Efficient Vehicle Patrol Scheduling (VPS) is essential for improving urban safety, ensuring security, and optimizing operational performance. Traditional scheduling methods often struggle to balance multiple objectives and constraints, such as limited vehicles, mandatory rest periods, and strict revisit cadences under dynamic traffic conditions. To address these challenges, this study proposes a dual methodological approach: first, a formalized optimization model is introduced to obtain exact solutions for small-scale instances; second, two novel scalable heuristics are developed, Adaptive Hill-Climbing-Based Patrol Scheduling (AHBPS) and Genetic-Based Dynamic Vehicle Patrol Scheduling (GDVPS), designed to handle large-scale and dynamic urban networks. Extensive simulations using real-world urban maps demonstrate that GDVPS consistently outperforms AHBPS in both solution quality and scalability, achieving up to 80% coverage in networks with 1000 locations, while maintaining real-time feasibility. The results confirm that GDVPS provides a robust, dynamic, and scalable scheduling solution, making it a promising candidate for deployment in modern urban safety operations.
available multispectral satellite imagery enables frequent monitoring of lake Chlorophyll-a (Chl-a), but retrieval accuracy depends strongly on both atmospheric correction (AC) and model choice. Using more than 600 pixel-in situ matchups (2000-2023) from more than 240 Landsat 5, 7, and 8 and Sentinel-2 images in Western Lake Ontario (+/- 4-day window; 3 & times; 3 pixel means), we benchmarked four radiometric products (Level-1, Level-2, DOS, ACOLITE) with four machine-learning (ML) models: least absolute shrinkage and selection operator (LASSO), mixture density network (MDN), support vector regression (SVR), XGBoost). ACOLITE paired with XGBoost was consistently best on the held-out test sets: root-mean-squared logarithmic error (RMSLE) = 0.34 (L5), 0.43 (L7), 0.56 (L8), and 0.42 (S2), with slopes similar to 0.7, 0.6, 0.4, and 0.7, respectively. Minimally corrected products (Level-1, DOS) performed poorly (test RMSLE 0.6; slopes 0.1). For Landsat 8, the provisional aquatic reflectance (AR) generalized better than LaSRC (test slope approximate to 0.6 versus 0.1; RMSLE 0.59 versus 0.68), but ACOLITE remained strongest overall. Against semi-empirical indices, ML-especially XGBoost-reduced errors and produced fits closer to unity across sensors. We then mapped seasonal Chl-a in Hamilton Harbour (2000-2024) to showcase model application. Monthly means rose from late spring to midsummer (May 13.2 & micro;g L-1; June 11.5; July 19.2; August 20.2), with persistent nearshore hotspots near the Royal Botanical Gardens (RBGs) and Windermere Basin. Mann-Kendall trend analyses during bloom months indicated little monotonic change: mean Sen's slopes were near zero (|mean| <= 2 & times; 10(-4) & micro;g L-1 day(-1)) and <15% of pixels showed significant trends per month. Thus, interannual variability dominates over simple linear trends. Overall, harmonizing radiometry with ACOLITE and deploying XGBoost provides a practical, cross-sensor pathway for robust, long-term Chl-a mapping, demonstrated here for a meso-eutrophic embayment of Western Lake Ontario.
Using an international sample of firms and two country-level measures of financial literacy, we find robust evidence of a positive relation between financial literacy and firms' Environmental, Social and Governance (ESG) disclosures. In cross-sectional analyses, we find that the effect of financial literacy in enhancing ESG disclosures is more prominent in poorer information environments, weaker legal institutions and weaker ESG reporting environments. Lastly, we find that financial literacy also enhances ESG performance. Our study contributes to and extends the literature by providing strong evidence that citizens' financial literacy enhances firms' ESG disclosure and the associated ESG performance.
Reaction of n-chloro-2-hydroxypyridine and pyrazine with Cu(ClO4)2 square 6H2O in aqueous alcohols yielded three types of products: [Cu(3-Cl-2-pyone)4(H2O)2](ClO4)2 (1); [Cu(4-Cl-2-pyone)4(pz)](ClO4)2 (2) and [Cu(5-Cl-2-pyone)4(pz)](ClO4)2 (3); and [Cu(5-Cl-2-pyone)2(H2O)2(pz)](ClO4)2 (4) and [Cu(6-Cl-2-pyone)2(H2O)2(pz)](ClO4)2 (5) [n-Cl-2-pyone = n-chloro-2-pyridone]. The structures fall into the crystal systems of monoclinic [P2/n (1), P21/n (2, 5), C2/c (4)] or orthorhombic [Pnnm, 3]. In all cases, the pyridone ligands are kappa-O coordinated with ancillary pyrazine and/or water ligands also coordinated to the Cu ions. The perchlorate ions remain uncoordinated in all complexes.
