Donations in a recursive dynamic model

This paper studies how donations respond to unexpected permanent changes in income and tax rates in a recursive dynamic model. The dynamic approach yields several interesting insights. If marginal tax rates are progressive, a permanent jump in a household�s income increases its consumption and donations in the short run, but has no effect in the long run. The permanent income elasticity of current donations is likely to exceed one. If the marginal tax rate is flat, the jump in income raises consumption and donations in both the short and the long run. A permanent marginal tax rate cut raises consumption and donations in the long run if marginal tax rates are progressive, while it reduces donations in the short run if it has little direct impact on tax payments. If the marginal tax rate is flat, a tax cut has a positive effect on consumption in both the short and the long run, but has an ambiguous effect on donations.

Defining the Structural Consequences of Mechanism-Based Inactivation of Mammalian Cytochrome P450 2B4 Using Resonance Raman Spectroscopy

In view of the potent oxidizing strength of cytochrome P450 intermediates, it is not surprising that certain substrates can give rise to reactive species capable of attacking the heme or critical distal-pocket protein residues to irreversibly modify the enzyme in a process known as mechanism-based (MB) inactivation, a result that can have serious physiological consequences leading to adverse drug−drug interactions and toxicity. While methods exist to document the attachment of these substrate fragments, it is more difficult to gain insight into the structural basis for the altered functional properties of these modified enzymes. In response to this pressing need to better understand MB inhibition, we here report the first application of resonance Raman spectroscopy to study the inactivation of a truncated form of mammalian CYP2B4 by the acetylenic inhibitor 4-(tert-butyl)phenylacetylene, whose activated form is known to attach to the distal-pocket T302 residue of CYP2B4

Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network

The global navigation satellite systems (GNSS) play a vital role in transport systems for accurate and consistent vehicle localization. However, GNSS observations can be distorted due to multipath effects and non-line-of-sight (NLOS) receptions in challenging environments such as urban canyons. In such cases, traditional methods to classify and exclude faulty GNSS observations may fail, leading to unreliable state estimation and unsafe system operations. This work proposes a Deep-Learning-based method to detect NLOS receptions and predict GNSS pseudorange errors by analyzing GNSS observations as a spatio-temporal modeling problem. Compared to previous works, we construct a transformer-like attention mechanism to enhance the long short-term memory (LSTM) networks, improving model performance and generalization. For the training and evaluation of the proposed network, we used labeled datasets from the cities of Hong Kong and Aachen. We also introduce a dataset generation process to label the GNSS observations using lidar maps. In experimental studies, we compare the proposed network with a deep-learning-based model and classical machine-learning models. Furthermore, we conduct ablation studies of our network components and integrate the NLOS detection with data out-of-distribution in a state estimator. As a result, our network presents improved precision and recall ratios compared to other models. Additionally, we show that the proposed method avoids trajectory divergence in real-world vehicle localization by classifying and excluding NLOS observations.Comment: Accepted for the IEEE ITSC202

Selfishness in device-to-device communication underlaying cellular networks

In a device-to-device (D2D) communication underlaying cellular network, user equipments are required to operate cooperatively and unselfishly to transmit data as relays. However, most users behave in a more or less selfish way, which makes user selfishness a key factor that affects the performance of the whole communication system. We focus on the impact of node selfishness on D2D communications. By separating the user selfishness into two types in accordance with two D2D transmission modes – connected D2D transmission and opportunistic D2D transmission, we propose a time-varying graph model that characterizes the impacts of both individual and social selfishness on D2D communications. Simulation results obtained under the realistic networking settings indicate that the interaction between connected and opportunistic selfishness worsens the impairment caused by individual selfishness, while the harmful interaction caused by social selfishness can be alleviated

Secondary structure and thermal stability of the extrinsic 23 kDa protein of photosystem II studied by Fourier transform infrared spectroscopy

AbstractThe secondary structure and thermal stability of the extrinsic 23 kDa protein (OEC23) of spinach photosystem II have been characterized in solution between 25 and 75°C using Fourier transform infrared spectroscopy. Quantitative analysis of the amide I band (1700–1600 cm−1) shows that OEC23 contains 5% α-helix, 37% β-sheet, 24% turn, and 34% disorder structures at 25°C. No appreciable conformational changes occur below 45°C. At elevated temperatures, the β-sheet structure is unfolded into the disorder structure with a major conformational transition occurring at 55°C. Implications of these results for the functions of OEC23 in photosystem II are discussed

Ultrathin Oxide Wrapping of Plasmonic Nanoparticles via Colloidal Electrostatic Self-Assembly and their Enhanced Performances

Ultrathin and uniform oxide layer-wrapped plasmonic nanoparticles (NPs) have been expected in the fields of light energy conversion and optical sensing fields. In this chapter, we proposed a universal strategy to prepare such core-shell plasmonic NPs based on colloidal electrostatic attraction and self-assembly procedures. Based on the self-assembly strategy, laser ablation of metal targets in liquid medium was conducted at room temperature to one-pot fabricate the oxide-wrapped plasmonic NPs. It demonstrates that a series of core-shell nanostructured NPs such as Au@Fe2O3, Au@Al2O3, Au@CuO, Au@ZnO, Pt@TiO2, and Pd@TiO2, have been readily obtained free of contaminations. Technical analyses illustrate that those composite NPs possess uniform and symmetrical oxides layers with several nanometers in thickness. Furthermore, both the thickness and crystallinity of the oxides layer could be precisely tailored simply by controlling hydrolysis of precursors and irradiation durations. Finally, due to ultrathin wrapping of oxides, the as-obtained core-shell plasmonic NPs show excellent surface-enhanced Raman scattering (SERS) and gas-sensing performances compared with bare metal or oxides NPs

GNSS/Multi-Sensor Fusion Using Continuous-Time Factor Graph Optimization for Robust Localization

Accurate and robust vehicle localization in highly urbanized areas is challenging. Sensors are often corrupted in those complicated and large-scale environments. This paper introduces GNSS-FGO, an online and global trajectory estimator that fuses GNSS observations alongside multiple sensor measurements for robust vehicle localization. In GNSS-FGO, we fuse asynchronous sensor measurements into the graph with a continuous-time trajectory representation using Gaussian process regression. This enables querying states at arbitrary timestamps so that sensor observations are fused without requiring strict state and measurement synchronization. Thus, the proposed method presents a generalized factor graph for multi-sensor fusion. To evaluate and study different GNSS fusion strategies, we fuse GNSS measurements in loose and tight coupling with a speed sensor, IMU, and lidar-odometry. We employed datasets from measurement campaigns in Aachen, Duesseldorf, and Cologne in experimental studies and presented comprehensive discussions on sensor observations, smoother types, and hyperparameter tuning. Our results show that the proposed approach enables robust trajectory estimation in dense urban areas, where the classic multi-sensor fusion method fails due to sensor degradation. In a test sequence containing a 17km route through Aachen, the proposed method results in a mean 2D positioning error of 0.19m for loosely coupled GNSS fusion and 0.48m while fusing raw GNSS observations with lidar odometry in tight coupling.Comment: Revision of arXiv:2211.0540