The disaster response operational strategies requires tool that a may provide time bound clear picture of potential spots. Natural disaster sometimes comes slowly like increment of water level resulting flood or early warning of tsunami whereas in manmade disasters like 1CBRNE attack or chemical accident or industrial structure collapse or leakage through a pipeline of some toxic hazardous gas requires very accurate and quick response strategies. 2Unmanned Aerial System is commercially used tools present in the market and also used by military organization for various operations throughout the world. The accuracy and time bound pictorial representation of the spots by these tow tools makes them very useful for providing clear picture of potential disaster sites and enabling decision makers to take decision on these representation. Apart of these the potential to deliver important material faster than any other mode also makes these equipment and technology a very important asset for making disaster risk reduction strategies. This paper elaborates the efficacy and utility of Unmanned Aerial System or Drones in developing robust and time bound strategies for disaster risk reduction. This paper also evaluates some experiences and describes some initiatives using Unmanned Aerial System to support disaster risk reduction strategies and post disaster decision making efforts.

Various species of plants, bacteria, fungi, insects, invertebrates, and vertebrate animals produce unique small molecules, peptides, or proteins (biomolecules) for their defensive purposes. These biomolecules, also known as toxins, are poisonous (toxic) to humans and could be misused due to relatively easy accessibility and affordability. Toxins are not contagious and replicative; their effect or symptoms depend on their concentration (dose), which can take hours to days after exposure. Furthermore, for nefarious actors, preparing low-effort homemade explosives (HMEs) using household items or easily accessible chemical precursors is not difficult. To mitigate the biosecurity risks, biological scientists, law enforcement investigators, and public health response professionals should be aware of the new threats and challenges posed by low-effort toxins and HMEs. The chemical and biological investigation, evidence, and countermeasures training program was aimed at preparing biological scientists, law enforcement investigators, and public health representatives to mitigate the biosecurity risks from low-effort toxins and HMEs. The tailor-made eight-day training program containing lectures, round table discussions, immersion scenarios, practical exercises, practical assessment, and tabletop exercises helped participants to understand and close their skill gaps. The training also covered various elements associated with chemical, biological, radiological, nuclear, and explosive (CBRNE) threats from nefarious individuals and groups. Some highlights of the training are reported in this report.

Nerve agents have recently been used in battlefield operations, espionage wars and terrorist attacks. These compounds, like some pesticides, cause organophosphate poisoning. The rapid, non-contact detection of a sarin simulant has been demonstrated at the Diagnostics and Metrology Laboratory of the Italian National Agency for New Technologies, Energy and Sustainable Economic Development using laser photoacoustic spectroscopy, an infrared absorption technology. The first measurements, carried out with an experimental system based on a quantum cascade laser and developed for the assessment of food authenticity in the “fingerprint region”, show that a detection limit of 1 nanolitre is within the reach of the instrument when chemometric analysis is applied.

This paper presents the development of machine learning (ML) models to predict hypoxemia severity during emergency triage, especially in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) events, using physiological data from medical-grade sensors. Gradient Boosting Models (XGBoost, LightGBM, CatBoost) and sequential models (LSTM, GRU) were trained on physiological and demographic data from the MIMIC-III and IV datasets. A robust preprocessing pipeline addressed missing data, class imbalances, and incorporated synthetic data flagged with masks. Gradient Boosting Models (GBMs) outperformed sequential models in terms of training speed, interpretability, and reliability, making them well-suited for real-time decision-making. While their performance was comparable to that of sequential models, the GBMs used score features from six physiological variables derived from the enhanced National Early Warning Score (NEWS) 2, which we termed NEWS2+. This approach significantly improved prediction accuracy. While sequential models handled temporal data well, their performance gains didn’t justify the higher computational cost. A 5-minute prediction window was chosen for timely intervention, with minute-level interpolations standardizing the data. Feature importance analysis highlighted the significant role of mask and score features in enhancing both transparency and performance. Temporal dependencies proved to be less critical, as Gradient Boosting Models were able to capture key patterns effectively without relying on them. This study highlights ML's potential to improve triage and reduce alarm fatigue. Future work will integrate data from multiple hospitals to enhance model generalizability across clinical settings.