Global warming worsens natural disasters and humanitarian issues. Disaster prediction relies on satellites and weather stations. AI may help catastrophe management. AI reduces disaster risk in many ways. Early warning systems, weather forecasts, disaster recovery, and reconstruction improve. AI could help us predict, prepare, and recover from natural calamities. These technologies provide climate change mitigation and community protection hope. They propose a better future amid climate change catastrophes. DRR is aggressively adopting AI, notably ML. This field encompasses severe event prediction, hazard mapping, real-time detection, situational awareness, decision assistance, and more. Growing usage of AI in disaster management raises questions about its benefits. We face what issues? How can these difficulties be resolved and opportunities maximised? What can AI tell policymakers, stakeholders, and the public to reduce disasters? The chapter introduces catastrophe management AI, AI implementation issues, and solutions to make the world more peaceful will be addressed. 

Navigating the Crescendo of Challenges in Harnessing Artificial Intelligence for Disaster Management

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Background: The limb reconstruction system (LRS), rail road fixator, and Ilizarov fixator are used to treat open fractures with bone loss and infective non-union of long bones. It benefits the fracture site by permitting distraction, compression, and further enhancing micromotion at the level of fracture site which plays an important part in healing. The LRS is a straightforward, unidirectional arrangement that provides exceptional strength and rigidity. The limb rebuilding procedure has proven to be an effective treatment option in instances of fracture infective non-union of long bones.
Aims and Objectives: This study is to evaluate and to observe the use of limb reconstruction devices (such as the Ilizarov fixator and the rail road fixator) in the treatment of open fractures with bone loss and infective non-union of long bones.
Materials and Methods: Twenty patients (15 males and five females) with fracture non-union of long bones were involved in this study after taking permission from ethic committee and consents from all 20 individuals. These patients then underwent thorough debridement and resection of non-viable bone ends with the application of limb reconstruction devices which were then followed by bone transport to fill the bone gap. This was done by means of Ilizarov in 13 patients and by rail road fixator in the remaining seven patients. The average time of union and complications were evaluated.
Results: Mean duration of fracture union was 7.9 months. While one patient had failure resulting in non-union, other 19 patients achieved union by limb reconstruction devices. Majority of the cases had no complications. Mean period of hospital stay for Ilizarov was 68.13 days and for rail road fixator 53.17 days. After applying independent “t-test,” no statistically significant differences were observed in fracture union duration between Ilizarov and rail road fixator, since the calculated P value was observed to be >0.05. Mean union of Ilizarov and rail road fixator application was found to be 7.6 months and 8.2 months, respectively.
Conclusion: Limb reconstruction devices in our present study can be utilized as an effective way to tackle infective non-union of long bones and open fracture with bone loss for achieving better stabilization. Both Ilizarov and rail road fixator perform a crucial role in causing early mobilization of the patient and also in fracture union by distraction osteogenesis.

Management of fracture non-union long bones by limb reconstruction devices

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Management of fracture non-union long bones by limb reconstruction devices