This article postulates that social, emotional and environmental intelligences are interconnected as well as inextricably linked to the principle of relationships. It defines these and goes deeper into explaining the elements of Environmental Intelligence providing real life examples as well as provocative questions. It offers practical and positive examples of how the climate change Titanic may be turned around. Education is seen as the core component of change.
Koti Joy, Unnikrishnan Sureshkumar, Anand Narayanan
et al.
Using data from the Galaxy and Mass Assembly (GAMA) survey, we investigate how galaxy properties correlate with the local environment, focusing on the two southern regions of the survey (G02 and G23) that have not previously been examined in this context. We employ two-point and marked correlation functions to quantify the environmental dependence of galaxy color, stellar mass, luminosity across the $u$, $g$, $r$, $J$, and $K$ bands, as well as star formation rate (SFR) and specific star formation rate (sSFR). We also assess the impact of redshift incompleteness and cosmic variance on these clustering measurements. Our results show that $u-r$ and $g-r$ colors are most strongly correlated with local overdensity, followed by stellar mass. The sSFR exhibits a clear inverse relationship with density of the environment, consistent with the trend observed for $u$-band luminosity, which traces young stellar populations. In contrast, galaxies brighter in the $g$, $J$, and $K$ bands preferentially inhabit denser regions. By comparing our measurements from the southern regions with those from the equatorial regions of GAMA, we find that cosmic variance does not significantly influence our conclusions. However, redshift incompleteness affects the clustering measurements, as revealed through comparisons of subsets within the G02 region. The measured correlations provide key constraints for models of galaxy assembly across mass and environment, while the environmental trends in color and near-infrared luminosity offer a means to trace stellar mass growth and quenching with redshift.
This study examines the economic social and environmental impacts of electric vehicle adoption in Bangladesh using survey data from 57 respondents and secondary research. Findings show strong public perception of electric vehicles as cost effective with ninety three percent agreement and environmentally beneficial with eighty two point five percent agreement. Electric vehicles have potential to reduce fuel imports lower operational costs and create over fifty thousand jobs by 2030. Socially electric vehicles improve mobility for low income groups with seventy five point four percent agreement and increase safety although adoption remains mostly in urban areas. Environmentally electric vehicles could reduce carbon dioxide emissions by up to thirty percent per kilometre and lower particulate matter levels by twenty to twenty five percent in Dhaka along with a three to five decibel reduction in traffic noise. Main barriers include high purchase costs limited charging infrastructure and low public awareness of policies. Policy recommendations include tax incentives solar powered charging stations and battery recycling regulations. This research concludes that strategic electric vehicle adoption could advance Bangladesh's sustainable transportation economic resilience and public health goals.
All aspects of our society, including the life sciences, need a mechanism for people working within them to represent the concepts they employ to carry out their research. For the information systems being designed and developed to support researchers and scientists in conducting their work, conceptual models of the relevant domains are usually designed as both blueprints for a system being developed and as a means of communication between the designer and developer. Most conceptual modelling concepts are generic in the sense that they are applied with the same understanding across many applications. Problems in the life sciences, however, are especially complex and important, because they deal with humans, their well-being, and their interactions with the environment as well as other organisms. This work proposes a systemist perspective for creating a conceptual model of a life scientist's problem. We introduce the notion of a system and then show how it can be applied to the development of an information system for handling genomic-related information. We extend our discussion to show how the proposed systemist perspective can support the modelling of precision medicine. This research recognizes challenges in life sciences research of how to model problems to better represent the connections between physical and digital worlds. We propose a new notation that explicitly incorporates systemist thinking, as well as the components of systems based on recent ontological foundations. The new notation captures important semantics in the domain of life sciences. It may be used to facilitate understanding, communication and problem-solving more broadly. We also provide a precise, sound, ontologically supported characterization of the term system, as a basic construct for conceptual modelling in life sciences.
Continuum robots have been widely adopted in robot-assisted minimally invasive surgery (RMIS) because of their compact size and high flexibility. However, their proprioceptive capabilities remain limited, particularly in narrow lumens, where lack of environmental awareness can lead to unintended tissue contact and surgical risks. To address this challenge, this work proposes a flexible annular sensor structure integrated around the vertebral disks of continuum robots. The proposed design enables real-time environmental mapping by estimating the distance between the robotic disks and the surrounding tissue, thereby facilitating safer operation through advanced control strategies. The experiment has proven that its accuracy in obstacle detection can reach 0.19 mm. Fabricated using flexible printed circuit (FPC) technology, the sensor demonstrates a modular and cost-effective design with compact dimensions and low noise interference. Its adaptable parameters allow compatibility with various continuum robot architectures, offering a promising solution for enhancing intraoperative perception and control in surgical robotics.
Nesrine Benchoubane, Olfa Ben Yahia, William Ferguson
et al.
In the face of adverse environmental conditions and cyber threats, robust communication systems for critical applications such as wildfire management and detection demand secure and resilient architectures. This paper presents a novel framework that considers both adversarial factors, building resilience into a heterogeneous network (HetNet) integrating Low Earth Orbit (LEO) satellite constellation with High-Altitude Platform Ground Stations (HAPGS) and Low-Altitude Platforms (LAPS), tailored to support wildfire management operations. Building upon our previous work on secure-by-component approach for link segment security, we extend protection to the communication layer by securing both Radio Frequency (RF)/Free Space Optics (FSO) management and different links. Through a case study, we quantify how environmental stressors impact secrecy capacity and expose the system to passive adversaries. Key findings demonstrate that atmospheric attenuation and beam misalignment can notably degrade secrecy capacity across both short- and long-range communication links, while high-altitude eavesdroppers face less signal degradation, increasing their interception capability. Moreover, increasing transmit power to counter environmental losses can inadvertently improve eavesdropper reception, thereby reducing overall link confidentiality. Our work not only highlights the importance of protecting networks from these dual threats but also aligns with the IEEE P3536 Standard for Space System Cybersecurity Design, ensuring resilience and the prevention of mission failures.
Wireless Sensor Networks (WSN) and Vehicular Ad-hoc Networks (VANET) have been extensively used in IoT applications for environmental monitoring, especially in rural and agricultural areas. In this paper we present a novel combined approach which uses both WSN and VANET clustered structures for the efficient gathering and processing of environmental parameters in long eNodeB/RSU-enabled roads/highways, which cross large rural areas. The former (WSNs) is used for traditional sensing and data gathering, whereas the latter (VANET) is used for both (a) performing intermediate processing based on modern edge computation offloading techniques, and (b) propagating the data (either raw data or computed results) to the residing eNodeB/RSUs. Extended experimental measurements, taken via the combined use of SUMO and Veins simulation platforms (and focusing in the air quality monitoring experimental case), demonstrate the high efficiency and scalability of the presented approach over very large deployment areas.
Stephen Serjeant, James Pearson, Hugh Dickinson
et al.
Major European Union-funded research infrastructure and open science projects have traditionally included dissemination work, for mostly one-way communication of the research activities. Here we present and review our radical re-envisioning of this work, by directly engaging citizen science volunteers into the research. We summarise the citizen science in the Horizon-funded projects ASTERICS (Astronomy ESFRI and Research Infrastructure Clusters) and ESCAPE (European Science Cluster of Astronomy and Particle Physics ESFRI Research Infrastructures), engaging hundreds of thousands of volunteers in providing millions of data mining classifications. Not only does this have enormously more scientific and societal impact than conventional dissemination, but it facilitates the direct research involvement of what is often arguably the most neglected stakeholder group in Horizon projects, the science-inclined public. We conclude with recommendations and opportunities for deploying crowdsourced data mining in the physical sciences, noting that the primary goal is always the fundamental research question; if public engagement is the primary goal to optimise, then other, more targeted approaches may be more effective.
Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success. However, we find existing methods fall short in terms of reliability and efficiency when hallucinations are encountered. In this paper, we address these challenges with a framework called QueryAgent, which solves a question step-by-step and performs step-wise self-correction. We introduce an environmental feedback-based self-correction method called ERASER. Unlike traditional approaches, ERASER leverages rich environmental feedback in the intermediate steps to perform selective and differentiated self-correction only when necessary. Experimental results demonstrate that QueryAgent notably outperforms all previous few-shot methods using only one example on GrailQA and GraphQ by 7.0 and 15.0 F1. Moreover, our approach exhibits superiority in terms of efficiency, including runtime, query overhead, and API invocation costs. By leveraging ERASER, we further improve another baseline (i.e., AgentBench) by approximately 10 points, revealing the strong transferability of our approach.
Amanda Jurgelewicz, Rance Nault, Jack Harkema
et al.
Abstract2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is associated with metabolic syndrome (MetS) in humans and elicits pathologies in rodents that resemble non-alcoholic fatty liver disease (NAFLD) in humans through activation of the aryl hydrocarbon receptor (AHR) pathway. Dysregulation of cholesterol homeostasis, an aspect of MetS, is linked to NAFLD pathogenesis. TCDD exposure is also linked to the suppression of genes that encode key cholesterol biosynthesis steps and changes in serum cholesterol levels. In a previous experiment, treating mice with TCDD in the presence of simvastatin, a 3-Hydroxy-3-Methylglutaryl-CoA Reductase competitive inhibitor, altered lipid and glycogen levels, AHR-battery gene expression, and liver injury in male mice compared to TCDD alone. The aim of this study was to deduce a possible mechanism(s) for the metabolic changes and increased injury using single-nuclei RNA sequencing in mouse liver. We demonstrated that co-treated mice experienced wasting and increased AHR activation compared to TCDD alone. Furthermore, relative proportions of cell (sub)types were different between TCDD alone and co-treated mice including important mediators of NAFLD progression like hepatocytes and immune cell populations. Analysis of non-overlapping differentially expressed genes identified several pathways where simvastatin co-treatment significantly impacted TCDD-induced changes, which may explain the differences between treatments. Overall, these results demonstrate a connection between dysregulation of cholesterol homeostasis and toxicant-induced metabolic changes.
Can intentional concept development in the social sciences be understood in terms of conceptual engineering (CE)? To answer this question, I analyze various types of conceptual changes in the social changes-with a special attention to organizational research and the so-called (re)conceptualizations-and distinguish between CE as a theoretical practice and CE as a research program. I show that social scientists, from the point of view of their scientific practice, exercise CE in two versions: CE de novo is employed as new conceptualizations and moderately progressive CE-as reconceptualizations. Importantly, the second type of CE-rather neglected in philosophy of the social sciences-appears to be highly important for the incremental progress of inquiry. Still, both types appear to be equally significant also for CE understood as a research program and for its prospects in the social sciences. Here, I point to three possible paths that help bridging the gap between actual practices of concept development in the social sciences and normative, programmatic approaches to CE: best practice recommendations, institutional actions and uses of AI-agents.
Flávio Nakasato Cação, Anna Helena Reali Costa, Natalie Unterstell
et al.
Even though most of its energy generation comes from renewable sources, Brazil is one of the largest emitters of greenhouse gases in the world, due to intense farming and deforestation of biomes such as the Amazon Rainforest, whose preservation is essential for compliance with the Paris Agreement. Still, regardless of lobbies or prevailing political orientation, all government legal actions are published daily in the Brazilian Federal Official Gazette (BFOG, or "Diário Oficial da União" in Portuguese). However, with hundreds of decrees issued every day by the authorities, it is absolutely burdensome to manually analyze all these processes and find out which ones can pose serious environmental hazards. In this paper, we present a strategy to compose automated techniques and domain expert knowledge to process all the data from the BFOG. We also provide the Government Actions Tracker, a highly curated dataset, in Portuguese, annotated by domain experts, on federal government acts about the Brazilian environmental policies. Finally, we build and compared four different NLP models on the classfication task in this dataset. Our best model achieved a F1-score of $0.714 \pm 0.031$. In the future, this system should serve to scale up the high-quality tracking of all oficial documents with a minimum of human supervision and contribute to increasing society's awareness of government actions.
This paper investigates the application of environmental feature representations for room verification tasks and acoustic meta-data estimation. Audio recordings contain both speaker and non-speaker information. We refer to the non-speaker-related information, including channel and other environmental factors, as e-vectors. I-vectors, commonly used in speaker identification, are extracted in the total variability space and capture both speaker and channel-environment information without discrimination. Accordingly, e-vectors can be extracted from i-vectors using methods such as linear discriminant analysis. In this paper, we first demonstrate that e-vectors can be successfully applied to room verification tasks with a low equal error rate. Second, we propose two methods for estimating metadata information -- signal-to-noise (SNR) and reverberation (T60) -- from these e-vectors. When comparing our system to contemporary global SNR estimation methods, in terms of accuracy, we perform favorably even with low dimensional i-vectors. Lastly, we show that room verification tasks can be improved if e-vectors are augmented with the extracted metadata information.
Mahyuddin K. M. Nasution, Rahmat Hidayat, Rahmad Syah
Possible for science itself, conceptually, to have and will understand differently, let alone science also seen as technology, such as computer science. After all, science and technology are viewpoints diverse by either individual, community, or social. Generally, it depends on socioeconomic capabilities. So it is with computer science has become a phenomenon and fashionable, where based on the stream of documents, various issues arise in either its theory or implementation, adapting different communities, or designing curriculum holds in the education system.
Daniel Tejero-Martin, Chris Bennett, Tanvir Hussain
The increasing demand for more efficient and environmental-friendly gas turbines has driven the development of new strategies for material development. SiC/SiC ceramic matrix composites (CMCs) can fulfil the stringent requirements; however, they require protection from the operating environment and debris ingested during operation. Environmental barrier coatings (EBCs) are a protective measure to enable the CMCs to operate under harsh conditions. EBC-coated CMCs will enable an increased efficiency and reduced pollutant and CO2 emissions. In this review, the fundamentals of SiC/SiC ceramic matrix composites degradation in steam environments and under the presence of molten alkali salts, namely CaO-MgO-Al2O3-SiO2 (CMAS), are first presented. Then, a summary of EBCs along with a comprehensive summary of the current compositions and their interactions with steam and molten salts is presented. Finally, an overview of the latest research directions for the potential next generation of EBCs are outlined
Varshit Dusad, Denise Thiel, Mauricio Barahona
et al.
Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimisation principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.
This paper describes the development of iEnvironment, an open science software platform that supports monitoring and modeling of aspects of surface water. The platform supports science and engineering research, especially in the context of the creation, sharing, analysis and maintenance of big and open data. In this era of big data, iEnvironment facilitates access to open data resources and research collaboration among science and research disciplines supported by computer scientists and software developers.
Currently it is widely accepted that the language of science is mathematics. This book explores an alternative idea where the future of science is based on the language of algorithms and programs. How such a language can actually be implemented in the sciences is outlined in some detail. We start by constructing a simple formal system where statements are represented as programs and inference is based on computability as opposed to the classical notion of truth value assignments. The focus is on theories where the intrinsic properties and dynamic state of real world objects can be defined in terms of information and subject to laws based on simple deterministic rules and finite state arithmetic. Such models, it is argued, not only offer alternative simulation tools, as opposed to those based on discrete approximations of conventional continuum theories, but in themselves can be regarded as a language that describes the physical laws at a fundamental level. This book does not examine any specific application in detail but rather attempts to lay down a foundation for the validation of such theories by employing the inference scheme based on computability logic.