Hasil untuk "Environmental Science"

Catherine A. Peters

Nicodemo Abate, Diego Ronchi, Sara Elettra Zaia et al.

This study presents a multi-resolution and multi-temporal remote sensing approach to assess human-induced changes in cultural landscapes, with a focus on the archaeological site of Amrit (Syria) within the MapDam project. By integrating satellite archives (KH, Landsat series, NASADEM) with ancillary geospatial data (OpenStreetMap) and advanced analytical methods, four decades (1984–2024) of land-use/land-cover (LULC) change and shoreline dynamics were reconstructed. Machine learning classification (Random Forest) achieved high accuracy (Test Accuracy = 0.94; Kappa = 0.89), enabling robust LULC mapping, while predictive modelling of urban expansion, calibrated through a Gradient Boosting Machine, attained a Figure of Merit of 0.157, confirming strong predictive reliability. The results reveal path-dependent urban growth concentrated on low-slope terrains (≤5°) and consistent with proximity to infrastructure, alongside significant shoreline regression after 1974. A Business-as-Usual projection for 2024–2034 estimates 8.676 ha of new anthropisation, predominantly along accessible plains and peri-urban fringes. Beyond quantitative outcomes, this study demonstrates the replicability and scalability of open-source, data-driven workflows using Google Earth Engine and Python 3.14, making them applicable to other high-risk heritage contexts. This transparent methodology is particularly critical in conflict zones or in regions where cultural assets are neglected due to economic constraints, political agendas, or governance limitations, offering a powerful tool to document and safeguard endangered archaeological landscapes.

Daiki Mayumi, Hiroki Matsuda, Tetsuya Yokota et al.

This study presents the construction of the first digital twin utilizing non-identifiable television viewing history data. As the media landscape continues to evolve, understanding viewer behavior has become increasingly crucial. By simulating viewing behaviors based on real-time data, our approach enables the virtual reproduction of viewer preferences and behavior patterns, facilitating optimized advertising, content production, and marketing strategies. We propose a method for classifying user viewing tendencies using large-scale, non-identifiable data and develop a simulator based on these classifications. A detailed analysis of the data led to the extraction of tailored features for television viewing and the development of a highly accurate classification model. The weekday and weekend models achieved F1 scores of approximately 0.95, demonstrating their strong predictive capabilities. This study provides valuable insights into digital twin construction for television viewing and opens new avenues for data-driven media strategies.

Longfei Cui, Xinyu Niu, Haizhong Qian et al.

The extraction of shape features from vector elements is essential in cartography and geographic information science, supporting a range of intelligent processing tasks. Traditional methods rely on different machine learning algorithms tailored to specific types of line and polygon elements, limiting their general applicability. This study introduces a novel approach called “Pre-Trained Shape Feature Representations from Transformers (PSRT)”, which utilizes transformer encoders designed with three self-supervised pre-training tasks: coordinate masking prediction, coordinate offset correction, and coordinate sequence rearrangement. This approach enables the extraction of general shape features applicable to both line and polygon elements, generating high-dimensional embedded feature vectors. These vectors facilitate downstream tasks like shape classification, pattern recognition, and cartographic generalization. Our experimental results show that PSRT can extract vector shape features effectively without needing labeled samples and is adaptable to various types of vector features. Compared to the methods without pre-training, PSRT enhances training efficiency by over five times and improves accuracy by 5–10% in tasks such as line element matching and polygon shape classification. This innovative approach offers a more unified, efficient solution for processing vector shape data across different applications.

Muthmainnah Muthmainnah, Galuh Mega Kurnia, Avinka Nugrahani

Introduction With Indonesia ranking top in the Association of Southeast Asian Nations for the number of smokers aged 13–15 years, this study aims to analyze the factors associated with smoking prevention behavior among students of senior high school. Methods This cross-sectional pilot study, conducted in 2022 with 90 samples of grade 10–11 students at SMA Negeri 1 Taman Sidoarjo East Java Indonesia, examined variables such as perceived vulnerability (the belief about the risk of experiencing a health issue), severity (the belief about the seriousness of the health issue), benefits (the belief in the benefit of taking preventive actions), barriers (the perceived obstacles to performing preventive behaviors), self-efficacy (the confidence in one's ability to perform preventive behaviors successfully), and cues to action (factors that trigger the decision to engage) in relation to health behaviors. Data were analyzed using the chi-squared test. Results The chi-squared analysis showed significant associations between several factors and smoking prevention behavior. For perceived susceptibility, 34.4% with high susceptibility had good behavior, and 13.3% had not good behavior (p=0.000). For perceived severity, 33.3% with high severity exhibited good behavior, and 21% had not good behavior (p=0.002). Regarding perceived benefits, 28.9% with high benefits showed good behavior, while 22.6% had not good behavior (p=0.018). Self-efficacy indicated 36.7% with high self-efficacy demonstrated good behavior versus 25.8% with not good behavior (p=0.001). Cues to action revealed that 28.9% with high cues had good behavior, and 18.9% did not have good behavior (p=0.003). No association was found for perceived barriers (p=0.386). Conclusions The level of smoking prevention behavior is influenced by perceived susceptibility, severity, benefits, self-efficacy, and cues to action. Therefore, more intensive and targeted efforts are needed to promote awareness of the dangers of smoking and to enhance adolescents' self-efficacy in preventing smoking.

Runlong Yu, Shengyu Chen, Yiqun Xie et al.

Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional methods frequently struggle with the inherent complexity, interconnectedness, and limited data of such systems. Foundation models, with their large-scale pre-training and universal representations, offer transformative opportunities by integrating diverse data sources, capturing spatiotemporal dependencies, and adapting to a broad range of tasks. This survey presents a comprehensive overview of foundation model applications in environmental science, highlighting advancements in forward prediction, data generation, data assimilation, downscaling, model ensembling, and decision-making across domains. We also detail the development process of these models, covering data collection, architecture design, training, tuning, and evaluation. By showcasing these emerging methods, we aim to foster interdisciplinary collaboration and advance the integration of cutting-edge machine learning for sustainable solutions in environmental science.

Richard Littauer, Kris Bubendorfer

Community science observational datasets are useful in epidemiology and ecology for modeling species distributions, but the heterogeneous nature of the data presents significant challenges for standardization, data quality assurance and control, and workflow management. In this paper, we present a data workflow for cleaning and harmonizing multiple community science datasets, which we implement in a case study using eBird, iNaturalist, GBIF, and other datasets to model the impact of highly pathogenic avian influenza in populations of birds in the subantarctic. We predict population sizes for several species where the demographics are not known, and we present novel estimates for potential mortality rates from HPAI for those species, based on a novel aggregated dataset of mortality rates in the subantarctic.

Scott Humr, Mustafa Canan

Current definitions of Information Science are inadequate to comprehensively describe the nature of its field of study and for addressing the problems that are arising from intelligent technologies. The ubiquitous rise of artificial intelligence applications and their impact on society demands the field of Information Science acknowledge the sociotechnical nature of these technologies. Previous definitions of Information Science over the last six decades have inadequately addressed the environmental, human, and social aspects of these technologies. This perspective piece advocates for an expanded definition of Information Science that fully includes the sociotechnical impacts information has on the conduct of research in this field. Proposing an expanded definition of Information Science that includes the sociotechnical aspects of this field should stimulate both conversation and widen the interdisciplinary lens necessary to address how intelligent technologies may be incorporated into society and our lives more fairly.

Daniel Apai, Rory Barnes, Matthew M. Murphy et al.

The search for extraterrestrial life in the Solar System and beyond is a key science driver in astrobiology, planetary science, and astrophysics. A critical step is the identification and characterization of potential habitats, both to guide the search and to interpret its results. However, a well-accepted, self-consistent, flexible, and quantitative terminology and method of assessment of habitability are lacking. Our paper fills this gap based on a three year-long study by the NExSS Quantitative Habitability Science Working Group. We reviewed past studies of habitability, but find that the lack of a universally valid definition of life prohibits a universally applicable definition of habitability. A more nuanced approach is needed. We introduce a quantitative habitability assessment framework (QHF) that enables self-consistent, probabilistic assessment of the compatibility of two models: First, a habitat model, which describes the probability distributions of key conditions in the habitat. Second, a viability model, which describes the probability that a metabolism is viable given a set of environmental conditions. We provide an open-source implementation of this framework and four examples as a proof of concept: (a) Comparison of two exoplanets for observational target prioritization; (b) Interpretation of atmospheric O2 detection in two exoplanets; (c) Subsurface habitability of Mars; and (d) Ocean habitability in Europa. These examples demonstrate that our framework can self-consistently inform astrobiology research over a broad range of questions. The proposed framework is modular so that future work can expand the range and complexity of models available, both for habitats and for metabolisms.

Tai Le Quy, Gunnar Friege, Eirini Ntoutsi

Ensuring fairness is essential for every education system. Machine learning is increasingly supporting the education system and educational data science (EDS) domain, from decision support to educational activities and learning analytics. However, the machine learning-based decisions can be biased because the algorithms may generate the results based on students' protected attributes such as race or gender. Clustering is an important machine learning technique to explore student data in order to support the decision-maker, as well as support educational activities, such as group assignments. Therefore, ensuring high-quality clustering models along with satisfying fairness constraints are important requirements. This chapter comprehensively surveys clustering models and their fairness in EDS. We especially focus on investigating the fair clustering models applied in educational activities. These models are believed to be practical tools for analyzing students' data and ensuring fairness in EDS.

Balaram Panda

Data Science is a modern Data Intelligence practice, which is the core of many businesses and helps businesses build smart strategies around to deal with businesses challenges more efficiently. Data Science practice also helps in automating business processes using the algorithm, and it has several other benefits, which also deliver in a non-profitable framework. In regards to data science, three key components primarily influence the effective outcome of a data science project. Those are 1.Availability of Data 2.Algorithm 3.Processing power or infrastructure

Huifang Lian, Huifang Lian, Huifang Lian et al.

PurposeFungal keratitis is a sight-threatening corneal infection caused by fungal pathogens, and the pathogenic mechanisms have not been fully elucidated. The aim of this study was to determine whether NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome-mediated pyroptosis contributes to Candida albicans (C. albicans) keratitis and explore the underlying mechanism.MethodsAn in vivo mouse model of C. albicans keratitis and an in vitro culture model of human corneal epithelial cells (HCECs) challenged with heat-killed C. albicans (HKCA) were established in this study. The degree of corneal infection was evaluated by clinical scoring. Gene expression was assessed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blot analysis or immunofluorescence staining was performed to evaluate protein expression. TdT-mediated dUTP nick end labeling (TUNEL) staining was performed to examine the pyroptotic cell death. A lactate dehydrogenase (LDH) release assay was performed to assess cytotoxicity.ResultsCompared with the mock-infected group, we observed that the mRNA levels of NLRP3, caspase-1 (CASP1), interleukin (IL)−1β and gasdermin-D (GSDMD) in C. albicans-infected mice cornea was significantly increased. Our data also demonstrated that the protein expression of NLRP3 and the pyroptosis-related markers apoptosis-associated speck-like protein containing a CARD (ASC), cleaved CASP1, N-GSDMD, cleaved IL-1β and cleaved IL-18 as well as pyroptotic cell death were dramatically elevated in the mouse model of C. albicans keratitis. More importantly, NLRP3 knockdown markedly alleviated pyroptosis and consequently reduced corneal inflammatory reaction in C. albicans keratitis. In vitro, the presence of activated NLRP3 inflammasome and pyroptotic cell death were validated in HCECs exposed to HKCA. Furthermore, the potassium (K+) channel inhibitor glyburide decreased LDH release and suppressed NLRP3 inflammasome activation and pyroptosis in HCECs exposed to HKCA.ConclusionIn conclusion, the current study revealed for the first time that NLRP3 inflammasome activation and pyroptosis occur in C. albicans-infected mouse corneas and HCECs. Moreover, NLRP3 inflammasome-mediated pyroptosis signaling is involved in the disease severity of C. albicans keratitis. Therefore, This NLRP3 inflammasome-dependent pathway may be an attractive target for the treatment of fungal keratitis.

Teresa Fish, Teresa Fish, Nathan Wolf et al.

Developing a robust understanding of Pacific halibut reproductive biology is essential to understanding the different components (e.g. maturity) that determine the reproductive output of the species and, therefore, for estimating the relative female spawning biomass. With these, effective and proactive management strategies can be designed and implemented to face the large-scale environmental changes to which high-latitude spawning fish are particularly vulnerable. To date, reproductive studies of Pacific halibut have mainly focused on population or regional scales, leaving the specific details of organism-level reproductive development unexamined. The work described here aimed to address information gaps in Pacific halibut reproductive biology by conducting a detailed histological examination of temporal changes in ovarian development over an annual reproductive cycle with special attention to the use of biological indicators (e.g. oocyte diameter, gonadosomatic index, hepatosomatic index, Fulton’s condition factor, somatic fat) in characterizing female developmental stages and reproductive phases. Our results provide a foundation for future studies directed at improving current maturity estimations by histological assessment and explore models that test the utility of biological indicators to predict maturity in this important fish species.

Zhang S, Wang L, Wang L et al.

Shouping Zhang, Lei Wang, Lirong Wang, Nan Yu, Yongjun Dong, Jianhe Hu College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Xinxiang, 453003, People’s Republic of ChinaCorrespondence: Lei Wang, Jianhe Hu, College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Eastern HuaLan Avenue, Xinxiang, 453003, People’s Republic of China, Tel +86-373-3040718, Email wlei_007@163.com; vet_jianhe@sina.comIntroduction: Porcine circovirus type 2 (PCV2) causes immune repression and intercurrent infections in pigs, resulting in a huge economic loss to the pig breeding industry. Additionally, the spread of PCV2 in pig farms can pollute the living environment of the residents in the farm’s vicinity, which increases the rate of infections. Therefore, rapid and sensitive detection methods are needed for disease prevention and timely environmental cleaning.Methods: This research describes a highly sensitive sandwich enzyme-linked immunosorbent assay (ELISA) that utilizes gold nanoparticles (AuNPs) in a functional, specific antibody labeled probe for the detection of PCV2. Due to their high specific surface area and histocompatibility, AuNPs were used as carriers of HRP labeled anti-PCV2 antibodies to amplify the detection signal.Results: Compared to conventional sandwich ELISA procedures, this method resulted in higher sensitivity (51-fold) and a shorter assay time with a limit of detection of 195 TCID50/mL. The cross-reactivity assay demonstrated that this assay was PCV2 specific.Conclusion: The amplified Ab (HRP) labeled AuNPs probe provides a sensitive analytical approach for the determination of the traces of the PCV2 antigen in early diagnosis.Keywords: PCV2, gold nanoparticles, ELISA, amplification, detection

Wenchao Yu, Wenchao Yu, Yisha Lu et al.

Feed efficiency (FE) is critical to the economic and environmental benefits of aquaculture. Both the intestines and intestinal microbiota play a key role in energy acquisition and influence FE. In the current research, intestinal microbiota, metabolome, and key digestive enzyme activities were compared between abalones with high [Residual feed intake (RFI) = −0.029] and low FE (RFI = 0.022). The FE of group A were significantly higher than these of group B. There were significant differences in intestinal microbiota structures between high- and low-FE groups, while higher microbiota diversity was observed in the high-FE group. Differences in FE were also strongly correlated to variations in intestinal digestive enzyme activity that may be caused by Pseudoalteromonas and Cobetia. In addition, Saprospira, Rhodanobacteraceae, Llumatobacteraceae, and Gaiellales may potentially be utilized as biomarkers to distinguish high- from low-FE abalones. Significantly different microorganisms (uncultured beta proteobacterium, BD1_7_clade, and Lautropia) were found to be highly correlated to significantly different metabolites [DL-methionine sulfoxide Arg-Gln, L-pyroglutamic acid, dopamine, tyramine, phosphatidyl cholines (PC) (16:0/16:0), and indoleacetic acid] in the high- and low-FE groups, and intestinal trypsin activity also significantly differed between the two groups. We propose that interactions occur among intestinal microbiota, intestinal metabolites, and enzyme activity, which improve abalone FE by enhancing amino acid metabolism, immune response, and signal transduction pathways. The present study not only elucidates mechanisms of variations in abalone FE, but it also provides important basic knowledge for improving abalone FE by modulating intestinal microbiota.

G. Baaklini, G. Baaklini, R. El Hourany et al.

<p>The eastern Mediterranean surface circulation is highly energetic and composed of structures interacting stochastically. However, some main features are still debated, and the behavior of some fine-scale dynamics and their role in shaping the general circulation is yet unknown. In the following paper, we use an unsupervised neural network clustering method to analyze the long-term variability of the different mesoscale structures. We decompose 26 years of altimetric data into clusters reflecting different circulation patterns of weak and strong flows with either strain or vortex-dominated velocities. The vortex-dominated cluster is more persistent in the western part of the basin, which is more active than the eastern part due to the strong flow along the coast, interacting with the extended bathymetry and engendering continuous instabilities. The cluster that reflects a weak flow dominated the middle of the basin, including the Mid-Mediterranean Jet (MMJ) pathway. However, the temporal analysis shows a frequent and intermittent occurrence of a strong flow in the middle of the basin, which could explain the previous contradictory assessment of MMJ existence using in-situ observations. Moreover, we prove that the Levantine Sea is becoming more and more energetic as the activity of the main mesoscale features is showing a positive trend.</p>

The Genomic Data Science Community Network, Rosa Alcazar, Maria Alvarez et al.

Over the last 20 years, there has been an explosion of genomic data collected for disease association, functional analyses, and other large-scale discoveries. At the same time, there have been revolutions in cloud computing that enable computational and data science research, while making data accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support students, faculty, and administrators at Underserved Institutions (UIs) including Community Colleges, Historically Black Colleges and Universities, Hispanic-Serving Institutions, and Tribal Colleges and Universities in taking advantage of these tools in local educational and research programs. We have formed the Genomic Data Science Community Network (http://www.gdscn.org/) to identify opportunities and support broadening access to cloud-enabled genomic data science. Here, we provide a summary of the priorities for faculty members at UIs, as well as administrators, funders, and R1 researchers to consider as we create a more diverse genomic data science community.

Rushil Anirudh, Rick Archibald, M. Salman Asif et al.

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future.

Amy McGovern, Imme Ebert-Uphoff, David John Gagne et al.

Given the growing use of Artificial Intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much can be learned from other domains where AI was introduced, often with the best of intentions, yet often led to unintended societal consequences, such as hard coding racial bias in the criminal justice system or increasing economic inequality through the financial system. A common misconception is that the environmental sciences are immune to such unintended consequences when AI is being used, as most data come from observations, and AI algorithms are based on mathematical formulas, which are often seen as objective. In this article, we argue the opposite can be the case. Using specific examples, we demonstrate many ways in which the use of AI can introduce similar consequences in the environmental sciences. This article will stimulate discussion and research efforts in this direction. As a community, we should avoid repeating any foreseeable mistakes made in other domains through the introduction of AI. In fact, with proper precautions, AI can be a great tool to help {\it reduce} climate and environmental injustice. We primarily focus on weather and climate examples but the conclusions apply broadly across the environmental sciences.

Imme Ebert-Uphoff, Ryan Lagerquist, Kyle Hilburn et al.

Neural networks are increasingly used in environmental science applications. Furthermore, neural network models are trained by minimizing a loss function, and it is crucial to choose the loss function very carefully for environmental science applications, as it determines what exactly is being optimized. Standard loss functions do not cover all the needs of the environmental sciences, which makes it important for scientists to be able to develop their own custom loss functions so that they can implement many of the classic performance measures already developed in environmental science, including measures developed for spatial model verification. However, there are very few resources available that cover the basics of custom loss function development comprehensively, and to the best of our knowledge none that focus on the needs of environmental scientists. This document seeks to fill this gap by providing a guide on how to write custom loss functions targeted toward environmental science applications. Topics include the basics of writing custom loss functions, common pitfalls, functions to use in loss functions, examples such as fractions skill score as loss function, how to incorporate physical constraints, discrete and soft discretization, and concepts such as focal, robust, and adaptive loss. While examples are currently provided in this guide for Python with Keras and the TensorFlow backend, the basic concepts also apply to other environments, such as Python with PyTorch. Similarly, while the sample loss functions provided here are from meteorology, these are just examples of how to create custom loss functions. Other fields in the environmental sciences have very similar needs for custom loss functions, e.g., for evaluating spatial forecasts effectively, and the concepts discussed here can be applied there as well. All code samples are provided in a GitHub repository.