Sri Lanka Institute of Information Technology

The primary focus of this study was on Differently Abled Employees’ (DAEs) work performance within Sri Lanka’s garment industry. Prior research revealed inadequate awareness among organizations regarding the provision of employment opportunities for DAEs. Notably, DAEs constitute a substantial portion of the economically inactive working-age population in Sri Lanka. In this setting, the study aimed to identify the crucial factors influencing the contribution of DAEs in the Sri Lankan garment industry. In this setting, the study sought to measure their impact and develop a framework that supports both DAEs and the garment industry, fostering a mutually beneficial work environment. Utilizing a mixed approach, the study encompassed a sample population of 270 DAEs. Data collection involved semi-structured interviews and a Likert scale questionnaire. Convenience sampling was deployed to interview 14 DAEs, while a sample of 159 DAEs was selected through simple random sampling for the distribution of the questionnaire. Thematic analysis and multiple linear regression analysis were employed to analyze qualitative and quantitative data. The results underscored the significance of the examined factors affecting DAEs’ contributions. Based on regression analysis results, the researchers developed a framework, which underwent further refinement through reviews and discussions. The findings proposed supportive strategies to achieve the overarching objective of the study to maximize DAEs’ contributions in the workplace.

Autism spectrum disorder is a developmental condition that affects the social and behavioral abilities of growing children. Early detection of autism spectrum disorder can help children to improve their cognitive abilities and quality of life. The research in the area of autism spectrum disorder reports that it can be detected from cognitive tests and physical activities of children. The present research reports on the detection of autism spectrum disorder from the facial attributes of children. Children with autism spectrum disorder show ambiguous facial expressions which are different from the facial attributes of normal children. To detect autism spectrum disorder from facial images, this work presents an improvised variant of the YOLOv7-tiny model. The presented model is developed by integrating a pyramid of dilated convolutional layers in the feature extraction network of the YOLOv7-tiny model. Further, its recognition abilities are enhanced by incorporating an additional YOLO detection head. The developed model can detect faces with the presence of autism features by drawing bounding boxes and confidence scores. The entire work has been carried out on a self-annotated autism face dataset. The developed model achieved a mAP value of 79.56% which was better than the baseline YOLOv7-tiny and state-of-the-art YOLOv8 Small model.

Macrocybe is a well-studied genus in the family Callistosporiaceae (Basidiomycota). Currently, the genus comprises eight species with worldwide distribution. All species in this genus are relatively large compared to other edible mushrooms and are commonly consumed by locals. Cultivation methodologies have been developed for several species of the genus, including M. gigantea, M. crassa, M. titans, and M. lobayensis. These mushrooms can be cultivated in lignocellulosic wastes such as sawdust, straw, and other agro-industrial wastes. The nutritional compositions have been identified for M. gigantea, M. crassa, and M. lobayensis, revealing that they are rich in fibers, proteins, and various vitamins. Although these mushrooms are of culinary significance, precautions should be taken when consuming them due to their potential cyanic toxicity. In addition to being rich in different nutrients, Macrocybe species possess medicinal properties such as antimicrobial, antioxidant, immunomodulatory, anticancer, anti-inflammatory, hepatoprotective, and several other beneficial effects. Several species are commercially available in countries like China and Thailand, and the commercial potential is high due to the large size, taste, and long shelf life of these mushrooms. There is significant potential for cultivating species of this genus and introducing their artificial cultivation practices to various counties worldwide. Diverse value-added products can also be produced using Macrocybe species.

Vehicular ad hoc networks (VANETs) involve the interconnection of numerous vehicles, enabling them to communicate vital information through a network designed for efficient vehicle-to-vehicle communication. This dynamic connectivity in VANETs allows for spontaneous communication among random vehicles, fostering real-time exchange of critical data such as traffic conditions, road hazards, and other relevant information. This cooperative network improves road safety and traffic efficiency by allowing vehicles to exchange information and respond to the ever-changing conditions in their proximity, leading to an overall enhancement in the transportation system. In this paper, we propose an authentication protocol that remains unconditionally secure against quantum attacks. This paper explores the integration of quantum authentication with blockchain technology to establish a secure framework for VANETs. We introduce a quantum blockchain framework aimed at augmenting the security of VANETs. The paper presents a comprehensive analysis of the quantum blockchain’s potential to mitigate common security threats in VANETs, including data tampering, eavesdropping, and unauthorized access.

Ocular burns due to accidental chemical spillage pose an immediate threat, representing over 20% of emergency ocular traumas. Early detection of the ocular pH is imperative in managing ocular chemical burns. Alkaline chemical burns are more detrimental than acidic chemical burns. Current practices utilize litmus, nitrazine strips, bromothymol blue, fluorescent dyes, and micro-combination glass probes to detect ocular pH. However, these methods have inherent drawbacks, leading to inaccurate pH measurements, less sensitivity, photodegradation, limited pH range, and longer response time. Hence, there is a tremendous necessity for developing relatively simple, accurate, precise ocular pH detection methods. The current review aims to provide comprehensive coverage of the conventional practices of ocular pH measurement during accidental chemical burns, highlighting their strengths and weaknesses. Besides, it delves into cutting-edge technologies, including pH-sensing contact lenses, microfluidic contact lenses, fluorescent scleral contact lenses, fiber optic pH technology, and pH-sensitive thin films. The study meticulously examines the reported work since 2000. The collected data have also helped propose future directions, and the research gap needs to be filled to provide a more rapid, sensitive, and accurate measurement of ocular pH in eye clinics. For the first time, this review consolidates current techniques and recent advancements in ocular pH detection, offering a strategic overview to propel ophthalmic-related research forward and enhance ocular burn management during a chemical spillage.

Food sustainability is crucial aspect in achieving several United Nations (UN) Sustainable Development Goals (SDGs). By integrating advanced technologies for reliable and accurate decision-making, we can advance food sustainability and, consequently, make significant advances toward achieving the UN SDGs. Rice, a staple crop in many Asian and some African nations, is crucial to Sri Lanka as well. Serving as the primary food for most Sri Lankans, it plays a vital role in sustaining the livelihoods of over 1.8 million farmers. In Sri Lanka, rice is grown during two distinct seasons of the year (Yala and Maha). This study focuses on ML with feature engineering for rice yield prediction using weather data: Rainfall, Maximum temperature, Minimum temperature, and Radiation. The data from two districts in Yala and Maha seasons collected from 1982 to 2019 were used for evaluating two sets of models respectively. Data were pre-processed to handle the outliers and missing values and scaled using normalization. The machine learning models considered are Linear Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Random Forest (RF). The performance of these models was evaluated using metrics: Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), and Mean Absolute Error (MAE). The results demonstrate that Random Forest Regression with less number of features can yield comparable results compared to the original set of features.

The complex relationship between economic growth and environmental sustainability remains one of the most pressing global challenges in the context of climate change. This study offers a comprehensive analysis of these trade-offs by leveraging a dynamic panel dataset spanning 170 countries from 2000 to 2020. Using advanced econometric methods, including dynamic panel data models and clustering techniques, this research rigorously tests the Environmental Kuznets Curve hypothesis while exploring the persistence of CO2 emissions and the role of renewable energy in mitigating environmental damage. The key findings indicate that a 1% increase in GDP leads to a 0.42% rise in CO2 emissions, highlighting the entrenched environmental costs of growth, particularly in developing and emerging economies. While renewable energy consumption is negatively correlated with emissions, it has yet to achieve the scale required to significantly offset this growth. Cluster analysis uncovers distinct sustainability profiles, revealing that high-GDP economies continue to face challenges in decoupling growth from emissions, while low-GDP countries show smaller carbon footprints but struggle with scaling renewable technologies. These insights demand differentiated policy strategies: advanced economies must accelerate decarbonization and clean energy innovation, while developing nations should prioritize leapfrogging to renewable technologies with international support. This study contributes to the global debate on sustainability by providing actionable policy recommendations aligned with the Paris Agreement, emphasizing the need for stronger international financial flows to assist low-income countries in achieving sustainable development.

Electrical distribution and communication cables cease to function for transmission when their length is insufficient, and it is considered as it approaches the end of their useful lives. Further, the disposal techniques are not eco-friendly. This study aimed to evaluate the feasibility of cement mortar systems with the inclusion of aluminium fibre extracted from electrical distribution cables. Two diameters of 1.35 mm and 1.70 mm and two lengths of 10 mm and 15 mm fibres were used while incorporating four volume ratios, particularly 0.5%, 1.0%, 1.5%, and 2.0% to evaluate the effect of the length, diameter and volume ratios. The compression test and density test were performed to study the behaviour of Metal Fibre Reinforced Mortar (MFRM) systems under both dry and wet states. Compared to conventional mortar, the ultimate compressive strength of MFRM systems was increased up to 39.4% in 1.5% of fibre addition under the 28-day dry state, where the 1.5% volume ratio showed the best performance under compressive loads. Strain at ultimate strength, modulus of elasticity and strain energy also showed improvements with the fibre inclusion up to 74.4%, 87.3%, and 106.6% respectively. Fibres with higher aspect ratios showed significant effectiveness among the aforementioned fibre variations. The overall results highlighted that the MFRM with 1.5% of fibres performed expertly with 15 mm length and 1.35 mm diameter under compression loads.

Optical Coherence Tomography (OCT) emerged as a technology for the detection of retinal disease. Owing to its excellent performance and ability to provide in-vivo high-resolution images, the popularity increased dramatically across various application domains. Consequently, OCT has been widely used in other branches of medical applications, i.e., oncology and otolaryngology, industry, and agriculture. Despite its widespread use, OCT image analysis is an inherently subjective, laborious, and time-intensive task that requires expertise. Deep learning (DL) stands as the current state-of-the-art method for image analysis. Hence, several research groups have directed their efforts toward incorporating DL algorithms with OCT imaging to reduce the time as well as the subjectivity. This article comprehensively reviews the principal technological advancements in DL methods applied across various OCT applications. Additionally, it explores the latest trends in developing DL methods for OCT, highlights their limitations, and discusses future opportunities in a comprehensive manner.

The fact that on a live bed, boulders tend to sink during scouring is usually ignored, weakening the true understanding of hydrodynamics in boulder beds. In this paper, flume experiments were conducted to investigate the hydrodynamics around a boulder over a movable bed with a particle tracking velocimetry (PTV) system. By measuring the velocity field, the major flow characteristics, such as velocity distribution, turbulent kinetic energy (TKE) and bed shear stress, were analyzed. The results show that the sinking boulder apparently mediates the local flow structure and turbulence pattern. The near wake region is located in the range of 2D (D is the particle size of the boulder) downstream of the boulder. There is a near-bed countercurrent in the near wake region, the TKE increase sharply, and the velocity distribution deviates from the logarithmic distribution. Compared with the flat bed, the turbulent kinetic energy extreme point of the boulder riverbed in the near wake area deviate from the bed surface to the water depth at the top of the boulder, and the direction reversal and extreme point appear at the top of the boulder. The bed shear stress increases sharply in the near wake region of 1.5 ~ 2D.

This study explores the use of virtual reality (VR) as an educational tool to enhance the understanding of electricity and magnetism among 10th-grade students in India. A VR application was developed using Unity and Meta Quest 2 (https://www.meta.com/quest/products/quest-2/; Access Date: 22nd January 2024), featuring thirteen interactive activities-six on electricity and seven on the magnetic effects of electric current. Thirty participants engaged with the VR platform, which offers a hands-on learning experience not provided in the current curriculum. The methodology involved pre- and post-intervention assessments to evaluate the effectiveness of the VR application. Results indicate a significant improvement in students’ comprehension of electricity and magnetism, suggesting that VR can be a cost-effective and engaging supplement to traditional laboratory setups.

Introduction and objectives Sri Lankans do not have a specific cardiovascular (CV) risk prediction model and therefore, World Health Organization(WHO) risk charts developed for the Southeast Asia Region are being used. We aimed to develop a CV risk prediction model specific for Sri Lankans using machine learning (ML) of data of a population-based, randomly selected cohort of Sri Lankans followed up for 10 years and to validate it in an external cohort. Material and methods The cohort consisted of 2596 individuals between 40–65 years of age in 2007, who were followed up for 10 years. Of them, 179 developed hard CV diseases (CVD) by 2017. We developed three CV risk prediction models named model 1, 2 and 3 using ML. We compared predictive performances between models and the WHO risk charts using receiver operating characteristic curves (ROC). The most predictive and practical model for use in primary care, model 3 was named “SLCVD score” which used age, sex, smoking status, systolic blood pressure, history of diabetes, and total cholesterol level in the calculation. We developed an online platform to calculate the SLCVD score. Predictions of SLCVD score were validated in an external hospital-based cohort. Results Model 1, 2, SLCVD score and the WHO risk charts predicted 173, 162, 169 and 10 of 179 observed events and the area under the ROC (AUC) were 0.98, 0.98, 0.98 and 0.52 respectively. During external validation, the SLCVD score and WHO risk charts predicted 56 and 18 respectively of 119 total events and AUCs were 0.64 and 0.54 respectively. Conclusions SLCVD score is the first and only CV risk prediction model specific for Sri Lankans. It predicts the 10-year risk of developing a hard CVD in Sri Lankans. SLCVD score was more effective in predicting Sri Lankans at high CV risk than WHO risk charts.

Tea [Camellia sinensis (L.) O. Kuntze] is a major commercially important crop cultivated in both tropical and subtropical areas for the production of the famous beverage "tea". Gray blight is one of the commonly encountered foliar fungal diseases caused by Pestalotiopsis-like taxa. In this study, fungi isolated from symptomatic tea leaves were characterized based on morphological and molecular data. A combined phylogenetic analysis of the internal transcribed spacer 1, 5.8S and the internal transcribed spacer 2 of the ribosomal RNA gene cluster (ITS), β-tubulin (TUB2) and translation elongation factor 1-α (TEF1-α) revealed multiple species of Pseudopestalotiopsis associated with gray blight in Sri Lanka. Among them, the currently known species of Ps. daweiana, Ps. annellata and Ps. chinensis were identified based on the molecular data derived from ex-type isolates. Additionally, four new species of Pseudopestalotiopsis viz Ps. petchii, Ps. ratnapurensis and Ps. rossmaniae and Ps. srilankensis are introduced herein with descriptions and illustrations. Since young leaves and buds of tea plants are used in manufacturing tea, prevention of the occurrence of foliar diseases is crucial to minimize annual yield loss. This study demonstrates the significance of DNA-aided identification and enhances our understanding of the diversity and distribution of fungi associated with tea, which are crucial for implementing effective disease management strategies.

In today's construction industry, supply chains are subject to much greater disruption than they were in the past, resulting in a greater need for resilience. However, there is a gap in the literature that examines the resilience of construction small and medium scale Enterprises (SMEs) specifically focusing on developing countries. This article is a step towards identifying the factors influencing the resilience of construction SME supply chains taking the case of Sri Lanka: a developing country which is currently amidst a major economic crisis. This research study adopted a mixed-method approach, employing 08 structured interviews with employees ranging from executive level to top level management of 08 construction SMEs followed by a questionnaire survey considering a sample of 195 construction SMEs also with executive level to top level management of each construction SME. The findings indicated that Collaboration, Entrepreneurial Orientation (EO), Internal Integration, and Outsourcing have a positive significant impact on the resilience of Sri Lankan construction SMEs' supply chains during an economic crisis, while 'collaboration' and 'EO' are the most influential factors respectively. Therefore, construction SMEs must prioritize and enhance collaboration and EO when devising supply chain strategies to strengthen resilience during economic crises. This paper contributes to filling the research gap by investigating factors influencing construction SME supply chains in a developing country during an economic crisis. Moreover, it contributes to the knowledge by being one of the latest empirical studies focusing on the construction SME supply chains in Sri Lanka. The findings provide a valuable reference for both policymakers and practitioners seeking to improve the resilience of construction SME supply chains.