Indonesian Journal of Innovation and Applied Sciences (IJIAS)

Assessment of Workplace Automation and Employee Performance: The Synergy Between Artificial Intelligence Tools and Human Efforts in the Nigerian Educational System

Jegede Adepitan Adebowale, Gedu Godwin Toritseju, Ojike Chinyere Perpetua — Sat, 28 Jun 2025 00:00:00 +0700

This research examines the correlation between workplace automation and employee performance, focusing on the interplay between AI technologies and human contributions. The study uses a descriptive research approach and focuses on a population of workers from diverse sectors, such as banking, healthcare, and manufacturing. A sample size of 300 respondents was established via Krejcie and Morgan’s technique, and data were gathered through structured questionnaires sent via internet channels. The questionnaire included sections on demographic data, kinds of AI technologies used, and their reported effects on productivity, work satisfaction, and task efficiency. Validity and reliability were established by expert evaluations and a pilot study, with Cronbach's Alpha computed to evaluate internal consistency. The data study used statistical methods like descriptive statistics, correlation analysis, and multiple regression analysis using SPSS to assess the impact of AI tools on employee performance and the interplay between AI automation and human contributions. The findings demonstrate a substantial positive correlation between AI tools and employee performance, with AI contributing to 53.6% of the variation in performance. The data demonstrates a strong synergy between AI automation and human efforts, accounting for 62.3% of the variation in performance results. These results highlight the need to amalgamate AI technologies with human competencies to augment productivity and cultivate a cooperative work atmosphere. The report advocates for continuous training and a learning culture to maximise the advantages of AI in the workplace, ensuring people see AI as an enhancement tool rather than a substitute.

Overcoming Challenges of Peer Assessment Implementation in Online Collaborative Learning in Higher Institutions of Learning

Job Kalema, Jane Katusiime, Simon Kawuma, Deborah Natumanya — Sat, 28 Jun 2025 00:00:00 +0700

Online collaborative learning has become an essential component of higher education, offering students opportunities to engage in peer-based learning activities. Peer assessment, as a tool for enhancing collaborative learning, has demonstrated significant potential but also presents several implementation challenges. The primary objectives of this paper are to identify and analyze the key challenges encountered in the implementation of peer assessment within online collaborative learning environments. This study employed a mixed-methods approach, combining qualitative and quantitative research methods to explore the challenges faced in implementing peer assessment in online collaborative learning environments. Approximately 35% of students in our study reported difficulties related to the peer assessment platforms, including usability issues and limited integration with other learning management systems.35% of students in our study found the assessment rubrics unclear or insufficiently detailed. The challenges identified in this study, particularly those concerning technological limitations, lack of training, and issues with fairness and trust, have significant implications for the implementation of peer assessment in online collaborative learning. Based on the results and expert review, it is clear that overcoming these challenges requires a multifaceted approach. Technological, pedagogical, and social challenges all play a role in hindering the effectiveness of peer assessment, but these challenges can be addressed through targeted strategies.

Enhancing Knowledge Production through the Management of Technology and Innovation in Higher Education: Challenges and Opportunities

Oluwatoyin Ayodele Ajani — Sat, 28 Jun 2025 00:00:00 +0700

This study explores the enhancement of knowledge production in higher education through the effective management of technology and innovation. Using a scoping review methodology, 278 articles were accessed from both Scopus and Google Scholar. Of these, only 68 peer-reviewed articles published in English were selected for thematic analysis. The review focused on identifying key challenges and opportunities related to technology and innovation management within higher education institutions. The analysis revealed several core challenges, including limited resources, cultural resistance, skill gaps, and concerns around data privacy and sustainability. Despite these obstacles, significant opportunities were identified. These include the potential for personalised learning, the use of artificial intelligence (AI) in research, enhanced global collaboration, and improvements in accessibility and administrative efficiency. Furthermore, the role of innovation hubs and public-private partnerships in fostering interdisciplinary research and development emerged as critical areas of focus. The findings underscore the importance of strategic leadership, policy development, and continuous evaluation in successfully managing technological innovation in higher education. By addressing these challenges and seizing the identified opportunities, institutions can play a pivotal role in advancing knowledge production and contributing to broader societal progress.

Managing Diabetes Using Machine Learning and Digital Twins

Sanele Hadebe, Belinda Ndlovu, Kudakwashe Maguraushe — Sat, 28 Jun 2025 00:00:00 +0700

Diabetes is a major public health problem worldwide, and early diagnosis will remain pivotal for intervention and management. This Systematic Literature Review (SLR), therefore, attempts to explore the prospects of integrating Machine Learning (ML) and Digital Twins (DT) to enable diabetes treatment through prediction and patient-specific modeling. This SLR contributes to the body of literature by examining how ML and DTs are being applied in diabetes treatment, identifying the opportunities and challenges that exist, and determining which algorithms are most commonly used. In contrast to SLRs that have been reviewed previously, this study considers Digital Twin-based technological perspectives, along with algorithmic evaluations of ML models, to provide an overall view of the potential for combined use in diabetes care. Following PRISMA guidelines, 11 relevant studies were selected from major academic databases. The study identified Random Forests, Gradient-Boosted Decision Trees, K-Nearest Neighbors, Time Series and Structured Analysis, Regression-based algorithms, and Artificial Neural Networks as machine learning algorithms commonly used to predict diabetes risk. The integration of ML and DT for diabetes management enables the personalization of patient management through virtual representations, real-time monitoring of an individual's glucose levels, simulation of disease progression, and prediction of subsequent treatment steps for proactive and immediate decision-making. Through this collaboration, simulations of various situations are performed, and the interventions are optimized to correspond with unique human physiological profiles for better patient outcomes. Based on the results, policymakers must balance data quality and patient privacy.

The Effect of Adding Rosemary Powder on the Physiological and Biochemical Characteristics of Blood and Antioxidant Status of Female Quails

Gulala W. Ameen, H. A. Noaman, Diyar Jaafar Mohammed — Sat, 28 Jun 2025 00:00:00 +0700

This study was conducted at the poultry farm of the Animal Production Department, College of Agriculture / University of Kirkuk, during the period from 01/02/2023 to 24/2/2023, to determine the effect of adding rosemary powder on the physiological and biochemical characteristics of blood and the status of antioxidants in the blood serum of female quails. 64 female Japanese quails, eight weeks old, were used. The birds were randomly distributed into four treatments, each treatment had four replicates, and each replicate had four female Japanese quails. The birds were fed free feed, and the treatments were distributed as follows: The first treatment was a control treatment, i.e., without addition, the second treatment added 0.25% rosemary powder, the third treatment added 0.50% rosemary powder, and the fourth treatment added 0.75% rosemary powder. The results of the statistical analysis led to: The results led to an improvement in the percentage of antioxidants in the blood serum, as well as a significant decrease in the percentage of blood fats (cholesterol HDL-LDL-VLDL and also in the percentage of liver enzymes compared to the control treatment.

Density and Mechanical Properties of Casuarina equisetifolia Wood Grown in Oromia Regional State, Bishoftu, Ethiopia

Mahadi Mussa, Seyoum Kelemwork, Mesganaw Wale — Mon, 30 Jun 2025 00:00:00 +0700

Ethiopia's rapid expansion of urbanization, industrialization, modernization, and population growth has led to an insistently increase in the demand for wood for construction, furniture, and other end products. This study aimed to investigate the effect of tree height and the diameter tree section (sapwood and heartwood) on the density and mechanical properties of Casuarina equisetifolia timber. For this study, ten sample of C. equisetifolia trees were selected and harvested from Bishoftu, Oromia Regional State, Ethiopia. The collected sample logs were sawn into 3-cm-thick lumber, and sample specimens were prepared for density and mechanical properties testing. Sample specimens of heart and sapwood sections were prepared from the bottom, middle, and top portions of the tree for testing density, static bending (MOE and MOR), compression strength parallel to the grain, impact bending strength, and hardness strengths in tangential and radial directions according to ISO standards. The results revealed that tree height had significantly affected density and hardness strengths in tangential and radial directions. On the other hand, the diameter section (heart and sapwood) significantly affected the density, impact bending, and hardness strength in the radial direction. On the contrary, tree height and tree diameter section didn’t show a significant effect on MOE, MOR, and compression strength parallel to the grain. Due to the high density of Casuarina equisetifolia wood, it is very hard to convert into lumber, and the lumber is also difficult to machine. Based on the basic properties studied, Casuarina equisetifolia wood could be used for wood flooring, chipwood, particleboard, furniture, and other products such as tool handles.

Effect of Normal Force, Sliding Speed, Temperature, and Al₂O₃ Addition on Wedge and Conventional Disc Brake Performance

Mon, 30 Jun 2025 00:00:00 +0700

The investigation probed the structure of the material of the brake shoe, the roughness of the surface, and wear behavior to understand its influence on system efficiency. Regression analysis and Design of Experiments (DOE) were utilized to examine dependencies of variables. The findings indicate that Al₂O₃ significantly improves thermal stability and wear in wedge-type and disc brakes. In addition, the optimal choice of sliding velocity, normal load, and temperature can produce better braking and reduced wear. These results are important in developing more efficient and durable brake systems for automotive, aerospace, and industrial applications. The study also emphasizes the importance of the design and material composition of brake shoes for obtaining ultimate brake performance under several operating conditions.

Investigation of Erosion-Induced Groundwater Pollution in Selected Wells Around Kwarin Awaja, Birnin Kudu, Nigeria

Mon, 30 Jun 2025 00:00:00 +0700

Soil erosion in semi-arid regions contributes significantly to groundwater contamination, particularly through sediment-laden runoff entering shallow aquifers. The intensification of rainfall events has accelerated surface erosion, leading to increased transport of sediments and contaminants into shallow wells, which serve as the primary water source for many households. This study investigated the extent and mechanisms of groundwater contamination in twelve selected wells around Kwarin Awaja, focusing on changes in water quality before and after major rainfall events. Water samples were analyzed for turbidity, electrical conductivity (EC), pH, coliform bacteria, nitrates, and heavy metals (cadmium and chromium). Results showed significant increases in turbidity, nitrates, total dissolved solids, chloride, sulphate, and electrical conductivity after rainfall, indicating erosion-induced pollutant infiltration. Paired sample t-tests revealed significant post-rainfall increases in turbidity (up to 12.1 NTU) and EC (up to 675 µS/cm), with values exceeding national standards such as Nigeria Standards for Drinking Water Quality (NSDWQ) in wells closest to active erosion sites. Pearson correlation analysis showed strong associations between proximity to erosion features and elevated pollution indicators. Microbial contamination, absent before rainfall, was detected in several wells after rainfall events. The findings highlight the vulnerability of shallow groundwater to erosion-driven pollution and underscore the need for targeted interventions to protect water quality in erosion-prone rural environments.