Inayat Moazzma, Abbas Muhammad, Yousaf Muhammad Zain
et al.
This study investigates the optical soliton solutions to the generalized third-order nonlinear Schrödinger equation involving the Caputo fractional derivative using new mapping method. The fractional generalized third-order nonlinear Schrödinger equation is frequently utilized in various fields, including mathematical physics, nonlinear optical phenomena, optical communication technologies and plasma physics. The obtained solutions have different solitons including bell shape, w-shape, anti bell, kink, dark and periodic wave solution. Bifurcation analysis is performed to further explore the behaviour of the system. For this analysis planar dynamical system is obtained by using Galilean transformation. This analysis offers valuable insights into the phase portraits, time series, chaotic behaviour and the sensitivity of the model to the external perturbations. The sensitivity and dynamics of optical solitons are thoroughly investigated that offers significant insights into their behavior within fractional models.
Using a recognition model of atmospheric gravity waves (AGWs), we identified 519 AGW events from the OH airglow images observed at the Dandong and Lhasa stations from 2015 to 2017. The 317 AGW events detected at the Dandong station have wavelengths ranging from 30 to 60 km, periods from 14 to 20 min, horizontal speeds from 30 to 60 m/s, and relative intensities from 0.4% to 0.6%, respectively. The parameters of 202 events recorded at the Lhasa station mainly vary within 15–35 km in horizontal wavelength, 4–6 min in period, 40–100 m/s in horizontal velocity, and 0.1%–0.3% in relative intensity. The occurrence rate peaks in winter and summer at Dandong and the peak in summer are absent at Lhasa because of the lack of convective weather. The seasonal propagation directions of the waves are influenced by both the wind field-filtering effect and the distribution of wave sources. In spring, because of the southeastward background wind field, fewer southeastward events are observed at the Dandong station. The situation at the Lhasa station is similar. In summer, both the Lhasa and Dandong stations are dominated by northeastward AGWs, which can be attributed to the southwestward wind. In autumn, ray-tracing results show that the events at Dandong mainly originate from wind shear, whereas the events at the Lhasa station are triggered by convective weather. The location of the wave sources determines the trend of the propagation directions at the Dandong and Lhasa stations in autumn. In winter, because of the eastward wind, more events are propagating to the southwest at the Dandong station.
Abstract Background The advancement of nanotechnology underscores the imperative need for establishing in silico predictive models to assess safety, particularly in the context of chronic respiratory afflictions such as lung fibrosis, a pathogenic transformation that is irreversible. While the compilation of predictive descriptors is pivotal for in silico model development, key features specifically tailored for predicting lung fibrosis remain elusive. This study aimed to uncover the essential predictive descriptors governing nanoparticle-induced pulmonary fibrosis. Methods We conducted a comprehensive analysis of the trajectory of metal oxide nanoparticles (MeONPs) within pulmonary systems. Two biological media (simulated lung fluid and phagolysosomal simulated fluid) and two cell lines (macrophages and epithelial cells) were meticulously chosen to scrutinize MeONP behaviors. Their interactions with MeONPs, also referred to as nano-bio interactions, can lead to alterations in the properties of the MeONPs as well as specific cellular responses. Physicochemical properties of MeONPs were assessed in biological media. The impact of MeONPs on cell membranes, lysosomes, mitochondria, and cytoplasmic components was evaluated using fluorescent probes, colorimetric enzyme substrates, and ELISA. The fibrogenic potential of MeONPs in mouse lungs was assessed by examining collagen deposition and growth factor release. Random forest classification was employed for analyzing in chemico, in vitro and in vivo data to identify predictive descriptors. Results The nano-bio interactions induced diverse changes in the 4 characteristics of MeONPs and had variable effects on the 14 cellular functions, which were quantitatively evaluated in chemico and in vitro. Among these 18 quantitative features, seven features were found to play key roles in predicting the pro-fibrogenic potential of MeONPs. Notably, IL-1β was identified as the most important feature, contributing 27.8% to the model’s prediction. Mitochondrial activity (specifically NADH levels) in macrophages followed closely with a contribution of 17.6%. The remaining five key features include TGF-β1 release and NADH levels in epithelial cells, dissolution in lysosomal simulated fluids, zeta potential, and the hydrodynamic size of MeONPs. Conclusions The pro-fibrogenic potential of MeONPs can be predicted by combination of key features at nano-bio interfaces, simulating their behavior and interactions within the lung environment. Among the 18 quantitative features, a combination of seven in chemico and in vitro descriptors could be leveraged to predict lung fibrosis in animals. Our findings offer crucial insights for developing in silico predictive models for nano-induced pulmonary fibrosis.
Abstract Background SrMnO3 demonstrates high efficiency in degrading chlorinated volatile organic compounds (CVOCs). However, the accumulation of chlorine species and the loss of active sites limit the further enhancement of its catalytic performance. Purpose To improve the catalytic and chlorine poisoning resistance properties of SrMnO3-based catalyst. Methods A modified hydrothermal method was employed to synthesize a multi-metal-oxides catalyst based on SrMnO3 with Ce introduced to lattice to increase surface defect density. Influences of catalyst dosage, relative humidity, pollutant concentration and airspeed on chlorobenzene (CB) removal efficiency were systematically investigated. Results The results revealed the great removal efficiency of the multi-metal-oxides catalyst based on SrMnO3 with T90 of 247 ℃, T95 of 269 ℃, and the mineralization rate of 71%. The catalytic mechanism on the catalyst was explored through comprehensive characterizations and the potential degradation pathways of CB were inferred. Conclusion This work provides new insights into the design of metal-doped perovskite catalysts, highlighting the critical role of surface defects and oxygen vacancies in catalytic performance. Graphical Abstract
Water resources from the Indus Basin sustain over 270 million people. However, water security in this region is threatened by climate change. This is especially the case for the upper Indus Basin, where most frozen water reserves are expected to decrease significantly by the end of the century, leaving rainfall as the main driver of river flow. However, future precipitation estimates from global climate models differ greatly for this region. To address this uncertainty, this paper explores the feasibility of using probabilistic machine learning to map large-scale circulation fields, better represented by global climate models, to local precipitation over the upper Indus Basin. More specifically, Gaussian processes are trained to predict monthly ERA5 precipitation data over a 15-year horizon. This paper also explores different Gaussian process model designs, including a non-stationary covariance function to learn complex spatial relationships in the data. Going forward, this approach could be used to make more accurate predictions from global climate model outputs and better assess the probability of future precipitation extremes.
Carme Carrion, Camilla Alay Llamas, Eka Dian Safitri
et al.
Abstract Background Planetary Health studies the impact of the global environmental crisis on health. Urgent transdisciplinary, intersectoral, and holistic solutions adapted to local realities are needed. Designing training programs attuned to contextual needs of diverse groups and geographical areas is crucial. Planetary health programs are emerging worldwide, but little is known about their scope and learning outcomes. A systematic scoping review is needed to shed light on the state of planetary health education. Objectives This review aims to identify existing frameworks, competencies, content, and teaching methods in planetary health education. Methods Following PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines, we included studies targeting undergraduate and postgraduate students, focusing on skills, knowledge, and abilities related to planetary health, published in English or Spanish. No exclusions were made based on geographic area, study design, or publication period. Databases consulted were MEDLINE via PubMed, Scopus, Web of Science, and ProQuest. Selection and data extraction processes were conducted systematically. Results We included 73 articles, with 88% from high-income countries and 49% focused on health professionals. Conceptual frameworks identified include "One Health," "Sustainable Development Goals," and the "Planetary Health Education Framework." Transversal skills (complex problem-solving, systemic thinking, collaboration, interdisciplinary) and specific competencies (understanding health interactions with climate change, pollution) were outlined in 45% of studies. Half of the studies described 23 general topics and 93 specific content areas. Teaching methods included in-person (59%), virtual (12%), and hybrid models (29%). Conclusions This review highlights the heterogeneity in conceptual frameworks, competencies, content, and teaching methods in planetary health education for health professionals. Future research should focus on developing and evaluating evidence-based educational models to address the evolving challenges of planetary health. Recommendations include enhancing collaboration among stakeholders and integrating innovative teaching methods to improve planetary health education. Trial registration The protocol has been registered in the Open Science Framework database (registration number: osf.io/h2b3j, March 2024). Clinical trial number: not applicable.
Large language models (LLMs) are rapidly being integrated into computational social science research, yet their blackboxed training and designed stochastic elements in inference pose unique challenges for scientific inquiry. This article argues that applying LLMs to social scientific tasks requires explicit assessment of uncertainty-an expectation long established in both quantitative methodology in the social sciences and machine learning. We introduce a unified framework for evaluating LLM uncertainty along two dimensions: the task type (T), which distinguishes between classification, short-form, and long-form generation, and the validation type (V), which captures the availability of reference data or evaluative criteria. Drawing from both computer science and social science literature, we map existing uncertainty quantification (UQ) methods to this T-V typology and offer practical recommendations for researchers. Our framework provides both a methodological safeguard and a practical guide for integrating LLMs into rigorous social science research.
Information value, a measure for decision sensitivity, can provide essential information in engineering and environmental assessments. It quantifies the potential for improved decision-making when reducing uncertainty in specific inputs. By contrast to other sensitivity measures, it admits not only a relative ranking of input factors but also an absolute interpretation through statements like ''Eliminating the uncertainty in factor $A$ has an expected value of $5000$ Euro''. In this paper, we present a comprehensive overview of the information value by presenting the theory and methods in view of their application to engineering and environmental assessments. We show how one should differentiate between aleatory and epistemic uncertainty in the analysis. Furthermore, we introduce the evaluation of the information value in applications where the decision is described by a continuous parameter. The paper concludes with two real-life applications of the information value to highlight its power in supporting decision-making in engineering and environmental applications.
Since the Industrial Revolution, the world economy has experienced rapid development, and China's economy has also achieved an unprecedented takeoff in the past. Behind the economic growth, population surge, and continuous improvement of people's living standards lies the enormous consumption of fossil energy and environmental pollution. This kind of pollution has caused irreparable damage to the world. The most concerned environmental issue globally at present is the global warming caused by carbon dioxide emissions. China is in a stage of rapid development, and as the largest developing country, China's development path has a significant impact on global climate change. At the same time, the global community also puts pressure on China to limit carbon dioxide emissions. To address energy shortages and environmental issues, countries around the world have introduced corresponding energy and environmental regulations. Due to different culture and government systems, the effects of energy and environmental regulations in various countries are also different. Therefore, it is still necessary to discuss China's energy and environmental regulations.This paper uses data from prefecture-level cities between 2003 and 2008 to discuss the impact of the "Eleventh Five-Year Plan" environmental regulations on urbanization rates. It first provides a theoretical analysis of the relationship between environmental regulation and urbanization, finding that environmental regulation can influence urban population mobility through both crowding-in and crowding-out effects.
Priscilla Muheki, Mirjana Pović, Somaya Saad
et al.
In preparation for the International Astronomical Union (IAU) General Assembly (GA) 2024, the first GA held in Africa, the African Network of Women in Astronomy (AfNWA) embarked on a visionary project: the creation of an inspiring storytelling book that showcases the remarkable journeys of professional female astronomers in Africa. This book is not merely a collection of biographies; it is a tapestry of resilience, passion, and scientific excellence woven through the lives of women who have ventured into the cosmos from the African continent. The primary aim of this book is twofold. Firstly, it seeks to bring greater visibility to women astronomers in Africa, highlighting their groundbreaking research and the personal stories that have shaped their careers. By shining a light on their achievements and awards, we hope to acknowledge their contributions to the field of astronomy and underscore the importance of diversity in science. Secondly, this book aspires to inspire and empower the next generation of scientists, particularly young women and girls across Africa. Through the personal narratives and professional achievements of these trailblazing astronomers and students in astronomy, we aim to spark curiosity, foster a love for science, and demonstrate that the sky is not the limit but just the beginning for those who dare to dream. As you delve into the stories within these pages, you will encounter a rich array of experiences and insights that reflect the unique challenges and triumphs women face in astronomy. From overcoming societal barriers to making groundbreaking discoveries, these women have carved paths that others can follow, proving that with determination and passion, the stars are within reach for everyone.
Living environments play a vital role in the prevalence and progression of diseases, and understanding their impact on patient's health status becomes increasingly crucial for developing AI models. However, due to the lack of long-term and fine-grained spatial and temporal data in public and population health studies, most existing studies fail to incorporate environmental data, limiting the models' performance and real-world application. To address this shortage, we developed SatHealth, a novel dataset combining multimodal spatiotemporal data, including environmental data, satellite images, all-disease prevalences estimated from medical claims, and social determinants of health (SDoH) indicators. We conducted experiments under two use cases with SatHealth: regional public health modeling and personal disease risk prediction. Experimental results show that living environmental information can significantly improve AI models' performance and temporal-spatial generalizability on various tasks. Finally, we deploy a web-based application to provide an exploration tool for SatHealth and one-click access to both our data and regional environmental embedding to facilitate plug-and-play utilization. SatHealth is now published with data in Ohio, and we will keep updating SatHealth to cover the other parts of the US. With the web application and published code pipeline, our work provides valuable angles and resources to include environmental data in healthcare research and establishes a foundational framework for future research in environmental health informatics.
Vietnam is a coastal country with a coastline stretching more than 3,260 km. Marine resources are important for the development of Vietnam. In Vietnamese seas, there are about 20 typical ecosystems spreading over 1 million square kilometers in the East Sea consisting of mangrove forests, coral reefs, lagoons, seagrasses in intertidal areas and estuaries, and living species in 155,000 hectares, 1,300 square kilometers, 500 square kilometers, 16,000 hectares, and 11,000 living species, respectively. At present, the impact of climate change, socio-economic development, and environmental pollution are considered as the main causes of degradation of Vietnam’s marine ecosystems. This paper presents and discusses the pressure of socio-economic activities including industry, tourism, marine transportation and services, aquaculture and fishery on marine ecosystems. In Vietnam, compared to the early 2000s a total of 12% of coral reefs, and 48% of other coral reefs are vulnerable to degradation. So far, about 100 species of marine life in Vietnam are at risk of being threatened due to over-exploitation and fishing. The seagrass-bed ecosystem is currently being degraded with only over 5,580 ha remaining. In some areas, such as Cat Ba, Ha Long, and Quang Nam, seagrass beds have almost no chance to recover naturally due to serious impacts from tourism and aquaculture activities. From the findings, orientations that aim at effective management and protection of marine ecosystems to cope with adverse impacts of anthropogenic activities, climate change, and the pressure of socioeconomic development were proposed.
The compute requirements associated with training Artificial Intelligence (AI) models have increased exponentially over time. Optimisation strategies aim to reduce the energy consumption and environmental impacts associated with AI, possibly shifting impacts from the use phase to the manufacturing phase in the life-cycle of hardware. This paper investigates the evolution of individual graphics cards production impacts and of the environmental impacts associated with training Machine Learning (ML) models over time. We collect information on graphics cards used to train ML models and released between 2013 and 2023. We assess the environmental impacts associated with the production of each card to visualize the trends on the same period. Then, using information on notable AI systems from the Epoch AI dataset we assess the environmental impacts associated with training each system. The environmental impacts of graphics cards production have increased continuously. The energy consumption and environmental impacts associated with training models have increased exponentially, even when considering reduction strategies such as location shifting to places with less carbon intensive electricity mixes. These results suggest that current impact reduction strategies cannot curb the growth in the environmental impacts of AI. This is consistent with rebound effect, where the efficiency increases fuel the creation of even larger models thereby cancelling the potential impact reduction. Furthermore, these results highlight the importance of considering the impacts of hardware over the entire life-cycle rather than the sole usage phase in order to avoid impact shifting. The environmental impact of AI cannot be reduced without reducing AI activities as well as increasing efficiency.
Gaurav Shinde, Tiana Kirstein, Souvick Ghosh
et al.
The rapid expansion of records creates significant challenges in management, including retention and disposition, appraisal, and organization. Our study underscores the benefits of integrating artificial intelligence (AI) within the broad realm of archival science. In this work, we start by performing a thorough analysis to understand the current use of AI in this area and identify the techniques employed to address challenges. Subsequently, we document the results of our review according to specific criteria. Our findings highlight key AI driven strategies that promise to streamline record-keeping processes and enhance data retrieval efficiency. We also demonstrate our review process to ensure transparency regarding our methodology. Furthermore, this review not only outlines the current state of AI in archival science and records management but also lays the groundwork for integrating new techniques to transform archival practices. Our research emphasizes the necessity for enhanced collaboration between the disciplines of artificial intelligence and archival science.