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Embedding sustainability into pedagogical approaches is a key priority in higher education. Equipping students with knowledge, understanding, and skills, and developing the next generation of innovators and leaders, can potentially provide the change needed and create a real impact in the journey to a sustainable future. Advancement in Science, Technology, Engineering, and Mathematics (STEM), and corresponding industries can be seen as vital for the success of meeting a majority of the sustainable development goals, and hence the approaches taken to embed sustainability in learning and teaching in STEM higher education programmes can be considered significant in many ways. This paper is based on published literature over the last two decades and a semi-structured interview with 12 university academics from a developing country. Hence, the paper reviews approaches taken to embed sustainability in learning, teaching and assessments in STEM programmes. It also aims to investigate the actions taken by universities to integrate sustainability in STEM education and the remedies taken to minimise the impact of the pandemic on the effectiveness of the learning pedagogies used to integrate sustainability concepts.

Diversity in use of Question and Answering (Q/A) is evolving as a popular application in the area of Natural Language Processing (NLP). The alive unsupervised word embedding approaches are efficient to collect Latent-Semantic data on number of tasks. But certain methods are still unable to tackle issues such as polysemous-unaware with task-unaware phenomena in NLP tasks. GloVe understands word embedding by availing information statistics from word co-occurrence matrices. Nevertheless, word-pairs in the matrices are taken from a pre-established window of local context, which may result in constrained word-pairs and also probably semantic inappropriate word-pairs. SemGloVe employed in this paper, refines semantic co-occurrences from BERT into static GloVe word-embedding with Bidirectional-Long-Short-Term-Memory (BERT- Bi-LSTM) model for text categorization in Q/A. This method utilizes the CR23K and CR1000k datasets for the effective text classification of NLP. The proposed model, with SemGloVe Embedding on BERT combined with Bi-LSTM, produced better results on metrics like accuracy, precision, recall, and F1 Score as 0.92, 0.79, 0.85, and 0.73, respectively, when compared to existing methods of Text2GraphQL, GPT-2, BERT and SPARQL. The BERT model with Bi-LSTM is better in every way for responding to different kinds of questions.

Exploration of blend polymer membrane with desire performances is a highly essential research topic because it makes them as key component of the battery technologies. However, the blend membrane cannot simultaneously meet out the high electrical insulation and ion conductivity. Here, we report porous PVdF-HFP based polymer membrane blended with P123 polymer which is prepared by phase inversion technique and along with solution casting technique for comparison. Especially, the major improvements can be claimed with porous PVdF-HFP blend with P123 matrix by phase inversion process that could be mainly due to increasing the disorganize crystallinity of the PVdF-HFP, thereby enhancing the ionic conductivity of PVdF-HFP/P123 than the same prepared by traditional solution casting. More importantly, our studies reveals that, besides having an effect of crystallinity and high ionic conduction PVdF-HFP/P123 blend matrix yield homogeneous pores with higher pore density when prepared by phase inversion approach. We attribute this to remarkable enhancement in higher electrolyte uptake and higher cation transport in the blend membrane. This facilitates high ionic conductivity of 7.06 × 10−5 S/cm and electrochemical stability of 2.8 V,while the solution casting approach display low ionic conductivity of 1.63 × 10−6 S/cm and electrochemical stability of 2.2 V, respectively. A symmetric supercapacitor device is fabricated with the membrane prepared by phase inversion process, resulting in a high specific capacitance of 80 F/g at the current density of 0.25 A/g with 90.51% specific capacitance retention at 0.5 A/g. The energy density of 90.9 W/kg can be achieved at the power density of 0.375 W/kg.

The impact of the COVID-19 pandemic was diverse and disproportionate among nations, and population segments. The impacts of the disease and the containment strategies adopted are broad and cut across multiple facets of life, society, and the economy, which are intimately interlinked. Therefore, a large household survey was conducted to ascertain the socioeconomic impact and human behavior changes due to the pandemic and the containment strategies covering all provinces of Sri Lanka. The ramifications on mobility and human behavior, income, economic status, food consumption, education, access to health services and information, and cultural and psychological changes were explored, and the data are reported in this paper. The survey was conducted on 3020 households, selected using a multistage clustering technique, to assess the impacts of the pandemic through three distinctly identified waves/phases of the pandemic in Sri Lanka. This dataset will enable researchers and policymakers to analyze the impact of the pandemic through a multifaceted perspective enabling a more holistic approach to decision-making.

A great number of universities worldwide are having their education interrupted, partially or fully, by the spread of the novel coronavirus (COVID-19). Consequently, an increasing number of universities have taken the steps necessary to transform their teaching, including laboratory workshops into an online or blended mode of delivery. Irrespective of the measures taken, universities must continue to maintain their high academic standards and provide a high-quality student experience as required for delivery of learning outcomes associated with each degree programme. This has created a challenge across the higher education landscape, where academics had to switch to remote teaching and different approaches to achieving laboratory delivery. As a result, students have not been receiving face-to-face teaching, and access to laboratory facilities has been limited or nearly impossible. This paper reviews numerous approaches taken by universities to deliver teaching and laboratory practices remotely, in consideration of the COVID-19 pandemic, whilst also considering the potential impacts on the student learning experience. This review is primarily focused on the fields of engineering, science and technology, based on published literature including books, reviewing web-based provision of selected universities, institutional and national policy documents.

Early diagnosis of pneumonia is crucial to increase the chances of survival and reduce the recovery time of the patient. Chest X-ray images, the most widely used method in practice, are challenging to classify. Our aim is to develop a machine learning tool that can accurately classify images as belonging to normal or infected individuals. A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing—classical, quantum, and hybrid—an attractive approach to explore. In this study, we offer a comparison between different methods: (1) a classical state-of-the-art implementation of SVM (LibSVM); (2) solving SVM with a classical solver (Gurobi), with and without decomposition; (3) solving SVM with simulated annealing; (4) solving SVM with quantum annealing (D-Wave); and (5) solving SVM using Graver Augmented Multi-seed Algorithm (GAMA). GAMA is tried with several different numbers of Graver elements and a number of seeds using both simulating annealing and quantum annealing. We found that simulated annealing and GAMA (with simulated annealing) are comparable, provide accurate results quickly, competitive with LibSVM, and superior to Gurobi and quantum annealing.

This school-based cross-sectional study, conducted among 872 grade thirteen students in Sri Lanka, assessed the complex relationship between burnout and work engagement using the validated Maslach Burnout Inventory-Student Survey (MBI-SS) and Utrecht Work Engagement Scale-Student Version (UWES-S). In canonical correlation analysis (CCA), three subscale scores of MBI-SS [Exhaustion (EX), Cynicism (CY), reduced Professional Efficiency (rPE)] and three subscale scores of UWES-S [Vigor (VI), Dedication (DE), Absorption (AB)] were used as the criterion and predictor variables respectively. The full model across three functions yielded by CCA was statistically significant (p < 0.001), explaining 64.7% of the variance between the variable sets. The dimension reduction analysis considered the first two functions accounted for 52.9% and 22.7% of shared variance respectively. In Function 1, EX was the most relevant criterion variable contributing to burnout [structure coefficient (rs) = 0.998], while all three variables were positively related. Furthermore, VI (rs = -0.959) and AB (rs = -0.899) variables were the primary contributors to work engagement. The canonical correlation between the burnout and work engagement variables was found to be high (0.727). This complex, strong relationship between burnout and work engagement among students provides a platform to develop evidence-informed policies focusing on student engagement as a means of promoting mental well-being of students., Author(s): Nuwan Darshana Wickramasinghe [sup.1] , Devani Sakunthala Dissanayake [sup.2] , Gihan Sajiwa Abeywardena [sup.3] Author Affiliations: (1) grid.430357.6, 0000 0004 0433 2651, Department of Community Medicine - Faculty of [...]

Abstract Background Treatment decisions in prostate cancer (PCa) rely on disease stratification between localised and metastatic stages, but current imaging staging technologies are not sensitive to micro-metastatic disease. Circulating tumour cells (CTCs) status is a promising tool in this regard. The Parsortix® CTC isolation system employs an epitope-independent approach based on cell size and deformability to increase the capture rate of CTCs. Here, we present a protocol for prospective evaluation of this method to predict post radical prostatectomy (RP) PCa cancer recurrence. Methods We plan to recruit 294 patients diagnosed with unfavourable intermediate, to high and very high-risk localised PCa. Exclusion criteria include synchronous cancer diagnosis or prior PCa treatment, including hormone therapy. RP is performed according to the standard of care. Two blood samples (20 ml) are collected before and again 3-months after RP. The clinical team are blinded to CTC results and the laboratory researchers are blinded to clinical information. Treatment failure is defined as a PSA ≥ 0.2 mg/ml, start of salvage treatment or imaging-proven metastatic lesions. The CTC analysis entails enumeration and RNA analysis of gene expression in captured CTCs. The primary outcome is the accuracy of CTC status to predict post-RP treatment failure at 4.5 years. Observed sensitivity, positive and negative predictive values will be reported. Specificity will be presented over time. Discussion CTC status may reflect the true potential for PCa metastasis and may predict clinical outcomes better than the current PCa progression risk grading systems. Therefore establishing a robust biomarker for predicting treatment failure in localized high-risk PCa would significantly enhance guidance in treatment decision-making, optimizing cure rates while minimizing unnecessary harm from overtreatment. Trial registration ISRCTN17332543.

Introduction: Bullying is a significant peer problem among school going adolescents which has many reported undesirable consequences. Identification of contributory factors of victimization is beneficial in addressing the bullying prevention programs. Objectives: To identify and explore the contributory factors of bullying victimization among school going adolescents in selected educational zone in Sri Lanka. Methods: Focus group discussions (FGD) were conducted among grade eight students from six randomly selected schools from each category of schools in the Kegalle educational zone. A convenient sampling technique was used to select the participants for each FGD. Thematic analysis was performed manually on the collected data. Results: Five main themes, namely, external appearance, problems with speech, suffering from illnesses, socioeconomic background and being a ‘newcomer’ were identified in the thematic analysis. Conclusions & Recommendations: Multiple contributory factors of in-school bullying victimization were identified in these focus group discussions. Therefore, the necessity of addressing the above factors, when addressing them in prevention programs is highlighted.

The growing demand for environmentally friendly materials in energy storage has led to a significant focus on using biopolymer membranes derived from renewable resources. This study focuses on creating eco-friendly biopolymer electrolytes for Electric Double Layer Capacitors (EDLC) by blending Magnesium trifluoromethanesulfonate (Mg(CF3SO3)2) with Cellulose Acetate (CA) through a solution casting method. To enhance performance, plasticized membranes were developed using nontoxic Poly(ethylene glycol) (PEG) as a plasticizer. The addition of PEG reduced membrane crystallinity, as shown by X-ray diffraction (XRD). Fourier Transform Infrared (FTIR) spectroscopy indicated complexation among electrolyte constituents. The optimal composition, containing 25 wt% PEG, exhibited the highest ion conductivity (3.76 × 10−4 S/cm) according to Electrochemical Impedance Spectroscopy (EIS). EIS data allowed determination of important ion transport parameters, such as Diffusion Coefficient, Ionic Mobility, and Carrier Density. The EDLC device showed a Specific Capacitance (Csp) of 13.14 F/g at a scan rate of 5 mV/s, with excellent stability (3.2 V) in Linear Sweep Voltammetry (LSV). Cyclic Voltammetry (CV) and Galvanostatic Charge Discharge (GCD) tests confirmed no redox processes, yielding a Csp of 12.94 F/g at 0.1 A/g. The EDLC device demonstrated exceptional cyclic stability, high coulombic efficiency, and maintained consistent results in terms of Power Density (Pd) and Energy Density (Ed) over 1000 cycles. Incorporating PEG into biopolymer membranes enhances the electrochemical energy storage of EDLC devices, contributing to sustainable energy storage solutions.

Abstract Objectives To test the feasibility of a randomised controlled trial (RCT) of aspirin and/or vitamin D3 in active surveillance (AS) low/favourable intermediate risk prostate cancer (PCa) patients with Prolaris® testing. Patients and Methods Newly‐diagnosed low/favourable intermediate risk PCa patients (PSA ≤ 15 ng/ml, International Society of Urological Pathology (ISUP) Grade Group ≤2, maximum biopsy core length