In post-disaster scenarios, government and non-governmental organizations (NGOs) work to coordinate disaster relief efforts efficiently, aiming to meet humanitarian needs promptly. Utilizing available data such as satellite imagery, sensor data, and social media, alongside data mining and big data analytics, can significantly enhance disaster management efforts. However, timely access to these often fragmented and incomplete data presents challenges. Artificial Intelligence (AI) techniques, including machine learning, deep learning, and natural language processing, offer solutions by enabling faster response times for government agencies, NGOs, and other authorities. This research examines 72 studies on the application of AI in various stages of disaster management, including the simulation, detection, prediction, and handling of post-disaster situations. AI-based systems improve the issuance of early warnings, streamline risk communication, optimize relief logistics, and support evacuation plans while also aiding in the decision-making processes for the issuance of building permits and grants. The analysis highlights the transformative potential of AI across all disaster management phases, from preparedness and response to prevention/mitigation and recovery, and identifies future challenges in this domain. In conclusion, the study underscores the power of AI not only in forecasting the occurrence and impact zones of disasters but also in identifying the most vulnerable communities and assessing the viability of disaster response strategies.

Among recent technological advances, microfluidic biochips have been leading a prominent solution for healthcare and miniaturized bio-laboratories with the assurance of high sensitivity and reconfigurability. On increasing more unreliable communication networks day-by-day, technological shifts in the fields of communication and security are now converging. In today's cyber threat landscape, these microfluidic biochips are ripe targets of powerful cyber-attacks from different hackers or cyber-criminals. Hence, securing such systems is of paramount importance. This paper presents the security aspects of digital microfluidic biochip layout to protect the confidentiality of layout data from unscrupulous people and man-in-the-middle attacks. We propose an authentication mechanism with an error control mechanism that provides reliability, authentication, trustworthy and safety for both storage and communication of GDS, i.e., Graphical Design System, file generally used for digital microfluidic biochip layouts. Simulation results articulate the efficacy of the proposed security model without the overhead of the bioprotocol completion time. The proposed scheme, which used AES as an encryption algorithm with a 256-bit encryption key, has also shown a speedup of 6.0 (with 85% efficiency) faster than the prior efficient scheme. We hope to develop a secure layout design flow for biochips to achieve better resistance to any attack.

Microfluidic biochips have recently emerged with significant promise and versatility in automating a variety of biochemical protocols on a tiny chip. Sample preparation, which involves mixing of fluids with a specified target ratio in the minuscule scale, is an essential component of these protocols. Algorithms that optimize on-chip sample-preparation cost and time are closely intertwined with the underlying mixing model, mixing sequence, and fluidic architecture. Although numerous mixing models have been studied in the literature, their impact on the dynamics of mixing steps is hitherto not fully understood. In this paper, we show that various mixing models can be envisaged in the light of prime factorization of integers thus establishing a connection among mixing algorithms, chip architectures, and performance. This insight has led to the development of the proposed factorization-based dilution algorithm ( FacDA) considering a generalized mixing model suitable for micro-electrode-dot-array (MEDA) biochips. It further leads to target volume oriented dilution algorithm ( TVODA) to cater to user’s demand for an output with a given volume. We formulate the optimization problem on the fabric of the Satisfiability Modulo Theory (SMT) while determining mixing sequences. Simulation results on a large number of test-cases reveal that FacDA and TVODA outperform the state-of-the-art dilution algorithms for MEDA biochips with respect to reactant cost, mixing time, and waste production.