Explore the vast range of titles available.

The use of anisotropic NMR data, such as residual dipolar couplings (RDCs) and residual chemical shift anisotropies (RCSAs), has emerged as a powerful technique for structural characterization of organic small molecules. RDCs typically report the relative orientations of different 1 H– 13 C bonds; RCSAs report the relative orientations of different carbon chemical shielding tensors and hence are more useful for proton-deficient molecules. This information is complementary to that obtained from conventional NMR data such as J couplings, isotropic chemical shifts, and nuclear Overhauser effects (NOEs)/rotational frame nuclear Overhauser effects (ROEs). Obtaining anisotropic NMR data requires the creation of an anisotropic sample environment through an alignment medium. Here, we focus on the use of compressed or stretched polymeric gels as two different but fundamentally equivalent methods for introducing sample anisotropy. Protocols are provided for the synthesis of the chloroform-compatible poly(methyl methacrylate) and dimethyl sulfoxide (DMSO)-compatible poly(2-hydroxyethyl methacrylate) gels and sample setup with a preparation time of 2–3 d. The bond-specific RDC data and the atom-specific RCSA data are extracted as changes in 1 H– 13 C couplings and 13 C chemical shifts, respectively, between two measurements under different alignment conditions, with a total experimental time of 0.5–4 d. NMR data acquisition and important considerations are described in detail. We also provide step-by-step procedures for the density functional theory (DFT) calculations involved and data analysis using the commercial software MSpin. We use three example compounds, namely cryptospirolepine (505 Da), retrorsine (351 Da), and estrone (270 Da), to demonstrate some important aspects of the workflow, such as input data preparation, handling of structural flexibility, and RCSA data correction when necessary. Liu et al. expand the toolset available for NMR characterization of organic compounds by providing a protocol for the generation and analysis of anisotropic NMR data (residual dipolar couplings and residual chemical shift anisotropies).

Background In the setting of diabetes mellitus, mitochondrial dysfunction and oxidative stress are important pathogenic mechanisms causing end organ damage, including diabetic kidney disease (DKD), but mechanistic understanding at a cellular level remains obscure. In mouse models of DKD, glomerular endothelial cell (GEC) dysfunction precedes albuminuria and contributes to neighboring podocyte dysfunction, implicating GECs in breakdown of the glomerular filtration barrier. In the following studies we wished to explore the cellular mechanisms by which GECs become dysfunctional in the diabetic milieu, and the impact to neighboring podocytes. Methods Mouse GECs were exposed to high glucose media (HG) or 2.5% v/v serum from diabetic mice or serum from non-diabetic controls, and evaluated for mitochondrial function (oxygen consumption), structure (electron microscopy), morphology (mitotracker), mitochondrial superoxide (mitoSOX), as well as accumulation of oxidized products (DNA lesion frequency (8-oxoG, endo-G), double strand breaks (γ-H2AX), endothelial function (NOS activity), autophagy (LC3) and apoptotic cell death (Annexin/PI; caspase 3). Supernatant transfer experiments from GECs to podocytes were performed to establish the effects on podocyte survival and transwell experiments were performed to determine the effects in co-culture. Results Diabetic serum specifically causes mitochondrial dysfunction and mitochondrial superoxide release in GECs. There is a rapid oxidation of mitochondrial DNA and loss of mitochondrial biogenesis without cell death. Many of these effects are blocked by mitoTEMPO a selective mitochondrial anti-oxidant. Secreted factors from dysfunctional GECs were sufficient to cause podocyte apoptosis in supernatant transfer experiments, or in co-culture but this did not occur when GECs had been previously treated with mitoTEMPO. Conclusion Dissecting the impact of the diabetic environment on individual cell-types from the kidney glomerulus indicates that GECs become dysfunctional and pathological to neighboring podocytes by increased levels of mitochondrial superoxide in GEC. These studies indicate that GEC-signaling to podocytes contributes to the loss of the glomerular filtration barrier in DKD. CcDz7iHMjDYmrcJBznsPoe Video abstract Graphical abstract

This work proposes an analysis of the capability of three deep learning models—the feedforward neural network (FFNN), long short-term memory (LSTM) network, and physics-informed neural network (PINN)—to identify the parameters of a flexible fixed-wing aircraft using in-flight data. These neural networks, composed of multiple hidden layers, are evaluated for their ability to perform system identification and to capture the nonlinear and dynamic behavior of the aircraft. The FNN and LSTM models are compared to assess the impact of temporal dependency learning on parameter estimation, while the PINN integrates prior knowledge of the system’s governing of ordinary differential equations (ODEs) to enhance physical consistency in the identification process. The objective is to exploit the generalization capability of neural network-based models while preserving the accurate estimation of the physical parameters that characterize the analyzed system. The neural networks are evaluated for their ability to perform system identification and capture the nonlinear behavior of the aircraft. The results show that the FFNN achieved the best overall performance, with average Theil’s inequality coefficient (TIC) values of 0.162 during training and 0.386 during testing, efficiently modeling the input-output relationships but tending to fit high-frequency measurement noise. The LSTM network demonstrated superior noise robustness due to its temporal filtering capability, producing smoother predictions with average TIC values of 0.398 (training) and 0.408 (testing), albeit with some amplitude underestimation. The PINN, while successfully integrating physical constraints through pretraining with target aerodynamic derivatives, showed more complex convergence, with average TIC values of 0.243 (training) and 0.475 (testing), and its estimated aerodynamic coefficients differed significantly from the conventional values. All three architectures effectively captured the coupled rigid-body and flexible dynamics when trained with distributed wing sensor data, demonstrating that neural network-based approaches can model aeroelastic phenomena without requiring explicit high-fidelity flexible-body models. This study provides a comparative framework for selecting appropriate neural network architectures based on the specific requirements of aircraft system identification tasks.

The motivation to accurately model the dynamics of flexible aircraft grew with the development of energy-efficient aircraft, consequently, great aspect ratio aircraft. The development of an accurate model that represents the flight dynamics of a flexible aircraft has been pursued by industry and aeronautical research organizations during the last decades. One of these approaches is to find a flexible aircraft model using systems identification methods. This research aims to apply an integrated model containing longitudinal and lateral directional rigid body dynamics, coupled to the first four flexible body modes, for identification and validation from flight test data. The Unmanned Aerial Vehicle (UAV) Eolo with the flexible wing is used during the experiments. Initially, a finite element structural model (FEM) based on beam elements, concentrated masses, and rigid bars was used. The quasi-stationary panel model based on the Vortex Lattice Method (VLM) was adopted for the aerodynamic model. Two diagonal matrices were used to correct the aerodynamic influence coefficients (AIC) matrix obtained via VLM before and post-multiplication for aircraft identification. The estimation of the main diagonal elements of each matrix was obtained through the Output Error Method in the time domain. A model validation analysis was carried out, which shows a good correlation between the model and measurement data. In conclusion, getting correction matrices instead of stability derivatives is beneficial because matrices can be used more directly during the aeronautical design and observe the behavior concerning loads.

The procedures to be performed after sudden engine failure of a single-engine helicopter impose high workload on pilots. The maneuver to regain aircraft control and safe landing is called autorotation. The safety limits to conduct this maneuver are based on the aircraft height versus speed diagram, which is also known as \"Dead Man’s Curve”. Flight-test pilots often use subjective methods to assess the difficulty to conduct maneuvers in the vicinity of this curve. We carried out an extensive flight test campaign to verify the feasibility of establishing quantitative physiological parameters to better assess the workload endured by pilots undergoing those piloting conditions. Eleven pilots were fully instrumented with sensors and had their physiological reactions collected during autorotation maneuvers. Our analyses suggested that physiological measurements (heart rate and electrodermal activity) can be successfully recorded and useful to capture the most effort-demanding effects during the maneuvers. Additionally, the helicopter’s flight controls displacements were also recorded, as well as the pilots’ subjective responses evaluated by the Handling Qualities Rate scale. Our results revealed that the degree of cognitive workload was associated with the helicopter’s flight profile concerning the Height-Speed diagram and that the strain intensity showed a correlation with measurable physiological responses. Recording flight controls displacement and quantifying the pilot's subjective responses show themselves as natural effective candidates to evaluate the intensity of cognitive workload in such maneuvers.

The purpose of this research is to develop an exergy-based method to evaluate and compare different aircraft propulsion systems architectures to assist the design engineer at the early stages of product development. The method was successfully applied to a case study comprised of a baseline regional aircraft powered by gas turbines, which was compared to a hybrid-electric propulsion (HEP) version comprised of the gas turbines hybridized with batteries. The highest exergy efficiency of 33.5% was obtained for a configuration that presented a 5% degree of hybridization (DOH), defined as “power coming from batteries divided by total power”, and 800Wh/kg battery density. This corresponds to an increase of 0.7% when compared to the 32.8% efficiency of the baseline gas turbine. On the other hand, the aircraft total weight increased 2,160 kg, or 7.1%. Also, both the exergy consumption and exergy destruction increased with hybridization. For the flight mission, a remarkable increase of 2% to 7% was obtained for these parameters, as hybridization increased from 5% to 15%. On top of that, the HEP configuration saves 23 kg of jet fuel or 1% of fuel burn along the mission in comparison with the baseline. CO 2 emissions reduction was around 70 kg per flight mission, as expected, since emissions increase proportionately with fuel consumption. Exergy-based emission costs and exergy destroyed in the kerosene refinery plant and in the electric power generation plant were also evaluated. Finally, some possible means to re-use the exergy lost in the aircraft propulsion system were presented and discussed.

Every human being experiences emotions daily, e.g., joy, sadness, fear, anger. These might be revealed through speech—words are often accompanied by our emotional states when we talk. Different acoustic emotional databases are freely available for solving the Emotional Speech Recognition (ESR) task. Unfortunately, many of them were generated under non-real-world conditions, i.e., actors played emotions, and recorded emotions were under fictitious circumstances where noise is non-existent. Another weakness in the design of emotion recognition systems is the scarcity of enough patterns in the available databases, causing generalization problems and leading to overfitting. This paper examines how different recording environmental elements impact system performance using a simple logistic regression algorithm. Specifically, we conducted experiments simulating different scenarios, using different levels of Gaussian white noise, real-world noise, and reverberation. The results from this research show a performance deterioration in all scenarios, increasing the error probability from 25.57% to 79.13% in the worst case. Additionally, a virtual enlargement method and a robust multi-scenario speech-based emotion recognition system are proposed. Our system’s average error probability of 34.57% is comparable to the best-case scenario with 31.55%. The findings support the prediction that simulated emotional speech databases do not offer sufficient closeness to real scenarios.

Purpose While computational methods are prevalent in aircraft conceptual design, recent advances in mechatronics and manufacturing are lowering the cost of practical experiments. Focussing on a relatively simple property, the lift curve, this study aims to increase understanding of how basic aerodynamic characteristics of a complex stealth configuration can be estimated experimentally using low-cost equipment, rapid prototyping methods and remotely piloted aircraft. Design/methodology/approach Lift curve estimates are obtained from a wind tunnel test of a three-dimensional-printed, 3.8%-scale model of a generic fighter and from flight testing a 14%-scale demonstrator using both a simple and a more advanced identification technique based on neural networks. These results are compared to a computational fluid dynamics study, a panel method and a straightforward, theoretical approach based on radical geometry simplifications. Findings Besides a good agreement in the linear region, discrepancies at high angles of attack reveal the shortcomings of each method. The remotely piloted model manages to provide consistent results beyond the physical limitations of the wind tunnel although it seems limited by instrumentation capabilities and unmodelled thrust effects. Practical implications Physical models can, even though low-cost experiments, expand the capabilities of other aerodynamic tools and contribute to reducing uncertainty when other estimations diverge. Originality/value This study highlights the limitations of commonly used aerodynamic methods and shows how low-cost prototyping and testing can complement or validate other estimations in the early study of a complex configuration.

17α-ethinylestradiol (EE2), a synthetic oestrogen in oral contraceptives, is one of many pharmaceuticals found in inland waterways worldwide as a result of human consumption and excretion into wastewater treatment systems. At low parts per trillion (ppt), EE2 induces feminisation of male fish, diminishing reproductive success and causing fish population collapse. Intended water quality standards for EE2 set a much needed global precedent. Ozone and activated carbon provide effective wastewater treatments, but their energy intensities and capital/operating costs are formidable barriers to adoption. Here we describe the technical and environmental performance of a fast- developing contender for mitigation of EE2 contamination of wastewater based upon small- molecule, full-functional peroxidase enzyme replicas called “TAML activators”. From neutral to basic pH, TAML activators with H 2 O 2 efficiently degrade EE2 in pure lab water, municipal effluents and EE2-spiked synthetic urine. TAML/H 2 O 2 treatment curtails estrogenicity in vitro and substantially diminishes fish feminization in vivo . Our results provide a starting point for a future process in which tens of thousands of tonnes of wastewater could be treated per kilogram of catalyst. We suggest TAML/H 2 O 2 is a worthy candidate for exploration as an environmentally compatible, versatile, method for removing EE2 and other pharmaceuticals from municipal wastewaters.

With increasing pressure to lower pollutant emissions, the aerospace industry has turned its attention to the design of more efficient aircraft. Electric airplanes are seen as one of the most promising solutions to this problem, and significant investments are being made to develop this type of aircraft. Since the electric propulsion system is distinct from those based on internal combustion engines, the performance characteristics of all-electric airplanes can be significantly different from that of regular aircraft. An important element of this new type of propulsion system, and one of the reasons for its unique characteristics, is the power source. Fuel cells are one of the main embedded power sources employed to provide electricity in vehicles, and its use to power electric airplanes is currently being researched. This work presents an analytical investigation of fuel and oxidizer consumption during the cruise flight of all-electric aircraft powered by fuel cells. This study is relevant because cruise flight usually is the crucial phase that drives aircraft design requirements in what concerns energy requirements. A novel formulation is developed, and parametric models are provided for the airplane relevant systems. New analytical solutions are derived in parametric, closed form, allowing quick calculations and eliminating the need for numerical solvers and possible convergence issues. Also, simulations are provided to illustrate the method developed in the article. The results show that the optimal velocities for minimal consumption can be higher than predicted by conventional methods.