Indonesian Journal of Innovation and Applied Sciences (IJIAS)

Evaluation of Impact on Public Transportation Headwinds: The Case of eThekwini Municipality Department of Transport

Charles Tony Simphiwe Ngwenya — 2025-10-30

The challenges and constraints confronting public transportation efficiency within the regulatory local government stewardship in the municipality’s spheres continue to have an inventory of misdemeanours such as mismanagement, dilapidating built environment in infrastructure, subdued digitalisation integration and transformation, contestation from privately owned transport entities, and poor control over transport operations. The public transport operations and its associated assets or infrastructure provide accessibility and act as wheels of economic growth and prosperity. These deliver a critical component of the envisaged good quality of life and standard of living from both the business perspectives and citizenry livelihoods state of affairs. The study investigated the public transport ecosystem and risks with associated resources. A structured questionnaire was used to collect data from pertinent stakeholders’ respondents to predict and explain insights and trends from both primary and secondary stakeholders within the public transport fraternity in the city. The positivist philosophical approach of the quantitative methodology, with its deductive paradigmatic stance, is selected in the study. The main findings indicate the lack of comprehensive consultation and buy-in within the broader stakeholder involvement. The findings further signified a gap in risk mitigation and resource optimisation and the significance of public service accessibility as a priority for policy-makers and practitioners. Findings indicate that the Transport Division realised the need to inculcate pragmatic execution in investing resources and capabilities from a problem-solving and decision-making perspective. This could yield sustained public service deliverables in desirable and sustainable dimensions within the complex public transportation sector.

Digital Platform Transformation and Socio-economic Resilience Among Indonesia’s Motorcycle Ride-hailing Drivers

Hannin Pradita Nur Soulthoni — 2025-10-30

The Fourth Digital Revolution has transformed Indonesia's informal transportation sector, particularly traditional motorcycle taxi drivers transitioning to digital ride-hailing platforms. This shift represents movement from location-bound, passive income generation to algorithm-driven, flexible employment. Despite widespread Grab adoption across secondary cities, limited research examines multidimensional impacts on drivers' socio-economic well-being beyond metropolitan areas. This study investigates how digital transformation affects the income and social well-being of former traditional motorcycle taxi drivers in Kendari City, a representative secondary urban area. The research employed a qualitative exploratory case study design, utilizing in-depth interviews with eight primary informants who transitioned to the Grab platform, supplemented by community leaders and driver representatives. Data collection used methodological triangulation, combining semi-structured interviews, participant observation, and document analysis over three months. Analysis followed Braun and Clarke's thematic framework, integrating Digital Transformation Theory, Platform Economy Theory, and Social Impact Theory. Findings reveal substantial positive impacts across multiple dimensions. Economically, drivers experienced significant income improvements, enhancing financial stability and predictability. Beyond monetary gains, digitalization strengthened social well-being through improved access to children's education and healthcare, greater work-life balance, and enhanced psychological well-being through reduced income uncertainty. However, challenges emerged regarding platform commission fees and technological dependence. Digital transformation through ride-hailing platforms serves as a powerful economic empowerment instrument, significantly improving income security and multidimensional social well-being. The transition constitutes a structural transformation reducing informal sector uncertainties while providing occupational autonomy, offering evidence-based insights for inclusive digital transformation policies.

Extending the Technology Acceptance Model: Contextualizing Trust and Usability in Shaping Online Consumer Behavior in Dhaka, Bangladesh

Rumana Afrin — 2025-10-30

With the rapid growth of e-commerce in the Asian region, understanding the drivers of online purchasing decisions is crucial for businesses and policymakers. Hence, this study extends the Technology Acceptance Model (TAM) to examine key factors shaping online consumer behavior in Dhaka, Bangladesh, an emerging e-commerce market marked by low trust and infrastructural limitations. Building on TAM’s core constructs, ‘perceived usefulness’ and ‘perceived ease of use’, researchers incorporate trust/security and delivery reliability as critical contextual variables. Data from 200 respondents reveal that price discounts and website usability significantly influence purchase intentions, followed by delivery time and customer reviews. Regression and ANOVA analyses confirm these variables’ predictive power. Trust and security concerns emerge as major antecedents to perceived usefulness, reflecting the unique challenges of digital adoption in Dhaka. The findings validate TAM’s relevance while demonstrating the need for its contextual adaptation in emerging markets. Young, tech-savvy consumers dominate the online shopping landscape, suggesting that targeted design and trust-building initiatives are essential. This research offers strategic insights for e-commerce platforms and policymakers aiming to optimize consumer engagement and satisfaction in Bangladesh’s rapidly evolving digital economy.

The Digital Divide Revisited: Connectivity, Devices, and the Hidden Barriers to Global EdTech Equity

Sayed Mahbub Hasan Amiri — 2025-10-30

This article interrogates the persistent inequities in global educational technology (EdTech) deployment, arguing that traditional “access gap” frameworks fail to address the complex architecture of digital exclusion. Through mixed-methods desk research analyzing 140+ scholarly works, institutional reports, and case studies across 15 countries, we identify five interdependent hidden barriers undermining EdTech equity: (1) the affordability mirage of hidden data/repair costs, (2) digital literacy deserts among teachers/students, (3) infrastructure fragility (electricity/ connectivity), (4) cultural-linguistic irrelevance, and (5) policy-governance gaps. Empirically grounded in contexts from Rwanda’s One Laptop Per Child program to India’s DIKSHA platform, findings reveal how these barriers disproportionately exclude marginalized learners, particularly in low-income and remote communities. The study advances a transformative solution framework centered on zero-cost connectivity architectures, situated teacher capacity building, adaptive hybrid infrastructure, decolonized content co-creation, and agile multistakeholder governance. We contend that only by addressing these systemic, human, and socio-technical dimensions can EdTech fulfil its promise as an educational equalizer. Urgent implementation of these evidence-based strategies could prevent an estimated $17 trillion in lost GDP by 2040 while reclaiming the democratic potential of digital learning.

Effects of Climate Change and Artificial Intelligence on Mental Health and Academic Performance in Kenya

Michael Keari Omwenga — 2025-10-30

Mental health conditions such as Stress, anxiety, trauma, and existential issues are all made worse by the increased frequency of catastrophic weather events and environmental degradation brought on by climate change. Potential solutions to lessen the negative effects of climate change on mental health are provided by digital advancements, especially artificial intelligence (AI) and digital phenotyping. Access and solution adoption concerns must be carefully considered when integrating digital tools into climate-related mental health care. The objective of the study was to address the effects of climate change on mental health and the scalability of digital interventions through cooperation amongst students. The study adopted a cross-sectional study design targeting college students in the Kisii region, Kenya. The convenience sampling method was used to sample 359 participants who were distributed questionnaires. Variables were examined using partial least squares-structural equation modelling (PLS-SEM), and data analysis was conducted using the specialized statistical programme SmartPLS in conjunction with multiple linear regression and confirmatory factor analysis (CFA). The results demonstrate that students' educational achievement and mental wellness are impacted by both the environment and artificial intelligence (AI). Additionally, the positive effects of AI and climate change on academic performance and mental health are amplified by digital learning, which serves as a positive moderating factor. These findings contribute to the discussion about using technology to improve education by showing how implementing AI and addressing climate change might benefit student performance and well-being.

Developing a Logistic Regression Machine Learning Model that Predicts Viral Load Outcomes for Children Living with HIV in Gutu District, Zimbabwe

Belinda Ndlovu — 2025-10-30

HIV remains a major public health issue globally, particularly in poor resource settings such as the Gutu district of Zimbabwe. The study aimed to develop a predictive viral load outcome model for HIV children based on the CRISP-DM research process. Secondary clinical data for children aged 0–17 years in Gutu were retrieved from the Demographic Health Information System (DHIS2). The study identified age, adherence status, gender, and geographical location as correlated with viral load outcomes. A supervised machine learning logistic regression model was trained with data balance and proper management of complexities. Grid search-based hyperparameter tuning was performed to improve model performance further. The evaluation metrics were accuracy, sensitivity, F1 Score, and area under the receiver operating characteristic curve (AUC-ROC). The model’s performance resulted in 89% accuracy, with all the metrics showing a strong performance. A confusion matrix was used to visualize the results. The findings add to the knowledge on viral load outcome prediction and HIV care in Zimbabwe. The findings suggest that early diagnosis and targeted interventions can improve viral load outcomes in children in Gutu. For future research, the development of the model will be based on more representative data sets and applied to other settings to determine differences in other regions and understand the dynamics of HIV care in children.

Behavior of Highway Bridge PSC Girder with Different Support Arrangements Under Dynamic Loading

Akshay M. R — 2025-10-30

This article presents a detailed finite element analysis of a three-span PSC I-girder bridge using MIDAS Civil software. The primary objective is to investigate the structural behavior of the bridge girder under different support configurations, namely simply supported and continuous systems, combined with elastomeric Bearings and roller Supports. The analysis incorporates loading conditions as per IRC:6-2017, IRC: 112-2011, and IRC SP: 114-2018. The bridge model was subjected to IRC Class A and Class 70R vehicular loads, in addition to seismic loads, to evaluate critical performance parameters of deflections, bending moments, shear forces, torsional effects, and dynamic characteristics, such as natural periods, which were comprehensively studied. The results highlight that Continuous Girders with elastomeric bearings provide superior stiffness, reduced deflections, better control of torsion, and improved seismic performance compared to other configurations. The findings demonstrate that the selection of an efficient girder-support system plays a vital role in ensuring safety, serviceability, and resilience of medium to long-span PSC I-girder bridges.

Molasses and Its Additives for Fodder and Green Plants for Ruminant Nutrition

Zirak M. R. Palani — 2025-10-30

Molasses, as a by-product of sugar production, is a feed widely used in ruminant diets, which is rich in available sugars and minerals. The present review paper, under this context, evaluates in detail the chemical characterization and nutritional aspects of molasses with an emphasis on it as a rich source of energy, enhancing the feed palatability and encouraging ruminal microbial fermentation. Molasses has been successful in green and conserved forage systems, particularly with improved quality silage and a reduction in feed losses. Feeding molasses with additives such as urea, minerals, and probiotics enhances rumen function, nitrogen utilization efficiency, and fiber digestion. Research has also shown that it has a positive impact on production performance (such as weight gain, higher milk yield, and feed intake). However, limitation still exists with the factors of the risk from sub-acute ruminal acidosis, imbalancing of electrolytes , and variation of animal species and age. Overconsumption leads to ruminal pH and microbial imbalance. It suggests moderate (usually no more than 10% of dry matter) molasses inclusion with the product introduced slowly and balanced with other feed ingredients. It also provides farmers and nutritionists with working tools to realize these advantages without jeopardizing animal health. The need for further research on the activity of molasses in the long term, its interaction with rumen microbiota, and its activity with other additives in sustainable ruminant production systems is emphasised. The future requirements for research into the long-term activity of molasses, its interaction with rumen microbiota, and its activity with other additives in sustainable ruminant production systems is emphasized.

Foodborne Pathogens and Antibiotic Resistance in School Meals: Implications for Public Health

Shamsudeen Muhammad — 2025-10-30

Food safety is a critical public health concern, particularly in school feeding programs, where improper handling and contamination pose significant health risks. This study investigates the bacteriological quality and antibiotic susceptibility patterns of Escherichia coli isolates from food samples obtained from two boarding schools in Zuru, Kebbi State, Nigeria. A total of 12 food samples were analyzed for microbial contamination, with Total Plate Count (TPC), Coliform Count, and Salmonella Count determined using standard bacteriological methods. The antibiotic susceptibility patterns of E. coli isolates were assessed using the disk diffusion method. The TPC ranged from 2.6 × 10³ to 5.6 × 10⁴ cfu/g in FSTC Zuru and 2.5 × 10³ to 8.0 × 10³ cfu/g in GSTC Zuru. Coliform counts ranged from 2.0 × 10² to 9.3 × 10² cfu/g, while Salmonella counts varied between 1.0 × 10¹ and 4.0 × 10¹ cfu/g. Escherichia coli, Salmonella, Shigella, Staphylococcus aureus, and Klebsiella pneumoniae were identified among the isolates, with E. coli and Salmonella each accounting for 33.3% of isolates. Antibiotic susceptibility testing of E. coli isolates revealed high resistance to Amikacin (100%), Streptomycin (75%), and Tetracycline (62.5%), while full susceptibility was observed for Gentamicin and Cefotaxime (100%). These findings highlight the microbial risks associated with school feeding programs and the prevalence of antibiotic-resistant bacteria, emphasizing stringent food safety measures, improved hygiene practices, and routine surveillance to mitigate foodborne infections and the spread of resistant pathogens.

Molecular Docking of Allicin with PBP2a in MRSA: Insights into Antibacterial Mechanisms

Onche, E. P — 2025-10-30

Methicillin-resistant Staphylococcus aureus (MRSA) is a significant universal public health challenge owing to its resistance to β-lactam antibiotics, mostly methicillin. Primarily, resistance is mediated through the acquisition of the mecA gene encoding penicillin-binding protein 2a (PBP2a), which diminishes the effectiveness of β-lactams. A sulfur-containing compound known as allicin, often derived from Allium sativum (garlic), has proven broad-spectrum properties of antibacteria; nevertheless, its information regarding interactions with resistance protein PBP2a and its likes remains understudied. This study made use of molecular docking analysis to examine the binding interactions between allicin and PBP2a. The binding score of allicin was -3.9 kcal/mol, which shows that allicin has a moderate affinity for the PBP2a active site when compared to vancomycin (positive control) binding energy of -15.6 kcal/mol. The interaction of allicin with key amino acids in the PBP2a catalytic site showed no hydrogen bond formation. The outcome of this research shows that allicin can serve as a potential adjunct or alternative intervention against MRSA by targeting PBP2a. The study reveals insights into allicin's mode of action and presents a basis for further research on the application of compounds derived from plants in combating antibiotic resistance.