This research paper investigates the effectiveness of using computer graphics(CG) based synthetic data for deep learning in military vehicle detection. In particular, we explore the use of synthetic image generation techniques to train deep neural networks for object detection tasks. Our approach involves the generation of a large dataset of synthetic images of military vehicles, which is then used to train a deep learning model. The resulting model is then evaluated on real-world images to measure its effectiveness. Our experimental results show that synthetic training data alone can achieve effective results in object detection. Our findings demonstrate the potential of CG-based synthetic data for deep learning and suggest its value as a tool for training models in a variety of applications, including military vehicle detection.

The present computational study provides a benchmark of density functional theory (DFT) methods in describing hydrogen evolution processes catalyzed by [Cp*Rh]-containing organometallic complexes. A test set was composed of 26 elementary reactions featuring chemical transformations and bonding situations essential for the field, including the emerging concept of non-innocent Cp* behavior. Reference values were obtained from a highly accurate 3/4 complete basis set and 6/7 complete PNO space extrapolated DLPNO-CCSD(T) energies. The performance of lower-level extrapolation procedures was also assessed. We considered 84 density functionals (DF) (including 13 generalized gradient approximations (GGA), nine meta-GGAs, 33 hybrids, and 29 double-hybrids) and three composite methods (HF-3c, PBEh-3c, and r2SCAN-3c), combined with different types of dispersion corrections (D3(0), D3BJ, D4, and VV10). The most accurate approach is the PBE0-DH-D3BJ (MAD of 1.36 kcal mol-1) followed by TPSS0-D3BJ (MAD of 1.60 kcal mol-1). Low-cost r2SCAN-3c composite provides a less accurate but much faster alternative (MAD of 2.39 kcal mol-1). The widely used Minnesota-family M06-L, M06, and M06-2X DFs should be avoided (MADs of 3.70, 3.94, and 4.01 kcal mol-1, respectively).

This study examines the impact of rapid urbanization on the green spaces of Baghdad, highlighting the environmental, economic, and social challenges posed by unplanned urban sprawl in developing countries. Using historical and descriptive methodologies, along with comparative analysis of urban growth, the research reveals that increased migration and weak governmental oversight have significantly reduced green areas, transforming potential parks into residential and commercial zones. The findings underscore the need for stringent urban planning and policy reforms to preserve and expand green spaces, aligning with global sustainability standards to enhance urban quality of life and ecological balance in Baghdad. Highlights: Urban Expansion: Baghdad's rapid growth has significantly reduced its green spaces. Weak Oversight: Lack of strict regulations has led to the conversion of green areas into urban developments. Sustainability Strategies: Urges policy reforms and sustainable planning to enhance urban green spaces. Keywords: Urbanization, Basic Design, Green Spaces, Overpopulation, Land Use

Artillery gun tube experiences very high pressure according to the blast of propellant charge. Therfore, it is essential to guarantee the structural safety of the gun tube. On the other hand, weight reduction of gun tube is also a crucial design factor since the agility of artillery vehicle directly leads to its survivability. In this line of thought, this work proposed an efficient design procedure which utilizes the convex combination of breech pressure and projectile base pressure time histories. Its efficiency is verified by comparing with other procedures. Other procedures utilize different computed max pressure rather than the convex combination design pressure. Additionally, a transient analysis is carried out considering the projectile movement and the corresponding pressure distribution through the newly developed ABAQUS user-subroutine. The analysis confirms the structural safety of the lightweight gun tube designed by the proposed method.

Development of organic nonlinear optical materials has become progressively more important due to their emerging applications in new-generation photonic devices. A novel series of chromophores based on innovative thiophene and furan-fused cyclopentadienyl bridge with various powerful donor and acceptor moieties were designed and theoretically investigated for applications in nonlinear optics. To unravel the structure-property relationship between this new push-pull conjugated systems and their nonlinear optical property, multiple methods, including density of states analysis, coupled perturbed Kohn-Sham (CPKS) method, sum-over-states (SOS) model, the two-level model (TSM), hyperpolarizability density analysis, and the (hyper)polarizability contribution decomposition, were performed to comprehensively investigated the nonlinear optical and electronic properties of this new π-system. Due to excellent charge transfer ability of new bridge and distinctive structure of donor and acceptor, the designed chromophores exhibit deep HOMO levels, low excitation energy, high dipole moment difference and large hyperpolarizability, indicating the appealing air-stable property and remarkable electrooptic performance of them. Importantly, THQ-CS-A3 and PA-CS-A3 shows outstanding NLO response properties with βtot value of 6953.9 × 10-30 and 5066.0 × 10-30 esu in AN, respectively. The influence of the push-pull strength, the heterocycle and the π-conjugation of new bridge on the nonlinear optical properties of this novel powerful systems are clarified. This new series of chromophores exhibit remarkable electro-optical Pockels and optical rectification effect. More interestingly, PA-CS-A3 and THQ-CS-A2 also show appealing SHG effect. This study will help people understand the nature of nonlinear optical properties of innovative heteroarene-fused based cyclopentadienyl chromophores and offer guidance for the rational design of chromophores with outstanding electrooptic (EO) performance in the future.

Easy and effective usage of computational resources is crucial for scientific calculations. Following our recent work of machine-learning (ML) assisted scheduling optimization [J. Comput. Chem. 2023, 44, 1174], we further propose (1) the improved ML models for the better predictions of computational loads, and as such, more elaborate load-balancing calculations can be expected; (2) the idea of coded computation, that is, the integration of gradient coding, in order to introduce fault tolerance during the distributed calculations; and (3) their applications together with re-normalized exciton model with time-dependent density functional theory (REM-TDDFT) for calculating the excited states. Illustrated benchmark calculations include P38 protein, and solvent model with one or several excitable centers. The results show that the improved ML-assisted coded calculations can further improve the load-balancing and cluster utilization, owing primarily profit in fault tolerance that aims at the automated quantum chemical calculations for both ground and excited states.

To build an automated system detecting toxic chemicals from Raman spectra, we have to obtain sufficient data of toxic chemicals. However, it usually costs high to gather Raman spectra of toxic chemicals in diverse situations. Tackling this problem, we develop methods to generate synthetic Raman spectra of DMMP and 2-CEES without actual experiments. First, we propose certain mathematical transforms to augment few original Raman spectra. Then, we train deep generative models to generate more realistic and diverse data. Analyzing synthetic Raman spectra of toxic chemicals generated by our methods through visualization, we qualitatively verify that the data are sufficiently similar to original data and diverse. For conclusion, we obtain a synthetic dataset of DMMP and 2-CEES with the proposed algorithm.