Asian Journal of Advanced Research and Reports

Technical and Soft Skills Preparedness of Electrical Technology Students for Industry Immersion

Dustin Lloyd D. Gabriel — 2025-12-03

This study aimed to examine the technical and soft skills preparedness of Bachelor of Science in Industrial Technology students majoring in Electrical Technology for their upcoming industry immersion. Using a descriptive research design, the study purposively sampled 43 second-year students from a university in Northern Philippines offering technical courses. Data was collected through a validated survey questionnaire assessing students’ competencies in technical domains—tools and equipment handling, electrical installation, technical knowledge, safety compliance, troubleshooting—and soft skills including communication, teamwork, time management, problem-solving, and professionalism, rated on a 4-point Likert scale. Results revealed that students exhibited a very high level of preparedness overall, with the highest mean scores in electrical installation (3.70) and tools handling (3.67) among technical skills, and professionalism (3.67) and teamwork (3.60) among soft skills, while lower preparedness was noted in troubleshooting (3.23) and communication (3.12). Findings suggest that the program’s outcomes-based and experiential learning strategies effectively integrate theoretical knowledge with hands-on practice, fostering industry-ready graduates. Nevertheless, targeted improvements in technical documentation, complex troubleshooting, communication, and problem-solving skills are recommended to enhance adaptability and workplace effectiveness. The results have practical implications for curriculum reform, highlighting the need for scenario-based exercises, documentation-intensive activities, and advanced troubleshooting modules, as well as strengthened industry partnerships for mentorship and authentic project experiences. By addressing these gaps, technical education programs can better equip students with both the technical and interpersonal competencies essential for professional success, promoting employability, confidence, and adaptability in dynamic industrial environments.

Motivation and Coping Mechanisms of Female Students in Automotive Technology: A Case Study in Northern Philippines

Felimer V. Abes — 2025-12-11

This study aimed to explore the motivation, gender-related challenges, and coping mechanisms of female students pursuing an Automotive Technology degree in Northern Philippines, highlighting how personal, social, and institutional factors influence their persistence in a male-dominated field. A qualitative case study was employed to capture in-depth, contextualized experiences of participants within their real-life educational setting. The study was conducted at a state university in Northern Philippines with seven purposively selected female students enrolled in the Automotive Technology program during the semester of data collection. Data was gathered through semi-structured interviews and analyzed using thematic analysis following six-step framework to identify recurring patterns related to motivation, gender bias, coping strategies, and institutional support. Findings revealed that intrinsic motivation, such as personal interest and passion for mechanical work, alongside extrinsic influences from family and role models, significantly shaped students’ engagement, consistent with Self-Determination Theory and Expectancy-Value Theory. Participants also expressed a desire to challenge gender stereotypes, reflecting high self-efficacy and agency as explained by Social Cognitive Theory. Despite encountering gender-related challenges, including skepticism from peers and instructors, students employed adaptive coping strategies, such as cognitive reframing, goal-oriented focus, social support, and emotional regulation, which reinforced resilience, persistence, and skill mastery. Furthermore, participants emphasized the importance of mentorship, visible female role models, hands-on workshops, and inclusive institutional practices in fostering empowerment and motivation. The interplay of intrinsic interest, social support, and self-efficacy underscores the need for gender-sensitive policies and structured support in technical education to sustain female students’ motivation, confidence, and professional development in male-dominated programs.

Revitalizing an Endangered Language Through Intangible Cultural Heritage: Evidence from the Hezhe Ethnic Group in China

Chunying Shi — 2025-11-28

Aims: The study investigated means for protecting Hezhe endangered language through oral literature.

Study Design: The study was based on Anthropology Linguistics approach.

Place and Duration of Study: The study was taken place in Heilongjiang Province, China, between May 2023 and July 2024.

Methodology: The study is a field investigation study. A total of 115 participants took part in it. The study employs various research methods, including questionnaire surveys, interviews, and field observations which were analyzed using descriptive statistics.

Results: The study reveals that the language proficiency of the Hezhe people has improved. About 28 participants could almost understand Hezhe language and 27 participants could almost speak Hezhe language. In recent years, as Hezhe Imakan and Jialingkuo have been recognized as part of the World Intangible Cultural Heritage List and the National Intangible Cultural Heritage List, various language and culture training centers have been established both online and offline in the Hezhe region. The establishment of cultural ecological protection zones in Hezhe ethnic settlements has provided a supportive environment for the survival and development of their language and culture.

Conclusion: The development model that integrates culture and tourism has injected new momentum into the sustainable development of the Hezhe language and culture.

Innovative Aspects and Practical Implications of This Study: The findings in the current delivered a comprehensive model for preserving the endangered language and culture.

Advanced Nanocomposite Polymer-Biomaterial Catalysts for Integrated Biodiesel and Battery Systems in Sustainable Energy Applications

Alamutu Mayowa Habeeb — 2025-12-01

The rising issues of pollution and the depletion of fossil fuels have also accelerated the search for sustainable alternatives. The aim of this research is to improve the efficiency of the reaction, the reusability of the catalyst, and the purity of the produced fuel by producing biodiesel from waste vegetable oil through the use of a polymer-CaO-TiO2 nanocomposite catalyst, as described, but without the two “nanocomposite catalyst” instances. The catalyst was produced through the Sol-gel assisted incorporation process, where calcium oxide, extracted from calcined eggshells, was compounded with titanium oxide nanoparticles and the biodegradable polymer. The physico-chemical properties of the WVO and the properties of the catalyst were assessed through standardized procedures. The optimization of the transesterification reaction was carried out by varying three significant factors, namely temperature (55-75°C), amount of catalyst (1-5 wt%, CaO), and molar ratio of methanol-to-oil (6:1-10:1) while other factors kept constant in each test run. Methanol acted as both the reactant and solvent, thereby giving FAME as products. The experimental works revealed an increase in the amount of produced biodiesel and reaction time by the polymer-CaO-TiO₂ catalyst compared to CaO catalyst. This work illustrates the possibility of combining nanocomposite catalyst development techniques and the processes associated with the generation of waste-to-energy as a viable and environmentally friendly means of producing biodiesel. The proposed process ensures an efficient, cleaner, and recyclable method of generating high-quality biodiesel from waste vegetable oil.

Occurrence and Distribution of Some Contaminants of Emerging Concern in the Sediments from Imo River Nigeria: A Non-Targeted Analysis Approach

Humphrey – Ebenezer, J.N. — 2025-12-03

This is a non–targeted assessment of the occurrence and distribution of contaminants of emerging concern in sediment samples of the Imo River, Nigeria. Three (3) sampling locations—Ekenobizi (Imo State), Owerrinta (Abia State), and Oyigbo (Rivers State) — along the Imo River were selected for the study based on their high human population and numerous anthropogenic activities. Sediment samples (9) were collected from upstream, midstream, and downstream of each of the sampled locations using the grab sampling method and taken to the laboratory for analysis within three hours after sampling. A non-targeted screening for Contaminants of Emerging Concerns (CECs) was done using gas chromatography – mass spectrometry (GC-MS) after preparation of the samples. The results showed contamination of the river with eighty-four (84) unique CECs distributed as follows: 21 CECs at Ekenobizi axis, 32 CECs at Owerrinta axis and 34 CECs at Oyigbo axis across the upper stream (US), midstream (MS) and downstream (DS) points. Some of the CECs were location-specific, while some were detected across the three sampling points of each location. Three of the detected CECs were common to both the Owerrinta and Oyigbo axes. Among the detected compounds were siloxanes, fatty acids, amines, hydrocarbons, and other organic chemicals. These chemicals are traceable to pharmaceutical and personal care products, agro-chemicals, and other industrial chemicals, and they exhibit varying degrees of ecological risks. There was no significant difference among the CECs concentrations of the sampled locations (where p > 0.05, p = 0.909). The detection and concentration CECs in the study area indicate a critical trend of environmental contamination and potential ecological risks, consistent with global studies.

Design and Development of a Fuel Injector Nozzle Cleansing Machine for Small Scale Workshops

Popoola, Ibrahim — 2025-12-05

The development of a fuel injector nozzle cleansing machine addresses the challenge of clogged and inefficient nozzles in internal combustion engines, particularly within small-scale workshops. Conventional cleaning methods are slow, hazardous, and often ineffective, leading to poor spraying patterns and reduced engine efficiency. This study presents the design, fabrication, and performance evaluation of a dual-process injector nozzle cleansing machine capable of both cleaning and spray testing. The machine consists of a mild steel frame stand, fuel tank, submersible fuel pump, nozzle rail, and 12V DC power supply. Fabrication involved welding, fastening, and soldering to achieve a rigid and safe structure. Performance evaluation was conducted using anti-rust thinner for cleansing and petrol (PMS) for spray testing at a pressure of 8 psi. Experimental results revealed an average cleaning efficiency of 88% and spraying efficiency of 95% across nozzles with two, three, and four holes. Findings indicate that nozzles with a higher number of holes demonstrated faster cleaning and more effective spray distribution. The study concludes that the proposed machine provides a cost-effective, safe, and reliable alternative to industrial injector cleaning systems, making it suitable for small-scale workshops and automotive maintenance applications.

Feeding Pattern of Alosa fallax in the Marine Waters off the Latakia Coast, Eastern Mediterranean: A Two-Year Seasonal Study

Reem Al-Sheikh Rasheed — 2025-12-05

Understanding fish feeding patterns is crucial for assessing trophic interactions and sustainable fisheries management. This study examines the dietary composition of Alosa fallax, a commercially significant species within the Clupeidae family, in the marine waters off the coast of Latakia, in the eastern Mediterranean. A total of 2,400 specimens were collected using locally employed fishing techniques, including gillnets and purse nets, between October 20, 2021, and September 14, 2023. The sampled individuals exhibited a standard-length range of 10.4–21.19 cm (mean 14.27±3 cm) and a weight range of 15.72–102.81 g (mean 43.78±27.61 g), with a maximum body height averaging 3.01±0.59 cm. Dietary analysis revealed that A. fallax is a planktivorous species, consuming 12 different planktonic organisms classified into four major taxonomic groups: Crustaceans ranked first in frequency (52.51%) and relative importance (49.59%), with a total of 8141 points. The crustaceans consisted of copepods (mainly Calanoidea), as well as malacostracans, including species from the orders Euphausiacea, Isopoda, Amphipoda, and Decapoda (such as Zoea, Penaeus, Crab megalopa, and Mysidacea)., Phytoplankton: Ranked second, with a frequency of 8.94% and a relative importance of 14.07%, totaling 6262 points. Organic Detritus: Ranked third, with a frequency of 14.68% and a relative importance of 12.37%, totaling 1348 points. Fish: Ranked fourth, with a frequency of 9.10% and a relative importance of 5.04%, totaling 1459 points. The fish consumed included species such as Anguilliformes (eel), Ophidion rochei (shriat), and Trachurus trachurus (horse mackerel). Polychaete Worms: Ranked last, with a frequency of 3.43% and a relative importance of 0.18%, totaling 85 points. This study enhances our ecological understanding of A. fallax feeding habits, providing valuable insights for fisheries management and the conservation of marine biodiversity along the Latakia coastline.

Structure-Based Docking and Interaction Analysis of Indonesian Phytochemicals Targeting SAP2 (1EAG) for Antifungal Drug Discovery

Nya Daniaty Malau — 2025-12-06

Aims: To evaluate the inhibitory potential of twenty Indonesian phytochemicals against Candida albicans Secreted Aspartic Protease 2 (SAP2; PDB ID: 1EAG) with the biological rationale of inhibiting SAP2-mediated virulence in C. albicans, as this enzyme is essential for fungal tissue invasion and pathogenicity.

Study Design: An in silico, structure-based molecular docking study employing a validated flexible-pocket docking model focused on the catalytic Asp32–Asp218 dyad of SAP2.

Place and Duration of Study: Computational analyses were performed at the Department of Physics Education, Universitas Kristen Indonesia, Jakarta, between January and October 2025.

Methodology: Twenty phytochemicals from Indonesian medicinal plants were prepared using ADFRsuite 1.0 and docked to SAP2 using AutoDock Vina (version 1.2.3). Each ligand underwent five independent docking replicates using a flexible-pocket model encompassing the catalytic and anchoring residues (Asp32, Asp218, Ser282, Leu297, Arg312). Binding free energies (ΔG, kcal mol⁻¹) were evaluated, and protein–ligand interactions were visualized using PyMOL and BIOVIA Discovery Studio Visualizer to identify hydrogen bonds and hydrophobic contacts.

Results: All ligands exhibited reproducible binding profiles (SD ≤ 0.25 kcal mol⁻¹), with ΔG values ranging from −4.1 to −6.0 kcal mol⁻¹. Demethoxycurcumin (−5.98 ± 0.04 kcal mol⁻¹), curcumin (−5.90 ± 0.06 kcal mol⁻¹), and bisdemethoxycurcumin (−5.60 ± 0.06 kcal mol⁻¹) showed the strongest affinities through hydrogen bonds with Ser282, Glu278, and Arg312 near the catalytic dyad. Sesquiterpenoids such as germacrone and β-bisabolene bound stably through compact hydrophobic interactions, while smaller ligands like 6-shogaol and citral localized in a peripheral sub-pocket, suggesting a potential allosteric site.

Conclusion: Curcuma-derived curcuminoids and Zingiber-derived terpenoids are promising natural SAP2 inhibitors. The flexible-pocket docking strategy enhances reproducibility and establishes a robust computational foundation for subsequent molecular dynamics and in vitro antifungal validation. These findings highlight the potential of identified compounds for lead optimization in future antifungal drug development.

Psychometric Evaluation of a Perception Scale Measuring 21st-Century Skills in Pupil Teachers

Subuhi Rahman — 2025-12-06

This paper is constructed to determine the psychometric properties of perception scale on 21^st Century Skills for pupil teachers. In this paper an attempt was made to assess the validity and reliability as well as to provide the appropriate standards for interpretating the results of perception scale on 21^st Century Skills. The scale includes 41 items to measure twelve types of skills on a large sample of 328 pupil-teachers of different teacher training colleges. Content validity was determined by various experts of reputed universities and construct validity was calculated using Pearson inter-items correlation between items was calculated. Furthermore, Pearson coefficient of correlation between dimensions and total value was calculated. The reliability of the test was tested by calculating Alpha Cronbach. Quartile Deviation was calculated in order to ascertain the levels of perception on 21^st Century Skills. Overall, it is concluded that the perception scale has good construct and discrimination validity. Moreover, all the values of reliability coefficient for each dimension are highly significant.

Leveraging Blockchain, Artificial Intelligence, and Data Analytics for Sustainable and Transparent Resource Management

Ezekiel Oluwagbemileke Ilori — 2025-12-06

The rising complexity in current resource management projects has generated demand for transparent, efficient, and sustainable operational systems. Current research work focuses on identifying the importance of Blockchain, Artificial Intelligence, and Data Analytics in improving transparency, efficiency, and sustainability in resource management projects from 2018 to 2025. Quantitative research methodology was adopted for analysis, incorporating a structured data pool with 1,200 projects in ten geographic regions and eight different resource management types. Indexes such as Transparency Index, Emission Reduction, Budget, Data Volume, and Project Duration were assessed for analysis with Python-based analysis tools.

This research evaluated individual and collective impact of Blockchain and AI adoption on project performance. The result shows that adoption of Blockchain technology leads to improved transparency, while adoption of AI improves sustainability performance, specifically in emission reduction. Joint adoption of Blockchain and AI showed best overall performance in projects, although with enhanced financial, processing, and timeline costs. Visualization techniques such as scatter plots and box plots identified correlations regarding impact levels in data size, transparency, and performance, emphasizing importance in having overall technology systems. These findings indicate that the combined use of Blockchain, AI, and analysis is resulting in more responsible and data-driven resource management. At the same time, there is an increase in costs for implementation, coupled with extended project schedules. It is proposed to embrace overall digital architectures, skill development in information technology, and policy initiatives in support of data transparency for achievement in resource management. On the whole, there emerges experimental validation in support of strategic integration for efficient, transparent, and sustainable resource management outcomes.

Exploring Green Entrepreneurship in Business Climate: Towards Change Adaptation in South-South Nigeria

Godwin Wonah Ogar — 2025-12-08

This study examined the effect of green entrepreneurship on climate change adaptation. The specific objectives were to examine how waste management and recycling affect community-based adaptation; and unveil how resource efficiency affect adaptive capacity of enterprises. The study employed a survey research design. The research focused on business enterprises located in Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and Rivers states, which are recognized for their diverse industrial activities, including manufacturing, agriculture, and services. The population for this study consisted of 220,765 enterprises. In this study, a sample size of 384 was established. A multi-stage sampling technique was utilized. The study employed both descriptive and inferential statistical statistics for data analysis. All hypotheses were tested using regression at a significance level of 5%, with regression models facilitating the hypothesis testing process. Findings show that waste management and recycling have a strong and statistically significant positive effect on community-based adaptation, suggesting that communities that integrate structured recycling systems and effective waste practices are better equipped to respond to environmental and socio-economic stresses. Finding also reveals that resource efficiency has a strong and statistically significant positive effect on the adaptive capacity of enterprises. The study concludes that green entrepreneurship plays a foundational role in advancing climate change adaptation, as it promotes innovation, environmental responsibility, and sustainable resource use across communities and enterprises. The study therefore recommended that SMEs should invest in structured recycling programs, strengthen waste-segregation systems, and promote public awareness initiatives to enhance resilience against climate-related challenges. SMEs should also prioritize eco-efficient production processes, invest in technologies that reduce material and energy waste, and embed resource optimization strategies into their long-term sustainability plans to strengthen organizational resilience.

AI-driven Green Logistics: Optimizing Last-mile Delivery Networks with Electric Vehicles for Carbon Neutrality in U.S. Metropolitan Areas

Aderibigbe Michael Oluwaseyi — 2025-12-08

The last-mile delivery process has continued to be the costliest, time-consuming, and environmentally challenging part of logistics systems, and this challenge has been further amplified in the context of quickly increasing large cities worldwide. The conventional logistics delivery chain also faces issues such as traffic congestion, increasing fuel prices, downtime, route inefficiency, and high emissions. Artificial intelligence optimization in logistics systems and electric vehicles have emerged as new approaches to improve last-mile delivery performance. The current research aims to investigate to what degree last-mile delivery can be improved by artificial intelligence optimization in routing and electric vehicles. The research followed a descriptive and analytical research study design, mainly depending on secondary sources with research data collected from peer-reviewed journals, reports published from the logistics industry, case research, and transport data published by concerned government bodies from 2018 to 2025. The data were systematically filtered to determine their appropriateness, sound methodology, measurable variables, and level of empirical research. Important variables such as delivery time, cost of delivery, distance travelled, emissions from vehicles, energy utilization, traffic congestions, and customer density were utilized for comparative analysis and cause-effect analysis. Analysis of the results shows that there was a great improvement in delivery performance through routing via artificial intelligence. The average time reduced by 34.6 percent, cost of delivery fell by 31.5 percent, and distance travelled to complete delivery reduced by 34 percent. The percentage of successful deliveries rose to 89 percent. Failed deliveries fell to less than half. The reduction in carbon dioxide emissions reached 86.6 percent for electric delivery vehicles compared to gasoline vehicles. The emissions of nitrogen oxides were eliminated entirely. Analysis of customer density indicated that areas of high density receive even greater advantages from artificial intelligence clustering and electric vehicles. The trend of congestions also indicated that predictive models of artificial intelligence result in reduced delays within peak hours. All case study comparisons among large cities across America confirmed these results. The paper concludes that combining Artificial Intelligence and electric vehicles represents a scalable, cost-efficient, and sustainable solution for last-mile delivery route optimization. The combination of these solutions improves route optimization performance and helps to avoid disruptions in last-mile delivery operations. In conclusion, it can be seen that modern cities require intelligent route optimization solutions and environmentally sustainable last-mile delivery vehicles to meet the demands of modern cities.

A Note on Dual Generalized Adrien Numbers

Feyza Demirci — 2025-12-12

In this study, we introduce and develop the concept of Dual Adrien numbers, with particular emphasis on two fundamental cases: the Dual Adrien sequence and the Dual Adrien–Lucas sequence. We conduct a systematic investigation of their structural and analytical properties, encompassing algebraic identities, matrix representations, recurrence relations, Binet-type formulas, generating functions, exponential expressions, Simson-type identities, and summation formulas. By establishing these results, we aim to construct a coherent and mathematically rigorous framework for the study of Dual Adrien numbers. Furthermore, we highlight their intrinsic connections with classical recurrence families, thereby situating them within the broader landscape of hypercomplex sequence analysis. This work not only extends the theory of generalized number sequences into the dual-number algebraic setting but also provides new tools and perspectives that may inspire further research in recurrence relations, combinatorial identities, and hypercomplex algebraic structures.

Ethical Governance of Agricultural Biotechnology in Kenya: A Critical Narrative Review on Innovation, Inclusion, and Indigenous Systems

Omweri Fred Siambe — 2025-12-06

This study critically examines the ethical governance of agricultural biotechnology in Kenya, focusing on the regulatory frameworks, stakeholder engagement, and socio-cultural considerations shaping policy implementation. Drawing on document analysis and expert interviews, the research identifies significant gaps in ethical oversight, including limited public participation, inadequate risk communication, and fragmented institutional mandates. The findings reveal that while Kenya has made strides in biosafety legislation, ethical governance remains underdeveloped, particularly in addressing distributive justice, indigenous knowledge systems, and long-term ecological impacts. The study argues for a pluralistic and anticipatory governance model that integrates ethical reflexivity, inclusive deliberation, and adaptive regulation. These insights have implications for policy reform, capacity building, and the democratization of biotechnology governance in Kenya and similar contexts.