The effects of agricultural activities and atmospheric acid deposition on carbonate weathering in a small karstic agricultural catchment, Southwest China

In order to quantify the sources and fluxes of DIC, the effects of the use of N-fertilizers and acid deposition on carbonate weathering have been quantified by hydrochemistry and δ13CDIC of groundwater in Qingmuguan underground river system (QURS) – a small karstic agricultural catchment of Southwest China. The results show that: (1) the significant temporal variations for major element concentrations and δ13CDIC of groundwater in different months were observed, especially, of which the groundwater showed significant high concentrations of DIC, Ca2+, Mg2+, NO3−, SO42− and δ13CDIC in rainy season and fertilizing period in the QURS; (2) the contributions of carbonate dissolution by carbonic acid to total concentrations of (Ca2++Mg2+) and HCO3− of groundwater in different months averaged 68.5 % and 81.0 %, respectively. While the contributions of carbonate dissolution by nitric acid originated from the use of N-fertilizers and atmospheric acid deposition to total concentrations of (Ca2++Mg2+) and HCO3− of groundwater in different months averaged 11.1 % and 6.7 %, respectively, and the contributions of carbonate dissolution by sulphuric acid originated from the atmospheric acid deposition to total concentrations of (Ca2++Mg2+) and HCO3− of groundwater in different months averaged 20.4 % and 12.3 %, respectively; (3) the δ13CDIC increased obviously with (Ca2++Mg2+)/HCO3− of groundwater in the rainy season and fertilizing period indicated that the use of N-fertilizers and atmospheric acid deposition should be responsible for the elevated the δ13CDIC and the molar ratio of (Ca2++Mg2+)/HCO3− of groundwater in the QURS.Key words: carbonate weathering, karst groundwater, agricultural activities, atmospheric acid deposition, Qingmuguan, Southwest China

Relativistic calculations of quasi-one-electron atoms and ions using Laguerre and Slater spinors

A relativistic description of the structure of heavy alkali atoms and alkali-like ions using S-spinors and L-spinors has been developed. The core wavefunction is defined by a Dirac-Fock calculation using an S-spinors basis. The S-spinor basis is then supplemented by a large set of L-spinors for the calculation of the valence wavefunction in a frozen-core model. The numerical stability of the L-spinor approach is demonstrated by computing the energies and decay rates of several low-lying hydrogen eigenstates, along with the polarizabilities of a $Z=60$ hydrogenic ion. The approach is then applied to calculate the dynamic polarizabilities of the $5s$, $4d$ and $5p$ states of Sr$^+$. The magic wavelengths at which the Stark shifts between different pairs of transitions are zero are computed. Determination of the magic wavelengths for the $5s \to 4d_{\frac32}$ and $5s \to 4d_{\frac52}$ transitions near $417$~nm (near the wavelength for the $5s \to 5p_j$ transitions) would allow a determination of the oscillator strength ratio for the $5s \to 5p_{\frac12}$ and $5s \to 5p_{\frac32}$ transitions.Comment: 2 figures, 23 page

Effective oscillator strength distributions of spherically symmetric atoms for calculating polarizabilities and long-range atom-atom interactions

Effective oscillator strength distributions are systematically generated and tabulated for the alkali atoms, the alkaline-earth atoms, the alkaline-earth ions, the rare gases and some miscellaneous atoms. These effective distributions are used to compute the dipole, quadrupole and octupole static polarizabilities, and are then applied to the calculation of the dynamic polarizabilities at imaginary frequencies. These polarizabilities can be used to determine the long-range $C_6$, $C_8$ and $C_{10}$ atom-atom interactions for the dimers formed from any of these atoms and ions, and we present tables covering all of these combinations

Automatic recognition of radar signals based on time-frequency image shape character

Radar signal recognition is one of the key technologies of modern electronic surveillance systems. Time-frequency image provides a new way for recognizing the radar signal. In this paper, a series of image processing methods containing image enhancement, image threshold binarization and mathematical morphology is utilized to extract the shape character of smoothed pseudo wigner-ville time-frequency distribution of radar signal. And then the identification of radar signal is realized by the character. Simulation results of eight kinds of typical radar signal demonstrate that when signal noise ratio (SNR) is greater than -3 dB, the Legendre moments shape character of the time-frequency image is very stable. Moreover, the recognition rate by the character is more than 90 per cent except for the FRANK code signal when SNR > -3 dB. Test also show that the proposed method can effectively recognize radar signal with less character dimension through compared with exitsing algorithms.Defence Science Journal, 2013, 63(3), pp.308-314, DOI:http://dx.doi.org/10.14429/dsj.63.240

Gamified Double-Edged Sword: Exploring the Different Social Comparison Motives of Mobile Fitness App Users - Research in Progress

Mobile fitness applications (a.k.a. “apps”) are widely used to manage personal health records. The success of fitness apps hinges on their ability in promoting users’ exercise activities. The gamified design element has been widely employed by fitness apps as an effective approach to motivate users to exercise more. However, the efficacy of different gamified elements in influencing users’ subsequent exercise behaviors is still under debate in both research and practice. In this research-in-progress paper, we anchor the social comparison mechanisms to accordingly design gamification elements and demonstrate the dual impact of gamification on users’ exercise behavior change. In addition, we argue that the improvement of users’ exercise performance hinges on the extent to which users’ dispositional approach avoidance temperament is aligned with user’ gamification-enabled social comparison motives. The theoretical inference will guide a future field experiment by testing the effect of gamification on the users’ exercise performance change

CAME: Contrastive Automated Model Evaluation

The Automated Model Evaluation (AutoEval) framework entertains the possibility of evaluating a trained machine learning model without resorting to a labeled testing set. Despite the promise and some decent results, the existing AutoEval methods heavily rely on computing distribution shifts between the unlabelled testing set and the training set. We believe this reliance on the training set becomes another obstacle in shipping this technology to real-world ML development. In this work, we propose Contrastive Automatic Model Evaluation (CAME), a novel AutoEval framework that is rid of involving training set in the loop. The core idea of CAME bases on a theoretical analysis which bonds the model performance with a contrastive loss. Further, with extensive empirical validation, we manage to set up a predictable relationship between the two, simply by deducing on the unlabeled/unseen testing set. The resulting framework CAME establishes a new SOTA results for AutoEval by surpassing prior work significantly.Comment: ICCV2023 main conferenc