Although climate and nature related scenario analysis is increasingly important in finance, operational implementations remain limited for translating long horizon environmental scenarios into counterparty credit risk measures used in pricing and regulatory capital. We propose an environmental valuation adjustment framework for CVA with three components: (i) a scenario to credit translation that maps environmental scenario drivers into hazard rates; (ii) nature specific tail generators that quantify model risk in scenario generation; and (iii) a distributionally robust wrong way risk bound based on Kullback Leibler (KL) divergence. We compute climate CVAs using transition scenarios and nature CVAs using biodiversity indicators. Our results show that nature CVAs can vary materially across alternative ecosystem generators, highlighting an additional source of model uncertainty.
Entrepreneurial small to medium enterprises face significant cybersecurity challenges when developing valuable intellectual property (IP). This paper addresses the critical gap in research on how E-SMEs can protect their IP assets from cybersecurity threats through effective threat intelligence and IP protection activities. Drawing on Dynamic Capabilities and Knowledge-Based View theoretical frameworks, we propose the Threat Intelligence-driven IP Protection (TI-IPP) model. This conceptual model features to modes of operation, closed IP development and open innovation, enabling E-SMEs to adapt their IP protection and knowledge management strategies. The model incorporates four key phases: sensing opportunities and threats, seizing opportunities, knowledge transfer, and organizational transformation. By integrating cybersecurity threat intelligence with IP protection practices, E-SMEs can develop capabilities to safeguard valuable IP while maintaining competitive advantage. This research-in-progress paper outlines a qualitative research methodology using multiple case studies to validate and refine the proposed model for practical application in resource-constrained entrepreneurial environments.
Recent image protection mechanisms such as Glaze and Nightshade introduce imperceptible, adversarially designed perturbations intended to disrupt downstream text-to-image generative models. While their empirical effectiveness is known, the internal structure, detectability, and representational behavior of these perturbations remain poorly understood. This study provides a systematic, explainable AI analysis using a unified framework that integrates white-box feature-space inspection and black-box signal-level probing. Through latent-space clustering, feature-channel activation analysis, occlusion-based spatial sensitivity mapping, and frequency-domain characterization, we show that protection mechanisms operate as structured, low-entropy perturbations tightly coupled to underlying image content across representational, spatial, and spectral domains. Protected images preserve content-driven feature organization with protection-specific substructure rather than inducing global representational drift. Detectability is governed by interacting effects of perturbation entropy, spatial deployment, and frequency alignment, with sequential protection amplifying detectable structure rather than suppressing it. Frequency-domain analysis shows that Glaze and Nightshade redistribute energy along dominant image-aligned frequency axes rather than introducing diffuse noise. These findings indicate that contemporary image protection operates through structured feature-level deformation rather than semantic dislocation, explaining why protection signals remain visually subtle yet consistently detectable. This work advances the interpretability of adversarial image protection and informs the design of future defenses and detection strategies for generative AI systems.
Human activities degrade the Earth environment at an unprecedented scale and pace, threatening Earth-system stability, resilience and life-support functions. We can of course deny the facts, get angry about them, or try to bargain. Or we may overcome these stages of grief and move towards accepting that human activities need to change, including our own ones. The purpose of this paper is to support astronomers in this transition, by providing insights into the origins of environmental impacts in astronomical research and proposing changes that would make the field sustainable. The paper focuses on the environmental impacts of research infrastructures, since these are the dominant sources of greenhouse gas emissions in astronomy, acknowledging that impact reductions in other areas, for example professional air travelling, need also to be achieved.
Ana Clara Araújo Gomes da Silva, Gilmar Teixeira Junior, Lívia Mancine C. de Campos
et al.
Environmental sustainability in Systems-of-Systems (SoS) is an emerging field that seeks to integrate technological solutions to promote the efficient management of natural resources. While systematic reviews address sustainability in the context of Smart Cities (a category of SoS), a systematic study synthesizing the existing knowledge on environmental sustainability applied to SoS in general does not exist. Although literature includes other types of sustainability, such as financial and social, this study focuses on environmental sustainability, analyzing how SoS contribute to sustainable practices such as carbon emission reduction, energy efficiency, and biodiversity conservation. We conducted a Systematic Mapping Study to identify the application domains of SoS in sustainability, the challenges faced, and research opportunities. We planned and executed a research protocol including an automated search over four scientific databases. Of 926 studies retrieved, we selected, analyzed, and reported the results of 39 relevant studies. Our findings reveal that most studies focus on Smart Cities and Smart Grids, while applications such as sustainable agriculture and wildfire prevention are less explored. We identified challenges such as system interoperability, scalability, and data governance. Finally, we propose future research directions for SoS and environmental sustainability.
The Deep Underground Neutrino Experiment (DUNE) is a proposed long-baseline neutrino oscillation experiment that will project an on-axis wide-band neutrino beam over a distance of 1300 km to determine the unknowns in the neutrino sector. Given the baseline of 1300 km and the intense beam facility, DUNE is a promising experiment to study the sub-leading effects such as environmental decoherence, matter induced non-standard interactions (NSIs), neutrino decay, etc. In this study, we investigate how NSI and environmental decoherence affect the neutrino oscillation probabilities simultaneously. Considering the modified probabilities we obtain the updated mass hierarchy (MH) and CP violation (CPV) sensitivities of DUNE. Furthermore, we demonstrate the sensitivity of DUNE to distinguish between the effects of NSI and environmental decoherence.
The sustainability of reforestation initiatives depends on the involvement of local communities, whose lack of ownership compromises efforts to combat deforestation in the Lubumbashi Charcoal Production Basin. This study assesses reforestation activities in two village areas (Milando and Mwawa), based on individual interviews (50 individuals/village area) and floristic inventories carried out in two types of habitats (reforested and unexploited) for each village area. The hypotheses tested were the following: (i) Reforested habitats and tree species were selected collaboratively, ensuring an inclusive approach; (ii) ecological parameters—density per hectare, quadratic mean diameter, basal area, and floristic diversity—of reforested sites were comparable to those of unexploited <i>miombo</i> due to protection allowing natural recovery; and (iii) ethnobotanical and floristic patterns reflect varying levels of anthropogenic disturbance and the limited diversity of species used in reforestation. Thus, the interviews gathered data on habitat and woody species selection for reforestation and management practices, while the inventories assessed the condition of these reforested habitats in terms of density per hectare, basal area, quadratic mean diameter, and floristic diversity. The results show that in both village areas, the selection of habitats for reforestation was carried out concertedly (22.00–44.00% of citations). Woody species were chosen according to the needs of local communities (40–52%) and the availability of seeds (18.00–44.00%). Furthermore, management practices for these reforested habitats include planning/assessment meetings (26.00–38.00%) and maintenance activities, such as firebreaks (38.00–46.00%) and surveillance of reforested habitats (24.00%). Additionally, these practices are being increasingly neglected, jeopardizing reforestation efforts. However, density/ha, basal area, quadratic mean diameter, and floristic diversity did not show significant differences between reforested and unexploited habitats, particularly at Milando (<i>p</i> > 0.05). Furthermore, floristic similarity is 55.56% for reforested habitats and 93.75% for unexploited habitats but remains low between reforested and unexploited habitats (40.00–47.62%). This similarity between ethnobotanical and floristic lists is also low (43.75–31.58%). Finally, a total of 442 woody individuals were recorded in reforested habitats and 630 in unexploited ones, with Fabaceae dominating both habitat types. Despite some cited reforestation species like <i>Acacia polyacantha</i> being absent, <i>Brachystegia spiciformis</i> emerged as the most prevalent species in both reforested and unexploited areas. The results of the present study suggest a sustainable and continuous management of these reforested habitats for an effective reconstitution of the forest cover. To reinforce the sustainable management of these reforested habitats, it is recommended that decision-makers conduct awareness-raising campaigns and establish payment for environmental service mechanisms to motivate communities.
IntroductionPhytoremediation is a promising strategy for cleaning up polycyclic aromatic hydrocarbon (PAH)-contaminated soils. This study investigated the effectiveness of four plant species—cotton, ryegrass, tall fescue, and wheat—in enhancing PAH removal from soils contaminated with diesel oil, PAHs, and aged oily sludge.MethodsAged oily sludge-contaminated soil was artificially prepared, and the selected plants were cultivated in different hydrocarbon-contaminated soils (diesel oil, PAHs, and oily sludge). The fate of PAHs was analyzed by measuring their distribution in rhizospheric soil and plant tissues. Root concentration factors (RCFs) and transpiration stream concentration factors (TSCFs) were used to evaluate PAH translocation and accumulation in plant tissues and their interactions with the rhizosphere.ResultsThe study demonstrated that plants enhanced PAH removal by 20%–80%, with wheat showing the highest efficiency. PAH removal was generally more effective in oily sludge-contaminated soil than in diesel oil or PAH-contaminated soil. Plant uptake of PAHs accounted for 2%–10% of total removal and exhibited a strong linear correlation with root weight. RCFs were linearly correlated with LogKow (3–6), indicating that the four plant species did not significantly concentrate PAHs in their roots.DiscussionThe findings confirm the potential of phytoremediation for PAH-contaminated soils, particularly using wheat as an effective species. The low RCFs and TSCFs suggest that PAH uptake was limited, implying that rhizodegradation and microbial interactions may play a more critical role than direct plant accumulation. This study supports phytoremediation as a cost-effective and eco-friendly alternative to conventional soil remediation methods, reducing economic and environmental burdens.
With the transformation of the global energy structure, photovoltaic power generation, as a clean and renewable energy form, is receiving increasing attention and from many countries. Especially in the agricultural sector, integrating the planting of traditional Chinese medicinal plants with clean energy construction and environmental protection plays a significant role. This not provides the electricity needed for agricultural facilities but also helps to achieve energy conservation and emission reduction, promoting sustainable agricultural development.
Oana Alina Nitu, Elena Stefania Ivan, Adnan Arshad
Vegetables such as lettuce, tomato, carrot, and beet are vital to the global food industry, providing essential nutrients and supporting sustainable agriculture. Their cultivation in greenhouses across diverse climatic zones (temperate, Mediterranean, tropical, subtropical, and arid) has gained prominence due to controlled environments that enhance yield and quality. However, these crops face significant threats from climate change, including rising temperatures, erratic light availability, and resource constraints, which challenge optimal growth and nutritional content. This study investigates the influence of microclimatic conditions—temperature, light intensity, and CO<sub>2</sub> concentration—on the growth, physiology, and biochemistry of these vegetables under varying greenhouse types and climatic zones, addressing these threats through a systematic review. The methodology followed the PRISMA guidelines, synthesizing peer-reviewed articles from 1995 to 2025 sourced from Web of Science, Pub Med, Scopus, Science Direct, Springer Link, and Google Scholar. Search terms included “greenhouse microclimate”, “greenhouse types”, “Climatic Zones, “and crop-specific keywords, with data extracted on microclimatic parameters and analyzed across growth stages and climatic zones. Eligibility criteria ensured focus on quantitative data from greenhouse studies, excluding pre-1995 or non-peer-reviewed sources. The results identified the following optimal conditions: lettuce and beet thrive at 15–22 °C, 200–250 μmol·m<sup>−2</sup>·s<sup>−1</sup>, and 600–1100 ppm CO<sub>2</sub> in temperate zones; tomatoes at 18–25 °C, 200–300 μmol·m<sup>−2</sup>·s<sup>−1</sup>, and 600–1100 ppm in Mediterranean and arid zones; and carrots at 15–20 °C, 150–250 μmol·m<sup>−2</sup>·s<sup>−1</sup>, and 600–1000 ppm in subtropical zones. Greenhouse types (e.g., glasshouses, polytunnels) modulate these optima, with high-tech systems enhancing resilience. Conclusively, tailored microclimatic management, integrating AI-driven technologies and advanced greenhouse designs, is recommended to mitigate threats and optimize production across climatic zones.
<p>During the Quaternary, ice sheets experienced several retreat–advance cycles, strongly influencing climate patterns. In order to properly simulate these phenomena, it is preferable to use physics-based models instead of parameterizations to estimate the surface mass balance (SMB), which strongly influences the evolution of the ice sheet. To further investigate the potential of these SMB models, this work evaluates the BErgen Snow SImulator (BESSI), a multi-layer snow model with high computational efficiency, as an alternative to providing the SMB for the Earth system model iLOVECLIM for multi-millennial simulations as in paleo-studies. We compare the behaviors of BESSI and insolation temperature melt (ITM), an existing SMB scheme of iLOVECLIM during the Last Interglacial (LIG). Firstly, we validate the two SMB models using the regional climate model Modèle Atmosphérique Régional (MAR) as forcing and reference for the present-day climate over the Greenland and Antarctic ice sheets. The evolution of the SMB over the LIG (130–116 ka) is computed by forcing BESSI and ITM with transient climate forcing obtained from iLOVECLIM for both ice sheets. For present-day climate conditions, both BESSI and ITM exhibit good performance compared to MAR despite a much simpler model setup. While BESSI performs well for both Antarctica and Greenland for the same set of parameters, the ITM parameters need to be adapted specifically for each ice sheet. This suggests that the physics embedded in BESSI allows better capture of SMB changes across varying climate conditions, while ITM displays a much stronger sensitivity to its tunable parameters. The findings suggest that BESSI can provide more reliable SMB estimations for the iLOVECLIM framework to improve the model simulations of the ice sheet evolution and interactions with climate for multi-millennial simulations.</p>
Personalized concept generation by tuning diffusion models with a few images raises potential legal and ethical concerns regarding privacy and intellectual property rights. Researchers attempt to prevent malicious personalization using adversarial perturbations. However, previous efforts have mainly focused on the effectiveness of protection while neglecting the visibility of perturbations. They utilize global adversarial perturbations, which introduce noticeable alterations to original images and significantly degrade visual quality. In this work, we propose the Visual-Friendly Concept Protection (VCPro) framework, which prioritizes the protection of key concepts chosen by the image owner through adversarial perturbations with lower perceptibility. To ensure these perturbations are as inconspicuous as possible, we introduce a relaxed optimization objective to identify the least perceptible yet effective adversarial perturbations, solved using the Lagrangian multiplier method. Qualitative and quantitative experiments validate that VCPro achieves a better trade-off between the visibility of perturbations and protection effectiveness, effectively prioritizing the protection of target concepts in images with less perceptible perturbations.
Recent advancements in diffusion models revolutionize image generation but pose risks of misuse, such as replicating artworks or generating deepfakes. Existing image protection methods, though effective, struggle to balance protection efficacy, invisibility, and latency, thus limiting practical use. We introduce perturbation pre-training to reduce latency and propose a mixture-of-perturbations approach that dynamically adapts to input images to minimize performance degradation. Our novel training strategy computes protection loss across multiple VAE feature spaces, while adaptive targeted protection at inference enhances robustness and invisibility. Experiments show comparable protection performance with improved invisibility and drastically reduced inference time. The code and demo are available at https://webtoon.github.io/impasto
Real-world systems are shaped by both their complex internal interactions and the changes in their noisy environments. In this work, we study how a shared active bath affects the statistical dependencies between two interacting Brownian particles by evaluating their mutual information. We decompose the mutual information into three terms: information stemming from the internal interactions between the particles; information induced by the shared bath, which encodes environmental changes; a term describing information interference that quantifies how the combined presence of both internal interactions and environment either masks (destructive interference) or boosts (constructive interference) information. By studying exactly the case of linear interactions, we find that the sign of information interference depends solely on that of the internal coupling. However, when internal interactions are described by a nonlinear activation function, we show that both constructive and destructive interference appear depending on the interplay between the timescale of the active environment, the internal interactions, and the environmental coupling. Finally, we show that our results generalize to hierarchical systems where asymmetric couplings to the environment mimic the scenario where the active bath is only partially accessible to one particle. This setting allows us to quantify how this asymmetry drives information interference. Our work underscores how information and functional relationships in complex multi-scale systems are fundamentally shaped by the environmental context.