Agriculture soils are an important source of greenhouse gases (GHGs) emissions which is leading to climate change and
global warming. Urea is an important source of nitrogen (N), however, most of urea applied to crops is lost to environment.
The use of slow-release nitrogen (SRN) has emerged as an excellent approach to increase crop productivity and reduce N
losses. Therefore, this research was conducted to reveal the effects of different types of urea fertilizers on rice productivity, N
dynamics and GHG emissions.The study was comprised of various types of coated urea: normal urea (NU), neem oil coated
urea (NOCU), zinc coated urea (ZCU), and sulphur coated urea (SCU) and various rates of urea application; control (no N
application), 70 kg N ha−
1, 140 kg N ha−
1, and 210 kg N ha−
1). The results indicate that coated urea application significantly
reduced GHG emissions and improved the rice productivity compared to uncoated urea. However, SCU application (140 kg
ha−
1) resulted in lowest CH4
(18.32% and 54.47%), N2O
(12.01 and 19.44%) and CO2
(48.43% and 8.77%) emissions during
both years as compared to uncoated urea. Further, SCU (140 kg N ha−
1) also reduced the GWP by 44.61% and 46.31%
respectively, in 2021 and 2022 and increased the rice yield by 87.42%, and 82.58% as compared to uncoated urea. Therefore,
the application of SCU can be a promising approach to mitigate the GHG emissions and enhance the rice productivity, and
nitrogen use efficiency (NUE) in semi-arid climates.

Monitoring traffic flow across large-scale transportation networks is essential for effective traffic management, yet comprehensive sensor deployment is often infeasible due to financial and practical constraints. The traffic sensor location problem (TSLP) aims to determine the minimal set of sensor placements needed to achieve full link flow observability. Existing solutions primarily rely on algebraic or optimization-based approaches, but often neglect the impact of sensor measurement errors and struggle with scalability in large, complex networks. This study proposes a new scalable and robust methodology for solving the TSLP under uncertainty, incorporating a formulation that explicitly models the propagation of measurement errors in sensor data. Two nonlinear integer optimization models, Min-Max and Min-Sum, are developed to minimize the inference error across the network. To solve these models efficiently, we introduce the BBA Algorithm (BBA) as an adaptive metaheuristic optimizer, not as a subject of comparative study, but as an enabler of scalability within the proposed framework. The methodology integrates LU decomposition for efficient matrix inversion and employs a node-based flow inference technique that ensures observability without requiring full path enumeration. Tested on benchmark and real-world networks (e.g., fishbone, Sioux Falls, Barcelona), the proposed framework demonstrates strong performance in minimizing error and maintaining scalability, highlighting its practical applicability for resilient traffic monitoring system design.