In this study, we wanted to see how as a representative of a society newspaper portrayed COVID-19. For this purpose, this study considered two countries United Kingdom (UK) and Bangladesh (BD), and analyzed how COVID-19 as an external event was focused in the newspapers. To conduct the analysis, we handpicked a set of covid related feature terms, and using Latent Dirichlet Allocation (LDA) we verified the coherency of the chosen terms. The main finding of this study is that, initially as a new event COVID-19 found huge importance in the newspapers, but with gradual time progression, this became a new normal event and lost its initial insurgence of focus and took a stable condition. We also observed that despite being quite different demographically, geographically, economically along with being affected differently due to covid, UK and BD exhibit quite similar characteristics in portraying covid in newspapers. The decisions were arrived at by applying different types of Natural Language Processing (NLP) tools and statistical analysis. For experimentation, we collected all the published news articles from two newspapers, the Guardian and the Daily Star from January 1, 2020, to March 31, 2021.

Quadratic Unconstrained Binary Optimization (QUBO) is a versatile approach used to represent a wide range of NP-hard Combinatorial Optimization (CO) problems through binary variables. The transformation of QUBO to an Ising Hamiltonian is recognized as an effective method for solving key optimization problems using quantum algorithms. Recently, PI-GNN, a generic framework, has been proposed to address CO problems over graphs based on QUBO with Hamiltonian loss function to train the underlying GNN architecture. Though PI-GNN is highly scalable, it exhibits a noticeable decrease in terms of the number of satisfied constraints with higher graph densities. In this paper, firstly, we identify the limitations and empirically present our strategy to improve PI-GNN’s performance. Secondly, we formulate and evaluate two strategies to integrate QUBO-Hamiltonian as the generic loss function in Reinforcement learning-based (RL) frameworks. The major contribution of our work lies in understanding the feasibility and quality of the QUBO-based generic reward function in an unsupervised RL setup in addressing graph-based CO problems. Empirically, through our empirical evaluation ^a , we have observed up to 44% improvement in terms of the number of satisfied constraints over PI-GNN in a representative Max-Cut problem.

The effective realization of the United Nations’ 2030 Agenda relies on precise, timely monitoring; yet, for many low-resource nations, the lack of consistent data remains a critical barrier. In these contexts, traditional household surveys are often infrequent, resource-intensive, and prone to organizational gaps, hindering the granular assessment of Sustainable Development Goals (SDGs). To bridge this gap, this paper proposes the Sustainable Composite Indicator for Policy Enforcement (SCIPE) , a robust hybrid framework designed to quantify quality of life by integrating traditional statistical sources with high-frequency non-traditional data (NTD), including satellite imagery and nighttime lights. Designed explicitly for data-scarce environments, we demonstrate the efficacy of SCIPE through a representative case study in Bangladesh , organizing the analysis at a granular, district-level resolution. This manuscript provides a policy-aligned conceptual mapping that connects specific quality of life domains to relevant SDGs and alike targets. We consider the SDGs as a standardized benchmark for categorising domains to specify necessary determinants for a holistic measurement. The methodological blueprint articulates rigorous protocols for normalization, participatory weighting, and validation, while explicitly incorporating governance measures to mitigate privacy risks and representational bias. We lay out a complete path to establish a system supporting timely and spatially resolved insights to support initiatives such as Five-Year Plans and Voluntary National Reviews in the context of Bangladesh or a country with a similar socio-economic category. Ultimately, the study demonstrates that while the hybrid SCIPE index offers superior responsiveness and spatial resolution, its responsible deployment requires prudent implementation, strong data governance, and continuous fairness audits to ensure equitable policy enforcement.