This experiment was conducted to evaluate the performance of doubled haploid maize hybrids under normal and drought condition. Fifteen doubled haploid maize hybrids were sown in Research Area of Plant Breeding and Genetics, University of Agriculture Faisalabad by using Randomized Complete Block Design during spring 2019. The experimental area was divided into two blocks. Both blocks contain two replications of 15 hybrids. One out of these two blocks was treated with normal irrigations and second block was treated with drought. Data was recorded for various growth and yield related traits. To estimate the performance of doubled haploid maize hybrids under normal and drought conditions the recorded data was subjected to ANOVA by using the STATISTIX 8.1 software. LSD mean comparison test at 0.05% level of significance for hybrids and hybrids × treatment interaction was also calculated. Analysis of variance showed the significant difference among all the hybrids and also in hybrid × treatment interaction. Hybrids DH-26S × 3B and DH-100A × 21 showed maximum 100 grain weight (31.9g) under drought condition. Hybrid DH-100A × 21 showed maximum biomass (278.9g) under drought condition. Genetic advancement and heritability percentage were also calculated for all parameter and listed in the tables. The results showed that maximum genetic advancement was found in biomass; (56), (47) respectively under normal and drought condition.

As artificial intelligence systems increasingly influence critical aspects of human life, the demand for their legal accountability, transparency, and jurisdictional defensibility has become urgent. This paper proposes DevLawOps, a novel framework that integrates legal principles directly into the DevOps pipeline to engineer AI systems that are auditable, explainable, and compliant with legal obligations across multiple jurisdictions. Drawing on interdisciplinary literature in law, philosophy, and software engineering, the study develops a layered system architecture that operationalizes legal norms through compliance middleware, jurisdiction-aware modules, and real-time legal databases. The framework reimagines law as a dynamic software component rather than an external constraint, addressing the limitations of existing legal personhood debates and liability models. Through comparative analysis and conceptual modeling, the paper illustrates how DevLawOps anticipates regulatory variation, localizes compliance, and embeds ethical safeguards as executable logic. It argues for a proactive approach to AI governance that makes legal accountability a continuous, automated, and traceable function of system design. The study concludes by offering practical recommendations for implementation and international collaboration, positioning DevLawOps as a forward-looking strategy for governing AI in a fragmented legal world.