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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]

Studies indicate that racial disparities in STEM achievement or equity grade gaps are associated with faculty fixed mindset beliefs; however, whether specific instructional beliefs are linked to student academic achievement remains unclear. We surveyed 216 STEM faculty to assess their mindset and instructional beliefs and linked these to detailed student transcript data (n = 31,361). Results reveal that faculty with fixed mindset beliefs also endorsed more traditional instructional beliefs regarding assessment, grading, and diversity. Further, the endorsement of these beliefs was associated with larger equity grade gaps. Analysis of faculty characteristics indicate that male faculty, full professors, and instructors in Physical Sciences tended to hold instructional beliefs that are linked to larger equity grade gaps. [ABSTRACT FROM AUTHOR]

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]

We explore the role of organizational factors in research collaboration networks among European universities. The study of organizational drivers in shaping collaboration patterns is crucial for policy design aimed at reducing research fragmentation and fostering knowledge creation and diffusion. By using Exponential Random Graph Models (ERGMs) and controlling for spatial factors, we investigate the role of two main mechanisms guiding the partners' selection process: organizational attributes and homophily. We investigate two distinct scientific collaboration networks (i.e., projects and publications) and two research domains (Physical Sciences and Engineering, and Life Sciences) over the 2011–2016 time period. Our empirical evidence reveals that, among the main dimensions indicated by the literature, research capability (measured by the dimension of doctoral programs) has the clearest and most stable impact either on the tendency to establish collaboration ties or as homophily effect. In terms of policy implications, it emerges that organizational similarity in research capability matters and policy makers should consider doctoral programs as a strategic variable to promote successful collaborations in scientific research. [ABSTRACT FROM AUTHOR]

This study explored the impact of interactive e-books on the viewing behaviors of children in science demonstrations at museums. To conduct this study, an interactive e-book on a pendulum experiment was developed and integrated into a physical science demonstration at a museum. Two scientific demonstrations were conducted in this study. The first demonstration was conducted using the conventional science demonstration method, without integrating the interactive e-book into the demonstration. The second was a science demonstration with an integrated interactive e-book. The study analyzed and compared the children's learning motivation, viewing behaviors, and knowledge gained in both demonstrations. The results showed that the different science demonstrations did not significantly affect the children's current knowledge gain or learning motivation. However, the sequence analysis revealed that in the conventional science demonstration without the interactive e-book, children were more engaged in observing the science demonstration, experiment, and instrument operation and interacting with neighboring viewers. In contrast, in the science demonstration with the integration of the interactive e-book, children had more physical involvement, including pointing at the answers in the e-book, swinging their arms with the pendulum animation, and nodding when confused about the expected results. In addition, the children who joined the demonstration with the interactive e-book made more attempts to learn about procedural knowledge. These results demonstrated that both learning modes have similar learning effects on children, indicating the feasibility of the two learning modes. Furthermore, the results revealed that children's needs might differ when engaged in different modes of science demonstrations; for example, conventional modes need more support from companions, whereas e-book modes need more children's physical participation. [ABSTRACT FROM AUTHOR]

In this research work, we extracted the variety of newly optical soliton solutions which describe the wave propagation in nonlinear low-pass electrical transmission lines model by utilizing the auxiliary equation method. The secured solitons solutions yield a variety of typical soliton shapes, including dark solitons, periodic singular optical solitons, combined bright and dark solitons, kink wave solitons, bright solitons, ant-kink wave solitons, and solitary waves. The physical structure of extracted soliton solutions visualized in three different graphically structures such as three-dimension, two-dimensional and contour plotting on the choices of some constant parameters by utilizing the numerical simulations. This study explored optical solitons, solitary wave solutions, exact solitons for improving the performance of nonlinear low-pass electrical transmission systems. It provides an overview of solitons, their relevance, and stability principles. It also presents the mathematical formulation of the nonlinear low-pass electrical transmission lines model and discusses its implications for signal propagation. The secured soliton solutions have many applications in engineering and science such as nonlinear optics, fiber optics, laser optics, nonlinear dynamics, ocean engineering, electronic engineering, electrical engineering, computing engineering, power engineering and several other different kinds of physical sciences. The whole study shows that the suggested method is more powerful, effective, simple, and strong for looking into different types of nonlinear models involve in nonlinear sciences and the engineering presentation field. [ABSTRACT FROM AUTHOR]

Curriculum reform worldwide has often reflected changing perspectives on the teaching and learning of science. Such perspectives underline the notion that not only the teaching of science content knowledge is relevant but also the aims and methods scientists use to further their knowledge and the social context in which science is applied. We call this the 'cognitive-epistemic' and 'social institutional aspects' of science. They form the hard core of the nature of science (NOS) concept for curriculum reform. In South Africa, after years of Apartheid, a key focus of school science curriculum reform is for importance to be given to NOS due to the potential benefits of learning and understanding of NOS for students. This study analyses cognitive-epistemic and social-institutional categories of NOS in the South African Life Sciences and Physical Sciences school curricula. A critical finding for both Life Sciences and Physical Sciences curricula was the imbalance in the NOS categories. In the curricula, there is less presence of the social-institutional categories compared to the cognitive-epistemic categories. In general, there was poor interconnectedness amongst NOS categories, with the strongest interconnectedness revealed for the cognitive-epistemic categories 'scientific practices' and 'methods and methodological rules'. Implications for the design of future curricula and recommendations for future research are discussed. [ABSTRACT FROM AUTHOR]

The primary objective of this article is to construct novel solitary wave solutions for the nonlinear (3+1)-dimensional modified Korteweg-de Vries-Zakharov-Kuznetsov equation with a conformable derivative by employing the subsidiary ordinary differential equation method and 1 φ (ς) , φ ′ (ς) φ (ς) method. The solutions obtained encompass diverse bright, dark, singular, and periodic solitary waves. Additionally, the article thoroughly discusses the sufficient conditions for the existence of these solutions. To visually represent the obtained solutions, we include 2D and 3D graphical images using the computational software Maple 18. We also included a comparison of fractional derivatives at different values of fractional order and conducted an analysis to understand the influence of nonlinear parameters on wave behaviors. The significance of these solutions lies in their wide-ranging applications in applied sciences and mathematical physics. Furthermore, the proposed methods are invaluable in efficiently solving nonlinear partial differential equations prevalent in engineering and physical science. [ABSTRACT FROM AUTHOR]

Background: Children with chronic kidney disease (CKD) usually present with disease impact on growth besides cardiovascular problems that not only impact the patient's health during childhood but also affect their adult life. We aimed to identify the diagnostic role of diffusion tensor imaging (DTI) in CKD in pediatric using its metrics: apparent diffusion coefficient (ADC) and fraction anisotropy (FA). Results: This prospective study was performed on thirty-five CKD patients (16 girls, 19 boys; mean age 12.3 ± 2.6 years) and 19 sex- and age-matched controls. Both groups underwent renal DTI and renal function tests. Based on renal biopsy, patients with CKD were further categorized into sclerotic CKD (n = 25) and non-sclerotic CKD (n = 10). Mean FA renal medulla/cortex in CKD (0.18 ± 0.18 and 0.20 ± 0.17) was lower significantly (p = 0.001) than volunteers' (0.31 ± 0.19, 0.27 ± 0.18). The cutoff FA of renal medulla/cortex used for CKD diagnosis was 0.22 and 0.23 with AUC of 0.828, 0.838 and accuracy of 80.8%, 82.8%. Mean of renal medulla/cortex ADC in CKD (2.13 ± 0.23 and 1.93 ± 0.22 × 10−3 mm2/s) was higher significantly (p = 0.001) than that of volunteers' (1.67 ± 0.15 and 1.64 ± 0.133 × 10−3 mm2/s. ADC cutoff value of renal medulla/cortex used for CKD diagnosis was 1.86 and 1.74 × 10−3 mm2/s with AUC of 0.827, 0.82, 0.827, and 0.911, and accuracy of 80.6%, 79.6%, 82.8%, and 84.2%. Renal medulla/cortex FA in sclerotic CKD was significantly different (p = 0.001) from non-sclerotic CKD (0.25 ± 0.07 and 0.26 ± 0.08). Cortical and medullary FA in CKD patients correlated with e-GFR (r = 0.363, r = 0.317) and serum creatinine (r = − 0.467, r = − 0.383). Conclusions: Renal cortical/medullary FA can assist in diagnosing pediatric CKD, predict sclerotic CKD, and correlate with some serum biomarkers. [ABSTRACT FROM AUTHOR]

While many university health science programs include physics courses to raise knowledge and understanding of physical science concepts, they are still far from addressing the needs of that science health profession. This study aimed to investigate the effect of an introductory physics course on first-year physiotherapy and rehabilitation (PR) students' conceptual understanding of simple electric circuits. The study participants were students enrolled in the Physics II course. Eighty-two students registered for the course. Sixty students (73%) took the pretest, and 67 (81%) completed the posttest. 53 students (64%) took the pretest and posttest. This study adopts an exploratory research methodology that includes a one-group pretest-posttest design. The Simple Electric Circuits Diagnostic Test (SECDT) was used to assess students' conceptual understanding. The prevalence of misconceptions was relatively low (before and after instruction), and very few students developed sound conceptual understanding after instruction. The local reasoning model was the most frequent misconception PR students held. After instruction, students' overall confidence in their SECDT responses increased significantly. Interestingly, when the students were grouped into three achievement groups, the medium-achievement group fell into more misconceptions as their achievement increased compared to low- and high-achievement groups. These findings suggested that students' low SECDT scores were due to a lack of knowledge rather than misconceptions. [ABSTRACT FROM AUTHOR]

The Journal of Materials Science is a reputable journal that publishes articles on various topics in materials science. It receives numerous submissions each year, which are reviewed by editors who are active researchers themselves. The journal has a strong emphasis on bio and medical materials science, as well as chemical routes to materials. The document provides summaries of the research interests and achievements of several scientists in the field, including Catalin Croitoru, Yi (David) Ju, Steven E. Naleway, Ghanshyam Pilania, Subha Narayan Rath, and Annela M. Seddon. The summaries highlight their contributions to the field, including their research focus, affiliations, publications, and awards. This resource is designed to assist library patrons in determining whether they would like to explore these scientists' work further. [Extracted from the article]

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]

The article discusses the role of physics in logical empiricism, a philosophical movement that was primarily concerned with the philosophy of science. The editors of the volume argue that there has been relatively little research on the logical empiricists' philosophy of the natural sciences, particularly physics. The volume aims to explore the traditions, methods, concepts, and ideas that influenced the logical empiricists' philosophy of physics, as well as their various and sometimes contradictory ideas about the physical sciences. While the volume contains some interesting insights, it also has some flaws, such as a lack of unity and contextualization in certain chapters. Overall, the volume may be valuable for experts in the field, but may not be the best starting point for those unfamiliar with logical empiricism. [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]

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]

Recent work in science education has emphasized the importance of the conceptual, social, and epistemic dimensions in science learning. But what about the material dimension—the actual physical materials that students use? The way their characteristics and affordances limit or determine instruction has thus far not been as thoroughly explored. Despite the vast body of work that exists on science and engineering practices, there are relatively few examples of how physical science materials are linked to and can support engagement with such practices for sensemaking. In this paper, we propose a framework for analyzing the design of these physical materials and the activities associated with them that can serve as lenses for educators and researchers to consider when planning and reflecting on classroom instruction. To validate our framework, we apply it to analyze activities developed and implemented by teachers during a four-year research-practice partnership project conducted in a city in Brazil, and investigate potential connections between the design of physical materials and students' epistemic agency. We conclude with recommendations for the development of physical resources in labs and makerspaces for science learning. [ABSTRACT FROM AUTHOR]

The simplified modified Camassa Holm (SMCH) equation is an essential nonlinear model for classifying several wave phenomena in physical science. In this study, the new m + 1 / G ′ -expansion method has been applied to the SMCH equation. Based on the given method, we have obtained some typical analytical solutions, such as exponential and rational function and complex function solutions of the SMCH equation. The three-dimensional and contour graphs of the obtained results are drawn for appropriate values of parameters. Getting the results and graphic drawings have been made using the Wolfram Mathematica. All the obtained solutions are crucial for understanding the nonlinear phenomena of dispersive waves. The solutions have shown that the presented method is powerful, practical, and suitable for investigating nonlinear models. [ABSTRACT FROM AUTHOR]

Numerous variations of cognitive challenges, such as those in fluid mechanics, plasma physics and nonlinear optics as well as in engineering and mathematics, involve nonlinear partial differential equations. In this study, we explore the (3+1)-dimensional generalized Yu–Toda–Sasa–Fukuyama (YTSF) equation with application in engineering and physical science. Three (1+1)-dimensional nonlinear partial differential equations can be acquired from the YTSF equation. Using a N-fold Darboux transformation technique of Lax pair to obtain the multi-soliton, resonant and complex soliton solutions of the equation. Also, by showing the solutions graphically, the completeness of the outcome was confirmed. The conclusions in this study might be useful for understanding the soliton solutions in mathematics and physics. [ABSTRACT FROM AUTHOR]

An editorial is presented, discussing the significance of the Nano Research Young Innovators Awards in recognizing the achievements of 33 outstanding researchers under 45 years of age in the field of bio-inspired nanomaterials. It highlights key topics covered by the awardees, including innovative approaches to treat Parkinson's disease, advancements in nanotranslacers, and the development of organic thin-film transistors for disease-related biomarker detection.

Remote Sensing (RS) provides significant insights for the monitoring of natural disasters like earthquakes for pre-and post-seismic precursors around the seismogenic regions. The integrated analysis of atmosphere data from satellites has been vital in this progress, providing detection of anomalies associated with the seismic activities. This study provides a comprehensive study of a statistical analysis for possible atmospheric precursors over the forth coming seismic breeding zone around the world. In this study, we have investigated Outgoing Longwave Radiation (OLR) and Relative Humidity (RH) anomalies associated with 10 large magnitude (Mw ≥ 6.5) earthquakes around the world. We examined pre-and post-seismic anomalies in OLR and RH from the data of the National Oceanic and Atmospheric Administration, Physical Sciences Laboratory (NOAA-PSL). This research confirms the existence of OLR and RH precursors as candidate parameters for large earthquakes precursors over the epicenters. The study findings indicate that the OLR and RH anomalies were evident in each of the 10 seismic events, with anomalous windows occurring between 7 and 5 days before and in few cases 2 to 3 days after the earthquake. Furthermore, this study suggests that OLR and RH hold promise as reliable space-based precursors for earthquake forecasting. The identified anomalies exhibit a notable consistency with the stress-induced activation of proxy defects at the interface between the lithosphere and atmosphere within the seismic breeding zone. [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]

The article discusses the critical hydrologic impacts of climate change and the urgent need to address them. It highlights the accelerating climate crisis, driven by greenhouse gas emissions, and the lack of preparation by policymakers and governments. The shifting hydrologic cycle, caused by climate change, threatens freshwater availability, biodiversity, food production, and human health. The article emphasizes the need for adaptation strategies, particularly for vulnerable communities, and calls for actions such as early warning systems, nature-based solutions, and improved governance. It also emphasizes the importance of protecting the hydrologic cycle and ocean sustainability, enhancing regional capacity, transforming agriculture, and providing support to vulnerable populations. The article concludes by stressing the need for rapid and wide-ranging efforts to improve community resilience and mitigate greenhouse gas emissions. [Extracted from the article]

In this manuscript, an analysis is carried out on the dynamic behavior of the ill-posed Boussinesq equation, which arises in nonlinear lattices and shallow water waves. The simplified Hirota method is employed to obtain multi-wave structures, such as one-soliton, two-soliton and three-soliton solutions. Some solutions are visually demonstrated through 3D, 2D and density plots. Furthermore, a comprehensive discussion on the stability analysis of the equation under study is presented. These results are innovative and have not been previously investigated in the context of this equation. These results show that the methodology used is concise, straightforward, and efficient, and as a result, it makes a significant contribution to comprehending the complexity of multi-wave profiles in nonlinear physical science models. [ABSTRACT FROM AUTHOR]

Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome by introducing stochasticity into the drift, but this approach requires specific assumptions for modelling uncertainty. We remove this constraint by presenting a purely data-driven framework for modelling probabilistic drift in flexible environments. Using ocean circulation model simulations, we generate probabilistic trajectories of object location by simulating uncertainty in the initial object position. We train an emulator of probabilistic drift over one day given perfectly known velocities and observe good agreement with numerical simulations. Several loss functions are tested. Then, we strain our framework by training models where the input information is imperfect. On these harder scenarios, we observe reasonable predictions although the effects of data drift become noticeable when evaluating the models against unseen flow scenarios. [ABSTRACT FROM AUTHOR]

EPA's DSSTox chemical structure database: a curated chemistry resource supporting computational toxicology research. As I shifted my focus to cleaning up datasets and chemical structure-identifier associations, I became alarmed at the scope of the problem across public datasets, undermining toxicity modeling efforts. Or consider a chemical-activity HTS result where the chemical was originally misidentified by a major supplier (yes, this happens) or, unbeknownst to the experimenter, the chemical reacted, volatilized, or degraded under the testing conditions. My hope for the near future is that public data resources supporting chemical-toxicity evaluation and regulation not only continue to expand, but that quality chemical curation becomes the norm and is demanded and expected by the scientific and regulatory communities. [Extracted from the article]

Developing students' understanding of and about science is an important educational goal. Learning the nature of science (NOS) has been recognized as a critical component of science literacy, affecting how students (our future citizens) make informed decisions. Textbooks can be useful teaching materials if the content presented aligns with curriculum guidelines, but they may not completely satisfy students' learning needs. The reconceptualized FRA to NOS (RFN) offers a framework for teachers and students seeking to unpack and construct a comprehensive understanding of NOS. The present research analysed how NOS was represented in three chapters addressing magnetism and electricity in three high school textbooks published in South Africa. Using the 11 RFN categories and four levels of information explicitness criteria as analytical tools, we found that scientific practices, scientific knowledge, and social values were the three most frequently used NOS representations. Textbook excerpts representing these three RFN categories at different levels of information explicitness were also discussed in order to show how the target RFN were represented on this topic. Chronological diagrams were employed to denote how NOS representations interacted with one another, as well as reveal the level of information explicitness. Finally, the identified learning goals proposed by the textbooks were analysed to see how the NOS-related content aligned with the learning goals from the RFN perspective. [ABSTRACT FROM AUTHOR]

Sparse neural networks can achieve performance comparable to fully connected networks but need less energy and memory, showing great promise for deploying artificial intelligence in resource-limited devices. While significant progress has been made in recent years in developing approaches to sparsify neural networks, artificial neural networks are notorious as black boxes, and it remains an open question whether well-performing neural networks have common structural features. Here, we analyze the evolution of recurrent neural networks (RNNs) trained by different sparsification strategies and for different tasks, and explore the topological regularities of these sparsified networks. We find that the optimized sparse topologies share a universal pattern of signed motifs, RNNs evolve towards structurally balanced configurations during sparsification, and structural balance can improve the performance of sparse RNNs in a variety of tasks. Such structural balance patterns also emerge in other state-of-the-art models, including neural ordinary differential equation networks and continuous-time RNNs. Taken together, our findings not only reveal universal structural features accompanying optimized network sparsification but also offer an avenue for optimal architecture searching. Deep neural networks have shown remarkable success in application areas across physical sciences and engineering science and finding such networks that can work efficiently with less connections (weight parameters) without sacrificing performance is thus of great interest. In this work the authors show that a large number of such efficient recurrent neural networks display certain connectivity patterns in their structure. [ABSTRACT FROM AUTHOR]

The presence of snow, ice, and permafrost creates an intricate environment that uses the cold as a resource to develop unique forms of infrastructures. The articles featured in this special section highlight the crucial role of snow, ice, and permafrost in building, maintaining and sustaining human-environment relations in cold environments, particularly in the Arctic. Recent studies show that permafrost degradation poses a significant threat to built infrastructure (Hjort et al. [15]), resulting in substantial costs (Streletskiy et al. [33], [32]). The study of infrastructures has seen a growing interest in recent years across academic disciplines and global regions. [Extracted from the article]

The acidity constant values (Ka or pKa) and polyionic species distribution are valuable tools to identify the extent of ionization and complexing properties of polyionic species. They are of great importance to predict the charged state of analyte and choose the appropriate condition in the design of functional materials for their intended purposes. Polyphosphates, being one of the anionic types of polyelectrolytes, have attracted interest for utilization in a quite wide range of applications in the boundaries of nanomaterial, bio-analytical chemistry, and physical sciences. In this study, it is aimed to estimate the intrinsic pKao of polyphosphoric acid (PPH) with high molar mass and the charge of its polyanionic conjugate base (PP−), (Z PP -) , according to activity coefficient estimation by Davies using potentiometric titration along with the theoretical consideration of Gran's approach. Besides, a series of PPH titration was simulated via CurTiPot and the data were further treated in MATLAB to visualize the 3-D pH surfaces of PPH/PP− pairs. As a result, the outcomes of experimental work and simulations were consistent very well, and pKao of PPH and Z PP - were found as 2.22 ± 0.01 and − 1.105 ± 0.003, respectively, at a 95% confidence interval. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslides seriously threaten hilly environments, leading local authorities to implement various mitigation measures to decrease disaster risk. However, there is a significant gap in the current literature regarding evaluating their effectiveness and the associated community risk perception. To address this gap, we used an interdisciplinary and innovative approach to analyse the slope stability of landslides, evaluate the effectiveness of existing structural mitigation measures, and assess the risk perception of those living in danger zones. Our case study focused on the Kutupalong Rohingya Camp (KRC) in Cox's Bazar, Bangladesh, which is home to over one million Rohingya refugees from Myanmar. Although various structural and non-structural countermeasures were implemented in the KRC to mitigate the impact of landslides, many of them failed to prevent landslides from occurring. We utilised a variety of methods from the physical sciences, including the infinite slope, limit equilibrium (LEM), and finite element (FEM) approaches, to calculate the factor of safety (FoS) for specific slopes. Additionally, in the social sciences domain, we conducted a questionnaire survey of approximately 400 Rohingya participants to assess the community's perception of the interventions and the degree of disaster risk. Our findings indicated that slopes with a gradient greater than 40° were unstable (FoS < 1), which was present throughout the entire KRC area. The effectiveness of the LEM and FEM methods was evaluated for four dominant slope angles (40°, 45°, 50°, and 55°) under varying loads (0, 50, and 100 kN/m2). The slopes were found to be stable for lower slope angles but unstable for higher slope angles (> 50°) and increased overburden loads (50–100 kN/m2). Different mitigation measures were tested on the identified unstable slopes to assess their effectiveness, but the results showed that the countermeasures only provided marginal protection against landslides. Survey results revealed that at least 70% of respondents believed that concrete retaining walls are more effective in reducing landslide occurrence compared to other measures. Additionally, about 60% of the respondents questioned the reliability of the existing structural mitigation measures. The study also found that the cohesion and friction angle of lower sandstone and the cohesion of upper soil layers are important factors to consider when designing and implementing slope protection countermeasures in the KRC area. [ABSTRACT FROM AUTHOR]

The paper by Kiss and Szalay ([12]) titled I Sensitivity of buildings' carbon footprint to electricity decarbonization: a life cycle-based multi-objective optimization approach i examines how changes in electricity mix impacts GHG emissions. The Intergovernmental Panel on Climate Change (IPCC) emphasizes the need to immediately tackle climate change by significantly reducing greenhouse gas (GHG) emissions and capping global warming at 1.5 °C above pre-industrial levels (IPCC, [8]). The study found that total GHG emissions from maize production were on average 429 ± 27 kg CO2-eq/ton grain, with the highest emissions occurring during the dry season. This SI further contributes to raising awareness to the topic of measuring GHG emissions, decarbonization measures as well as climate neutrality. [Extracted from the article]

This study investigated how an intervention consisting of a series of physical science lessons embedded within the elementary science methods course impacted elementary preservice teachers' (N = 473) science content knowledge as evidenced in their scaled scores for the science content component of a standardized subject certification examinations for elementary teachers (TExES Core Subjects EC-6, Science (804) exam). The science content component of the certification exam was the instrument used to generate data for this study. The independent variables for this study were the timing of the exam attempt compared to the participation in the science intervention. The dependent variables included the scaled score for the exam and the science content competencies. Results for an independent sample t-test indicated that the difference between the mean scores for these two groups was statistically significant (t = − 4.21, df = 102, p <.001) with the preintervention group scoring lower compared to the postintervention group. Mean scores for the exam were higher (MS = 57.7%, SD = 29.8%) than the mean score for exam attempts occurring before the intervention (MS = 44.4%, SD = 29.2%). The results suggest that the intervention implemented as part of the science methods course had a positive impact on prospective teachers. That is, their science content knowledge resulted in an increased passing rate on the certification exam. [ABSTRACT FROM AUTHOR]

Background: Learning assistants (LAs) increase accessibility to instructor–student interactions in large STEM lecture classes. In this research, we used the Formative Assessment Enactment Model developed for K-12 science teachers to characterize LA facilitation practices. The Formative Assessment Enactment Model describes instructor actions as eliciting or advancing student thinking, guided by their purposes and the perspective they center as well as by what they notice about and how they interpret student thinking. Thus, it describes facilitation practices in a holistic way, capturing the way purposes, perspectives, noticing, interpreting, and actions are intertwined and working together to characterize different LA actions. In terms of how perspectives influence actions, eliciting and advancing moves can be enacted either in authoritative ways, driven by one perspective that has authority, or in dialogic ways, driven by multiple perspectives. Dialogic practices are of particular interest because of their potential to empower students and center student thinking. Results: Our analysis of video recordings of LA–student interactions and stimulated recall interviews with 37 introductory physical science lectures' LAs demonstrates that instead of as a dichotomy between authoritative and dialogic, LA actions exist along a spectrum of authoritative to dialogic based on the perspectives centered. Between the very authoritative perspective that centers on canonically correct science and the very dialogic perspective that centers the perspectives of the students involved in the discussion, we find two intermediary categories. The two new categories encompass a moderately authoritative perspective focused on the LA's perspective without the claim of being correct and a moderately dialogic perspective focused on ideas from outside the current train of thought such as from students in the class that are not part of the current discussion. Conclusions: This spectrum further adds to theory around authoritative and dialogic practices as it reconsiders what perspectives can drive LA enactment of facilitation other than the perspective of canonically correct science and the perspectives of the students involved in the discussion. This emerging characterization may be used to give LAs and possibly other instructors a tool to intentionally shift between authoritative and dialogic practices. It may also be used to transition towards more student-centered practices. [ABSTRACT FROM AUTHOR]

Noise and uncertainty are usually the enemy of machine learning, noise in training data leads to uncertainty and inaccuracy in the predictions. However, we develop a machine learning architecture that extracts crucial information out of the noise itself to improve the predictions. The phenomenology computes and then utilizes uncertainty in one target variable to predict a second target variable. We apply this formalism to PbZr0.7Sn0.3O3 crystal, using the uncertainty in dielectric constant to extrapolate heat capacity, correctly predicting a phase transition that otherwise cannot be extrapolated. For the second example – single-particle diffraction of droplets – we utilize the particle count together with its uncertainty to extrapolate the ground truth diffraction amplitude, delivering better predictions than when we utilize only the particle count. Our generic formalism enables the exploitation of uncertainty in machine learning, which has a broad range of applications in the physical sciences and beyond. [ABSTRACT FROM AUTHOR]

Khattab et al. ([10]) revealed that Si and Se increased the expression of two TFs - DREB2A (dehydration-responsive element-binding protein) and NAC5 gene - and some drought-specific genes (e.g., I OsCMO i encoding rice choline monooxygenase and dehydrin I OsRAB16b i ). With urbanization, rapid industrialization, and uncontrolled anthropogenic activities, abiotic stresses have plagued the world - from more intense hurricanes to record-breaking temperatures, waterlogging, salinity, potentially toxic metals, mineral toxicity, and persistent droughts - threatening the stability of food-production systems. 2021; Cambridge University PressIn Press 7 Jiao L, Zhang L, Zhang Y, Wang R, Lu B, Liu X. Transcriptome analysis provides new insight into the distribution and transport of selenium and its associated metals in selenium-rich rice. Furthermore, the application of selenium (Se) fertilizers [selenite (SeO SB 3 sb SP 2- sp ), fermented Se, and potassium selenocyanoacetate (Se-AAF)] at 30 g ha SP -1 sp during different stages of rice growth significantly enhanced total biomass, yield, and total Se content (Yuan et al. [28]). [Extracted from the article]

In this study, the performance of several daily gridded climatic variables, e.g., maximum and minimum temperatures "Tmax (CPC) and Tmin (CPC)," relative humidity "RH (CDC)," wind speed "WS (CPC)," and precipitation collected from National Oceanic and Atmospheric Administration, Physical Science Laboratory "NOAA (PSL)," was evaluated with ground observed data in 9 different districts of Punjab. It was concluded, after in-depth investigations, that Tmax (CPC), Tmin (CPC), and RH (CDC) follow the same quality and performance as ground observations. The performance of gridded precipitation in different geospatial approaches, e.g., point-to-point and polygon-to-point, was also evaluated. Results revealed that the performance of daily mean gridded precipitation of district "RF (CPC DM)" is most reliable and accurate with gauge-based observation. The accuracy of monthly long-termed mean precipitation RFM (CPC) in the remaining 27 districts of Punjab was predicted by using techniques of statistical and geostatistical interpolations. The model was optimized based on minimum root mean square error (RMSE), and a monthly spatial accuracy ranking matrix was developed using a new pixel-based approach. This research formulated the district-wise accuracy of RFM (CPC) by using the first rank in the spatial accuracy ranking matrix. Accuracy mapping revealed that RFM (CPC) is most accurate in South Punjab districts (Bahawalpur, Bahawalnagar, RY Khan, Rajanpur, Muzaffargarh, DG Khan, Layyah, Multan, Lodhran, Khanewal, and Vehari) throughout the year. It performed well in Central Punjab districts (Lahore, Sheikhupura, Kasur, Nankana, Sahiwal, Pakpattan, Okara, Faisalabad, Chiniot, T.T. Singh, Narowal, Gujrat, Gujranwala, and Sialkot) during winter, spring, and autumn seasons, while in Monsoon season, its accuracy is moderate. RFM (CPC) poorly performed in Northern Punjab districts (Rawalpindi, Jhelum, Chakwal, Attock, Mianwali, Khushab, Bhakkar, Jhang, Sargodha, and Hafizabad) throughout the time series of data. [ABSTRACT FROM AUTHOR]

In physical sciences, dynamic systems are modeled using their parameters within governing equations that often form a system of ordinary differential equations (SODE). This system consists of multiple equations, each of which relates the time derivative of a single parameter to several parameters. A parameter can appear in multiple equations, and this parameter potentially links the equations to each other. Although in certain cases the SODE can be written by domain experts, it is often unknown. With advances in sensor technology, large quantities of data can be sampled from dynamic systems, thus enabling the data-driven discovery of closed-form SODEs. State-of-the-art approaches are based on sparse single-task learning, which means that each equation from the SODE is learned independently. Omitting the coupling features of equations leads to SODEs that weakly identify the dynamic system. Furthermore, the convexity of the sparse penalty included in the learning criterion gives an SODE that is biased with respect to the true SODE. To reduce such a bias, we propose a multitask learning (MTL) based penalty which can learn the closed-form SODE with unbiasedness. The purpose of each task is to discover a single equation. But discovering an SODE is nontrivial, as dynamic systems are often nonlinear and the available data are noisy. Our proposal improves SODE identification by harnessing a nonconvex sparse matrix-structured penalty which takes into account the coupling feature as well as addresses the bias issue. Experimental results, based on noisy data simulated from known SODEs, confirm that, compared to single-task learning, MTL is more effective for recovering the closed-form SODE, and the proposed nonconvexity ensures that it can be estimated with unbiasedness. We also show the benefits of our approach on a real-world public dataset sampled from a laboratory-based ecological experiment. [ABSTRACT FROM AUTHOR]

In FIMS, (bridge 1) HT ht and (bridge 2) HT ht . In SIMS, (bridge 1) HT ht and (bridge 3) HT ht . Finally, in SISS, (bridge 4) HT ht and (bridge 6) HT ht . [Extracted from the article]

In the structure of METTL9 SP M6 sp bound with the SLC39A5 peptide, the substrate histidine (His375) is snugly fitted into the catalytic pocket. Altogether, the binding assay and MS data demonstrate that both METTL9 SP 46-318 sp and METTL9 SP M6 sp recognize and efficiently methylate peptide substrates. Then, we synthesized peptides derived from previously identified substrates, including SLC39A5 SP 369-380 sp and mouse S100A9 SP 101-111 sp [9],[10], and examined their binding affinities for METTL9 SP 46-318 sp and METTL9 SP M6 sp by isothermal titration calorimetry (ITC) binding assay. SLC39A5 His375 ("H1"), as the substrate histidine, is accommodated into a deep pocket of METTL9 SP M6 sp composed of Asn210, Asp213, Arg214, Val241, and Tyr295. [Extracted from the article]

Jonathan Hopkins, CC BY-ND The experimental study lays the foundation for AI materials that can be applied in the construction of buildings, airplanes, and imaging technologies. For more information: https://engineering.lehigh.edu/news/article/cooler-approach-making-new-materials-can-stand-heat AI Capable of Predicting Properties of Multifaceted Metamaterials In a new study by the University of Amsterdam's Institute of Physics and research institute AMOLF, the researchers tested how well artificial intelligence (AI) can estimate the properties of so-called combinatorial mechanical metamaterials. Courtesy of A. Kuzmin/University of Latvia and A. Smekhova/HZB To investigate the local environment of individual components, the team used multi-edge x-ray absorption spectroscopy (EXAFS) at BESSY II and then the reverse Monte Carlo method to analyze the collected data. High-entropy alloys are a new class of alloys that are composed of four or more metallic elements in approximately equal amounts. [Extracted from the article]

We investigate the degree to which impact in science and technology is associated with surprising breakthroughs, and how those breakthroughs arise. Identifying breakthroughs across science and technology requires models that distinguish surprising from expected advances at scale. Drawing on tens of millions of research papers and patents across the life sciences, physical sciences and patented inventions, and using a hypergraph model that predicts realized combinations of research contents (article keywords) and contexts (cited journals), here we show that surprise in terms of unexpected combinations of contents and contexts predicts outsized impact (within the top 10% of citations). These surprising advances emerge across, rather than within researchers or teams—most commonly when scientists from one field publish problem-solving results to an audience from a distant field. Our approach characterizes the frontier of science and technology as a complex hypergraph drawn from high-dimensional embeddings of research contents and contexts, and offers a measure of path-breaking surprise in science and technology. Here, using hypergraph modeling the authors show that surprising research (in terms of unexpected combinations of research contents and contexts) is associated with impact and arises from scientific outsiders solving problems in distant disciplines. [ABSTRACT FROM AUTHOR]

Although the first-order liquid–liquid phase transition (LLT) has been reported to exist in various systems (i.e., phosphorus, silicon, water, triphenyl phosphite, etc.), it is still one of the most challenging problems in the field of physical science. Recently, we found that this phenomenon occurs in the family of trihexyl(tetradecyl)phosphonium [P666,14]+ based ionic liquids (ILs) with different anions (Wojnarowska et al in Nat Commun 13:1342, 2022). To understand the molecular structure–property relationships governing LLT, herein, we examine ion dynamics of two other quaternary phosphonium ILs containing long alkyl chains in cation and anion. We found that IL with the anion containing branched –O–(CH2)5–CH3 side chains does not reveal any signs of LLT, while IL with shorter alkyl chains in the anion brings a hidden LLT, i.e., it overlaps with the liquid-glass transition. Ambient pressure dielectric and viscosity measurements revealed a peculiar behavior of ion dynamics near Tg for IL with hidden LLT. Moreover, high-pressure studies have shown that IL with hidden LLT has relatively strong pressure sensitivity compared to the one without first-order phase transition. At the same time, the former exposes the inflection point indicating the concave-convex character of logτσ(P) dependences. [ABSTRACT FROM AUTHOR]

The article discusses the importance of open science in the field of space biology and biomedical health research. The NASA Open Science Data Repository (OSDR) is highlighted as a crucial resource for accessing comprehensive space-related datasets, including data from model organisms and human astronauts. OSDR provides metadata contextualizing each experiment and has worked with the scientific community to standardize data generation and formatting. NASA's Biological and Physical Science Division is actively promoting open science through its "Transform to Open Science" program, aiming to train 20,000 scientists in open science practices by 2028. The article emphasizes the value of open data in driving scientific progress and its potential for advancements in human health and technology on Earth. [Extracted from the article]

This paper shares findings from a teacher designed physics and computing unit that engaged students in learning physics and computing concurrently thru inquiry. Using scientific inquiry skills and practices, students were tasked with assessing the validity of local rollercoaster g-force ratings as posted to the public. Students used computational electronic textile circuits (e-textiles) to engage in "myth busting" amusement park g-force ratings. In doing so, students engaged computing and computational thinking skills in service to answering their scientific inquiry. Findings from this study indicate that physics classes are ideal spaces for engaging in computing's Big Ideas as laid out by Grover and Pea (Educational Researcher 42, 38–43, 2013) as well as the pillars of computational thinking (Wing, Communications of the ACM 49, 33–35, 2006). However, essential to this dual engagement is a need for computing content to act in service to the better acquisition of physics content within the physics classroom space. Findings indicate that the teachers' use of e-textiles to integrate physics and computing broadened and deepened student learning by providing affordances for computational thinking within the structure of physical science inquiry. [ABSTRACT FROM AUTHOR]

There are two key categories of moves that drive interaction in science classrooms; initiating moves and rejoinders. Closed-ended initiating questions have always been associated with low student engagement and open-ended initiating questions with prolonged interaction in science classroom. In this paper, we report on a qualitative case study conducted in South Africa with three physical sciences teachers. The purpose of the study was to understand the nature of initiating and rejoinder moves and their effect in the science classrooms. We draw on a hybridised framework combining Analysing Teacher Moves (ATM) with teacher discursive moves. We present data that contradicts the claim that closed-ended initiating questions always lead to minimised interaction and that only open-ended initiating questions lead to prolonged interaction depending on the nature of rejoinders. We discuss the value of understanding this intricacy of teacher move patterns and its implications for teacher education and professional development of science teachers. [ABSTRACT FROM AUTHOR]

The rise of China as a scientific research superpower has been frequently discussed in media and literature. However, past analyses are usually based on the geographical database and they ignore how the millions of emigrated Chinese students, who are now being considered the major research workforce in many countries, affect their academic outputs. Here we quantitatively analyze the contribution of Chinese scholars in physical science around the globe by their publications in a country's papers from 2010 to 2021 as well as their citations. Contrary to common perception, we find that increasing the number of Chinese scholars does not correlate with the net publication growth or decline in their host countries before the Chinese population exceeds a critical value. On the other hand, increasing Chinese authors in a paper improves its citations. The phenomena, though anomalous, are observed in many subfields of physics across the globe. Our analysis suggests that although Chinese scholars do not change the perceived publication capabilities of many countries but may have reshaped their research culture as well as workforce distributions. The results would be valuable for R&D, higher education, and immigration policymakers. [ABSTRACT FROM AUTHOR]

Identifying underlying governing equations and relevant information from high-dimensional observable data has always been a challenge in physical sciences. With the recent advances in sensing technology and available datasets, various machine learning techniques have made it possible to distill underlying mathematical models from sufficiently clean and usable datasets. However, most of these techniques rely on prior knowledge of the system and noise free data obtained by simulation of physical system or by direct measurements of the signals. Hence, the inference obtained by using these techniques is often unreliable to be used in the real world where observed data are noisy and require feature engineering to extract relevant features. In this work, we provide a deep-learning framework that extracts relevant information from real-world videos of highly stochastic systems, with no prior knowledge and distills the underlying governing equation representing the system. We demonstrate this approach on videos of confined multi-agent/particle systems of ants, termites, fishes as well as a simulated confined multi-particle system with elastic collision interactions. Furthermore, we explore how these seemingly diverse systems have predictable underlying behavior. In this study, we have used motion tracking to extract spatial trajectories of individual agents/particles in a system, and by using LSTM VAE we projected these features on to a low-dimensional latent space from which the underlying differential equation representing the data was extracted using SINDy framework. [ABSTRACT FROM AUTHOR]