Physical Representation Research Articles

Improved Snow Albedo Evolution in Noah-MP Land Surface Model Coupled with a Physical Snowpack Radiative Transfer Scheme

Abstract The widely used community Noah-MP land surface model currently adopts snow albedo parameterizations that are semiphysical in nature and have systematic biases which impact the accuracy of weather and climate modeling systems that use Noah-MP as the land component. We hypothesized that integrating the snowpack radiative transfer scheme from the latest version of the Snow, Ice, and Aerosol Radiative (SNICAR) model can improve the physical representation of snow albedo processes and reduce corresponding land model uncertainties. Therefore, we evaluate Noah-MP simulations employing the SNICAR scheme and compare model accuracy to a Noah-MP simulation using the default semiphysical Biosphere-Atmosphere Transfer Scheme (BATS) scheme using in situ spectral snow albedo observations at three Rocky Mountain field stations. The agreement between simulated and in situ observed ground snow albedo is significantly enhanced in NoahMP–SNICAR simulations relative to NoahMP–BATS simulations (root-mean-square error reductions from 0.116 to 0.103). Especially, NoahMP–SNICAR improves modeled snow albedo variability for fresh snow and aged snowpack (correlation increase from 0.42 to 0.67). The underestimated variability of snow albedo in NoahMP–BATS is a result of inadequate representation of physical linkages between snow albedo evolution and environmental/snowpack conditions (temperature, snow density, snow water equivalent, and light-absorbing particles), which is substantially improved by the NoahMP–SNICAR scheme. This new development of NoahMP–SNICAR physics provides a means to improve snow albedo accuracy and reduce corresponding uncertainties while providing new modeling capabilities such as hyperspectral snow albedo and effects of snow grain size, snow grain shape, and light-absorbing particles in future studies. Significance Statement The widely used community Noah-MP land surface model utilizes simplified snow albedo parameterizations that are semiphysical and have large uncertainties that affect the accuracy of weather and climate modeling systems. We aim to reduce uncertainties by incorporating a radiative transfer snow albedo model (i.e., SNICAR) into Noah-MP. The newly coupled NoahMP–SNICAR model shows a significant enhancement in simulating snow albedo at three Rocky Mountain stations when compared to the Noah-MP configuration with the default snow albedo scheme (i.e., BATS). It also introduces new modeling capabilities for future studies, such as hyperspectral snow albedo and the effects of snow grain size, snow grain shape, and light-absorbing particles.

An Analysis of a Historical House in Alaçatı, Çeşme

Alaçatı, located on the Çeşme Peninsula in Türkiye, is a historic and touristic coastal settlement. In recent years, despite the growing influence of tourism on its historic center, the settlement has managed to partially preserve its urban fabric and architectural heritage. In the historic center of the settlement, the civil architecture from the Ottoman period stands out as a prominent physical representation of cultural heritage. Historical houses are particularly significant for understanding the socio-cultural history of the settlement. Today, most of them have been restored and lost their original functions. On the other hand, some historical buildings hold a significant place in the city's memory, not only for their residential function but also for their commercial role in the settlement. A notable example is atwo-storied stone house named “Yürük Grocery”, situated in the Hacı Memiş Neighborhood, Alaçatı. Among the other historical houses, it stands out with its corner position, shared courtyard design and ground-floor function. This study aims to document the architectural features and cultural heritage values of this historical building, trace its evolution over time, and contribute to the literature. The methodology of this study includes documentation, literature review, archival research, fieldwork, and oral expressions. As a result, this study has identified three periods by examining the building's architectural features, history, construction techniques, and material characteristics in its evolution. In this way, the study contributes to maintain its historical identity.

Youth-Initiated Moments: Making Visible Youth Bids for Rightful Presence in Informal STEM Learning

Although informal science, technology, engineering, and mathematics (STEM) learning (ISL) has served as a catalyst for inclusive lifelong STEM engagement, research indicates that participation in ISL can still remain inequitable, especially for youths from minoritized communities. To address the continued inequities, youths’ rightful presence should be placed at the core of informal STEM education knowledge and practice. Rightful presence refers to youths legitimately belonging in learning spaces by calling out the limits of equity as inclusion. Our study explores the insights youths offered to inform pedagogical practices in support of equitable learning in ISL spaces. We seek such insights, particularly attending to the moments we call ‘youth-initiated,’ the instances in which youths made visible their bids for rightful presence by seeking shifts in and through their ISL experiences. Drawing on a research practice partnership project in which we co-generated and analyzed data with youths and educator partners, we identify three forms of youths’ bids for rightful presence: reorganizing physical and social representations within learning space, mattering beyond educator imagination, and foregrounding the sociopolitical dimension of learning. We illustrate these findings with three focal youth-initiated moments that emerged in the context of three respective ISL programs. We also elaborate on how educators and peers responded to the bids in ways that brought immediate and consequential shifts in ISL opportunities. We discuss our findings and implications regarding research and practice of justice-oriented pedagogies in and beyond ISL spaces.

Language and Visual Representation in Physics: Enhancing Understanding Through Multimedia

The incorporation of multimedia resources in physics education has become a crucial approach for improving student understanding by combining verbal and visual components. This investigation utilizes a mixed-methods design to assess the impact of multimedia tools on student participation and comprehension in physics courses. Quantitative information was gathered through assessments before and after the intervention, while qualitative data was obtained from student interviews and in-class observations. The study involved creating tailored multimedia materials, which were subsequently integrated into lessons. Quantitative data underwent statistical analysis, including paired t-tests, while qualitative findings were examined using thematic analysis. The results revealed a substantial increase in student engagement from 45% without multimedia to 85% with its use. Additionally, the post-test mean score (79.3 ± 8.81) exceeded the pre-test average (58.5 ± 12.65), suggesting enhanced understanding and uniformity among students. Qualitative outcomes highlighted multimedia's contribution to clarifying intricate concepts, improving communication abilities, and promoting collaborative learning. The research concludes that the strategic integration of multimedia tools within cooperative frameworks can establish a vibrant, interactive learning environment, considerably enhancing students' conceptual grasp and preparing them for future academic and career challenges.

Arctic soil carbon insulation averts large spring cooling from surface–atmosphere feedbacks

The insulative properties of soil organic carbon (SOC) and surface organic layers (moss, lichens, litter) regulate surface-atmosphere energy exchanges in the Arctic through a coupling with soil temperatures. However, a physical description of this process is lacking in many climate models, potentially biasing their high-latitude climate predictions. Using a coupled surface-atmosphere model, we identified a strong feedback loop between soil insulation, surface air temperature, and snowfall. Without insulation, the latent heat needed for soil ice thawing leads to a late spring and summer cold bias in surface air temperature (above 2 °C) over Arctic regions. The integration of soil insulation eliminates this bias and significantly improves the simulation of permafrost dynamics. Our findings, including the potential consequences of large perturbations (e.g., fires), highlight the importance of combining soil water freezing with a physical representation of SOC and surface organic layer insulation in Earth system models, to improve Arctic climate predictions.

Conceptual service architecture to synchronise research data management services using machine-actionable data management plans

Researchers of all disciplines produce, share, and reuse data as part of everyday research. Most funders require them to manage and document their data using data management plans (DMPs). DMPs are often static documents that researchers create by answering questions in predefined templates at the beginning of the research and, therefore, may become outdated and obsolete as the project progresses. It is essential to keep the DMP up to date at all stages of the research lifecycle since numerous stakeholders and various services participate in data management that depend on information from them. In this paper, we propose a conceptual service architecture that uses machine-actionable data management plans to automate the exchange and synchronization of information between different semi-automated research data management (RDM) services acting on behalf of different stakeholders. To solve the stated problem, we analyze typical use cases in which the DMPs change and formulate requirements based on which we developed the conceptual architecture. We depict the designed architecture through a set of views, namely physical, development, logical, and process, using UML and BPMN representation that describe the processes required to synchronize DMP information among multiple services. We instantiate it by implementing a service that connects a data repository and a DMP tool. Thus, we evaluate to what extent the defined processes help in keeping DMP contents up to date and which criteria must be fulfilled to keep them highly automated. The result of the paper feeds into a larger discussion on streamlining interconnectivity and machine-actionability across planning, tracking, and assessing research phases. It also facilitates consensus building on enhancing the Research Data Alliance’s recommendation for machine-actionable DMPs.

Concreteness of energy and bonding concepts in 9th grade chemistry – examining learning among high and low achievers

ABSTRACT Concreteness fading was applied in a curricular unit on energy changes in chemical reactions. Students explored bond breaking and formation by physically interacting with magnets, observing these processes through computer simulations and diagrams, and applying them to chemical equations. The participants included 36 ninth graders from a regular public school and 56 from a science magnet school. Conceptual knowledge was assessed using questionnaires administered before and after the unit, and 11 students were interviewed to support the quantitative findings.We observed significant pretest-to-posttest gains among both groups, with magnet school students achieving college-level averages. Regular school students showed smaller gains on items related to the definition of bonds or those involving purely symbolic chemical representations. However, both groups made similar progress in understanding macro-scale observations of energy change and in applying bonding concepts to nano-scale visual representations. Consistent with other studies, our findings indicate that while concreteness fading is an effective teaching strategy, it does not reduce the performance gap between high and low achievers. Interviews suggest that this gap stems from the difficulty low achievers face in adopting bonding concepts as visual and physical representations are phased out.

Multiple soliton and singular wave solutions with bifurcation analysis for a strain wave equation arising in microcrystalline materials

A microcrystalline material refers to a crystallized substance or rock comprised of tiny crystals that can only be observed under a microscope. The strain wave equation is a fourth-order nonlinear partial differential equation encountered in the examination of non-dissipative strain wave propagation within microstructured solids. In this paper, the transmission of waves in microcrystalline materials is dictated by the non-dissipative case of strain wave equation’s structure, accounting for multiple dimensions within microcrystalline structures. The simplest equation method is employed to extract multi-soliton solutions, while the modified Sardar subequation method is applied to identify additional soliton solutions, including bright, combined dark–bright, combined dark-singular, periodic singular, and singular solitons. Furthermore, the dynamical system bifurcation theory approach is utilized to investigate the phase diagrams of the governing equation. Further elaboration on the physical dynamical representation of the presented solutions is provided through profile illustrations. A comparison with the existing literature is also provided, highlighting the efficacy of our work. The significance of the acquired outcomes lies in their capacity to portray a wide array of intricate and diverse phenomena observed in both mathematical and physical systems.

Machine Learning Advances in High-Entropy Alloys: A Mini-Review.

The efficacy of machine learning has increased exponentially over the past decade. The utilization of machine learning to predict and design materials has become a pivotal tool for accelerating materials development. High-entropy alloys are particularly intriguing candidates for exemplifying the potency of machine learning due to their superior mechanical properties, vast compositional space, and intricate chemical interactions. This review examines the general process of developing machine learning models. The advances and new algorithms of machine learning in the field of high-entropy alloys are presented in each part of the process. These advances are based on both improvements in computer algorithms and physical representations that focus on the unique ordering properties of high-entropy alloys. We also show the results of generative models, data augmentation, and transfer learning in high-entropy alloys and conclude with a summary of the challenges still faced in machine learning high-entropy alloys today.

Numerical Study of Nanomaterial Flow with Joule Effect, Coefficient of Rate of Chemical Reaction along with Viscous Dissipation and Heat Source

This research, investigate the flow of nanomaterials with heat radiation, viscous dissipation, Joule effect. The paper also investigates the rate of chemical reactions, which reveals how the concentration of nanoparticles in the nanofluid varies over time and space. A physical representation guided us to a nonlinear paired partial differential equation, which was then reduced to an ordinary differential equation using the similarity approach for different presumptions on the real situation. The computational effort is carried out by compressing the Runge-Kutta-Felberg method with the Newton-Raphson iterative strategy to solve non-linear linked ordinary differential equations by changing the boundary value issue to an initial value problem by selecting a guess value. Later, computations for numerous non-dimensional factors relevant in the investigation of fluid flow, such as temperature, are anticipated. It has been noted that the reaction rate parameter had a major impact on the concentration profiles, and that when the reaction rate parameter rises, the boundary layer’s concentration thickness increases.

Multiple Representations in The Context of Education in The 21st Century

Multiple representation is an important part of practically any human experience. Literature is being extensively used to study how important multiple representations are for pupils’ in understanding a concept. Moreover, we explored whether particular traits in this group were related to participants, the physics concepts, or multiple representation. The eligibility requirements have been encountered in 47 articles studies from Scopus and WoS indexed articles. The review examined the Springer, Sage, Elsevier, Willey, relevant journals using a qualitative research technique. We conducted a search to find papers published from 2019 to 2024. Then, we use descriptive statistics and content analysis to analyse the data. Our qualitative content analysis revealed five key themes: multiple representations, external representations, and multiple representations in physics. The categories and frequencies have each been examined separately. We have been assessed the research's inadequacies in order to direct future efforts toward a deeper comprehension of physics phenomena. In the current reformation of physics education, multiple representation has been highlighted as a new trend in understanding a concept. As a result, the findings of this study may be used as a starting point for all stakeholders involved in physics education in the future, notably educators, professors, and researchers.

Intelligent aerodynamic modelling method for steady/unsteady flow fields of airfoils driven by flow field images based on modified U-Net neural network

An intelligent modelling method driven by flow field images for predicting steady and unsteady flow filed around aerofoils has been developed. Signed Distance Field (SDF) images achieve dimensionality enhancement of aerofoil geometric information, and ‘synthesised images’ achieve dimensionality enhancement of the angle of attack of the aerofoil and Mach number. An intelligent aerodynamic model for steady flow field of aerofoils is constructed based on the U-Net neural network architecture, and further incorporating a long short-term memory (LSTM) module to construct a U-Net-LSTM neural network architecture to extract the temporal features. Typical NACA aerofoils results show that, the prediction error for steady flow is less than 1.98%, while the prediction error for unsteady flow is less than 2.56%. Additionally, the model demonstrates good generalization capability, with a generalization error for steady flow less than 2.45% and a generalization error for unsteady flow less than 3.34%. This research provides a new method for intelligent aerodynamic modelling based on physical representations. Compared to existing methods, this method avoids the need for extracting aerofoil geometry information and eliminates the necessity of predicting the flow field point by point, making it more concise and efficient. Highlights 1. An aerodynamic model was constructed using U-Net to rapidly predict the steady flow field around airfoils. 2. A Long Short-Term Memory (LSTM) module was incorporated to capture temporal information, enabling the rapid prediction of the unsteady flow field around airfoils. To address the problem of ‘dimension loss’ in the modelling datasets, effective data dimensionality enhancement was achieved using SDF images and ‘synthesized images’.

Descriptor-Free Collective Variables from Geometric Graph Neural Networks.

Enhanced sampling simulations make the computational study of rare events feasible. A large family of such methods crucially depends on the definition of some collective variables (CVs) that could provide a low-dimensional representation of the relevant physics of the process. Recently, many methods have been proposed to semiautomatize the CV design by using machine learning tools to learn the variables directly from the simulation data. However, most methods are based on feedforward neural networks and require some user-defined physical descriptors. Here, we propose bypassing this step using a graph neural network to directly use the atomic coordinates as input for the CV model. This way, we achieve a fully automatic approach to CV determination that provides variables invariant under the relevant symmetries, especially the permutational one. Furthermore, we provide different analysis tools to favor the physical interpretation of the final CV. We prove the robustness of our approach using different methods from the literature for the optimization of the CV, and we prove its efficacy on several systems, including a small peptide, an ion dissociation in explicit solvent, and a simple chemical reaction.

The Mapping of Sensemaking Territories Through Physical and Social Media: A Case of the Historical Bazaar of Kemeraltı, Izmir

The paper aims to analyze and make visible the intertwined layers of a palimpsest territory, such as the historical bazaar of Kemeraltı in Izmir, Turkey, through the lens of architecture students concerning their perceptions of producing Instagrammable visual data to influence and attract the prospective visitors and to compensate the lack of interest in the bazaar as mentioned by local institutions. Through this analysis, we also aim to conduct a methodological experiment that recognizes social media as a cognitive tool by combining digital and physical representations. The study encompasses a one-month workshop for students of the visualization in an elective architecture course. The technique of theme-based cognitive mapping both in the space of places and the space of flows in Manuel Castells’ sense was utilized to investigate how the students perceive the historical and socio-cultural qualities of the region through different realms. The workshop briefs were accompanied by Instagram hashtag research and the design of visual journals, consisting of the photographs and videos taken by the students to share their influencer/sensemaker routes specific to the selected themes. The students tailored various influencer/sensemaker roles and generated place-based scenarios in combination with the themes. Ultimately, Kemeraltı’s multifaceted genius could be reflected on cognitive and sensory grounds through digital and physical cognitive maps, social media journals, and analyses. It was observed that the students could integrate the intersubjective character of the readily presented data on social media into the subjective and authentic character of the data produced mindfully on the site.

Physics-guided deep learning for skillful wind-wave modeling.

Modeling sea surface wind-waves is crucial for both scientific research and engineering applications. Nowadays, the most accurate wave models are based on numerical methods, which primarily concern the wave spectrum evolution by solving wave action balance partial differential equations. These methods are computationally expensive and limited by incomplete physical representations of wave spectral evolution. Here, we present a deep learning-based wave model trained using observation-merged wave hindcasts. Guided by the physics knowledge that waves are either generated by local current winds or by remote historical winds, this method can directly model significant wave height, bypassing the need for wave spectral information. This feature engineering effectively reduces the complexity of model inputs and outputs. The resulting artificial intelligence method can model 1 year of global significant wave heights at a 0.5° × 0.5° × 1-hour resolution within half an hour on a personal computer, achieving higher accuracy than state-of-the-art numerical wave models.

Exploring Household Preferences for Visualising Consumption Information: Towards Data Physicalizations to Promote Sustainable Practices

Abstract This paper explores household preferences for visualizing consumption data and investigates the potential of data physicalizations to enhance user engagement and promote sustainable practices within households. We conducted semi-structured interviews with thirteen households, utilizing a combination of images and low-fidelity prototypes to gain insights into participants’ preferences, ideas and feedback on visualizing consumption information. We requested participants to discuss an object in their home that symbolizes sustainability. The results revealed different emotional responses, from empathy to repulsion, triggered by different visual representations. Our findings also identified certain physical objects and locations within the household that play a role in fostering family collaboration towards sustainable practices. Participants preferred having a physical representation of their consumption data within their homes over a screen-based display. In light of these findings, we open space to consider designing physicalizations that encourage collaboration, enhance user engagement and motivate households to reduce their consumption.

A study on solutions of fractional order equal-width equation using novel approach

The time-fractional equal-width equation plays a vital role in plasma physics, serving as an essential tool for elucidating the dynamics of hydromagnetic waves in cold plasma, a critical component for the comprehensive understanding of plasma-related phenomena. Furthermore, this equation finds application in fluid mechanics, which models the transmission of nonlinear waves across shallow waters. In this study, we employ an innovative technique to solve time-fractional equal-width equation, demonstrating the q-homotopy analysis transform technique. This considered technique was implemented to find the effective approximated solution of the considered equations. The obtained results are discussed through the 3D plots and graphs that express the physical representation. The results derived from the q-homotopy analysis transform method are presented in a series form, exhibiting swift convergence and capturing the behavior of the equal-width equation’s solution with minimal error, and the convergence analysis and uniqueness of the solution using the projected method have been secured using the fixed-point theory. The projected method we used does not require linearization, perturbations, or discretization, which significantly reduces computations. The results disclose that the proposed technique is highly effective, methodical, and easy to apply for complex and nonlinear systems, helping us to capture the associated behavior of diverse classes of phenomena. The numerical simulations presented ensure higher accuracy and are compared with other techniques to evaluate approximate errors. In comparison with other techniques, the considered method is a competent tool to get an analytical solution of an considered equation and the methodology employed herein for the considered equation is demonstrated to be both efficient and dependable, marking a significant advancement in the field.

Electrophysiology Reveals That Intuitive Physics Guides Visual Tracking and Working Memory.

Starting in early infancy, our perception and predictions are rooted in strong expectations about the behavior of everyday objects. These intuitive physics expectations have been demonstrated in numerous behavioral experiments, showing that even pre-verbal infants are surprised when something impossible happens (e.g., when objects magically appear or disappear). However, it remains unclear whether and how physical expectations shape different aspects of moment-by-moment online visual scene processing, unrelated to explicit physical reasoning. In two EEG experiments, people watched short videos like those used in behavioral studies with adults and infants, and more recently in AI benchmarks. Objects moved on a stage, and were briefly hidden behind an occluder, with the scene either unfolding as expected, or violating object permanence (adding or removing an object). We measured the contralateral delay activity, an electrophysiological marker of online processing, to examine participants' working memory (WM) representations, as well as their ability to continuously track the objects in the scene. We found that both types of object permanence violations disrupted tracking, even though violations involved perceptually non-salient events (magical vanishing) or new objects that weren't previously tracked (magical creation). Physical violations caused WM to reset, i.e., to discard the original scene representation before it could recover and represent the updated number of items. Providing a physical explanation for the violations (a hole behind the occluder) restored object tracking, and we found evidence that WM continued to represent items that disappeared 'down the hole'. Our results show how intuitive physical expectations shape online representations, and form the basis of dynamic object tracking.

The need for carbon-emissions-driven climate projections in CMIP7

Abstract. Previous phases of the Coupled Model Intercomparison Project (CMIP) have primarily focused on simulations driven by atmospheric concentrations of greenhouse gases (GHGs), for both idealized model experiments and climate projections of different emissions scenarios. We argue that although this approach was practical to allow parallel development of Earth system model simulations and detailed socioeconomic futures, carbon cycle uncertainty as represented by diverse, process-resolving Earth system models (ESMs) is not manifested in the scenario outcomes, thus omitting a dominant source of uncertainty in meeting the Paris Agreement. Mitigation policy is defined in terms of human activity (including emissions), with strategies varying in their timing of net-zero emissions, the balance of mitigation effort between short-lived and long-lived climate forcers, their reliance on land use strategy, and the extent and timing of carbon removals. To explore the response to these drivers, ESMs need to explicitly represent complete cycles of major GHGs, including natural processes and anthropogenic influences. Carbon removal and sequestration strategies, which rely on proposed human management of natural systems, are currently calculated in integrated assessment models (IAMs) during scenario development with only the net carbon emissions passed to the ESM. However, proper accounting of the coupled system impacts of and feedback on such interventions requires explicit process representation in ESMs to build self-consistent physical representations of their potential effectiveness and risks under climate change. We propose that CMIP7 efforts prioritize simulations driven by CO2 emissions from fossil fuel use and projected deployment of carbon dioxide removal technologies, as well as land use and management, using the process resolution allowed by state-of-the-art ESMs to resolve carbon–climate feedbacks. Post-CMIP7 ambitions should aim to incorporate modeling of non-CO2 GHGs (in particular, sources and sinks of methane and nitrous oxide) and process-based representation of carbon removal options. These developments will allow three primary benefits: (1) resources to be allocated to policy-relevant climate projections and better real-time information related to the detectability and verification of emissions reductions and their relationship to expected near-term climate impacts, (2) scenario modeling of the range of possible future climate states including Earth system processes and feedbacks that are increasingly well-represented in ESMs, and (3) optimal utilization of the strengths of ESMs in the wider context of climate modeling infrastructure (which includes simple climate models, machine learning approaches and kilometer-scale climate models).

The Distributed Xin’anjiang Model Incorporating the Analytic Solution of the Storage Capacity Under Unsteady-State Conditions

Developing a functional linkage between hydrological variables and easily accessible terrain and soil information is a novel concept for distributed hydrological models. This approach aims to address limitations imposed by data scarcity and high computational demands. The model hypothesizes that the relationship between the evaporation flux and the absolute value of the matric potential follows a power exponential pattern. Analytic solutions for the groundwater depth, the evaporation capacity, and the storage capacity are derived with respect to the topographic index, considering the relationship between the groundwater depth and the topographic index and the influence of setting off. Subsequently, a distributed Xin’anjiang Model using the analytic solution of the storage capacity under unsteady-state conditions is constructed. This new model is employed to simulate soil moisture and discharge in the Tarrawarra Watershed. The simulation results for soil moisture and discharge are compared with those from the Storage Capacity Model and the DHSVM. Additionally, the computational speeds of all three models are compared. The findings indicate that the simulation accuracy of the new model for soil moisture and discharge surpasses that of the Storage Capacity Model and the DHSVM. Meanwhile, the computational speed of the new model is significantly faster than the DHSVM and slightly slower than the Storage Capacity Model. It offers a balance between computational efficiency, predictive accuracy, and physical mechanism representation. The data requirements of the new model are minimal and easy to procure, and it requires less computational effort. Moreover, it accurately captures the spatial and temporal dynamics of soil moisture and the discharge process of the watershed.