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This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

In recent times, there has been a lot of research focused on performance measurement (PM) in project-based sectors. However, there are very few studies that were reported on the significance of PM in the construction sector. Keeping track of an organization in achieving organizations goals and objectives seems an important way. One of the major challenges faced by the industry is unavailable of an appropriate PM system for assessing organizational performance. Most of the PM approaches consider the traditional project triangle assessment of project success. Based on the limitations identified in existing PM models, the purpose of this paper is to develop a comprehensive PM model, i.e. Modified Project Quarter Back Rating (MPQR) applicable for construction projects.

A detailed list of performance areas as a method for PM is analyzed in the construction industry context. Also, industry-specific professionals conducted semi-structured interviews to assess whether these performance areas are sufficient to measure and understand the PM systems.

The research finding focuses on developing the MPQR model that considers both financial and non-financial areas for performance assessment to provide a holistic assessment of project performance.

MPQR model provides an opportunity to set the benchmark for overall performance for construction organizations.

The findings of the study are expected to provide guidelines to construction professionals for implementing the performance model that will improve performance in the construction industry.

Optimal navigation and trajectory planning are in high demand because of the rise in automated systems. This study aims to focus on implementing an intelligent regression-based chaotic Harris Hawk optimization (LR-CHHO) to achieve a globally optimal path free from collisions.

This study removes the drawbacks of the existing HHO model in terms of its exploration and exploitation behaviors. After the threat is encountered, the improved controller is activated. The LR tool, here, avoids the issue related to the sensitivity of the model. The virtual Hawks, as per the HHO technique, are generated and trained to enhance the diversity in Hawks population. The final controller then calculates the optimal turn angle for the humanoid to avoid threats before reaching the goal.

Model showed an overall improvement greater than 4% in the path and 9% in time compared with standard models in Terrains 1 and 2. Regarding energy efficiency, a significant improvement of more than 20% in the hip, 14% in the knee and 30% in the ankle was observed on both even and uneven terrains.

The originality of this study focuses on improving the diversity in the HHO population by introducing the LR-based model to help the humanoids find an optimal path to the goal. Although the basic model lacked an optimal solution because of sensitivity, less diversity, etc., the proposed model helped resolve the issue and achieve an optimal turning angle for the humanoids to trace the optimal path.