Reassessment of public awareness and prevention strategies for HIV and COVID-19 co-infections through epidemic modeling.

A co-infection model between HIV and COVID-19 that takes into account COVID-19 vaccination and public awareness is discussed in this article. Rigorous analysis of the model is conducted to establish the existence and local stability conditions of the single-infection models. We discover that when the corresponding reproduction number for COVID-19 and HIV exceeds one, the disease continues to exist in both single-infection models.

Water saving rice cultivation using sheet-pipe subsurface irrigation.

Water saving in rice cultivation has assumed paramount importance, especially in the context of climate change. The introduction of sheet-pipe technology in Indonesia heralded as an innovative subsurface irrigation and drainage system, is poised to revolutionize how to manage this vital resource. Our study was designed with two primary objectives: first, to investigate how rice plants respond when water levels are deliberately reduced using the sheet-pipe technology; and second, to comprehensively analyze water productivity and water use efficiency in comparison to conventional flooded rice cultivation systems.

Evaluating the effect of climate change on rice production in Indonesia using multimodelling approach.

Achieving global food security in the face of climate change is a critical challenge, particularly in vulnerable countries like Indonesia. To effectively address this challenge, a systems-based approach utilizing climate-hydrological-crop models has emerged as an integral approach. These models integrate climate, hydrological, and crop components to understand and predict the complex interactions within agricultural systems and their responses to climate variables.

An aggregate method for thorax diseases classification.

A common problem found in real-word medical image classification is the inherent imbalance of the positive and negative patterns in the dataset where positive patterns are usually rare. Moreover, in the classification of multiple classes with neural network, a training pattern is treated as a positive pattern in one output node and negative in all the remaining output nodes. In this paper, the weights of a training pattern in the loss function are designed based not only on the number of the training patterns in the class but also on the different nodes where one of them treats this training pattern as positive and the others treat it as negative.