Search results

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

The objective of this study is to explore the impact of a government-supported initiative for operational security, specifically the establishment of the national security emergency industry demonstration base, on the profitability of local publicly traded companies. Additionally, the study investigates the significance of firms' blockchain strategies and technologies within this framework.

Using the differences-in-differences (DID) approach, this study evaluates the impact of China's national security emergency industry demonstration bases (2015–2022) on the profitability of local firms. Data from the China Research Data Service (CNRDS) platform and investor Q&As informed our analysis of firms' blockchain strategy and technology, underpinned by detailed data collection and a robust DID model.

Emergency industry demonstration bases have notably boosted enterprise profitability in both return on assets (ROA) and return on equity (ROE). Companies adopting blockchain strategies and operational technology see a clear rise in profitability over non-blockchain peers. Additionally, the technical operation of blockchain presents a more pronounced advantage than at the strategic level.

We introduced a new perspective, emphasizing the enhancement of corporate operational safety and financial performance through the pathway of emergency industry policies, driven by the collaboration between government and businesses. Furthermore, we delved into the potential application value of blockchain strategies and technologies in enhancing operational security and the emergency industry.

This research aims to build on the pre-existing corpus of literature through the integration of the technology acceptance model (TAM) and usage habit to more accurately capture the determinants associated with social media addiction among university students. This study seeks to delineate how usage habit and TAM may be used as predictors for addiction potential, as well as provide greater insight into current trends in social media usage across this population demographic.

A cross-sectional research design was employed to investigate the determinants of social media addiction among university students in Malaysia at the onset of their tertiary education. A self-administered survey, adapted from prior studies, was administered to a sample of 217 respondents. The hypotheses on social media addiction were subsequently tested using a partial least squares structural equation modeling (PLS-SEM) approach.

Usage habit was found to be a direct and strong predictor of this type of addiction, as well as all TAM variables considered in the research. Additionally, by integrating TAM with usage habit, the study revealed a comprehensive and multi-faceted understanding of social media addiction, providing an important insight into its complexity in the Malaysian context. Although several other factors have been identified as potential contributors to social media reliance and addictive behavior, it appears that usage habit is paramount in driving these addictive tendencies among university students.

This expanded model holds significant implications for the development of interventions and policies that aim to mitigate the adverse effects of social media addiction on students' educational and psychological well-being. The study illustrates the applicability of the TAM in examining addictive behaviors within emerging contexts such as the Malaysian higher education sector, thus contributing to the extant literature on the subject.

The integrated TAM and habit model is an effective predictor of social media addiction among young adults in developing countries like Malaysia. This highlights the importance of actively monitoring and controlling users' interactions with technology and media platforms, while promoting responsible usage habits. Educators can use these findings to create tailored educational programs to educate students on how to use technology responsibly and reduce their risk of becoming addicted to social media.

This study provides a unique perspective on social media addiction among university students. The combination of TAM and usage habit has the potential to shed significant light on how variables such as perceived usefulness (PU) and perceived ease of use (PEOU) may be associated with addictive behaviors. Additionally, by considering usage habit as an explanatory factor, this research offers a novel approach to understanding how addictions form over time.