We report the most precise measurements to date of the strong-phase parameters between $D^0$ and $\bar{D}^0$ decays to $K^0_{S,L}\pi^+\pi^-$ using a sample of 2.93 fb$^{-1}$ of $e^+e^-$ annihilation data collected at a center-of-mass energy of 3.773 GeV with the BESIII detector at the BEPCII collider. Our results provide the key inputs for a binned model-independent determination of the Cabibbo-Kobayashi-Maskawa angle $\gamma/\phi_3$ with $B$ decays. Using our results, the decay model sensitivity to the $\gamma/\phi_3$ measurement is expected to be between 0.7$^{\circ}$ and 1.2$^{\circ}$, approximately a factor of three smaller than that achievable with previous measurements. The improved precision of this work ensures that measurements of $\gamma/\phi_3$ will not be limited by knowledge of strong phases for the next decade. Furthermore, our results provide critical input for other flavor-physics investigations, including charm mixing, other measurements of $CP$ violation, and the measurement of strong-phase parameters for other $D$-decay modes.

As software security threats continue to evolve, the demand for innovative ways of securing coding has tremendously grown. The integration of Generative AI (GenAI) into software development holds significant potential for improving secure coding practices. This paper aims at systematically studying the impact of GenAI in enhancing secure coding practices from improving software security, setting forth its potential benefits, challenges, and implications. To outline the contribution of AI driven code generation tools, we analyze via a structured review of recent literature, application to the industry, and empirical studies on how these tools help to mitigate security risks, comply with the secure coding standards, and make software development efficient. We hope that our findings will benefit researchers, software engineers and cybersecurity professionals alike in integrating GenAI into a secure development workflow without losing the advantages GenAI provides. Finally, the state of the art advances and future directions of AI assisted in secure software engineering discussed in this study can contribute to the ongoing discourse on AI assisted in secure software engineering.

The cubic perovsike strontium titanate SrTiO$_3$ (STO) is one of the most studied, polarizable transition metal oxides. When excess charge is introduced to this material, e.g., through doping or atomic defects, STO tends to host polarons: Quasi-particles formed by excess charge carriers coupling with the crystal phonon field. Their presence alters the materials properties, and is a key for many applications. Considering that polarons form preferentially on or near surfaces, we study small polaron formation at the TiO$_2$ termination of the STO(001) surface via density functional theory calculations. We model several supercell slabs of Nb-doped and undoped STO(001) surfaces with increasing size, also considering the recently observed as-cleaved TiO$_2$ terminated surface hosting Sr-adatoms. Our findings suggest that small polarons become less stable at low concentrations of Nb-doping, in analogy with polarons localized in the bulk. Further, we inspect the stability of different polaron configurations with respect to Nb- and Sr-impurities, and discuss their spectroscopic properties.

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.

This work presents a comprehensive theoretical comparison between absorption-based and electromagnetically induced transparency (EIT)-based atomic gradient sensing in a four-level tripod system. Both methods were evaluated under identical and optimized physical conditions to ensure a fair and unbiased comparison. The analysis demonstrates that EIT, driven by its steep dispersion response, consistently outperforms conventional absorption detection across a wide range of super-Gaussian beam profiles. Under optimal detuning, EIT achieved up to an order-of-magnitude enhancement in gradient sensitivity and maintained a twofold advantage even under identical detuning. Both approaches reached sub-diffraction spatial resolution in the range of 0.29lambda-0.40lambda, with EIT exhibiting sharper edge contrast and higher localization accuracy. These results confirm EIT as a fundamentally superior approach for precision atomic gradient sensing and sub-wavelength localization, offering clear guidance for the design of next-generation optical and quantum metrology systems.