Multi-Formalism Modeling for Disaster Resilience, Forecasting, and Response

Disasters are major events that have significant effects on individuals, communities, and health care systems. Nurses are valuable resources that have a role in disaster response. However, barriers in disaster education in nursing programs negatively affect nurses' knowledge and skills in disaster preparedness and response. A literature review was conducted to better understand the disaster response educational needs of nurses and to explore gaps in nurses' preparedness for disaster response and nursing education and training. Findings indicated disaster response education is a vital component of nursing education. When provided with effective disaster education, nurses are better equipped for disaster management and disaster response. Challenges in educational needs continue, and nurses lack specific disaster knowledge and preparedness. Further research is needed to identify effective teaching strategies for disaster response and preparedness. Emphasis should be placed on integrating disaster education into nursing education programs at all levels. [J Nurs Educ. 2025;64(4):235-238.].

Assessment of U.S. helicopter emergency medical services' planning and preparedness for disaster response

PurposeSemantic segmentation is a fundamental part of the surgical application of deep learning. Traditionally, segmentation in vision tasks has been performed using convolutional neural networks (CNNs), but the transformer architecture has recently been introduced and widely investigated. We aimed to investigate the performance of deep learning models in segmentation in robot-assisted radical prostatectomy (RARP) and identify which of the architectures is superior for segmentation in robotic surgery.Materials and MethodsIntraoperative images during RARP were obtained. The dataset was randomly split into training and validation data. Segmentation of the surgical instruments, bladder, prostate, vas and seminal vesicle was performed using three CNN models (DeepLabv3, MANet, and U-Net++) and three transformers (SegFormer, BEiT, and DPT), and their performances were analyzed.ResultsThe overall segmentation performance during RARP varied across different model architectures. For the CNN models, DeepLabV3 achieved a mean Dice score of 0.938, MANet scored 0.944, and U-Net++ reached 0.930. For the transformer architectures, SegFormer attained a mean Dice score of 0.919, BEiT scored 0.916, and DPT achieved 0.940. The performance of CNN models was superior to that of transformer models in segmenting the prostate, vas, and seminal vesicle.ConclusionsDeep learning models provided accurate segmentation of the surgical instruments and anatomical structures observed during RARP. Both CNN and transformer models showed reliable predictions in the segmentation task; however, CNN models may be more suitable than transformer models for organ segmentation and may be more applicable in unusual cases. Further research with large datasets is needed.

Use of raloxifene and tamoxifen by breast cancer risk level in a Medicare-eligible cohort

The Pediatric Acute Care Cardiology Collaborative (PAC3) was established to improve acute care cardiology outcomes through the development of an accurate and well-validated clinical registry. We report the validation results of the initial PAC3 registry audits and describe a novel regional audit format developed to accommodate a rapidly expanding membership facilitate collaborative learning and allow for necessary modification due to the COVID-19 pandemic. Six hospitals were audited using a regional audit format and three hospitals were subsequently audited virtually. Critical and challenging-to-collect data elements were audited among at least 40 randomly selected cases. Discrepancies were categorised as either major or minor depending on their relative importance to patient outcomes and clinical care. Results were tabulated and reported. We audited 386 encounters and 27,086 individual data fields across 9 hospitals. The aggregate overall accuracy rate was 99.27% and the aggregate major discrepancy rate was 0.51%. The overall accuracy rate ranged from 98.77% to 99.59%, and the major discrepancy rate ranged from 0.26% to 0.88% across the cohort. No appreciable difference was seen between audit formats. Both the regional and virtual audit methods were viewed favourably by participants. A low data discrepancy rate was found demonstrating that the PAC3 registry is a highly accurate data source for use in quality improvement, benchmarking, and research. Regional audits and virtual audits were both successfully implemented.

Outpatient physician billing data for age and setting specific syndromic surveillance of influenza-like illnesses

A new capability has been developed for ESSENCE systems that allow local administrators to publish their data sources for use by another jurisdiction's ESSENCE users. This presentation will describe the new federated query capability in ESSENCE and how it could affect public health practice in the future. Specifically, this presentation will describe how a federated set of disease surveillance systems across the country could help improve national disease surveillance situational awareness along with its potential to connect non-ESSENCE systems in the future for even more complete coverage.

Recently, the introduction of Convolutional Neural Network (CNNs) has advanced the way of solving image segmentation tasks. Semantic image segmentation has considerably benefited from employing various CNN models. The most widely used network in this field is U-Net and its different variations. However, these models require significant number of trainable parameters, floating-point operations per second, and great computational power to be trained. These factors make real-time semantic segmentation in low powered devices very hard. Therefore, in the present paper, we aim to modify particular aspects of the U-Net model to improve its performance through developing a fast U-Net (FU-Net) relying on bottleneck convolution layers in the contraction and expansion paths of the model. The proposed model can be utilized in semantic segmentation applications even on the devices with limited computational power and memory by ensuring the state-of-the-art performance. The amount of memory required by the proposed model is reduced by 23 times when compared with the original U-Net. Moreover, the modifications allowed achieving better performance. In conducted experiments, we assessed the performance of the proposed model on two biomedical image segmentation datasets, namely 2018 Data Science Bowl and ICIS 2018: Skin Lesion Analysis Towards Melanoma Detection. FU-Net demonstrated the state-of-the-art results in biomedical image segmentation, requiring the number of trainable parameters reduced by eight times compared with the original U-Net model. In addition, using bottleneck layers decreased the number of computations, resulting in nearly 30% speed-up at the training, validation and test stages. Furthermore, despite relying on fewer parameters FU-Net achieved a slight improvement of the performance in terms of pixel accuracy, Jaccard index, and dice coefficient evaluation metrics.

Classification of synthetic aperture radar data has been attempted by researchers in the past for several years, with varying degrees of success. One of the most challenging problem in such classifications is the segregation of urban (built-up region) and tall vegetation (TV). This is due to lack of difference in their scattering power. Therefore, an attempt has been made to improve urban and TV classification accuracy by exploring the use of Sentinel-1 interferometric coherence images as an additional feature along with other popularly used scattering features. A convolutional neural network (CNN) is trained on these features to obtain a good accuracy for classifying urban, TV and other classes. Obtained results are analyzed in both visual and quantitative ways. It is observed that when interferometric coherence images are added as an additional input feature in the CNN, producer and user accuracy for the urban and TV are improved from 15 to 25%, and overall accuracy(OA) is reached from 72.5% to 79.3%. Results oabtained with the CNN model is also compared with the traditional machine learning models such as support vector machine(SVM) and k-nearest neighbors(KNN). CNN over performes with 79.3% OA, followed by KNN with 66% OA while the performance of SVM is the worst among them all with 60% OA.

The human brain is one of the body's most major organs. If there are problems within the human brain, they may cause serious consequences, and even endangers the human life. One of the most fatal diseases for humans is a brain tumor. In the old days, tumor detection was done manually by doctors through reading magnetic resonance images, which might not be time efficient, and sometimes may even produce inaccurate results. Nowadays, with the development of science and technology, Artificial Intelligence (AI) is present in many fields in human life, including medical field. Tumor detection with AI is one of the applications that technology changes human life. The Convolution Neural Network (CNN), a prominent algorithm in deep learning, is widely employed in tumor identification. In this study, a CNN model is proposed. Over 7000 brain tumor magnetic resonance images, including glioma, meningioma, no tumor and pituitary are used in this study. The images are also preprocessed to improve the accuracy of the proposed models. In this study, the well-known VGG16 model, which is a pretrained deep learning model, is utilized to compare with the proposed model. The proposed model and the VGG16 model are trained and evaluated using both the original (uncropped) images and preprocessed (cropped) images. The results of the experiment indicate that the suggested model exceeds the VGG16 model in the light of loss and accuracy.

Recent research in disaster informatics demonstrates a practical and important use case of artificial intelligence to save human lives and suffering during natural disasters based on social media contents (text and images). While notable progress has been made using texts, research on exploiting the images remains relatively under-explored. To advance image-based approaches, we propose MEDIC (https://crisisnlp.qcri.org/medic/index.html), which is the largest social media image classification dataset for humanitarian response consisting of 71,198 images to address four different tasks in a multi-task learning setup. This is the first dataset of its kind: social media images, disaster response, and multi-task learning research. An important property of this dataset is its high potential to facilitate research on multi-task learning, which recently receives much interest from the machine learning community and has shown remarkable results in terms of memory, inference speed, performance, and generalization capability. Therefore, the proposed dataset is an important resource for advancing image-based disaster management and multi-task machine learning research. We experiment with different deep learning architectures and report promising results, which are above the majority baselines for all tasks. Along with the dataset, we also release all relevant scripts (https://github.com/firojalam/medic).

Abstract. Wildfires, intensified by climate change and anthropogenic factors, represent a major threat to ecosystems, infrastructure, and human life. Early detection is critical for effective prevention and mitigation. This study presents a Convolutional Neural Network (CNN)-based system designed to detect wildfires from Sentinel-2 satellite imagery. The methodology integrates advanced preprocessing techniques—including atmospheric correction, cloud masking, and spatial normalization—with a tailored CNN architecture optimized for spectral–spatial feature extraction. Training utilized a curated dataset of wildfire events in Türkiye (2016–2025), annotated at the pixel level to distinguish fire-affected and unaffected regions. The CNN model, employing LeakyReLU activation and AdamW optimization, achieved robust performance, with precision, recall, and F1-scores all averaging 0.97, and an overall accuracy of 97%. High fire-class precision (1.00) minimized false alarms, while strong recall (0.93) ensured reliable detection. The model’s lightweight design allows deployment on cloud platforms and edge devices, enabling real-time monitoring across forestry, agriculture, and urban planning applications. Analysis of Turkish wildfire statistics (1988–2023) revealed that over 90% of fires stem from human negligence or arson, underscoring the need for public education, stricter enforcement, and technology-driven surveillance. Long-term data further indicate a rising severity in fire events, strengthening the case for AI-enhanced early warning systems. This research demonstrates that CNNs applied to Sentinel-2 imagery offer a scalable, accurate, and cost-effective solution for wildfire detection. By bridging remote sensing and deep learning, the proposed system supports both immediate disaster response and long-term policy planning for wildfire risk reduction.

Researchers have established the prominent role digital volunteers play during crises and disasters. From self‐organizing to annotating public data, these volunteers are now a fixture in disaster research. However, we know much less about how these volunteers function, behind the public scene, when using private social media as a disaster unfolds and people need to be rescued. This qualitative study identified the emergent helping roles along with the skillsets and abilities that helped volunteers perform these behind‐the‐scene roles during the Hurricane Harvey flooding in 2017. Using in‐depth interviews along with captured images in private social media, we find these volunteers resembled organizational knowledge workers. We identify nine specific communicating and coordinating actions that these disaster knowledge workers performed. The contributions of these findings centre on implications for disaster response and management.

Financial markets, such as the stock market, bond market and foreign exchange market, are important channels for fund transfer. As a graphical analysis tool, candlestick charts use graphs to display the open, high, low, and close prices in a specific period. In the past, there have been attempts to identify the characteristics of candlesticks based on Gramian Angular Field (GAF) images, but they are not perfect. In this study, we implemented Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), AdaBoost, Random Forest (RF) and XGBoost models, we found that the use of deep learning models is not the best choice for the recognition of candlestick features based on GAF images. Comparing these models, MLP and CNN are better than AdaBoost and RF, but worse than XGBoost. Our results show that for the candlestick pattern classification problem based on GAF images, it is unnecessary to use complex CNNs and traditional machine learning models can also achieve satisfactory results with much less computation resources.

ObjectiveUsing the phase synchronization of EEG signals, two different phases, PLI and PLV, were used to construct brain network analysis and graph convolutional neural network, respectively, to achieve automatic identification of Alzheimer’s disease (AD) and to assist in the early diagnosis of Alzheimer’s disease.MethodsIn this paper, we selected outpatients (16 AD subjects, 20 mild cognitive impairment (MCI) subjects and 21 healthy control (HC) subjects) from the outpatient clinic of Yangpu Mental Health Center in Shanghai, China, from January 2023 to December 2023, and collected resting-state EEG data. To collect resting-state EEG data, each patient was asked to sit down with eyes closed for 5 min. Firstly, the acquired EEG data were preprocessed to extract the data in the α-band at 8–13 Hz; secondly, the phase lag index (PLI) and phase-locked value (PLV) were used to construct the brain functional network, and the brain functional connectivity map was visualized by brain functional connectivity analysis. Finally, the constructed PLI and PLV were input into the graph convolutional neural network (GCN) model as node features for training and classification, respectively.ResultsHealthy controls had relatively strong mean brain functional connectivity in the PLV brain network compared to AD and MCI patients. MCI patients showed lower mean brain functional connectivity in the brain network of PLI, while all three groups showed significant differences in brain functional connectivity between parietal and occipital lobes. The GCN model improved classification accuracy by more than 10% compared to using a machine learning classifier. When PLV was used as the nodal feature in the GCN model, the model achieved an average classification accuracy of 77.80% for the three groups of AD, MCI and HC, which was an improvement over the accuracy of choosing raw EEG data and PLI as the nodal feature. The performance of the model was further validated.ConclusionsThe experimental results show that the GCN model can effectively identify the graph structure compared with the traditional machine learning model, the GCN-PLV model can better classify AD patients, and the alpha band is proved to be more suitable for AD resting-state EEG by tenfold cross-validation. The brain network map constructed based on PLI and PLV can further capture the local features of EEG signals and the intrinsic functional relationships between brain regions, and the combination of these two models has certain reference value for the diagnosis of AD patients.