National Institute of Technology Tiruchirappalli

Softmax is commonly used in neural network- based classification systems to convert output values into probabilities, but its conventional implementation involves complex operations such as exponentials and division, which are inefficient for FPGA and VLSI-based hardware due to high area, power, and latency requirements. This project presents a hardware- efficient approximate softmax architecture designed for low-power FPGA systems using a simplified Top-1 approximation approach. The proposed design identifies the dominant output class using comparator-based logic and fixed- point arithmetic, thereby eliminating computationally expensive operations while maintaining correct decision-making. The approximate softmax module is implemented using synthesizable SystemVerilog and validated through simulation and synthesis using Xilinx Vivado, with Python used only for basic numerical verification. The developed design is hardware-ready and suitable for integration into FPGA-based neural network accelerators and embedded VLSI systems, with scope for future hardware implementation.

Purpose Enhancing construction site productivity (CSP) is essential, as the industry's growth is closely linked to the economy's development. Prior research on CSP has demonstrated its role, including enhancing project planning and execution. However, existing research has not investigated factors impacting CSP in India. Therefore, this study aims to develop and validate a CSP model for the Indian construction industry. Design/methodology/approach Structural equation modeling (SEM) was employed to analyze the relationships among latent variables and test the proposed theoretical framework. In this study, 8 latent constructs operationalized through 28 indicators were examined to assess their impact on CSP. A total of 204 valid responses were obtained and subjected to rigorous statistical examination. The analytical procedures comprised reliability assessment, chi-square testing, correlation analysis, confirmatory factor analysis and SEM to validate the proposed model. Findings The SEM analysis revealed that site management (SM) (ß = 0.38, p < 0.001) and safety and quality procurement (ß = 0.32, p < 0.001) exerted the strongest significant impact on CSP. These findings indicate that standardized safety and quality measures are critical determinants of site productivity. Additionally, three indicators emerged as particularly important: workforce stability, skill development and working conditions. Research limitations/implications These findings furnish construction practitioners with empirical evidence regarding the hierarchical impact of factors impacting CSP. Critically, the results establish a framework for analyzing site operations and administrative processes, thereby enhancing evidence-based decision-making in project management. Moreover, this study advances construction performance management theory by empirically validating that site-level managerial capabilities and human resource dimensions constitute the primary determinants of productivity in developing economies. Originality/value This study contributes to the productivity management body of knowledge by developing and validating a CSP model in the context of developing economies. By establishing SM as a strong influencing factor, this study provides evidence that calls for further research on management practices and their mechanisms for enhancing productivity at construction sites.

ABSTRACT This research examines the impact of the ionic liquid 1‐butyl‐3‐methylimidazolium bromide ([Bmim]Br) and DPPC (1,2‐dipalmitoyl‐sn‐glycero‐3‐phosphocholine) vesicles on the kinetics of outer‐sphere electron transfer (OSET) inside cobalt(III) metallo‐surfactant systems. The systems studied were 1 cis ‐[Co(A1) 2 (A) 2 ](ClO 4 ) 3 , 2 cis ‐[Co(A2) 2 (A) 2 ](ClO 4 ) 3 , and 3 cis ‐[Co(A3) 2 (A) 2 ](ClO 4 ) 3 , (where A1 = imidazo[4,5‐f][1,10]phenanthroline, A2 = dipyrido[3,2‐d:2′‐3′‐f]quinoxaline, A3 = dipyrido[3,2‐a:2′,4′‐c]phenazine, and A = Dodecylamine) alongside [Fe(CN) 6 ] 4− ions. Kinetic data conducted across various concentrations of DPPC and [Bmim]Br, as well as different temperatures, validate a second‐order OSET mechanism. The electron‐transfer rate constants rise in direct correlation with [Bmim]Br concentration, indicating the influence of ionic liquid‐induced microenvironmental effects. Conversely, DPPC vesicles impede the reaction below their phase transition temperature due to limited reactant mobility, whereas they accelerate the rate beyond the transition due to enhanced membrane fluidity and advantageous reactant arrangement. Activation parameters obtained from OSET theory indicate negative ΔS ‡ values, signifying a more ordered transition state. The results illustrate how ionic liquids (ILs) and lipid assemblies can affect electron‐transfer kinetics via regulated self‐aggregation, hydrophobic interactions, and reactant placement. The results provide insights for the rational design of metallo‐surfactant systems in catalysis, biomimetic redox processes, and soft‐matter electrochemistry, supporting SDG goals through advances in sustainable electron‐transfer control.