Na Yu¹, Changzheng Gao², Xiuna Wang², Dongwei Li^2,*, Weiyang You²

Energy Engineering, Vol.120, No.12, pp. 2879-2901, 2023, DOI:10.32604/ee.2023.029426 - 29 November 2023

Abstract With the goal of “carbon peaking and carbon neutralization”, it is an inevitable trend for investing smart grid to promote the large-scale grid connection of renewable energy. Smart grid investment has a significant driving effect (derivative value), and evaluating this value can help to more accurately grasp the external effects of smart grid investment and support the realization of industrial linkage value with power grid investment as the core. Therefore, by analyzing the characterization of the derivative value of smart grid driven by investment, this paper constructs the evaluation index system of the derivative value… More >

Milovan Kovač¹, Snežana Tadić¹, Mladen Krstić^1,*, Violeta Roso²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 1019-1046, 2023, DOI:10.32604/cmes.2023.027909 - 23 April 2023

Abstract Overcoming the global sustainability challenges of logistics requires applying solutions that minimize the negative effects of logistics activities. The most efficient way of doing so is through intermodal transportation (IT). Current IT systems rely mostly on road, rail, and sea transport, not inland waterway transport. Developing dry port (DP) terminals has been proven as a sustainable means of promoting and utilizing IT in the hinterland of seaport container terminals. Conventional DP systems consolidate container flows from/to seaports and integrate road and rail transportation modes in the hinterland which improves the sustainability of the whole logistics… More >

Meenu Chaudhary¹, Loveleen Gaur¹, NZ Jhanjhi^2,*, Mehedi Masud³, Sultan Aljahdali³

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1009-1024, 2022, DOI:10.32604/iasc.2022.023417 - 08 February 2022

Abstract Employee categorisation differentiates valuable employees as eighty per cent of profit comes from twenty per cent of employees. Also, retention of all employees is quite challenging and incur a cost. Previous studies have focused on employee churn analysis using various machine learning algorithms but have missed the categorisation of an employee based on accomplishments. This paper provides an approach of categorising employees to quantify the importance of the employees using multi-criteria decision making (MCDM) techniques, i.e., criteria importance through inter-criteria correlation (CRITIC) to assign relative weights to employee accomplishments and fuzzy Measurement Alternatives and Ranking… More >