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SAR ATR Performance Evaluation on Spatially Perturbed Synthetically Generated Signatures
No full textAutomatic target recognition (ATR) on synthetic aperture radar (SAR) data can be a challenging task due to the limited availability of publicly available measured datasets. Prior work has focused on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and the Synthetic and Measured Paired and Labeled Experiment (SAMPLE/SAMPLE+) datasets. The use of synthetic or modeled data in training AI has increased, both as modeling tools become more effective and also with increasing understanding of how to train AI with synthetic data. In this paper, we investigate the challenge of training an ATR with limited real-world data, focusing on two main problems: i) how best to use training tools to effectively train a robust ATR with combinations of measured and synthetic data, and ii) what is the effect of synthetic model uncertainty when using synthetic data to train ATRs. Model uncertainty could come from, for example, incomplete target data—say, a few images—or uncertain intelligence. Hence, we will show how noise augmentation and dropout affect ATR performance for varying proportions of measured versus synthetic training data. Then we will show how model uncertainty, produced by injecting uniform noise in to the vertices of a CAD model used to simulate SAR imagery, affects ATR performance. The target we will use is SLICY, an MSTAR target that was constructed of radar reflector primitives, which enables interpretability of ATR performance, both in terms of algorithm accuracy but also in saliency of the ATR features. Several experiments are performed that address how noise augmentation, dropout, and model uncertainty affect a canonical ATR used on MSTAR data. Results suggest some rules of thumb in how to train SAR ATRs with limited real-world training data and potential synthetic model uncertainty
Generative artificial intelligence for construction: Use cases, trends, challenges, and opportunities
No full textRecently, generative artificial intelligence (AI) technologies such as Generative Adversarial Networks (GANs), large language models (LLMs), Generative Pre-trained Transformers (GPT), and diffusion models have been increasingly applied to address challenges and inefficiencies within the Architecture, Engineering, Construction (AEC) workflows, particularly in design, planning, and construction management. However, its current research landscape remains fragmented, with limited synthesis of trends and unclear pathways for adoption in the construction industry. To address these gaps, a mixed-method review is conducted, combining bibliometric analysis to quantitatively map research trends with qualitative thematic synthesis for in-depth contextual insights. A total of 148 publications were retrieved from Scopus and Google Scholar (2014–2024). The bibliometric analysis identified 49 high-frequency keywords, grouped into six thematic clusters, characterizing the quantitative research landscape. Complementing this, the qualitative synthesis examined five dominant application domains: (1) proactive safety monitoring and risk prevention, (2) generative AI for sustainable construction, (3) automating design through generative intelligence, (4) construction education, and (5) construction management, within which key research gaps and practical challenges are critically examined. Building upon these insights, the study proposes four-level research roadmap spanning (1) industry-level considerations, (2) organizational and stakeholder perspectives, (3) project-level perspectives, and (4) technological integration. Unlike prior reviews that concentrated on isolated single-model technologies or narrowly defined domains, this study offers a comprehensive, cross-domain analysis of generative AI for construction. By employing a mixed-method review—integrating quantitative bibliometric mapping and qualitative thematic synthesis—it bridges technical, organizational, and implementation perspectives to deliver a holistic understanding of the field. Hence, this review offers clear avenues for future investigation, empowering researchers to expand and refine Generative AI toward achieving a more efficient, resilient, and sustainable construction
Mapping peatland extent and condition in the conterminous United States and Hawaii to inform peatland protection and restoration
No full textPeatland conservation and restoration are globally important goals because of peatlands’ potential to sequester and store carbon for millennia, regulate hydrology, and emit greenhouse gases (GHGs) when degraded. To provide information that can be used to identify possible targets for restoration, we have developed a peatland condition map for the conterminous US and Hawaii using existing GIS-based information. We intersected gSSURGO histosols and histic epipedons (HE) with layers for land use, crops, ditches, roads, and railroads (within 150 m buffers for the last three), land protection classes, and USDA Natural Resources Conservation Service (NRCS) wetland easements. Of the 94,750 km2 of histosols and 13,533 km2 of HE analyzed, 7 % (7709 km2) were under agricultural use. Of 100,415 km2 of histosols and HE not in agricultural use, 19 % were within 150 m of ditches, roads, or railroads. Of mapped histosols, 38 % (36,042 km2) were legally protected from extractive use, and 635 km2 were in NRCS wetland easements. Based on IPCC tier 1 emission factors, the greatest reduction of CO2-e emissions per unit area and nationally would be from rewetting of peatlands under agriculture. In non-agricultural areas, rewetting peatlands affected by ditching alone is likely more cost-effective than if they are also affected by roads and railroads. Total potential emission reduction associated with rewetting of currently analyzed drained peatlands is estimated at \u3e36.8 Tg CO2-e yr−1. Future gap filling of the map will likely increase this estimate. This map can be used to evaluate potential peatland restoration opportunities at a variety of scales
Influence of communication channels on forestry programs participation: Evidence from smallholders in Vietnam
No full textCommunication plays a pivotal role in enhancing rural households\u27 engagement in forestry programs, yet its impacts have not been thoroughly explored. In this study, we examine the influence of communication channels on the participation decisions of 300 randomly sampled households across 30 communities in Thanh Hoa Province, Vietnam. Semi-structured interviews were used to collect data on communication aspects, household characteristics, socioeconomic status, institutional considerations, farm attributes, and biophysical factors. Logistic regression and Bayesian model averaging were utilized for data analysis. Our findings revealed that government-led formal communication channels were more influential in increasing forestry participation than informal farmer-to-farmer communication. Further, information on forestry programs, planting knowledge, forest area, formal communication channels, and household wealth ranking jointly predicted landholders\u27 participation decisions. As for policy interventions, we suggested improving transparency and monitoring of formal communication channels, extending educational outreach initiatives, and utilizing informal networks to disseminate technical information/supports to local households. Implementing tailored support instruments for resource-constrained households is further recommended. Collectively, our study offers a foundation for understanding communication\u27s role in forestry program participation, contributing to the development of context-tailored strategies to enhance the efficacy and socioeconomic viability of forestry development projects in rural Vietnam
The influence of human presence and footprint on animal space use in US national parks
No full textGiven the importance of protected areas for biodiversity, the growth of visitation to many areas has raised concerns about the effects of humans on wildlife. In 2020, the COVID-19 pandemic led to temporary closure of national parks in the United States, offering a pseudonatural experiment to tease apart the effects of permanent infrastructure and transient human presence on animals. We compiled GPS tracking data from 229 individuals of 10 mammal species in 14 parks and used third-order hierarchical resource selection functions to evaluate the influence of the human footprint on animal space use in 2019 and 2020. Averaged across all parks and species, animals avoided the human footprint, whether the park was open or closed. However, although animals in remote areas showed consistent avoidance, on average those in more developed areas switched from avoidance to selection when protected areas were closed. Findings varied across species: some responded consistently negatively to the footprint (wolves, mountain goats), some positively (mule deer, red fox) and others had a strong exposure-mediated response (elk, mountain lion). Furthermore, some species responded more strongly to the park closure (black bear, moose). This study advances our understanding of complex interactions between recreation and wildlife in protected areas
ESTIMATING UNCONFINED COMPRESSIVE STRENGTH (UCS) OF ROCKS FROM MEASUREMENT WHILE DRILLING (MWD) DATA
No full textMeasurement-while-drilling (MWD) technology enables the real-time monitoring of drilling parameters during the drilling process. MWD data can provide valuable insight into the mechanical behavior of intact rocks and rock masses, which is essential for geotechnical applications such as foundation design and underground construction. The aim of this study is to correlate real-time drilling parameters, recorded using a laboratory-developed MWD system, with the unconfined compressive strength (UCS) of rocks. A custom laboratory MWD system was designed to simultaneously record four key drilling parameters: thrust (axial force, crowd), torque, penetration rate, and rotational speed. The custom-built MWD system was used to continuously measure the drilling parameters while coring into five different materials, i.e., gypsum, sandstone, dolomite, basalt, and concrete. Core samples collected during these corings were then tested for UCS under both dry and water-saturated conditions. Two compound parameters, i.e., specific energy and drillability, were calculated from the raw MWD data and compared with UCS values. Both indices showed a good correlation with UCS, with specific energy providing slightly more accurate predictions. These results demonstrate the potential of laboratory-based MWD systems as practical tools for estimating rock strength in geotechnical investigations
DESIGN AND DEVELOPMENT OF FLUORESCENT PROBES FOR SENSING AND BIOIMAGING OF NAD(P)H, INTRACELLULAR VISCOSITY, AND SO2
No full textUnderstanding the dynamic biochemical and biophysical changes within living systems is vital for advancing disease diagnosis and therapeutic development. This work presents the design and development of innovative fluorescent probes, based on cyanine and coumarin scaffolds, for the highly sensitive and selective detection of key metabolic and biophysical markers, NAD(P)H, mitochondrial viscosity, and sulfur dioxide (SO₂) in live cells and fruit fly larvae. A series of cyanine-based probes were engineered to undergo strong fluorescence enhancement upon reduction by NAD(P)H, enabling real-time visualization of metabolic fluctuations in cancer cells and Drosophila larvae. These probes exhibit excellent sensitivity, mitochondrial targeting capability, and near-infrared emission, making them powerful tools for studying redox dynamics under conditions such as glycolysis, hypoxia, and drug treatment. Additionally, a dual-channel coumarin-based probe was developed to simultaneously detect mitochondrial viscosity and SO₂. This multifunctional probe offered ratiometric SO₂ detection and near-infrared fluorescence for viscosity changes, enabling dynamic monitoring of mitochondrial responses to stress induced by agents such as nystatin, monensin, and LPS. Together, these fluorescent probes provide robust and versatile platforms for real-time bioimaging and metabolic analysis in both physiological and pathological settings, offering valuable insights into cellular function, disease mechanisms, and treatment responses
DESIGN OF ALUMINUM-COPPER ALLOY WITH SCANDIUM IN SOLUTION
No full textThe 2xxx series of precipitation-strengthened aluminum-copper alloys are commonly used in high-temperature applications (up to 300°C) where lightweight metals are required. Scandium additions to aluminum-copper alloys have gained recent academic interest due to improved high-temperature mechanical properties, by forming coarsening- resistant Al3Sc dispersoids and stabilization of θ′ precipitates. However, gaps in knowledge about the effects of scandium present a challenge to successful commercialization of aluminum-copper-scandium alloys. This work seeks to explore these knowledge gaps and ultimately design and test a commercially feasible aluminum- copper alloy with scandium. To better understand the processing conditions required to avoid the detrimental W-phase (Al8Cu4Sc), castings were produced with a wide range of solidification rates and heat treatments. It was shown that the only feasible heat treatment is a single-step homogenization with copper below 4 wt%, and that higher copper or multi-step homogenization will lead to the formation of W-phase. W-phase results precipitate-free zones which lower the hardness of the resulting microstructure. Using a 3.5 wt% copper sample combined with a single-step homogenization, the effects of scandium in solution on precipitation of copper-rich precipitates are studied. Scandium in solution stabilizes θ″ and θ‴ precipitates over θ′ when aging at 160°C, which is supported by density functional theory calculations that show a significant reduction in the interaction energy of θ″, θ‴, and GP zones with the addition of scandium. Finally, an engineered alloy with low copper and scandium in solution is tested in comparison to AA2219 and is shown to have similar high-temperature strength while having a 60% increase in the fracture toughness. Transmission electron microscopy shows that θ′ precipitates in the scandium-containing alloy have larger aspect ratios and a preference for {100} semi-coherent interfaces. This work expands knowledge of the processing conditions required for a commercially feasible aluminum-copper-scandium alloy and demonstrates an alloy design strategy for this system
Does energy policy scholarship consider energy resilience? A bibliometric analysis and agenda for reform
No full textThis study investigates the extent to which the concept of energy resilience is integrated into energy policy scholarship and proposes future research agenda to strengthen engagement with energy resilience in energy policy making. Our study shows that energy resilience is absent in energy policy scholarship, or framed the other way round, energy policy scholarship is absent in the discussion of energy resilience topic. We used bibliometric analysis of literature across Scopus and ProQuest databases and applied keyword co-occurrence mapping and journal-keyword analysis techniques to assess how themes of energy resilience intersect with “community,” “disaster,” and “policy.” Our analysis reveals that energy resilience is predominantly framed within technical, systemic and infrastructural contexts, with limited interdisciplinary attention to its socio-economic, governance, and equity dimensions. Notably, resilience-related terms are underrepresented in leading policy journals, suggesting a disconnect between energy resilience as a concept and energy policy scholarship. We argue that advancing energy resilience as a structured policy agenda requires a holistic framework that integrates technical reliability with community-level resilience, institutional capacity, and justice considerations. This research provides empirical evidence of the thematic silos in the energy policy literature and offers a roadmap for incorporating energy resilience more substantively into energy policy design, implementation, and evaluation
A Novel, Metal-Based Approach to Identify Residences with Lead Service Lines
No full textThere is an urgent need for rapid, cost-effective approaches to identify residences with lead service lines (LSLs). We evaluated whether analyzing water for corrosion-related metals could accurately identify residences with LSLs without relying on potentially inaccurate property records. We applied principal component analysis logistic regression (PCA-LR) and classification tree models using 28 analytes per bottle (including Pb, Cu, Zn, Fe, Al, and others) measured in 216 water samples collected in Flint, Michigan, in August 2015. The PCA-LR model achieved 87% accuracy (AUROC = 0.93) with 81% sensitivity and 90% specificity, while the classification tree model achieved 80% accuracy (AUROC = 0.77) with 74% sensitivity and 84% specificity. The classification tree provided interpretable decision rules identifying key predictive metals, primarily relying on 1 min flush Pb concentrations with Zn and Al as secondary predictors. It also revealed distinct metal co-occurrence patterns between LSLs and premise plumbing, offering insights into Pb source identification. The tree’s interpretable structure makes it particularly valuable for practical implementation by utilities. Although additional work is needed to extend these models to other water systems, our results suggest that metal analysis provides an accurate, cost-effective, and minimally invasive tool that complements existing approaches for predicting the presence of an LSL