The role of generational diversity in enhancing employee competency development at Malaysia public universities: A literature review

Generational diversity within organisations, particularly in higher education, is increasingly recognised as a critical factor influencing the development of employee competencies. Public universities in Malaysia face a pressing challenge: effectively managing this diversity to foster an inclusive and productive working environment. This study investigates the role of generational diversity in enhancing employee competencies and workplace dynamics in academic settings. By examining the interactions among different generations and the impact of their varying values and approaches on team collaboration and effectiveness, the research provides insights into how generational diversity shapes teaching, learning, and organisational performance. A systematic literature review of high-impact journal publications was conducted to explore the relationship between generational diversity and competency development. The findings reveal that generational diversity enriches perspectives in teaching and learning processes, while also influencing workplace dynamics and employee collaboration. Distinct generational approaches and beliefs are shown to drive innovation and improve educational outcomes. Moreover, the study highlights the importance of generationally sensitive management practices in enhancing job satisfaction and productivity. The results suggest that educational institutions should implement strategies to integrate generational diversity effectively, leveraging its potential to enhance competency development. Understanding and valuing generational differences are essential for creating an inclusive and competitive work environment, particularly in the context of Malaysian technical universities

Exploring student perspectives on ChatGPT: Knowledge, attitudes, concerns, and usage patterns at Universiti Teknikal Malaysia Melaka

This study explores the prevalence and perspectives of ChatGPT usage among students at Universiti Teknikal Malaysia Melaka, encompassing their knowledge, attitudes, concerns, perceived ethics, and usage patterns. A total of 367 students from five faculties participated in the survey, which was conducted via Google Forms. The findings reveal a high level of awareness and familiarity with ChatGPT, with 87.2% of respondents having heard about it and 79.6% understanding its workings. However, significant concerns were identified, including the potential undermining of educational goals (mean = 3.90, SD = 0.95) and negative impacts on creative writing skills (mean = 3.46, SD = 0.91). Ethical apprehensions were also prominent, with 49.1% of students recognizing ethical or legal considerations and 64.8% expressing comfort in interacting with ChatGPT. Despite these concerns, the utility of ChatGPT in enhancing productivity and aiding academic tasks is acknowledged, evidenced by high mean scores for assistance in drafting essays (mean = 3.87, SD = 0.87) and resolving academic doubts (mean = 3.83, SD = 0.85). This study underscores the need for ethical guidelines and controlled integration of ChatGPT into educational practices to balance its benefits with the maintenance of academic integrity and critical thinking skills

Visualizing anthocyanins: Colorimertic analysis of blue maize

Anthocyanin, vibrant pigments found in a wide range of plants, including maize, contribute to the red, blue, and purple hues observed in fruits, vegetables, and grains. The inherent color variations in maize, including natural shades of purple, red, blue, and even rainbow colors, pose a significant challenge in accurately assessing maize maturity. This study recognizes the importance of visualizing the distinct blue purplish anthocyanin coloration to determine the optimal harvest time for blue maize, particularly among small-scale producers. To address this crucial need, this research project presents the development of the MaizeMeter, an advanced colorimeter specifically designed to analyze maize color based on anthocyanin pigmentation. Leveraging the power of Internet of Things (IoT) implementation, the MaizeMeter provides real-time monitoring and interpretation of anthocyanin color values. The proposed methodology encompasses the calibration of the color sensor and the prototyping of the MaizeMeter, culminating in the establishment of a comprehensive database of anthocyanin color profiles in blue maize. The generated anthocyanin color database by the MaizeMeter will serve as a vital tool for small-scale farmers and researchers, enabling more efficient and accurate assessment of maize maturity in the future

Integrating water quality model and aeration with IoT technology in water quality management: A conceptual framework

Eutrophication poses a significant threat to both human population growth and aquatic life. It gives rise to a range of issues, including algae blooms, loss of habitat, reduced self-purification capacity, and changes in the biodiversity system. In order to restore the ecosystem, it is imperative to implement water quality management measures to combat eutrophication. Common methods for mitigating eutrophication include the use of water quality models, aeration, and IoT technology. Water quality model simulations have been demonstrated to accurately predict future water quality. Aeration, on the other hand, increases oxygen concentration in water through dispersion. Furthermore, the utilization of IoT in water quality monitoring provides users with precise and real-time data. Despite research findings that suggest the effectiveness of water quality models, aeration, and IoT technologies in addressing eutrophication, their current integration is inadequate. Therefore, the aim of this paper is to develop a conceptual framework that incorporates water quality models, aeration, and IoT technology to regulate water quality, with a specific focus on preventing the eutrophication issue. The conceptual framework was created by studying existing research on frameworks for water treatment, water quality modelling, aerators, and IoT technologies. Several adjustments were made to tailor the general framework to the specific requirements of this study. The discussion emphasizes the advantages of conceptual framework development in managing water quality, which integrates water quality models, aerators, and IoT technologies. This framework is expected to serve as an effective tool for managing eutrophication in water, while also promoting sustainable measures to address water contamination

Interphase investigation of modified McLachlan model and the 3D finite element method for electrical conductivity

This paper explores the electrical conductivity interphase of Ag/Epoxy composite using modified McLachlan theory and 3D finite element composite model through experimental verification. The model characteristic presents conductivity as a dynamic function influenced by particle content, particle electrical properties, electrical properties transition, and an exponent. This model was meticulously crafted, considering the intricate interplay between the polymer matrix and silver particles, the tunnelling distance between adjacent silver particles, and the interphase regions around particles. This model has proven its mettle through rigorous analysis of experimental results and the impact of various parameters on conductivity. The predictions have shown impressive alignment with the experimental data, highlighting the crucial roles played by the parameters in the conductivity of silver composites where the percolation threshold reached 6 vol % of filler loading. The experimental study demonstrated that the electrical conductivity was 3.84 × 10−1 S/cm for micro-sized particles and 1.32 × 10−2 S/cm for nano-sized particles. Notably, a large tunnelling distance drastically reduces conductivity, while higher and slighter surface energies of the polymer matrix and filler enhance conductivity. Furthermore, a thin interphase yields minimal conductivity, whereas a thick interphase and low waviness improve conductivity. The McLachlan-modified model falls slightly short in accuracy compared to the 3D finite element method models. Adjustments to the equations can enhance its alignment with experimental data

Numerical simulation of blood flow dynamics in a stenosed artery enhanced by copper and alumina nanoparticles

Nanotechnology holds immense importance in the biomedical field due to its ability to revolutionize healthcare on a molecular scale. Motivated by the imperative of enhancing patient outcomes, a comprehensive numerical simulation study on the dynamics of blood flow in a stenosed artery, focusing on the effects of copper and alumina nanoparticles, is conducted. The study employs a 2-dimensional Newtonian blood flow model infused with copper and alumina nanoparticles, considering the influence of a magnetic field, thermal radiation, and various flow parameters. The governing differential equations are first non-dimensionalized to facilitate analysis and subsequently solved using the 4th order collocation method, bvp4c module in MATLAB. This approach obtains velocity and temperature profiles, revealing the impact of relevant parameters crucial in the biomedical field. The findings of this study underscore the significance of understanding blood flow dynamics in stenosed arteries and the potential benefits of utilizing copper and alumina nanoparticles in treatment strategies. The incorporation of nanoparticles introduces novel avenues for enhancing therapeutic interventions, particularly in mitigating the effects of stenosis. The elucidation of velocity and temperature profiles provides valuable insights into the behavior of blood flow under different conditions, thereby informing the development of targeted biomedical applications. The arterial curvature flow parameter influences temperature profiles, with increased parameters promoting more efficient heat dissipation. The elevated values of Prandtl number and thermal radiation parameter showcase the diminished temperature profiles, indicating stronger dominance of momentum diffusion over thermal diffusion and radiative heat transfer mechanism. Sensitivity analysis of the pertinent physical parameters reveals that the Prandtl number has the most significant impact on blood flow dynamics. A statistical analysis of the present results and existing literature has also been included in the study. Overall, this research contributes to advancing our understanding of vascular health and lays the groundwork for innovative approaches in stenosis treatment and related biomedical fields

Investigation of malware redline stealer using static and dynamic analysis method forensic

Redline Stealer is a malware variant discovered in early March 2020 by proof point analyst. Redline is famous for its ability to bypass the antivirus scan. Redline Stealer was created by hacker with the purpose to steal victim's information such as login data, password and credit card information from the browser application that used in infected computer. This research uses static and dynamic methods to analyze redline stealers. The process of static analysis is carried out by observing the malware's sample file, while dynamic analysis is carried out by monitoring malware's activity when the malware is running on the system. This research show that Redline Stealer uses the obfuscation feature based on .net, which can run only when there is an internet connection, stealing sensitive information, especially in a browser application. The conclusion of this research is Redline Stealer can be classified as a stealer malware that can steal important data on the infected system. The result of the analysis using the strings extract and decompile did not find any information because this malware uses the obfuscation feature, so the static analysis did find fewer information than the dynamic method

A forensic intelligence system for identification of data originality based on signature files

The difficulty of maintaining the authenticity of files is a security problem that must be corrected in the process of developing information technology. One example of a case that often occurs is the modification of the file extension. This happens due to human error or deliberate and automatic factors. The method used for analysing the extension of a file is signature file analysis. This method is used to detect crimes that use techniques to change file extensions to hide content in its original form. Research related to the modification of file extensions using file signature analysis has been done before. However, this research still has many weaknesses, one of which is that the process of checking the file signature and the appropriate file extension is done manually and is too time-consuming. So, the forensic investigation process carried out in this case was not efficient. In this research, as a solution to the above problems, the forensic intelligence system was created to identify file types by automatically matching file extensions and signatures. If the file entered is modified, the output given is the name of the file entered, the size of the file, the file signature, the original extension of the file, and the time the file was uploaded to the application. In addition, this application can restore files with modified extensions to their original extensions. The extensions used for this research experiment amounted to 22 types out of a total of 130 types of extensions

Numerical investigation on photovoltaic thermal panel using various nanofluids concentrations

Increasing the efficiency of solar panels is crucial for effective use of renewables. The present numerical study deals with improving the performance of a PVT system with nanofluid using CFD FLUENT software. ZnO-water and SiO2-water nanofluids are investigated and correlation are established between the PVT efficiency and various nanofluid volumetric concentrations ranging from 1% to 10%. Validation of the present results is verified by comparison with experimental data. Comprehensive research is conducted to evaluate the correlation between the thermophysical properties of nanofluids such as density, thermal conductivity, specific heat capacity and dynamic viscosity. The results demonstrate that the overall efficiency of the ZnO-water nanofluid and SiO2-water nanofluid increases by 0.44% and 0.24%, respectively, as the volumetric concentration of the nanofluid rises from 1% to 10%. The ZnO-water nanofluid reveals enhanced thermal and electrical efficiency compared to the SiO2- water nanofluid due to its superior thermal conductivity and enhanced heat transfer capabilities along the absorber tube. The ZnO-water nanofluid exhibits a greater heat transfer coefficient, thereby facilitating the cooling mechanism of the PV panel and reducing the PV cell temperature, hence enhancing power generation

Emotional intelligence and online learning readiness among students in a technical university in Malaysia

The main aim of this study is to investigate the influence of emotional intelligence on students’ online learning readiness in a public university in Malaysia. Four hundred and ninety-six students from eight faculties at the Universiti Teknikal Malaysia Melaka (UTeM) participated in this study which was conducted fully online using Google Forms due to the pandemic issue. The students completed the Online Learning Readiness (OLR) Scale and the Trait Emotional Intelligence Scale – Short Form (TEIS-SF) to assess their online learning readiness and emotional intelligence. According to the descriptive results, all dimensions related to students' online learning readiness scored above medium mean scores, and almost all dimensions of emotional intelligence, with the exception of the social dimension, scored above-medium mean scores. Correlation analysis reveals a significant positive relationship between students' emotional intelligence and their readiness for online learning. The key findings in this study could be used to facilitate online learning by educators and students