University of Moratuwa

SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.

One of the key problems arises in binary logistic regression model is that explanatory variables being considered for the logistic regression model are highly correlated among themselves. Multicollinearity will cause unstable estimates and inaccurate variances that affects confidence intervals and hypothesis tests. Aim of this was to discuss some diagnostic measurements to detect multicollinearity namely tolerance, Variance Inflation Factor (VIF), condition index and variance proportions. The adapted diagnostics are illustrated with data based on a study of road accidents. Secondary data used from 2014 to 2016 in this study were acquired from the Traffic Police headquarters, Colombo in Sri Lanka. The response variable is accident severity that consists of two levels particularly grievous and non-grievous. Multicolinearity is identified by correlation matrix, tolerance and VIF values and confirmed by condition index and variance proportions. The range of solutions available for logistic regression such as increasing sample size, dropping one of the correlated variables and combining variables into an index. It is safely concluded that without increasing sample size, to omit one of the correlated variables can reduce multicollinearity considerably.

The ongoing COVID-19 disease significantly affects not only human health, it also affects the wealth of country' economy and everyday routine of human life. To control the spread of the virus, face mask is used as primary personal protective equipment (PPE). Thus, the production and usage of face masks significantly increase as the COVID-19 pandemic still escalating. Further, most of these masks contain plastics or other derivatives of plastics. Therefore, this extensive usage of face masks generates million tons of plastic wastes to the environments in a short span of time. This study aims to investigate the environmental impact induced by face mask wastes and sustainable solution to reduce this waste. An online survey was carried out to identify the types of face mask and number of masks used per week by an individual from 1033 people. Based on this survey and available literature, this study quantifies the amount of plastics waste generated by face masks. However, this survey was limited with certain ages, country and durations (July-August 2020). Thus, the prediction of plastic waste generation, only provide fundamental knowledge about the mask wastes. Results revealed that there is a huge plastic waste remained in land and marine environment in the form of mask waste, which will contribute to micro-plastic pollution. Therefore, this paper also highlights the sustainable approach to the mask production by integrating the use of natural plant fiber in the woven face mask technology to reduce the plastic waste induced by masks. Further, upcycling the mask waste and producing construction materials also discussed.

Abstract Additive manufacturing technology has attracted unprecedented attention in industry because it provides infinite possibilities for rapid prototyping and enormous potential and opportunities for the production of electronic devices of various complex shapes. This paper focuses on the progress of additive manufacturing of piezoelectric materials in recent years, summarizing the advantages of additive manufacturing technology and its technical impact on the production of piezoelectric materials. It identifies the likely future research development direction of additive manufacturing technology, as well as the limitations of the current additive manufacturing technology in the context of piezoelectric materials. First, the various processes involved in additive manufacturing of piezoelectric devices and the impact of various processes on device performance are systematically summarized. Then, the development of additive manufacturing technology and slurry preparation of piezoelectric ceramic materials, piezoelectric polymer materials, and piezoelectric composites are introduced. Finally, the technical difficulties and potential research directions associated with additive manufacturing of piezoelectric materials are discussed. With the continuous development of additive manufacturing technology and piezoelectric materials, additive manufacturing of piezoelectric devices with excellent performance, high energy storage density, and high electromechanical conversion efficiency will be applied in a wider range of fields in the future.

Abstract Recent urban microclimate studies in Colombo, Sri Lanka, indicate that the maximum daily temperature within street canyons decreases with increasing height to width (H/W) ratio, but higher H/W ratio negatively affects street‐level wind flow. There is also evidence pointing to the cooling effect of sea breeze. The nocturnal heat island is small in contrast to daytime urban–rural differences. In this paper, we use the software ENVI‐met to simulate the effect of different urban design options on air and surface temperatures, as well as on outdoor thermal comfort. The latter is expressed as the physiologically equivalent temperature (PET), an index based on air and radiant temperatures as well as wind and humidity. It is found that high albedo at street level gives the lowest air temperature during daytime, although the reduction is only about 1 °C. The lowest daytime mean radiant temperatures result from high H/W ratios of streets. This has a positive effect on thermal comfort; the increase of H/W ratio from about 1 to 3 leads to a decrease in PET by about 10 °C. Differences in air and surface temperatures, as well as PET, are small during the night. The results show that strategies that lead to better air temperature mitigation may not necessarily lead to better thermal comfort. However, shade enhancement through increased H/W ratios is clearly capable of significant reductions in PET, and thus, improved outdoor thermal comfort. Consequently, a critical urban design task in the humid tropics will be to guide the rapid urban growth towards efficient ‘shade growth’. Copyright © 2007 Royal Meteorological Society

In this paper, we investigate the two-way communication between two users assisted by a reconfigurable intelligent surface (RIS). The scheme that two users communicate simultaneously over Rayleigh fading channels is considered. The channels between the two users and RIS can either be reciprocal or non-reciprocal. For reciprocal channels, we determine the optimal phases at the RIS to maximize the signal-to-interference-plus-noise ratio (SINR). We then derive exact closed-form expressions for the outage probability and spectral efficiency for single-element RIS. By capitalizing the insights obtained from the single-element analysis, we introduce a gamma approximation to model the product of Rayleigh random variables which is useful for the evaluation of the performance metrics in multiple-element RIS. Asymptotic analysis shows that the outage decreases at (log(ρ)/ρ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sup> rate where L is the number of elements, whereas the spectral efficiency increases at log(ρ) rate at large average SINR p. For non-reciprocal channels, the minimum user SINR is targeted to be maximized. For single-element RIS, closed-form solution is derived whereas for multiple-element RIS the problem turns out to be non-convex. The latter one is solved through semidefinite programming relaxation and a proposed greedy-iterative method, which can achieve higher performance and lower computational complexity, respectively.

In the present day, there are many diseases which need to be identified at their early stages to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason, there is a need of analysing complex medical data, medical reports, and medical images at a lesser time but with greater accuracy. There are even some instances where certain abnormalities cannot be directly recognized by humans. In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden relationships or abnormalities which are not visible to humans. Implementing algorithms to perform such tasks itself is difficult, but what makes it even more challenging is to increase the accuracy of the algorithm while decreasing the required time for the algorithm to execute. In the early days, processing of large amount of medical data was an important task which resulted in machine learning being adapted in the biological domain. Since this happened, the biology and biomedical fields have been reaching higher levels by exploring more knowledge and identifying relationships which were never observed before. Reaching to its peak now the concern is being diverted towards treating patients not only based on the type of disease but also their genetics, which is known as precision medicine. Modifications in machine learning algorithms are being performed and tested daily to improve the performance of the algorithms in analysing and presenting more accurate information. In the healthcare field, starting from information extraction from medical documents until the prediction or diagnosis of a disease, machine learning has been involved. Medical imaging is a section that was greatly improved with the integration of machine learning algorithms to the field of computational biology. Nowadays, many disease diagnoses are being performed by medical image processing using machine learning algorithms. In addition, patient care, resource allocation, and research on treatments for various diseases are also being performed using machine learning-based computational decision making. Throughout this paper, various machine learning algorithms and approaches that are being used for decision making in the healthcare sector will be discussed along with the involvement of machine learning in healthcare applications in the current context. With the explored knowledge, it was evident that neural network-based deep learning methods have performed extremely well in the field of computational biology with the support of the high processing power of modern sophisticated computers and are being extensively applied because of their high predicting accuracy and reliability. When giving concern towards the big picture by combining the observations, it is noticeable that computational biology and biomedicine-based decision making in healthcare have now become dependent on machine learning algorithms, and thus they cannot be separated from the field of artificial intelligence.

Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds. Self-supervised learning, which operates without any human labeling, is a promising approach to address this issue. We observe in the real world that humans are capable of mapping the visual concepts learnt from 2D images to understand the 3D world. Encouraged by this insight, we propose CrossPoint, a simple cross-modal contrastive learning approach to learn transferable 3D point cloud representations. It enables a 3D-2D correspondence of objects by maximizing agreement between point clouds and the corresponding rendered 2D image in the invariant space, while encouraging invariance to transformations in the point cloud modality. Our joint training objective combines the feature correspondences within and across modalities, thus ensembles a rich learning signal from both 3D point cloud and 2D image modalities in a self-supervised fashion. Experimental results show that our approach outperforms the previous unsupervised learning methods on a diverse range of downstream tasks including 3D object classification and segmentation. Further, the ablation studies validate the potency of our approach for a better point cloud understanding. Code and pretrained models are available at https://github.com/MohamedAfham/CrossPoint.

The demand for fresh water in Hambantota District, Sri Lanka is rapidly increasing with the enormous amount of ongoing development projects in the region. Nevertheless, the district experiences periodic water stress conditions due to seasonal precipitation patterns and scarcity of surface water resources. Therefore, management of available groundwater resources is critical, to fulfil potable water requirements in the area. However, exploitation of groundwater should be carried out together with artificial recharging in order to maintain the long term sustainability of water resources. In this study, a GIS approach was used to delineate potential artificial recharge sites in Ambalantota area within Hambantota. Influential thematic layers such as rainfall, lineament, slope, drainage, land use/land cover, lithology , geomorphology and soil characteristics were integrated by using a weighted linear combination method. Results of the study reveal high to moderate groundwater recharge potential in approximately 49% of Ambalantota area.

Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since the early 2000s and has already entered a mature phase. While considered the most widely used solution for Machine Translation, its performance on low-resource language pairs remains sub-optimal compared to the high-resource counterparts due to the unavailability of large parallel corpora. Therefore, the implementation of NMT techniques for low-resource language pairs has been receiving the spotlight recently, thus leading to substantial research on this topic. This article presents a detailed survey of research advancements in low-resource language NMT (LRL-NMT) and quantitative analysis to identify the most popular techniques. We provide guidelines to select the possible NMT technique for a given LRL data setting based on our findings. We also present a holistic view of the LRL-NMT research landscape and provide recommendations to enhance the research efforts further.

Purpose The purpose of this study is to explore employability skills that employers, university lecturers and graduates value to bring to the workplace, when graduates are applying for entry‐level graduate jobs in the field of computer science in Sri Lanka. Design/methodology/approach A total of three samples were selected for this exploratory study, namely, graduates, employers, and university lecturers. Three self‐administered survey questionnaires were developed targeting the three groups. In addition to descriptive statistics, paired sample t ‐test, Analysis of Variance (ANOVA) and correlation analysis were used for the data analysis. Findings The findings suggested that there are differences in the priorities given for employability skills by the four groups – male graduates, female graduates, employers, and university lecturers. Further, the findings suggest that employability skills are influenced by the gender of the graduates. Overall, the findings of the study could be used to assist universities, graduates, employers, and career advisers in applying strategic decisions in managing graduates' careers. Originality/value Although a considerable amount of the literature addresses employability skills, much of the information is theoretical in nature and offers policy recommendations and prescriptive advice. Further, a majority of the research studies has primarily examined the experiences of a particular higher educational institute where remedial actions were taken to impart employability skills. The paper presents findings of a survey that investigated and compared employability skills that employers, university lecturers and graduates value to bring to the workplace when graduates are applying for entry‐level graduate jobs.

With the predicted depletion of natural resources and alarming environmental issues, sustainable development has become a popular as well as a much-needed concept in modern process industries. Hence, manufacturers are quite keen on adopting novel process monitoring techniques to enhance product quality and process efficiency while minimizing possible adverse environmental impacts. Hardware sensors are employed in process industries to aid process monitoring and control, but they are associated with many limitations such as disturbances to the process flow, measurement delays, frequent need for maintenance, and high capital costs. As a result, soft sensors have become an attractive alternative for predicting quality-related parameters that are ‘hard-to-measure’ using hardware sensors. Due to their promising features over hardware counterparts, they have been employed across different process industries. This article attempts to explore the state-of-the-art artificial intelligence (Al)-driven soft sensors designed for process industries and their role in achieving the goal of sustainable development. First, a general introduction is given to soft sensors, their applications in different process industries, and their significance in achieving sustainable development goals. AI-based soft sensing algorithms are then introduced. Next, a discussion on how AI-driven soft sensors contribute toward different sustainable manufacturing strategies of process industries is provided. This is followed by a critical review of the most recent state-of-the-art AI-based soft sensors reported in the literature. Here, the use of powerful AI-based algorithms for addressing the limitations of traditional algorithms, that restrict the soft sensor performance is discussed. Finally, the challenges and limitations associated with the current soft sensor design, application, and maintenance aspects are discussed with possible future directions for designing more intelligent and smart soft sensing technologies to cater the future industrial needs.

With the explosive growth in the number of vehicles in use, automated license plate recognition (ALPR) systems are required for a wide range of tasks such as law enforcement, surveillance, and toll booth operations. The operational specifications of these systems are diverse due to the differences in the intended application. For instance, they may need to run on handheld devices or cloud servers, or operate in low light and adverse weather conditions. In order to meet these requirements, a variety of techniques have been developed for license plate recognition. Even though there has been a notable improvement in the current ALPR methods, there is a requirement to be filled in ALPR techniques for a complex environment. Thus, many approaches are sensitive to the changes in illumination and operate mostly in daylight. This study explores the methods and techniques used in ALPR in recent literature. We present a critical and constructive analysis of related studies in the field of ALPR and identify the open challenge faced by researchers and developers. Further, we provide future research directions and recommendations to optimize the current solutions to work under extreme conditions.

Using a micro-scale urban simulation program, we examined the sensitivity of air temperature and mean radiant temperature (MRT) of built-up urban cores to urban-area geometry (the density of buildings), thermal properties of human-made surfaces (albedo) and green cover (street trees), in 2 warm-climate cities: Pettah, Colombo (Sri Lanka) and downtown Phoenix, Arizona (USA). Air temperature and MRT are indicative of human thermal comfort, and their rural/urban gradients signify the urban heat island (UHI) effect. Although high albedo values lead to low daytime temperatures in both cities, the best thermal comfort, quantified by both the air temperature and MRT, was found in high-density development. Thus, density enhancement is a viable UHI mitigation option in built-up areas of warm climate cities. Manipulation of thermal properties is an alternative strategy, but the practical utility of high albedo surfaces is questionable. Additionally, some UHI mitigation options are more likely to bring improvements in MRT than in air temperature. Urban designers should use mitigation options that are based on human comfort, which is determined by both MRT and air temperature, rather than simply attempting to control air temperature alone.

Abstract Integrating electronics with clothing and the human body to support people's lifestyle is quickly becoming a reality. Some of these electronics, including health sensors, communication devices, and personal electronics, contain the potential to revolutionize life in the future. One of the most demanding aspects of such electronic designs is their power supply systems, which necessitate not only a continuous power supply, but also wearable characteristics and durability, which many conventional power‐supply methods have failed to fulfill. The triboelectric nanogenerator (TENG), which depends on static charging between materials, can convert mechanical vibrations into electricity. TENGs are foreseen as a leading candidate to power wearable electronics due to their advantages such as high instantaneous power outputs and efficiency, low cost, ease of fabrication, lightweight, and wearability. This paper is a comprehensive review on the most prominent wearable TENG categories; textiles‐based TENGs for clothing applications, footwear‐incorporated TENG designs, and other TENG accessories. Herein, the most important developments in these categories, with a focus on their materials, fabrication, features, advantages, and drawbacks, are examined. Finally, a detailed analysis is provided on the main challenges impeding the progress of wearable TENGs with the insights into potential improvement techniques, targeting the widespread commercialization of this technology.

Purpose The construction industry consumes large amounts of natural resources, which are not properly utilised owing to the generation of waste. Construction waste has challenged the performance of the industry and its sustainable goals. The majority of the causes underlying material waste are directly or indirectly affected by the behaviour of the construction workforce. Waste occurs on site for a number of reasons, most of which can be prevented, particularly by changing the attitudes of the construction workforce. Therefore, the attitudes and perceptions of the construction workforce can influence the generation and implementation of waste management strategies. The research reported in this paper is based on a study aimed at evaluating the attitudes and perceptions of the construction workforce involved during the pre‐ and post‐contract stages towards minimising waste. Design/methodology/approach A structured questionnaire survey was carried out to understand and evaluate the attitudes and perceptions of the workforce. Four types of questionnaires were prepared for project managers/site managers, supervisors, labourers, and estimators. Findings The findings indicate the positive perceptions and attitudes of the construction workforce towards minimising waste and conserving natural resources. However, a lack of effort in practising these positive attitudes and perceptions towards waste minimisation is identified. The paper further concludes that negative attitudes towards subordinates, attitudinal differences between different working groups, and a lack of training to reinforce the importance of waste minimisation practices have obstructed proper waste management practices in the industry. Originality/value The paper reveals the effect of the attitudes and perceptions of the construction workforce towards waste management applications, which would be of benefit to construction managers in designing and implementing better waste management practices.

Urbanisation leads to increased thermal stress in hot-humid climates due to increased surface and air temperatures and reduced wind speed. We examined the influence of urban morphology and sea breeze on the microclimate of Colombo, Sri Lanka. Air and surface temperatures, humidity and wind speed were measured at 1 rural and 5 urban sites during the warmest season. The urban sites differed in their height to width (H/W) ratio, ground cover and distance to the sea. Intra-urban air temperature differences were greatest during the daytime. A maximum intra-urban difference of 7 K was recorded on clear days. Maximum temperatures tended to decrease with increasing H/W ratio and proximity to the sea. All urban sites experienced a nocturnal urban heat island (UHI) when the sky was clear or partly cloudy. The temperature differences between sunlit and shaded urban surfaces reached 20 K, which shows the importance of shade in urban canyons (reducing long-wave radiation from surfaces). Within the urban areas, the vapour pressure was high (> 30 hPa) and showed little diurnal variation. Wind speeds were low (< 2 m s -1 ) and tended to decrease with increasing H/W ratio. Shading is proposed as the main strategy for lowering air and radiant temperatures; this can be achieved by deeper canyons, covered walkways and shade trees. It is also suggested to open up wind corridors perpendicular to the sea to facilitate deeper sea breeze penetration.

Abstract With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, Web-mined text datasets covering hundreds of languages. We manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4). Lower-resource corpora have systematic issues: At least 15 corpora have no usable text, and a significant fraction contains less than 50% sentences of acceptable quality. In addition, many are mislabeled or use nonstandard/ambiguous language codes. We demonstrate that these issues are easy to detect even for non-proficient speakers, and supplement the human audit with automatic analyses. Finally, we recommend techniques to evaluate and improve multilingual corpora and discuss potential risks that come with low-quality data releases.

Opinion mining and sentiment analysis is a fast growing topic with various world applications, from polls to advertisement placement. Traditionally individuals gather feedback from their friends or relatives before purchasing an item, but today the trend is to identify the opinions of a variety of individuals around the globe using microblogging data. This paper discusses an approach where a publicised stream of tweets from the Twitter microblogging site are preprocessed and classified based on their emotional content as positive, negative and irrelevant; and analyses the performance of various classifying algorithms based on their precision and recall in such cases. Further, the paper exemplifies the applications of this research and its limitations.

This paper examines the effect of building shape, zones, orientation and window to wall ratio (WWR) on the lighting energy requirement and the thermal comfort in the naturally ventilated houses in tropical climate. The lighting electricity and the adaptive thermal discomfort hours (ASHRAE 55 80% acceptability) of 300 different models of two-storey houses were obtained using Design Builder simulation software. The models were developed for three building shapes (square, rectangle and L-shaped) and the orientation of each model was changed for 24 orientations and four window to wall ratios. Results indicate that the rectangular shape with staircase positioned in the middle of the house will provide higher thermal comfort for WWR of 20 and for other WWRs the L-shaped models provide higher thermal comfort when the staircase is positioned at the short corner or middle. The square-shaped houses with staircase at the middle have the highest lighting electricity and the L shape has the lowest lighting electricity. Further, WWR changes the thermal comfort by 20–55% and the percentage change in lighting electricity due to WWR is only 1.5–9.5%. Therefore, thermal comfort should receive more attention in deciding the WWR. Moreover, the results show an effect when the zone sizes and location change.