Journal of the Sri Lanka Association for the Advancement of Science - Vol. 7 Issue 01 (2025)

ISSN 1391-0256 Journal of the Sri Lanka Association for the Advancement of Science Volume 7 Issue 1, 2025 Founded in 1944 and incorporated by the Act of Parliament No 11 of 1966. JSLAAS 1 Journal of the Sri Lanka Association for the Advancement of Science is a biannual publication. Selected research work from annual research sessions (based on scientific merit) as well as other research articles are invited to submit research manuscripts as per the guidelines provided by SLAAS. SLAAS members may also separately submit their papers for publication. The Journal can be accessed on-line to view and download the full text of the articles published respective to the volumes free of charge Submission Manuscript Only online submission, Web: https://journal.slaas.lk, e-ISSN: 2682-6992 Members of the Editorial Board Editor in Chief Prof. K P S Chandana Jayaratne Department of Physics University of Colombo, Sri Lanka. email: chandanajayarat[email protected] CoEditor Dr. R Chinthaka L De Silva Materials Technology Section Industrial Technology Institute Sri Lanka. 363, Bauddhaloka Mw, Colombo, Sri Lanka. email: [email protected] General President SLAAS 2025 Prof. Udeni P. Nawagamuwa Department of Civil Engineering University of Moratuwa Katubedda 10400,Moratuwa email: [email protected] Exchanges: Please address all requests to the Secretary, Sri Lanka Association for the Advancement of Science, ”Vidya Mandiraya” 120/10, Wijerama Mawatha Colombo 07, Sri Lanka Managing Editor Dr. Lochandaka Ranathunga Department of Information Technology Faculty of Information Technology University of Moratuwa, Sri Lanka email: [email protected] Sectional Representatives Dr. Jeevani Dahanayake Dr. Rochana Weerasinghe Dr Ruminda Wimalasiri Mr Prabhath Dharmasena Dr. Monika Madhavi Prof. Hemamala Karunadasa Dr. Thilina Thanthiriwatte Dr Lakmini Gamage Editorial Board Prof. Mahesh Jayaweera Prof. B C Liyanage Athapattu Prof. Chandana Abeysinghe Prof. G M K B Gunaherath Prof. S Vasanthapriyan Prof. Prasanthi Gunawardene Dr. K M G P Yahampath Dr. Thanuja Paragoda Dr. Jasotha Prabagar International Editorial Advisory Board Prof. Don Nalin Nilusha Wijayawardene Dr. Udara Abeysekara Prof. Hemamala Karunadasa Dr. M Wasim Siddiqui ISSN 1391-0256 Copyright © 2023 by the Sri Lanka Association for the Advancement of Science, Sri Lanka. All rights reserve. e-ISSN: 2682-6992 2 Table of Contents Page 1 Molecular docking of potential antifungal compounds from Ulva fasciatamethanolic extract gainst Pseudopestalotiopsistheae A. H. D. Alahakoon, B.K. D. M. Rodrigo, B.M. Chathuranga, M. Balasooriya, H. M. Herath, R. P. Wanigatunge 03 2 Dosimetric Impact on IMRT Plans of Altering Per Control Point Statistical Uncertainty in Monaco TPS K. L. I. Gunawardhana, J. Jeyasugiththan, P. De Silva and D. Satharasinghe 16 3 Development of a Solar - Powered, Automated Water Ionizer Using Graphite-Based Electrodes for Alkaline and Acidic Water Production A.M.K.L Abeykoon, M.D.Y Milani, H.M. B. I. Gunathilaka ,R. C. W. Arachchige, D.M.K Muthumala 40 3 Molecular docking of potential antifungal compounds from Ulva fasciatamethanolic extract against Pseudopestalotiopsistheae A. H. D. Alahakoon1, B.K. D. M. Rodrigo1, B.M. Chathuranga M. Balasooriya2, H. M. Herath1, R. P. Wanigatunge1* 1Department of Plant and Molecular Biology, Faculty of Science, University of Kelaniya, Sri Lanka 2School of Science, Mae Fah Luang University, Thailand. ABSTRACT Plant diseases caused by fungal pathogens significantly threaten global food security, accounting for nearly 40% of annual crop losses and incurring over US$220 billion in management costs worldwide. Among these, Pseudopestalotiopsistheae has emerged as notable phytopathogen in Sri Lanka, causing chlorosis in Solanum melongena. Its virulence is largely attributed to the secretion of pectinase enzymes, which degrade plant cell walls and facilitate host colonization. Excessive use of synthetic fungicides to manage such pathogens has led to environmental degradation, health risks, and the emergence of fungicide-resistant strains. Consequently, there is a growing interest in eco-friendly alternatives such as natural products derived from marine organisms. Marine macroalgae, particularly Ulva fasciata, commonly found in Thalpe reef, are known to produce a wide range of bioactive secondary metabolites with antifungal potential. In a previous study, methanolic extract of U. fasciata revealed numerous bioactive compounds with potential antifungal activity. The present studyaimed to evaluate the inhibitory potential of these compounds against the pectinase enzyme of P. theae using molecular docking, a powerful in silico approach for predicting interactions between small molecules and target proteins. The findings are expected to contribute to the development of sustainable, ecofriendly strategies for managing plant diseases, offering a cost-effective alternative to synthetic fungicides.This study highlights the potential of marine bioresources and computational tools in the discovery of novel antifungal agents targeting emerging phytopathogens. Key words-Antifungal compounds, Ulva fasciata, Molecular docking, Pseudopestalotiopsistheae INTRODUCTION Approximately 40% of global crop production is lost each year due to attacks by pests and pathogens, including numerous bacterial and fungal species. To combat these plant diseases, more than US$ 220 billion is spent annually worldwide (FAO, 2022). Among emerging fungal pathogens, Pseudopestalotiopsistheaeishas been identified as a significant threat in Sri Lanka, causing chlorosis in Solanum melongena (Koshila et al., 2023). Its virulence is primarily attributed to secretion of extracellular pectinase enzymes which degrade plant cell walls and facilitate host colonization(Sopalun&Iamtham, 2020). Pectinases are a group of enzymes that hydrolyze glycosidic linkages in pectic polymers and are functionally categorized into polygalacturonases, pectin esterases, pectin lyases and pectate lyase (Aryaet al., 2022). Pseudopestalotiopsis, Neopestalotiopsis, and Pestalotiopsis are closely related genera within the family Amphisphaeriaceaeand are known to cause various plant diseases, including cankers, shoot dieback, leaf spots, blights, severe chlorosis, and fruit A. H. D. Alahakoon et al., JSLAAS,Vol. 7, Issue 1 (2025) 03-15 4 rot(Maharachchikumbura, 2014;Sane et al., 2019). Although chemical fungicides are widely used to control fungal infections,their excessive usage leads to serious environmental consequences, including contamination of aquatic ecosystems, residue accumulation in crops, and the emergence of resistant fungal strains. Moreover, fungicides pose risks to non-target organisms and human health(Goswami et al., 2018). Biocontrol has been explored as a natural and sustainable alternative to chemical fungicides for managing various fungal infections in agriculture (Bubiciet al., 2019). It involves mechanisms such as competition for space and nutrients, production of antifungal compounds and secondary metabolites (Rashad & Moussa, 2020), and the biological triggering of plant resistance (Hermosa et al., 2013). Plants, animals, and marine organisms are sources of natural products with inherent fungicidal activity (Dong et al., 2020). Marine macroalgae (seaweeds) are multicellular, eukaryotic and photosynthetic organisms known to be rich in bioactive compounds (Makkar et al., 2016).Ulva fasciata, a common macroalgae in Thalpe reef of Sri Lanka, showed potent antifungal activity against P. theaein a previous study through its methanolic extract (Rodrigo et al., 2025).Gas Chromatography-Mass Spectrometry (GC-MS) analysis of the extract revealed several potential antifungal compounds, includingPhenylephrine, Palmitic acid, 17-Octadecenal, 4-Hydroxy-2-butanone, Heptadecene and 3Methoxyamphetamine. However, the specific mechanism by which these compounds inhibit the fungal activityremain unclear. Molecular docking has become a valuable computational technique for exploring the therapeutic potential of natural products. This method simulates the interactions between bioactive compounds and target proteins, predicting binding affinity and interaction modes. By virtually testing thousands of molecules, molecular docking enables the identification of promising compounds efficiently, and costeffectively, significantly reducing the need for extensive laboratory screening (Agu et al., 2023). In antifungal research, docking is particularly useful for identifying inhibitors of fungal enzymes or proteins that contribute to pathogenicity. It provides insights into how candidate molecules interact with target sites at the atomic level, assessing the strength and stability of these interactions (Hendra et al., 2024). Hence, the present study aimed to employ molecular docking techniques to investigate the binding interactions between the most potent bioactive compounds from the methanolic extract of U. fasciataand the extracellular enzymes of P. theae, with the objective of inhibiting their enzymatic activity. Though the fungus P. theae secretes pectinase as an extracellular enzyme to maintain its pathogenicity, the amino acid sequences or 3D structures of pectinase enzymes from P. theaeare not currently available in databases. Therefore, the polygalacturonase sequence from Pestalotiopsis sp. NC0098 (KAI0138346.1) was used to construct a homology model for subsequent analysis as it is the only available related amino acid sequence in the databases. METHODOLOGY Homology modeling of polygalacturonase enzyme of Pestalotiopsis sp. Polygalacturonase enzyme of Pestalotiopsis sp. NC0098 (KAI0138346.1) was used for generating the homology model as amino acid sequences or 3D structures of pectinases of the fungus P. theaewere not available inthe NCBI GenBank protein database. Polygalacturonaseamino acid sequence was searched against the Protein Data Bank (PDB) using the NCBI Protein BLAST tool to identify suitable homologous templates. Four template structures with A. H. D. Alahakoon et al., JSLAAS,Vol. 7, Issue 1 (2025) 03-15 5 sequence identities ranging from 54.87% to 55.46% were retrieved. Multiple sequence alignment was performed using the CLUSTALW online tool and homology modeling was carried out using MODELLER software (version 10.1).From the generated models, the one with the lowest DOPE (Discrete Optimized Protein Energy) score was selected for further analysis, as lower DOPE scores indicate higher model reliability (Selvam et al., 2017). The selected model was further refined in MODELLER, and energy minimization was performed using the GROMOS simulation package within Swiss-PdbViewer. Model validation was conducted using several structure assessment tools: PROCHECK, Verify3D, and ERRAT to assess stereochemical quality and 3D structure compatibility. Additionally, PROSA was used to calculate the Z-score, and the QMEAN score was evaluated using its corresponding web server to assess the overall quality and stability of the predicted structure (Selvam et al., 2017). Active compound identification in the U. fasciata– methanolicextract Potential antifungal compounds present in U. fasciata-methanolic extract were identified by GC-MS analysis as described by Kamal et al. (2011) in our previous study (Rodrigo et al., 2025). Molecular docking Molecular docking analysis was carried out using the AutoDock Vina software (Version 1.1.2). The homology-modeled polygalacturonase protein served as the receptor, and the receptor was prepared using Auto Dock Tools software (Version 1.5.7). The molecule was checked for adding polar H molecules and missing amino acid residues. Kollman charges were added to the molecule by equally distributing the charge across the protein surface (Phosrithong & Ungwitayatorn, 2010). Ligand structures were based on the chemical compounds previously identified through GC-MS analysis (Rodrigo et al., 2025). Structures of the selected chemical molecules were obtained from the PubChem database, and energy was minimized using AVOGADRO software (Version 1.2.0). The minimized structures were then converted into a Protein Data Bank file format (pdb) using Open Babel software (Version 3.1.1). Potential ligand-binding pockets on the receptor were identified using the DoGSiteScorertool of the ProteinsPlus server. The binding pocket with the highest drug score value was selected for docking the ligands (Selvam et al., 2017). Nine independent docking runs were carried out for each ligand and the best binding mode with the lowest (most negative) binding free energy was selected as the best conformation (Phosrithong & Ungwitayatorn, 2010). RESULTS AND DISCUSSION Homology modeling of the Polygalacturonase enzyme The extracellular enzymes are the pathogenicity determinant factors in many plant pathogens as they facilitate host invasion by degrading plant cell wall components. Enzymes are proteins that catalyze chemical reactions in living organisms, and their activity can be inhibited by certain bioactive compounds. Marine algae are known to produce diverse secondary metabolites capable of interferingwith such enzymes present in the plant pathogenic fungi and lead to the inhibition of their activity (Agu et al., 2023). In this study, a homology model of the polygalacturonase enzyme was generated with 4 similar crystal structures available in the protein data bank using the MODELLER software (Figure 1). Then the loops of the structures were refined, and energy was minimized. The best model was evaluated using online servers of PROCHECK, Verfiy3D, ERRAT, PROSA, and QMEAN. A. H. D. Alahakoon et al., JSLAAS,Vol. 7, Issue 1 (2025) 03-15 6 Figure 1. Homology model of polygalacturonase enzyme of Pestalotiopsis sp. (a) cartoon diagram (b) surface view diagram of the energy-minimized protein model Each tool assesses different aspects of protein structure quality. PROCHECK evaluates the stereochemical quality of a protein structure including parameters like bond lengths, bond angles and planarity using Ramachandran plot analysis (Figure 2) (Wlodawer, 2017). A high percentage of residues in the most favored regions is indicative of a well-refined model. Values above 90% are considered excellent, while those exceeding 80% are generally acceptable. In this study, PROCHECK analysis revealed that 84.4% of residues (Table 1) were located in the most favoured regions of the Ramachandran plot, which falls within the acceptable range and is comparable to previous models developed for Aspergillus nigerenzymes (Gundampatiet al., 2012).This suggests that the overall stereochemical quality of the model is acceptable. Furthermore, no residues were observed in the generally allowed or disallowed regions (0.0%), proving the reliability of the model. The additional allowed regions (%) ideally range between 1–15%, and this model exhibited 15.6% (Table 1), which, although at the upper threshold, still falls within the acceptable range. This value is slightly higher than the percentage reported in the additionally allowed regions for A. niger (Gundampatiet al., 2012). However, the overall results support the structural validity of the predicted model. A. H. D. Alahakoon et al., JSLAAS,Vol. 7, Issue 1 (2025) 03-15 7 Figure 2. Ramachandran plot of the model Table 1. Model evaluation results of PROCHECK, Verfiy3D, and ERRAT Program PROCHECK Verfiy3D ERRAT Most favored regions Additional allowed regions Generally allowed regions Disallowed regions 3D-ID Score Quality Factor Value 84.4% 15.6% 0.0% 0.0% 81.07% 76.03% Table 2. Model evaluation results of QMEAN and PROSA Program QMEAN PROSA QMEAN4 Value Z-Score Value -0.46 -6.49 Verfiy3D assesses the compatibility of the 3D model with its own amino acid sequence by assigning a 3D environment score to each residue and compares it with known preferences based on experimentally determined structures (Eisenberg et al., 1997).A model is generally considered reliable if a 3D-1D scoreis more than 80%.In this study, Verfy3D analysis showed 81.07% of the residues had an acceptable 3D-1D score (Table 1), indicating that the residue environments are biochemically plausible and structurally consistent. ERRAT analyzes non-bonded atomic interactions to identify statistical deviations by comparing the input protein structure to high-resolution crystallographic data, and it computes an overall error function that reflects the model’s reliability. A quality factor above 90% is indicative of an excellent model, while values between 70% and 90% are generally considered acceptable. In this study, the model achieved 76.03% quality factor,suggesting that the non-bonded interactions are largely consistent with those found in experimentally validated structures. Although this value is slightly lower A. H. D. Alahakoon et al., JSLAAS,Vol. 7, Issue 1 (2025) 03-15 8 than the 83.97% reported for Trichoderma longibrachiatum (Tamboliet al., 2017), it remains within the acceptable range for functional docking studies, thereby supporting the structural plausibility of the model. QMEAN (Qualitative Model Energy Analysis) is another important tool used to assess the quality of predicted protein structures. It is a composite scoring function that evaluates local geometry (torsion angles, solvation, hydrogen bonding), long-range interactions and agreement with high-resolution structures (Benkert et al., 2008). The QMEAN score typically ranges from 0 to –4, with values closer to 0 indicating a high-quality model. In this study, the QMEAN score was -0.46 (Table 2), which is close to 0 and comparable to the QMEAM values reported for Aspergillus ficuum, where scores were -3 or higher (Chikkeruret al., 2018).This suggests that the modeled structure is of good quality and comparable to experimentally determined protein structures. PROSA provides a Z-score that indicated the energy separation of the native and average of the misfolds in the units of standard deviation (Heydari-Zarnaghet al., 2015). If z-score falls –4 to –10 typically indicates that global structure resembles real proteins. In our study, the PROSA Z-score was – 6.49 (Table 2), which falls well within this acceptable range, suggesting that the modeled structure is realistic and reliable. This is comparable to the Z-score reported in the PROSA analysis for Trichoderma longibrachiatumwhich had a Z-score of -6.78 (Tamboliet al., 2017). Figure 3. ProSA Z-score plot of the model. The value of Z-score is highlighted as a black dot and is in the range of native conformations This multi-angle validation is essential to build trust in the accuracy of a predicted or experimentally determined protein model before using it in downstream applications like molecular docking, drug design, or structural biology research. The scores received for these tests indicate that the model generated was of good quality, and it has higher reliability (Selvam et al., 2017). Biologically active compounds in the U. fasciata– methanolic extract Nine different chemical compounds were identified in our previous study by Rodrigo et al. (2025) using GC-MS analysis (Table 3). Various aromatic and non-aromatic compounds were found in different abundances (Figure 3). The most abundant compounds in the extract were 4-hydroxy-2-butanone (30.75%) followed by hydroxylamine/methylamine (37.37%). A. H. D. Alahakoon et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 03 - 1 5 15 [24] Rashad, Y.M. & Moussa, T.A.A. (2020). Biocontrol agents for fungal plant diseases management. In: El-Wakeil, N., Saleh, M., Abuhashim, M. (Eds.), Cottage Industry of Biocontrol Agents and Their Applications (pp. 101–122). Springer, Cham, Switzerland. [25] Rodrigo, B.K. D. M., Alahakoon, A. H. D., Balasooriya, B.M.C. M., Edirisinghe, P., Herath, H. M., &Wanigatunge, R. P. (2025). Antifungal activity of extracts from Ulva, Sargassum, and Gracilaria against three fungal pathogens and GC-MS analysis of the most effective extracts. International Journal of Secondary Metabolite, 12(2), 331-342. https://doi.org/10.21448/ijsm.1506431 [26] Sane, S., Sharma, S., Konduri, R. & Fernandes, M. (2019). Emerging corneal pathogens: First report of: Pseudopestalotiopsistheae: keratitis. Indian Journal of Ophthalmology, 67(1), 150-152. http://dx.doi.org/10.4103/ijo.IJO_791_18 [27] Selvam, K., Senbagam, D., Selvankumar, T., Sudhakar, C., Kamala-Kannan, S., Senthilkumar, B. &Govarthanan, M. (2017). Cellulase enzyme: homology modeling, binding site identification and molecular docking. Journal of Molecular Structure, 1150, 61-67. https://doi.org/10.1016/j.molstruc.2017.08.067 [28] Shobier, A.H., Ghani, S.A.A. & Barakat, K.M. (2016). GC/MS spectroscopic approach and antifungal potential of bioactive extracts produced by marine macroalgae. The Egyptian Journal of Aquatic Research, 42(3), 289-299. https://doi.org/10.1016/j.ejar.2016.07.003 [29] Sopalun, K. &Iamtham, S. (2020). Isolation and screening of extracellular enzymatic activity of endophytic fungi isolated from Thai orchids. South African Journal of Botany, 134, 273-279. https://doi.org/10.1016/j.sajb.2020.02.005 [30] Tamboli, A. S., Waghmare, P. R., Khandare, R. V., &Govindwar, S. P. (2017). Comparative analyses of enzymatic activity, structural study and docking of fungal cellulases. Gene Reports, 9, 54-60. 10.1016/j.genrep.2017.08.008 [31] Wlodawer, A. (2017). Stereochemistry and Validation of Macromolecular Structures. Methods in Molecular Biology, 1607, 595-610. https://doi.org/10.1007/978-1-4939-7000-1_24 A. H. D. Alahakoon et al., JSLAAS,Vol. 7, Issue 1 (2025) 03-15 16 Dosimetric Impact on IMRT Plans of Altering Per Control Point Statistical Uncertainty in Monaco TPS K. L. I. Gunawardhana∗1, J. Jeyasugiththan2, P. De Silva1 and D. Satharasinghe2 1Department of Radiotherapy, National Hospital Galle, Sri Lanka 2Department of Nuclear Science, University of Colombo, Colombo, 00300, Sri Lanka ABSTRACT Intensity-Modulated Radiation Therapy (IMRT) uses computer-controlled linear accelerators to deliver precise radiation doses to benign or malignant tumors or specific areas within tumors while minimizing exposure to healthy tissues and organs at risk. The purpose of this study is to evaluate the dosimetric impact on IMRT plans of changing the per control point Statistical Uncertainty (SU) from 1% to 6% in 1% increments using the Monaco Treatment Planning System (TPS) for three different diagnoses: Larynx, Oesophagus, and Prostate. The per control point SU is a key factor in determining dose calculation accuracy and calculation time. In this study, 54 IMRT plans were generated by varying the per control point SU as 1%, 2%, 3%, 4%, 5%, and 6%, using nine patients for each diagnosis. Dosimetric indices, including Conformity Index, Heterogeneity Index, Target Dose (PTV), Organ At Risk doses, Dose Calculation Time, Treatment Delivery results, and Dose Volume Histogram, were used to evaluate the generated plans. No significant differences were observed across all dosimetric indices, and an exponential relationship was found between Dose Calculation Time and the per control point SU. For IMRT plans, a 3% per Control Point SU is acceptable, providing shorter and adequately accurate dose calculation times without compromising plan quality or deliverability. Keywords: Radiotherapy, IMRT, dosimetric impact, Per Control Point, Statistical Uncertainty * [email protected] K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 17 INTRODUCTION Radiotherapy is a medical treatment that uses high doses of radiation (gamma rays, highenergy X-rays, and Electrons) to kill or damage benign and malignant tumors (Hall & Giaccia, 2019) . It is a crucial component of cancer treatment and is employed either as radiotherapy itself or in combination with surgery, chemotherapy, or immunotherapy (Delaney, Jacob, Featherstone, & Barton, 2005). Radiotherapy can be given inside or outside of our bodies. The most common kind is External Beam Radiation Therapy (EBRT). It uses a large machine called a Linear Accelerator (Linac) to treat cancer patients using high-energy X-rays and electrons. At present, there are other advanced types of machines used for radiotherapy treatment too. Such as Tomotherapy machine, Cyberknife machine, MR-Lianc, PET-Linac, ProBeam machine, GammaKnife machine, and ZAP-X machine (Palta & Mackie, 2011). There are ordinary and advanced treatment techniques currently used in the oncology field using those advanced machines. Such as three-dimensional conformal Radiotherapy (3DCRT), IntensityModulated Radiation Therapy (IMRT), Image-Guided Radiation Therapy (IGRT), Volumetric Modulated Arc Therapy (VMAT), Stereotactic Body Radiation Therapy (SBRT), Proton Therapy, and Adaptive Radiation Therapy (ART) (Khan & Gibbons, 2014). Apart from those machines, the cobalt 60 machine is used to treat cancer patients using Gamma-rays, and it is an older version of a raditherapy treatment machine (Podgorsak, 2005) . Intensity-Modulated Radiation Therapy, also called IMRT, is an advanced type of radiation therapy technique, and it is an inverse planning technique (Bortfeld, 2006) . Inverse planning is a technique that uses a computer program to automatically achieve a treatment plan that has an optimal merit. Here, it is less dependent on the geometric parameters but more on the specification of volumes of tumor targets and organs at risk, as well as their dose constraints. IMRT uses computer-controlled linear accelerators to deliver precise radiation doses to a benign or malignant tumor or specific areas within the tumor. IMRT allows for the radiation dose to conform more precisely to the three-dimensional (3-D) shape of the tumor by modulating or controlling the intensity of the radiation beam in multiple small segments. Also, IMRT allows higher radiation doses to be focused on the tumor while minimizing the dose to surrounding normal critical structures. Because the ratio of normal tissue dose to tumor dose is reduced to a minimum with the IMRT approach, higher and more effective radiation doses can safely be delivered to tumors with fewer side effects compared with conventional radiotherapy techniques. IMRT also has the potential to reduce treatment toxicity, even when doses are not increased. Due to its complexity, IMRT does require slightly longer daily treatment times, additional planning, and safety checks before starting the patient treatment (IMRT Patient Specific Quality Assurance) when compared with conventional radiotherapy (Gupta, Agarwal, Ghosh-Laskar, & Shrivastava, 2009) . IMRT was first conceptualised in the 1960s (Intensity Modulated Radiation Therapy Collaborative Working Group, 2001) . Although the concept of IMRT and early algorithms for planning were developed in Sweden, clinical application did not begin until a fully integrated IMRT planning and delivery system, namely, the NOMOS Peacock system, was invented and commissioned in 1993 by the collaborated effort between NOMOS and Baylor College of Medicine/the Methodist Hospital (Houston, TX, USA) (Mohan, 2005). After obtaining investigational device exemptions and protocol approval by Baylor’s Investigational Review Board, the first patient with brain metastases was to have three brain tumors treated simultaneously using IMRT in September 1993 (Lawrence & Cox, 1995). In 1994, the NOMOS Peacock system was introduced as the first commercial IMRT delivery unit. The Peacock system required the use of a beam modulation device known as a dynamic multivane intensityK. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 18 modulating collimator (MIMiC). This particular form of IMRT, called serial tomotherapy, could be treated by a continuously rotating gantry (Carol, 1995). Step and shoot IMRT represents another commonly used technique whereby multiple static beams are subdivided into ‘segments’ (LoSasso, Chui, & Ling, 1998). In the sliding window technique (dynamic Multileaf CollimatordMLC), a window defined by the MLC leaves sweeps across the treatment field at variable speed, while the monitor units are delivered continuously (Zhang et al., 2007). Dose calculation accuracy in IMRT is an important and crucial factor to prevent mistreatment of radiation treatment delivery using linear accelerator machines (Papiez & Langer, 2006). Among the commercially available dose calculation algorithms, Monte Carlo (MC) is considered to be potentially more accurate and complex than others. Although MC dose calculation algorithms are recognized as the most accurate dose computation algorithms for treatment planning, their inherent Statistical Uncertainty (SU) determines the accuracy of the dose calculation and the time span of the dose calculation (Taleei & Tabrizi, 2019) . The SU decreases inversely with the square root of the time span of the dose calculation. By decreasing the SU, one can increase the dose calculation accuracy. But the SU decreases, resulting in a significant increase in the time span of the dose calculation. Therefore, it should be a compromise between the SU and the dose calculation accuracy, with a suitable time span for the dose calculation in IMRT planning. Therefore, by studying this, it is possible to get an idea about how to optimize the accuracy of the dose calculation and the SU with a suitable time span of the dose calculation in IMRT planning (Chetty et al., 2007) . There are many Treatment Planning Systems (TPSs) that can be used to develop IMRT plans, utilizing their own dose calculation algorithm (Vassiliev et al., 2010) . The Elekta’s Monaco TPS is one of the most powerful tool that bring increased automation, intelligent workflows, and high-quality treatment planning to a wide range of radiotherapy treatment delivery systems. The Monaco TPS combines t h e MC dose calculation algorithm with robust optimization tools to provide high-quality radiotherapy treatment plans for IMRT, VMAT, and SBRT (Goodall & Ebert, 2020) . The Monaco 5.11.02 TPS used two kinds of SUs. Such as per control point SU and per calculation SU, and the planner can manually select one of them. In this research, we used a per control point SU to generate IMRT plans. Also, the Monaco 5.11.02 TPS has an option to choose different percentage values between 0.1 % - 10 % (Kry et al., 2019). In the per control point SU, the percentage uncertainty is based on the per voxel on a per segment. Also, the uncertainty was not the same in all voxels. The low-dose voxels in the peripheral regions of the patient had a higher uncertainty of dose than the voxels in the region of the maximum dose (PTV) (Keall, Siebers, Jeraj, & Mohan, 2000) . The dose uncertainty in the target volume (PTV) for the final plan was calculated and appeared in the TPS console window after the second stage dose calculation. There are a few other studies that have previously evaluated the overall effect of SU on dose calculation. But not about altering the per control point SU in IMRT plans. In 2000, Keall, Siebers, Jeraj, and Mohan (2000) found that the dose in Monte Carlo (MC) calculation does not significantly affect isodose lines and Dose Volume Histogram (DVH) for SU of 2% or lesser values. In 2004, Cheong, Suh, and Cho (2004) investigated the effect of SU on photon dose calculation using BEAMnrc and DOXXYZnrc MC simulation systems and evaluated SU based on DVH, isodose comparison, and root mean-square. In 2005, Ma et al. (2005) studied the issues related to the statistical analysis of MC dose calculations for realistic clinical beams using various variance reduction or time-saving techniques. Also, they discussed the effect of statistical uncertainties on dose prescription and monitor unit calculation for conventional treatment and IMRT based on MC simulations. K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 19 In 2016, Sarkar et al. (2016) investigated the interplay between Monte Carlo Variance (MCV) and Fluence Smoothing Factor (FSF) in VMAT for carcinoma of esophagus patients using a CMSMonaco TM Treatment Planning System (TPS). They reported that variation in FSF causes a difference in dosimetric and physical parameters for the treatment plan. In 2019, Palanisamy et al. (2019) explored the dosimetric impact of varying SU when calculating the dose of VMAT plans, and in 2021, Rembish et al. (2021) determined the severity of the effects on VMAT dose calculations caused by varying per control point SU in an MC-based TPS. Also, they assessed the impact of the uncertainty during DVH evaluation. The goal of IMRT planning is to shape the radiation dose to avoid or reduce exposure of healthy tissue and limit the side effects of treatment while delivering a therapeutic dose to the cancer. According to the best of our knowledge, no precise data are available for the optimal acceptance level of SU% % per control point for different diagnoses in IMRT. Also, no one studied the Dosimetric Impact on IMRT Plans of altering the per control point SU in Monaco TPS. Therefore, the purpose of this study is to evaluate the dosimetric impact on IMRT plans of altering the per control point SU (1% - 6%) using Elekta’s Monaco TPS for three different diagnoses (Larynx, Esophagus, and Prostate). METHODOLOGY CT Simulation and Radiotherapy Treatment Machine In this research work, three different diagnoses, which have high diversity, such as the Larynx, Oesophagus, and Prostate, were planned using the IMRT technique. A total of nine patients, three from each diagnosis, were selected for this study. The necessary CT image sets of all nine patients were obtained using a CT simulator (Siemens Healthineers, SOMATOM Confidence). The CT slice thickness of 5 mm was obtained for each clinical case for treatment planning. All generated IMRT plans were delivered using a 6 MV photon beam of Elekta Synergy Platform linear accelerator and its having a 1 cm multi-leaf collimator (MLC) at the iso-center. Contouring and Dose Prescription The tumor volume (Planning Target Volume - PTV) and Organs At Risk (OARs) volumes were contoured, and the doses prescribed to the Larynx, Oesophagus, and Prostate were 66 Gy/30 fractions, 50.4 Gy/28 fractions, and 78 Gy/39 fractions, respectively. Treatment Planning System (TPS) In this research work, the Monaco 5.11.02 TPS (IMPAC Medical System, Inc., Maryland Heights, MU, USA) was used to generate IMRT plans. It has a two-stage process of optimizing dose distribution. At the first stage, the ideal fluence distribution of a beam is optimized to meet a user-defined prescription for a single set of beams. At the second stage, the ideal distribution is transmitted into a set of segments where the shapes and weights are optimized based on the same prescription. For this research work, the Monte Carlo (MC) algorithm was used for dose calculation to generate an IMRT plan. K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 20 The Dosimetric Parameters used in Monaco TPS All the 54 IMRT plans were generated using the dosimetric parameters, which are given in Table 1 below. By keeping these parameters constant, IMRT plans were generated using MC dose calculation algorithm only by varying per control point SU 1%, 2%, 3%, 4%, 5%, and 6%. To analyze IMRT plans, different dosimetry indices were used as mentioned below. Table 1. The Dosimetric Parameters used in Monaco TPS Parameter Value Delivery Mode dMLC CT Slice Thickness 0.5 cm Grid Size 0.3 cm Beamlet Width 0.3 cm Control Points 40 Segment Width 0.5 cm Auto Flash Margin 0.2 cm Surface Margin 0.3 cm Target Margin 0.8 cm Fluence Smoothing Medium Dosimetric Indices used for IMRT Plan Evaluation In this study, we used several dosimetric indices for IMRT Plan evaluation. Such as, Conformity Index (CI), Heterogeneity Index (HI), Target Dose and Critical Organ Doses, Dose Calculation Time (DCT), Treatment Delivery Results (IMRT Patient Specific QA and Gamma Indices), and Dose Volume Histogram (DVH). • Conformity Index (CI) The Conformity Index (CI) describes the degree to which the prescribed isodose volume conforms to the shape and size of the target volume. This value is only reported for Monaco plans (Elekta, 2017) . The CI formula is given in equation 1:   2 * Rx RI V CI TV V  (1) where, TV = Structure Volume, V Rx = is the structure volume covered by the Dose of Interest and V RI is the total volume of the Dose of Interest. K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 21 • Heterogeneity Index (HI) The Heterogeneity Index (HI) describes the uniformity of dose within a target volume and is directly calculated from the statistics of the DVH. This value is only reported for Monaco plans (Elekta, 2017). The HI formula is is given in equation 2: 5% 95% D HI D  (2) where, D5% is the dose delivered to the hottest 5% of the tissue volume. D95% is the minimum dose received by 95% of the tissue volume. • Target Dose and Organ At Risk (OAR) Doses The target dose (Dose to PTV) was analyzed as D95% (the dose received by 95% of the volume of PTV and V95% (the volume received 95% of the prescribed dose). Moreover, the maximum dose (Dmax) and mean dose (Dmean) for PTV and Organ at Risk (OAR) were analyzed for all three clinical cases. The OAR dose constraints (Table 2) were taken from the Practical Radiotherapy Planning book (5th Edition) (Morris, Roques, Ahmad, & Loo, 2023). K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 22 Table 2: Organ at Risk (OAR) Dose Constraints Organ Constraint Optimal Mandatory Brainstem Dmax whole organ 54 Gy (60 Gy if PRV used) Spinal Cord Dmax to PRV (cord +5 mm or spinal 50 Gy (48 Gy if concomitant chemothercanal) apy) Parotid Gland Mean Dose 24 Gy Lens Dmax 10 Gy Heart Mean Dose 25 Gy 30 Gy Heart V30 Gy 45 % Heart V40 30 % Lungs V20 Gy 35 % (25 % if risk factors) Lungs Mean Dose 18 Gy Bladder V50 Gy 50 % Bladder V60 Gy 25 % 50 % Femoral Heads V50 Gy 5 % 50 % Kidney (each) V20 Gy 25 % 30 % Kidney (both) V20 Gy 30 % 35 % Rectum V30 Gy 70 % 80 % Rectum V40 Gy 51 % 65 % Rectum V50 Gy 38 % 50 % Rectum V60 Gy 27 % 35 % Rectum V70 Gy 15% 20 % • Dose Calculation Time (DCT) The dose calculation time was measured from the Monaco TPS optimization console window, which could give the dose calculation start and end time for all IMRT plans with different per control point SU values ranging from 1% to 6%. The total calculation time was defined as the time difference between the start and finish time of the MC dose calculation. For this research work, HP Z840 workstations, 128 GB RAM, Intel(R) Xeon(R) CPU E5-2697 v3 @ 2.60GHz (2 processors), and the 64-bit Operating system were used. K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 23 • Treatment Delivery Results (IMRT Patient Specific QA and Gamma Indices The MatriXXEvolution (IBA) instrument and myQA software (Version 2019-002 (2.12.15.0), IBA Dosimetry GmbH, Germany) were used for the IMRT patient-specific QA with Elekta Synergy Platform Linear Accelerator. The MatriXX Evolution has 1020 air vented pixel ionization chambers arranged in a 32 x 32 grid (except for the four corner positions where chambers are missing) that cover an active field of 24.4 cm x 24.4 cm at 100 cm Source to Detector Distance (SDD). The distance between the individual chambers is 7.62 mm center to center. The diameter is 4.5 mm. Also, this MatriXXEvolution includes a temperature and pressure sensor to perform an automated k (t, p) correction of the chamber signal. The effective point of measurement is 3 mm below the MartiXXEvolution housing surface. The measured data is then transmitted to a PC or laptop via a standard Ethernet interface in the PC or laptop. The two-dimensional (2D) Gamma indices were compared at the isocenter between measured dose and TPS planned dose based on the dose to distance agreement (3%, 3mm) with 5% threshold value. • Dose Volume Histogram A Dose Volume Histogram (DVH) is a histogram that represents radiation dose (cGy or %) in the x-axis and volume (%) in the y-axis in radiation therapy planning. DVHs are most commonly used as a plan evaluation tool. Also, it is used to compare doses from different plans or to different structures. DVHs were introduced by Michael Goitein and Verhey in 1979 (Shipley et al., 1979) . The ”volume” referred to in DVH analysis is a target of radiation treatment, a healthy organ near a target, or an arbitrary structure, and DVH summarizes 3D dose distributions in a graphical 2D format. In present radiation therapy, 3D dose distributions are typically created in a computerized TPS based on a 3D reconstruction of a CT scan. STATISTICLE ANAYLISYS The Statistical analysis was performed for all three diagnoses (for 54 IMRT Plans) using the percentage variation technique. Also, the comparison results were represented using tables, figures, and charts with the aid of OriginPro and overleaf softwares. K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 24 RESULTS & DISCUSSION The dosimetric parameters were evaluated using the results of dosimetric indices. Some similarities and differences were observed due to the impact of Monte Carlo (MC) dose calculation uncertainty (per control point). The comparison results were analyzed using descriptive and inferential statistics. For the comparison process, we used several statistical terms in Monaco 5.11.02 Treatment Planning System (TPS), like mean dose, and max dose. The mean dose (cGy or Gy) is the averaged sampled dose within the total volume of the structure that is within the calculation volume, and the max dose (cGy or Gy) is the largest sampled dose within the total volume of the structure that is within the calculation volume (ICRU, 2010). According to the measured average results shown in Table 3-5, the PTV dose coverage slightly changed as the per control point Statistical Uncertainty (SU) increased from 1% - 6% for PTV Dmean, PTV D95% (dose received by 95% volume of PTV), and V95% ( the volume received 95% of the prescribed dose). This is primarily due to the nature of the MC dose calculation algorithm used in the Monaco Treatment Planning System. The MC algorithm simulates particle interactions to calculate dose. A higher SU allows fewer particle histories (simulations), which speeds up the calculation but introduces greater stochastic noise. With higher SU, random dose fluctuations can slightly raise or lower the average depending on spatial variation of statistical noise (Miften et al., 2018). However, the maximum dose to PTV (PTV Dmax) increased as the per control point SU increased from 1% to 6% with a significant dose difference. This is a well-known and expected behavior of MC-based dose calculation systems. Unlike D95% or Dmean (which are averages), Dmax is based on one or a few voxels. When SU increases, even a single voxel with a random high dose due to low sampling is enough to raise Dmax significantly. This makes Dmax particularly unstable at high SU values (Low, Moran, Dempsey, Dong, & Oldham, 2011). The Global Max of the plan slightly increased with per control point SU. This is also a characteristic effect of MC dose calculation noise. The Global Maximum Dose is usually defined as the dose in the single highest-dose voxel in the entire 3D dose matrix. With higher SU, random fluctuations (positive outliers) in individual voxels are more likely. Therefore, one or a few voxels may report artificially high dose values, raising the Global Max (Low, Moran, Dempsey, Dong, & Oldham, 2011). According to the measured average results given in Table 3, the mean dose to Organs at Risk (OARs) such as the left parotid and right parotid showed a small variation. The max dose to brain stem, spinal cord, left lens, and right lens also showed small dose differences. But no clinically and statistically significant dose differences were observed. K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 31 Table 4. Average Comparison Results of Dosimetric Indices for Different Per Control Point Statistical Uncertainty (SU) Levels for Oesophagus Target and OARs Larynx SU 1% SU 2% SU 3% SU 4% SU 5% SU 6% PTV 50.4 D95% (cGy) 4950.47 ± 8.43 4963.87 ± 16.73 PTV 50.4 Dmax (cGy) 5189.47 ± 8.83 5209.37 ± 17.56 PTV 50.4 Dmean (cGy) 4997.70 ± 8.50 5013.07 ± 16.90 4952.93 ± 25.12 5210.03 ± 26.44 5002.13 ± 25.37 4955.83 ± 33.41 5212.10 ± 35.14 5010.37 ± 33.79 4939.50 ± 38.88 5226.63 ± 44.16 4999.80 ± 42.23 4949.03 ± 49.71 5261.10 ± 52.85 5011.73 ± 50.34 PTV 50.4 V95% 98.67 ± 0.17 98.71 ± 0.34 98.68 ± 0.51 98.73 ± 0.67 98.70 ± 0.84 98.71 ± 1.00 PTV 50.4 Heterogeneity Index 1.03 ± 0.002 1.03 ± 0.004 1.03 ± 0.005 1.03 ± 0.007 1.03 ± 0.009 1.03 ± 0.010 PTV 50.4 Conformity Index 0.68 ± 0.001 0.68 ± 0.002 0.68 ± 0.003 0.68 ± 0.005 0.70 ± 0.006 0.68 ± 0.007 Heart Dmean (cGy) 2303.07 ± 3.93 2278.23 ± 7.70 2285.87 ± 11.64 2276.40 ± 15.38 2273.73 ± 19.26 2290.83 ± 23.09 Spinal Cord Dmax (cGy) 3560.20 ± 6.23 3584.77 ± 12.43 3576.33 ± 18.70 3558.87 ± 24.73 3572.70 ± 31.15 3547.00 ± 36.66 Rt Lung Dmean (cGy) 1523.03 ± 2.61 1516.70 ± 5.11 1522.23 ± 7.74 1522.50 ± 10.28 1511.73 ± 12.79 1518.63 ± 15.28 Lt Lung Dmean (cGy) 1458.57 ± 2.49 1456.60 ± 4.92 1455.77 ± 740 1455.80 ± 9.83 1448.57 ± 12.26 1456.53 ± 14.65 Lt Kidney Dmean (cGy) 168.80 ± 0.38 167.77 ± 0.78 164.23 ± 1.12 162.93 ± 1.50 165.97 ± 1.91 162.77 ± 2.22 Rt Kidney Dmean (cGy) 40.70 ± 0.07 40.73 ± 0.15 40.80 ± 0.22 40.67 ± 0.29 40.57 ± 0.36 40.60 ± 0.43 Global Max of the Plan (%) 102.97 ± 0.17 103.36 ± 0.34 103.38 ± 0.51 103.42 ± 0.67 103.70 ± 0.84 104.39 ± 1.00 Dose Calculation Time (mins) 90.8 ± 4.66 26.06 ± 1.32 12.18 ± 0.62 8.42 ± 0.43 5.85 ± 0.30 4.89 ± 0.25 Gamma Pass Rate 3%, 3 mm 97.5 ± 2.93 98.3 ± 2.95 97.9 ± 2.94 98.1 ± 2.94 97.7 ± 2.93 98.4 ± 2.95 K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 32 Table 5. Average Comparison Results of Dosimetric Indices for Different Per Control Point Statistical Uncertainty (SU) Levels for Prostate Target and OARs Larynx SU 1% SU 2% SU 3% SU 4% SU 5% SU 6% PTV 78 D95% (cGy) 7657.57 7672.97 7681.73 7662.20 7633.23 7615.20 ± 18.89 ± 38.12 ± 56.61 ± 75.37 ± 91.90 ± 109.41 PTV 78 Dmax (cGy) 8046.73 8075.70 8118.27 8139.67 8132.50 8202.93 ± 19.85 ± 40.12 ± 59.83 ± 80.06 ± 97.93 ± 117.88 PTV 78 Dmean (cGy) 7793.60 7811.20 7822.13 7810.90 7813.40 7827.07 ± 19.23 ± 38.80 ± 57.65 ± 76.83 ± 94.07 ± 112.47 PTV 78 V95% (cm3) 99.43 ± 0.2 99.54 ± 0.50 99.53 ± 0.74 99.39 ± 0.98 99 ± 1.20 98.77 ± 1.44 PTV 78 Heterogeneity Index 1.03 ± 0.003 1.03 ± 0.005 1.03 ± 0.008 1.03 ± 0.010 1.04 ± 0.012 1.05 ± 0.014 PTV 78 Conformity Index 0.72 ± 0.002 0.70 ± 0.004 0.70 ± 0.005 0.71 ± 0.007 0.72 ± 0.009 0.71 ± 0.011 Rectum Dmean (cGy) 4109.13 4164.23 4144.10 4132.20 4098.40 4083.50 ± 9.89 ± 20.69 ± 30.53 ± 40.63 ± 49.35 ± 58.68 Bladder Dmean (cGy) 3778.87 3811.00 3801.77 3768.13 3741.43 3741.30 ± 9.40 ± 19.09 ± 28.27 ± 37.42 ± 45.53 ± 54.08 Lt Pelvic Bone Dmean (cGy) 2168.93 2180.77 2170.37 2159.70 2181.27 2193.07 ± 5.39 ± 10.92 ± 16.11 ± 21.40 ± 26.47 ± 31.65 Rt Pelvic Bone Dmean (cGy) 2189.97 2189.70 2199.67 2187.13 2200.87 2223.80 ± 5.46 ± 11.02 ± 16.41 ± 21.80 ± 26.84 ± 32.26 Global Max of the Plan (%) 103.21 103.73 104.08 104.45 104.37 105.16 ± 0.25 ± 0.50 ± 0.74 ± 0.98 ± 1.20 ± 1.44 Dose Calculation Time (mins) 81.5 ± 4.11 24.74 ± 1.24 14.03 ± 0.71 9.12 ± 0.47 7.06 ± 0.36 5.92 ± 0.30 Gamma Pass Rate 3%, 3 mm 97.1 ± 2.91 97.7 ± 2.93 97.5 ± 2.93 97.6 ± 2.93 97.2 ± 2.92 97.6 ± 2.93 K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 33 DISCUSSION The Monte Carlo (MC) methods are mainly used in three distinct problem classes: optimization, numerical integration, and generating draws from a probability distribution. In Monaco 5.11.02 TPS, MC was used for optimization, and the MC method has been identified as the gold standard for dose calculation (Clements, Schupp, Tattersall, Brown, & Larson, 2018) . At present, MC simulation calculates the dose very closely to reality, taking into account the contribution of secondary photons and electrons scattering and dose absorption, especially in homogeneous environments (Tugrul, 2021). The per control point SU is the percentage (%) SU per voxel, on a per-segment basis, that is willing to accept for the final dose calculation. So, the mean, per voxel, uncertainty in a central region of the dose of a segment is equal to the user-specified SU at the end of the dose calculation. A voxel is a measurement of volume in a structure that is to be imaged. Each voxel represents a defined volume and can be localized by coordinates on a three-dimensional (3D) grid. Here, the very important fact is that the smaller the per control point SU, the longer the dose calculation time. Also, when we used per control point SU values between 0.1% - 10%, the results should be a final dose uncertainty of approximately 1% for the plan in the central region of the target volume. The main difference between Per Calculation SU and Per Control Point SU is based on the number of histories (Uyar & Günekbay, 2023) and the voxel. In other words, Per Calculation SU is fast because it estimates the number of histories for the entire plan (recommended value 1%). Per control Point SU gives better resolution because it uses percentage uncertainty per voxel on a per-segment basis. So it should vary for the number of control points. In this work, we used 40 control points to generate each IMRT plan. The MC dose calculation without any SU is the most worthwhile in an IMRT plan from the accuracy point of view. However, it would take infinite time to calculate. So the planner should accept a certain range for this calculation uncertainty. The SU of MC is inversely proportional to the volume of the dose voxel (Mohan, Antolak, & Hendee, 2001) . For example, when decreasing the voxel size from 5 mm to 3 mm, it caused to increase in the Monte Carlo calculation time of approximately fivefold. Also, when reducing the SU by a factor of two, it caused the MC calculation time to be fourfold (Figure 6). So it was very crucial in decreasing/ increasing voxel size or SU in both ways. Overall analysis of this study suggests that there were no diagnosis-specific dosimetric variations. As reported by Jiang et al. (Jiang, Pawlicki, & Ma, 2000) , large Statistical Uncertainties (SUs) are expected to blur the Dose Volume Histogram (DVH) curves and may become unreliable. The statistical noise should have practically no effect on inverse treatment planning (as IMRT) because the intensity along a ray is affected by the average of dose values over a large number of voxels lying along the ray and not by the dose in any one voxel (Mohan, Antolak, & Hendee, 2001) . It was suggested that large SU can be used for large tumors and OARs such as parallel organs (Palanisamy, David, Durai, Bhalla, & Puri, 2019) . The effect of the per control point SU in this study showed no significant dose differences on the mean dose to the target and OAR volumes. So, it is suggested that SU can be used up to 5% for parallel organs. For the structures with small volumes (such as small tumors, lens), Monaco does not recommend using per control point SU higher than 5%. If we use a higher value (higher than 5%), it causes the system to underestimate the cost function value assigned to that structure (Elekta, 2017). Significant variation was observed in average dose calculation time and the per control point SU. There is an exponential relationship observed between average dose K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 34 calculation time and the per control point SU (Figure 6). The dose calculation time may not be too long. Also, the gamma index showed a good pass rate for all three different diagnoses (such as Larynx, Oesophagus, and Prostate), and there are no significant variations were observed in the gamma pass rate for all three diagnoses. The final dose uncertainty should be approximately 1% for the entire plan (Elekta, 2017), and it shouldn’t be greater than 1% for the entire plan (Figure 7). Finally, based on all the measured average results, as well as considering other important factors and constraints, we recommend maintaining a control point SU value of 3% without compromising the quality or delivery of the plan. Additionally, we analyzed the percentage variations concerning the 3% per control point SU for the Larynx, Oesophagus, and Prostate (refer to Tables 6-8). Table 6. Percentage Variation values with respect to 3% for Larynx Target and OARs Larynx (wrt.3%) SU 1% SU 2% SU 4% SU 5% SU 6% PTV 66 D95% (cGy) 0.01 0.48 0.17 0.07 0.25 PTV 66 Dmax (cGy) 0.24 0.01 0.11 0.53 0.83 PTV 66 Dmean (cGy) 0.05 0.33 0.18 0.09 0.34 PTV 66 V95% (%) 0.09 0.69 0.05 0.05 0.46 PTV 66 Heterogeneity Index 0 0 0 0 0 PTV 66 Conformity Index 0 1.25 1.25 0 1.25 Brain Stem Dmax (cGy) 6.95 4.31 1.39 4.37 1.03 Spinal Cord Dmax (cGy) 0.06 0.02 0.24 0.92 0.05 Lt Parotid Dmean (cGy) 3.13 0.52 1.9 0.69 0.4 Rt Parotid Dmean (cGy) 2.66 0.66 0.75 1.87 2.19 Lt Lens Dmax (cGy) 1.62 0.91 3.33 0.97 3.72 Rt Lens Dmax (cGy) 0.16 1.09 2.5 0.39 4.01 Global Max of the Plan (%) 0.23 0 0.18 0.53 0.99 Gamma Pass Rate 3%, 3 mm 0.20 0.20 0.31 0.10 0.51 K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 35 Table 7: Percentage Variation values with respect to 3% for Oesophagus Target and OARs Oesophagus (wrt.3%) SU 1% SU 2% SU 4% SU 5% SU 6% PTV 50.4 D95% (cGy) 0.05 0.22 0.06 0.27 0.08 PTV 50.4 Dmax (cGy) 0.39 0.01 0.04 0.32 0.98 PTV 50.4 Dmean (cGy) 0.09 0.22 0.16 0.05 0.19 PTV 50.4 V95% 0.01 0.03 0.05 0.02 0.03 PTV 50.4 Heterogeneity Index 0.00 0.00 0.00 0.00 0.00 PTV 50.4 Conformity Index 0.00 0.00 0.00 2.94 0.00 Heart Dmean (cGy) 0.75 0.33 0.41 0.53 0.22 Spinal Cord Dmax (cGy) 0.45 0.24 0.49 0.10 0.82 Rt Lung Dmean (cGy) 0.05 0.36 0.02 0.69 0.24 Lt Lung Dmean (cGy) 0.19 0.06 0.00 0.49 0.05 Lt Kidney Dmean (cGy) 2.78 2.16 0.79 1.06 0.89 Rt Kidney Dmean (cGy) 0.25 0.17 0.32 0.56 0.49 Global Max of the Plan (%) 0.40 0.02 0.04 0.31 0.98 Gamma Pass Rate 3%, 3 mm 0.41 0.41 0.20 0.20 0.51 Table 8. Percentage Variation values with respect to 3% for Prostate Target and OARs Prostate (wrt.3%) SU 1% SU 2% SU 4% SU 5% SU 6% PTV 78 D95% (cGy) 0.31 0.11 0.25 0.63 0.87 PTV 78 Dmax (cGy) 0.88 0.52 0.26 0.18 1.04 PTV 78 Dmean (cGy) 0.36 0.14 0.14 0.11 0.06 PTV 78 V95% (cm3) 0.1 0.01 0.14 0.53 0.76 PTV 78 Heterogeneity Index 0 0 0 0.97 1.94 PTV 78 Conformity Index 2.86 0 1.43 2.86 1.43 Rectum Dmean (cGy) 0.84 0.49 0.29 1.1 1.46 Bladder Dmean (cGy) 0.6 0.24 0.88 1.59 1.59 Lt Pelvic Bone Dmean (cGy) 0.07 0.48 0.49 0.5 1.05 Rt Pelvic Bone Dmean (cGy) 0.44 0.45 0.57 0.05 1.1 Global Max of the Plan (%) 0.84 0.34 0.36 0.28 1.04 Gamma Pass Rate 3%, 3 mm 0.41 0.21 0.10 0.31 0.10 According to the calculated percentage variation values (Table 6-8) with respect to a 3% per control point SU, showed no any significant variation between the dosimetric indices and the per control point SU (%) values for all three diagnoses (Larynx, Oesophagus, and Prostate). K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 36 CONCLUTION This study proposed an optimal acceptable range for the per Control Point Statistical Uncertainty (SU) in Monte Carlo Dose calculations during IMRT planning in Monaco 5.11.02 Treatment Planning System (TPS). Based on the measured average results, a 3% per control point SU is acceptable for all three diagnoses (Larynx, Oesophagus, and Prostate) in IMRT planning, allowing for reduced calculation time without compromising target coverage, Organ at Risk (OAR) doses, or plan delivery. Conflict of Interest None declared. Financial Disclosure The authors did not receive any kind of grant or financial support from any organization for the present study. REFERENCES [1] Bortfeld, T. (2006). IMRT: A review and preview. Physics in Medicine and Biology, 51(13), R363– R379. https://doi.org/10.1088/0031-9155/51/13/R21 [2] Carol, M. P. (1995). 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Comparison of the number of history in Monte Carlo simulation programs. arXiv. https://arxiv.org/abs/2301.05916 K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 39 [36] Vassiliev, O. N., Wareing, T. A., McGhee, J., Failla, G., Salehpour, M., & Mourtada, F. (2010). Validation of a new grid-based Boltzmann equation solver for dose calculation in radiotherapy with photon beams. Physics in Medicine & Biology, 55(3), 581–598. https://doi.org/10.1088/0031-9155/55/3/003 [37] Zhang, M., Moiseenko, V., Liu, M., Yan, D., & Fraass, B. A. (2007). Dose calculation accuracy in IMRT: Impacts on patient-specific QA and treatment. Medical Physics, 34(5), 1807–1814. K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 40 Development of a Solar-Powered, Automated Water Ionizer Using GraphiteBased Electrodes for Alkaline and Acidic Water Production A.M.K.L Abeykoon1*, M.D.Y Milani1, H.M. B. I. Gunathilaka1,R. C. W. Arachchige1, D.M.K Muthumala2 1Materials Technology Section of Industrial Technology Institute, No. 363, Bauddhaloka Mawatha, Colombo 07, Sri Lanka 2 Faculty of Technology, Wayamba University of Sri Lanka, Kuliyapitiya, Sri Lanka. ABSTRACT This study presents the development and automation of a novel water ionizer, designed to produce ionized water with precisely controlled pH levels through an advanced electrolysis process. The primary objective is to generate both alkaline and acidic water for various applications, including sterilization, cleaning, and drinking. With the increasing demand for alkaline water due to its potential human health benefits in reducing oxidative stress caused by free radicals, this research introduces a cost-effective, eco-friendly system that integrates innovative graphite-based electrode materials, automated control mechanisms, and a PV solar power system. These electrodes contribute to cost reduction, while the automation system enables precise regulation of pH levels, significantly enhancing the reliability and user convenience of the ionizer. Additionally, the system is powered by a solar cell setup, optimizing the use of renewable energy and aligning with sustainability goals by reducing dependence on conventional power sources. Keywords: Water Ionizer, Graphite-Based Electrodes, Electrolysis Automation [email protected]* INTRODUCTION Water ionization technology has gained significant attention in recent years due to the increasing demand for alkaline water, which is valued for its potential health benefits, including its ability to neutralize oxidative stress caused by free radicals[1][2][3]. Alkaline water, with its elevated pH level, is widely believed to improve hydration, enhance detoxification, and contribute to overall wellness[4][5]. This has led to its use in various applications such as drinking water, cleaning, and sterilization[6][7][8]. Water ionizers, which alter the pH of water through an electrolysis process, are the primary devices responsible for producing ionized water without adding any chemical or ingredient [9][10][11]. Conventional water ionizers typically utilize expensive electrode materials, such as platinumcoated titanium, to facilitate the electrolysis process [12][13]. While these materials are effective, they significantly increase the cost of the devices, making them less accessible for broader consumer use[14].Additionally, these devices often face challenges such as high energy consumption, limited electrode lifespan, and the risk of metal ion migration, which can compromise the quality and safety of the ionized water, pH stability, and user convenience. In response to these limitations, there is a need for more efficient, cost-effective, and automated systems that can deliver high-quality ionized water with improved performance and reliability[15]. A.M.K.L Abeykoon et al., JSLAAS,Vol. 7, Issue 1 (2025) 40-48 47 heavy metals and toxic elements, including lead, arsenic, cadmium, mercury, and chromium, were not detected in both inlet and outlet samples. This indicates that the ionizer does not introduce any harmful heavy metal contaminants into the water. The aluminum concentration remained consistent at a low level of 0.02 mg/L, suggesting that the device’s components do not contribute to metal leaching. The consistent absence of heavy metals and toxic elements confirm that the device is safe and suitable for producing alkaline water drinking purpose. CONCLUTION This study successfully developed and automated a high-efficiency water ionizer, capable of producing ionized water with precisely controlled pH levels through an advanced electrolysis process. The ionizer's ability to generate both alkaline and acidic water makes it suitable for various applications, including sterilization, cleaning, and drinking. The integration of innovative graphite-based electrodes and selective ion-exchange membranes significantly enhanced the accuracy in pH regulation, providing a more reliable and user-friendly experience. Moreover, the inclusion of pH sensors and automated control mechanisms enabled real-time monitoring and adjustments, ensuring consistent output quality. The use of a solar-powered energy system further emphasizes the sustainability of the ionizer, reducing its reliance on traditional power sources and supporting environmental goals. ACKNOLOWLEDGEMENT Financial support from the Sri Lanka Treasury (Grant No. TG/21/196)is highly appreciated. REFERENCE [1] R.M.C. Ignacio, K.-B. Joo, K.-J. Lee, Clinical effect and mechanism of alkaline reduced water, J. Food Drug Anal. 20 (2012) 33. [2] F.S.L.G. Delos Reyes, A.C.C. Mamaril, T.J.P. Matias, M.K. V Tronco, G.R. Samson, N.D. Javier, A. Fadriquela, J.M. Antonio, M.E.J. V Sajo, The search for the elixir of life: On the therapeutic potential of alkaline reduced water in metabolic syndromes, Processes 9 (2021) 1876. [3] J.A. Koufman, N. Johnston, Potential Benefits of pH 8.8 Alkaline Drinking Water as an Adjunct in the Treatment of Reflux Disease, Ann. Otol. Rhinol. Laryngol. 121 (2012) 431– 434. https://doi.org/10.1177/000348941212100702. [4] S. Lal, A.K. Kakodia, S.K. Verma, Alkaline water and human health: Significant hypothesize, J. Appl. Sci. Educ. 2 (2022) 1–11. [5] B. Benelam, L. Wyness, Hydration and health: a review, Nutr. Bull. 35 (2010) 3–25. [6] Y.-R. Huang, Y.-C. 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Satharasinghe 3 Development of a Solar-Powered, Automated Water Ionizer Using Graphite-Based Electrodes for Alkaline and Acidic Water Production A.M.K.L Abeykoon, M.D.Y Milani, H.M. B. I. Gunathilaka ,R. C. W. Arachchige, D.M.K Muthumala Edited and Published by the Sri Lanka Association of for the Advancement of Science ISSN 1391-0256