The use of biodegradable materials in 3D printing has gained attention due to its potential in addressing environmental concerns in the manufacturing industry. This paper aims to explore the current state of research and development in sustainable 3D printing using biodegradable materials. The research found that biodegradable materials, such as bioplastics, are being increasingly used in 3D printing as an eco-friendly alternative to traditional materials. Various types of biodegradable materials have been tested, including Polylactic Acid (PLA), cellulose-based materials, and starch-based materials. One of the main advantages of using biodegradable materials in 3D printing is its potential to reduce the carbon footprint of the production process. These materials are derived from renewable resources and have a lower environmental impact compared to non-biodegradable materials, such as petroleum-based plastics. However, the use of biodegradable materials in 3D printing also presents challenges, including limited availability and higher production costs, as well as the need for specific print settings and post-processing methods. Further research is needed to optimize the use of biodegradable materials in 3D printing and to develop new materials with improved properties. Collaboration between material scientists and 3D printing manufacturers is crucial to advancing sustainable 3D printing using biodegradable materials.

The field of 3D printed electronics has been rapidly growing in recent years, with the potential to revolutionize industries such as healthcare, aerospace, and consumer electronics. Polymer 3D printing has emerged as a promising technique for fabricating electronic devices due to its versatility, scalability, and cost-effectiveness. However, there are several challenges that need to be addressed in order to fully unlock the potential of polymer 3D printed electronics. This research paper discusses the current state of the art in this field, highlighting the current challenges and proposing potential solutions. These challenges include material selection, design considerations, printing techniques, and post-processing methods. In addition, the paper explores the limitations of existing polymer materials and presents recent advances in the development of new functional materials for 3D printing. Furthermore, the integration of various components and multi-material printing techniques are also discussed as key factors in advancing the capabilities of 3D printed electronics. Finally, this paper provides insights and recommendations for future research directions in order to fully realize the potential of polymer 3D printed electronics.

Malaria is a significant public health challenge in the Democratic Republic of the Congo (DR Congo), with a high prevalence and mortality rate, particularly among children under five years old. This study examines the impact of malaria in eastern DR Congo, where armed conflict and humanitarian crises have resulted in overcrowded refugee and internally displaced persons (IDP) camps, exacerbating malaria transmission. Malnutrition, limited access to healthcare, and poor living conditions makes children under the age of five particularly vulnerable. Despite attempts by organizations such as the World Health Organization and various non-governmental organizations to supply insecticide-treated bed nets and antimalarial drugs, implementation in refugee camps is impeded by war, resource constraints, and insufficient healthcare facilities. A focused elimination approach that includes integrated vector control, enhanced diagnostic access, healthcare professional training, and community engagement is critical. Surveillance and research are critical for determining malaria prevalence and resistance patterns. Effective malaria treatment in refugee camps necessitates broad strategies to protect vulnerable children and lower disease burdens.

Brain tumors are a life-threatening condition, and their early detection is crucial for effective treatment and improved survival rates. Traditional manual evaluation techniques, such as expert radiologist assessments and visual inspections, are widely used for diagnosing brain tumors. While these methods can be highly reliable, they are often time-consuming, prone to human error, and challenging to scale for large datasets. Consequently, there is a growing demand for Computer-Aided Diagnostic (CAD) systems to overcome these limitations and deliver fast, accurate, and scalable solutions. Despite these promising advancements, the study highlights potential limitations, including susceptibility to overfitting due to the limited availability of labeled data and the need for extensive hyperparameter tuning to generalize across diverse datasets. This study proposes a scalable multi-class brain tumor classification framework optimized for small-form-factor devices. We introduced a novel, lightweight custom convolutional neural network (CNN) that maintains high classification accuracy while significantly reducing computational complexity. We evaluated the model's capacity by training and testing it on five different datasets, and it performed well on all five. We observed a significant improvement in performance with the model on larger datasets, but it struggled with smaller and imbalanced datasets. We achieved significant scores on the datasets, and we had the highest testing accuracy on Dataset-5 (99.67% training accuracy, 98.17% validation accuracy, and 98.30% testing accuracy). What is important to note is that we had the lowest testing accuracy on Dataset-3 (99.99% training accuracy, 74.11% validation accuracy, and 75.63% testing accuracy). The proposed framework leverages state-of-the-art pretrained deep learning models, including EfficientNetb3, ResNet-101, ResNet-50, Xception, AlexNet, DenseNet121, Swin Transformer, and our custom lightweight CNN model. Experimental evaluations demonstrate that EfficientNetb3 achieves the highest accuracy of 99.11%, while the custom lightweight CNN attains 98% accuracy with 4.1 × fewer parameters and reduced training time. These results highlight the effectiveness of computer-aided approaches in achieving near-expert performance, making them suitable for integration into clinical workflows. This research paves the way for deploying efficient and scalable deep learning models in real-world medical applications, thereby expanding accessibility to accurate brain tumor diagnosis.