Hasil untuk "Environmental sciences"

W. Dill

C. T. Chiou, P. Porter, D. Schmedding

W. Callister

B. Adam

M. Jacobson, H. Hansson, K. Noone et al.

D. Demeritt

F. von der Kammer, P. L. Ferguson, P. Holden et al.

Yiqing Zhou, Xule Zhou, Zecan Cheng et al.

In WSN/IoT, node localization is essential to long-running applications for accurate environment monitoring and event detection, often covering a large area in the field. Due to the lower time resolution of typical WSN/IoT platforms (e.g., 1 microsecond on ESP32 platforms) and the jitters in timestamping, packet-level localization techniques cannot provide meter-level resolution. For high-precision localization as well as world-wide interoperability via 2.4-GHz ISM band, a new variant of LoRa, called LoRa 2.4 GHz, was proposed by semtech, which provides a radio frequency (RF) time of flight (ToF) ranging method for meter-level localization. However, the existing datasets reported in the literature are limited in their coverages and do not take into account varying environmental factors such as temperature and humidity. To address these issues, LoRa 2.4 GHz RF ToF ranging data was collected on a sports field at the XJTLU south campus, where three LoRa nodes logged samples of ranging with a LoRa base station, together with temperature and humidity, at reference points arranged as a 3x3 grid covering 400 square meter over three weeks and uploaded all measurement records to the base station equipped with an ESP32-based transceiver for machine and user communications. The results of a preliminary investigation based on a simple deep neural network (DNN) model demonstrate that the environmental factors, including the temperature and humidity, significantly affect the accuracy of ranging, which calls for advanced methods of compensating for the effects of environmental factors on LoRa RF ToF ranging outdoors.

Tian Liu, Han Liu, Boyang Li et al.

Unmanned Aerial Vehicles (UAVS) are limited by the onboard energy. Refinement of the navigation strategy directly affects both the flight velocity and the trajectory based on the adjustment of key parameters in the UAVS pipeline, thus reducing energy consumption. However, existing techniques tend to adopt static and conservative strategies in dynamic scenarios, leading to inefficient energy reduction. Dynamically adjusting the navigation strategy requires overcoming the challenges including the task pipeline interdependencies, the environmental-strategy correlations, and the selecting parameters. To solve the aforementioned problems, this paper proposes a method to dynamically adjust the navigation strategy of the UAVS by analyzing its dynamic characteristics and the temporal characteristics of the autonomous navigation pipeline, thereby reducing UAVS energy consumption in response to environmental changes. We compare our method with the baseline through hardware-in-the-loop (HIL) simulation and real-world experiments, showing our method 3.2X and 2.6X improvements in mission time, 2.4X and 1.6X improvements in energy, respectively.

Bing Wang, Ximing Li, Yiming Wang et al.

The proliferation of misinformation across diverse social media platforms has drawn significant attention from both academic and industrial communities due to its detrimental effects. Accordingly, automatically distinguishing misinformation, dubbed as Misinformation Detection (MD), has become an increasingly active research topic. The mainstream methods formulate MD as a static learning paradigm, which learns the mapping between the content, links, and propagation of news articles and the corresponding manual veracity labels. However, the static assumption is often violated, since in real-world scenarios, the veracity of news articles may vacillate within the dynamically evolving social environment. To tackle this problem, we propose a novel framework, namely Misinformation detection with Dynamic Environmental Representations (MISDER). The basic idea of MISDER lies in learning a social environmental representation for each period and employing a temporal model to predict the representation for future periods. In this work, we specify the temporal model as the LSTM model, continuous dynamics equation, and pre-trained dynamics system, suggesting three variants of MISDER, namely MISDER-LSTM, MISDER-ODE, and MISDER-PT, respectively. To evaluate the performance of MISDER, we compare it to various MD baselines across 2 prevalent datasets, and the experimental results can indicate the effectiveness of our proposed model.

Darpan Aswal, Manjira Sinha

Transformer based models, especially large language models (LLMs) dominate the field of NLP with their mass adoption in tasks such as text generation, summarization and fake news detection. These models offer ease of deployment and reliability for most applications, however, they require significant amounts of computational power for training as well as inference. This poses challenges in their adoption in resource-constrained applications, especially in the open-source community where compute availability is usually scarce. This work proposes a graph-based approach for Environmental Claim Detection, exploring Graph Neural Networks (GNNs) and Hyperbolic Graph Neural Networks (HGNNs) as lightweight yet effective alternatives to transformer-based models. Re-framing the task as a graph classification problem, we transform claim sentences into dependency parsing graphs, utilizing a combination of word2vec \& learnable part-of-speech (POS) tag embeddings for the node features and encoding syntactic dependencies in the edge relations. Our results show that our graph-based models, particularly HGNNs in the poincaré space (P-HGNNs), achieve performance superior to the state-of-the-art on environmental claim detection while using up to \textbf{30x fewer parameters}. We also demonstrate that HGNNs benefit vastly from explicitly modeling data in hierarchical (tree-like) structures, enabling them to significantly improve over their euclidean counterparts.

Sophia M. Walls, Adam Bloss, Ian J. Ford

Quantum state diffusion is a framework within which measurement may be described as the continuous and gradual collapse of a quantum system to an eigenstate as a result of interaction with its environment. The irreversible nature of the quantum trajectories that arise may be characterised by the environmental stochastic entropy production associated with the measurement. We consider a system of two spin 1/2 particles undergoing either single particle measurements or measurements of the total z-spin component S_{z}. The mean asymptotic rates of environmental stochastic entropy production associated with collapse can depend on the eigenstate of S_{z} selected, and on the initial state of the system, offering an additional avenue for characterising quantum measurement.

M. Cristina Rodriguez, Analia Zwick, Gonzalo A. Alvarez

Quantum probes offer a powerful platform for exploring environmental dynamics, particularly through their sensitivity to decoherence processes. In this work, we investigate the emergence of critical behavior in the estimation of the environmental memory time $τ_c$, modeled as an Ornstein-Uhlenbeck process characterized by a Lorentzian spectral density. Using dynamically controlled qubit-based sensors -- realized experimentally via solid-state Nuclear Magnetic Resonance (NMR) and supported by numerical simulations -- we implement tailored filter functions to interrogate the environmental noise spectrum and extract $τ_c$ from its spectral width. Our results reveal a sharp transition in estimation performance between short-memory (SM) and long-memory (LM) regimes, reflected in a non-monotonic estimation error that resembles a phase transition. This behavior is accompanied by an avoided-crossing-like structure in the estimated parameter space, indicative of two competing solutions near the critical point. These features underscore the interplay between control, decoherence, and inference in open quantum systems. Beyond their fundamental significance, these critical phenomena offer a practical diagnostic tool for identifying dynamical regimes and optimizing quantum sensing protocols. By exploiting this criticality, our findings pave the way for adaptive control strategies aimed at enhancing precision in quantum parameter estimation -- particularly in complex or structured environments such as spin networks, diffusive media, and quantum materials.

Jacem Zidani, Latifa Tajounte, Abdellah Benzaouak et al.

The review highlights the advancements in flexible lead-free piezoelectric materials, emphasizing their potential for energy harvesting and sustainable energy. Although normal piezoelectric materials such as lead zirconate titanate (PZT) have great efficiency, their lead content causes environmental issues. This research focuses on replacement materials like biodegradable polymers and bismuth sodium titanate (BNT), which not only show interesting piezoelectric capabilities but also have advantages in terms of flexibility and biocompatibility. In order to increase piezoelectric performance while maintaining flexibility, it is advised to include inorganic fillers into polymer matrices, therefore qualifying these materials for usage in biomedical and wearable electronics applications. The evaluation also covers the issues resulting from the great usage of these resources, including e-waste and the need of sustainable solutions. The general message of the research underlines the need of developing new piezoelectric materials able to effectively gather mechanical energy from different sources, therefore promoting self-sustaining systems and reducing reliance on traditional power sources. The review also underlines how lead-free piezoelectric materials can boost power density and chemical oxygen demand (COD) removal rates in microbial fuel cells (MFCs), therefore promoting sustainable energy solutions that turn organic waste into bioelectricity.

Alexandros Amprazis, Penelope Papadopoulou

Societal Impact Statement Lack of plant awareness represents a significant phenomenon characterized by the underestimation of plants, with clear implications for sustainability. This study explores the potential of key competencies in education for sustainable development as an effective framework for mitigating this phenomenon. Through conceptual analysis, these competencies emerge as a valuable tool for enhancing plant awareness. This has significant implications for both the educational community and the general public, as it offers an additional pathway for fostering plant awareness, which can ultimately lead to increased public pressure and stronger mobilization by policymakers on critical issues such as biodiversity conservation and climate change. Summary The phenomenon of “plant blindness” or “lack of plant awareness” has received much attention from researchers over the last years. Recognizing education as both a contributing factor to and a potential solution for this issue, this study explores key competencies in education for sustainable development as a framework to enhance plant awareness. A conceptual analysis was conducted to identify thematic relationships between this framework and plant awareness. The analysis suggests that enhancing systems thinking and integrated problem‐solving competencies can help learners better recognize and understand the importance of plants for both human welfare and planet Earth. Through the development of critical thinking, normative, and self‐awareness competencies, learners are encouraged to question existing personal and societal perspectives on plants, thereby reshaping their perception of flora. Moreover, the application of anticipatory, strategic, and collaboration competencies allows learners to explore the intrinsic values of the plant world more deeply, fostering respect and empathy, which can lead to a broader shift in attitudes toward flora. Through the integration of these elements into plant education, botany classes can become more engaging and relevant to real‐world issues. This approach can help bridge the gap between traditional science education and the development of pro‐conservation behaviors, while also enriching the evaluation methods used to assess plant awareness. Additionally, key competencies in education for sustainable development within plant education can promote not only a less utilitarian perspective of plants as organisms but also a more holistic approach to science education, reducing its emphasis on instrumentalization.

Weiwei Wang, Elena Blanc-Betes, Madhu Khanna et al.

Abstract Energy crops will be critical for scaling up production of Sustainable Aviation Fuel in the United States and reducing greenhouse gas emissions. Here we examine the economic incentives for the extent and type of land conversion needed to scale up fuel production from a mix of cellulosic feedstocks and quantify its greenhouse gas intensity. We show that even with the availability of marginal non-cropland, there will be incentives for converting cropland to produce energy crops as the price of sustainable aviation fuel increases. But contrary to expectations, we find that scaling up fuel production by converting more cropland and more non-cropland from existing uses to energy crops lowers its net greenhouse gas intensity, due to high soil carbon sequestration rate of energy crops, even after considering land use change emissions.The potential savings in emissions are larger than the foregone soil carbon accumulation benefits from keeping that land in current uses.

Elias Bojago, Gemechu Tadila, Mamush Masha

Abstract Understanding spatiotemporal changes in land use, land cover (LULC), and vegetation dynamics is crucial for sustainable environmental management and planning. This study investigated LULC and vegetation changes in the Gambela region of Ethiopia using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data from 2004 to 2024. This study relied on MOD13A3 (NDVI, 1 km, monthly) to track vegetation changes from to 2004–2024, as well as Landsat image classification was used to LULC estimation. The IGBP was refined using a random forest with NDVI thresholding to identify shifts. The accuracy was 87% through Sentinel-2 and ground truth, and NDVI deviations were associated (0.80) with yields. Geospatial and statistical techniques were employed to detect and quantify transitions between land cover classes and fluctuations in greenness in the study area. Six LULC classes, namely forest, agricultural land, grassland, irrigated land, built‑up area, and water bodies, were mapped and analyzed. Between 2004 and 2024, forest cover declined by 2 693.9 km2 (from 74.2% to 65.3%), agricultural land expanded by 4 618.4 km2 (from 5.3% to 20.6%), and grasslands contracted by 2 397.8 km2 (from 19.5% to 11.5%). Irrigated areas more than tripled (0.4% to 1.2%), and built‑up extent grew nearly five‑fold (0.2% to 0.9%), whereas water bodies remained largely stable during this period. NDVI analysis revealed a 12% reduction in high-greenness areas, typically corresponding to NDVI values ≥ 0.6 (often 0.6–0.8), and a mean NDVI drop from 0.62 to 0.59 in non-forest zones, indicating declining vegetation health in converted landscapes. The study found significant LULC changes driven by agricultural expansion, settlement growth, and climate variability, with declining natural vegetation and increasing cultivated and built-up areas in the western and central regions. MODIS data are valuable for environmental monitoring, offering insights into land management and climate adaptation.

David W. Cash, S. Moser

Chungang Shi, Yan Lv, Wei Wang

The small mass limit is derived for a McKean-Vlasov equation subject to environmental noise with state-dependent friction. By applying the averaging approach to a non-autonomous stochastic slow-fast system with the microscopic and macroscopic scales, the convergence in distribution is obtained.

Yi Zheng

For controlling pollution of the marine environment while developing coastal economy, the coastal environmental performance was proposed and measured in static and dynamic methods combined with DEA and efficiency theory in this paper. With the two methods, 16 harbor cities were evaluated. The results showed the index designed in this paper can better reflect the effect to the marine environment for economy of the coastal cities.