Job Restructuring and Socio-Economic Security in Sri Lanka

After the economic liberalization in Sri Lanka, employment practices in the manufacturing industry and tertiary sector have changed gradually from traditional contractual arrangement and other terms and conditions of the work to nonstandard work arrangement or atypical employment practices. First part of this article distinguishes different types of atypical employment patterns and explains key features of the new type of employment relationship in Sri Lanka. Second part of the article, describes the major changes in the job security and its impact on socio-economic security in Sri Lanka by using the data from 600 Manufacturing Enterprises of the Enterprises Labor Flexibility & Security Survey (ELFS) for 2005-2006 conducted in manufacturing industry by the Social Policy Analysis and Research Center (SPARC) of the University of Colombo. Key Words: Atypical Employment, Typical Employment, Job Security, Job Restructuring, Human Resource Managemen

Moving from Offline to Online: Addressing the Cancer Spectrum Through the National Strategic Plan on Cancer Prevention and Control, Sri Lank - 2020-2024

Introduction: Sri Lanka is committed to achieving the global targets at the end of 2025 by a 25% reduction in premature mortality from cancer by intervening from prevention, early detection, treatment palliative care, and surveillance. Current Sri Lankan evidence shows a gradual increase of cancers where many are preventable by risk factor reduction. According to the data of the National Cancer Registry, Sri Lanka in 2019, there were 14,845 (47%) males and 16,989 (53%) females diagnosed with cancer with a total of 31844. One of the recommendations of the imPACT review 2019 was to develop the NSP to achieve quality of life by preventing and control of cancer in Sri Lanka. National Cancer Control Programme, Ministry of Health, Sri Lanka gave the leadership to develop the National Strategic Plan (2020-2024) that provided a direction for the country for cancer prevention and control. Methods: Based on the local and international evidence initial draft was prepared by the NCCP. Feedback was obtained via emails and virtual meetings from all the stakeholders (medical professional colleges, UN organizations, non-government organizations, community-based organizations) due to COVID 19 physical meeting restrictions. This was followed by a series of virtual meetings with the same stakeholders and finalizing the document. Results: The NSP was developed for the year 2020-2024 as a guiding document for Sri Lanka’s response to cancer control and prevention with a vision and a goal to be achieved in cancer prevention and control in the country. This document has laid down seven strategic objectives and seven strategic directions to serve as pillars for the cancer spectrum with a detailed activity plan under each strategic direction. Conclusion and recommendations: The NSP was developed to reduce cancer incidence and mortality via evidence-based strategic objectives of prevention, early detection, diagnosis, treatment and palliation, surveillance, and research with an activity plan. It was recommended to have close monitoring, mid-term and end of five-year reviews to assess its progress

Green Human Resource Management Practices: A Review

The objective of this review is to explore green human resource management practices of organisations based on the existent literature. In this emerging field, it has been generally observed that the existent literature has to be extended further from the perspective of functions of Human Resource Management (HRM). It reveals that much of the past research focused on a few functions of HRM such as recruitment, training and development, performance evaluation and reward management in integrating environmental management with HRM though HRM has more potential and scope in improving organisation’s environmental performance. Hence, this review incorporates diverse functions of HRM to explore the respective green HRM practices under those functions. The findings of the review have identified and highlighted several green HRM practices under the 12 functions of HRM such as job design, job analysis, human resource planning, recruitment, selection, induction, performance evaluation, training and development, reward management, discipline management, health and safety management and employee relations. The contribution of this paper lies in extending the scope and depth of green HRM in materializing sustainable environmental performance of organisations. Key Words: Green, Human Resource Management, Green HRM, Practices, Organisatio

Employee Green Performance of Job: A Systematic Attempt towards Measurement

Though there is a growing interest among academicians and practitioners in respect of green aspects of Human Resource Management (HRM) there is a gap in the theoretical as well as empirical knowledge about environmental related job performance of an employee in an organization. A systematic review of literature reveals that less attention has been paid in valuing, developing, measuring and making use of employee green performance of job by the organizations even though this phenomenon exists in practice. The objectives of this paper are: to introduce a key concept called „employee green performance of job, to define it nominally, and to identify key dimensions of the concept for the purpose of measuring it in reality based on systematic review of literature. Our attempt resulted in identifying three key dimensions and eleven elements which will be useful for developing an instrument to measure the concept.  Key Words: Green Performance of Job, Green Human Resource Management, Employee, Corporate Environmental Managemen

Molecular determinants of allosteric modulation at the M1 muscarinic acetylcholine receptor

Background: BQCA is a selective allosteric modulator of the M1 mAChR. Results: Residues that govern BQCA activity were identified using mutagenesis and molecular modelling. Conclusion: BQCA likely occupies a pocket overlapping prototypical mAChR modulators and gains selectivity through coop- erativity with orthosteric ligands. Significance: Understanding the structural basis of BQCA function can provide insight into the design of more tailored allosteric ligands

Self-building Artificial Intelligence and machine learning to empower big data analytics in smart cities

YesThe emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the selfbuilding AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications.Supported by the Data to Decisions Cooperative Research Centre (D2D CRC) as part of their analytics and decision support program and a La Trobe University Postgraduate Research Scholarship

Compassionate communities and cities in low and middle-income countries : a systematic review protocol to identify transferrable lessons for implementation in the primary care context

Introduction: Compassionate Communities are neighbourhood or village level organizations that addresses the holistic concept of health, physical, psychological, spiritual and social well-being. It is estimated that out of the 40 million individuals who require palliative care each year, 78 per cent reside in low- and middle-income countries. Objective: Compassionate Communities and Cities are based on a health promotion approach to palliative care, with the goal of supporting individuals in solidarity at the end of life. This systematic review aims to analyse Compassionate Communities and Cities initiatives in low- and middle-income countries to identify transferrable lessons for implementation in the primary care context. Methods: A comprehensive, search for studies indexed in Medline, EMBASE, PsycINFO, Web of Science and Scopus databases and grey literature will be conducted using an electronic search strategy. The search strategy will include terms (and synonyms) describing Compassionate Communities and Cities, palliative care and low- and middle-income countries. Article screening will be conducted in two rounds. First, a title and abstract screening will be conducted followed by a full-text screening. Article selection will be conducted by two independent reviewers. Studies or articles fulfilling the inclusion criteria will be evaluated using a data extraction form. Data on objectives, study population, study setting, study characteristics and outcomes will be independently extracted by two reviewers. The reviewers will independently assess the methodological quality of included studies. A narrative synthesis approach will be employed to summarise and explain the extracted data. Results: The systemic review will be carried out and documented in accordance with the Preferred Reporting Items for Systematic Reviews and MetaAnalyses. Conclusions: This systematic review will summarise the evidence and provide recommendations for the development and the implementation of Compassionate Communities and Cities approach in other low resource settings

Kidney Tumor Detection using Attention based U-Net

The advancement of deep learning techniques has provoked the potential of using Medical Image Analysis (MIA) for disease detection and prediction in numerous ways. This has been mostly useful in identifying tumours and abnormalities in many organs of the human body. Particularly in kidney diseases, the treatment options such as surgery have largely benefitted by the ability to detect tumours in early stages, thereby shifting towards more efficient methods including conservative nephron procedures. Therefore, to enable the early detection of kidney tumours, we propose a convolutional neural network based U-Net architecture which is able to detect tumours using an attention mechanism. The proposed architecture was evaluated using KiTS19 Challenge dataset that includes a collection of multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes for 300 patients who underwent nephrectomy for kidney tumours. The outcomes demonstrate the ability of the proposed architecture to distinguish images with tumours in the kidney and support early tumour detection