Nicolas Rothbacher, Kit T. Rodolfa, Mihir Bhaskar
et al.
Advances in Artificial Intelligence (AI) have generated widespread enthusiasm for the potential of AI to support our understanding and protection of the environment. As such tools move from basic research to more consequential settings, such as regulatory enforcement, the human context of how AI is utilized, interpreted, and deployed becomes increasingly critical. Yet little work has systematically examined the role of such organizational goals and incentives in deploying AI systems. We report results from a unique case study of a satellite imagery-based AI tool to detect dumping of agricultural waste, with concurrent field trials with the Wisconsin Department of Natural Resources (WDNR) and a non-governmental environmental interest group in which the tool was utilized for field investigations when dumping was presumptively illegal in February-March 2023. Our results are threefold: First, both organizations confirmed a similar level of ground-truth accuracy for the model's detections. Second, they differed, however, in their overall assessment of its usefulness, as WDNR was interested in clear violations of existing law, while the interest group sought to document environmental risk beyond the scope of existing regulation. Dumping by an unpermitted entity or just before February 1, for instance, were deemed irrelevant by WDNR. Third, while AI tools promise to prioritize allocation of environmental protection resources, they may expose important gaps of existing law.
Artificial intelligence (AI) is often presented as a key tool for addressing societal challenges, such as climate change. At the same time, AI's environmental footprint is expanding increasingly. This report describes the systemic environmental risks of artificial intelligence, in particular, moving beyond direct impacts such as energy and water usage. Systemic environmental risks of AI are emergent, cross-sector harms to climate, biodiversity, freshwater, and broader socioecological systems that arise primarily from AI's integration into social, economic, and physical infrastructures, rather than its direct resource use, and that propagate through feedbacks, yielding nonlinear, inequitable, and potentially irreversible impacts. While these risks are emergent and quantification is uncertain, this report aims to provide an overview of systemic environmental risks. Drawing on a narrative literature review, we propose a three-level framework that operationalizes systemic risk analysis. The framework identifies the structural conditions that shape AI development, the risk amplification mechanisms that propagate environmental harm, and the impacts that manifest as observable ecological and social consequences. We illustrate the framework in expert-interview-based case studies across agriculture and biodiversity, oil and gas, and waste management.
Jose Marie Antonio Minoza, Rex Gregor Laylo, Christian F Villarin
et al.
Machine learning inference occurs at a massive scale, yet its environmental impact remains poorly quantified, especially on low-resource hardware. We present ML-EcoLyzer, a cross-framework tool for measuring the carbon, energy, thermal, and water costs of inference across CPUs, consumer GPUs, and datacenter accelerators. The tool supports both classical and modern models, applying adaptive monitoring and hardware-aware evaluation. We introduce the Environmental Sustainability Score (ESS), which quantifies the number of effective parameters served per gram of CO$_2$ emitted. Our evaluation covers over 1,900 inference configurations, spanning diverse model architectures, task modalities (text, vision, audio, tabular), hardware types, and precision levels. These rigorous and reliable measurements demonstrate that quantization enhances ESS, huge accelerators can be inefficient for lightweight applications, and even small models may incur significant costs when implemented suboptimally. ML-EcoLyzer sets a standard for sustainability-conscious model selection and offers an extensive empirical evaluation of environmental costs during inference.
This paper introduces AIJIM, the Artificial Intelligence Journalism Integration Model -- a novel framework for integrating real-time AI into environmental journalism. AIJIM combines Vision Transformer-based hazard detection, crowdsourced validation with 252 validators, and automated reporting within a scalable, modular architecture. A dual-layer explainability approach ensures ethical transparency through fast CAM-based visual overlays and optional LIME-based box-level interpretations. Validated in a 2024 pilot on the island of Mallorca using the NamicGreen platform, AIJIM achieved 85.4\% detection accuracy and 89.7\% agreement with expert annotations, while reducing reporting latency by 40\%. Unlike conventional approaches such as Data-Driven Journalism or AI Fact-Checking, AIJIM provides a transferable model for participatory, community-driven environmental reporting, advancing journalism, artificial intelligence, and sustainability in alignment with the UN Sustainable Development Goals and the EU AI Act.
Nghiem Thanh Pham, Tung Kieu, Duc-Manh Nguyen
et al.
Small Language Models (SLMs) offer computational efficiency and accessibility, yet a systematic evaluation of their performance and environmental impact remains lacking. We introduce SLM-Bench, the first benchmark specifically designed to assess SLMs across multiple dimensions, including accuracy, computational efficiency, and sustainability metrics. SLM-Bench evaluates 15 SLMs on 9 NLP tasks using 23 datasets spanning 14 domains. The evaluation is conducted on 4 hardware configurations, providing a rigorous comparison of their effectiveness. Unlike prior benchmarks, SLM-Bench quantifies 11 metrics across correctness, computation, and consumption, enabling a holistic assessment of efficiency trade-offs. Our evaluation considers controlled hardware conditions, ensuring fair comparisons across models. We develop an open-source benchmarking pipeline with standardized evaluation protocols to facilitate reproducibility and further research. Our findings highlight the diverse trade-offs among SLMs, where some models excel in accuracy while others achieve superior energy efficiency. SLM-Bench sets a new standard for SLM evaluation, bridging the gap between resource efficiency and real-world applicability.
Generative AI (GenAI) systems and chatbots rely on vast corpora of consumer data. The use of such data for training GenAI has raised concerns around data ownership, copyright issues, and potential harm to consumers. In this work, we explore a related but less examined angle: the ownership and privacy of data originating from deceased individuals. We propose three post mortem data management principles to guide the protection of deceased individual's data, and analyze popular GenAI chatbots policies and answers to legacy requests. We plan to systematically audit consumer GenAI chatbots on their behavior regarding post-mortem data management
This paper presents a comprehensive dataset of LoRaWAN technology path loss measurements collected in an indoor office environment, focusing on quantifying the effects of environmental factors on signal propagation. Utilizing a network of six strategically placed LoRaWAN end devices (EDs) and a single indoor gateway (GW) at the University of Siegen, City of Siegen, Germany, we systematically measured signal strength indicators such as the Received Signal Strength Indicator (RSSI) and the Signal-to-Noise Ratio (SNR) under various environmental conditions, including temperature, relative humidity, carbon dioxide (CO$_2$) concentration, barometric pressure, and particulate matter levels (PM$_{2.5}$). Our empirical analysis confirms that transient phenomena such as reflections, scattering, interference, occupancy patterns (induced by environmental parameter variations), and furniture rearrangements can alter signal attenuation by as much as 10.58 dB, highlighting the dynamic nature of indoor propagation. As an example of how this dataset can be utilized, we tested and evaluated a refined Log-Distance Path Loss and Shadowing Model that integrates both structural obstructions (Multiple Walls) and Environmental Parameters (LDPLSM-MW-EP). Compared to a baseline model that considers only Multiple Walls (LDPLSM-MW), the enhanced approach reduced the root mean square error (RMSE) from 10.58 dB to 8.04 dB and increased the coefficient of determination (R$^2$) from 0.6917 to 0.8222. By capturing the extra effects of environmental conditions and occupancy dynamics, this improved model provides valuable insights for optimizing power usage and prolonging device battery life, enhancing network reliability in indoor Internet of Things (IoT) deployments, among other applications. This dataset offers a solid foundation for future research and development in indoor wireless communication.
The Truyen Tran, Thu Minh Tran, Xuan Tung Nguyen
et al.
This study aims to present the results of anticipation of lightweight concrete durability when exposed to a chloride environment under pre-compressive load. The research employs Keramzit aggregate as the coarse aggregate for lightweight concrete. Following a 28-day curing period in water, the concrete specimens undergo varying levels of pre-compressive stress. Rapid Chloride Permeability Testing is then conducted to ascertain the chloride diffusion coefficient. The study posits a correlation between the chloride diffusion coefficient and precompressive stress levels, drawing from the experimental findings. Furthermore, Monte-Carlo simulation is employed to assess the influence of stochastic variables on the corrosion likelihood of concrete structures using
lightweight aggregates. These stochastic variables encompass the chloride diffusion coefficient, surface chloride concentration, critical chloride concentration, concrete protection layer thickness, and a coefficient contingent
on environmental conditions, to appraise the operational lifespan of lightweight concrete structures.
Engineering (General). Civil engineering (General), Chemical engineering
Elaina M. Kenyon, Michael J. Devito, Grace Patlewicz
et al.
PFASs are widely present and persistent in the environment, and exposure can occur via multiple pathways. Human and animal PFAS exposures have been associated with alterations in thyroid hormones, hepatotoxicity, and other adverse effects. This study evaluated the subchronic toxicities of four specific PFASs in 90-day oral rat studies. Studies were conducted in male and female Sprague–Dawley rats exposed to PFASs in corn oil via oral gavage. The PFASs studied were 1H,1H,9H-perfluorononyl acrylate (PFNAC), 1H,1H,2H,2H-perfluorohexyl iodide (PFHI), methyl heptafluoropropyl ketone (MHFPK), and 2-chloro-2,3,3,3-tetrafluoropropanoic acid (CTFPA). High doses were 10 mg/kg-day (male) and 30 mg/kg-day (female) for PFNAC, 200 mg/kg-day for PFHI, 300 mg/kg-day for MHFPK, and 30 (male) and 100 mg/kg-day (female) for CTFPA. The four lower doses for each PFAS were spaced at two- or threefold dose increments. The most consistent effect was dose-dependent increases in the relative and absolute liver weights for PFNAC, PFHI, and CTFPA but not for MHFPK. Increased liver weights were correlated with findings of hepatocellular hypertrophy. Increased kidney weights for PFNAC and PFHI were correlated with increased incidence of minimal tubule epithelial hypertrophy (PFNAC) or increased incidence and severity of chronic progressive nephropathy and hyaline droplet accumulation (PFHI). There were no compound-related effects on morbidity and mortality or overt signs of toxicity.
Michael Petrides, Aliki Peletidi, Evangelia Nena
et al.
Background Approximately 50 million individuals across the globe are impacted by epilepsy, leading to fear, discrimination, psychiatric issues, high costs, and social stigma. Proper diagnosis and treatment could allow up to 70% of those affected to live seizure-free. Community pharmacists have significant potential to actively participate in epilepsy patient care, beyond merely dispensing medications. The objective of this study was to systematically review and assess the roles of pharmacists in epilepsy care, focusing on pharmacist-led interventions and services for patients with epilepsy.Methods Following PRISMA 2020 guidelines, the review included cross-sectional, retrospective cohort, and qualitative/quantitative studies on pharmacist-led epilepsy interventions in community and outpatient settings. Searches were conducted in Scopus, PubMed Central, and Science Direct for studies published through the end of 2023. Two evaluators independently reviewed and chose studies, and the data was analysed using Microsoft Excel®. Quality assessment was performed using the MMAT tool.Results Five eligible studies were included, covering 457 participants. Studies originated from the USA (n = 3), Netherlands (n = 1), and Palestine (n = 1). They evaluated pharmacist-led interventions in epilepsy, including medication adherence, quality of life, and pharmacist’s integration in epilepsy care.Conclusion This review underscores the possible contributions of pharmacists in epilepsy care, stressing the importance of pharmacist-led interventions to enhance medication adherence and the quality of life for individuals with epilepsy. Future research should evaluate the effectiveness and cost-effectiveness of these services, including disease management and patient education. Increasing awareness among pharmacists and patients about pharmacists’ contributions is crucial for improving epilepsy care.
Therapeutics. Pharmacology, Pharmacy and materia medica
Michał Fajt, Grzegorz Machowski, Bartosz Puzio
et al.
Abstract Low-field NMR (LF-NMR) is a widely applied technique for evaluating pore size distribution (PSD) in porous materials. Conventional approaches typically assume surface-controlled spin-spin relaxation and negligible diffusion contributions under the fast-diffusion regime, which introduces systematic errors when applied to nano- and microporous systems. In this work, we present the Effective Diffusion Cubic (EDC) model, a new framework for LF-NMR-based PSD estimation in tight rocks. The EDC method incorporates pore-size dependence of both the effective diffusion coefficient and the induced internal magnetic field gradient. Crucially, the effective diffusion coefficient, D(d), is parameterized by a logistic function that faithfully approximates the Padé form, enabling a precise quantification of diffusion-related effects on T2 relaxation. Applied to nine siliciclastic core samples, the EDC approach produced PSDs corrected for diffusion-induced distortions and in closer agreement with independent reference data compared to conventional models. These results demonstrate that the EDC methodology provides a physically consistent and more accurate means of quantifying pore systems, thereby enhancing NMR-based petrophysical characterization of tight rock formations.
Cynthia C. Becker, Allison R. Cauvin, Karen L. Neely
et al.
Abstract Stony coral tissue loss disease (SCTLD) is widespread within the Caribbean and affects at least 22 species of reef-building coral. Bacteria have been implicated in the etiology of SCTLD, but the community of bacteria and archaea may also contribute to SCTLD resistance. To identify potential mechanisms through which microbes contribute to SCTLD resistance, we sequenced metagenomes from 41 colonies of the threatened coral, Orbicella faveolata, in the lower Florida Keys. All colonies were fate-tracked for three to five years and disease lesions were treated with amoxicillin. By 2024, 20% were never diseased, 10% had lesions before sampling but recovered, 22% were apparently healthy but were eventually susceptible to infection, and 49% had regular repeated infections. Within the coral microbiome, diseased and yet-to-be diseased colonies exhibited higher variability in functional genes. In contrast, corals that remained unaffected or recovered had less variable microbiomes with greater abundances of vitamin and antibiotic biosynthesis, secretion system, and quorum sensing genes that may support host health and resilience to pathogens. Though on some colonies antibiotic treatments were applied repeatedly, there was no effect on the diversity of beta-lactamases, antibiotic resistance genes that may confer amoxicillin resistance. Additional potentially probiotic gene clusters for the production of antimicrobial and bioactive compounds were present in many colonies regardless of fate. Taken together, we find significant probiotic potential in the coral microbiome to armor host O. faveolata corals against SCTLD infection, which may underpin intraspecific variation in stony coral tissue loss disease resilience and susceptibility.
Environment Sound Classification has been a well-studied research problem in the field of signal processing and up till now more focus has been laid on fully supervised approaches. Over the last few years, focus has moved towards semi-supervised methods which concentrate on the utilization of unlabeled data, and self-supervised methods which learn the intermediate representation through pretext task or contrastive learning. However, both approaches require a vast amount of unlabelled data to improve performance. In this work, we propose a novel framework called Environmental Sound Classification with Hierarchical Ontology-guided semi-supervised Learning (ECHO) that utilizes label ontology-based hierarchy to learn semantic representation by defining a novel pretext task. In the pretext task, the model tries to predict coarse labels defined by the Large Language Model (LLM) based on ground truth label ontology. The trained model is further fine-tuned in a supervised way to predict the actual task. Our proposed novel semi-supervised framework achieves an accuracy improvement in the range of 1\% to 8\% over baseline systems across three datasets namely UrbanSound8K, ESC-10, and ESC-50.
Mohammad Erfatpour, Dustin MacLean, Rachid Lahlali
et al.
The ovule is a plant structure that upon fertilization, transforms into a seed. Successful fertilization is required for optimum crop productivity and is strongly affected by environmental conditions including temperature and precipitation. Climate change refers to sustained changes in global or regional climate patterns over an extended period, typically decades to millions of years. These shifts can result from natural processes like volcanic eruptions and solar radiation fluctuations, but in recent times, human activities—especially the burning of fossil fuels, deforestation, and industrial emissions—have accelerated the pace and scale of climate change. Human-induced climate change impacts the agricultural sector mainly through global warming and altering weather patterns, both of which create conditions that challenge agricultural production and food security. With food demand projected to sharply increase by 2050, urgent action is needed to prevent the worst impacts of climate change on food security and allow time for agricultural production systems to adapt and become more resilient. Gaining insights into the female reproductive part of the flower and seed development under extreme environmental conditions is important to oversee plant evolution, agricultural productivity, and food security in the face of climate change. This review summarizes the current knowledge on plant reproductive development and the effects of temperature and water stress, soil salinity, elevated carbon dioxide, and ozone pollution on the female reproductive structure and development across grain legumes, cereal, oilseed, and horticultural crops. It identifies gaps in existing studies for potential future research and suggests suitable mitigation strategies for sustaining crop productivity in a changing climate.
Nutrition. Foods and food supply, Food processing and manufacture
Phoebe Koundouri, Georgios I. Papayiannis, Electra V. Petracou
et al.
In this paper we propose a consensus group decision making scheme under model uncertainty consisting of an iterative two-stage procedure and based on the concept of Fréchet barycenter. Each step consists of two stages: the agents first update their position in the opinion metric space by a local barycenter characterized by the agents' immediate interactions and then a moderator makes a proposal in terms of a global barycenter, checking for consensus at each step. In cases of large heterogeneous groups the procedure can be complemented by an auxiliary initial homogenization step, consisting of a clustering procedure in opinion space, leading to large homogeneous groups for which the aforementioned procedure will be applied. The scheme is illustrated in examples motivated from environmental economics.
Deterministic communication means reliable packet forwarding with close to zero packet loss and bounded latency. Packet loss or delay above a threshold caused by, e.g., equipment failure or malfunction could be catastrophic for applications that require deterministic communication. To meet loss related targets, per-packet service protection has been introduced by deterministic communications standards; it is provided by Frame Replication and Elimination for Reliability (FRER) for Layer 2 Ethernet networks and by Packet Replication, Elimination, and Ordering Functions (PREOF) for Layer 3 IP/MPLS networks. We have implemented FRER with two conceptually different methods: (1) in eBPF/XDP as a lightweight software implementation; and (2) in userspace. We evaluate our XDP FRER via an experimental analysis and compare the two FRER implementations.
This document is an abbreviated version of the law review, led by Alexander Q. Gilbert, entitled: "Major Federal Actions Significantly Affecting the Quality of the Space Environment: Applying NEPA to Federal and Federally Authorized Outer Space Activities." Here, we discuss the future of the space environment, and how it is increasingly becoming a human environment with regard to continued robotic and human presence in orbit, planned and proposed robotic and human presence on bodies such as the Moon and Mars, planned space mining projects, the increase use of low-Earth orbit for communications satellites, and other human uses of space. As such, we must evaluate and protect these environments just as we do on Earth. In order to prioritize mitigating threat of contamination, avoiding conflict, and promoting sustainability in space, all to ensure that actors maintain equal and safe access to space, we propose applying the National Environmental Policy Act, or NEPA, to space missions. We put forward three examples of environmental best practices for those involved in space missions to consider: adopting precautionary and communicative structure to before, during, and after missions taking place off-world, environmental impact statements, and transparency in tools that may impact the environment (including radioisotope power sources, plans in case of vehicle loss or loss of trajectory, and others). For additional discussion related to potential space applications of NEPA, NEPA's statutory text, and NEPA's relation to space law and judicial precedent for space, we recommend reading the full law review.