectional parking maneuvers. Vehicle models and sensors have been identified for this application. Several odometry versions are designed using a modular approach, which was developed in this paper to help users to design state estimators. Different odometry versions have been implemented and validated both in the simulation environment and in real driving tests. The evaluated results show that the versions using more models and using state variables in models provide both more accurate and more robust estimation.

13C-13C spin-coupling constants in crystalline 13C-labeled saccharides: conformational effects interrogated by solid-state 13C NMR spectroscopy.

Solid-state 13C NMR spectroscopy has been used in conjunction with selectively 13C-labeled mono- and disaccharides to measure 13C-13C spin-couplings (JCC) in crystalline samples. This experimental approach allows direct correlation of JCC values with specific molecular conformations since, in crystalline samples, molecular conformation is essentially static and can be determined by X-ray crystallography. JCC values measured in the solid-state in known molecular conformations can then be compared to corresponding JCC values calculated in the same conformations using density functional theory (DFT). The latter comparisons provide important validation of DFT-calculated J-couplings, which is not easily obtained by other approaches and is fundamental to obtaining reliable experiment-based conformational models from redundant J-couplings by MA'AT analysis. In this study, representative 1JCC, 2JCCC and 3JCOCC values were studied as either intra-residue couplings in the aldohexopyranosyl rings of monosaccharides or inter-residue (trans-glycoside) couplings in disaccharides. The results demonstrate that (a) accurate JCC values can be measured in crystalline saccharides that have been suitably labeled with 13C, and (b) DFT-calculated JCC values compare favorably with those determined by solid-state 13C NMR when molecular conformation is a constant in both determinations.

Dual-Bayes Localization Filter Extension for Safeguarding in the Case of Uncertain Direction Signals.

In order to run a localization filter for parking systems in real time, the directional information must be directly available when a distance measurement of the wheel speed sensor is detected. When the vehicle is launching, the wheel speed sensors may already detect distance measurement in the form of Delta-Wheel-Pulse-Counts (DWPCs) without having defined a rolling direction. This phenomenon is particularly problematic during parking maneuvers, where many small correction strokes are made. If a localization filter is used for positioning, the restrained DWPCs cannot process in real time. Without directional information in the form of a rolling direction signal, the filter has to ignore the DWPCs or artificially stop until a rolling direction signal is present. For this reason, methods for earlier estimation of the rolling direction based on the pattern of the incoming DWPCs and based on the force equilibrium have been presented. Since the new methods still have their weaknesses and a wrong estimation of the rolling direction can occur, an extension of a so-called Dual-Localization filter approach is presented. The Dual-Localization filter uses two localization filters and an intelligent initialization logic that ensures that both filters move in opposite directions at launching. The primary localization filter uses the estimated and the secondary one the opposite direction. As soon as a valid rolling direction signal is present, an initialization logic is used to decide which localization filter has previously moved in the true direction. The localization filter that has moved in the wrong direction is initialized with the states and covariances of the other localization filter. This extension allows for a fast and real-time capability to be achieved, and the accumulated velocity error can be dramatically reduced.

Pavement type and wear condition classification from tire cavity acoustic measurements with artificial neural networks.

Tire road noise is the major contributor to traffic noise, which leads to general annoyance, speech interference, and sleep disturbances. Standardized methods to measure tire road noise are expensive, sophisticated to use, and they cannot be applied comprehensively. This paper presents a method to automatically classify different types of pavement and the wear condition to identify noisy road surfaces. The methods are based on spectra of time series data of the tire cavity sound, acquired under normal vehicle operation. The classifier, an artificial neural network, correctly predicts three pavement types, whereas there are few bidirectional mis-classifications for two pavements, which have similar physical characteristics. The performance measures of the classifier to predict a new or worn out condition are over 94.6%. One could create a digital map with the output of the presented method. On the basis of these digital maps, road segments with a strong impact on tire road noise could be automatically identified. Furthermore, the method can estimate the road macro-texture, which has an impact on the tire road friction especially on wet conditions. Overall, this digital map would have a great benefit for civil engineering departments, road infrastructure operators, and for advanced driver assistance systems.