Target tracking is an important application type for wireless sensor networks (WSN). Recently, various approaches are proposed to maintain the accurate tracking of the targets as well as low energy consumption. Clustering is a fundamental technique to manage the scarce network resources. The message complexity of an application can be significantly decreased when it is redesigned on top of a clustered network. Clustering has provided an efficient infrastructure in many existing studies. The clusters can be constructed before the target enters the region which is called the static method or clusters are created by using received signal strength (RSS) from target which is called the dynamic method. In this paper we provide simulations of static and dynamic clustering algorithms against various mobility models and target speeds. The mobility models that we applied are random waypoint model, random direct model and Gauss Markov model. We provide metrics to measure the tracking performance of both approaches. We show that the dynamic clustering is favorable in terms of tracking accuracy whereas the energy consumption of static clustering is significantly smaller. We also show that the target moving with Gauss Markov model can be tracked more accurately than the other models.

Recent advances in artificial intelligence (AI) research, particularly in image processing technologies, have shown promising applications across various domains, including health care. There is a significant effort to use AI for the early diagnosis and detection of diseases, offering cost-effective and timely solutions to enhance patient outcomes. This study introduces a deep learning network aimed at analyzing pathology images for the accurate diagnosis of pancreatic cancer, specifically pancreatic ductal adenocarcinoma (PDAC). Utilizing a novel dataset comprised of cases diagnosed with PDAC and/or chronic pancreatitis, this study applies deep learning algorithms to assess the effectiveness and reliability of the diagnostic process. The dataset was enhanced through image duplication and the creation of a second dataset with varied dimensions, facilitating the training of advanced transfer learning models including InceptionV3, DenseNet, ResNet, VGG, EfficientNet, and a specially designed deep neural network. The study presents a convolutional neural network model, optimized for the rapid and accurate detection of pancreatic cancer, and conducts a comparative analysis with other models to select the most accurate algorithm for a decision support system. The results from Dataset 1 show that EfficientNetB0 achieved a high success rate of 92%. In Dataset 2, VGG16 was found to have high performance, with a success rate of 92%. On the other hand, ResNet50 achieved a remarkable success rate of 96% despite a moderate training time and showed high precision, recall, F1 score, and accuracy. These results provide valuable data to demonstrate and share the relevance of different deep learning models in pancreatic cancer diagnosis.

Emergency triage artificial intelligence (AI) is usually evaluated as a centralized prediction problem, yet many hospitals cannot pool patient-level records across governance boundaries. We studied a narrower and safety-relevant task: within-dataset federated simulation for detecting Emergency Severity Index (ESI)-2/ESI-1 encounters using information available before or during triage. Zendal is a reproducible within-dataset federated evaluation framework for critical emergency triage escalation. It defines a triage-time feature boundary, site-aligned cross-silo splits, training-split-only imputation and scaling, validation-selected operating thresholds, bootstrap uncertainty, decision-curve analysis, calibration analysis, local-only baselines, per-site and subgroup diagnostics, and leave-one-site-out transfer stress testing. In 558,029 public emergency department encounters, the endpoint was binary critical-band ESI assignment (ESI-2/ESI-1 vs. ESI-3/ESI-4/ESI-5), not an independently adjudicated clinical outcome. The feature set contained 240 pre-triage variables: demographics, arrival metadata, previous utilization counts, triage vital signs, and chief-complaint indicators; disposition, diagnoses, medications, procedures, imaging, laboratories, and target-derived imputations were excluded. A centralized logistic reference reached AUROC 0.879 (95% CI: 0.876–0.882), average precision 0.767, sensitivity 0.900 (95% CI: 0.895–0.905), specificity 0.654 (95% CI: 0.649–0.658), and positive predictive value (PPV) 0.530 at a validation-selected threshold targeting at least 90% sensitivity. Federated logistic training across three site-aligned clients nearly matched the centralized reference: AUROC 0.878 (95% CI: 0.875–0.880), average precision 0.767, sensitivity 0.900 (95% CI: 0.896–0.904), specificity 0.650 (95% CI: 0.645–0.655), and PPV 0.527. Post hoc Platt scaling reduced the federated expected calibration error from 0.160 to 0.011, supporting recalibration before direct risk interpretation. Model-guided escalation produced a positive mean decision-curve net benefit across thresholds 0.05–0.50 (0.164 for federated logistic vs. −0.003 for flag-all and 0 for flag-none). In leave-one-site-out transfer tests, federated source-site models retained AUROC 0.846–0.876 and sensitivity 0.871–0.909 on held-out sites, but the low-prevalence site had PPV 0.107 and negative net benefit. An open MIMIC-IV-ED Demo smoke test supported schema feasibility but was too small for external validation. Zendal is therefore a transparent framework for no-raw-row-exchange triage model evaluation, not a deployment-ready clinical system or a demonstration of formal privacy protection.