Potential Space Research Articles

The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses

Abstract. Despite their recognized significance on global climate and extensive research efforts, the mechanism(s) driving Heinrich events remain(s) a subject of debate. Here, we use the 3D thermomechanically coupled glacial systems model (GSM) to examine the Hudson Strait ice stream surge cycling and the role of three factors previously hypothesized to play a critical role in Heinrich events: ice shelves, glacial isostatic adjustment, and sub-surface ocean temperature forcings. In contrast to all previous modeling studies examining HEs, the GSM uses a transient last glacial cycle climate forcing, global viscoelastic glacial isostatic adjustment model, and sub-glacial hydrology model. The results presented here are based on a high-variance sub-ensemble retrieved from North American history matching for the last glacial cycle. Over our comparatively wide sampling of the potential parameter space (52 ensemble parameters for climate forcing and process uncertainties), we find two modes of Hudson Strait ice streaming: classic binge–purge versus near-continuous ice streaming with occasional shutdowns and subsequent surge onset overshoot. Our model results indicate that large ice shelves covering the Labrador Sea during the last glacial cycle only occur when extreme calving restrictions are applied. The otherwise minor ice shelves provide insignificant buttressing for the Hudson Strait ice stream. While sub-surface ocean temperature forcing leads to minor differences regarding surge characteristics, glacial isostatic adjustment does have a significant impact. Given input uncertainties, the strongest controls on ice stream surge cycling are the poorly constrained deep geothermal heat flux under Hudson Bay and Hudson Strait and the basal drag law. Decreasing the geothermal heat flux within available constraints and/or using a Coulomb sliding law instead of a Weertman-type power law leads to a shift from the near-continuous streaming mode to the binge–purge mode.

Space cybersecurity challenges, mitigation techniques, anticipated readiness, and future directions

Space Cybersecurity (SC) is becoming critical due to the essential role of space in global critical infrastructure – enabling communication, safe air travel, maritime trade, weather monitoring, environmental surveillance, financial services, and defence systems. Simultaneously, involving diverse stakeholders in space operations further amplifies this criticality. Similarly, previous research has identified isolated vulnerabilities in SC and proposed individual solutions to mitigate them. While such studies have provided useful insights, they do not offer a comprehensive analysis of space cyber-attack vectors and a critical evaluation of the effectiveness of mitigation strategies. This study addresses this problem by holistically examining the scope of potential space cyber-attack vectors, encompassing the ground, space, user, cloud, communication channels, and supply chain segments. Furthermore, the study evaluates the effectiveness of legacy security controls and frameworks and outlines SC-vector-aligned counterstrategies and mitigation techniques to tackle the unique SC threats. Based on the analysis, the study proposes future research directions to develop and test advanced technological solutions and regulatory and operational frameworks to establish international standards policies and foster stakeholder collaboration. The study contributes a multi-disciplinary foundation and roadmap that researchers, technology developers, and decision-makers can draw on in shaping a robust and sustainable SC framework.

Adaptive CVgen: Leveraging reinforcement learning for advanced sampling in protein folding and chemical reactions

Enhanced sampling techniques have traditionally encountered two significant challenges: identifying suitable reaction coordinates and addressing the exploration-exploitation dilemma, particularly the difficulty of escaping local energy minima. Here, we introduce Adaptive CVgen, a universal adaptive sampling framework designed to tackle these issues. Our approach utilizes a set of collective variables (CVs) to comprehensively cover the system's potential evolutionary phase space, generating diverse reaction coordinates to address the first challenge. Moreover, we integrate reinforcement learning strategies to dynamically adjust the generated reaction coordinates, thereby effectively balancing the exploration-exploitation dilemma. We apply this framework to sample the conformational space of six proteins transitioning from completely disordered states to folded states, as well as to model the chemical synthesis process of C60, achieving conformations that perfectly match the standard C60 structure. The results demonstrate Adaptive CVgen's effectiveness in exploring new conformations and escaping local minima, achieving both sampling efficiency and exploration accuracy. This framework holds potential for extending to various related challenges, including protein folding dynamics, drug targeting, and complex chemical reactions, thereby opening promising avenues for application in these fields.

Novel application of citric acid based natural deep eutectic solvent in drilling fluids for shale swelling prevention

Swelling of shale in clastic reservoirs poses a significant challenge, causing instability in wellbores. Utilizing water-based drilling mud with shale inhibitors is preferable for environmental reasons over oil-based mud. Ionic liquids (ILs) have garnered interest as shale inhibitors due to their customizable properties and strong electrostatic features. However, widely used imidazolium-based ILs in drilling fluids are found to be toxic, non-biodegradable, and expensive. Deep Eutectic Solvents (DES), considered a more economical and less toxic alternative to ILs, still fall short in terms of environmental sustainability. The latest advancement in this field introduces Natural Deep Eutectic Solvents (NADES), renowned for their genuine eco-friendliness. This study explores NADES formulated with citric acid (as a Hydrogen Bond Acceptor) and glycerine (as a Hydrogen Bond Donor) as additives in drilling fluids. The NADES based drilling mud was prepared according to API 13B-1 standards and their efficacy was compared with KCl, imidazolium based ionic liquid, and Choline Chloride: Urea-DES based mud. A thorough physicochemical characterization of the in-house prepared NADES is detailed. The research evaluates rheological, filtration and shale inhibition properties of the mud, demonstrating that NADES enhanced the yield point to plastic viscosity ratio (YP/PV), reduced mudcake thickness by 26%, and decreased filtrate volume by 30.1% at a 3% concentration. Notably, NADES achieved an impressive 49.14% inhibition of swelling and improved shale recovery by 86.36%. These outcomes are attributed to NADES’ ability to modify surface activity, zeta potential, and clay layer spacing which are discussed to understand the underlying mechanism. This sustainable drilling fluid promises to reshape the drilling industry by offering a non-toxic, cost-effective, and highly efficient alternative to conventional shale inhibitors, paving the way for environmentally conscious drilling practices.

The Importance of the Teachers’ Role in Imaginative Play

This study of Norwegian kindergartens investigates how teachers may support imaginative play, drawing on Vygotsky’s approaches to play and teachers’ positioning in play in terms of different roles. For this study, the empirical investigations were organized as both observations and focus group interviews. The sample was two kindergartens with in total 12 teachers and 44 children aged 3-5 years. The study was conducted in November 2021-April 2022. The analyses point to three main findings; firstly, changes according to daily schedule and content may have effect on play, secondly, the teacher’s role may be an obstacle in play, and thirdly, the teacher’s role may be a driving force in play. In the discussion of these findings, questions are raised about traditions and a lack of competence together can lead to children not receiving sufficient support in imaginative play. However, when the teachers are a play partner who both add new fictional elements and support the fiction by »lending« their experiences, the play’s potential and opportunity space is utilized. The conclusion is that the quality of the teachers’ role and involvement will have an impact on children’s cultural expression in play.

Exploring the Practical Teaching Mode of Language Laboratory based on Artificial Intelligence

With the rapid development of artificial intelligence (AI) technology, its application in the field of education is becoming more and more extensive, especially in the teaching mode of language laboratory, AI technology shows great potential and innovation space. The purpose of this paper is to explore the practical teaching mode of language lab based on artificial intelligence, and to reveal its advantages and challenges in language learning by analysing the current status of the application of AI technology in teaching. This study aims to explore the in-depth analysis of the research on the practical teaching mode of language labs based on artificial intelligence. Firstly, it introduces the problems of language labs and analyses the limitations and problems of traditional labs. Secondly, the language laboratory solution measures are elaborated in detail to construct the practical teaching mode of language laboratory based on artificial intelligence. Then, the practical teaching mode of language laboratory based on artificial intelligence is explored. Finally, the application value of the language laboratory of artificial intelligence in the aspects of linguistic research and teaching practicability is discussed. As an innovative application of educational technology, the language laboratory of artificial intelligence has a positive role in promoting the transformation of the modern language teaching mode, and provides new perspectives and methods for foreign language teaching. This paper focuses on the design and construction of the practical language teaching mode of artificial intelligence. With the rapid development of artificial intelligence technology, its application in the field of education has become increasingly widespread. In language teaching, the construction of an AI-based practical teaching model can help to improve teaching efficiency and quality and stimulate students’ interest in learning. This paper elaborates the design principles, construction elements, implementation process and effect evaluation of this teaching mode from various aspects.

The Srgb Color Space Based Density Analysis for Brain Tumor Segmentation

One of the important areas of study in medical image processing is early disease detection and diagnosis. A subfield of medical image processing includes the brain tumour segmentation procedure. Medical professionals have an efficient means of diagnosing illnesses because to computer vision and machine learning technology. The Srgb based density analysis is used in this study to isolate the area of brain tumours in MRI images. To differentiate the tumour area from the surrounding area, the intensity values of the input are normalised using the Gaussian filter and the Srgb colour space. In brain MRI samples, the adaptive threshold approach aids in locating potential tumour space. By calculating region parameters like area and density function, the actual space occupied by brain tumours is recovered. Ultimately, by applying morphological functions and removing potential false positives, the accurate tumour space is identified. Recall, accuracy, and F-measure are a few of the performance indicators that are used to evaluate how effective the suggested strategy.

Nonknowledge in Computation. Reflecting on Irrevocable Uncertainty

Abstract My paper approaches the theme of computational creativity by looking at uncertainty as an epistemic and aesthetic tool that must be examined to address the challenges brought to critical practice by planetary computation. It positions uncertainty as central to how the encounter of the human practitioner with non-human machines is conceptualized, and as a resource for building speculative-pragmatic paths of resistance against algorithmic capture. It proposes ways to cultivate uncertainty and use it as a design material to produce new types of knowledge that question machines’ pre-emptying manoeuvres and resist their capture of potential. The argument proposed is that uncertainty affords the production of new imaginaries of the human-machine encounter that can resist the foreclosure of futures (what will be) and are sustained instead by the uncertainty of potential (what might be) (Munster 2013). Dwelling in a space of potential – Deleuze’s virtual, or what I call a space of ‘maybes’, requires of the practitioner a repositioning of their epistemic perspective and reflecting on the following questions: how can material knowledge be made by engaging with modes of un-knowing and not-knowing in machine interaction? How can these modes of un-knowing and not-knowing be fostered as a critical and political onto-epistemological project of reinventing critical practice for the algorithmic age? (Horl et al. 2021; Hansen 2021, 2015, Pasquinelli and Joler 2020). The paper argues that the machinic unknown should be engaged with - not through the conventional paradigm that pitches human vs machine creativity and attempts to rank and score them through similarities, but rather through a (paradoxical) deepening of the unknowability at the core of the machine (Parisi) and machine’s own incommensurability (Fazi 2020). It then proposes the Chinese notion of wu wei (active non action) (Jullien 2011, 2004, 2000, 1995; Allen 2015, 2011) as a stratagem to experiment with to craft speculative-pragmatic interventions, and to augment the ‘power of maybes’ as a space of anti-production, and resisting reduction (Ito 2019).

Study on the Coupling Coordination Degree and Driving Mechanism of “Production-Living-Ecological” Space in Ecologically Fragile Areas: A Case Study of the Turpan–Hami Basin

One of the key conditions for achieving superior regional growth is ensuring the harmonious development of both the layout and functions of territorial space. Territorial space, which includes production space, living space, and ecological space, serves as a critical system and venue for economic, cultural, and social activities in a region. The harmonized growth of production–living–ecological space (PLES) is essential for attaining sustainable development goals. Research on PLES offers a fresh perspective on promoting sustainable development of the spatial domain and the sustainable use of resources. However, studies on PLE functions in ecologically fragile areas are lacking. Therefore, in this study, which adopts a PLES perspective, land-use data are used to classify land according to the dominant functions of production, ecology, and living. Integration of point-of-interest (POI) data with socio-economic data was established to spatially describe indicators at the grid level and create a scoring system for PLES indicators in ecologically fragile areas. Finally, the entropy weight method, holistic assessment methods, coupling coordination degree model (CCDM), and geodetector were employed to explore the coupling coordination relationships and factors influencing PLESs in the Turpan–Hami Basin from 2010 to 2020. The results indicate that the Turpan–Hami Basin consists predominantly of potential ecological space, mainly in the central and northern regions, which are characterized by the Gobi Desert and bare rock landforms. Over the past decade, the PLES framework has seen a notable rise in the allocation of residential and ecological areas, whereas the portion dedicated to production spaces has diminished. The overall coupling coordination degree (CCD) of PLES in the Turpan–Hami Basin is at a coordinated level and gradually increasing. The most significant impact on the degree of PLES coupling coordination is exerted by population and natural factors. The research findings provide theoretical support for the sustainable utilization of resources in the Turpan–Hami Basin and other ecologically fragile areas while also offering scientific evidence to promote the coupling coordination of PLES, thereby contributing to high-quality regional development.

Knowledge-Based Perturbation LaF-CMA-ES for Multimodal Optimization

Multimodal optimization presents a significant challenge in optimization problems due to the existence of multiple attraction basins. Balancing exploration and exploitation is essential for the efficiency of algorithms designed to solve these problems. In this paper, we propose the KbP-LaF-CMAES algorithm to address multimodal optimization problems based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) framework. The Leaders and Followers (LaF) and Knowledge-based Perturbation (KbP) strategies are the primary components of the KbP-LaF-CMAES algorithm. The LaF strategy is utilized to extensively explore the potential local spaces, where two cooperative populations evolve in synergy. The KbP strategy is employed to enhance exploration capabilities. Improved variants of CMA-ES are used to exploit specific domains containing local optima, thereby potentially identifying the global optimum. Simulation results on the test suite demonstrate that KbP-LaF-CMAES significantly outperforms other meta-heuristic algorithms.

Preselection of potential target spaces based on partial information by the supplementary eye field

Before selecting a saccadic target, we often acquire partial information about the location of the forthcoming target and preselect a region of visual space even before the target becomes visible. To determine whether the supplementary eye field (SEF) represents information signifying the potential target space, we examined neuronal activity in the SEF of monkeys performing a behavioral task designed to isolate the process of visuospatial preselection under uncertainty from the process of selecting a specified location. Our data showed that the activity of SEF neurons represented information about the potential target space instructed by symbolic cues. Increased activity of visuospatially selective SEF neurons encoded the potential target space, which could be a mechanism facilitating subsequent selection of an appropriate target. Furthermore, electrical stimulation of the SEF during the preselection period disrupted subsequent target selection. These results demonstrate that the SEF contributes to the preselection of potential target spaces based on partial information.

A Note on the Fourier Magnitude Data and Sobolev Embeddings

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Optimization of an Urban Microgreen Space Distribution Based on the PS-ACO Algorithm: A Case Study of Shenyang, China

In this study, optimization of the microgreen space distribution through multistage regulation is investigated, with the goal of alleviating the imbalance between the supply and demand of green resources in the central urban area of Shenyang. An optimized evaluation model of green space supply and demand is employed to calculate the green space accessibility index at a 100-m grid scale and identify different levels of green space resource supply and demand. Priority is given to supplementing resources for the elderly population by balancing the green space supply in vulnerable areas. Particle swarm—ant colony optimization (PS-ACO) is used to select microgreen space sites within each priority level. On the basis of the “important-urgent” quadrant analysis, S1-priority residential areas account for 8.12% of the grid, S2-priority areas account for 67.01%, and S3-priority areas account for 24.87%. The PS-ACO algorithm outputs potential microgreen space sites within each priority level to accurately regulate the green space distribution in residential areas with different supply pressures and limited land availability. A spatial correlation analysis of the new sites reveals good spatial dispersion within service units, effectively alleviating demand pressures, and good aggregation at a regional scale to address imbalances in the supply of green space in a targeted manner. Thus, the optimized results of the PS-ACO algorithm are effective, providing reliable site-selection references for subsequent urban microgreen space distributions.

Enhanced carrier transfer performance on blade-coating PbS quantum dot solar cells by coordinated operation of iodine ligands

The significant breakthrough in the photovoltaic conversion efficiency of lead sulfide (PbS) quantum dot solar cells has primarily been achieved on small substrates using the spin-coating method. However, the blade-coating technique offers a scalable alternative for large-scale fabrication of absorption film, addressing the limitations of spin-coating for commercial applications. Given the inherent challenges of the blade-coating method, which often produces low-quality and defective films. Thus, an iodide ligand passivation strategy to realize the formation of high-quality films with reduced trap states was proposed. Typically, the iodide ligand is incorporated into the precursor solution with the aim of fine-tuning the nanoparticle’s functionality, facilitating the formation of systematically aligned and densely coupled film configurations during the blade coating procedure. The functional mechanisms of the aligned and compact-coupled configurations, as well as the effect of iodide introduction on defect mitigation, have been systematically investigated. The results reveal that iodide ligand introduction modulates the interparticle electrostatic potential and inter-dot space, facilitating the formation of a uniform structure with enhanced energy level alignment and reduced trap states. The highly aligned and compact structure improves interfacial integration with the ZnO electron transport layer, enabling efficient photon-electron extraction and transfer. As a result, PbS quantum dot solar cells fabricated using the blade coating technique have achieved an optimized efficiency of 4.68 %, offering significant potential for the development and production of directly-synthesized ink for quantum dot solar cells.

The effects of climate and population on human land use patterns in Europe from 22ka to 9ka ago

Between 22ka and 9ka ago, after the Last Glacial Maximum (LGM) and during the transition to the Holocene, mobile hunter-gatherer populations, differentiated by their stone tool assemblages, periodically dispersed and contracted across Europe. It is well understood that climate played an important role in human distributions and population sizes during the post-LGM Late Pleistocene and Early Holocene, but the question remains as to whether increasing population sizes drove early human dispersal. Here, using a spatiotemporal species distribution model to infer the fundamental and potential human climatic niche space of Late Upper Paleolithic, Final Paleolithic and Early Mesolithic Europe, and hypotheses derived from the Ideal Distribution Model, we test i) how changes in climate affected the size and extent of the projected potential human niche space, ii) for effects of changes in size of projected potential niche on regional human population sizes, iii) whether increasing human population sizes drove human dispersal into less climatically suitable habitats, and iv) whether populations associated with different high-order material culture groupings (macro-level technocomplexes) occupied different climatic spaces. We find that changes in climate correlate strongly with the size and extent of the projected potential human niche space, that increases in the projected human niche space correlate with increases in human population, that human population size is just beginning to become large enough to influence land use and dispersal patterns at the Pleistocene-Holocene transition, and that archaeological technocultural entities overlap in their fundamental climatic niche space. This overlap implies that changing tools forms cannot readily been seen as reflecting adaptations to changing climates.

Wake-Sleep Consolidated Learning.

We propose wake-sleep consolidated learning (WSCL), a learning strategy leveraging complementary learning system (CLS) theory and the wake-sleep phases of the human brain to improve the performance of deep neural networks (DNNs) for visual classification tasks in continual learning (CL) settings. Our method learns continually via the synchronization between distinct wake and sleep phases. During the wake phase, the model is exposed to sensory input and adapts its representations, ensuring stability through a dynamic parameter freezing mechanism and storing episodic memories in a short-term temporary memory (similar to what happens in the hippocampus). During the sleep phase, the training process is split into nonrapid eye movement (NREM) and rapid eye movement (REM) stages. In the NREM stage, the model's synaptic weights are consolidated using replayed samples from the short-term and long-term memory and the synaptic plasticity mechanism is activated, strengthening important connections and weakening unimportant ones. In the REM stage, the model is exposed to previously-unseen realistic visual sensory experience, and the dreaming process is activated, which enables the model to explore the potential feature space, thus preparing synapses for future knowledge. We evaluate the effectiveness of our approach on four benchmark datasets: CIFAR-10, CIFAR-100, Tiny-ImageNet, and FG-ImageNet. In all cases, our method outperforms the baselines and prior work, yielding a significant performance gain on continual visual classification tasks. Furthermore, we demonstrate the usefulness of all processing stages and the importance of dreaming to enable positive forward transfer (FWT). The code is available at: https://github.com/perceivelab/wscl.

A board game ludography analysis for Game-Based Learning in Astronomy, Astrophysics and Space Science

Space exploration and astronomical objects are extensively used in modern board games. These games can be an entertaining way to present educational contents on Astrophysics-related topics, by acknowledging the importance and beauty of Space. However, careful consideration of mechanics, messages, and themes is required to achieve this goal. We present a classification system for the presence of Astronomy, Astrophysics, and Space Science in the 2000 most popular modern board games, where games are categorised according to their complexity and actual scientific contents. This initial mapping defines a potential Astronomy, Astrophysics and Space Science ludography, i.e. a set of board games for STEM educators to create learning activities about such topics, using easily attainable commercial products.

“We’re no longer the senzala”: Race, space, and difference in the Ethnic Capital of Brazil

Drawing on ethnographic research conducted in Ijuí, a small rural city in Rio Grande do Sul, this work examines how history, race, and power over space articulate in the construction of a multiethnic heritage tourism festival. Ijuí has recently garnered significant national and international heritage recognition based on its unique history of multiethnic European colonization and the metanarrative that this produced an exceptionally tolerant and harmonious society. Contrasting with this narrative, the reality for the city’s Black, indigenous, and mixed residents reveals how tourism is often situated within a zone of ambivalence, serving both as an enduring source of precarity and a critical site of hope. Drawing from Michel De Certeau’s theories of strategies and tactics, along with Susan Gal and Judith Irvine’s work on rhematization, this article aims to (1) trace the precarity involved when colorblind multiethnic tourism operates within contexts of marked racial inequality, (2) interrogate the ideological, material, and semiotic processes by which these precarities become recursively inscribed within the space of tourism, and (3) show how the space of tourism can be reappropriated and utilized through tactics that defy categorization as tourism, serving ends that threaten the very basis of the precarities themselves. Thus, even as the multiethnic tourism landscape has been a source of ongoing marginalization, I argue in this work that it can also be reimagined as a space of potential where new communities, relations, visibilities, and histories can circulate and take root.

A Reparative Lens for Exploring Youth Aspirations in South African Universities

ABSTRACT In South Africa, low-income rural black youth are assumed to exhibit endurance in their higher education agency while yet not really being agentic, because they accommodate an oppressive university and settle for less ambitious aspirational horizons. Drawing on illustrative empirical data from a longitudinal study of black youth without hot knowledge of higher education to deploy from their families’ biographies, the project explored students’ accessing, participating in and moving on from their studies at five South African universities. In exploring youth aspirations and agency, the concept of repair is employed to position aspirations as reparative and temporal where past injustices, present and future intersect in shaping agency and in projecting imagined futures for youth. The cultural lens of aspiration offers a space to interrogate and dismantle past exclusions and unsettle norms of the “disadvantaged student” to avoid reinscribing past injustice in ways which are globally relevant to marginalised university students from poor backgrounds compared to better off students. It shows how day to day negotiated conditions of living in universities by these students are agentic; enduring poverty and exclusion is an active aspirational struggle for dignity and recognition, even if these students are not able to undo relations of power. Ubuntu is mobilised as an ontological means to further deepen how we understand aspirations and agency as interdependent and interacted between person and community and as refusal of the neoliberal university. Finally, features of universities as potential spaces of aspirations and repair for transformative change and transformative futures are sketched.

Multi-task oriented team formation in online collaborative learning

Rational and personalized team formation can greatly enhance the efficiency of online collaborative learning. However, in the process of forming learning teams and completing learning tasks, online synchronous communication among team members is often overlooked, and the abilities of team members may change during task execution, which may have an effect on the completion of team tasks. To address these problems, this paper comprehensively considers the impact of learners’ online time and skill levels on team formation. We quantify the potential development space of learners’ skills and design optimization objectives accordingly based on the Zone of Proximal Development theory. Additionally, we propose a Near-Informer deep learning model to predict learners’ future online states, aiming to measure communication efficiency. Then, we propose a multi-task oriented multi-objective optimization team formation problem, and transform it into a single-objective one by using weighted aggregation of all objectives. After that, we present a Three-dimensional Monte Carlo Tree Search algorithm (TDMCTS). The experiments are conducted based on two real datasets. Compared with the baselines, our Near-informer model achieved the highest number of optimal predictions, with 22 out of 32. Besides, TDMCTS algorithm achieved more comprehensive team formation results, with average skill satisfaction degrees of 99.8% and 98.9% on two task sets, respectively, while satisfying the task requirements.