Hasil untuk "Environmental Science"

Christopher D. Pfledderer, Emma J. Mullane, Denver M.Y. Brown et al.

Objective: To examine rural-urban differences in perceived social and built environment characteristics and assess associations with the frequency of meeting physical activity guidelines among United States youth. Methods: We used data from the 2022-2023 National Survey of Children’s Health, a nationally representative sample of United States children and adolescents. Weighted cumulative logit models were used to determine associations between parental perceptions of social/built environments and number of days youth met physical activity guidelines, stratified by rural-urban status. Results: The total sample included 55,551 (Representative N=41,792,444, 11.9 ± 3.5 years, 48% female) youth. Positive perceptions of neighborhood support were associated with higher odds of meeting physical activity guidelines (OR:1.43; 95% CI:1.32,1.54) as were perceptions of school safety (OR:1.29; 95% CI:1.08,1.54). Access to neighborhood amenities was associated with higher odds of meeting guidelines (OR:1.18; 95% CI:1.05,1.34). For rural youth, neighborhood support (OR:1.62; 95% CI: 1.34,1.95) and neighborhood amenities (OR:1.26; 95% CI:1.05,1.52) were positively associated with odds of meeting guidelines. For urban youth, neighborhood support (OR:1.40; 95% CI:1.29,1.53) and school safety (OR:1.31; 95% CI:1.07,1.59) were positively associated with odds of meeting guidelines. Conclusions: Perceived social/built environmental factors are associated with youth physical activity, although associations differ by urbanicity.

Anushka Narayanan, Karianne J. Bergen

Explainable AI (XAI) is essential for understanding machine learning (ML) decision-making and ensuring model trustworthiness in scientific applications. Prototype-based XAI methods offer an intrinsically interpretable alternative to post-hoc approaches which often yield inconsistent explanations. Prototype-based XAI methods make predictions based on the similarity between inputs and learned prototypes that represent typical characteristics of target classes. However, existing prototype-based models are primarily designed for standard RGB image data and are not optimized for the distinct, variable-specific channels commonly found in geoscientific image and raster datasets. In this study, we develop a prototype-based XAI approach tailored for multi-channel geospatial data, where each channel represents a distinct physical environmental variable or spectral channel. Our approach enables the model to identify separate, channel-specific prototypical characteristics sourced from multiple distinct training examples that inform how these features individually and in combination influence model prediction while achieving comparable performance to standard neural networks. We demonstrate this method through two geoscientific case studies: (1) classification of Madden Julian Oscillation phases using multi-variable climate data and (2) land-use classification from multispectral satellite imagery. This approach produces both local (instance-level) and global (model-level) explanations for providing insights into feature-relevance across channels. By explicitly incorporating channel-prototypes into the prediction process, we discuss how this approach enhances the transparency and trustworthiness of ML models for geoscientific learning tasks.

CAI Huiwen, LIU Lihong, YU Jing et al.

Innovative marine talents are the cornerstone of developing a leading maritime nation. Cultivating high-quality talent in marine science has become an urgent priority for higher education. Within this process, pedagogy remains a critical determinant of educational quality. Based on the theory of “micro-thinking”, this paper re-evaluates the fundamental concepts of teaching. From this perspective, innovation in higher education should originate from the core components of education: educators and students, teaching materials, and instructional processes. The aim of this paper is to further promote the course reform in environmental oceanography and foster educational innovations that contribute to cultivating highly qualified, well-trained marine science professionals for the society.

Aidan D. Meade, Adrian Maguire, Adrian Maguire et al.

Although significant advances in understanding the molecular drivers of acquired and inherited radiosensitivity have occurred in recent decades, a single analytical method which can detect and classify radiosensitivity remains elusive. Raman microspectroscopy has demonstrated capabilities in the objective classification of various diseases, and more recently in the detection and modelling of radiobiological effect. In this study, Raman spectroscopy is presented as a potential tool for the detection of radiosensitivity subpopulations represented by four lymphoblastoid cell lines derived from individuals with ataxia telangiectasia (2 lines), non-Hodgkins lymphoma, and Turner’s syndrome. These are classified with respect to a population with mixed radiosensitivity, represented by lymphocytes drawn from both healthy controls, and prostate cancer patients. Raman spectroscopic measurements were made ex-vivo after exposure to X-ray doses of 0 Gy, 50 mGy and 500 mGy, in parallel to radiation-induced G2 chromosomal radiosensitivity scores, for all samples. Support vector machine models developed on the basis of the spectral data were capable of discrimination of radiosensitive populations before and after irradiation, with superior discrimination when spectra were subjected to a non-linear dimensionality reduction (UMAP) as opposed to a linear (PCA) approach. Models developed on spectral data acquired on samples irradiated in-vitro with a dose of 0Gy were found to provide the highest level of performance in discriminating between classes, with performances of F1 = 0.92 ± 0.06 achieved on a held-out test set. Overall, this study suggests that Raman spectroscopy may have potential as a tool for the detection of intrinsic radiosensitivity using liquid biopsies.

Brittany N. Hupp, Mohammed S. Hashim, Raquel Bryant et al.

Scientific ocean drilling (SciOD) has been invaluable in advancing our understanding of Earth history. However, the most recent international SciOD programme ended in 2024, alongside the non-renewal of the riserless drilling vessel, the JOIDES Resolution. The US has not committed to joining a new SciOD programme despite prior efforts focused on important scientific priorities (e.g. climate change, assessing natural hazards). During this critical juncture, we argue that incorporating accessibility, justice, equity, diversity and inclusion (AJEDI) efforts will further develop a cohesive community that is well prepared to tackle questions critical to the US and global community. Herein we provide recommendations to develop a knowledgeable and diverse community of scientists in the changing landscape of US SciOD, as informed by historical participation data and recent efforts by early career scientists. Recommendations focus on accessible training opportunities, enhanced stewardship of archived materials, additional funding for research at all academic levels, inclusion of cultural advisors and social scientists, and a commitment to continuing SciOD education. By pursuing these recommendations, the US SciOD community could become a leader for modelling AJEDI principles and ensuring equitable knowledge transfer that is needed to reimagine and rebuild a new, inclusive SciOD programme.

Jianfei Sun, Xiaoting Jie, Sujuan Chen et al.

Robust, national-scale quantification of soil organic carbon (SOC) dynamics in China’s paddy fields has been hindered by widely divergent estimates and a lack of comprehensive driver attribution. To address this, we developed a new empirical model from a comprehensive database of 746 long-term field observations (125 sites) to identify predominant drivers and quantify national-scale SOC stock dynamics from 1980 to 2018. The model explained 43% of the variance in topsoil SOC change. Organic matter input was the dominant driver (21.83% variance), with livestock manure demonstrating the highest C sequestration efficiency, followed by green manure and straw. Soil pH, latitude (as a climate proxy), and initial SOC content were also critical controllers. We estimate that China’s paddy topsoils (0–20 cm) acted as a significant C sink from 1980 to 2018, accumulating 242.51 ± 85.80 Tg C (an average rate of 6.65 Tg C yr⁻¹), bringing the 2018 national stock to 1220.48 ± 85.80 Tg C. Spatially, sequestration was highest in central (e.g., Hunan) and northeastern (e.g., Heilongjiang) China, while Chongqing experienced a net SOC loss. Crucially, our study provides a new long-term benchmark that reconciles previous, higher estimates from shorter timeframes, empirically demonstrating that sequestration rates are non-linear and diminish over time. These findings confirm that the C sequestration potential of paddy soils, while substantial, is finite and requires spatially targeted management of organic inputs and soil pH to maintain.

Zachary T. Rewolinski, Bin Yu

Data science is a pillar of artificial intelligence (AI), which is transforming nearly every domain of human activity, from the social and physical sciences to engineering and medicine. While data-driven findings in AI offer unprecedented power to extract insights and guide decision-making, many are difficult or impossible to replicate. A key reason for this challenge is the uncertainty introduced by the many choices made throughout the data science life cycle (DSLC). Traditional statistical frameworks often fail to account for this uncertainty. The Predictability-Computability-Stability (PCS) framework for veridical (truthful) data science offers a principled approach to addressing this challenge throughout the DSLC. This paper presents an updated and streamlined PCS workflow, tailored for practitioners and enhanced with guided use of generative AI. We include a running example to display the PCS framework in action, and conduct a related case study which showcases the uncertainty in downstream predictions caused by judgment calls in the data cleaning stage.

Chenxu Wang, Huaping Liu

Adversarial attacks and robustness in Deep Reinforcement Learning (DRL) have been widely studied in various threat models; however, few consider environmental state perturbations, which are natural in embodied scenarios. To improve the robustness of DRL agents, we formulate the problem of environmental state perturbation, introducing a preliminary non-targeted attack method as a calibration adversary, and then propose a defense framework, named Boosted Adversarial Training (BAT), which first tunes the agents via supervised learning to avoid catastrophic failure and subsequently adversarially trains the agent with reinforcement learning. Extensive experimental results substantiate the vulnerability of mainstream agents under environmental state perturbations and the effectiveness of our proposed attack. The defense results demonstrate that while existing robust reinforcement learning algorithms may not be suitable, our BAT framework can significantly enhance the robustness of agents against environmental state perturbations across various situations.

Andrew Chang, Yike Li, Iran R. Roman et al.

Audio DNNs have demonstrated impressive performance on various machine listening tasks; however, most of their representations are computationally costly and uninterpretable, leaving room for optimization. Here, we propose a novel approach centered on spectrotemporal modulation (STM) features, a signal processing method that mimics the neurophysiological representation in the human auditory cortex. The classification performance of our STM-based model, without any pretraining, is comparable to that of pretrained audio DNNs across diverse naturalistic speech, music, and environmental sounds, which are essential categories for both human cognition and machine perception. These results show that STM is an efficient and interpretable feature representation for audio classification, advancing the development of machine listening and unlocking exciting new possibilities for basic understanding of speech and auditory sciences, as well as developing audio BCI and cognitive computing.

Anu Shrestha, Narayan Babu Dhital

Urban air quality shows notable variability across various microenvironments. Transport-related microenvironments often have one of the highest air pollution levels, where commuters have significant exposure to air pollutants. In Kathmandu Valley (KV), Nepal, passengers’ exposure to air pollutants in such microenvironments remain a poorly understood issue. In this study, we analyzed cabin air quality and ventilation rates in public buses operated in KV. In-cabin and in-route ambient air pollution levels were monitored during real-world bus operations in 32 trips on a test route selected in the core city area. A CO2 mass balance model was applied to estimate cabin ventilation rates. The mean in-cabin and outdoor PM2.5 concentrations were 95.9 ± 40.4 (mean ± standard deviation) and 94.7 ± 32.4 μg/m3, respectively. Likewise, mean in-cabin and outdoor PM1 concentrations were 72.5 ± 31.3 and 69.7 ± 25.3 μg/m3, respectively, with mean PM1 to PM2.5 ratio >0.75, indicating that a majority of particle mass was in the sub-micron size range, which is often more health-hazardous. We estimated passengers’ inhalation dose for PM2.5 and PM1 as 5.65 ± 2.32 and 4.27 ± 1.79 μg/km, respectively. Among various factors considered, traffic hour significantly affected both in-cabin and ambient particulate matter concentrations. The trip-average in-cabin CO2 concentration ranged from 513 to 1230 ppm, whereas cabin ventilation rates ranged from 8.0 to 72.4 L/s/person. Ventilation levels in a majority of bus trips were inadequate, especially considering the recommended thresholds to reduce the transmission risk of airborne diseases. Setting standards for ventilation and air conditioning systems in public buses is crucial to ensure thermal comfort, protect bus commuters from the harmful impacts of air pollution, and to improve public transport services in KV.

Lira GVDAG, Silva GAPD, Bezerra PGDM et al.

Georgia Véras de Araújo Gueiros Lira,1,2 Giselia Alves Pontes da Silva,2 Patricia Gomes de Matos Bezerra,3 Emanuel SC Sarinho1,2 1Allergy and Immunology Research Centre, Federal University of Pernambuco, Recife, PE, Brazil; 2Department of Paediatrics, Federal University of Pernambuco, Recife, PE, Brazil; 3Department of Pediatric Pulmonology, Instituto de Medicina Integral Prof. Fernando Figueira, Recife, PE, BrazilCorrespondence: Georgia Véras de Araújo Gueiros Lira, Allergy and Immunology Research Centre, Federal University of Pernambuco, Av. Prof. Morais Rego, 1235 – University City, Recife, PE, CEP: 50670-901, Brazil, Tel +81 2126-8000 ; +81 3268-9336, Email georgiaveras@uol.com.brAbstract: Much is known about the role of aeroallergens in asthma, but little is described about the damage caused by inhaled pollutants and irritants to the respiratory epithelium. In this context, the most frequent pollutants and irritants inhaled in the home environment were identified, describing the possible repercussions that may occur in the respiratory tract of the pediatric population with asthma and highlighting the role of the caregiver in environmental control through a salutogenic perspective. Searches were carried out in the MEDLINE/PubMed, Web of Science, Lilacs and Scopus databases for articles considered relevant for the theoretical foundation of this integrative review, in which interactions between exposure to pollutants and inhaled irritants and lung involvement. Articles published in the last 10 years that used the following descriptors were considered: air pollution; tobacco; particulate matter; disinfectants; hydrocarbons, fluorinated; odorants; chloramines; pesticide; asthma; and beyond Antonovsky’s sense of coherence. Exposure to smoke and some substances found in cleaning products, such as benzalkonium chloride, ethylenediaminetetraacetic acid and monoethanolamine, offer potential risks for sensitization and exacerbation of asthma. The vast majority of the seven main inhaled products investigated provoke irritative inflammatory reactions and oxidative imbalance in the respiratory epithelium. In turn, the caregiver’s role is essential in health promotion and the clinical control of paediatric asthma. From a salutogenic point of view, pollutants and irritants inhaled at home should be carefully investigated in the clinical history so that strategies to remove or reduce exposures can be used by caregivers of children and adolescents with asthma.Keywords: air pollution, tobacco, particulate matter, disinfectants, asthma, sense of coherence

Mihai-Ovidiu Degeratu, René Schönenberger, Nikolai Huwa et al.

Aicha Khemili, Djamel Bensizerara, Haroun Chenchouni et al.

Infectious diseases, including vector-borne and antibiotic-resistant infections, present significant global health challenges, necessitating the exploration of natural alternatives for disease control. In this study, we investigated the essential oil (EO) profile as well as larvicidal and antibacterial properties of two wild Apiaceae species used in Algeria: <i>Daucus carota</i> L. (DCEO) and <i>Foeniculum vulgare</i> Mill. (FVEO). EO was extracted from the aerial parts by steam distillation and analyzed using Gas Chromatography Mass Spectrometry (GC/MS). Major constituents identified in DCEO were geranyl acetate (50.07%) and elemicin (10.77%), while FVEO contained estragole (24.93%), fenchone (20.20%), and α-phellandrene (17.96%). Both EOs were highly effective towards <i>Culex pipiens</i> larvae, with low LC₅₀ values of 30.6 ± 1.06 ppm for DCEO and 34.7 ± 1.06 ppm for FVEO, indicating their potential as bioinsecticides due to their bioactivity and bioavailability. Additionally, the two Eos demonstrated moderate antibacterial efficacy against gram-positive bacteria, <i>Staphylococcus aureus</i> ATCC 25923 and <i>Staphylococcus aureus</i> MRSA ATCC 43300, respectively, with DCEO showing MIC values of 10 and 20 mg/mL, respectively, and FVEO exhibiting MIC values > 20 mg/mL. However, both EOs showed limited effectiveness against gram-negative bacteria, <i>Escherichia coli</i> ATCC 25922 and <i>Klebsiella pneumonia</i> ATCC 700603. These results highlight the potential applications of DCEO and FVEO as natural bioinsecticides and antibacterial agents, offering promising avenues for further research and development in pest control and food preservation.

Elenna Capote, Louis Dartez, Derek Davis

The next generation of ground-based gravitational-wave interferometers is expected to generate a bounty of new astrophysical discoveries, with sensitivities and bandwidths greatly improved compared to current-generation detectors. These detectors will allow us to make exceptional advancements in our understanding of fundamental physics, the dynamics of dense matter, and the cosmic history of compact objects. The fundamental design aspects of these planned interferometers will enable these new discoveries; however, challenges in technical noise, data quality, and calibration have the potential to limit the scientific reach of these instruments. In this work, we evaluate the requirements of these elements for next-generation gravitational-wave science, focusing on how these areas may impact the proposed Cosmic Explorer observatory. We highlight multiple aspects of these fields where additional research and development is required to ensure Cosmic Explorer reaches its full potential.

Peter Fettke, Wolfgang Reisig

Process science is a highly interdisciplinary field of research. Despite numerous proposals, process science lacks an adequate understanding of the core concepts of the field, including notions such as process, event, and system. A more systematic framework to cope with process science is mandatory. We suggest such a framework using an example. The framework itself addresses three aspects: architecture, statics, and dynamics. Corresponding formal concepts, based on established scientific theories, together provide an integrated framework for understanding processes in the world. We argue that our foundations have positive implications not only for theoretical research, but also for empirical research, e.g., because hypothesized relationships can be explicitly tested. It is now time to start a discussion about the foundations of our field.

Edafetanure-Ibeh Faith, Evah Patrick Tamarauefiye, Mark Uwuoruya Uyi

The aim of attending an educational institution is learning, which in turn is sought after for the reason of independence of thoughts, ideologies as well as physical and material independence. This physical and material independence is gotten from working in the industry, that is, being a part of the independent working population of the country. There needs to be a way by which students upon graduation can easily adapt to the real world with necessary skills and knowledge required. This problem has been a challenge in some computer science departments, which after effects known after the student begins to work in an industry. The objectives of this project include: Designing a web based chat application for the industry and computer science department, Develop a web based chat application for the industry and computer science and Evaluate the web based chat application for the industry and computer science department. Waterfall system development lifecycle is used in establishing a system project plan, because it gives an overall list of processes and sub-processes required in developing a system. The descriptive research method applied in this project is documentary analysis of previous articles. The result of the project is the design, software a web-based chat application that aids communication between the industry and the computer science department and the evaluation of the system. The application is able to store this information which can be decided to be used later. Awareness of the software to companies and universities, implementation of the suggestions made by the industry in the computer science curriculum, use of this software in universities across Nigeria and use of this not just in the computer science field but in other field of study

Fahu Chen, Jie Chen, Wei Huang

E. Salganik, E. Salganik, B. A. Lange et al.

<p>Sea-ice ridges constitute a large fraction of the total Arctic sea-ice area (up to 40 %–50 %); nevertheless, they are the least studied part of the ice pack. Here we investigate sea-ice melt rates using rare, repeated underwater multibeam sonar surveys that cover a period of 1 month during the advanced stage of sea-ice melt. Bottom melt increases with ice draft for first- and second-year level ice and a first-year ice ridge, with an average of 0.46, 0.55, and 0.95 m of total snow and ice melt in the observation period, respectively. On average, the studied ridge had a 4.6 m keel bottom draft, was 42 m wide, and had 4 % macroporosity. While bottom melt rates of ridge keel were 3.8 times higher than first-year level ice, surface melt rates were almost identical but responsible for 40 % of ridge draft decrease. Average cross-sectional keel melt ranged from 0.2 to 2.6 m, with a maximum point ice loss of 6 m, showcasing its large spatial variability. We attribute 57 % of the ridge total (surface and bottom) melt variability to keel draft (36 %), slope (32 %), and width (27 %), with higher melt for ridges with a larger draft, a steeper slope, and a smaller width. The melt rate of the ridge keel flanks was proportional to the draft, with increased keel melt within 10 m of its bottom corners and the melt rates between these corners comparable to the melt rates of level ice.</p>

Xinrui He, Muhammad Arif, Muhammad Arif et al.

The spatial distribution pattern of species diversity and its driving variables are essential to understanding how biodiversity will respond to the threat of future climatic instability. This study examines how the alpha and beta diversity indices vary along the elevation gradient and which factors are more responsible for arid and semi-arid plant diversity and community assembly. There were 121 species from 90 genera and 42 families found at elevations ranging from 1,200 to 2,600 m within the Ningxia Helan Mountain National Nature Reserve in China. These were primarily shrubs and herbs with characteristics adapted to extreme temperatures. The diversity of both the shrub layer and the herb layer was highest at mid-elevation, underscoring the importance of protecting mid-elevation ecosystems. Climatic distance and topographic distance were more influential than spatial distance in driving the species composition change of shrubs and herbs. In the herb layer, climatic, topographic, and spatial factors contributed more to community assembly than shrub communities. In this case, improving the environment in the community or introducing herbs could help the restoration of these places. These findings provide insight into biogeographic patterns, biodiversity growth mechanisms, and community formation processes. Moreover, it can improve projections of climate change within and across ecosystems and provide the scientific basis for the use and management of plant resources in arid and semi-arid regions.