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Monitoring urban green space using remote sensing derived vegetation indices in Colombo district, Sri Lanka
Get PDFThis study investigated the use of Remote Sensing (RS)-derived vegetation indices for monitoring urban green spaces (UGSs) from
Sentinel-2 RS multispectral imagery with a spatial resolution of up to 10 meters. Six vegetation indices, including the Normalized
Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil
Adjusted Vegetation Index (MSAVI), Green Ratio (GR), and Transformed Vegetation Index (TVI) by using Google Earth Engine
(GEE) platform. Derived the original and enhanced images were used to compare the aforementioned vegetation indices in order
to assess four image quality parameters: Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), Standard Deviation
(SD), and Correlation Coefficient (CC). The findings demonstrated the range of values for each vegetation index: NDVI (-0.398163
to 0.888742), EVI (-0.287905 to 0.615649), SAVI (-0.597015 to 1.00000), MSAVI (-0.495483 to 0.795898), GR (-0.199505 to
0.444334), and TVI (0.058906 to 2.32316). Among the indices, MSAVI exhibited the best fit with image quality parameters of
PSNR, RMSE, SD, and CC measuring at 45.31, 0.0197, 0.06, and 0.9641, respectively. This outcome suggests MSAVI's better
performance in estimating green vegetation areas compared to other indices in the study area. The study also discussed the
limitations of using vegetation indexes to monitor UGS, including the influence of atmospheric conditions, sensor calibration, and
data preprocessing techniques. Overall, this study is insightful which is valuable in terms of the effectiveness of different vegetation
indices for UGS. The findings of this study can be used to inform futur
Long-run volatility memory dynamics and inter-market linkages in GCC equity markets: application of DCC-FIGARCH models
No full textThe study investigates volatility persistence, long-term memory and time-varying conditional correlations among the stock markets of the Gulf Cooperation Council (GCC) countries. Daily equity index data between 2012 and 2024 were analyzed using univariate fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models to examine long-memory behavior and multivariate dynamic conditional correlation (DCC) models to assess conditional correlations between these markets. For each of the GCC equity markets, the analysis highlighted large degrees of long-memory and volatility persistence. Finally, the DCC model shows that strong and dynamic Intermarket links among the GCC, especially between KSA and UAE, exist and reflect significant volatility spillover from good economic ties. This study fills the gap in the literature by providing a comprehensive understanding of long-run volatility memory and inter-market associations in the GCC stock markets
Investigate the larvicidal efficiency of selected botanical plant extracts against dengue vector Aedes sp.
Get PDFThe global rise in mosquito-borne diseases, particularly dengue fever, emphasizes the
need for eco-friendly vector control methods. This study investigates the larvicidal
potential of five medicinal plants such as Nicotiana tabacum (Tobacco), Acorus
calamus (Sweet Flag), Pongamia pinnata (Pungam), Calotropis sp., and Aristolochia
bracteolate (Worm Killer) against Aedes sp. larvae. All selected plant extracts were
prepared by using methanol as a solvent and larvicidal mortality was assessed 24 hrs
with different concentration ranges from 25-500 ppm for each extract. The highest
mortality was observed in Acorus calamus with 98.00% ± 2.45 reduction at 500 ppm
alongside the lowest LC50 of 58.07 ppm ± 0.80, LC90 of 557.82 ± 0.75 and LC99 of
1103.22 ± 0.75. Calotropis sp. demonstrate moderate effectiveness with a 93.00% ±
2.45 mortality rate at 500 ppm and an LC50 of 97.27 ppm ± 0.75. In contrast,
Aristolochia bracteolate was the least effective, resulting in a mortality rate of 67.00%
± 5.10 at 500 ppm, an LC50 of 330.47 ppm ± 0.4, an LC90 of 1208.20 ± 0.63, and an
LC99 of 2166.09 ± 1.02. One-way ANOVA confirmed significant differences in
mortality across extracts (p < 0.001), and Tukey’s post-hoc test revealed Acorus
calamus was significantly more effective than Pongamia pinnata and Aristolochia
bracteolate (p < 0.05). Regression analysis indicated a strong dose-response
relationship for all extracts, with Acorus calamus showing the highest regression
coefficient (0.22) and Aristolochia bracteolate the lowest (0.08). Phytochemical
screening identified key bioactive compounds such as phenols, terpenoids, and
alkaloids. Effect size analysis (Cohen’s d = 1.72) between Acorus calamus and
Aristolochia bracteolate highlighted substantial differences in efficacy. These findings
demonstrate the strong potential of Acorus calamus as a promising natural larvicide,
while Aristolochia bracteolate is less effective, suggesting that a higher concentration
or combination of plant extracts might be needed for optimal mosquito control
Lotteries buying behavior in Sri Lanka
Get PDFPurpose: The main purpose of this study is to generate additional revenue to the
government for public expenditure to the welfare facilities of the people in the country
from the sale national lottery products. The lotteries buying behavior of people
specially in the Northern & Eastern provinces are very less in compare with other
districts even though there is no price variance in regional wise. It is a very big
question alarming in the mind of researcher why this much of difference of buying
lottery tickets in the districts of North and Easter Provinces. There may be some
specific reasons for the less sales performance in these districts. It was motivated the
researcher to study this problem.
Methodology: This non-experimental quantitative study is based on the research
questions mentioned above. The study used the lottery buying behavior as dependent
variable and its determinants – demographic, economic, social, cultural, psychological
and advertisement factors as independent variables. Testing of hypotheses employs
statistical procedures to draw inferences about the population to seeks answer. The
sampling size cover lottery buyers from 8 districts of Northern and Eastern provinces.
According to the population, a sample size of 384 respondents (Lottery buyers) was
identified by using online sample size calculator at 95% confidence interval. Collected
data was analyzed by means of statistical method using SPSS. In the analysis
descriptive statistics, Chi-squared test, correlation test and multiple regression
techniques were performed to meet the objective of the study.
Findings: The demographic factors analysis, the urban and the rural areas did not
contribute, but gender contribute marginally to buying behavior of lottery tickets
while age group showed differently.
Implication of the research: The findings of the study would be useful for
government and the senior marketing managers of the DLB & NLB improve sales
performance. Finally, the public can understand the important and the philosophy
behind the corporate social responsibility for the wellbeing of the public
Pioneering disease prediction in cinnamon leaves using machine learning: a systematic literature review
Get PDFThe integration of Machine Learning (ML) in
agricultural disease prediction has become
increasingly prominent. This review paper
explores the evolution of techniques used for
predicting diseases in cinnamon leaves and
analyzes common cinnamon leaf diseases,
drawing on research conducted up to 2023. The
paper
highlights
the
evolution
of
ML
methodologies, particularly in the areas of image
processing, feature extraction, and classification
algorithms. It provides an in-depth analysis of
various approaches, such as Convolutional
Neural Networks (CNNs), Support Vector
Machines (SVMs), and Random Forests,
evaluating
their
effectiveness
in
disease
prediction. From an initial set of 100 studies, 22
were selected for detailed analysis based on their
relevance and contribution to the field.
Additionally, the review addresses the challenges
associated with developing reliable ML models.
Through the synthesis of findings from multiple
studies, this paper offers a comprehensive
overview of current research in cinnamon leaf
disease and prediction, identifying existing gaps
and proposing directions for future investigations
to improve the precision and applicability of ML
driven solutions in agriculture
The nature of the Muslim marriage and divorce act (MMDA) of Sri Lanka
Get PDFIn the past, there had been a serious of arguments put forwarded to bring the
amendments to the Muslim Marriage and Divorce Act. One of the key debates
revolved as to whether the Muslim Marriage and Divorce Law in Sri Lanka adheres
to Islamic Law or Customary Law. it’s essential to grasp this distinction. As such
the Sri Lankan Muslim Marriage and Divorce Act functioning as both customary
law and Special legislation. With a rich legal heritage obtaining in Sri Lanka. The
Legal system could general been categorised in various ways of this application.
For an instance, some countries follow civil law system while others having
common law system. Such as the English common law based on the adversary
system where as civil law system based on inquisitorial system. In general, the law
is divided into substantial/ procedural/ Adjective law. with principal consideration
the Muslim Marriage and Divorce Act Law is 90% of procedure/ Adjective Law.
Furthermore, there's a pressing need to expand the legal framework in this domain.
It's noteworthy that the lack of attention from the majority of judges and legal
scholars poses a barrier to incorporating fundamental laws into relevant
legislation
Compatibility of flour of cassava, sweet potato, jackfruit and breadfruit for partial replacement of wheat flour in dinner bun production
Get PDFThis study explores the potential of utilizing composite flours derived from
Cassava (Manihot esculentav), Sweet potato (Ipomoea batatas), Jackfruit
(Artocarpus heterophyllus Lam), and Breadfruit (Artocarpus altilis) in dinner
bun production by partially replacing wheat flour. Composite flour, offers
tailored functional properties and nutritional benefits. The study involves
characterizing the flour properties, determining optimal composition ratios,
and assessing consumer acceptance. Cassava, Sweet potato, Jackfruit, and
Breadfruit were processed into flour and used for dinner bun production.
Several treatments of dinner bun samples were prepared by incorporating
four types of composite flour of Cassava, Sweet potato, Jackfruit, and
Breadfruit with flours of soybean and wheat, each having three treatments
(T1, T2, and T3). According to the sensory evaluation the best composite
flour samples were selected (Composite flour of Cassava T1, Sweet potato
T1, Jackfruit T1, and Breadfruit T3) for this combination, proximate
chemical analyses were performed. Proximate analysis revealed for each
selected composite flour type: cassava (moisture:0.81±0.04%, protein:
0.091±0.030%, ash: 1.48±0.17%, fat: 1.71±0.00%, fiber: 0.02±0.00%),
sweet potato (moisture: 0.80±0.03%, protein: 2.157±0.136%, ash:
1.47±0.07%, fat: 3.12±0.05%, fiber: 0.21±0.01%), jackfruit (moisture:
0.81±0.04%, protein: 0.004±0.002%, ash: 1.53±0.13%, fat: 3.52±0.06%,
fiber: 0.02±0.00%), and breadfruit (moisture: 0.81±0.03%, protein:
0.567±0.009%, ash: 1.51±0.04%, fat: 4.14±0.05%, fiber: 0.04±0.00%). The
initiative aligns with the Food and Agriculture Organization's composite
flour program, aiming to utilize locally available materials for bakery
products
Impact of payee tax on the performance of state University Executive Staff in Sri Lanka
Get PDFPurpose: This study explores the impact of the Pay as You Earn (PAYE) tax on the
performance of executive staff at Sri Lankan state universities. The research aims to
identify how the current PAYE tax system affects motivation, job satisfaction, and
productivity among these executive staff.
Design/methodology/approach: A sample size of 320 executive staffs from 17 state
Universities was studied using the stratified sampling method. To ensure validity and
reliability, the survey was pre-tested, and Cronbach’s alpha was used to confirm
internal consistency. Factor analysis identified the key dimensions of job satisfaction
and motivation affected by the PAYE tax, while correlation analysis revealed
significant negative relationships between tax burden and performance. Multiple
regression analysis further highlighted that higher PAYE tax burdens significantly
reduce job satisfaction and motivation.
Findings: The findings underscore the substantial financial strain caused by the
PAYE tax system, which directly impacts work performance and commitment among
university executive staff.
Practical implications: The study offers policy recommendations aimed at balancing
taxation and employee welfare, ensuring that tax policies do not unintentionally
compromise the operational efficiency of university leadership.
Originality value: By illuminating the relationship between taxation and human
resource performance, this study contributes to a broader understanding of taxation
policies and their effects on human resource performance in the public higher
education sector of Sri Lanka, with broader implications for tax reform and employee
welfare programs
Role of cloud computing in modernizing the accounting methods
Get PDFPurpose: This study explores the role of cloud computing in modernizing accounting
methods, focusing on operational efficiency, accessibility, and real-time financial
analysis. It highlights how cloud-based systems automate accounting tasks, reduce
manual errors, enhance team collaboration across locations, and strengthen data
security through encrypted storage and regular backups.
Design/methodology/approach: A survey methodology was employed, targeting
100 finance professionals utilizing cloud accounting software. Data collected from
the survey were analyzed using SPSS, leveraging descriptive statistics and regression
analysis to measure the impact of cloud computing on accounting practices and
decision-making.
Findings: Cloud accounting significantly enhances decision-making by providing
real-time access to financial data, reducing operational costs, and enabling scalability.
However, challenges such as data privacy concerns, integration with legacy systems,
and the need for user training were also identified.
Practical implications: The findings offer practical guidance for organizations
adopting cloud accounting systems, emphasizing the necessity of addressing data
security, system integration, and user readiness to optimize benefits and ensure
successful implementation.
Originality value: This research establishes cloud computing as a pivotal factor in
modernizing accounting methods, providing enhanced flexibility, security, and
operational efficiency. It offers actionable recommendations for leveraging cloud
technology to transform accounting practices