Original Observations Research Articles

Observation of high frequency Alfvén eigenmodes excited by RF-accelerated minority hydrogen ions in the EAST tokamak

Abstract The paper presents the original observation of high frequency Alfvén eigenmode excited by fast minority hydrogen ions accelerated by ion cyclotron range of frequency waves in EAST. The frequency of high frequency Alfvén eigenmodes is around 0.75~8*Ω H, where Ω H is the cyclotron frequency of hydrogen ions evaluated at the low-field side plasma edge. It tracks the on-axis magnetic field B t and the edge plasma electron density n e via the Alfvenic relation ω~ B t *n e -1/2. The high frequency Alfvén eigenmodes are always accompanied by sub-cyclotron modes in several experiments at EAST, consistent with the high frequency Alfvén eigenmode detected on other conventional tokamaks, namely ASDEX Upgrade and DIII-D. Based on the previous study, these sub-cyclotron modes are consistent with the spectrum splitting produced by toroidal mode numbers[Nuclear Fusion 43, 228 (2003)]. We also found that the location of eigenmode excitation of high frequency Alfvén eigenmode driven by RF-accelerated minority hydrogen ions remains unchanged even though the cyclotron resonance location of hydrogen ions varies with B t.

Revisiting the earliest hyperscanning study: power and functional connectivity in the alpha band may link brains far apart.

This brief report revisits the earliest known hyperscanning study published in 1965, which examined simultaneous EEG recordings of identical twins separated by six meters. The original study's findings suggested that eye closure in one twin elicited alpha activity in the other, despite physical separation. Leveraging contemporary signal processing techniques, we reanalyzed the digitized data to validate their findings. Spectral analysis confirmed alpha activity in the twins' EEG signals, aligning with the original observations. Multitapering along with background noise subtraction also revealed the alpha activity in the unrelated subject, which could not be observed by visual inspection alone. Coherence analysis revealed significant alpha band synchrony between a twin and an unrelated subject, differing from the initial study's conclusions. Our findings indicate that even historical data can yield new insights when revisited with contemporary data analysis tools and highlight the potential for future large-scale studies using advanced techniques to explore nonlocal brain interactions.

Enhancing BDS-3 PPP-B2b real-time positioning performance by tightly integrating MEMS IMU and LiDAR in GNSS-Degraded environment

Currently, BDS-3 can provide satellite-based real-time precise point positioning (PPP) service via its PPP-B2b signal. However, in a complex Global Navigation Satellite System (GNSS)-degraded environment, the BDS-3 PPP-B2b positioning performance is severely influenced. A tightly coupled PPP/microelectromechanical system inertial measurement unit (MEMS IMU)/Light Detection and Ranging (LiDAR) combination is proposed, in which the original pseudorange and carrier observations, IMU measurements, and LiDAR planar features are fused via an extended Multi-State Constraint Kalman Filter (MSCKF). Moreover, the Galileo satellites are further utilized using the broadcast ephemerides to increase the number of available satellites. And to improve the real-time computing efficiency, a planar feature tracking model based on ground segmentation is adopted. The model consists of point cloud segmentation, planar feature extraction and fusion, and data correlation. To evaluate the effectiveness of the proposed method, the experiment is conducted at both a campus and a park. The result shows that approximately sub-meter-level positioning accuracy can be achieved using tightly coupled BDS-3 PPP-B2b, IMU, and LiDAR, while the positioning errors of BDS-3 PPP-B2b and BDS-3 PPP-B2b/IMU are both larger than 5 m. Furthermore, the MEMS IMU and LiDAR measurements show the capability of bridging the gaps in the GNSS data, which enables continuous and stable positioning. Moreover, compared to the state-of-the-art A-LOAM, LEGO-LOAM, and LIO-SAM frameworks, absolute trajectory errors of less than 1.0 and 2.5 m are achieved in the campus and park, respectively, using the proposed algorithm, with improvements of more than 3.40 and 1.58 times.

Anatomy of null contractions

We introduce null contractions of the Poincare and relativistic conformal algebras. The longitudinal null contraction involves writing the algebra in lightcone coordinates and contracting one of the null directions. For the Poincare algebra, this yields two non-overlapping co-dimension one Carroll algebras. The transverse contraction is a limit on the spatial dimensions and yields two non-overlapping co-dimension one Galilean algebras. We find, similar to Susskind’s original observation of the non-relativistic case, that the Poincare algebra, written in the lightcone coordinates, naturally contains Carrollian sub-algebras in one lower dimension. The effect of the longitudinal contraction, which essentially focusses on the null direction, is to disentangle the two Carroll algebras that now correspond to the symmetries of the two null boundaries. The transverse contraction similarly separates the overlapping Galilean sub-algebras of the original Poincare algebra. We discuss aspects of the conformal case, where we get lower dimensional Carroll Conformal algebras and Schrödinger algebras.

Data transformation and model selection in bivariate allometry.

Students of biological allometry have used the logarithmic transformation for over a century to linearize bivariate distributions that are curvilinear on the arithmetic scale. When the distribution is linear, the equation for a straight line fitted to the distribution can be back-transformed to form a two-parameter power function for describing the original observations. However, many of the data in contemporary studies of allometry fail to meet the requirement for log-linearity, thereby precluding the use of the aforementioned protocol. Even when data are linear in logarithmic form, the two-parameter power equation estimated by back-transformation may yield a misleading or erroneous perception of pattern in the original distribution. A better approach to bivariate allometry would be to forego transformation altogether and to fit multiple models to untransformed observations by nonlinear regression, thereby creating a pool of candidate models with different functional form and different assumptions regarding random error. The best model in the pool of candidate models could then be identified by a selection procedure based on maximum likelihood. Two examples are presented to illustrate the power and versatility of newer methods for studying allometric variation. It always is better to examine the original data when it is possible to do so.

Stabilisation of the swing pattern of an anisotropic simple pendulum

The suppression of the effects of anisotropy on a pendulum by use of a rotating mount was initially envisaged by Léon Foucault, based on his observations of the vibrations of a rod clamped in a lathe. However, the method seems to never have been tried due to the practical difficulties involved. We report a computational study of the stabilisation of the swing pattern of a simple pendulum, showing anisotropic behaviour in a static configuration, by rotation of the system mount. When the mount is static, for most initial conditions the swing patterns quickly evolves into unstable, complex Lissajous-like patterns. When the pendulum mount is rotated faster than the pendulum frequency effects of anisotropy are suppressed, and the swing pattern stabilises to that of an isotropic 3D simple pendulum. Suppression of mount anisotropy influence occurs for relatively low rotation rates. We also study swing evolution in the presence of random variations in the orientation of the mount principal axes. The use of computational techniques confirms Foucault’s original observations and hypothesis and provides an interesting avenue for students to engage meaningfully with this historically important and inspiring experiment in a novel and challenging manner.

Predictive modelling of freeway space utilising clinical history, normalised muscle activity, dental occlusion, and mandibular movement analysis

This study aimed to predict dental freeway space by examining the clinical history, habits, occlusal parameters, mandibular hard tissue movement, soft tissue motion, muscle activity, and temporomandibular joint function of 66 participants. Data collection involved video-based facial landmark tracking, mandibular electrognathography, surface electromyography of mandibular range of motion, freeway space, chewing tasks, phonetic expressions, joint vibration analysis, and 3D jaw scans of occlusion. This resulted in a dataset of 121 predictor features, with freeway space as the target variable. Six models were trained on synthetic data ranging from 500 to 25,000 observations, with 65 original observations reserved for testing: Linear Regression, Random Forest, CatBoost Regressor, XGBoost Regressor, Multilayer Perceptron Neural Network (MPNN), and TabNet. Explainable AI indicated that key predictors of freeway space included phonetics, resting temporalis muscle activity, mandibular muscle activity during clenching, body weight, mandibular hard tissue lateral displacements, and dental arch parameters. CatBoost excelled with a test error of 0.65 mm using 5000 synthetic data points, while a refined MPNN achieved the best performance with 25,000 synthetic data points and 121 unique predictors, yielding an absolute error of 0.43 mm on the 65 original observations.

A strainmeter array as the fulcrum of novel observatory sites along the Alto Tiberina Near Fault Observatory

Abstract. Fault slip is a complex natural phenomenon involving multiple spatiotemporal scales from seconds to days to weeks. To understand the physical and chemical processes responsible for the full fault slip spectrum, a multidisciplinary approach is highly recommended. The Near Fault Observatories (NFOs) aim at providing high-precision and spatiotemporally dense multidisciplinary near-fault data, enabling the generation of new original observations and innovative scientific products. The Alto Tiberina Near Fault Observatory is a permanent monitoring infrastructure established around the Alto Tiberina fault (ATF), a 60 km long low-angle normal fault (mean dip 20°), located along a sector of the Northern Apennines (central Italy) undergoing an extension at a rate of about 3 mm yr−1. The presence of repeating earthquakes on the ATF and a steep gradient in crustal velocities measured across the ATF by GNSS stations suggest large and deep (5–12 km) portions of the ATF undergoing aseismic creep. Both laboratory and theoretical studies indicate that any given patch of a fault can creep, nucleate slow earthquakes, and host large earthquakes, as also documented in nature for certain ruptures (e.g., Iquique in 2014, Tōhoku in 2011, and Parkfield in 2004). Nonetheless, how a fault patch switches from one mode of slip to another, as well as the interaction between creep, slow slip, and regular earthquakes, is still poorly documented by near-field observation. With the strainmeter array along the Alto Tiberina fault system (STAR) project, we build a series of six geophysical observatory sites consisting of 80–160 m deep vertical boreholes instrumented with strainmeters and seismometers as well as meteorological and GNSS antennas and additional seismometers at the surface. By covering the portions of the ATF that exhibits repeated earthquakes at shallow depth (above 4 km) with these new observatory sites, we aim to collect unique open-access data to answer fundamental questions about the relationship between creep, slow slip, dynamic earthquake rupture, and tectonic faulting.

Design of Fast Radio Burst Signal Recognition System based on Deep Learning

In order to identify fast radio burst signals from the original observation data of FAST radio telescopes, this paper designs a fast radio burst signal recognition system based on deep learning object detection algorithm. The system uses the incoherent achromatization algorithm and the YOLO series target recognition algorithm to realize the recognition of fast radio burst signals, and provides users with a friendly graphical system interface. In view of the different performance of users' computers, the system has the function of selecting different algorithm models. Experiments have proved that the system achieves 86% recall and 83% accuracy in the FRB20201124A real-world data test set.

Perioral dermatitis: nuances of the clinical picture and therapy in different age periods

Perioral dermatitis is a common dermatosis that predominantly affects women aged 25–40 but is also observed in children and adolescents. The distinct localization of this condition in the perioral, perinasal, and periocular zones contributes to apparent psycho-emotional distress, markedly impairing patients’ quality of life. Perturbations in the skin biotope, local immune dysregulation, and topical glucocorticosteroid use are among the primary etiological factors. Effective management involves addressing causative factors, discontinuation of topical steroids, adopting gentle skincare practices, and employing localized treatments like metronidazole, pimcrolimus, and activated zinc pyrithione. Maintenance therapy may be warranted in select cases. The purpose of this work is to review the current literature on the problem and to present original clinical observations concerning the utilization of activated zinc pyrithione in treating patients with this condition. The article presents the results of the use of topical activated zinc pyrithione in patients spanning various age groups and exhibiting diverse degrees of perioral dermatitis severity. Significant clinical evidence substantiates the remarkable efficacy and safety profile of activated zinc pyrithione in managing perioral dermatitis, both as a standalone therapeutic modality and as an integral component of combination therapy. The use of activated zinc pyrithione in the treatment of perioral dermatitis emerges as an efficacious and secure approach for treating mild to moderate forms of the condition.

Gendered labor: Appearance management and the unequal extraction of effort and time among ambassadors

This article examines the gendered appearance management ambassadors do to properly represent states. Drawing on feminist sociologist theorizations of labor, and attentive to intersections with sexuality, class, and Western standards, the study shows the unequal extraction of time and effort from men and women as they manage their appearance in diplomacy. Theoretically, I argue that the gendered management of dress, hair, and adornments are better conceived of as labor than as habitus or capital. Empirically, the study focuses on appearance management among ambassadors posted to Washington D.C. 2018–2020. Relying on a wealth of data—an original time survey, ambassador interviews, and observations—the study makes three empirical claims about the diplomatic embodiment of states: (a) diplomatic appearance standards are largely gender binary, with male uniformity and female variation; (b) more time and effort is demanded from female than male ambassadors; and (c) diplomatic appearance standards demand gender labor that is inextricably linked with the management of sexuality, upper class conventions, and Western physical appearance norms for representing states. In short, individuals presenting as men or women (although apparently not nonbinary) may now represent states as ambassadors, but more labor is extracted from women in doing so.

Integration of surface-based and space-based atmospheric CO2 measurements for improving carbon flux estimates using a new developed 3-GAS inversion model

The ongoing necessity and developmental trajectory in atmospheric CO2 assimilation system revolve around enhancing the optimization efficacy of assimilation inversions through the synergistic integration of distinct observational datasets. In this study, we developed an new inversion system-Global Greenhouse-Gases Assimilation System (3-GAS), by coupling the global atmospheric transport model GEOS-Chem with the four-dimensional local ensemble transform Kalman filter (4D-LETKF) algorithm. We compiled an integrated dataset utilizing the widely recognized carbon observation data from ObsPack (Observation Package), GOSAT (The Greenhouse Gases Observing Satellite), and OCO-2 (Orbiting Carbon Observatory-2) to estimate global surface fluxes. The integration of multiple observational data significantly amplifies spatial coverage, enhances the variability in flux inversion, and fosters improvements in localized assimilation efficacy. The spatial distribution of the carbon flux from the combined assimilation emerges as a comprehensive outcome, born from the assimilation with individual observational data. This distribution is primarily influenced by the coverage attributes of the original observations and the meticulous selection of observations during the assimilation process. The validation with surface and aircraft observations reveals that the results of combined assimilation possess composite advantages, which are manifested in alleviating certain intrinsic errors inherent to individual assimilation. The combined assimilation improves the accuracy based on root-mean-square error reduced by 9%, 22% and 33% in comparison to surface-only, GOSAT-only and OCO-2-only assimilation. Eventually, predicated upon preliminary inversion results and regional characteristics, we endeavor to propose a more refined filtration scheme and error determination method for the integrated dataset, as the initiative for the efficient assimilation of multi-source data.

Jonathan Hutchinson – A multispecialist of medicine

Sir Jonathan Hutchinson was a renowned dermatologist, surgeon, syphilologist, neurologist, and medical educator. Although his fields of interest were medicine, surgery, and ophthalmology, he contributed tremendously in the field of dermatology. He had made many original observations and published articles; many of the signs and diseases are named after him. A few to name are Hutchinson teeth, triad, nail sign, facies. Along with being a prolific author, he was a collector and teacher of many subjects. He had a feather on cap of publishing more than 600 articles and case reports in dermatology. He wrote the 10 volumes of Archives of Surgery, a remarkable singlehanded labor. He had earned honorary degrees from Universities of Glasgow, Cambridge, Edinburgh, Oxford, Dublin, and Leeds. He was knighted in 1908.

GRACE-FO attitude determination: Star camera installation matrix calibration and incremental quaternion integrator

The satellite attitude error is transmitted to the gravity field through KBR antenna phase center correction and non-conservative force conversion, which affects the accuracy of gravity field inversion. Therefore, obtaining high-precision satellite attitude is one of the important research contents of gravity field inversion. Each satellite of GRACE-FO is equipped with three SCs and an IMU for satellite attitude measurement, and fusion of these two types of data can obtain high-precision satellite attitude. In this paper, we use kalman filter to fuse the original GRACE-FO 1A observation data, and take into account the calibration of the installation matrix of the SCs during the fusion process. Meanwhile, a quaternion integrator that takes angle increments as inputs is derived from the equivalent rotation vector differential equation to address the computational inefficiency of first-order quaternion integrator, the accuracy and computational efficiency are verified using measured data. The experimental results show that after the calibration of the installation matrix of the SCs, the system bias of 20′′ caused by switching from three SCs observations to two SCs observations can be eliminated. The standard deviation of the attitude difference between the obtained attitude in this paper and the attitude calculated by JPL is at most 6′′ and the proposed incremental quaternion integrator has the same accuracy as the first-order quaternion integrator but with computational efficiency improved by at least 35%.

Critical observations in model-based diagnosis

In this paper, we address the problem of finding the part of the observations that is useful for the diagnosis. We define a sub-observation as an abstraction of the observations. We then argue that a sub-observation is sufficient if it allows a diagnoser to derive the same minimal diagnosis as the original observations; and we define critical observations as a maximally abstracted sufficient sub-observation. We show how to compute a critical observation, and discuss a number of algorithmic improvements that also shed light on the theory of critical observations. Finally, we illustrate this framework on both state-based and event-based observations.

Book Review of Global Drought and Flood: Monitoring,Prediction,and Adaptation

Global Drought and Flood: Monitoring, Prediction, and Adaptation is an American Geophysical Union monograph (published by Wiley) that introduces the latest advancements in physical and remote sensing models developed for drought and flood issues. The book consists of 18 chapters in 3 parts, which introduce global remote sensing monitoring of droughts and floods, modeling and prediction of global drought and flood, as well as risk assessment, management, and socioeconomic responses to global drought and flood. Current remote sensing observation research suggests that merging climate data can generate longer records suitable for drought assessment and monitoring, while some variables such as snow and relative humidity can be integrated into drought monitoring models to improve the monitoring and estimation of drought initiation, respectively. However, changes in satellite sensors also introduce considerable unquantifiable uncertainty to drought modeling. An ideal approach to this issue is to provide uncertainty boundaries based on top of the original observations. This uncertainty and the structural and parameter uncertainties generated by model-based simulations can be combined to assist decision-making in operational applications. The monograph not only elaborates on the dominant models, methods, techniques, and products used in practical applications internationally but also provides planning and deployment for flood disaster prediction. The drought and flood modeling and remote sensing methods presented in this book can be used for reference and use by global emergency organizations and decision-makers.

First Report of Golovinomyces bolayi Causing Powdery Mildew on Lettuce in Mexico.

In August 2022, powdery mildew symptoms were detected on lettuce (Lactuca sativa) in a commercial field located in Quecholac, Puebla, Mexico. Signs appeared as whitish powdery masses on leaves. Disease incidence was about 100% and signs covered up to 40% of leaf surface. Mycelium was amphigenous forming white patches. Hyphal appressoria were indistinct or nipple-shaped and solitary. Conidiophores (n= 30) were hyaline, erect, arising from the upper surface of hyphal mother cells or lateral, and of 90 to 201 μm long. Foot cells were cylindrical, of 49 to 92 × 10-15 μm, followed by 1-3 shorter cells, and forming conidia in chains. Conidia (n= 100) were hyaline, ellipsoid-ovoid, doliiform-subcylindrical, 27 to 40 × 14 to 20 μm. Conidial germination belonging to the Euoidium type. Chasmothecia were not observed. The morphological characters were consistent with those of Golovinomyces bolayi (Braun et al. 2019). A voucher specimen was deposited in the Herbarium of the Department of Agricultural Parasitology at the Chapingo Autonomous University under accession number UACH451. To confirm the identification of the fungus, genomic DNA was extracted from conidia and mycelium following the CTAB method (Doyle and Doyle 1990), and the internal transcribed spacer (ITS) region was amplified by PCR using the primers ITS5/ITS4 (White et al. 1990) and sequenced. The resulting 506 bp sequence had 100% identity to those of G. bolayi (LC417109 and LC417106). Phylogenetic analyses using the Maximum Likelihood and Maximum Parsimony methods were performed and confirmed the results obtained in the morphological analysis. The isolate UACH451 grouped in a clade with isolates of G. bolayi. The ITS sequence was deposited in GenBank under accession number OR467546. Pathogenicity was confirmed by gently dusting conidia onto ten leaves of healthy lettuce plants. Five non-inoculated leaves served as controls. The plants were maintained in a greenhouse at 25 to 30 ºC, and relative humidity of 70%. All inoculated leaves developed similar symptoms to the original observation after 10 days, whereas control leaves remained disease free. Microscopic examination of the fungus on inoculated leaves showed that it was morphologically identical to that originally observed. Based on morphological data and phylogenetic analysis, the fungus was identified as G. bolayi. This pathogen has been previously reported causing powdery mildew on lettuce in Argentina, Canada, Chile, Ecuador, Peru, USA and Venezuela (Braun et al. 2019; Mieslerová et al. 2020). To our knowledge, this is the first report of G. bolayi causing powdery mildew on lettuce in Mexico.

Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs

Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs. Our codes and datasets are available at: https://github.com/CurryTang/Graph-LLM .

Deep Active Learning with Noise Stability

Uncertainty estimation for unlabeled data is crucial to active learning. With a deep neural network employed as the backbone model, the data selection process is highly challenging due to the potential over-confidence of the model inference. Existing methods resort to special learning fashions (e.g. adversarial) or auxiliary models to address this challenge. This tends to result in complex and inefficient pipelines, which would render the methods impractical. In this work, we propose a novel algorithm that leverages noise stability to estimate data uncertainty. The key idea is to measure the output derivation from the original observation when the model parameters are randomly perturbed by noise. We provide theoretical analyses by leveraging the small Gaussian noise theory and demonstrate that our method favors a subset with large and diverse gradients. Our method is generally applicable in various tasks, including computer vision, natural language processing, and structural data analysis. It achieves competitive performance compared against state-of-the-art active learning baselines.

Case Report: Inflammatory orbital pseudotumor revealing Behçet's disease: an original observation

Objective To provide an original observation of Behçet’s disease, revealed by an inflammatory pseudotumor of the orbit. Methods We reported an observation of an inflammatory pseudotumor of the orbit that revealed Behçet’s disease. Results Twenty-Eight years old patient was admitted to the internal medicine department for painful edema in the right eye with headache and loss of visual acuity. Ophthalmologic examination revealed eye protrusion with conjunctival hyperemia. Orbital MRI resonance imaging revealed periorbital inflammatory thickening associated with inflammatory myositis. Behçet’s was diagnosed based on a history of recurrent oral aphthous since childhood, pseudofolliculitis, pathergy test positivity, and negativity of the rest of the etiological investigations. The evolution was spectacular with boli methylprednisolone and colchicine prescriptions. Conclusion Although the association is rare, Behçet’s disease should be included in the workup of inflammatory pseudotumors of the orbit.