Hasil untuk "Environmental Science"

J. Korhonen, A. Honkasalo, J. Seppälä

T. Kelley, J. Knowles

The global urgency to improve STEM education may be driven by environmental and social impacts of the twenty-first century which in turn jeopardizes global security and economic stability. The complexity of these global factors reach beyond just helping students achieve high scores in math and science assessments. Friedman (The world is flat: A brief history of the twenty-first century, 2005) helped illustrate the complexity of a global society, and educators must help students prepare for this global shift. In response to these challenges, the USA experienced massive STEM educational reforms in the last two decades. In practice, STEM educators lack cohesive understanding of STEM education. Therefore, they could benefit from a STEM education conceptual framework. The process of integrating science, technology, engineering, and mathematics in authentic contexts can be as complex as the global challenges that demand a new generation of STEM experts. Educational researchers indicate that teachers struggle to make connections across the STEM disciplines. Consequently, students are often disinterested in science and math when they learn in an isolated and disjoined manner missing connections to crosscutting concepts and real-world applications. The following paper will operationalize STEM education key concepts and blend learning theories to build an integrated STEM education framework to assist in further researching integrated STEM education.

Emma L. Schymanski, Junho Jeon, Rebekka Gulde et al.

J. Benayas, A. Newton, A. Diaz et al.

B. B. Jensen, K. Schnack

E. Dockner, S. Jørgensen, N. Long et al.

V. Plumwood

Haixin Chang, Hongkai Wu

Ziruo Hao, Tao Yang, Xiaofeng Wu et al.

The extraction of invariant causal relationships from time series data with environmental attributes is critical for robust decision-making in domains such as climate science and environmental monitoring. However, existing methods either emphasize dynamic causal analysis without leveraging environmental contexts or focus on static invariant causal inference, leaving a gap in distributed temporal settings. In this paper, we propose Distributed Dynamic Invariant Causal Prediction in Time-series (DisDy-ICPT), a novel framework that learns dynamic causal relationships over time while mitigating spatial confounding variables without requiring data communication. We theoretically prove that DisDy-ICPT recovers stable causal predictors within a bounded number of communication rounds under standard sampling assumptions. Empirical evaluations on synthetic benchmarks and environment-segmented real-world datasets show that DisDy-ICPT achieves superior predictive stability and accuracy compared to baseline methods A and B. Our approach offers promising applications in carbon monitoring and weather forecasting. Future work will extend DisDy-ICPT to online learning scenarios.

Wahid Akhsin Budi Nur Sidiq, Tjaturahono Budi Sanjoto, Nasir Nayan et al.

The coastal areas of Semarang City have experienced land conversion due to development activities that threaten mangrove sustainability in recent years. The urgency of this research is the need to monitor mangrove density levels to be used as input in its management. The purpose of this study is to analyze changes in mangrove density levels and the occurrence of abrasion in time series using the Google Earth Engine cloud computing model. The research method used visual interpretation and spectral transformation of NDVI and MNDWI to identify spatial distribution, mangrove density and abrasion. The results showed that there was a significant decrease in mangrove area in 2019-2023 with an area of 111.74 hectares. Furthermore, the level of mangrove density is quite dynamic, especially for high density with a decrease in area from 2019 - 2023 with an area of 260.25 hectares, besides that the decline in high density mangroves also occurred in 2015 - 2023 with a decrease in area of 38.73 hectares. Abrasion in the research location was identified in 2 coastal villages, namely Mangunharjo Village with abrasion along 0.88 km (2015 - 2023) and Tugurejo Village with abrasion along 1.04 km, where both areas also experienced a decrease in mangrove area at a high-density level. In conlusion, there has been a decrease in the area and density of mangroves in the study site, one of which has an impact on abrasion.

Yao Li, Qiu Chen, Tingting Wang et al.

Abstract As the prevalence of metabolic diseases such as diabetes and obesity continue to rise, the search for more effective and convenient treatments has become a crucial issue in medical research. Microneedles (MNs), as an innovative drug delivery system, have shown advantages in the treatment of metabolic diseases in recent years. MNs-based drug delivery system, which use MNs to deliver drugs directly to the subcutaneous tissue, improve drug bioavailability and reduce systemic side effects. This review aims to summarize the latest concepts, designs, and types of MNs, and to investigate the materials and manufacturing methods used in their construction. Subsequently, the mechanisms of drug delivery and graded release of MNs and recent research progress are further summarized. This article focuses on the application of MNs in the treatment of common metabolic diseases, with a special emphasis on the progress and optimization of diabetic and anti-obesity MNs. The main challenges and future perspectives in the production and evaluation of MNs, as well as in enhancing treatment efficacy and improving safety, are elucidated.

Hao Li, Yongkang Qi, Minru Su et al.

Electrodes with high activation efficiency and stability are critical for the electrochemical activation of persulfate. In this study, the effective degradation of acid orange 74 (AO 74) was achieved using the electrochemically activated peroxydisulfate (PDS) with a Ag nanoparticle–modified carbon paper (AgNPs@CP) electrode, demonstrating a 2.5-fold enhancement in the AO 74 degradation rate compared with the CP electrode. The impact of reaction conditions, including AgNPs dosage, PDS content, current density, initial solution pH, and agitation rate, as well as water matrices, such as Cl−, CO32−, and SO42−, on AO 74 degradation was systematically investigated to establish optimal parameters. Radical quenching experiments and electron paramagnetic resonance analysis identified sulfate radical and hydroxyl radical as the dominant reactive species. Gas chromatography–mass spectrometry analysis revealed that AO 74 was primarily transformed into aliphatic organic compounds during electrochemical degradation. Remarkably, the 3-h AO 74 degradation efficiency remained stable over five consecutive cycles through alternating use of the AgNPs@CP anode and CP cathode, facilitated by Ag0/Ag+/Ag2+ redox cycling that enabled Ag recovery and minimized Ag leaching. The electrochemically activated PDS with the AgNPs@CP electrode shows promise as a pretreatment technology for dyeing wastewater with low biodegradability.

Lei Liu, Yuchao Lu, Ling An et al.

As human activities intensify, environmental systems such as aquatic ecosystems and water treatment systems face increasingly complex pressures, impacting ecological balance, public health, and sustainable development, making intelligent anomaly monitoring essential. However, traditional monitoring methods suffer from delayed responses, insufficient data processing capabilities, and weak generalisation, making them unsuitable for complex environmental monitoring needs.In recent years, machine learning has been widely applied to anomaly detection, but the multi-dimensional features and spatiotemporal dynamics of environmental ecological data, especially the long-term dependencies and strong variability in the time dimension, limit the effectiveness of traditional methods.Deep learning, with its ability to automatically learn features, captures complex nonlinear relationships, improving detection performance. However, its application in environmental monitoring is still in its early stages and requires further exploration.This paper introduces a new deep learning method, Time-EAPCR (Time-Embedding-Attention-Permutated CNN-Residual), and applies it to environmental science. The method uncovers feature correlations, captures temporal evolution patterns, and enables precise anomaly detection in environmental systems.We validated Time-EAPCR's high accuracy and robustness across four publicly available environmental datasets. Experimental results show that the method efficiently handles multi-source data, improves detection accuracy, and excels across various scenarios with strong adaptability and generalisation. Additionally, a real-world river monitoring dataset confirmed the feasibility of its deployment, providing reliable technical support for environmental monitoring.

Yuichiro Kitajima

Bell's inequality is derived from three assumptions: measurement independence, outcome independence, and parameter independence. Among these, measurement independence, often taken for granted, holds that hidden variables are statistically uncorrelated with measurement settings. Under this assumption, the violation of Bell's inequality implies that either outcome independence or parameter independence fails to hold, meaning that local hidden variables do not exist. In this paper, we refer to this interpretive stance as the nonfactorizable position. In contrast, superdeterminism represents the view that measurement independence does not hold. Despite its foundational role, this assumption has received relatively little philosophical scrutiny. This paper offers a philosophical reassessment of measurement independence through three major frameworks in the philosophy of science: de Regt's contextual theory of scientific understanding, Kuhn's criteria for theory choice, and Lakatos's methodology of scientific research programmes. Using these lenses, we evaluate the two major responses to the violation of Bell's inequality, the nonfactorizable position and superdeterminism, and argue that the nonfactorizable position currently fares better across all three criteria. Beyond this binary, we introduce a spectrum of intermediate positions that allow for partial violations of measurement independence, modeled via mutual information. These positions modify the ``positive heuristic'' of superdeterminism, a crucial component in Lakatos's definition of research programmes, offering avenues for progressive research. This analysis reframes the debate surrounding Bell's inequality and illustrates how methodological tools can effectively guide theory evaluation in physics.

Xiaoli Ren, Tao Feng, Juan Lei et al.

The activated carbon was prepared with the Cephalosporin Mycelia Residue (CMR) obtained from Wichita Pharmaceutical Co., Ltd., and the adsorption kinetics and thermodynamics of Cefoperazone Sodium and Sulbactam Sodium (CS & SS) onto the activated carbon derived from the CMR were investigated. The results of activated carbon characterization showed that the iodine value of the activated carbon derived from the CMR was 1044 mg·g−1, the SBET was 596 m2·g−1, electron microscope scanning showed that the surface of activated carbon was porous and heterogeneous. Four kinds of kinetic models and four kinds of thermodynamic models were used to fit the experimental data of adsorption kinetics and thermodynamics, respectively. In addition, the ΔG0, ΔH0 and ΔS0 was calculated and analysised. The results showed that pseudo-second order model fitted the best to the adsorption kinetic data, indicating that the adsorption rate was proportional to the square of the adsorption site. Redlich-Peterson and Langmuir isotherm model fitted the best to the adsorption thermodynamic data, indicating that CS & SS was mainly adsorbed in single molecular layer on the surface of activated carbon. ΔH0> 0 indicated that the adsorption was endothermic, high temperature was conductive to the adsorption of CS & SS.

Xiaoliang Shi, Jiayin Xin, Aruna Aria et al.

This systematic study on the international research trends in carbon neutrality underscores its critical role in combating global warming and advancing sustainable development. By leveraging the “Web of Science Core Collection” databases and employing CiteSpace software for visual analysis, we examined 2223 research papers to track the influence and trends of key countries, institutions, and authors. Our results reveal a significant increase in publication volume, indicating a robust development potential for carbon neutrality research. The study also identifies environmental science, environmental research, and energy and fuel science as central interdisciplinary hubs, highlighting the importance of cross-disciplinary collaboration. Notably, China leads in publication output but has room for improvement in citation impact, suggesting a need for enhanced research quality and international visibility. The study's findings are instrumental for guiding future research directions, policy-making, and interdisciplinary cooperation, particularly in the fields of environmental science and energy, to accelerate progress towards carbon neutrality and global climate governance.

Ruoxi Xu, Yingfei Sun, Mengjie Ren et al.

Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research, leading to various studies exploring the combination of these two fields. In this survey, we systematically categorize previous explorations in the combination of AI and social science into two directions that share common technical approaches but differ in their research objectives. The first direction is focused on AI for social science, where AI is utilized as a powerful tool to enhance various stages of social science research. While the second direction is the social science of AI, which examines AI agents as social entities with their human-like cognitive and linguistic capabilities. By conducting a thorough review, particularly on the substantial progress facilitated by recent advancements in large language models, this paper introduces a fresh perspective to reassess the relationship between AI and social science, provides a cohesive framework that allows researchers to understand the distinctions and connections between AI for social science and social science of AI, and also summarized state-of-art experiment simulation platforms to facilitate research in these two directions. We believe that as AI technology continues to advance and intelligent agents find increasing applications in our daily lives, the significance of the combination of AI and social science will become even more prominent.

Alexander Wieser, Johannes Lachner, Martin Martschini et al.

The detection of low abundances of $^{135}$Cs in environmental samples is of significant interest in different fields of environmental sciences, especially in combination with its shorter-lived sister isotope $^{137}$Cs. The method of Ion-Laser InterAction Mass Spectrometry (ILIAMS) for barium separation at the Vienna Environmental Research Accelerator (VERA) was investigated and further improved for low abundance cesium detection. The difluorides BaF$_2^-$ and CsF$_2^-$ differ in their electron detachment energies and make isobar suppression with ILIAMS by more than 7 orders of magnitude possible. By this method, samples with ratios down to the order of $^{135,137}$Cs/$^{133}$Cs $\approx 10^{-11}$ are measurable and the $^{135}$Cs/$^{137}$Cs ratios of first environmental samples were determined by AMS.

Pengfei Li, Yejia Liu, Jianyi Yang et al.

The sharply increasing sizes of artificial intelligence (AI) models come with significant energy consumption and environmental footprints, which can disproportionately impact certain (often marginalized) regions and hence create environmental inequity concerns. Moreover, concerns with social inequity have also emerged, as AI computing resources may not be equitably distributed across the globe and users from certain disadvantaged regions with severe resource constraints can consistently experience inferior model performance. Importantly, the inequity concerns that encompass both social and environmental dimensions still remain unexplored and have increasingly hindered responsible AI. In this paper, we leverage the spatial flexibility of AI inference workloads and propose equitable geographical load balancing (GLB) to fairly balance AI's regional social and environmental costs. Concretely, to penalize the disproportionately high social and environmental costs for equity, we introduce $L_q$ norms as novel regularization terms into the optimization objective for GLB decisions. Our empirical results based on real-world AI inference traces demonstrate that while the existing GLB algorithms result in disproportionately large social and environmental costs in certain regions, our proposed equitable GLB can fairly balance AI's negative social and environmental costs across all the regions.

Ramanakumar Sankar, Shawn Brueshaber, Lucy Fortson et al.

The Jovian atmosphere contains a wide diversity of vortices, which have a large range of sizes, colors and forms in different dynamical regimes. The formation processes for these vortices is poorly understood, and aside from a few known, long-lived ovals, such as the Great Red Spot, and Oval BA, vortex stability and their temporal evolution are currently largely unknown. In this study, we use JunoCam data and a citizen-science project on Zooniverse to derive a catalog of vortices, some with repeated observations, through May 2018 to Sep 2021, and analyze their associated properties, such as size, location and color. We find that different colored vortices (binned as white, red, brown and dark), follow vastly different distributions in terms of their sizes and where they are found on the planet. We employ a simplified stability criterion using these vortices as a proxy, to derive a minimum Rossby deformation length for the planet of $\sim1800$ km. We find that this value of $L_d$ is largely constant throughout the atmosphere, and does not have an appreciable meridional gradient.