Lance Fiondella

Commercial and recreational Unmanned Aerial Systems (UAS) have gained popularity across a wide range of applications and are anticipated to expand throughout civilian airspace. UAS are a form of cyber-physical system and are therefore susceptible to cyberattacks on their hardware, software, and communication subsystems. Left unaddressed, such attacks can trigger a functional loss such as loss of command-and-control/data link or an uncontrolled fly-away that in turn opens an accident pathway capable of causing harm to people on the ground, property, and other airspace users. Cyberattack is therefore one causation pathway among several that a UAS safety case must address, not a standalone subject assessed in isolation from mission safety. Formal risk models are needed to quantify the nature and severity of these consequences so mitigation strategies can be identified, compared, implemented, and validated. This paper develops a quantitative cyber risk management framework that traces attacks through functional loss to safety consequence across pre-flight, in-flight, and post-flight UAS mission operations. Attacks are mapped to the MITRE Common Attack Pattern Enumeration and Classification (CAPEC), and a stoplight chart method for cyber risk assessment, adapted from the safety domain, translates attack likelihood and impact into acceptability categories. The framework considers attacks, their likelihood and impact, and alternative deterrent and defensive countermeasures, and formulates a countermeasure allocation problem to compare mitigation strategies under budget constraints.

Chronic diabetic wounds remain a major clinical challenge because persistent inflammation, hypoxia, and immune dysregulation prevent the transition of macrophages from pro‐inflammatory M1 states to pro‐regenerative M2 phenotypes. Here, we report an immunomodulatory colloidal bioink that integrates zein‐based oxygen‐generating microparticles and human mesenchymal stem cells within a porous, mechanically robust matrix for adaptive in situ bioprinting. Implemented with the autonomous, intelligent, visually guided in situ robotic bioprinting platform, this bioink enables patient‐specific deposition directly onto wound defects with high spatial conformity and stable tissue integration. Sustained oxygen release alleviates local hypoxic stress, enhances stem cell survival, and reshapes the wound microenvironment to favor regenerative immune responses. In diabetic wound models, the printed constructs promote angiogenesis, accelerate wound closure, and improve tissue regeneration. Spatial transcriptomic analysis further reveals macrophage heterogeneity across wound compartments and identifies a coordinated M1‐to‐M2 transition spatially and transcriptionally associated with the combined contribution of oxygen modulation and stem cell‐derived paracrine signaling. Together, this study establishes a therapeutic strategy that combines intelligent biomaterial design, controlled hypoxic conditioning, and adaptive robotic bioprinting to achieve precision immunomodulation and personalized regenerative treatment for chronic diabetic wounds with clinical translational potential and broad future applicability.

Large-scale software exhibits periods of increased defect discovery when blocks of less thoroughly tested code are introduced into an existing codebase. For example, the mission systems schedule of software intensive government acquisition programs includes multiple overlapping software blocks associated with various capabilities. Software reliability researchers have proposed changepoint models to characterize periods of increased defect discovery. However, these models attempt to identify the location of these changepoints after testing has been performed, which is counter-intuitive because conscious decisions such as testing new functionality drive software changepoints. Existing changepoint models are therefore difficult to employ in a predictive manner. To overcome this limitation, this paper proposes a covariate software defect discovery model capable of explaining changepoints in terms of common software testing activities and metrics such as software size estimation, code coverage, and defect density. The proposed and past changepoint models are compared with respect to their predictive accuracy and computational efficiency. Our results indicate that the proposed approach is more computationally efficient and enables accurate prediction of the time needed to achieve a desired defect discovery intensity or mean time to failure despite the occurrence of changepoints during software testing.

Recent efforts have dedicated significant attention to agent‐based simulation models in a spectrum of fields, including modern warfare, with less attention paid to nonviolent campaigns. A type of computer experiment used to study complex scenarios, these models simulate agent actions subject to nonlinear dynamics, generating nondeterministic outcomes. An agent‐based simulation model developed by Chenoweth of Harvard Kennedy School to study the dynamics of nonviolent resistance was reconstructed, and sensitivity analysis was conducted with the intent of identifying conditions most beneficial to its success. However, the Chenoweth model involves 25 factors, making interpretation difficult. Each iteration of the model produces a pass/fail/draw outcome, effectively censoring stalemates and requiring that each case be run sufficient times to estimate the nonviolent success probability for those conditions. Indiana University's high‐performance supercomputer was available for computation, but an efficient test plan was needed. A series of Latin hypercube designs was constructed for this wargaming application, leveraging their space‐filling qualities to obtain better visibility into factor effects and interrelationships. The study plan evolved to address findings about model functionality and within‐case repeatability and in order to locate viable regions.

Traditional software reliability growth models (SRGM) characterize defect discovery with the Non‐Homogeneous Poisson Process (NHPP) as a function of testing time or effort. More recently, covariate NHPP SRGM models have substantially improved tracking and prediction of the defect discovery process by explicitly incorporating discrete multivariate time series on the amount of each underlying testing activity performed in successive intervals. Both classes of NHPP models with and without covariates are parametric in nature, imposing assumptions on the defect discovery process, and, while neural networks have been applied to SRGM models without covariates, no such studies have been applied in the context of covariate SRGM models. Therefore, this paper assesses the effectiveness of neural networks in predicting the software defect discovery process, incorporating covariates. Three types of neural networks are considered, including (i) recurrent neural networks (RNNs), (ii) long short‐term memory (LSTM), and (iii) gated recurrent unit (GRU), which are then compared with covariate models to validate tracking and predictive accuracy. Our results suggest that GRU achieved better overall goodness‐of‐fit, such as approximately 3.22 and 1.10 times smaller predictive mean square error, and 5.33 and 1.22 times smaller predictive ratio risk in DS1G and DS2G data sets, respectively, compared to covariate models when of the data is used for training. Moreover, to provide an objective comparison, three different proportions for training data splits were employed to illustrate the advancements between the top‐performing covariate NHPP model and the neural network, in which GRU illustrated a better performance over most of the scenarios. Thus, the neural network model with gated recurrent units may be a suitable alternative to track and predict the number of defects based on covariates associated with the software testing process.

Rapid progress in terrain-aware autonomous ground navigation has been driven by advances in supervised semantic segmentation. However, these methods rely on costly data collection and labor-intensive ground truth labeling to train deep models. Furthermore, autonomous systems are increasingly deployed in unrehearsed, unstructured environments where no labeled data exists and semantic categories may be ambiguous or domain-specific. Recent zero-shot approaches to unsupervised segmentation have shown promise in such settings but typically operate on individual frames, lacking temporal consistency-a critical property for robust perception in unstructured environments. To address this gap we introduce Frontier-Seg, a method for temporally consistent unsupervised segmentation of terrain from mobile robot video streams. Frontier-Seg clusters superpixel-level features extracted from foundation model backbones-specifically DINOv2-and enforces temporal consistency across frames to identify persistent terrain boundaries or frontiers without human supervision. We evaluate Frontier-Seg on a diverse set of benchmark datasets-including RUGD and RELLIS-3D-demonstrating its ability to perform unsupervised segmentation across unstructured off-road environments.

Artificial neural network pruning is a method in which artificial neural network sizes can be reduced while attempting to preserve the predicting capabilities of the network. This is done to make the model smaller or faster during inference time. In this work we analyze the ability of a selection of artificial neural network pruning methods to generalize to a new cybersecurity dataset utilizing a simpler network type than was designed for. We analyze each method using a variety of pruning degrees to best understand how each algorithm responds to the new environment. This has allowed us to determine the most well fit pruning method of those we searched for the task. Unexpectedly, we have found that many of them do not generalize to the problem well, leaving only a few algorithms working to an acceptable degree.