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Asian Journal of Science, Technology, Engineering, and Art
Published by Lembaga Yasin Alsys
ISSN : 30255287     EISSN : 30254507     DOI : https://doi.org/10.58578/AJSTEA
Core Subject : Science, Art, Social, Engineering,
Arts Computer Science & IT Engineering Social Sciences
Asian Journal of Science, Technology, Engineering, and Art [3025-5287 (Print) and 3025-4507 (Online)] is a double-blind peer-reviewed, and open-access journal to disseminating all information contributing to the understanding and development of Science, Technology, Engineering, and Art. Its scope is international in that it welcomes articles from academics, researchers, graduate students, and policymakers. The articles published may take the form of original research, theoretical analyses, and critical reviews. AJSTEA publishes 6 editions a year in February, April, June, August, October and December. This journal has been indexed by Harvard University, Boston University, Dimensions, Scilit, Crossref, Web of Science Garuda, Google Scholar, and Base. AJSTEA Journal has authors from 5 countries (Indonesia, Nigeria, Pakistan, Nepal, and India).
Arjuna Subject : Umum - Umum
Articles 231 Documents
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Genotypic Detection of Dominant Bacteria in Dental Caries in Uyo, Nigeria Udoh, Mary Athanasius; Onwuezobe, Ifeanyi Abraham; Abdulkadir, Rasheedat; Abubakar, Auwal; Yahaya, Musbau Adekunle; Onah, Daniel Oche
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 3 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i3.5799

Abstract

Dental caries remains one of the most prevalent and persistent oral health challenges globally, with nearly universal incidence across populations. The disease is increasingly complicated by the emergence of antibiotic-resistant bacteria, a phenomenon largely driven by biofilm formation and the acquisition of resistance genes. This study aimed to identify the predominant bacterial species implicated in the etiology of dental caries in Uyo, Nigeria, and to characterize their associated antibiotic resistance genes. A total of 120 clinical samples were analyzed using the VITEK 2 Compact System (bioMérieux) for bacterial identification and antibiotic susceptibility testing. Molecular detection of three extended-spectrum β-lactamase (ESBL) genes—CTX-M, TEM, and OXA—was performed via PCR using standard thermal cycling conditions on an ABI 9700 Applied Biosystems platform. Among the 27 isolates recovered, Gram-negative bacteria constituted 66.7%, with Burkholderia cepacia complex being the most prevalent (25.9%). Burkholderia cepacia exhibited high sensitivity to Amikacin and Tobramycin but showed marked resistance to Ceftazidime. Of the B. cepacia isolates, 6 (85.7%) underwent 16S rRNA sequencing, confirming their identity as Burkholderia cepacia (n=4) and Burkholderia cenocepacia (n=2). CTX-M genes were detected in all sequenced isolates (100%), while TEM genes were present in one isolate (16.7%) and OXA genes were absent. These findings underscore the potential public health threat posed by ESBL-producing B. cepacia complex strains in dental caries, highlighting the urgent need for targeted antimicrobial stewardship and enhanced surveillance in oral healthcare settings.
Detecting Cardiac Arrhythmias through Electrocardiography: Current Advancement and Future Direction from the Standpoint of Deep Learning Sabo, Abdulhafiz; Gital, Abdulsalam Y.; Babayaro, Abass; Waziri, Jamilu Usman; Muhammad, Sabo Sani; Nazif, D. M.
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 3 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i3.5902

Abstract

Cardiac arrhythmia remains a leading cause of mortality worldwide and is a significant risk factor for the development of various cardiovascular diseases. Electrocardiography (ECG) is a widely utilized diagnostic tool for the early detection of cardiac arrhythmias, and recent advancements in deep learning (DL) have demonstrated notable success in automating and enhancing this process. Despite the growing body of research, there remains a lack of a focused and comprehensive literature review dedicated specifically to the application of deep learning techniques in ECG-based arrhythmia detection. Addressing this gap, the present study systematically reviews recent contributions that apply deep learning algorithms to ECG data for the identification and classification of cardiac arrhythmias. The review categorizes relevant studies based on architectural approaches, datasets used, performance metrics, and clinical relevance. A novel taxonomy is proposed to classify the domains of deep learning applications in ECG, including supervised, unsupervised, and hybrid learning models, as well as real-time and offline diagnostic systems. The review also identifies current limitations in model generalizability, data quality, and interpretability. Based on these insights, future research directions are proposed to guide the development of more robust, transparent, and clinically applicable deep learning systems for cardiac arrhythmia detection. This review serves as a foundational reference for researchers and practitioners seeking to advance the intersection of artificial intelligence and cardiovascular diagnostics.
Modified Cardiac Arrhythmia Classification from Electrocardiography Signals Using a Convolutional Neural Network Model Abdulhafiz, Sabo; Gital, Abdulsalam Ya’u; Mohammed, Sani Sabo; Nazif, D. M.
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 4 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i4.5905

Abstract

Manual classification of cardiac arrhythmias from electrocardiogram (ECG) signals is a labor-intensive and error-prone process due to the complex and variable nature of cardiac waveforms. Convolutional Neural Networks (ConvNets), widely recognized for their success in image classification, offer a promising solution for automating this task. This study proposes an enhanced ConvNet-based approach for the classification of cardiac arrhythmias, leveraging AlexNet as a feature extractor. The features obtained from the convolutional layers are input into a backpropagation neural network for final classification. The proposed model was evaluated on four distinct arrhythmia conditions using ECG waveforms from the MIT-BIH Arrhythmia Database. Comparative analysis against traditional models revealed the superior performance of the proposed ConvNet architecture, achieving high scores across multiple evaluation metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. The feature extractor demonstrated robust performance, with classification accuracies of 1.00 and 0.99 on training and testing datasets, respectively. These findings underscore the potential of ConvNet-based models to serve as efficient, accurate, and fully automated tools for arrhythmia diagnosis, contributing significantly to advancements in cardiovascular disease detection and clinical decision support systems.
Modeling Volatility Using Bayesian GARCH with Student-t and Generalized Error Distributions: A Case Study of Bitcoin Daniel, Adashu Jacob; Josaphat, Anule Aondolum
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 4 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i4.5926

Abstract

This study investigates the optimal model for capturing and forecasting volatility in the cryptocurrency market, with a specific focus on Bitcoin (BTC). Various Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models are evaluated to determine the most effective approach for modeling the stylized facts commonly observed in financial time series data. While the Maximum Likelihood Estimation (MLE) method is widely employed for estimating GARCH model parameters, this study introduces a Bayesian framework, utilizing the Metropolis-Hastings algorithm to estimate parameters of the symmetric GARCH(1,1) model. Under this approach, model parameters are treated as random variables with known prior distributions. The analysis is based on 2,000 daily BTC observations from January 2018 to June 2023, obtained from Yahoo Finance. Model selection criteria, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Hannan–Quinn Criterion (HQC), identified the EGARCH(1,1) model under the Student-t and Generalized Error Distributions as the most suitable for capturing BTC volatility. Results further indicate the presence of volatility asymmetry and persistence, characteristic of cryptocurrency markets. In terms of predictive performance, the Bayesian GARCH(1,1) model under the Generalized Error Distribution and the EGARCH(1,1) model under the Student-t distribution exhibited the lowest values for RMSE, MAE, MAPE, and ME, confirming their suitability for future volatility forecasting in the cryptocurrency space.
Health Risk Assessment of Pesticide Residue in Millet, Maize, Sorghum and Rice Cultivated in Wukari, Nigeria Imbasire, Nuhu; Raphael, Odoh; Asabe, Magomya M.; Ogu, Odiba Emmanuel; Samaila, Danjuma; Tutuwa, Adamu Nashuka; Patience, Jonathan; Agbu, Tsoken Danji; Shingu, Jesse Polly
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 3 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i3.5942

Abstract

The widespread application of pesticides in agriculture has raised global concerns about the accumulation of chemical residues in staple food crops and their implications for human health. This study quantitatively assessed the health risks associated with pesticide residues in millet, sorghum, and rice cultivated in Wukari, Nigeria. Using a cross-sectional design, grain samples were systematically collected from local farms and analyzed through gas chromatography–mass spectrometry (GC-MS) to identify and quantify residual pesticides. Detected compounds included organophosphates, carbamates, and pyrethroids, with variable concentrations across crop types. Health risk assessments were conducted by calculating the Estimated Daily Intake (EDI), Hazard Quotient (HQ), and Health Risk Index (HRI) for each pesticide, and comparing these values with the Maximum Residue Limits (MRLs) established by international regulatory bodies such as the FAO/WHO Codex Alimentarius. The results indicated that multiple pesticide residues exceeded permissible thresholds, with HQ and HRI values surpassing 1 in several cases, suggesting potential non-carcinogenic health risks, especially for sensitive groups including children and pregnant women. These elevated values are likely attributable to the extensive and often unregulated use of pesticides during cultivation and post-harvest storage, leading to bioaccumulation in the grains. The findings underscore the urgent need for regulatory enforcement, public health surveillance, and farmer education to mitigate dietary exposure to hazardous residues in commonly consumed cereals.
Assessing the Role of Information and Communication Technology (ICT) as a Tool for Organizational Communication Abdu, Khalid Fatima; Idris, Zago Salisu; Binni, Muhammad Amina
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 4 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i4.5998

Abstract

This study examines the role of Information and Communication Technology (ICT) as a critical enabler of organizational communication, with a focus on its implementation within a commercial bank in Nigeria. ICT encompasses a wide range of technologies used for the processing and dissemination of information, and its integration has profoundly influenced communication practices, organizational structures, and managerial operations across various sectors. Within organizational contexts, ICT tools such as computers, telephones, email, databases, video conferencing, and search engines have significantly enhanced internal and external communication efficiency. This study aims to assess the extent to which ICT facilitates organizational communication and supports collaboration, information dissemination, and employee engagement. A structured questionnaire was randomly distributed among employees of the selected bank, and the collected data were analyzed using descriptive statistics, including mean scores and percentages, with the aid of SPSS software. The results indicate a positive perception of ICT’s role in enhancing communication processes, boosting employee morale, supporting collaboration, and contributing to the achievement of organizational goals. These findings underscore the strategic importance of ICT in modern organizational communication and highlight the need for continued investment in technological infrastructure to improve communication effectiveness and overall organizational performance.
Bacteriological Assessment of Locally Prepared Beverage Drinks Sold in Aliero and Jega, Kebbi State, Nigeria I., Muawuya; M., Shamsudeen M.; M., Gumi A.
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 4 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i4.6042

Abstract

Beverage drinks produced and sold by street vendors and small-scale producers are an essential source of nutrition and livelihood for millions in developing countries. However, their preparation and sale often occur under unhygienic conditions and without adequate regulatory oversight, increasing the risk of foodborne pathogen transmission. This study aimed to evaluate the bacteriological quality of three commonly consumed, locally prepared beverages—kunu, zobo, and soymilk, sold in Aliero and Jega towns in Kebbi State, Nigeria. A total of 30 samples (15 from each town; 5 per beverage type) were collected and analyzed for total viable count (TVC), total coliform count (TCC), pH levels, and the presence of bacterial pathogens using standard microbiological methods. All beverage types exhibited high microbial loads, with mean TVC ranging from 2.9 × 10⁵ to 6.5 × 10⁶ CFU/mL and mean TCC ranging from 1.3 × 10⁴ to 2.6 × 10⁵ CFU/mL, both exceeding WHO/FAO permissible limits for ready-to-drink beverages. Among the beverages, soymilk recorded the highest microbial loads, whereas zobo, with a more acidic pH (4.1–4.2), showed relatively lower counts. The identified bacterial species included Escherichia coli (31.1%), Staphylococcus aureus (26.7%), Salmonella spp. (17.8%), Shigella spp. (13.3%), and Pseudomonas aeruginosa (11.1%). These findings indicate significant microbial contamination, likely stemming from inadequate hygiene during processing, handling, and storage. The study underscores the critical need for enhanced sanitary practices, targeted public health education, regulatory enforcement, and routine microbial monitoring of street-vended beverages. Ensuring the microbiological safety of traditional drinks is imperative for safeguarding public health in low-resource settings.
Developing Biofilms with Pathogenic Bacteria Mohsen, Rana Talib
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 4 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i4.6049

Abstract

Microbial communities that develop on living and nonliving surfaces, such as dental tissues or artificial implants, often form complex, structured assemblies known as biofilms. These biofilms enhance microbial survival by providing protection against environmental stressors, including antimicrobial agents. The formation of biofilms contributes significantly to the antibiotic resistance observed in many bacterial populations. Bacillus cereus, a known foodborne pathogen, is capable of forming biofilms and producing toxins that cause gastrointestinal illnesses, including vomiting and diarrhea. Preventing the initial development of biofilms may be more effective than attempting to eliminate mature biofilms, which are notoriously difficult to eradicate. A range of strategies, such as chemical disinfectants, antibiotic therapies, and the application of nanoparticles has been explored to inhibit or disrupt biofilm formation. The significance of microbial biofilms spans various sectors, notably the food and pharmaceutical industries, where contamination and persistent infections pose major concerns. Growing recognition of the link between biofilms and chronic disease has intensified research interest, as bacteria residing in biofilms are often shielded from immune responses and conventional treatments. Current insights into biofilm-associated pathogenesis highlight multiple mechanisms through which biofilms contribute to disease development and persistence.
Effects of Smoking on Hematological Parameters among Baghdad City Mohammed, Wed Abbas
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 4 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i4.6072

Abstract

Cigarette smoking is widely recognized as a major risk factor in the development and progression of various chronic diseases, including atherosclerosis and chronic obstructive pulmonary disease. In Iraq, smoking remains prevalent across both rural and urban populations. It has been associated with significant alterations in inflammatory markers and hematological parameters. This study aimed to examine smoking habits and their effects on key blood parameters, including Total Leukocyte Count (TLC), Differential Leukocyte Count, Total Red Blood Cell Count (TRBC), Hemoglobin (Hb) concentration, and Packed Cell Volume (PCV). A total of 80 healthy adult males aged 20 to 56 years from Baghdad were enrolled, comprising 40 smokers and 40 non-smokers. Participant classification was based on a self-administered questionnaire. The results revealed that TLC, TRBC, Hb concentration, PCV, eosinophils, and lymphocytes were elevated in both light and heavy smokers compared to non-smokers, while neutrophil and monocyte levels were reduced. These findings suggest that cigarette smoking induces measurable changes in hematological profiles, which may contribute to its role in disease pathogenesis.
Virgin Coconut Oil Ameliorates Cognitive Impairment in Alzheimer’s-Like Rats Induced with Aluminium Chloride (AlCl₃) + D-Galactose (D-Gal) Mahdi, Onesimus; Benjamin, Simeon; Bobbo, Khadijat Abubakar; Agbon, Abel Nosoreme; Aliyu, Lawan Ibrahim; Mathias, Kuchahyells; Alhassan, Anathoth; Dilla, Deborah; Reseph, Raphael
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 4 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i4.6088

Abstract

Neurodegenerative diseases are a group of disorders marked by the progressive deterioration of neurons in the brain and spinal cord, with age-related cognitive dysfunctions, particularly in Alzheimer’s disease (AD) strongly associated with neurotransmission abnormalities. Aluminium (Al), the third most abundant metal in the Earth's crust, is recognized for its neurotoxic properties, while D-galactose (D-gal), a reducing sugar, induces cellular senescence through its interaction with amino acid residues in proteins. The combined administration of Al and D-gal has been established as a model for inducing neurotoxicity and studying AD mechanisms. Virgin Coconut Oil (VCO), a natural supplement rich in medium-chain triglycerides convertible to ketone bodies for cerebral energy metabolism, has demonstrated potential in promoting neurogenesis in aging models. This study investigates the neuroprotective effects of VCO in a rat model of cognitive dysfunction induced by Aluminium Chloride (AlCl₃) and D-gal. Thirty-five healthy male albino Wistar rats (150–200 g) were administered D-gal (60 mg/kg, intraperitoneally) and AlCl₃ (200 mg/kg, orally). Rats in treatment groups received VCO at doses of 1 and 3 ml/kg/day, while a positive control group was treated with donepezil (1 mg/kg) alongside AlCl₃ and D-gal. Cognitive performance was assessed using the Novel Object Recognition test; oxidative stress was evaluated by measuring hippocampal malondialdehyde (MDA) levels, and histological analysis of the CA1 region was conducted to assess neuronal integrity. Rats exposed to AlCl₃ and D-gal exhibited significant cognitive deficits, elevated MDA levels, and hippocampal neuronal loss (p < 0.05). VCO administration significantly attenuated these impairments by reducing oxidative stress and preserving hippocampal cytoarchitecture. These findings suggest that VCO possesses neurotherapeutic potential for mitigating AD-related cognitive impairments.

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