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Faculty online learning communities (FOLCs) can help faculty effectively adopt and persist in using research-based curricula. This paper documents faculty perspectives on the value they gained from participating in a multi-year FOLC designed to help them implement an inquiry-based physical science curriculum. Personal value narratives were collected from 11 volunteer FOLC participants. In the narratives, participants responded to prompts regarding their initial reasons for participation, activities they engaged in, and values they gained, as well as how participation impacted their profession, social connections, professional practice, and ability to influence their world as teachers. Qualitative analysis shows that the values faculty gained through their participation in the FOLC could be associated with four major areas of impact: teaching, community, leadership and personal development. The first two aligned with the initial goals of the FOLC, while the latter two emerged organically through participation over its four-year duration. The methods used to assess value creation in the NGPET FOLC and the findings from this study can inform other professionals who aspire to create and sustain a long-standing faculty development community and understand its impacts.

This document is an editorial for a special issue of the journal "Technologies" focused on data science and big data in various fields such as biology, physical science, and engineering. The editorial highlights the importance of analyzing large amounts of data generated by digital technologies and the need for data scientists to use artificial intelligence and machine learning to extract valuable knowledge. The special issue includes 12 papers covering topics such as machine learning techniques for customer churn prediction, agile program management in the U.S. Navy, deep learning for cybersecurity in Industry 5.0, self-directed learning during the COVID-19 era, decision tree-based neural networks for data classification, data-driven governance in technology companies, and more. The papers explore different approaches, models, and tools in the context of data science and big data. [Extracted from the article]

This paper evaluates how the contribution of scientific publications on climate change from the University of São Paulo (USP) evolved between 1989 and 2022. The study conducted a bibliometric analysis of 2874 research articles collected from the Web of Science and Scopus databases to explore main trend topics and reveal influential journals and collaboration networks using the Bibliometrix software. The study shows an increasing and more significant temporal publication distribution from 2010 onwards. Based on the author's affiliations, results indicate that the most important partnerships are national (67%), while the international co-authorships are predominantly with research collaborators of the Global North (North America and Europe). Several papers published in the period are in high-impact factor journals, evidence on climate change from USP. Environmental services, Atlantic Forest, Tropical Forest, Amazon, biodiversity, and Land use are the hotspot research topics to which USP researchers contribute. The evolution of three thematic groups was identified: (i) physical science, (ii) causes and effects, and (iii) strategies, discussed in themes such as organic matter, deforestation, and mitigation, respectively. This research is helpful to get insights into the current research development trends, to show the broadness of the scientific production, and the importance of the USP role in the climate change theme. Ultimately, it provides valuable information for further studies and suggests the possibility of advancing the research agenda on climate change with the Global South. [ABSTRACT FROM AUTHOR]

This document is a compilation of various scientific papers and preprints covering a wide range of topics, including protein folding, cell migration, machine learning, deep neural networks, neural scaling laws, representations and generalization in artificial and brain neural networks, and the neuron as a direct data-driven controller. The papers discuss different aspects of these subjects, providing a comprehensive overview of the current research in these fields. This document can be a valuable resource for library patrons conducting research on these topics, particularly in the fields of neuroscience and artificial intelligence. [Extracted from the article]

Faculty online learning communities (FOLCs) can help faculty effectively adopt and persist in using research-based curricula. This paper documents faculty perspectives on the value they gained from participating in a multi-year FOLC designed to help them implement an inquiry-based physical science curriculum. Personal value narratives were collected from 11 volunteer FOLC participants. In the narratives, participants responded to prompts regarding their initial reasons for participation, activities they engaged in, and values they gained, as well as how participation impacted their profession, social connections, professional practice, and ability to influence their world as teachers. Qualitative analysis shows that the values faculty gained through their participation in the FOLC could be associated with four major areas of impact: teaching, community, leadership and personal development. The first two aligned with the initial goals of the FOLC, while the latter two emerged organically through participation over its four-year duration. The methods used to assess value creation in the NGPET FOLC and the findings from this study can inform other professionals who aspire to create and sustain a long-standing faculty development community and understand its impacts. [ABSTRACT FROM AUTHOR]

This article discusses the potential of new environmental data to improve the governance and sustainability of cities. It highlights the use of distributed sensors and real-time data to monitor and manage urban systems, inform policy and planning, and address urban challenges. The article also explores the practicalities, politics, and governance of using environmental data in cities. The document includes eight papers that cover topics such as urban observatories, governance issues, and public perceptions of real-time data. The authors emphasize the need for interdisciplinary collaboration and new modes of governance to fully realize the benefits of these technologies in urban settings. [Extracted from the article]

The ordinary 3D wave equation for nondissipative, homogeneous, isotropic media admits solutions where the point sources are permitted to move, but as shown in this paper, it does not admit solutions where the receiver is allowed to move. To overcome this limitation, a new wave equation that permits both the receiver and the source to move is derived in this paper. This new wave equation is a generalization of the standard wave equation, and it reduces to the standard wave equation when the receiver is at rest. To derive this new wave equation, we first mathematically define a diverging spherical wave caused by a stationary point source. From this purely mathematical definition, the wave equation for a stationary source and a moving receiver is derived, together with a corresponding free-space Green function. Utilizing the derived Green function, it is shown that unlike the standard wave equation this new wave equation also permits solutions where both the receiver and the source are permitted to move. In conclusion, this paper demonstrates that, instead of an ordinary wave equation, the wave equation for a moving source and a moving receiver governs the waves emitted by moving point sources and received by moving receivers. This new wave equation has possible applications in acoustics, electrodynamics, and other physical sciences. [ABSTRACT FROM AUTHOR]

Manifold visualisation techniques are commonly used to visualise high-dimensional datasets in physical sciences. In this paper, we apply a recently introduced manifold visualisation method, slisemap, on datasets from physics and chemistry. slisemap combines manifold visualisation with explainable artificial intelligence. Explainable artificial intelligence investigates the decision processes of black box machine learning models and complex simulators. With slisemap, we find an embedding such that data items with similar local explanations are grouped together. Hence, slisemap gives us an overview of the different behaviours of a black box model, where the patterns in the embedding reflect a target property. In this paper, we show how slisemap can be used and evaluated on physical data and that it is helpful in finding meaningful information on classification and regression models trained on these datasets. [ABSTRACT FROM AUTHOR]

This document provides a summary of a special issue on smart sensors for structural health monitoring (SHM) and nondestructive evaluation (NDE). It discusses the importance of developing defect detection and monitoring techniques for structures to ensure their integrity and prevent failure. The document highlights the challenges faced in implementing SHM and NDE, as well as the various techniques and smart sensors used in these fields. It also provides an overview of 33 papers published in the special issue, covering topics such as sensor technology, signal processing, and field applications. The authors hope that these papers will inspire future research in these rapidly developing areas. [Extracted from the article]

This document is an interview with Calin Dragoi, the first author of a paper titled "The oscillation of mitotic kinase governs cell cycle latches in mammalian cells" published in the Journal of Cell Science. The paper explores the cell cycle and how it can be experimentally perturbed to produce abridged cell cycle variants. The researchers constructed a cell cycle model and used computer simulations to propose a mechanism for the alternation of DNA replication and mitosis. The findings were confirmed in a non-transformed cell line. Calin Dragoi discusses their research, mentors, motivation for pursuing a career in science, and future plans. [Extracted from the article]

Among about a thousand objects obtained by the Science Museum from Stephen Hawking's office is a blackboard full of cartoons and puns, both childishly silly and deeply scientifically meaningful. This blackboard is now one of the Museum's most popular objects thanks to its playful imagery and perhaps also its perceived fragility. But it is also one of the objects that best illustrates the scientific life of Stephen Hawking and his generation, and more broadly, it is an exemplar of the social and material culture of the postmodern epoch of theoretical physics in the decades around the turn of the twenty-first century. In this article, the curator of the Hawking Collection explains the importance of this blackboard, first through an account of its unexpected trajectory to becoming the Hawking Collection's most charismatic object, followed by a discussion of the general historical role of blackboards: their iconography, material culture, and value as historical sources and how their conservation requirements contribute to their perceived value. The author then delves deeper into the specific content of this blackboard, showing how it constitutes a window into the highs and lows of a generation of physicists inspired by Hawking's work, as they attempted, and ultimately failed, to unify the two great theories of the twentieth century: general relativity and quantum mechanics. What we now call the superspace and supergravity blackboard is a microcosm of one of those moments in physics when theory broke away from what was empirically verifiable. The scribblings on the blackboard were collectively drawn by an up-and-coming generation of supersymmetry theorists. It immortalises the promising early years of supergravity, one of the hopeful candidates at the time for the long-wished-for Theory of Everything. Preserved in chalk are the seeds of one of the most brilliant theoretical discoveries of the turn of the century: what is now called the holographic principle. But also, seen with the benefit of hindsight, supersymmetric approaches seeded the identity crisis that theoretical physics is experiencing today, especially around the role of empirical anchoring of proposed fundamental theories. The contested status of the blackboard's content and its humorous, self-ironic style, make it a key piece of a unique zeitgeist of physics at the turn of the twenty-first century, which one could call the era of postmodern physics. In addition to its significance to the history of specific ideas, this paper also argues that the blackboard reflects the collective, social nature of theoretical research, while also inviting reflection on what constitutes success and failure in the physical sciences. [ABSTRACT FROM AUTHOR]

The aim of this paper was to explore how teaching and learning of physical sciences using rural blended learning strategy (RBLS) in rural schools occurred. A qualitative case study design was used to collect data from a from a rural school. Hence, it was a purposeful sampling strategy with one physical science teacher and his 45 learners. Data was collected through classroom observation, semi-structured, and focus group interviews. The study found that issues such as digital divide, poor training of teacher, incorrect teaching approaches, workloads, poor parental involvement remain challenges in rural areas, even though the teacher experienced effective institutional support and exposure to e-learning. As such, we noted his ability to create Google Classroom (GC) and WhatsApp platforms and their effective use, even though the same could not be said about the use of video conferencing (VC) platforms. Issues like workload were cited as the barriers to the implementation of RBLS. The teacher also carried on with teacher centered methods, which were not effective not only for the learning of science but also for the implementation of blended learning. Therefore, the teacher failed to flip the classroom. Learners on the other hand found RBLS to be inducing their self-directed learning and motivation. We recommend that more prolonged training on the use of RBLS should be provided, not only to teacher but also to the learners. Department of basic education should engage network providers on the provision of zero-rated data for educational applications such as GC and VC platforms while school governing bodies provide institutional support to both learners and teacher. [ABSTRACT FROM AUTHOR]

Holism evolution states that although energy, matter, life, consciousness and the human personality are independent and different substances/levels of evolution, they are holistically and intimately related. This paper presents the findings from teaching chemistry to high school science students and post-graduate university science-education students using poetry constructed from physical science ideas embedded in holism evolution. The research findings are based on observations of student behaviour and their written feedback from being taught chemistry using poems on the periodic table and acids and bases. Both poems demonstrate the holism intimacy between various substances/levels of evolution, with the periodic table poem also presenting groups of chemical elements as representing the characteristics of infant, teenager, adult and old age of matter, presenting a holistic relationship between matter and life. The findings indicate that using holism evolution infused content led to a personally meaningful engagement with chemistry even among students with limited previous interest in the subject. Many post-graduate students expressed new levels of engagement with chemistry as well as new insights to enliven their teaching practice. [ABSTRACT FROM AUTHOR]

Mathematics plays a fundamental role in ecological research, yet its uses remain strikingly separate from advances in the environmental social sciences and humanities. In this paper, I work to address this impasse and outline the motivation and scope for an 'ecological mathematics', an approach to doing mathematics in environmental research which foregrounds relationship, embodiment and human difference. I begin by tracing the historical emergence of mathematics in ecology, noting how life processes have been conceptualised in a way which forces them to fit the ideals of mathematical models transplanted from the physical sciences. I then investigate the cultural factors shaping the evolution of mathematical thought, eliciting a malleability in how mathematical knowledge relates to the more-than-human world. This provides a place from which to rethink the role of abstraction in ecological thought, and develop mathematical methods grounded in ecological concepts. Drawing on ethnographic and perceptual accounts of space and time, I work with topological concepts from both mathematics and the social sciences to suggest a new correspondence between these subjects, elaborating a way of employing mathematical techniques which enliven, rather than deaden, the ecologies under study. The paper concludes with important philosophical clarifications to the approach of an ecological mathematics. [ABSTRACT FROM AUTHOR]

The gap between genomics and phenomics is narrowing. The rate at which it is narrowing, however, is being slowed by improper statistical comparison of methods. Quantification using Pearson's correlation coefficient (r) is commonly used to assess method quality, but it is an often misleading statistic for this purpose as it is unable to provide information about the relative quality of two methods. Using r can both erroneously discount methods that are inherently more precise and validate methods that are less accurate. These errors occur because of logical flaws inherent in the use of r when comparing methods, not as a problem of limited sample size or the unavoidable possibility of a type I error. A popular alternative to using r is to measure the limits of agreement (LOA). However both r and LOA fail to identify which instrument is more or less variable than the other and can lead to incorrect conclusions about method quality. An alternative approach, comparing variances of methods, requires repeated measurements of the same subject, but avoids incorrect conclusions. Variance comparison is arguably the most important component of method validation and, thus, when repeated measurements are possible, variance comparison provides considerable value to these studies. Statistical tests to compare variances presented here are well established, easy to interpret and ubiquitously available. The widespread use of r has potentially led to numerous incorrect conclusions about method quality, hampering development, and the approach described here would be useful to advance high throughput phenotyping methods but can also extend into any branch of science. The adoption of the statistical techniques outlined in this paper will help speed the adoption of new high throughput phenotyping techniques by indicating when one should reject a new method, outright replace an old method or conditionally use a new method. [ABSTRACT FROM AUTHOR]

Purpose: Entrepreneurs prioritise and act on purposeful endeavours instigated to actions by the visions of profits and benefits in the perceived opportunities. In the state of maximum entropy, with disorderliness and disequilibrium, entrepreneurs select the preferred pathway, through the profit-sensing mechanism, with the best probability of success to bet on. Therefore, this paper unpacks the forces at work in the mechanism to explain how entrepreneurs respond to opportunity and interpret the signals to coalesce into organised actions. Design/methodology/approach: This research is primarily a conceptual paper on entrepreneurial action and the mechanism leading to that action. It refers to thermodynamic principles and biological cases to explain the forces at work using mostly analogical comparisons and similarities. Findings: This paper aims to present an alternative theoretical scaffolding for entrepreneurship researchers to explore non-rational entrepreneurial behaviours and actions in uncertain, unstable and non-equilibrium environments, thereby creating new and competing hypotheses under the backdrop of adaptive evolution and thermodynamics phenomena. Research limitations/implications: The discussion featuring instinctively and naturally forming responses cannot fully explain the real entrepreneurial action as there is an element of free will and choices that are not discussed. While strategic choice and free-will shape decisions, they are preceded first by the attraction of the gradients and the biased motion in the direction of profit-attractant. Practical implications: There remain essential links and issues not addressed in this "natural science", constituting life science and physical science, oriented entrepreneurship research and exploration. Conceptualising opportunity-as-artefact and entrepreneurship as design, significant incidences of entrepreneurial actions can be explained by the presence of gradients stimulating entrepreneurial actions. Social implications: This viewpoint of information causality in opportunity-as-artefact casts a new look at the venerable question of what causes entrepreneurial actions. Shane and Venkataraman brought into focus this conversation, initiating the conceptual definition of opportunity. To have entrepreneurship, entrepreneurial opportunities must come first. Figuring the signals arising from these opportunities and cueing entrepreneurs to action is the main focus of this study. Originality/value: Considering the "mechanism" at work and the thermodynamical forces at play, the entrepreneurial design process appears to hold considerable promise for future research development. [ABSTRACT FROM AUTHOR]

Multimarket Multireservoir Hydro Scheduling Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout in Germany and the steady growth in photovoltaic capacity. The reduced profitability of peak/off-peak arbitrage forces hydropower producers to recover part of their original profitability on the reserve markets. In their paper titled "A Planner-Trader Decomposition for Multimarket Hydro Scheduling" Schindler, Rujeerapaiboon, Kuhn, and Wiesemann propose a bi-layer stochastic programming framework that jointly optimizes the trading strategies on the spot and reserve markets. The model faithfully accounts for uncertainty in electricity prices, water inflows, and reserve activations, and it ensures that the hydropower producers can fulfill their market commitments under any circumstances. The model is numerically challenging due to the various sources of uncertainty that are revealed at different time scales and that affect the problem's objective function and constraints, and the authors propose a new planner-trader decomposition and an information restriction for its solution. A case study based on real data from Austria reveals significant benefits of simultaneously participating in the spot and the reserve markets. Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout in Germany and the steady growth in photovoltaic capacity. The reduced profitability of peak/off-peak arbitrage forces hydropower producers to recover part of their original profitability on the reserve markets. We propose a bilayer stochastic programming framework for the optimal operation of a fleet of interconnected hydropower plants that sells energy on both the spot and the reserve markets. The outer layer (the planner's problem) optimizes end-of-day reservoir filling levels over one year, whereas the inner layer (the trader's problem) selects optimal hourly market bids within each day. Using an information restriction whereby the planner prescribes the end-of-day reservoir targets one day in advance, we prove that the trader's problem simplifies from an infinite-dimensional stochastic program with 25 stages to a finite two-stage stochastic program with only two scenarios. Substituting this reformulation back into the outer layer and approximating the reservoir targets by affine decision rules allows us to simplify the planner's problem from an infinite-dimensional stochastic program with 365 stages to a two-stage stochastic program that can conveniently be solved via the sample average approximation. Numerical experiments based on a cascade in the Salzburg region of Austria demonstrate the effectiveness of the suggested framework. Funding: This research was supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 [Grant MOE-2019-T3-1-010], the National University of Singapore [Grant A-0009135-01-00], and the Swiss National Science Foundation [Grant BSCGI0_157733] as well as the Engineering and Physical Sciences Research Council [Grant EP/R045518/1]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2456. [ABSTRACT FROM AUTHOR]

The field of model order reduction (MOR) has seen significant advancements in addressing the challenges associated with the complexity of mathematical models in engineering and physical sciences. This paper provides a schematic study of MOR techniques applicable to linear time-invariant integer order, fractional order and interval systems. Various classical and optimization methods are elaborated highlighting their effectiveness in reducing system dimension and maintaining the essential dynamic characteristics. Emphasis is given to the distinct approaches required for integer, fractional and interval systems, given their unique mathematical properties and application-specific challenges. The optimization techniques integral to the reduction process are schematically investigated, focusing on balancing accuracy, computational efficiency and stability in the reduced models. Through a detailed comparison of these methods, the advantages and challenges of each approach are presented, providing critical insights into their suitability for different classes of systems. Although classical techniques have been extensively employed independently and combined with soft computing techniques for integer-order systems, lesser emphasis is paid to fractional-order and interval-system’s order reduction. Several issues such as unstable systems, steady-state errors, passivity and non-minimum phase characteristics still pertain and not addressed simultaneously in existing techniques. The lack of generality of the methods proposed in literature further motivates researchers to work on this years old problem. This review serves as a valuable resource for researchers seeking to understand the state of the art in MOR methods and their applications, offering guidance on selecting appropriate techniques based on system characteristics and reduction objectives. The analysis concludes with suggestions for future research directions aimed at enhancing the robustness and applicability of MOR techniques across various domains. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue titled "Macroscopic and Microscopic Thermodynamics: From Fundamentals to Present Applications, 2nd Edition." It explores the broad field of thermodynamics and its applications in various scientific disciplines. The article discusses the historical development of thermodynamics and its expansion into fields such as cosmology, information science, and social sciences. It also highlights the importance of molecular thermodynamics in chemical engineering and the use of computational methods in solving thermodynamic problems. The document provides a list of the contributions included in the special issue, which cover topics such as vapor-liquid equilibrium, clathrate hydrates, binary mixtures, and surface tension. The authors emphasize the importance of thermodynamics in addressing theoretical and practical problems and highlight its potential for further research in fields like process engineering, human health, and environmental preservation. [Extracted from the article]

Sierpiński graphs are frequently related to fractals, and fractals apply in several fields of science, i.e., in chemical graph theory, computer networking, biology, and physical sciences. Functions and polynomials are powerful tools in computer mathematics for predicting the features of networks. Topological descriptors, frequently graph constraints, are absolute values that characterize the topology of a computer network. In this essay, Firstly, we compute the M-polynomials for Sierpiński-type fractals. We derive some degree-dependent topological invariants after applying algebraic operations on these M-polynomials. [ABSTRACT FROM AUTHOR]

This document is a QnAs (Questions and Answers) session with Miguel A. Garcia-Garibay, a professor of chemistry and biochemistry at the University of California, Los Angeles. Garcia-Garibay discusses his research on the chemical amplification of photons in crystalline solids, specifically focusing on the concept of quantum chain reactions. He explains how his team designed a crystal with a reactant and an antenna molecule to initiate a chain reaction, resulting in the amplification of photon effects. The potential applications of this research include improving particle detectors, increasing sensor sensitivity, and potentially impacting biological systems. [Extracted from the article]

The current research introduces a novel approach to address the computational challenges associated with solving the Lane–Emden‐type equations by transforming them from their conventional differential form to the corresponding integro‐differential form. These equations have wide‐ranging applications in physical sciences, including modeling diffusion phenomena and thermal gradients. We utilize the Volterra integro‐differential (VID) form to resolve computational challenges due to singularity issues. Through the Scale 3 Haar Wavelet (S3‐HW) algorithm, we transform the VID equations into algebraic form and obtain solutions using the Gauss‐elimination method. The quasilinearization technique is implemented whenever a nonlinearity is encountered. Comparative analysis against various techniques demonstrates the superior accuracy and efficiency of our method. Despite challenges such as the discontinuity of Scale 3 Haar Wavelets and singularity issues of Lane–Emden‐type equations, our algorithm paves the way for extending its application to a wide range of physical problems. [ABSTRACT FROM AUTHOR]

Teachers are often required to display or explicitly state learning objectives prior to beginning a lesson. In particular, many elementary classrooms are required to post or state "I CAN..." statements related to their standards. Unfortunately, this practice can "give away" what students should be figuring out, decrease students' "need to know" or motivation for learning about the phenomenon, and/or narrow what students notice or connect during the lesson. Here, we provide three strategies that may assist teachers in meeting their schools' requirements while preserving students' sense of wonder and ability to engage in sensemaking: (1) Delay sharing the learning objective until after the lesson and use it to metacognitively check understanding; (2) Frame learning objectives in terms of the crosscutting concept or/or science and engineering practice; and (3) Create a question map to think more deeply about the core ideas embedded in the standard(s). We encourage teachers to consider how to present the learning objective as a vital part of the lesson planning process and recommend that they try these strategies individually or in combination to find what works best for their classroom. [ABSTRACT FROM AUTHOR]

Global climate change is often thought of as a steady and approximately predictable physical response to increasing forcings, which then requires commensurate adaptation. But adaptation has practical, cultural and biological limits, and climate change may pose unanticipated global hazards, sudden changes or other surprises–as may societal adaptation and mitigation responses. These poorly known factors could substantially affect the urgency of mitigation as well as adaptation decisions. We outline a strategy for better accommodating these challenges by making climate science more integrative, in order to identify and quantify known and novel physical risks including those arising from interactions with ecosystems and society. We need to do this even–or especially–when they are highly uncertain, and to explore risks and opportunities associated with mitigation and adaptation responses by engaging across disciplines. We argue that upcoming climate assessments need to be more risk‐aware, and suggest ways of achieving this. These strategies improve the chances of anticipating potential surprises and identifying and communicating "safe landing" pathways that meet UN Sustainable Development Goals and guide humanity toward a better future. Plain Language Summary: Global risks may arise from many places, not all of which are being considered at the moment in the usual projections of future climate. Some risks are being overlooked, sometimes by most of the physical science community itself, or else by others who need information about future climate. We think physical climate sciences can do a lot to remedy this situation by looking more systematically at a broader array of risks and possible futures, narrowing them down, and collaboratively seeking better ways to express them. In this perspective we argue why this is important and explain a few ways it can be done. Key Points: Climate science needs to adjust to account systematically for low‐likelihood, high‐impact risks and comprehensive pathway interactionsAchieving this requires new strategies; several are outlined here, including increased model coupling and scenario explorationSuccess will require collaboration between physical sciences and other disciplines [ABSTRACT FROM AUTHOR]

Teaching physical sciences in English as the Language of Learning and Teaching (LOLT) has presented numerous challenges, particularly for learners who are not proficient in the language. The South African government is increasingly considering the use of indigenous languages as the medium of instruction for subjects such as physical sciences, sparking debates regarding the effectiveness of indigenous languages in teaching and learning. This study aimed to explore stakeholders' perceptions regarding the use of Tshivenḓa scientific language for the concept of electricity. Utilizing qualitative interpretative case study methodology, the researcher conducted interviews with two grade 10 teachers, three parents, and two groups of grade 10 learners from selected schools to investigate their views on using Tshivenḓa scientific language register to teach physical sciences. The findings indicate that stakeholders view the adoption of scientific language registers in Tshivenḓa as a positive initiative. They believe it will provide learners who struggle to understand physical sciences when taught in English with an opportunity to learn effectively in a language they are familiar with. Therefore, the study recommends expanding the development of scientific registers in Tshivenḓa to cover other topics in physical sciences. [ABSTRACT FROM AUTHOR]

This study examined the role of formative feedback in the teaching and learning of Physical Sciences. Several scholars have recognised the pedagogical value of formative feedback in science education. However, there is a need for significant improvement in the quality of formative feedback provided by teachers in science classrooms. The empirical investigation explored the nature of formative feedback, pedagogical challenges, and pedagogical practices employed by teachers when implementing formative assessment in Physical Sciences classrooms. The study is underpinned by the Assessment for Formative Purposes Cycle as the underlying theoretical framework. We utilised the interpretive paradigm and a qualitative approach, specifically employing phenomenology as a means of inquiry. The empirical investigation involved 12 Grade 10 Physical Sciences teachers selected purposively from Inkosi Sambane Circuit Schools, under UMkhanyakude District, KwaZulu-Natal, South Africa. Data were collected through semi-structured interviews and classroom observations, and thematic analysis was used to analyse the data. The findings revealed that meaningful formative feedback could have been enhanced by addressing contextual factors such as overcrowding and lack of resources in Physical Sciences classrooms. While teachers valued the quality of formative feedback, they expressed concern about learners' inability to utilise formative feedback to improve their learning. This paper recommends providing teachers with the necessary training to effectively utilise formative feedback and enhance learners' academic achievement in Physical Sciences.

In recent years, metasurface, as a representative of micro- and nano-optics, have demonstrated a powerful ability to manipulate light, which can modulate a variety of physical parameters, such as wavelength, phase, and amplitude, to achieve various functions and substantially improve the performance of conventional optical components and systems. Artificial Intelligence (AI) is an emerging strong and effective computational tool that has been rapidly integrated into the study of physical sciences over the decades and has played an important role in the study of metasurface. This review starts with a brief introduction to the basics and then describes cases where AI and metasurface research have converged: from AI-assisted design of metasurface elements up to advanced optical systems based on metasurface. We demonstrate the advanced computational power of AI, as well as its ability to extract and analyze a wide range of optical information, and analyze the limitations of the available research resources. Finally conclude by presenting the challenges posed by the convergence of disciplines. [ABSTRACT FROM AUTHOR]

Understanding of dusty fluids for different Brinkman numbers in porous media is limited. This study examines the Darcy–Brinkman model for two-dimensional magneto-hydrodynamic fluid flow across permeable stretching/shrinking surfaces with heat transfer. Water was considered as a conventional base fluid in which the copper (Cu), silver (Ag), and titanium dioxide ( T i O 2 ) nanoparticles were submerged in a preparation of a ternary dusty nanofluid. The governing nonlinear partial differential equations are converted to ordinary differential equations through suitable similarity conversions. Under radiation and mass transpiration, analytical solutions for stretching sheets/shrinking sheets are obtained. Several parameters are investigated, including the magnetic field, Darcy–Brinkman model, solution domain, and inverse Darcy number. The outcomes of the present article reveal that increasing the Brinkman number and inverse Darcy number decreases the velocity of the fluid and dusty phase. Increasing the magnetic field decreases the momentum of the boundary layer. Ternary dusty nanofluids have significantly improved the heat transmission process for manufacturing with applications in engineering, and biological and physical sciences. The findings of this study demonstrate that the ternary nanofluid phase's heat and mass transpiration performance is better than the dusty phase's performance. [ABSTRACT FROM AUTHOR]

It is well known that molecules confined very close to a surface arrange into molecular layers. Because solid-liquid interfaces are ubiquitous in the chemical, biological and physical sciences, it is crucial to develop methods to easily access molecular layers and exploit their distinct properties by producing molecular layered crystals. Here we report a method based on crystallization in ultra-thin puddles enabled by gas blowing, which allows to produce molecular layered crystals with thickness down to the monolayer onto a surface, making them directly accessible for characterization and further processing. By selecting four molecules with different types of polymorphs, we observed exclusive crystallization of polymorphs with Van der Waals interlayer interactions, which have not been observed with traditional confinement methods. In conclusion, the gas blowing approach unveils the opportunity to perform materials chemistry under confinement onto a surface, enabling the formation of distinct crystals with selected polymorphism. Molecules arranged in close proximity to a surface form molecular layers, exhibiting distinct properties. However, the creation of these layers is challenging. Here, the authors present a technique for generating molecular layers through crystallization induced by gas blowing onto a surface. [ABSTRACT FROM AUTHOR]

Physical laws manifest themselves through the amalgamation of mathematical symbols, numbers, functions, geometries, and relationships. These intricate combinations unfold within a mathematical model devised to capture and represent the "objective reality" of the system under examination. In this symbiotic relationship between physics and mathematics, the language of mathematics becomes a powerful tool for describing and predicting the behavior of the physical world. The language used and the associated concepts are in a perpetual state of evolution, mirroring the ongoing expansion of the phenomena accessible to our scientific understanding. In this contribution, written in honor of Richard Kerner, we delve into fundamental, at times seemingly elementary, elements of the mathematical language inherent to the physical sciences, guided by the overarching principles of symmetry and group theory. Our focus turns to the captivating realm of spheres, those strikingly symmetric entities that manifest prominently within our geometric landscape. By exploring the interplay between mathematical abstraction and the tangible beauty of symmetry, we seek to deepen our understanding of the underlying structures that govern our interpretation of the physical world. [ABSTRACT FROM AUTHOR]

The article discusses the book "Why Machines Will Never Rule the World" by Jobst Langrebe and Barry Smith, which argues against the possibility of achieving artificial general intelligence (AGI). While the article agrees with the authors' argument, it disagrees with their conclusion that machines will never rule the world. It explores the potential negative effects of technology, including the "degeneration effect," and references historical perspectives on technology. The article also discusses the relevance of philosophy in the AGI debate and presents opposing views on the benefits and risks of AI. It raises concerns about the limitations of computers, the impact of AI on individuals and society, and the potential loss of skills and deep understanding. The article concludes by suggesting that the book's title should be changed to address the omission of certain perspectives. [Extracted from the article]

The exploration of topologically-ordered states of matter is a long-standing goal at the interface of several subfields of the physical sciences. Such states feature intriguing physical properties such as long-range entanglement, emergent gauge fields and non-local correlations, and can aid in realization of scalable fault-tolerant quantum computation. However, these same features also make creation, detection, and characterization of topologically-ordered states particularly challenging. Motivated by recent experimental demonstrations, we introduce a paradigm for quantifying topological states—locally error-corrected decoration (LED)—by combining methods of error correction with ideas of renormalization-group flow. Our approach allows for efficient and robust identification of topological order, and is applicable in the presence of incoherent noise sources, making it particularly suitable for realistic experiments. We demonstrate the power of LED using numerical simulations of the toric code under a variety of perturbations. We subsequently apply it to an experimental realization, providing new insights into a quantum spin liquid created on a Rydberg-atom simulator. Finally, we extend LED to generic topological phases, including those with non-abelian order. Detection of topological phases in experiments is challenging, especially in the presence of incoherent noise. Cong et al. introduce a novel method combining error correction and renormalization-group flow and apply it to characterization of quantum spin liquid phases realized in a Rydberg-atom simulator. [ABSTRACT FROM AUTHOR]

This article explores the use of Neural Network Potentials (NNPs) to model prebiotic chemistries accurately and efficiently. The authors present a scalable and generalizable approach for designing NNPs that can handle chemical reactivity in solvated systems. Through active learning and enhanced sampling techniques, they generate free-energy landscapes and calculate committors, finding a preference for the dissociative mechanism and identifying HPO4 2− as the more reactive species under prebiotic conditions. This research has the potential to enhance our understanding of prebiotic chemistry and its connection to the origins of life. The study acknowledges the need for further improvements, such as incorporating machine learning approaches and training neural network potentials using collective variables. The work was supported by the US Department of Energy. [Extracted from the article]

The article discusses the uplift of the Puna Plateau in northwestern Argentina and challenges previous interpretations of its geological history. The authors argue that the field observations and paleoaltimetry analysis used to support significant deformation and uplift in the Miocene are flawed. They suggest that the interpretation of deformation in the Eastern Cordillera does not necessarily require synchronous deformation in the Puna Plateau. The authors emphasize the need for a comprehensive approach that considers all available data and acknowledges uncertainties in order to understand the complex uplift and paleoenvironmental history of the region. [Extracted from the article]

Introduction: Advances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states. Motivation: In addition to providing much needed insights into potential therapeutic protein targets, we also aim to demonstrate that small patient datasets have the potential to provide insights that usually require many samples (>5,000). There are many such datasets available and novel advancements in ML can provide valuable insights from these patient datasets. Problem statement: Using a public dataset made available by patient advocacy group AnswerALS and a multidisciplinary Open Science approach with a systems biology augmented ML technology, we aim to validate previously reported drug targets in ALS and provide novel insights about ALS subpopulations and potential drug targets using a unique combination of ML methods and graph theory. Methodology: We use NetraAI to generate hypotheses about specific patient subpopulations, which were then refined and validated through a combination of ML techniques, systems biology methods, and expert input. Results: We extracted 8 target classes, each comprising of several genes that shed light into ALS pathophysiology and represent new avenues for treatment. These target classes are broadly categorized as inflammation, epigenetic, heat shock, neuromuscular junction, autophagy, apoptosis, axonal transport, and excitotoxicity. These findings are not mutually exclusive, and instead represent a systematic view of ALS pathophysiology. Based on these findings, we suggest that simultaneous targeting of ALS has the potential to mitigate ALS progression, with the plausibility of maintaining and sustaining an improved quality of life (QoL) for ALS patients. Even further, we identified subpopulations based on disease onset. Conclusion: In the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual's needs. [ABSTRACT FROM AUTHOR]

Understanding symmetry-breaking states of materials is a major challenge in the modern physical sciences. Quantum atmosphere proposed recently sheds light on the hidden world of these symmetry broken patterns. Yet, no experiment has been performed to demonstrate its potential. In our experiment, we prepare time-reversal-symmetry conserved and broken quantum atmosphere of a single nuclear spin and successfully observe their symmetry properties. Our work proves in principle that finding symmetry patterns from quantum atmosphere is conceptually viable. It also opens up entirely new possibilities in the potential application of quantum sensing in material diagnosis. [ABSTRACT FROM AUTHOR]

Petrological assimilation is a key process in the evolution of high-grade metamorphic terrains in the continental crusts. This study examines the mechanisms of such macroscopic assimilation between felsic (F) and mafic (M) constituents as two petrologically interacting continuum phases, as observed in the Chotanagpur Granite Gneissic Complex (CGGC), India, which underwent amphibolite to granulite facies metamorphism (~775 to 900°C and 7 to 11 kb) between from the Paleoproterozoic to the late Mesoproterozoic. From field investigation we could recognized four interface patterns: planar, wavy, fingering and incoherent, which are generated at the interface between the Fand M units. We have adopted the Turing type reaction-diffusion (RD) approach, which is a well established theoretical model to interpret any complex auto-regulatory pattern in natural and physical sciences, to understand the physics of the self-organizing interface geometries observed across the CGGC. The RD model findings suggest that these patterns are constrained by a combination of: diffusion coefficients (DF, DM) of F and M, a linear or non-linear reaction term (R) that describes phase interactions and a pinning field (W) that introduces microscale heterogeneity. For linear interactions, F -- M undergo homogeneous mixing and show planar/wavy interfaces, when DF = DM and W =0. The mixing turns heterogeneous as DF * DM and W > 0, resulting in phase boundary migration with a fingering pattern. Non-linear reaction coupling enhances heterogeneous mixing and produces incoherent phase boundaries where F-phases host relics of M-phases, following a power-law size distribution. Striking similarities of interface patterns and fractal dimensions estimated from model and CGGC validate the proposed mechanism of macroscopic petrological assimilation. We argue that RD model provides a new insight into the genesis of hybrid rocks in metamorphic terrains. [ABSTRACT FROM AUTHOR]