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IMPROVED LONGEVITY OF ORTHOPEDIC IMPLANTS BY ADHESION OF PARYLENE C TO TITANIUM ALLOY PASSIVATION LAYERS VIA THE MULTICOMPONENT APPROACH TO SOLID SURFACE ENERGY
No full textThe purpose of this investigation is to determine the suitability of existing approaches to solid surface energy in increasing the adhesion of Parylene C to titanium, as well as some novel approaches. In transitioning from metal to metal oxide during passivation, the surface changes from one in which only dispersion forces are possible to one in which dispersive and either polar or acid-base forces must be present. Matching these components of the surface/interfacial energy as a means of maximizing the work of adhesion have been made in both academic and technical literature. Yet, few studies have attempted to connect multicomponent surface energy measurements with adhesion, and certainly not for the Parylene C-titanium system. Here, it is hypothesized that the titanium surface can be controlled through the partial pressure of oxygen in passivation or pretreatment to increase the work of adhesion (and thereby the practical adhesion) to Parylene C. This work begins with the careful characterization of the heat of fusion of Parylene C, which is currently missing from the literature. It is anticipated that this value will be highly valuable to researchers working with this material, as it enables the determination of the degree of crystallinity through simple differential scanning calorimetry (DSC) experiments. Next, non-trivial measurements of the multicomponent surface energy of both the adherend (titanium) and the coating (Parylene C) are taken under differing conditions of annealing and passivation/pretreatment, respectively. Finally, the application of contact angle (CA) measurement for calculating surface energy and electrochemical impedance spectroscopy (EIS) for coating adhesion and delamination is used to test the hypothesis and evaluate existing multicomponent approaches to adhesion. Additionally, the polarizability approach of Carré is also considered and then extended in what has been herein termed “Bidirectional Carré.” Results indicate that is extension may be as predictive as traditional surface energy models
Comparative Flood Risk Mapping Using Knowledge-Driven, Data-Driven, and Ensemble Models in a Humid Tropical River Basin in India
No full textFlooding is the most prevalent monsoon calamity in Kerala (a state in SW India), with the 2018 and 2019 events being the most catastrophic. Many tropical river basins in Kerala were severely battered by flooding during these two years. Thus, this study aims to create a flood risk map of the tropical Keecheri-Puzhakkal river basin in Kerala, which is usually flooded every monsoon, employing the Analytic Hierarchy Process (AHP), Fuzzy-AHP (F-AHP), Support Vector Machine (SVM), and SVM-Naïve Bayes (SVM-NB) stacking models to identify the model with better performance and to list the important conditioning factors (CFs). A total of nine CFs, such as slope, soil, stream density, aspect, land use/land cover (LULC), normalized difference water index (NDWI), stream power index (SPI), sediment transport index (STI), and topographic wetness index (TWI) have been selected for hazard modelling. The Area Under the Curve (AUC) values of the four hazard maps confirmed an acceptable performance (AUC ≥ 0.70) for the knowledge-driven AHP and F-AHP models, and an excellent performance (AUC ≥ 0.80) for the data-driven SVM and SVM-NB models. However, the SVM-NB model (AUC: 0.831) accomplished the highest performance, followed by the SVM model (AUC: 0.829), the F-AHP model (AUC: 0.769), and the AHP model (AUC: 0.768). The validation employing other metrics also supported this, substantiating our findings that data-driven models outperform the knowledge-driven models. Furthermore, the ensemble models—F-AHP and SVM-NB—exhibited slight enhancements over their respective standalone counterparts, AHP and SVM, underscoring the advantages of integrating models. Finally, exposure and vulnerability layers have been integrated with the hazard layers to produce risk maps. This study found that slope, LULC, SPI, TWI, and stream density are the top five important CFs. The outcomes of this modelling will help land use planners and policymakers identify suitable models for flood risk modelling in the future, so that the government will be equipped to deal with extreme events like the 2018 floods
Environmental and socio-economic Pareto-front trade-off analysis of U.S. PET packaging material in a circular economy
No full textVarious recycling technologies are emerging to implement circular economy in plastics supply chain systems. However, the environmental and socio-economic trade-offs of plastics in circular economy are not well understood at a systems level. Particularly, quantifying these trade-offs as a function of end-of-life (EOL) management decisions, including transition of recycling technologies, systems level metrics such as circularity, recycled content, and the need for fossil-derived plastics are not well understood. The present study addressed these research gaps by applying a systems analysis modeling approach that utilizes material flow analysis, life cycle assessment, socio-economic data, and system optimization techniques for polyethylene terephthalate (PET) packaging supply chains in the United States. Pareto-front trade-offs between conflicting environmental and socio-economic impacts as well as those between socio-economic impacts and circularity were explored using the epsilon constraint method. The Pareto-front trade-off analysis revealed the transition of EOL management strategies for PET packaging systems, including changes in selection of recycling technologies, to aid decision making process by quantifying studied system metrics. Transitioning from environmentally optimal to socio-economically optimal systems led to increased employment (by 17 %), wages (by 26 %), and revenues (by 6 %) but also led to increased global warming potential (GWP; by 65 %), energy consumption (by 59 %), and reliance on fossil PET in the system (by 78 %). Finally, the results show that there is not a unique set of recycling technologies to achieve a sustainable circular economy of PET packaging system, instead it depends on the decision maker\u27s objectives and targeted metrics of the system
High-content crumb rubber modified asphalt mixture via wet process: Laboratory evaluation and field application
No full textTo promote the recycling of waste tires in the asphalt industry, high-content crumb rubber-modified asphalt (CRMA) has received growing attention. This study evaluates the performance of high-content CRMA mixtures through two phases: (1) laboratory evaluation using laboratory-produced CRMA at 15 %, 22 % and 28 % rubber content, and factory-produced CRMA at 22 %; and (2) field construction using plant-produced CRMA at 22 %. All CRMA binders were modified from a base binder graded PG 52–28. Performance evaluation included binder performance and mixture-level assessments of resistance to rutting, cracking, and moisture damage. The Dynamic Shear Rheometer (DSR) and Multiple Stress Creep Recovery (MSCR) tests confirmed enhanced rutting resistance, while the Bending Beam Rheometer indicated an improvement in low-temperature performance grade (PG) to −34 to −40 °C. CRMA enhanced fatigue resistance but reduced storage stability. In the laboratory evaluation, the rutting resistance of the 22 % and 28 % CRMA mixtures was between those of the control mixtures with PG 70–28 and PG 58–34 binders. The factory 22 % CRMA ranked second lowest, and the laboratory 15 % CRMA performed the worst. All CRMA mixtures exhibited excellent cracking resistance, although higher rubber content did not consistently outperform the conventional 15 % CRMA. Field results showed a similar trend: plant 22 % CRMA mixture had strong cracking resistance but weaker rutting resistance than the control. In laboratory tests, the Tensile Strength Ratio was near the failure threshold due to the high natural sand content; however, it improved to above 90 % in the field after adjusting the aggregate type. Notably, A better DSR or MSCR rutting index (G*/sinδ, Jnr and %Recovery) did not necessarily indicate better mixture-level rutting resistance for CRMA when compared with control asphalt. In contrast, a lower low-temperature PG generally correlated with improved cracking resistance. Overall, high-content CRMA mixtures offer significantly improved cracking resistance while maintaining acceptable rutting performance, supporting waste tire recycling, and advancing circular economy goals
Effects of elevated nutrient supply on litter decomposition are robust to impacts of mammalian herbivores across diverse grasslands
No full textLitter decomposition is one of the largest carbon (C) fluxes in terrestrial ecosystems and links aboveground biomass to soil C pools. In grasslands, decomposition drivers have received substantial attention but the role of grassland herbivores in influencing decay rates is often ignored despite their potentially large effects on standing biomass and nutrient cycling. Recent work has demonstrated that nutrient addition increases early-stage decay and suppresses late-stage decay. Mammalian herbivores can mediate the effects of nutrient supply on biomass, suggesting herbivores may alter the effects of nutrients on decomposition, though this is largely unknown. We examined how herbivory mediates the effects of nutrient supply on long-term decomposition across 19 grassland sites of the Nutrient Network distributed experiment. At each site, a full-factorial experiment of combined nitrogen (N), phosphorus (P), and micronutrient (K) enrichment (‘control’ or ‘ + NPK’) and mammalian herbivore (\u3e ~ 50 g) exclusion (‘unfenced’ or ‘fenced’) was carried out in a randomized block design. We hypothesized that nutrient effects on litter decomposition would be strongest where herbivores caused the greatest reductions in aboveground plant biomass (i.e., at sites with more intense herbivory). After accounting for wide variation in decomposition rates across sites, we found that, within sites, elevated nutrients increased early-stage decay and suppressed late-stage decay. In contrast, neither herbivore exclusion (i.e., fencing) nor site level changes in aboveground biomass due to herbivory altered the nutrient effects on decomposition rates. Across grasslands, our results indicate that elevated nutrient supply modifies litter decomposition rates independent of herbivore impacts
Optical vortex spin-orbit control of refractive index in iron garnets
No full textThe interaction between light’s angular momentum (AM) and material systems has unlocked new avenues in structured photonics, including in magneto-optical (MO) materials. While spin angular momentum (SAM) effects in MO systems are well-established, orbital angular momentum (OAM) introduces novel opportunities for new nonreciprocal light-matter interactions. We demonstrate a unique optical phenomenon where OAM states undergo state-specific nonreciprocal operation within an MO medium, reducing Faraday rotation. In this study, we derive the perturbation to the Hamiltonian for the electronic transition in the presence of optical OAM. The reduction of Faraday rotation is verified experimentally using OAM Mach-Zehnder interferometry. This effect arises from transverse momentum transfer into the material, inducing spin-orbit coupling (SOC) at a perturbed electronic transition rate. The resulting OAM-dependent optical SOC modifies the material’s refractive index, directly linking structured light and MO response. Our findings extend previous observations of paraxial beams and reveal a deeper fundamental mechanism governing OAM-driven nonreciprocal interactions. These insights pave the way for OAM-selective nonreciprocal photonic devices, chiral optical logic, quantum memory elements, and ultrafast spintronic architectures. This work advances MO integration with structured light for enhanced control over photonic and spintronic systems
Energy-Aware Optimal Reconfiguration of a Heterogeneous Connected and Automated Vehicle Cohort on a Limited-Access Highway
No full textThis paper presents an optimized vehicular reordering methodology designed to minimize energy consumption within heterogeneous cohorts operating at constant velocity on limited-access highways. The approach addresses the challenge of optimizing vehicle sequencing by considering both aerodynamic drag reduction benefits and the energy costs of reconfiguring a cohort from a stochastic initial state. This study provides empirical validation through on-road vehicle tests, demonstrating significant energy savings, achieving up to 10% reduction in axle energy for optimally configured cohorts compared to independent operation. A System of Systems (SoS) simulation environment, integrating micro-traffic, validated powertrain, and aerodynamic drag reduction models, was developed to simulate complex reconfiguration maneuvers and quantify associated energy expenditures. The methodology examines how powertrain characteristics influence optimal arrangements and quantifies the impact of individual vehicle placement on overall cohort efficiency. Findings indicate that while reconfiguration incurs a minor energy cost (typically \u3c 0.45% of total trip energy for a 20 km trip), the net energy savings over relevant travel distances are substantial. The study also highlights the sensitivity of drag reduction estimators for heterogeneous platoons and the current limitations in available models. Ultimately, a predictive optimization framework is proposed that leverages connectivity-enabled information to select the most energy-efficient cohort configuration, considering factors such as distance to destination and reconfiguration energy, thereby offering a practical strategy for enhancing fuel economy in future connected and automated transportation systems
Attitudes toward the continued protection of gray wolves under the Endangered Species Act
Get PDFIn February 2024, the U.S. Fish & Wildlife Service announced their intention to develop – for the first time – a National Recovery Plan under the Endangered Species Act (ESA) for gray wolves in the lower 48 states. In August 2025, a federal judge vacated the U.S. Fish and Wildlife Service\u27s (FWS) decision denying ESA protections for gray wolves in the Western U.S.
Those events represent key context for this report, which describes the results of a web-based survey implemented in July and August of 2025 and designed to assess the attitudes of residents of the contiguous United States, as they pertain to the appropriateness of continuing protection of gray wolves under the ESA.
The survey\u27s central result is to reveal strong support for continuing protection among all examined socio-demographic groups. Statistics that exemplify this support include: (i) across the entire sample (n=1079), 78% ± 2.5% (95% CI) expressed support for continued protection of gray wolves under the Endangered Species Act, and (ii) for every person who strongly opposes continued protection, there more than nine people who strongly support continued protection
Evaluation of machine learning performance on source separated passive acoustic recordings
No full textThe United States National Park Service often deploys passive acoustic monitoring devices whose data are used to characterize the ecological health of the site and quantify the level of anthropogenic sources present. The analysis of the data is then used to inform conservation management policy that protects the natural soundscape of the National Parks. Traditional methods for analyzing the acoustic recordings involve human listeners annotating a subset of this data and labeling the acoustic sources present. Manual data processing is labor intensive and relies on human perception that varies between individuals, these limitations ultimately reduce the amount of data that can be analyzed. By augmenting the manual annotation of acoustic data with machine learning (ML) this research aims to increase the quantity of data that can be annotated while achieving higher annotation confidence for acoustic sources. Previous research has shown that separating the transient acoustic sources from the background acoustic environment reduces data complexity allowing for improved characterization of the sources. This paper will evaluate the performance of machine learning models using source separated data to understand the effects it has on data annotation accuracy, hyperparameter complexity, and ML model training time
Uncovering Risks of Data-Free Feature Vector Inversion Attacks Against Vector Databases
No full textThe vector database stores data as high-dimensional feature vectors. Some recently proposed attack techniques enable an adversary to launch feature vector inversion (FVI) attacks against vector databases. In FVI attacks, an adversary trains an FVI attack network to reconstruct the original private data from their feature vectors based on the assumption that an auxiliary dataset is available to the adversary. However, such a data-available assumption is too strong, making such FVI attacks unrealistic in many real-world scenarios. In this paper, we make the first systematic study on FVI attacks against vector databases in the data-free setting. To tackle the issue of no training data, we develop an output-to-input data generation technique that helps to generate synthetic fake samples for the FVI attack network training. In addition, to ensure the high quality of generated fake samples, we develop the accelerable complete bipartite graph (CBG) search strategy and the downstream-classifier-aided generator training strategy. Furthermore, as the key insight of this work, we find that the proposed output-to-input data generation technique can be employed to launch the other three ML attacks. Intriguingly, we find that the proposed FVI attack technique in the data-free setting can be directly employed to boost the attack performance of FVI attacks in the auxiliary-dataset-available setting. Finally, we propose and study defenses against the proposed attacks