Excessive salinity poses serious threats to crop productivity and global food security. Salt stress reduces crop growth and final productivity by inducing ionic, osmotic, and oxidative damage. Applying plant growth hormones is thought to be an efficient method of reducing the negative effects of salinity. Brassinosteroids (BRs) are plant hormones that have demonstrated appreciable effects against different abiotic stresses. BRs can mitigate the deleterious impacts of salt stress by improving water and nutrient uptake, membrane stability, antioxidant activities, and osmolyte synthesis while maintaining the hormonal balance. Furthermore, BRs can also improve the metabolic and molecular responses of plants to help them counter the toxic effects of salinity, and they interact with sugars, proteins, amino acids, and phytohormones to regulate metabolic functioning to increase adaptation against salinity. As vital hormones, BRs are also involved in the signaling pathways of genes, proteins, and enzymes that work to defend plant cells from the toxic effects of salinity. Transgenic plants with improved BR synthesis have shown improved salt tolerance, and genome-wide studies encoding BR genes suggest their roles in plant growth and salt tolerance. The present review discusses the role of BRs in mitigating salinity toxicity through different mechanisms, crosstalk, and the interactions of BRs with other osmolytes and phytohormones. This review will provide knowledge to support the development of salt-tolerant crops and ensure sustainable crop production.

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.