In today’s cities and urban regions, the proliferation of plastic waste has continued to pose an urgent environmental challenge that demands innovative and holistic solutions. The consequential impact on our planet's ecosystems has underscored the need for a concerted effort to address this crisis. In response, this paper introduces a smart system for recycling and plastic waste management. It will help to modernize contemporary waste management practices by seamlessly integrating informal sector collectors into a technologically advanced supply chain. It harnesses the transformative potential of data intelligence platforms, thereby enabling the reclamation of post-consumer and post-industrial plastic waste. It presents a dashboard for collecting and processing waste data in real-time. It provides an architectural and step-by-step design of a modern waste management and recycling system. The paper leverages a triad of cutting-edge technologies: mobile, cloud, and the Internet of Things (IoT). City leaders and municipalities can adapt this system and use it for responsible waste disposal, efficient recycling, and promoting circular economy practices.

Facial beauty perception is a complex area of study that has intrigued researchers across various disciplines. While some argue that it is subjective, influenced by personal and cultural factors, others propose that it is objective and rooted in evolutionary biology. This study explores the latter perspective, aiming to model facial beauty with an emphasis on racial fairness. Departing from black box convolutional deep learning approaches that are susceptible to racial biases, particularly arising from their holistic consideration of facial attributes such as skin tone, our focus lies solely on designing a more transparent machine learning model that integrates guardrails to prevent the introduction of such biases. By deliberately excluding skin tone and selecting specific features for the model to learn from, we aim to ensure a more equitable assessment across diverse racial and ethnic groups. Following rigorous training and evaluation, our hybrid model demonstrated impressive predictive performance, despite prioritizing transparency and racial fairness over complexity.

Over the past decade, the pharmaceutical and healthcare sectors have witnessed a notable transformation with the emergence of online pharmacies. There are some reviews on online pharmaceutical services, shedding light on user motivations and misconceptions. While concerns like counterfeit medicines persist, the debate revolves around the advantages of perpetual access to healthcare information, safeguarding user privacy, and the cost-effectiveness of online pharmacy services. In this paper, we present Medshopp, a comprehensive online pharmacy platform that fosters direct interaction between patients and healthcare providers. Medshopp goes beyond conventional transactions by offering a user-friendly interface, swift medication deliveries, and various patient benefits, envisioning a future where mental health treatments are integrated. With features such as online doctor appointments, first aid diagnosis and ambulance services, Medshopp is ideal for the management of emergency medications in Low- and Middle-Income Countries (LMIC). It provides accessible healthcare solutions, leveraging existing technology to enhance the overall healthcare experience.

In the ongoing battle against global pandemics, understanding the key determinants that fuel outbreaks are of paramount importance. With this focus, our study aims to assess and rank the predictive capabilities of a wide range of socio-economic, eco-climatic, and spatiotemporal variables in predicting Lassa Fever (LF) outbreaks, using data from previous Nigerian outbreaks (2012-2019). Employing machine learning methods, particularly XGBoost and Random Forest, our study aims to offer accurate and robust predictions concerning LF incidence rates. As a crucial add-on, we leverage the innovative SHAP (SHapley Additive exPlanations) technique as a post-processing tool to dissect and better understand the contributions of individual features towards the predictions generated by our machine learning models. This multi-layered approach allowed us to place a pronounced focus on healthcare infrastructure, population demographics, land cover, and other climatic covariates. Among the models evaluated, XGBoost performed the best; delivering an accuracy of 0.93, and AUC of 0.90, and an F1 score of 0.86 on 2018 data. For 2019 data, it maintained a strong accuracy of 0.90, an AUC of 0.89, and an F1 score of 0.75. Our SHAP analysis further emphasized precipitation seasonality, diagnostic center density, and land cover characteristics as pivotal influencers in predicting LF outbreaks. These findings shed light on the complex interplay between environmental conditions, urbanization, and healthcare infrastructure. Given these promising results, our work sets the stage for the development of an advanced early warning system for Lassa Fever in Nigeria: a system that could efficiently intertwine computational insights with on-ground interventions, ensuring timely and targeted responses to potential outbreaks.