Abstract

This article investigates the impact of artificial intelligence (AI) on intellectual property (IP) rights, addressing challenges in ownership and authorship of AI-generated creations while exploring legal and ethical dilemmas in traditional IP domains. It offers strategies for navigating these complexities, drawing on legal precedents, international agreements, and policy recommendations. The research emphasizes the urgent need for legislative updates to address these challenges effectively. Recommendations include the enactment of innovative constitutional provisions, updating IP legislation to encompass AI-related issues comprehensively, and advocating for effective judicial intervention. By implementing these strategies, Sri Lanka can foster a harmonious coexistence of AI and IP, ensuring the protection of intellectual property rights while stimulating innovation in the AI era.

Keywords: Artificial intelligence, Copyright Intellectual property, Trademarks

Download

Abstract

The Photovoltaics phenomenon is one of the major turning points in the battle against the depletion of fossil fuels. Sunlight is the main resource in photovoltaics, but there remains a quest to harvest it efficiently to generate electricity. This study is focused on designing basic, cost-effective prototype solar cells using ZnO/Cu2O nanoparticles (NPs) and Co(cobalt) co-doped Ag-ZnO/Cu2O NPs under normal university laboratory conditions. ITO-coated glass was used as the substrate of the solar cell and a modified low-temperature chemical bath deposition method was used for fabrication. Both ZnO and Cu2O NPs were synthesized by aqueous precipitation methods and the fabrication was performed successfully using ZnO and Cu2O NPs. However, the fabrication with Co co-doped Ag-ZnO and Cu2O NPs requires more research since the Co co-doped Ag-ZnO NPs were synthesized by solvothermal method, and it appeared as a fine powder which was not thick enough to hold onto the Cu2O layer. The UV-spectroscopic analysis confirmed the characteristic band of ZnO NPs at 367.5 nm, Cu2O NPs at 360 nm, and Co co-doped Ag-ZnO NPs at 378 nm. The FTIR spectrum showed sharp peaks at 460 cm-1 and 606 cm-1 for the corresponding Zn-O bond and Cu-O bond, respectively with a broad peak at 1329 cm-1 for Cu2O FTIR, due to the chemisorbed and/or physiosorbed H2O and CO2 molecules on the surface of the nanostructure. The EDX analysis showed the presence of slight carbon impurity in ZnO NPs which resulted in a deviated XRD pattern while Cu2O NPs showed the characteristic XRD pattern. The solar cell illuminated under three different lux conditions, had the characteristic J-V plot when measured through Gamry Potentiostat.

Keywords: Co co-doped Ag-ZnO NPs, Cu2O NPs, Photovoltaics, Solar cell, ZnO NPs

Download

Abstract

The COVID-19 pandemic, a persistent global health emergency that has affected almost all facets of daily life, was initially discovered in Wuhan, China, in December 2019. Since that time, the virus has rapidly spread over the globe, causing serious social and economic upheavals necessitating the need for reliable forecasting methods. This study compares ten distinct models to predict the number of confirmed COVID-19 cases in Sri Lanka, aiming to assess the performance of statistical models using limited and volatile real-world data characterized by trends, random peaks, and autocorrelations. In addition to the classical ARIMA model, various smoothing and filtering techniques were explored to capture the unique characteristics of the data. The model consistencies in multiple-day predictions were demonstrated, and robust evaluation criteria, along with non-robust measures, were utilized to enhance the effectiveness of the evaluation process. The results highlight the effectiveness of traditional smoothing and filtering strategies such as Simple Exponential Smoothing, Holt’s Exponential Smoothing, and the Smoothing Splines technique coupled with the ARIMA model. This study also discovered that the ARIMA model, when applied directly to the original data without using any smoothing or filtering approaches, failed to forecast adequately, thereby demonstrating the insufficiency of the ARIMA model on its own to provide credible forecasts when given a volatile set of data.

Keywords: Arima, Smoothing, Time series, Trend analysis

Download

Abstract

The global threat of antimicrobial resistance has spurred interest in discovering innovative antimicrobial agents from diverse sources. Amid the rise of new diseases, the quest for novel drug leads has intensified. This study explores the antibacterial potential of lichen-associated fungi in mangrove ecosystems, using NARA Regional Research Centre in Kalpitiya, Sri Lanka as the study site. Lichen-associated fungi were isolated from collected lichens and the antibacterial activities of the isolates were tested using two gram-positive bacteria: Staphylococcus aureus (ATCC 25923) and Bacillus cereus (ATCC 11778) and two gram-negative bacteria: Pseudomonas aeruginosa (ATCC 25853) and Escherichia coli (ATCC 25922). Putative fungal isolates were primarily screened using agar plug diffusion assay and ethyl acetate extracts of fungal isolates with marked activity were secondarily screened using the well diffusion assay in triplicate. Isolate LIF 0803 identified as Trichosporon faecale showed the most outstanding antibacterial activities as 2.58, 3.43, 4.2, 4.5 cm of zone diameter at 100 mg/mL, and 1.95, 3.08, 3.7, 4.3 cm of zone diameter at 50 mg/mL against P. aeruginosa, S. aureus, B. cereus, and E. coli respectively. All nine fungal isolates showed promising antibacterial activity against both gram-positive and negative bacteria. Therefore, this study showed that lichen-associated fungi in mangrove ecosystems have potent antibacterial activities. Hence, bioassay-guided fractionation of active compounds from lichen-associated fungi and structure elucidation are warranted.

Keywords: Broad-spectrum antibacterial activity, Bioactive secondary metabolites lichen, Mangrove, Trichosporon faecale

Download

Abstract

The textile industry is one of the largest worldwide polluters of clean water due to the heavy use of synthetic dyes. These chemicals negatively affect the environment, especially aquatic life due to their toxic and mutagenic properties. Synthetic dyes cause harm to human health such as skin allergies and respiratory sensitization. Several advantages such as ease of extraction, availability, high yields and no seasonal variation make microbial pigments the most ideal source of natural pigments. This study was done to isolate colour pigment producing bacteria and fungi from soil collected from organic farms from various locations in Sri Lanka. Out of 7 soil samples, 3 yielded pigment producing bacteria and fungi. In total, 9 pigment producing bacteria and 3 pigment producing fungi were isolated. Gause’s synthetic agar yielded the most pigmented isolates. Isolates were inoculated in broths and pigment production was observed. Extracellular pigments produced by 5 of the bacterial isolates were extracted by a water-based method. The antibacterial activity of the pigments in their crude and concentrated forms was tested using the well diffusion method against Escherichia coli ATCC 8739 and Staphylococcus aureus ATCC 6538P. Inhibition zone against S. aureus was observed for both crude (12.33±0.58mm) and concentrated pigments (9.67±0.58mm) extracted from purple pigment producing bacterial isolate (BPU). This pigment has the potential to be used in antibacterial textile preparation. Extracted pigments were used to dye scoured cotton fabric with the use of 3% alum as mordant. Pigment from BPU isolate resulted in better coloured fabric.

Keywords: Extracellular pigments, Microbial pigments, Natural pigments, Textile industry

Download