Asian Journal of Advanced Research and Reports
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Tuna Bone Waste (Thunnus sp.) as a Hydroxyapatite Source: Synthesis and Characterization for Biomaterial Applications
No full textTuna fish bone waste, a by-product of fish processing, exhibits promising potential for conversion into hydroxyapatite (HA), a high-value biomaterial with medical applications. This study successfully synthesized HA from tuna bone waste through a combined approach of calcination (900°C, 10 hours) and wet precipitation using (NH₄)₂HPO₄ at a Ca/P ratio of 1.67. The process achieved a conversion efficiency of 33.97%, producing HA with 81.7% phase purity along with a secondary whitlockite phase (18.3%) attributed to low pH conditions and Mg²⁺ ion presence. Comprehensive characterization revealed: (1) a hexagonal HA crystal structure with lattice parameters a = 9.424 Å and c = 6.879 Å (XRD analysis), and (2) spherical small particles (200-1000 nm) displaying agglomeration tendencies (SEM observation). The synthesized material demonstrates suitable characteristics for biomedical applications including bone grafts and tissue engineering scaffolds. This research presents an eco-friendly strategy for valorizing fishery waste while decreasing reliance on costly synthetic HA sources. Future studies should focus on optimizing the synthesis process to enhance both yield and purity
Advancing IoT Cybersecurity through AI and ML: A Comparative Study on Intrusion Detection and Privacy Protection
No full textThe wide use of Internet of Things (IoT) devices in residences and industries has brought unexpected ease, but concurrently, unprecedented new privacy attacks and cybersecurity threats. Classical security measures lag in tackling the dynamic and complex nature of IoT ecosystems due to limited resources and device variety. This study examines the use of Artificial Intelligence (AI) and Machine Learning (ML) methods to enhance the security posture of IoT ecosystems, specifically to counter data breaches and protect user privacy. Publicly available datasets, the TON_IoT and CICIDS2018 datasets, were used to benchmark the performance of several machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) networks. The models were trained and tested on classifying and labelling cyberattacks such as DoS attacks, reconnaissance, and data exfiltration attempts in IoT network traffic and telemetry logs. The findings indicate that CNN recorded the best detection accuracy (94.3% on TON_IoT and 96.2% on CICIDS2018) and performed better than traditional algorithms, whereas Random Forest recorded the best compromise between performance and computational cost and was thus appropriate for real-time use. The research affirms that intrusion detection in IoT networks can be dramatically enhanced through AI/ML methods and that model choice must be determined on the basis of deployment factors like available computational resources, as well as whether real-time processing is required
Assessment of Water Quality Parameters of Owala Dam
No full textWater quality monitoring is crucial for managing freshwater resources as it provides valuable data for local action planning and decision-making. This study assessed the water quality of Owala Dam, Osun State, Nigeria. Water samples were collected monthly from three stations along the Oba-Ile River (Owala Dam) in Olorunda between 2021 and 2024, and analyzed in the laboratory following standard protocols. In-situ measurements included water temperature (mercury-in-glass thermometer, 0–110°C, 0.1°C intervals), pH (Mueel meter), and electrical conductivity (Lovibond meter). Dissolved oxygen (DO) was measured using Winkler’s method, while biochemical oxygen demand (BOD) was calculated via the five-day BOD test. Total dissolved solids (TDS) were determined through filtration and evaporation, sulphate by the turbidimetric method, and chloride using the APHA titration method with potassium dichromate and AgNO₃. Phosphate levels were determined colorimetrically using a spectrophotometer after treatment with ammonium molybdate and stannous chloride. Results showed a slight, statistically insignificant rise in temperature (25.85°C in 2021/2022 to 26.39°C in 2023/2024). pH remained within the acceptable range (6.5–8.5) set by the Nigerian Standard for Drinking Water Quality (NSDWQ) and WHO. TDS remained constant at ~0.57 mg/L. BOD decreased significantly (p = 0.016), while DO levels were high (~66 mg/L), exceeding WHO\u27s minimum of 6 mg/L. Transparency declined significantly (p < 0.05). Electrical conductivity was stable (~0.34 ds/m), below the 1000 ds/m limit. Nitrate (~0.07 mg/L) and phosphate levels were consistently low, and chloride levels (~40 mg/L) were within safe limits. Except for DO, all parameters complied with WHO standards. Continuous monitoring is therefore recommended in this dam to ensure sustainable water resource management
A Review of Soil Erosion Risks in Somalia
No full textSoil erosion represents critical environmental and agricultural challenges in Somalia, where climate variability, recurrent droughts, and unsustainable land-use practices exacerbate vulnerability. This review synthesises findings from published and unpublished studies, reports, and geospatial assessments to evaluate rainfall erosivity, soil loss, and land degradation dynamics across different regions of Somalia. Data were collected from 2000 to 2024 and analyzed using established frameworks, including the Modified Fournier Index (MFI), Bagnouls–Gaussen Index (BGI), the Revised Universal Soil Loss Equation (RUSLE), and the CORINE methodology. The evidence indicates that high rainfall erosivity affects nearly 40% of Somalia, particularly in southern regions, where alternating drought and intense rainfall events increase erosion susceptibility. Soil loss estimations in Hirshabelle and Bay regions highlight moderate to high erosion risks, influenced by rainfall intensity, slope steepness, and land management practices. The Actual Soil Erosion Risk (ASER) assessment in Waqooyi Galbeed shows that 99% of the landscape faces moderate erosion risk, with localized hotspots of high vulnerability. Furthermore, widespread land degradation, driven by deforestation, overgrazing, and climate change, is diminishing soil fertility and undermining agricultural productivity, thereby worsening food insecurity. The findings underscore the urgent need for integrated soil and water conservation strategies, including water harvesting, vegetative cover restoration, and sustainable land management practices. By consolidating secondary data and modeling evidence, this review provides a comprehensive understanding of erosion dynamics in Somalia and offers decision support for policymakers, land managers, and development partners seeking to strengthen resilience and promote sustainable food systems
Levels of Progression of Reproductive Needs among Women in India: A Need for Evidence-based Intervention
No full textIntroduction: In order to promote gender equity and address the diverse needs of women in their reproductive years, it is essential to ensure access to economic opportunities, education and reproductive health care services. Sustainable Development Goals (SDGs) three and five focus on good health and well-being and gender equality respectively. Therefore, broadly, this paper focuses on the extent of progress in meeting the different needs of women in reproductive ages. Identification of states that are lagging in addressing these needs is essential for holistic well-being of the women in their reproductive years.
Methods: The paper is based on a quantitative and qualitative techniques in order to handle the large volume of collected data related to various dimensions of women. Microsoft Excel Spreadsheets and Statistical Package for the Social Sciences (SPSS) version 25, has been used for statistical and cartographic representation of the observation. Statistical techniques such as mean, standard deviation and \u27z\u27 has been applied to bring all the indicators to a comparable platform.
Results: The study finds that states in southern India perform better than most of the northern Indian states and most of the smaller states outperform larger states. It is probably because the former states have been implementing welfare policies more effectively than the latter ones.
Discussion: The low performing states may follow the best practices, programmes and policies of the states and UTs displaying better levels of progression. Therefore, in order to promote women\u27s well-being, empowerment, and the health of future generations, it is imperative that their reproductive needs be met. Addressing these needs is essential to ensure maternal and child care, and overall family and societal well-being. This in-turn leads to a balanced, progressive and more inclusive society
A Review on the Bonding Properties of the Old-New Concrete Interface
No full textThe bond performance at the interface between new and old concrete is critical for the repair and strengthening of concrete structures. This review systematically examines the core influencing factors and the resulting mechanical performance. The findings indicate that interface treatment through roughening methods is fundamental to ensuring bond quality, as it enhances mechanical interlock, often proving more effective than the sole use of bonding agents. Material modification, particularly the incorporation of ultra-high-performance concrete (UHPC), significantly improves the interfacial performance. The dense microstructure and high fiber content in UHPC effectively bridge micro-cracks, thereby substantially enhancing crack resistance, mitigating shrinkage, and increasing overall mechanical strength. Furthermore, the review underscores that optimal bond performance is achieved through the synergistic design of multiple factors, including surface roughness, bonding agents, fiber reinforcement, and curing regimes. Current research gaps are identified, particularly concerning the long-term durability under coupled environmental and mechanical loads, as well as the development of comprehensive theoretical models. Future research directions are briefly noted to address these challenges
A Literature Review about the Implementation of Artificial Intelligence (AI) Technology in the Hotel Industry in Indonesia
No full textIndonesia’s hospitality industry has experinced significant growth, attracting a growing number of both domestic and international tourist. This growth underscores an urgent need for service innovation, particularly through the adoption of new technologies in a competitive market. This study examines how Artificial Intelligence (AI) is being applied in Indonesia’s hotel industry, covering the various implementations of AI, the challenges encountered, and the impacts on hotel operations and guest satisfaction. This research uses a literature study approach, gathering and analyzing 20 scholarly sources from 2023 onward on this topic. The results indicate that AI technologies have started to be implemented in Indonesian hotels, for example through customer service chatbots and automation systems to improve operational efficiency. AI adoption helps to personalize guest services, streamlines processes, and enhances the overall guest experience. However, challenges remain, including limited investment capacity, the need for staff training, data privacy concerns, and resistance to change. These findings suggest that optimizing AI integration in Indonesia’s hospitality sector can bolster the industry’s competitiveness and service quality, provided that the identified challenges are properly addressed
The Role of Digital Twins in Construction Project Lifecycle Management: Enhancing Efficiency and Innovation in the United States
No full textDigital Twin (DT) technology use throughout the lifecycle of infrastructure projects is revolutionizing infrastructure planning, construction, and operation in the United States. The paper discusses systematically 138 publications (2016–2024) to analyse DT applications, advantages, and disadvantages in every phase of the lifecycle: design and planning, construction and execution, operation and maintenance, and decommissioning. The methodology is based on the PRISMA protocol and consists of scient metric analysis and thematic categorization. Adoption of DT is most developed in the design and maintenance stages, where it is combined with BIM, AI, and IoT to optimize efficiency, predictive analytics, and sustainability performance. Empirical research on large-scale U.S. projects like the Los Angeles Metro extension and Orlando Smart City indicates enhanced project visualization, cost management, and operational resilience. Nonetheless, lifecycle implementation at scale is constrained by data interoperability, cybersecurity, and standardization deficiencies. DTs are considered a driver of digital transformation for American construction, facilitating collaboration, minimizing rework, and advancing national sustainability goals under the Infrastructure Investment and Jobs Act. Results offer a strategic plan for leveraging DT technology to maximize project efficiency, lifecycle performance, and innovation in the U.S. built environment
Evaluation of Machine Learning Models for Predicting Flood Susceptibility Using Spatial and Socio-Environmental Attributes in Lagos, Nigeria
No full textFlooding is one of the major challenges to urban resilience in Lagos, Nigeria, which is one of the fast-growing coastal megacities globally experiencing high exposure to extreme rainfall and sea-level rise. In this paper, the performances of four machine learning classifiers, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Artificial Neural Network (ANN), are compared for flood extent mapping using multi-sensor and multi-temporal satellite data. Sentinel-1 SAR backscatter (VV, VH), Sentinel-2 NDVI, and elevation derived from SRTM were fused to create predictive features, whose ground-truth labels were derived from professional digitization of flooded and non-flooded regions that were validated using LASEMA flood reports (2022–2023). Preprocessing included speckle filtering, atmospheric correction, terrain correction, and co-registration to 10 m resolution. Models were trained and tested on spatial block 5-fold cross-validation to avoid spatial autocorrelation, and compared on accuracy, precision, recall, F1-score, and ROC-AUC, whose ROC curves were compared statistically using the DeLong test. Results indicate that ensemble models performed better than conventional classifiers. RF produced the highest recall (0.93) and ROC-AUC (0.972) and was therefore better at identifying flooded pixels, while XGBoost produced the highest precision (0.92), reducing false alarms. The two models performed better than SVM and ANN (accuracies < 0.90) on a consistent basis. Feature importance analysis indicated SAR backscatter as the strongest predictor, although NDVI and elevation were complementary. Spatial susceptibility mapping indicated that almost 50% of Lagos falls within high to very high flood-risk areas, specifically low-lying coasts like Lekki, Victoria Island, and Ajegunle.
This study proves that ensemble learning using combined multi-sensor satellite data offers a scalable and robust platform for detecting floods in intricate urban settings. The results support incorporating ML-based flood mapping into Lagos and other Sub-Saharan African cities\u27 cities\u27 disaster planning and urban planning policies
Pre-service Mathematics Teachers\u27 Cognition of “Senior High School Students Can Understand Knowledge but cannot Solve Problems”
No full textIn senior high school mathematics teaching, many students can understand the knowledge but cannot solve the problems. Existing studies mainly focus on front-line teaching, but no one has explored the cognition of pre-service mathematics teachers to this phenomenon. Whether pre-service mathematics teachers have clear cognition of this phenomenon will greatly affect the quality of mathematics teaching in senior high school in the future. As for the selection of sampling techniques, random sampling was used in this paper, and a total of 30 graduate students and undergraduates from the School of Mathematics and Statistics of Shandong Normal University who want to be senior high school mathematics teachers in the future are selected as the survey objects. As for the selection of investigation method, interview method was used to investigate their understanding of this phenomenon in this paper. The results are as follows: In terms of causes, 93. 33% pre-service mathematics teachers believed that students\u27 grasp of knowledge is not enough, 56. 67% believed that students\u27 thinking is narrow and limited, etc; In terms of countermeasures, 56. 67% believed that students\u27 ability to use knowledge is not enough, and 30% believed that teachers should pay attention to the inspiration of students\u27 ideas and methods, etc. The conclusions of this paper are as follows: (1) For the phenomenon that senior high school students can understand the knowledge but cannot solve the problem, most pre-service mathematics teachers’ cognition have shortcomings in both rationality and comprehensiveness. (2) It is necessary to train pre-service mathematics teachers