New Horizons of Science, Technology and Culture Vol. 8

Safeguarding Cultural Heritage: The Role of Narrative Transmission in Preserving Mt. Isarog's Indigenous Knowledge

2026-02-27T11:34:36+00:00

Literature is the oldest means of transferring knowledge and understanding the culture of the people in a community. However, the legend of Mt. Isarog is neither widely dispersed to the community nor in the canon for reading literature and understanding culture, unlike several myths and legends about mountains in the Philippines that are in printed books, such as “The Legend of Maria Makiling,” “The Legend of Daragang Magayon,” and “Bulusan and Aguingay.”

Despite technological advances in communication methods and information dissemination, it is vital to understand the impact of oral literature on a region’s cultural diversity. Yet, the transferring of knowledge through oral traditions—particularly those surrounding Mt. Isarog—presents challenges in accurately interpreting local cultures, traditions, languages, settlements, and histories. The study investigates the Role of Narrative Transmission in Preserving Mt. Isarog's Indigenous Knowledge.

Oral literature pertaining to Mt. Isarog is accounts of the mountain residents, who mostly worked with children and relatives as farmers, tenants, and labourers on farms, whose culture and tradition are becoming disoriented and varied because of the modern cultures viewed through social media and the migration of the natives and ecotourism. The study revealed that the compiled and recorded oral literature are beneficial reference paraphernalia in showcasing the lifestyle of the old natives in the mountains; valuable sources of old knowledge of the Camarinense in cultural-based ecotourism; links between Bicolanos’ past and present cultures; and mitigation tools to more complexities in conveying meanings in educating people, sharing mythological knowledge, and conserving the region’s unique cultural heritage. Findings suggest that the Camarinense people should cultivate a strong sense of responsibility toward protecting and valuing local oral literature while also actively participating in the preservation of Mt. Isarog’s unique biodiversity through ecotourism initiatives. This should be done in terms of future studies and will be stressed by collecting information about the studies done by the tourism council and tourism educators, and strengthening local identity.

Association between Farmers’ Profile Characteristics and the Effectiveness of Televised Farm Broadcast Programmes in Andhra Pradesh

2026-02-27T11:38:22+00:00

Television is the most powerful audiovisual medium, evolving from its humble beginnings in 1936. In today's world, it has grown into a huge network for mass information and mass entertainment. As an effective medium of mass communication, television plays a crucial role in the agricultural sector, making proper planning and execution of farm broadcast programmes essential for bringing about desired changes in agricultural communities. The present study was taken up to investigate the relationship between the profile characteristics of televiewing farmers and the effectiveness of farm broadcast programmes. The study was conducted in Andhra Pradesh using an ex-post facto research design. Out of the 13 districts of Andhra Pradesh, four districts were selected randomly. Two mandals from each district and two villages from each mandal were selected randomly. After listing out the total number of farmers who have television sets, 15 farmers from each village were chosen by a simple random sampling method; the total sample size became 240. The primary data were collected by using a pre–tested interview schedule. To convert the data into meaningful findings, statistical tools such as the Correlation coefficient (r) and Multiple Linear Regression (MLR) were used. The findings indicated that, out of sixteen profile characteristics selected, thirteen, viz., age, education, farming experience, socio-economic status, innovativeness, mass media exposure, marketing orientation, risk orientation, economic orientation, scientific orientation, extension contact, social participation and training exposure had significant relationship with the effectiveness of farm broadcasts. The other profile characteristics such as farm size, annual income and cropping intensity, did not show any relationship. The MLR analysis found that all 16 profile characteristics of televiewing farmers put together explained about 78.64 per cent variation in the effectiveness of farm broadcasts. The remaining 21.36 per cent is due to the extraneous profile characteristics, which were not considered under study. The profile characteristics, namely socio-economic status, mass media exposure, risk orientation, economic orientation, scientific orientation, extension contact and annual income were found to be positively significant at the 0.01 level of significance. The variable social participation was found to be positively significant at the 0.05 level of significance. This study concludes that farm broadcast programmes provide a comprehensive understanding of agricultural technologies and effectively supplement knowledge gained through training programmes. Due to its audio-visual component and comprehensive nature, the effectiveness of farm broadcasts increases with increasing training exposure. Future studies may include additional profile characteristics like achievement motivation, management orientation, previous knowledge, production orientation, planning orientation, price situation, input accessibility, etc., which are likely to affect them and which are beyond the present study, and may also be studied.

A Fractional-Order Pneumonia Transmission Model Using the Generalised Atangana–Baleanu Derivative: Analysis and Numerical Simulations

2026-02-27T11:40:40+00:00

Pneumonia remains a major global health burden, especially among children under five and the elderly, and is a leading cause of death in many low- and middle-income countries. In this paper, we formulate a novel fractional pneumonia model utilising the generalized Atangana–Baleanu (GAB) derivative. Pneumonia remains a global health threat, particularly in regions with limited medical infrastructure. We extend the traditional integer-order compartmental model into a fractional framework to capture the hereditary properties and memory effects of disease transmission. We establish the existence and uniqueness of solutions for all compartments (Vaccinated V, Susceptible S, Carrier C, Infected I, and Recovered R) using fixed-point theory. This mathematical foundation ensures the biological feasibility of our memory-dependent model, which has significant implications for understanding persistent transmission patterns in pneumonia epidemiology. A numerical scheme based on Newton polynomial interpolation is derived. Finally, we discuss the comparative dynamics of the model relative to data trends for Iowa and Mississippi, demonstrating that fractional-order derivatives provide superior flexibility in fitting localised epidemic spikes compared to traditional models. Our key finding shows that regions with higher vaccination efficacy experience significantly flattened epidemic curves, highlighting the critical importance of vaccination coverage in pneumonia control strategies.

Internet of Things Security: Requirements, Challenges and Mitigation Strategies

2026-02-27T11:44:25+00:00

Background: The Internet of Things (IoT) is one of the most promising technologies that aims to enhance humans’ quality of life (QoL). IoT plays a significant role in several fields such as healthcare, automotive industries, agriculture, education, and many cross-cutting business applications. Despite the growing body of literature on IoT security in recent years, critical gaps remain unaddressed.

Objectives: This study explores the multifaceted security issues of IoT environments, highlighting current gaps and presenting modern technological solutions. The key purpose of the study is to systematically explore the diverse security challenges in IoT environments, critically assess current technological solutions, and identify unresolved issues along with potential directions for future research.

Methods: A systematic literature review was conducted for the period 2020–2025, covering more than 50 studies from IEEE, ACM, Elsevier, and SpringerLink. Selected papers were evaluated based on relevance, peer-review status, and contribution to IoT security.

Findings: Security remains a major concern as IoT device connectivity expands across industries. IoT systems face unique challenges such as weak authentication, lightweight encryption requirements, and large-scale heterogeneity. This study synthesises current solutions like multi-factor authentication, blockchain, and machine learning for intrusion detection.

Conclusion: There is a critical need for integrated, multilayered security frameworks suitable for large-scale and resource-constrained IoT deployments. Future work should focus on real-time, context-aware, and energy-efficient security models validated on realistic testbeds.

Dynamics of Effects of Land Fragmentation on Food Security in Three Agro-ecological Zones of Embu County in Kenya

2026-03-04T10:58:12+00:00

Land fragmentation is a common agricultural phenomenon in many countries where a single large farm is subdivided into a large number of separate small land plots. Land fragmentation has been cited as one of the major causes of food insecurity in Kenya. This citation may be due to the fact that land fragmentation is rampant in most high agricultural potential areas in Kenya, mainly due to increasing population pressure, but there is limited evidence from empirical studies. This chapter is based on a study that was carried out to evaluate the impact of land fragmentation on food security in three agro-ecological zones (AEZs) of Embu County in Kenya from January to November 2016. The study used data collected from 384 farm households that were randomly selected from three AEZs in Embu County, using the 4-stage cluster sampling method. The AEZs were the Sunflower-Cotton Zone, the Coffee Zone and the Tea Zone, based on the official AEZs classification system in Kenya. The household caloric acquisition method was used to compute a household food security index (HFSI) that was used to measure the household food security status. HFSI < 1 indicates food insecurity, and HFSI ≥ 1 indicates food security based on daily calorie requirements. The effect of farm size on food security was evaluated using the Binary Logit Regression method. The results showed that the average number of people in a household was 3.73 in the Tea Zone, 3.59 in the Coffee Zone and 3.93 in the Sunflower Zone, and that farm size had a positive and significant effect on food security in the Sunflower (P=.029) and Tea zones (P=.007), but not in the Coffee Zone (P=.365). Further, it was found that the minimum farm size that could ensure the attainment of the minimum (cut-off) point for household food security (HFSI = 1) was above 2 ha in the Sunflower Zone and 0.5 ha in the Tea Zone. The study concluded that farm size has a positive impact on household food security in the study area. Based on the study findings, it is recommended that further fragmentation of farms below 0.5 ha in the Coffee and Tea zones and 2 ha in the Sunflower Zone should be discouraged to ensure sustainable food security in the study area. For the farms that are already below the minimum cut-off size for food security, measures to increase these farms’ productivity so that they can support more people per ha should be devised and implemented. This study focused on only three agro-ecological zones within Embu County; therefore, future research should expand the geographical scope to include other counties and agro-ecological zones in Kenya to more comprehensively evaluate the effects of land fragmentation on food security.

Economic Recovery through Corporate Social Responsibility during COVID-19: Lessons from Islamic Banks in Bangladesh

2026-03-04T11:01:45+00:00

Background: The COVID-19 pandemic has brought not only health but also extensive economic challenges. As a result, the rapid economic growth of Bangladesh has been disrupted. Currently, eight full-fledged Islamic banks (IBs) are functioning in Bangladesh, adhering to the underlying principles of Shariah; among them, seven are actively engaged in corporate social responsibility (CSR) activities to help the underprivileged segments of its citizens.

Purpose: The purpose of this study is to examine the economic impact of COVID-19 and analyse how the CSR initiatives of IBs can contribute to reducing the adverse economic impact in the context of Bangladesh.

Methodology: This study, through employing a content analysis method, examined the information available from these IBs as well as other government sources and published materials to address the COVID-19 economic impacts, especially the role of these IBs.

Findings: This study found that Bangladesh has been facing several major economic challenges, including the declining revenue from the Readymade Garments industry, decreasing inflow of foreign remittance, struggling small and medium-sized enterprises (SMEs) and start-ups, already crippling financial institutions, instability in the capital market, continuous trade deficit and a sharp increase in unemployment. Along the line with national and international funds, IBs’ CSR funds can also help address the economic downturn in Bangladesh caused by the COVID-19 pandemic. The study further identified that if IBs develop a consortium among themselves, the CSR funds could be better utilised for the socio-economic development of Bangladesh. It also demonstrated that IBs could spend USD 83.30 million annually, which means USD 417 in five years period.

Conclusion: This study is unique in the sense that it seeks to address the economic challenges of COVID-19 in the context of Bangladesh with support from the CSR initiatives of IBs. This study has created a new insight for IBs into developing an integrated CSR strategy, which is expected to bring significant contributions to the livelihood of the susceptible citizens of this country. The study recommended that all IBs work together to develop a joint CSR strategy for the socio-economic development of Bangladesh. Further research can be conducted in future, considering the total CSR funds by all conventional and Islamic banks.

Biomass-Derived Activated Carbon for CO₂ Mitigation in Compression Ignition Engine Exhaust Systems

2026-03-16T08:11:14+00:00

Rapid industrialisation has intensified environmental pollution and global warming, with the automotive sector being a major contributor to greenhouse gas emissions. This study investigates an effective post-combustion CO₂ reduction approach for compression ignition (CI) engines using a modified exhaust adsorption system containing waste biomass–derived adsorbents. Activated carbon and biochar produced from coconut shell, rice husk, and eucalyptus wood through carbonisation and activation processes were evaluated for their CO₂ adsorption performance. Integration of a single adsorption chamber into the exhaust system achieved up to a 48% reduction in CO₂ emissions compared to baseline operation, while the addition of a second chamber provided a further 16% reduction. Experiments were conducted on a single-cylinder, four-stroke diesel engine, where baseline CO₂ emissions increased with engine load, reaching a maximum of 13.5%. After installing biochar and activated carbon filters, CO₂ emissions decreased significantly, with biochar showing superior performance at higher loads. The optimal configuration using blended diesel with activated carbon distributed across dual compartments reduced CO₂ emissions from 6.2% to 0.4% at maximum load. Concurrent reductions in CO, HC, and NOₓ emissions confirmed the effectiveness of the proposed adsorption system.

Predicting Student Performance Using Deep Learning and Indian Knowledge Systems

2026-03-16T08:15:33+00:00

Background: Existing data-driven approaches have demonstrated promising predictive capabilities; however, many remain narrowly focused on technical optimisation. By treating behavioural data as context-free signals, these systems often overlook the cultural, social, and ethical dimensions that influence learning.

Aim: The aim of this chapter is to develop a culturally grounded and ethically responsible framework for predicting student academic performance by integrating mobile phone behavioural analytics with principles drawn from the Indian Knowledge System (IKS).

Objectives: The study seeks to (i) model student learning behaviour using temporally rich mobile usage data, (ii) enhance prediction accuracy and interpretability through deep learning architectures, (iii) operationalise IKS-inspired constructs to provide cultural and ethical context, and (iv) support fair, human-centred educational interventions.

Methods: An integrative multi-input deep learning framework is proposed that combines Temporal Convolutional Networks (TCN) for sequential behaviour modelling, attention mechanisms for feature prioritisation, and static psychometric and demographic feature fusion. Mobile phone data capturing usage patterns, activity rhythms, and engagement indicators are processed alongside IKS-informed contextual features. Model performance is evaluated against classical machine learning baselines using predictive and fairness-aware metrics, with interpretability analyses supporting transparent decision-making.

Results: Experimental validation on representative datasets demonstrates that the proposed framework consistently outperforms traditional machine learning models in terms of prediction accuracy and stability. Attention-based explanations reveal that IKS-inspired features contribute meaningfully to performance gains while reducing subgroup disparities. The results indicate improved fairness, enhanced interpretability for educators, and greater alignment with student well-being and learning rhythms.

Conclusion: The results indicate that embedding contextual and value-oriented dimensions improves not only predictive accuracy but also interpretability and fairness, making the system more aligned with real educational environments. The inclusion of explainability mechanisms further strengthens trust and transparency, which are critical for adoption in academic settings. Overall, this work contributes a meaningful step toward educational AI systems that support holistic learning, respect learner identity, and encourage responsible decision-making in data-driven education.

Novelty: The key novelty of this work lies in embedding indigenous knowledge principles directly into the design and interpretation of deep learning models for educational analytics. By bridging behavioural data, advanced neural architectures, and culturally rooted context, the framework advances a human-centric paradigm for academic performance prediction that emphasises ethical responsibility, cultural resonance, and holistic educational outcomes.