New Jersey Institute of Technology

Autonomous systems built on ROS2 are increasingly deployed in safety- and performance-critical domains such as autonomous driving and mobile robotics. While existing research has proposed various timing analyses and scheduling strategies for ROS2, many rely on simplified assumptions that do not hold in real-world applications. In this article, we present a detailed empirical study of ROS2-based autonomous applications, uncovering underexplored runtime behaviors that significantly impact both real-time and functional performance. These include the importance of partial cause-effect chains, dynamic execution paths and timing variability, non-FIFO data access patterns, and computation threads uncontrolled by ROS2 executors. We extend an existing tracing tool to support Transform Library and ROS2’s Action entity, enabling reconstruction and analysis of realistic cause-effect chains. Our findings are validated through experiments in simulated autonomous robot scenarios and a case study using the Autoware autonomous driving framework. Together, our results highlight the need for rethinking ROS2 modeling, scheduling and analysis to better reflect the realities of autonomous systems.

Lifestyle behaviors such as smoking, vaping, and physical activity can induce epigenetic modifications that influence health trajectories and may provide forensic value. DNA methylation signatures linked to these behaviors offer potential for behavioral inference, personalized health assessment, and improved investigative practices. This study aimed to characterize methylation patterns associated with nicotine exposure and exercise using buccal cell DNA profiling, and to evaluate the extent to which these patterns differentiate harmful and protective lifestyle habits. Buccal epithelial DNA was analyzed using the Illumina Infinium MethylationEPIC v2 BeadChip to assess genome-wide methylation. Participants were categorized by smoking status, vaping behavior, and exercise activity. Differentially methylated regions (DMRs) and CpG sites were identified through pairwise comparisons among smokers, vapers, non-smokers/non-vapers, athletes, and sedentary individuals. A threshold of p < 1 × 10-4 was applied for significant differentially methylated CpG sites. Distinct epigenetic profiles were associated with smoking/vaping and physical activity. Five DMRs differentiated smokers from non-smokers/non vapers, while 11 DMRs distinguished vapers from the same reference group. Twenty-eight DMRs displayed divergent methylation patterns between smokers and vapers. Exercise also showed measurable epigenetic influence: control athletes exhibited 26 significantly differentially methylated CpG sites relative to non-athletes, and smoker athletes demonstrated 126 suggestive differential sites compared to sedentary smokers. Additionally, 63 sites differentiated smoker athletes from non-smoker/non-vaper non-athletes, indicating interactions between risk-associated and health-promoting behaviors. Buccal cell DNA methylation profiling effectively captured signatures associated with smoking, vaping, and physical activity. These findings underscore the potential of epigenetic markers for lifestyle assessment in both personalized medicine and forensic investigations.

<strong class="journal-contentHeaderColor">Abstract.</strong> We present dynamic mid-latitude ionospheric changes associated with the Apr 8, 2024 solar eclipse, as observed in the eastern to central United States using wide-field scans from the Millstone Hill incoherent scatter radar. The radar&rsquo;s field of view covers a large portion of the continental US (25&ndash;55&deg; N, 75&ndash;95&deg; W), and is capable of observing altitude profiles from the E-region up to the topside ionosphere. We find that extensive ionospheric changes triggered by the eclipse along the US east coast were detected before the totality shadow entered the continent, and in particular when the eclipse obscuration percentage became greater than 0 %. During the time interval where the obscuration percentage is significant (&gt;75 %) over the radar scanning region, we find maximum electron density (<em>N_e</em>) drops of up to 50 % (2 &times;10¹¹ m^&minus;3) in the F1 region (200&ndash;250 km) to the north of the totality path, with a large <em>N_e</em> decrease lasting for 1 hour, from 19&ndash;20 UT. In the topside ionosphere, a delayed drop in <em>N_e</em> values occurred beginning 30 min&ndash;1 hour later, with electron density decreasing by 40&ndash;50 % (2.5&ndash;3 &times;10¹¹ m^&minus;3) below background levels, followed by a recovery to typical background values only after 22 UT. The electron temperature (<em>T_e</em>) showed a faster response and recovery rate which closely mirrored the eclipse obscuration level, with a visible decrease in <em>T_e</em> when the obscuration rate exceeded 0&ndash;30 % depending on altitude, with a faster response time at higher altitudes. A decrease in <em>T_e</em> of the order of 40 % (850 K) was observed at altitudes from 325&ndash;400 km between 18.75&ndash;19.25 UT. The ion temperature (<em>T_i</em>), similar to <em>T_e</em>, responded rapidly to the obscuration level as it exceeded 30 %, with up to 20 % or 225 K reduction in a narrow geographic area and at two distinct altitude regions: 200&ndash;225 km and 300&ndash;325 km between 19&ndash;20 UT.

<strong class="journal-contentHeaderColor">Abstract.</strong> We present dynamic mid-latitude ionospheric changes associated with the Apr 8, 2024 solar eclipse, as observed in the eastern to central United States using wide-field scans from the Millstone Hill incoherent scatter radar. The radar&rsquo;s field of view covers a large portion of the continental US (25&ndash;55&deg; N, 75&ndash;95&deg; W), and is capable of observing altitude profiles from the E-region up to the topside ionosphere. We find that extensive ionospheric changes triggered by the eclipse along the US east coast were detected before the totality shadow entered the continent, and in particular when the eclipse obscuration percentage became greater than 0 %. During the time interval where the obscuration percentage is significant (&gt;75 %) over the radar scanning region, we find maximum electron density (<em>N_e</em>) drops of up to 50 % (2 &times;10¹¹ m^&minus;3) in the F1 region (200&ndash;250 km) to the north of the totality path, with a large <em>N_e</em> decrease lasting for 1 hour, from 19&ndash;20 UT. In the topside ionosphere, a delayed drop in <em>N_e</em> values occurred beginning 30 min&ndash;1 hour later, with electron density decreasing by 40&ndash;50 % (2.5&ndash;3 &times;10¹¹ m^&minus;3) below background levels, followed by a recovery to typical background values only after 22 UT. The electron temperature (<em>T_e</em>) showed a faster response and recovery rate which closely mirrored the eclipse obscuration level, with a visible decrease in <em>T_e</em> when the obscuration rate exceeded 0&ndash;30 % depending on altitude, with a faster response time at higher altitudes. A decrease in <em>T_e</em> of the order of 40 % (850 K) was observed at altitudes from 325&ndash;400 km between 18.75&ndash;19.25 UT. The ion temperature (<em>T_i</em>), similar to <em>T_e</em>, responded rapidly to the obscuration level as it exceeded 30 %, with up to 20 % or 225 K reduction in a narrow geographic area and at two distinct altitude regions: 200&ndash;225 km and 300&ndash;325 km between 19&ndash;20 UT.

Ordered leaf attachment, Phylo2Vec, and HOP are three recently introduced vector representations for rooted phylogenetic trees where the representation is determined by an ordering of the underlying leaf set X. Comparing the vectors of two rooted phylogenetic X-trees T and for a fixed ordering on X leads to polynomial-time computable measure for the dissimilarity of T and , albeit dependent on the choice of the leaf ordering. For each of ordered leaf attachment, Phylo2Vec, and HOP, we compare this measure with the rooted subtree prune and regraft distance (rSPR), the hybrid number, and the temporal tree-child hybrid number of T and . Although there is no direct relationship between rSPR and any of the three vector-based measures, we show that, when minimized over all orderings, the hybrid number is equal to HOP and is an upper bound on the other two. Moreover, when minimized over all orderings induced by common cherry-picking sequences of T and , the temporal tree-child hybrid number of T and is equal to each of the three vector-based measures.

In recent years, gig platforms like Uber and DoorDash have implemented strategies to boost gig drivers' earnings during peak hours. Uber's 'back-to-back' feature allows drivers to accept new trips while still on route, and Uber Eats' 'Batch Order Route' initiative allows drivers to pick up multiple deliveries from different locations, which may result in multiple tops before one order is delivered. Despite revenue gains, these features lead to user complaints about extended waiting times. In response, platforms introduce features like Uber Eats' 'Priority Delivery' and Uber's 'Priority', where customers pay an extra subscription fee for guaranteed reduced waiting times. This paper focuses on designing matching policies to enhance system revenue while limiting customer waiting times. We present a hybrid model combining online matching and queue theory for quantitative analysis of users' waiting times. Additionally, we introduce an LP-based sampling framework and a unified queue-theory-based method for evaluating online performance. Comprehensive experiments on real datasets validate our theoretical findings, highlighting the efficiency of our matching framework in promoting profit and meeting committed waiting times.

This paper investigates the use of large language models for interpreting optimization failures and suggesting feasible adjustments to constraint settings in electrical power networks. Optimal power flow determines the most economical and reliable generation dispatch while satisfying network balance equations and operational limits such as voltage bounds, generator capacities, and transmission line ratings. However, these nonlinear optimization problems frequently fail to converge when system constraints are overly tight, conflicting, or incorrectly configured, producing complex numerical logs that are difficult to analyze. In this study, a pretrained large language model is employed as an advisory system to interpret such solver failure information and recommend minimal constraint modifications that can restore feasibility. The method is evaluated using the IEEE 14-bus test system, where infeasible cases are intentionally created by altering generator limits, voltage boundaries, and line capacities. Each failed case is represented in a structured textual format and processed by the language model to propose corrective actions. Results show that for single-constraint failures, the model consistently provides valid and minimal adjustments leading to full convergence (100% success rate), while for multi-constraint failures, performance drops to about 30%. These findings highlight the potential of language-based reasoning as a complementary interpretive layer in real-time operational diagnostics. Keywords: Constraint Adjustment, Feasibility Restoration, GPT-4o, LLMs, OPF Convergence, Prompt Engineering, Zero-Shot Reasoning.

Abstract The electron density of the solar corona is a fundamental parameter in many areas of solar physics. Traditionally, routine estimates of coronal density have relied exclusively on white-light observations. However, these density estimates, obtained by inverting the white-light data, require simplifying assumptions, which may affect the robustness of the measurements. Hence, to improve the reliability of coronal density measurements, it is highly desirable to explore other complementary methods. In this study, we estimate the coronal electron densities in the middle corona, between approximately 1.7 and 3.5 R ⊙ , using low-frequency radio observations from the recently commissioned Long Wavelength Array at the Owens Valley Radio Observatory (OVRO-LWA). The results demonstrate consistency with those derived from white-light coronagraph data and predictions from theoretical models. We also derive a density model valid between 1.7 and 3.5 r ⊙ , given by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>ρ</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msup> <mml:mrow> <mml:mi>r</mml:mi> </mml:mrow> <mml:mrow> <mml:mo accent="true">′</mml:mo> </mml:mrow> </mml:msup> <mml:mo stretchy="false">)</mml:mo> <mml:mo>=</mml:mo> <mml:mn>1.27</mml:mn> <mml:msup> <mml:mrow> <mml:mi>r</mml:mi> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mo accent="true">′</mml:mo> </mml:mrow> </mml:msup> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:mn>29.02</mml:mn> <mml:msup> <mml:mrow> <mml:mi>r</mml:mi> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mo accent="true">′</mml:mo> </mml:mrow> </mml:msup> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:mn>71.18</mml:mn> <mml:msup> <mml:mrow> <mml:mi>r</mml:mi> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mo accent="true">′</mml:mo> </mml:mrow> </mml:msup> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>6</mml:mn> </mml:mrow> </mml:msup> </mml:math> , where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi>r</mml:mi> </mml:mrow> <mml:mrow> <mml:mo accent="true">′</mml:mo> </mml:mrow> </mml:msup> <mml:mo>=</mml:mo> <mml:mi>r</mml:mi> <mml:mo>/</mml:mo> <mml:msub> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>⊙</mml:mo> </mml:mrow> </mml:msub> </mml:math> , with r the heliocentric distance. OVRO-LWA is a solar-dedicated radio interferometer that provides science-ready images with low latency, making it well suited for generating regular and independent estimates of coronal densities to complement existing white-light techniques.