Integrating the interpersonal theory of suicide into the relations between cyber-victimization and suicidality among adolescents:A short-term prospective study

The relation between cyber-victimization and suicidality among adolescents has been well documented; however, the mechanisms underlying this association have not been well investigated. Drawing upon the interpersonal theory of suicide, this study aimed to examine the mediating mechanisms (i.e., thwarted belongingness and perceived burdensomeness) underlying longitudinal, bidirectional relations between cyber-victimization and suicidal ideation/attempts among adolescents and explore gender differences in the mechanisms. Participants were 497 Chinese adolescents (46.1 Mage = 13.28, SD = .66), who completed the assessment of cyber-victimization, thwarted belongingness, and perceived burdensomeness, suicidal ideation/attempts at three-time points. The time interval between each two assessments is two weeks. Results showed the reciprocal relations between cyber-victimization and thwarted belongingness/perceived burdensomeness, between thwarted belongingness and suicidal ideation/suicide attempts, and between perceived burdensomeness and suicide attempts. Longitudinal mediation analyses indicated that Time 2 thwarted belongingness mediated the relation between Time 1 cyber-victimization and Time 3 suicidal ideation/suicide attempts. Besides, the reverse pathway from Time 1 suicidal ideation to Time 3 cyber-victimization was also mediated by Time 2 thwarted belongingness, but it was only significant in females, as suggested by multiple-group analyses. According to the aforementioned results, the interpersonal theory of suicide provides a useful framework for understanding relations between cyber-victimization and suicidality. Findings suggest that intervention targeted at improving the need to belong may help reduce suicide risk and lower cyber-victimization. Anti-cyber-victimization should be integrated into suicide intervention and prevention programs, and gender differences should be taken into account in order to enhance the program’s effectiveness

Electron-induced non-monotonic pressure dependence of the lattice thermal conductivity of {\theta}-TaN

Recent theoretical and experimental research suggests that $\theta$-TaN is a semimetal with high thermal conductivity ($\kappa$), primarily due to the contribution of phonons ($\kappa_\texttt{ph}$). By using first-principles calculations, we show a non-monotonic pressure-dependence of the $\kappa$ of $\theta$-TaN. $\kappa_\texttt{ph}$ first increases until it reaches a maximum at around 60 GPa, and then decreases. This anomalous behaviour is a consequence of the competing pressure responses of phonon-phonon and phonon-electron interactions, in contrast to the other known materials BAs and BP, where the non-monotonic pressure dependence is caused by the interplay between different phonon-phonon scattering channels. Although TaN has similar phonon dispersion features to BAs at ambient pressure, its response to pressure is different and an overall stiffening of the phonon branches takes place. Consequently, the relevant phonon-phonon scattering weakens as pressure increases. However, the increased electronic density of states around the Fermi level significantly enhances phonon-electron scattering at high pressures, driving a decrease in $\kappa_{\mathrm{ph}}$. At intermediate pressures ($\sim$20$-$70 GPa), the $\kappa$ of TaN surpasses that of BAs. Our work provides deeper insight into phonon transport in semimetals and metals where phonon-electron scattering is relevant.Comment: 5 pages, 4 figure

Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files

First-principles computational spectroscopy is a critical tool for interpreting experiment, performing structure refinement, and developing new physical understanding. Systematically setting up input files for different simulation codes and a diverse class of materials is a challenging task with a very high barrier-to-entry, given the complexities and nuances of each individual simulation package. This task is non-trivial even for experts in the electronic structure field and nearly formidable for non-expert researchers. Lightshow solves this problem by providing a uniform abstraction for writing computational x-ray spectroscopy input files for multiple popular codes, including FEFF, VASP, OCEAN, EXCITING and XSPECTRA. Its extendable framework will also allow the community to easily add new functions and to incorporate new simulation codes.Comment: 3 pages, 1 figure, software can be found open source under the BSD-3-clause license at https://github.com/AI-multimodal/Lightsho

Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles

As machine learning (ML) methods continue to be applied to a broad scope of problems in the physical sciences, uncertainty quantification is becoming correspondingly more important for their robust application. Uncertainty aware machine learning methods have been used in select applications, but largely for scalar properties. In this work, we showcase an exemplary study in which neural network ensembles are used to predict the X-ray absorption spectra of small molecules, as well as their point-wise uncertainty, from local atomic environments. The performance of the resulting surrogate clearly demonstrates quantitative correlation between errors relative to ground truth and the predicted uncertainty estimates. Significantly, the model provides an upper bound on the expected error. Specifically, an important quality of this uncertainty-aware model is that it can indicate when the model is predicting on out-of-sample data. This allows for its integration with large scale sampling of structures together with active learning or other techniques for structure refinement. Additionally, our models can be generalized to larger molecules than those used for training, and also successfully track uncertainty due to random distortions in test molecules. While we demonstrate this workflow on a specific example, ensemble learning is completely general. We believe it could have significant impact on ML-enabled forward modeling of a broad array of molecular and materials properties.Comment: 24 pages, 16 figure

Trends and future research in electronic marketing: a bibliometric analysis of twenty years

Artigo publicado em revista científica internacionalElectronic marketing (eM) is a flourishing phenomenon that is gaining intense concern because of a significant impact on organizational performance. Over the past few decades, the relevance of eM has been observed in numerous fields (e.g., consumers, organizational strategy, advertisement, and overall philosophy of management to understand the insights globally). To effectively maneuver the field, all stakeholders, particularly academicians and practitioners, must comprehend the current position of the eM theory and practices for dynamic utilization. A systematic bibliometric analysis can serve this issue by providing a holistic view of the publication trend and its trajectory in terms of various themes, including citations and publication metrics. This study analyzes the bibliometric data from 2000 to 2019 to reveal the most productive countries, universities, authors, journals, and prolific publications in electronic marketing. To this end, VOS viewer software was used to visualize the mapping based on co-citation, bibliographic coupling (BC), and co-occurrence (CC). The primary addition of this research is to provide an overview of eM tendencies and paths that may help researchers know the tendencies and future research directions worldwide.info:eu-repo/semantics/publishedVersio

First Principles Methods for Calculating Thermoelectric Transport Properties

Thermoelectricity, as a substantial energy form alternate to the traditional fossil fuels, has attracted tremendous attentions nowadays. The energy conversion efficiency of the thermoelectric device is mainly governed by the dimensionless thermoelectric figure of merit (aka zT) of thermoelectric materials, which consists of both electrical and phonon transport properties. Nowadays, the exploration of high figure of merit thermoelectric materials still rely greatly on the experimental efforts due to the lack of first principles methods for calculating the thermoelectric transport properties. Comparing with the computational methods for the phonon transport properties (aka lattice thermal conductivity), which can be calculated considering the phonon-phonon interactions as the scattering term in the Boltzmann transport equation (BTE), the first principles methods for calculating the electrical transport properties fall behind. Till now, the most common methods for calculating the electrical transport properties usually employ the combination of BTE along with the relaxation time approximation. The human-adjustable and single-value nature of the relaxation time makes this calculation scheme for the electrical conductivity lack physical meaning and predictive power. In this dissertation, we developed first principles algorithms for calculating the electrical transport properties using the electron-phonon interaction as the scattering term in the electron BTE, which can be combined with available methods for phonon transport properties to provide a full description of the thermoelectric figure of merit. The complete methodology is presented in Chapter 2. Although 3C-SiC possesses a simple structure, the polar nature of this material makes it a good candidate to examine the accuracy of our algorithms for calculating the electrical transport properties. The calculated charge carrier (both electron and hole) mobilities as a function of temperature agree well with the experimental results. Besides, a temperature dependent scattering mechanism is observed through our calculations in Chapter 3. Despite the excellent thermoelectric performance of n-type Mg3Sb2, the low thermoelectric figure of merit of the p-type counterpart prevents this material from practical applications. In Chapter 4 of this dissertation, we presented our work on the anisotropic transport properties of both n- and p-type Mg3Sb2, which are hard to explore experimentally. Our calculated n-type thermoelectric figure of merit using the methods developed in Chapter 2 is in excellent agreement with the experimental value, showing the excellent predictive power of our methods. Most importantly, strong anisotropic thermoelectric figure of merit of the p-type Mg3Sb2 is observed, with the out-of-plane figure of merit beyond unity, making it possible for device applications. Moreover, we further proposed through highly oriented polycrystalline samples, it is possible to greatly improve the p-type performance of Mg3Sb2 experimentally. Nanomaterials, especially the two-dimensional materials, have drawn great attentions these days after the discovery of graphene. Although it remains challenging to measure the thermoelectric transport properties of two-dimensional materials experimentally, it can be easily calculated using our algorithms developed in Chapter 2. In Chapter 5, we presented our work on the thermoelectric transport properties of two-dimensional α-Tellurium (α-Te). We found despite the thermoelectric figure of merits of both n-type and p-type two-dimensional α-Te are already promising compared with other two-dimensional materials, small tensile strain (less than 4%) could further boost the n-type thermoelectric performance. However, the tensile strain has a negative effect on the p-type thermoelectric properties. Lastly, in Chapter 6, we discussed possible future efforts following the vein of the first principles methods for calculating the thermoelectric transport properties

Strong tracking filter modeling for GPS robust navigation

Efficient implementation of positioning algorithm plays a crucial role in modern Global Positioning System (GPS). Conventional non-linear Least Squares (LS) method is applied iteratively by Taylor expansion, which doesn't combine different epoch-time for mutual restraint. While with respect to generally used extended Kalman filter (EKF), it requires an accurate system model estimation and exact stochastic Gaussian white noise, resulting in non-uniformly convergence with an unknown bias or model error. To solve this problem, this paper proposes a Strong Tracking Filter (STF) modeling. To achieve robustness about model uncertainty and tracking capability on the mutation status, the STF adjusts the real-time state prediction error covariance matrix and the corresponding gain matrix. It also makes use of Time-varying Fading Factor (TVFF) to deal with the past data, which weakens the stale data to the impact of current filtered value. Simulation shows that the proposed STF is capable of enhancing more than half precision than traditional EKF and LS.Engineering, Electrical & ElectronicTelecommunicationsEICPCI-S(ISTP)

Band manipulation for high thermoelectric performance in SnTe through heavy CdSe-alloying

Convergence of L and Σ valence bands through alloying with monotellurides, has successfully realized the significant zT-enhancement in SnTe thermoelectrics. CdTe, as a solvend introducing Cd-substitution at Sn site, has been demonstrated theoretically and experimentally as an effective agent to reduce the energy difference between two valence bands. However, the low solubility of ∼3% for CdTe in SnTe limits the possibility for further zT-enhancement in SnTe, which motivates the existing efforts on increasing solubility of CdTe or exploring Cd-based solvend with higher solubility. Inspired by the solubility as high as 14% for CdSe in SnTe at 1050 K, this work focuses on the effect of CdSe on thermoelectric properties of SnTe. The converged valence bands induced by the heavy Cd-substitution, results in a significant increase in electronic performance. Moreover, CdSe-alloying would simultaneously introduce cation and anion point defects and boundary interfaces of CdSe precipitates, which leads to a strong phonon scattering and therefore a reduced lattice thermal conductivity of ∼0.8 W/m-K. As a result, the synergistic effect of electronic and thermal performances results in a peak zT as high as 1.1 at 850 K, demonstrating (SnTe)1-x(CdSe)x as the promising thermoelectric materials