Sri Lankan Journal of Applied Sciences

Geochemical Analysis and Beryllium-Dating Studies of Marine Core Sediment Belong to the Central Western Bay of Bengal

R. Swetha, P.V.L. Narayana, P. Kumar, R. Sharma, S. Chopra, A.D.P. Rao — Sun, 31 Aug 2025 00:00:00 +0530

A gravity core of 4.12cm was collected from the marginal coastal waters of central western Bay of Bengal, covering the continental shelf off Krishna Delta (Nizampatnam Bay) during the 157 cruises of O. R.V. Sagar Kanya. It was subsampled with every 2cm interval, resulting in 212 samples. Half of each sample was dried at 60 ⁰C, ground and sieved through a 230 mesh size and studied for organic carbon (OC), calcium and magnesium carbonates (CaCo₃& MgCo₃) by titration methods. By following the 10Be/⁹Be dating study method, investigations about the rate of sedimentation was also carried out, and all the results were interpreted based on the existing phenomenon.

Keywords: Marine sediment, calcium and magnesium carbonates, Geochemical, Dating, Organic Carbon

Impacts of Different Fertilizer Application Approaches on Selected Soil Properties and Rice Yield in the Dry Zone of Sri Lanka

M.H.V.H.Y. Gunarathne, D M S Duminda — Sun, 31 Aug 2025 00:00:00 +0530

Abstract

There is a growing concern to apply sustainable agricultural practices to optimize crop productivity. Hence, this study examined the effects of various fertilization strategies on soil fertility and rice yield. The study was consisted with three different treatments: T1-conventional fertilizer input management system (IMS); Department of Agriculture inorganic fertilizer recommendation (DOA), 2013, T2- reduced IMS; 50% DOA+ organic manure, and T3- organic IMS; organic manure only. The study was conducted during the 2020/2021 Maha season, which marked the fifth season of the long-term cropping system research project at the farm premises of the Faculty of Agriculture, Rajarata University of Sri Lanka. A randomized complete block design was used with three replicates for the field experiment. Statistical analysis was carried out using the mixed procedure in SAS version 9.0. Mean comparisons were performed using Tukey's method, with a significance level at p≤0.05. Soil samples were collected from the surface (0-15cm) and subsurface (15-30 cm) soil depths during three growth stages of paddy: just after land preparation (initial stage), at the 50% flowering stage, and just after harvesting. Soil Total Nitrogen (STN), Soil Organic Matter content (SOM) and Soil Microbial Biomass Carbon (SMBC) were analyzed and paddy yield was measured. STN levels did not significantly differ among the three IMSs (p≥0.05). SOM content in the organic IMS showed stability across all growth stages. The three-way interaction of the IMS, plant growth stage, and soil depth showed a significant effect on SMBC content (p≤0.05). Rice grain yields under organic IMS were notably similar compared to yields obtained with conventional IMS. The highest grain yield was significantly higher with reduced IMS (p<0.05). This indicates the potential to increase yields and sustain soil fertility by replacing 50% of synthetic inorganic fertilizers (SIF) with organic manure. Further inquiry is required for definitive results.

Keywords: Input management systems, Rice Yield, Soil Carbon, Soil microbial biomass, Soil Nitrogen

A Review on Medicinal Plants Used in Certain Skin Diseases in Sri Lanka

H.M.S.K.H. Bandara, H.B.C. Harshini — Sun, 31 Aug 2025 00:00:00 +0530

This article aims to review the therapeutic potential, chemistry, and botanical aspects of certain medicinal plants used in indigenous medicine in Sri Lanka for treating skin diseases. Medicinal plants have been utilized for generations across various cultures, including Sri Lanka, reflecting a rich body of empirical knowledge regarding their efficacy and safety. Examining these traditional practices can provide valuable insights into potential new therapeutic agents and treatment approaches for skin diseases. This review article focuses on explaining the traditional medicinal usage of 15 medicinal plants, Curcuma longa L., Azadarachta indica (A.Juss), Coscinium fenestratum (Gaertn.) Colebr., Hemidesmus indicus (R.Br.), Ricinus communis L., Mimosa pudica L., Moringa oleifera Lam., Ixora coccina L., Atalantia ceylanica (Arn.) Oliv., Murraya koenigii L., Aloe vera L. Burm., Cassia fistula L., Carica papaya L., Cocos nucifera L., and Elaeocarpus serratus L. The review article is based on a literature review of selected studies published between 1990 to 2024. The databases used included PubMed and Google Scholar. The keywords used in the search included “medicinal plants [name of the plant] + skin diseases + Srilanka”, “[name of the plant] + phytochemicals”, “[name of the plant] +clinical studies +skin diseases” and “[name of the plant] + botany”.

Key words: Indigenous medicine, Medicinal plants, Phytochemicals, Skin diseases, Sri Lanka

Development of Fault Detection and Diagnosis model for Drilling Machines

A.T.C. Dhivya , R.M.T.C.B. Ekanayake, D.D.B. Senanayake — Sun, 31 Aug 2025 00:00:00 +0530

Drilling machines are essential in industrial applications as they are used to drill materials such as metal, plastic, and concrete and are now being incorporated into smart industries Such machinery needs to be maintained properly given that they are known to wear out very quickly. In its previous form, as a manual process, monitoring has served its purpose significantly. Nowadays, it is replaced by automated systems that utilize achievements in signal processing and machine learning. This work proposes fault detection for drilling machines through sound signals and Fine K Nearest Neighbour (Fine KNN). Fine KNN was selected due to its moderate accuracy and computational efficiency compared to other classifiers in real-time despite a slightly lower accuracy than Quadratic SVM or bagged trees. The dataset employed is obtained from a GitHub repository and contains sound signals under various fault scenarios: healthy, bearing, gear, and fan. In feature extraction, there is a total of 16 time-domain and frequency-domain features that are extracted from the chosen signal and then narrowed down to 14 by using the RelieF algorithm to improve the model. The Fine KNN model maintains an efficiency of operation while detecting faults at a rate of 94.7% which is indicative of the model’s accuracy. For this reason, feature selection and preprocessing serve a critical role to enhance the model performance and suitability for real-time applications as affirmed by this research. Thus, this research opens up the possibility for integrating more complex models for machine condition monitoring at the edge devices. The future work will focus on obtaining more sophisticated classifiers and better preprocessing for improving the fault detection performances in compact and power efficient platforms suitable for the industrial IoT environment.

Keywords: Fault detection, Fine KNN, Sound Signals, Monitoring, Accuracy

Optical Nonlinearity and Enhanced Second Harmonic Generation in Copper–doped Cadmium Iodide Nanocrystals

M. Idrish Miah — Sun, 31 Aug 2025 00:00:00 +0530

Cadmium iodide nanocrystals are grown and doping defect and quantum-confinement effects in optical susceptibility in the nanocrystals are investigated. The nanomaterials with various crystal sizes are pumped with intense laser beams. This pumpprobe experiment probes the doubled-frequency second harmonic generation (SHG). The second–order optical susceptibility is calculated from the experimentally measured SHG intensity. The results show that a significant enhancement in the optical susceptibility is achieved in nanomaterials with moderate doping. However, bulk and intrinsic crystals show no considerable SHG effect. The results were discussed within models of the impurity induced metallic-aggregators and photoinduced electronphonon anharmonic interactions.

Keywords: Nanomaterials; Nano-confined effect; Doping effect; Optical susceptibility

AI in Wearable Embedded Systems for Healthcare Monitoring: A Review

R.M.D.D. Rathnayake, A.M.P.R.B. Arawa, R.M.T.C.B. Ekanayake — Sun, 31 Aug 2025 00:00:00 +0530

Abstract
With an eye towards real-time health monitoring and early diagnosis, this review investigates how Artificial Intelligence (AI), Embedded Systems, and the Internet of Things (IoT) could be used in wearable healthcare technology. This study examines current developments in low-power embedded systems, edge artificial intelligence computing, sensor technologies, and IoT connection that all help to provide intelligent, energy-efficient wearable devices. Results show that although embedded microcontrollers offer continuous monitoring with low energy consumption, artificial intelligence-driven analytics increase diagnosis accuracy and enable predictive healthcare. IoT integration enables flawless data transfer for remote patient care, therefore supporting more responsive and easily available healthcare service. Important issues such data security, power constraints, ethical questions, and openness in AI decision-making still exist despite these developments. Emerging technologies such Explainable AI (XAI), federated learning, blockchain-based security, and self-powered wearables as hopeful paths for addressing these constraints are highlighted in this study. The last point underlines how important it is to combine IoT, embedded systems, and artificial intelligence in wearable technologies to turn reactive medical practices into preventive healthcare approaches. Future studies should concentrate on developing trust, increasing openness, and boosting energy efficiency in AI-driven healthcare wearables if we are to guarantee effective deployment and general acceptance.

Keywords: Wearable Healthcare Technology, Embedded Systems, Artificial Intelligence, Remote patient monitoring, Real time Data Transmission, Internet of Things