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The battery bubble
No full textThe global push for electrification has placed battery technology at the forefront of climate solutions, but this almost singular focus is creating a precarious economic and environmental bubble. This article presents a multidimensional analysis–encompassing economic, scientific, and anthropological perspectives–of the impending battery industry crisis. We examine the rise of a “battery bubble” driven by the electric vehicle (EV) revolution, using Granular Interaction Thinking Theory (GITT) to highlight how a narrow technological focus can backfire. Nickel, a critical metal for batteries, is explored as the first casualty of this bubble. Soaring demand for it and price volatility have led to severe environmental degradation and market instability. Drawing parallels to historical manias such as Tulip Fever and the Dot-com bubble, we discuss how hype and herd behavior inflate expectations of battery dominance, risking “immiserizing growth”–an economic expansion that paradoxically worsens social, economic, and environmental well-being. Finally, we propose a pathway to escape the battery bubble through a shift to an eco-surplus culture underpinned by the “semiconducting” principle of environmental-economic value exchange. This approach calls for reorienting our value system to prevent solving one environmental problem at the cost of exacerbating others. The analysis underscores the urgency of recalibrating climate strategies before the battery bubble busts, with potentially cascading consequences for global stability
From network degradation to mechanical brittleness: The aging response of epoxy vitrimers
No full textThe growing use of composite materials in engineering has intensified the need for sustainable alternatives to traditional thermoset polymers, which are difficult to recycle and contribute to environmental pollution. Vitrimers, a class of covalently adaptable network polymers capable of bond exchange reactions, offer a promising solution by combining the mechanical robustness of conventional thermosets with the potential for reprocessing and recyclability. However, their long-term stability under extreme environmental conditions remains underexplored. This study investigates the effects of oxidative and hydrolytic aging on a DGEBA-based vitrimer system formulated with glutaric anhydride and zinc acetylacetonate. By subjecting samples to accelerated aging conditions and analyzing changes in macromolecular structure, thermal behavior, and mechanical performance using FTIR, DMA, microscopy, nano-indentation, and tensile testing, we explored the degradation mechanisms that govern vitrimer durability in extreme environments and evaluated their potential for long-term structural applications. Although both oxidation and hydrolysis are identified as coupled diffusion-reaction processes in bulk polymers, their degradation mechanisms for the chosen vitrimer were found to differ significantly. Hydrolysis exhibited an initial period of mass gain due to water sorption, followed by a reaction-dominated phase characterized by substantial mass loss via bulk erosion. In contrast, oxidation, limited by the low diffusivity of oxygen at atmospheric pressure, did not show a diffusion-driven mass gain or an induction period. Instead, degradation initiated immediately, resulting in an overall mass loss and the localized formation of micro-pores near the material\u27s outer surface. While the two extreme environments provided two differing degradation mechanisms, they shared a similar macroscopic response of increased embrittlement as aging progresses, demonstrated by a significant reduction in peak stress and failure strain. These insights into the distinct degradation pathways and their converging mechanical consequences provide a critical foundation for evaluating the long-term viability of vitrimers in demanding structural applications and for guiding the design of more durable, recyclable polymer systems
Road Performance Evaluation of Preventive Maintenance Techniques for Asphalt Pavements
No full textPreventive maintenance treatments are widely applied to asphalt pavements to mitigate deterioration and extend service life. This study evaluated four common technologies: a high-elasticity ultra-thin overlay, an Stone Mastic Asphalt (SMA)-10 thin overlay, micro-surfacing (MS-III), and a chip seal. Laboratory testing focused on skid resistance, surface texture, and low-temperature cracking resistance. Skid resistance was measured with a tire–pavement dynamic friction analyzer under controlled load and speed, while surface macrotexture was assessed using a laser scanner. Low-temperature cracking resistance was determined through three-point bending beam tests at −10 °C. The results showed that chip seal achieved the highest initial friction and texture depth, immediately enhancing skid resistance but exhibiting rapid texture loss and gradual friction decay. Micro-surfacing also demonstrated good initial skid resistance but experienced a sharp reduction of over 30% due to fine aggregate polishing. By contrast, the high-elastic ultra-thin overlay and SMA thin overlay provided more stable skid resistance, lower long-term friction loss, and excellent crack resistance. The polymer-modified ultra-thin overlay achieved the highest low-temperature bending strain ≈40% higher than untreated pavement, indicating superior crack resistance, followed by the SMA thin overlay. Micro-surfacing with a chip seal layer only slightly improved low-temperature performance. Overall, the high-elastic ultra-thin overlay proved to be the most balanced preventive maintenance option under heavy-load traffic and cold climate conditions, combining durable skid resistance with enhanced crack resistance
Understanding the sensitivity of PV snow loss modeling to snow slide coefficients in standard snow loss model over Michigan
No full textAs utility-scale photovoltaic systems increase in northern climates, understanding when and how snow slides from panels has become an active area of research producing many prediction models. One widely used snow shedding model requires a snow sliding coefficient that controls how much the snow slides down the panel in one timestep. The slide coefficient is determined based on the photovoltaic system mounting style - roof or ground mount. The roof-mount coefficient has been used as a standard input, as it had more validation when the model was developed. In this research, we performed a sensitivity analysis on the roof- and ground-mount snow slide coefficients using historical weather data and a simulated utility-scale solar site in Michigan to better understand the effect of the snow slide coefficient on the hourly and annual losses over a ten-year period
Preservation of the Bernstein property for sums of independent random variables
No full textIt is shown that Bernstein-type conditions on independent random variables are preserved by their sum. Some optimality properties of such preservation are proved
Automated Semantic Segmentation of Arctic Surface Water Features with Very-High Resolution Satellite X-Band Radar Imagery and U-Net Deep Learning: Segmentation sémantique automatisée des caractéristiques des eaux de surface de l’Arctique à partir d’images radar satellite en bande X à très haute résolution et à l’aide de l’apprentissage profond U-Net
No full textRepeatable methods capable of quantifying Arctic surface water extent at high resolutions are important, but still require development. Here, we present a study using very-high resolution (VHR) X-band Synthetic Aperture Radar (SAR) imagery from Capella Space for fine-scale semantic segmentation of Arctic surface water features. Our study proposes a modified U-Net encoder-decoder model for this task, optimized using the Nadam algorithm. Otsu thresholding was leveraged to rapidly generate 512 × 512-pixel patches for the U-Net, resulting in an efficient and automated training pipeline. Within this study, we also quantitatively compared the deep learning (DL) U-Net to a shallow machine learning (ML) algorithm, XGBoost (XGB), and evaluated the Capella Space imagery against spatially and temporally coincident Sentinel-1 C-band. Performance evaluations showed the U-Net outperforms XGB measured under several statistical metrics, reaching an Intersection over Union (IoU) of 0.955. An explainability analysis was conducted to complement this finding, using Gradient-weighted Class Activation Mapping (Grad-Cam). Visual analysis also underscored the extreme detail of small water features captured by Capella Space imagery, which are at times omitted or lack clarity in conventional Sentinel-1. This research makes several contributions to Arctic surface water mapping, demonstrating the effectiveness of combining VHR SAR imagery with DL
Digital twin for sustainable decision-making in building net zero carbon retrofitting: a systematic review
No full textPurpose – Digital twin (DT) is an innovative concept within the construction sector that utilizes real-world performance data to create a virtual model, enabling optimized decision-making. Building net zero carbon (NZC) retrofitting offers an opportunity to reduce global carbon emissions. However, decision-makers face challenges in making smart and sustainable decisions in NZC retrofitting. DT can improve the smartness and sustainability of the decision-making practice in NZC retrofitting, providing promising solutions. Hence, this study assesses the potential of DT for smart and sustainable decision-making in NZC retrofitting. Design/methodology/approach – This study used a three-stage methodology, which included initial work, systematic review and analysis and discussion. Accordingly, the study investigated 29 relevant academic publications on DT and building retrofitting to NZC, using content analysis as the major analytical approach. Findings – The findings demonstrated the effective application of DT in assessing carbon emissions, energy usage, comfort levels and financial savings within the context of building NZC retrofitting. However, it highlighted a lack of integrated focus on the environmental, social and economic pillars of sustainability in NZC retrofitting. Furthermore, the review identified the technologies utilized in implementing DT for building NZC retrofitting as data-related, modeling-related and model simulation-related technologies. Centered on the identified gaps, the study provides recommendations for building NZC retrofitting. Originality/value – The identified gaps and proposed directions would guide future researchers interested in implementing DT to make sustainable decisions in building NZC retrofitting
Integrating feminist pedagogy into manufacturing education: a digital twin-based teaching module
No full textIntegrating feminist pedagogy into engineering education offers a novel pathway to make technical learning more inclusive, participatory, and socially responsive. This paper presents the design and classroom implementation of a digital twin-based teaching module that combines sustainable manufacturing concepts with student-centered learning. A CNC milling machine was retrofitted and linked to its virtual counterpart using CAD/CAM tools, open-source controllers, and a custom-developed graphical user interface (GUI). The system captures real-time data on energy consumption and tool vibration, enabling students to explore how machining parameters impact sustainability factors such as power usage and vibration − induced tool wear. Grounded in feminist pedagogical principles, emphasizing collaboration, reflexivity, and co-creation of knowledge, the module was deployed in Smart Manufacturing and Internet of Things (IoT) courses. The approach fostered a more inclusive learning environment by encouraging active participation, shared authority, and critical thinking around engineering practices. Student surveys and course evaluations indicated improved engagement, deeper conceptual understanding, and greater satisfaction. These results highlight the potential of integrating feminist pedagogy with digital twin technology to enhance manufacturing education and better prepare students for the demands of Industry 4.0 and sustainable engineering
HAWC, VERITAS, Fermi-LAT, and XMM-Newton Follow-up Observations of the Unidentified Ultra-high-energy Gamma-Ray Source LHAASO J2108+5157
Get PDFWe report observations of the ultra-high-energy gamma-ray source LHAASO J2108+5157, utilizing VERITAS, HAWC, Fermi-LAT, and XMM-Newton. VERITAS has collected ∼40 hr of data that we used to set ULs to the emission above 200 GeV. The HAWC data, collected over ∼2400 days, reveal emission between 3 and 146 TeV, with a significance of 7.5σ, favoring an extended source model. The best-fit spectrum measured by HAWC is characterized by a simple power law with a spectral index of 2.45 ± 0.11stat. Fermi-LAT analysis finds a point source with a very soft spectrum in the LHAASO J2108+5157 region, consistent with the 4FGL-DR3 catalog results. The XMM-Newton analysis yields a null detection of the source in the 2–7 keV band. The broadband spectrum can be interpreted as a pulsar and a pulsar wind nebula system, where the GeV gamma-ray emission originates from an unidentified pulsar, and the X-ray and TeV emissions are attributed to synchrotron radiation and inverse Compton scattering of electrons accelerated within a pulsar wind nebula. In this leptonic scenario, our X-ray upper limit provides a stringent constraint on the magnetic field, which is ≲1.5 μG
