Reviewerly/exHarmony huggingface dataset:
Exploration of Unggan weaving in Minang culture: An ethnomathematics study

Unggan weaving, an original cultural product of the Sijunjung people, especially in Unggan Village, is widely used as employee, traditional, and school uniforms once a week. However, the use of Unggan weaving in mathematics learning has yet to be widely explored. This research aims to reveal the mathematical elements of Unggan weaving as a learning vehicle more appropriate to students' culture. This research is qualitative research with an ethnographic approach. Data collection methods use in-depth interviews, documentation, and ethnographic notes. The interview subjects were Ernita, owner of Unggan Weaving (R-1), and Masniati, an employee of Unggan Weaving (R-2). Data analysis uses the results of interpretation and translation of the phenomena found, as well as based on the results of the informant's conception of the meaning and the informant's original language regarding the focus, which is combined verbally with the researcher's language after in-depth understanding through focus group discussions (FGD). The research results show that Unggan woven fabric contains mathematical elements of geometric transformation (translation, reflection, dilation, symmetry) and arithmetic number patterns. The results of this research can be recommended as material for developing school mathematics teaching materials based on local wisdom.
Computational thinking on concept pattern number: A study learning style Kolb

This research aimed to determine how the number pattern concept's computational thinking characteristic picture was reviewed from the Kolb model's learning style. The research method used in this study is qualitative descriptive. The research was conducted at one of the state's small schools in Bandung. The research subjects consisted of 29 students in the ninth grade. One of the 29 study issues is selected with assimilator learning styles. The data-gathering techniques used are questionnaire tests, test instruments, and interviews. Angket is used to group subjects into four groups of learning style types. The test instrument was used to describe the computational thinking characteristics of high school students on the concept of number patterns, and the interview was used to strengthen the test summary results of the subject. The results of this study show that the characteristics of computational thinking that each type of learning style dominates are different. Computational thinking students with an assimilator learning style in solving mathematical problems of number patterns can solve issues by involving decomposition, pattern identification, abstraction and generalization, and algorithms. They can generalize patterns using accurate, thorough, complete, and systematic problem-solving strategies.
Deep Attributes: Innovative LSTM-based seismic attributes

Summary Seismic attributes are derived measures from seismic data that help characterize subsurface geological features and enhance the interpretation of subsurface structures: we propose to exploit the hidden layers of Long-Short Time Memory neural network predictions as possible new reflection seismic attributes. The idea is based on the inference process of a neural network, which in its hidden layers stores information related to different features embedded in the input data and which usually are not considered. Neural network applications typically ignore such intermediate steps because the main interest lies in the final output, which is considered as the exclusive exploitable feature of the process. On the contrary, here we analyse the possibility to exploit the intermediate prediction steps, hereafter referred as “Deep Attributes” because they are produced by a deep learning algorithm, to highlight features and emphasize characteristics embedded in the data but neither recognizable by traditional interpretation, nor evident with classical attributes or multi-attribute approaches. Nowadays, classical signal attributes are numerous and used for different purposes; we here propose an original strategy to calculate attributes previously never exploited, which are potentially complementary or a good alternative to the classical ones. We tested the proposed procedure on synthetic and field 2-D and 3-D reflection seismic data sets to test and demonstrate the stability, affordability and versatility of the entire approach. Furthermore, we evaluated the performance of deep attributes on a 4-D seismic dataset to assess the applicability and effectiveness for time-monitoring purposes and comparing them with the sweetness attribute.
Examınatıon of famous use ın televısıon advertısements wıth electroencephalogram (eeg)

Objective: The research aims to analyze the changes in brain activity of emotions caused by the use of celebrities in television (TV) commercials using Electroencephalography (EEG). It tries to determine the positive (liking, having fun) and negative (disliking, bored) emotions of the audience with Electroencephalography (EEG). Method: We tried to determine the emotional states of the volunteers who watched the commercials by examining the theta activities in different parts of their brains. A questionnaire was applied to 36 volunteers in our research before and after watching the advertisements. EEG recordings were made while the volunteers were watching the commercials. Theta activities obtained as a result of EEG recording were tabulated as duration. The hypotheses were tested by using the responses of the volunteers to the questionnaires and the EEG results comparatively with frequency tables and the Independent-Samples T test. Originality/Relevance: Both the preparation and broadcasting of TV advertisements, which have a very important place in marketing communication, can be very costly for businesses. Neuromarketing, which encourages interdisciplinary work, allows the use of methods, designs, theories and technologies used in neuroscience in areas such as marketing and advertising. In this study, with the help of EEG, one of the techniques used in neuromarketing, it has been tried to determine the emotions that the advertisements will create in the audience. It is among the few experimental studies conducted in Turkey. Results: Although EEG results shows that the activity intensity in the left hemisphere and left frontal lobe was higher than the activity intensity observed in the right hemisphere and right frontal lobe in the volunteers watching TV commercials, and the activity intensity in the left hemisphere and left frontal lobe in the volunteers where the advertisements created positive emotions, and the activity intensity in the left hemisphere and left frontal lobe in the volunteers, where the advertisements created negative emotions, statistically, as our hypotheses suggest, no significant activity difference was observed. Theoretical/methodological contributions: The research contributes to the scientific community by deepening the knowledge on neuromarketing, which is under development in Turkey as in many countries. It also provides clues about the use of neuromarketing in advertising research. This study will contribute to the preparation of effective advertisements by understanding the emotions of the audience. Managerial implications: Research findings can help businesses and advertisers prepare more effective commercials, thereby saving businesses costs.
Solid-state nanopore-sized counting for lamp false positive differentiation

Nucleic acid detection is crucial in disease diagnostics and environmental monitoring. Loop-mediated isothermal amplification (LAMP) is a noteworthy method in this space due to its ability to work under isothermal conditions and its high sensitivity and specificity. LAMP has been applied to various applications, from identifying certain pathogens to detecting specific types of RNA and DNA. The LAMP reaction, triggered by the specific target and facilitated by 4 or 6 specific primers, results in the generation and elongation of characteristic cauliflower-like DNA amplicons. Yet, even with cautious primer design, LAMP may still yield false positives. Predominantly, LAMP assays apply intercalating dyes for a real-time assessment of amplification. This method, however, can't confidentially separate true positives from false positives due to a similar cycle threshold (Ct), which can result in incorrect determination. Several methodologies have been proposed to improve the fluorescence-based LAMP assays, involving fluorescence resonance energy transfer (FRET) and specialized probes. However, these approaches often exacerbate assay complexity and increase the cost of the assay, while not entirely avoiding false positives. To address the challenge of differentiating false positives in LAMP, our study introduces label-free nanopore sized counting technique as a novel approach for LAMP result analysis. This method facilitates the single-molecule level direct detection and sizing of LAMP amplicons. Notably, the variation in amplification efficiency and pathways between true and false positives leads to distinct differences in amplicon size and distribution. Therefore, with the tool of nanopore sized counting, we could effectively differentiate between the true and false positives in samples that remain ambiguous when using fluorescence-based detection. With this development, we aim to improve the accuracy of LAMP-based assays interpretations, advancing the reliability of nucleic acid detection across diverse applications.
Fast and robust 3D MINFLUX excitation with a variable phase plate

MINFLUX has achieved record resolution in superresolution imaging and single fluorophore tracking. It is based on localizing single fluorophores by rapid probing with a patterned beam that features a local intensity minimum. Current implementations, however, are complex and expensive and are limited in speed and robustness. Here, we show that a combination of an electro-optical modulator with a segmented birefringent element such as a spatial light modulator produces a variable phase plate for which the phase can be scanned on the MHz timescale. Bisected or top-hat phase patterns generate high-contrast compact excitation point-spread functions for MINFLUX localization in the x, y, and z-direction, respectively, which can be scanned around a fluorophore within a microsecond and alternated among different excitation wavelengths. We discuss how to compensate for non-optimal performance of the components and present a robust 3D and multi-color MINFLUX excitation module with record speed, which we envision as an integral component of a high-performance and cost-effective open-source MINFLUX.
How fast does a random walk fill a finite region?

The filling of a finite region of a lattice by a random walk is of interest, fundamentally and for biophysical applications. Filling is described by the cover time, defined as the time required for the walker to visit all conducting sites at least once, and the blanket time, defined as the time required for the walker to visit the sites uniformly for a specified level of uniformity. For the unobstructed case, a theorem states that the blanket time is proportional to the cover time. That is, the cost of uniformity is just a multiplicative constant, not a more stringent time dependence. The cover time is a measure of diffusive reaction times, and of the observation time needed for a single-particle tracking experiment to visualize the entire region. The blanket time is related to the work of Ellery et al. [J Chem Phys 144 (2016) 171104], who proposed a new method of obtaining the crossover time from anomalous to normal diffusion in obstructed diffusion. They claimed that an eigenvalue of the transfer matrix yields the crossover time. I claim on the basis of numerics and the math literature that this eigenvalue instead yields the characteristic time for approach to a uniform distribution. It is useful to compare the approach to uniformity in the Monte Carlo and transfer matrix methods. Monte Carlo requires averaging over many random walks for a particular initial position and many initial positions for a particular obstacle configuration, but the transfer matrix method generates a numerically exact average over both. Either method requires averaging over obstacle configurations at the prescribed obstacle concentration. The two methods have distinct asymptotic time dependencies: the transition matrix method as 1/t, and Monte Carlo as 1/sqrt t.
Edtech and Equity in Panama: Mobile Technology for Levelling the Learning Playing Field

Access to quality education in the Republic of Panama is a topic of growing concern. The COVID-19 pandemic served to highlight the importance of this issue and its connection with information and communications technology. As remote learning assumed a critical role during the global pandemic, education technology (edtech) became a familiar concept and a determinant of families’ abilities to access instruction for their school-aged children. The situation prompted complex questions about the political economy of edtech, digital inequality, and the future of learning. This chapter examines edtech and equity in Panama, focusing on mobile technology as a potential means to levelling the learning playing field. It discusses two collaborative low-tech projects to propel basic skills acquisition for children in their early years of schooling that were implemented nationwide in 2020 while schools were closed. It links the knowledge generated from the study of these projects to the broader discussion of global forces that shape education delivery and local responses that work to drive equity and inclusion.
Improved free energy calculations using multi topology ensemble of expanded ensemble

Protein binding interactions are difficult to adequately sample using computational methods due primarily to the relatively high degree of conformational flexibility of proteins. Our group has developed a novel method termed multi-topology ensemble of expanded ensemble (MTEEXE) which allows for expanded ensemble calculations to be run in parallel for iterative relative free energy calculations in GROMACS and to allow conformational swapping between the parallel transformations. These iterative transformations can be applied to any systems with a common back bone or central substructure. We have applied this method to calculate the relative binding free energy of acyl-ACP to the ketoacyl ACP synthase FabF for acyl chain lengths varying from 4 to 16 carbons. This system features both a flexible protein-protein binding interface and a highly flexible acyl chain which is covalently attached to ACP. The degree of conformational flexibility in our system poses a significant challenge to traditional computational free energy methods. We have also applied this method to multi-site perturbations of a small molecule core similar to multisite lambda dynamics in the CHARMM infrastructure, but without the need for modification of the MD code. We outline a method of setting up topologies in order to ensure separability of the partition function for each iterative transformation and provide instructions on how to perform inter-replicate coordinate modifications in order to generalize our proposed method. We demonstrate that MTEEXE not only increases efficiency of running iterative transformations, but also increases conformation sampling, especially when transforming bulky groups like the acyl chain, to produce more accurate and reproducible free energy estimates in GROMACS.
Marangoni spreading on liquid substrates in new media art

Abstract With the advent of new media art, artists have harnessed fluid dynamics to create captivating visual narratives. A striking technique known as dendritic painting employs mixtures of ink and isopropanol atop paint, yielding intricate tree-like patterns. To unravel the intricacies of that technique, we examine the spread of ink/alcohol droplets over liquid substrates with diverse rheological properties. On Newtonian substrates, the droplet size evolution exhibits two power laws, suggesting an underlying interplay between viscous and Marangoni forces. The leading edge of the droplet spreads as a precursor film with an exponent of 3/8, while its main body spreads with an exponent of 1/4. For a weakly shear-thinning acrylic resin substrate, the same power laws persist, but dendritic structures emerge, and the texture of the precursor film roughens. The observed roughness and growth exponents (3/4 and 3/5) suggest a connection to the quenched Kardar–Parisi–Zhang universality class, hinting at the existence of quenched disorder in the liquid substrate. Mixing the resin with acrylic paint renders it more viscous and shear-thinning, refining the dendrite edges and further roughening the precursor film. At larger paint concentrations, the substrate becomes a power-law fluid. The roughness and growth exponents then approach 1/2 and 3/4, respectively, deviating from known universality classes. The ensuing structures have a fractal dimension of 1.68, characteristic of diffusion-limited aggregation. These findings underscore how the non-linear rheological properties of the liquid substrate, coupled with the Laplacian nature of Marangoni spreading, can overshadow the local kinetic roughening of the droplet interface.
Bayesian filtering for macroscopic hidden Markov models: Potential and limits of minimal informative priors to improve parameter inference

Inferring the functional dynamics of ion channels from ensemble currents or combined with fluorescence data is daunting because the chemical reaction network (CRN) is only partially observable. We addressed this problem by applying a parallelized Bayesian filter to specify first-order CRNs for macroscopic current and fluorescence data leading to a more accurate likelihood (Muench 2022 eLife 11:e62714) than previous gold-standard algorithms. We generalized the previous signal model (Moffatt, Biophysical journal 93.1, 2007, 74–91) and derived approximate closed-form filtering equations beyond the Kalman filter solution even if state-dependent noise contributions in the signal are present. Incorporating the details of the single-channel Markov dynamics into the likelihood of a macroscopic signal supersedes classical deterministic approaches with rate equations in two ways. Firstly, the posterior is closer located to the true parameter values. Secondly, the uncertainty quantification is meaningful. In contrast, deterministic rate equations approaches underestimate the true parameter uncertainty even for large ensembles molecules. The likelihood misspecification intrinsic to rate equations approaches causes p values, confidence regions, or posteriors to be too confident for any finite data amount. Using Bayesian statistics requires defining a prior distribution. When little information about the parameter is known, and information in the data is poor, minimally informative priors are crucial to make the posterior as sensitive as possible to the data. Applying such a prior reduces the number of channels required for a reasonable inference by orders of magnitude compared to the standard uniform prior, which is often considered mistakenly uninformative. Nevertheless, the likelihood of HMMs contains usually a degree of practical parameter identifiability which poses challenges for minimally informative priors which we will discuss.
Trained image analysis techniques for characterizing cell phenotype in electron microscopy images of mouse thrombi

The thrombus formation following a puncture wound is integral to vascular healing. However, the mechanism(s) underlying blood platelet recruitment and their activation is still poorly understood. Platelet differentiation is fundamental to sealing leaks at sites of vascular injury during wound healing. Investigating platelet activation and distribution within thrombi can elucidate the mechanism behind clot formation. Furthermore, the application of deep learning to characterization of morphology-based cell phenotypes of tens of thousands of cells within a single thrombus, can be generalizable to other tissue types. Montages of 500–1,000 high-resolution TEM images of mouse femoral artery and jugular vein thrombi cross sections were our study models for mapping platelet activation states. After individual platelet auto-segmentation using Amira software, each platelet was assigned to one of five activation states: discoid (least activated), loosely adherent, tightly adherent, degranulated, and procoagulant (most activated). A trained machine-learning model was used to produce a heat-map localizing different platelets types. Remarkably, the thrombi formed in the femoral artery and jugular vein samples showed considerable consistency in organization across samples, which indicated that staged platelet activation resulted in a discoid platelet tethering zone that was pushed progressively outward as platelets converted from one activation state to another. Our analysis supports a model that we term “capture and activate,” in which the initial high platelet activation is directly linked to the exposed adventitia. All subsequent tethering of discoid platelets is to loosely adherent platelets that convert to tightly adherent platelets, and self-limiting, intravascular platelet activation over time is the result of decreased signaling intensity. In the future, we hope that this analysis technique can be expanded to other image processing applications for cellular characterization, as well as 3D imaging modalities.
High Spatiotemporal Remote Sensing Images Reveal Spatial Heterogeneity Details of Soil Organic Matter

Soil is the foundation of sustainable agricultural development. Soil organic matter (SOM) is a key indicator for characterizing soil degradation, and remote sensing has been applied in SOM prediction. However, the differences in SOM prediction from different remote sensing data and the ability to combine multi-source and multi-phase remote sensing data for SOM prediction urgently need to be explored. The following research employed Landsat-8, Sentinel-2, and Gaofen-6 satellite data, utilizing a random forest algorithm to establish a SOM prediction model. It aimed to explore the variations in SOM prediction capabilities among these satellites in typical black soil regions. Additionally, the study involved creating multi-phase synthetic images for SOM prediction using Landsat-8 and Sentinel-2 images captured during three years of bare soil periods. Finally, the research examined the ability to combine three satellites to construct high spatiotemporal remote sensing images for SOM prediction. The results showed that (1) using Landsat-8 and Sentinel-2 to extract the principal components of the three-year bare soil period to construct the multi-phase synthetic image for SOM prediction, higher prediction accuracies can be obtained compared with the single-phase images. (2) The highest accuracy can be obtained using multi-phase synthetic images and high spatial resolution images to construct high spatiotemporal remote sensing images and perform SOM prediction (R² is 0.65, RMSE is 0.67%, MAE is 0.42%). (3) Simultaneously, high spatiotemporal remote sensing images can reach 2 m spatial resolution to reveal the spatial heterogeneity of SOM. The causes of SOM spatial anomalies can be determined after analysis combined with soil degradation information. In subsequent research, SOM prediction should focus more on multi-sensor collaborative prediction.