New insight from modelling galactic deuterons over changing solar activity

ABSTRACT The measurement of temperature and O3 concentration in the near-space region (20–100 km) holds significant scientific and practical value. The 1.27 μm O2(a1Δg) dayglow, characterized by high radiative intensity and broad spatial coverage, serves as an important tracer. Existing limb-viewing forward models suffer from inaccuracies in photochemical calculations and radiative transfer treatment, particularly due to simplified photodissociation coefficients and neglect of self-absorption and scattering. Consequently, large discrepancies arise between modeled and satellite-observed volume emission rates (VERs) in the 20–40 km range. An optimized forward model for 1.27 μm O2(a1Δg) limb-viewing observations is proposed. The variation of the photodissociation coefficient J 3 with solar zenith angle (SZA) and altitude is explicitly calculated. Voigt line shapes are applied below 40 km to refine photon absorption coefficients g, thereby improving the precision of the photochemical model. The radiative transfer simulation incorporates both self-absorption and scattering effects. Validation shows that the simulated scattered absorption spectrum agrees well with the Moderate Resolution Atmospheric Transmission 5 (MODTRAN5) model, with a mean relative difference below 2%. Compared with Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) observations, the global mean relative difference of the simulated airglow integrated radiance is below 18.05% (23–100 km). Incorporating ozone profiles measured by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) further reduces the relative differences of local spectra to 6%. Inversion fitting based on the forward model yields mean relative differences of 2–5% between the fitted and SCIAMACHY-observed spectra. The mean residual between the retrieved and SABER temperature profiles is below 5 K. By incorporating both self-absorption and scattering processes into the radiative transfer model, this work establishes a more physically consistent forward model for the 1.27 μm O2(a1Δg) dayglow, enabling more accurate retrieval of atmospheric temperature and VER in the near-space region.

Glycolytic production of HDO from the metabolism of perdeuterated glucose provides a means for metabolic imaging with 2 H MRI. The present study compared HDO production from a cost-efficient [2,3,4,6,6- 2 H 5 ]glucose with [ 2 H 7 ]glucose in vitro and in vivo . 2 H NMR spectroscopy was performed to measure glucose consumption, lactate, and HDO production in the SFxL glioblastoma cell line. In vivo studies in healthy mice using 2 H magnetic resonance spectroscopy were performed at 11.1 T after administering a bolus of either metabolic contrast agent. In vivo metabolite levels were quantified using unlocalized and slice-selective localized spectra. Our in vitro results demonstrated similar glucose consumption and HDO production kinetics, although significant differences in lactate labeling were observed. The in vivo study showed comparable glucose consumption and HDO production kinetics following tail-vein bolus administration of either metabolic contrast agent, while lactate was not detected in the brain. [2,3,4,6,6- 2 H 5 ]glucose shows comparable HDO production to [ 2 H 7 ]glucose, while offering lower cost and reduced spectral complexity. These findings place [2,3,4,6,6- 2 H 5 ]glucose as an alternative to [ 2 H 7 ]glucose for HDO-based DMI studies.

Optical Frequency Domain Reflectometry (OFDR) has extensive application prospects in structural health monitoring, but faces technical challenges in simultaneously achieving high-precision continuous strain monitoring and large strain monitoring capabilities. This study proposes a novel OFDR machine learning approach targeting the requirements of structural health monitoring, aiming to develop a model with high-precision capabilities for both continuous strain and large strain demodulation. Based on the characteristics of OFDR local spectra, the proposed sequence exchange augmentation and coefficient scaling augmentation methods help the model focus on intrinsic spectral variation patterns rather than surface intensity fluctuations, enhancing generalization capability and prediction stability. The PCA dimensionality reduction method provides a novel interpretive perspective for spectral data, abstracting time-correlated spectral data into principal component feature information and extracting novel spectral features and variation patterns. This method achieves a 90% reduction in dimensions while preserving over 99% of the cumulative explained variance, providing machine learning models with a more structured and information-dense feature space. The weighted ensemble capability of the AutoGluon framework can integrate different high-performance models, maintaining excellent demodulation performance in both continuous strain and large strain scenarios by synthesizing the predictive capabilities of various models. Furthermore, the framework’s automated processing and multi-layer stacking architecture efficiently explore model combinations, significantly improving training efficiency and providing researchers with more comprehensive model performance perspectives and data evidence. The trained weighted ensemble model achieved a Mean Absolute Error (MAE) of 2.4 in continuous strain measurement and an MAE of 16.4 under 3000μϵ large strain conditions, simultaneously possessing high-precision demodulation capabilities for both continuous varying strain and large strain scenarios. This study provides a new technical paradigm for the deep integration of OFDR with high-performance machine learning algorithms, offering efficient and effective solutions for the entire OFDR machine learning pipeline from data augmentation to data processing, model training and optimization. The approach meets structural health monitoring application requirements and provides technical support for OFDR system applications in this field.

Abstract The local dimension spectrum provides a framework for quantifying the fractal properties of a measure, and it is well understood for non-overlapping self-similar measures. In this article, we study the local dimension spectrum for dominated self-affine measures. By analyzing exact dimensionality, we obtain deterministic results that extend the scope of the local dimension spectrum beyond the almost-sure setting.

Transmission spectroscopy has advanced our understanding of exoplanet atmospheres, but it can be hindered by contamination originating in stellar heterogeneities, mainly coming from line-of-sight effects. Therefore, probing how stellar spectra vary across the stellar surface is essential to accurately disentangling stellar and planetary spectral contributions in transit observations. Such observations can actually be used to reconstruct the local stellar spectra behind the planet's transit chord. These methodologies can help us learn more about the physics of stellar surface and how to tackle line-of-sight effects. In this paper, we study the centre-to-limb variations of line profiles across the surface of HD 189733 using the ESPRESSO spectrograph. We build on other works by analysing the same sets of lines, allowing for a direct comparison of results and an assessment of the feasibility of applying the Doppler shadow technique with ESPRESSO. We gain a better understanding of the variations in line profiles, while also making a comparison between the data of HD 189733 and synthetic spectra and solar data. Fe I We analysed spectra collected by ESPRESSO during two transits of HD 189733 b as separate sets of data. We performed a cross-correlation of each individual spectrum with two different masks made of selected spectral lines for a total of four sets of cross-correlation functions (CCFs) and employed a Doppler shadow methodology to retrieve profiles for local regions of the stellar surface. We then compared the results with previous works and with solar disc-resolved observations from IAG ATLAS. Finally, we compared the data with two separate transit simulations made using SOAPv4 with Turbospectrum synthetic spectra computed with MARCS stellar atmosphere models under local thermal equilibrium (LTE) and non-LTE (NLTE) conditions. Fe I For the profile depth of three sets of CCFs, we verified a statistically significant increase in line depth from the stellar limb to the centre. This variation was expected from simulations with MARCS models, although the solar data present a smaller gradient in the variation of line depth. In the case of the width of the line profiles, we verified that the profile width decreases from stellar limb to stellar centre for a set of CCFs. This result is consistent with the behaviour observed in solar data, but not reproduced by the simulations. Fe I Fe I These results highlight the abilities of ESPRESSO in providing the necessary precision and resolution to study centre-to-limb variations of spectral line profiles on the surface of other stars with the use of CCFs. The local CCF profiles of HD 189733 agree with the IAG ATLAS data, but disagree with simulations on line widths, indicating that important physical processes are missing and must be included to recover accurate profile widths.

We analyze the spectral stability of travelling waves in a δ-regularized dissipative sine-Gordon equation modelling refined long Josephson junction dynamics. Linearization about a wave yields a singularly perturbed fourth-order spectral problem with intrinsic slow–fast spatial structure. Using an Evans-function formulation on a domain of consistent spatial splitting, we establish a local factorization separating slow and fast modes and prove that the δ-induced fast subsystem remains uniformly hyperbolic and does not generate an additional point spectrum near λ=0. Hence, the local point spectrum coincides with that of the classical dissipative sine-Gordon equation. Numerical computations of the essential spectrum and Evans winding numbers confirm the analysis and show that the higher-order terms enhance high-frequency damping without altering low-frequency spectral stability.

Acute undifferentiated febrile illness (AUFI) is a challenging clinical condition in tropical regions, caused by a broad range of pathogens. In Villeta municipality, Colombia, data on neglected bacterial causes remain scarce, highlighting the need to expand understanding of the local etiological spectrum. Thus, the aim of the present study was to explore the presence of the neglected pathogens, Bartonella, Borrelia, and Coxiella burnetii, as potential causes of AUFI in Villeta. DNA was extracted from whole-blood samples from febrile patients. Quality and purity were assessed spectrophotometrically and by conventional polymerase chain reaction (PCR). Bartonella, Borrelia, and C. burnetii were detected using genus- and species-specific quantitative PCR (qPCR) assays. Bartonella-positive samples were further analyzed by multigene PCRs and sequencing for species identification. Anti-Bartonella and anti-C. burnetii immunoglobulin G (IgG) antibodies were evaluated by indirect immunofluorescence to assess recent or past exposure to these agents. A total of 41 febrile patients were evaluated. Bartonella DNA was detected in 9.8% (4/41) of samples. No Borrelia or C burnetii DNA was detected. Phylogenetic analysis revealed two distinct clades, although none could be assigned to species level. Serological analysis showed anti-Bartonella IgG antibodies in 29.3% (12/41) of cases, with 9.8% (4/41) exhibiting seroconversion. One patient presented both molecular and seroconversion evidence of recent Bartonella infection. None of the patients were seropositive for C. burnetii. This study provides the first molecular and serological evidence of Bartonella circulation among febrile patients in Villeta, Colombia, revealing genetically distinct lineages and indicating both active and past infections, underscoring its potential role in AUFI.

Abstract We present a radiation-hydrodynamics (RHD) scheme that enables 3D simulations resolving both protostellar interiors and their surrounding accretion flows within a single framework, to clarify how a protostar evolves while interacting with the accretion flow. The method builds on an explicit two-moment M1 closure scheme with a reduced speed of light approximation (RSLA) for massively parallel computation. Our scheme introduces a complementary non-RSLA radiation component that dominates in optically thick regions. This hybrid treatment restores physical energy conservation inside protostars, which would otherwise be violated under the RSLA, while retaining the advantage of large time steps. To overcome the limitation of the conventional M1 closure in solving radiative transfer in extremely optically thick regions inside protostars and across steep optical-depth gradients near their surfaces, we incorporate the optical-depth information of neighboring cells into the radiative transfer calculation. We further evolve photon-number densities in addition to radiation energy densities to reconstruct an effective local spectrum on the fly without resorting to costly multifrequency transport. We implement this scheme in the adaptive mesh refinement code SFUMATO and verify its validity through a series of test calculations. As an application, we follow the early evolution of a massive protostar formed at high redshift, within a full cosmological context. The results reveal a continuous structure connecting the swollen protostar and its surrounding disk, which cannot be captured in conventional 1D models. This RHD scheme opens a path to studies of protostellar evolution and its interaction with the accretion flow in realistic 3D environments.

The digital transformation of Industry 4.0 requires networking solutions that deliver ultra-low latency, energy efficiency, and robust security. Conventional 5G architectures face limitations such as high infrastructure costs, performance bottlenecks, and vulnerabilities in mission-critical environments. This study proposes the Private Hybrid Wireless Access Network (PHWAN) framework, a novel architecture that combines localized spectrum management, edge–cloud orchestration, and blockchain-based Zero Trust security. A comprehensive cost–benefit model and MATLAB-based simulation of an industrial IoT environment were used to evaluate PHWAN against traditional 5G deployments. Results show that PHWAN reduces latency by 50 % (0.5 ms to 0.25 ms), lowers energy consumption by 61 % (5.4 mJ to 2.1 mJ), and improves bandwidth utilization by 108 %. Security analysis further demonstrates improved access control and data integrity without incurring significant overhead. These findings establish PHWAN as a scalable and cost-effective alternative to 5G for delay-sensitive and resource-constrained industrial IoT applications. Future research will extend validation to standardized platforms such as NS-3 and 5G-LENA and explore integration with 6G spectrum slicing, quantum-secured communications, and industrial metaverse applications to enhance resilience and interoperability in next-generation smart factories.

Wireless bandwidth is in greater demand than ever before due to the Internet of Things' (IoT) applications' rapid expansion in fields including smart cities, autonomous and Industry 4.0. Traditional fixed spectrum allocation approaches can lead to inefficient utilization and excessive interference levels, particularly in densely populated areas. The purpose of this evaluation is to create an intelligent, decentralized, and privacy-preserving framework for optimizing spectrum detection and sharing among IoT devices utilizing machine learning (ML) techniques. The Cognitive Radio Networks (CRNs) Dataset is gathered from the Kaggle source. The procedure consists of four sequential steps. Each IoT node uses Extreme Gradient Support Vector with Adaptive Ant Colony Optimization (EGSV-AACO) to monitor spectrum occupancy and identify idle bands. Each node builds a local spectrum access model based on temporal spectrum patterns. Model weights are delivered to a nearby edge server on a regular basis to avoid exposing raw data using Federated Averaging (FedAvg). The server aggregates the locally trained models to form a global model and redistributes it to all participating devices. This updated global model will drive real-time, collision-free spectrum allocation among IoT devices. A smart campus simulation using MATLAB shows that the proposed EGSV-AACO framework ensures access convergence, improves spectrum usage, and prevents raw data leakage. The developed model outperforms all baseline methods and achieved an accuracy of 97%, precision of 97.5%, recall of 96%, and an F1-score of 96.5%. Overall, this research introduces a novel Federated EGSV-AACO framework that significantly enhances decentralized, privacy-preserving, and intelligent spectrum sensing and sharing in IoT networks.

Abstract In magnetic confinement fusion, toroidicity-induced Alfvén eigenmodes (TAEs) are well-studied, weakly stable solutions of the linearized ideal magnetohydrodynamic equations. Driven unstable by suprathermal populations of energetic particles, TAE pose a key vulnerability to the confinement of high-energy alpha particles generated by fusion reactions. Hence, it is paramount to understand TAE dynamics if a working reactor is to be realized. In this work, we detect and characterize signatures of nonstationary nonlinear coupling between TAE using a novel, time-resolved bispectral analysis; results are supported by analytic signal of band-passed data. Crucially, a stationary phase relationship between two TAE and a nascent low frequency fluctuation is observed precisely when the triple product of magnetic fluctuation amplitudes is enhanced. Local mode number and frequency spectrum, gleaned from beam-emission spectroscopy, corroborates simultaneous satisfaction of nonlinear matching conditions, and provides a tool to identify theorized pathways of energy transfer, e.g. TAE parametric instability.

Inadvertent ingestion of fishbone can lead to bowel perforation. Numerous cases reports have been published; therefore, we aimed to describe the localization and type of complication in a dedicated cohort. From 2009 to 2023, we queried our local informatic department (Cayenne Hospital, French Guiana) to search electronic patient records (ICD-10 code T18: Foreign body in alimentary tract) and we reviewed each case to identify abdominal complication caused by fishbone ingestion. Patients with complication occurring above the stomach without a computed tomography available (CT) were excluded. Clinical, biological, imaging and treatment related data were collected. The overall raw incidence was estimated. Forty-two patients (mean age: 60years old±15; female 36%) were included for analysis from May 2014 to June 2023. Abdominal pain was present in 41/42 patients (98%) and inflammatory biological syndrome was found in 34/42 patients (81%). On CT, perforation of the bowel wall was seen in 41/42 patients (98%), intra-abdominal collection in 17/42 patients (40%) and pneumoperitoneum in 2/42 patients (5%). The ileum (11/42, 26%) was the most common complication site, followed by the liver (6/42, 14%), and the right colon and stomach (5/42, 11% each). Treatment included surgery for 23/42 patients (55%), antibiotics alone for 14/42 patients (33%), endoscopic removal for 4/42 patients (10%), and percutaneous drainage for 1/42 patient (2%). The estimated incidence was 2.9 per 100,000 person-years. We report the largest analysis of bowel perforation secondary to fishbone ingestion, providing an exhaustive spectrum of localization and type.

Brucella canis is a zoonotic pathogen that infects both dogs and humans, yet its evolutionary and phylogenetic characteristics are poorly understood. Here, we comprehensively characterized an isolated strain of B. canis through integrated bacteriological, comparative genomic, and whole-genome sequencing-based core genome single-nucleotide polymorphism (WGS-cgSNP) analyses. B. canis YN20042 was isolated from a febrile patient (38 °C) with sweating and fatigue. The culture exhibited rough, grayish white, sticky, and opaque colonies. The isolate was identified as Brucella strain by a BCSP-31 polymerase chain reaction (PCR) assay, which yielded an amplicon of the expected 223-bp size, and was classified as a B. canis strain by conventional biotyping. The patient reported frequent contact with dogs and livestock. The strain showed a 99.99% average nucleotide identity to the B. canis reference strain ATCC 23365 (GCA_000018525.1). An in silico multilocus sequence typing (MLST) analysis showed that the strain belonged to sequence type 21, which was consistent with its classification within B. canis. The genome of strain YN20042 exhibited strong synteny with the reference strain and showed no detectable structural variations. It harbored 12 predicted virulence factors encompassing 71 associated genes, although it notably lacked the wbpL gene but contained a Brucella suis mprF gene. A further analysis identified predicted mutations in key virulence genes (eryA, pagN, bmaC, cfa1, and cfa2) and predicted multiple horizontally acquired genes, collectively suggesting a complex evolutionary trajectory involving both gene variants and potential recombination events. A WGS-SNP analysis revealed that YN20042 clustered closely with strains isolated from Zhejiang and Beijing, indicating a high degree of genetic relatedness. The first isolation of B. canis in the region expands the local spectrum of pathogenic Brucella and highlights the substantial infection risk for individuals with close dog and livestock contact. Enhanced surveillance, targeted screening of high-risk populations, and public health education are necessary to mitigate the risk of B. canis transmission.

Impact of Bi3+ co-doping on local structure, EPR spectra and luminescence properties of Yb3+ probe ions in CeO2 nanoparticles

To determine the frequency and clinical associations of hypermobility spectrum disorders in a central orthopedic hospital. A clinical epidemiological, prospective hospital-based cross-sectional design was employed. Kuwait Ministry of Health Ethics Board approved the study (ref. no. 2022/2233). The Beighton score and the 2017 hypermobility spectrum disorders classification framework were used for screening over a 7-month period. A total of 270 patients were examined, revealing a hypermobility spectrum disorder frequency of 21.1%; 31.7% in women and 4.7% in men. The mean age ± standard deviation was 39.4 ± 16.9 years, and 91.2% of identified cases were women. Among individuals with hypermobility spectrum disorders, 63.2% were classified as generalized hypermobility spectrum disorders, 26.3% as localized hypermobility spectrum disorders, and 10.5% as peripheral hypermobility spectrum disorders. The most frequent primary diagnosis or injury involved the knee (45.3%), hand (19.4%), and foot and ankle (19.4%). The most frequent secondary diagnosis or injury involved the knee (40.6%) and foot and ankle (28.1%). The most frequent tertiary diagnosis or injury involved the knee (50.0%) and the spine (20.0%). Management was most commonly directed toward the knee (45.7%), followed by the foot and ankle (12.7%). Statistically significant associations were identified between the Beighton score and primary diagnosis or injury (p = 0.032, r = 0.284), secondary management (p = 0.003, r = 0.516), and tertiary management (p = 0.027, r = 0.690). Hypermobility spectrum disorder was frequently observed in a central orthopedic hospital and showed significant associations with orthopedic diagnoses and management pathways, indicating a substantial clinical burden and implications for orthopedic service planning.

In the field of spectrum situation monitoring, space-based spectrum mapping can overcome the limitations of ground-based monitoring, such as restricted coverage, environmental constraints, and difficulties in equipment deployment. However, current research on radio map reconstruction methods based on space-based satellite data is insufficient. Traditional methods mostly set missing patterns based on the type of data collected on the ground, such as random sampling or strip sampling. These methods can meet the needs of generating spectrum maps for local spectrum monitoring on the ground, but they are hardly adaptable to the task of space-based global spectrum mapping. To address this challenge, we have designed a tensor reconstruction method for radio maps based on local low-rankness. Before conducting global completion, we first identify local regions in the map with more prominent low-rank characteristics for completion. This enables the method to handle radio map reconstruction in the context of space-based wide-area coverage. During the reconstruction phase, targeting the two-dimensional spatial completion problem of space-based spectrum maps, we extend the autoregressive process from the temporal dimension in classical spatiotemporal models to the spatial domain, proposing a low-rank tensor completion model based on dual autoregression (LRTCDAR). By incorporating a dual autoregressive structure to capture spatial correlations and combining it with the inherent low-rank property of radio maps, the model achieves high-precision reconstruction of incomplete radio maps. Extensive experimental results demonstrate that, compared to state-of-the-art methods, the proposed approach effectively reconstructs radio maps under various conditional backgrounds. Notably, LRTCDAR exhibits greater advantages when applied to satellite data acquisition scenarios.

Super-resolution (SR) in remote sensing is generally effective only when the resolution gap between inputs and targets is less than 10x, limiting its applicability for producing field-scale, high-resolution data from sensors such as Sentinel-2. Classical pansharpening, which relies on a high-resolution panchromatic band from the same platform, degrades sharply at large scale factors. We propose a spectral extension SR framework that enables >80x upscaling (10-20 m to 12.5 cm) of Sentinel-2 VNIR bands using co-registered UAS-RGB imagery as high-resolution side information. The framework has two stages. First, UAS hyperspectral data (400–1000 nm) are spectrally harmonized via non-negative regression to simulate high-resolution Sentinel-2 bands free of atmospheric noise. Second, these harmonized bands serve as ground truth to train a lightweight SRCNN-based regression model that maps Sentinel-2 spectral content onto UAS spatial textures. The method succeeds at extreme scale factors because the hyperspectral data exhibit a low-rank structure, with local spectra dominated by soil and vegetation components. UAS-RGB bands capture these dominant components, enabling—unlike pansharpening—spectral extension into red-edge and near-infrared bands. Despite the use of an SR model the problem is closer to a guided colorization model. Applied to precision farming sites across Maryland and Pennsylvania, the approach achieves a spectral fidelity greater than 30 dB on unseen crops. The harmonized data improve cover crop nitrogen prediction by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx 31\%$</tex-math></inline-formula> over Sentinel-2 and UAS RGB baselines, delivering multispectral UAS performance without specialized sensors. The SRCNN model generalizes across crops and seasons and supports flexible configurations, including RGB-only training, spatial resolution invariance (4-12.5cm) and advancing scalable, low-cost multimodal remote sensing.

Reflector temperature coefficient of molten salt fast reactor using moderating reflector

Context: Precocious puberty, clinically defined as the development of secondary sexual characteristics before 8 years of age in girls and 9 years in boys, represents a clinical spectrum that encompasses both pathological and non-pathological variants of early pubertal onset. Given the paucity of data from African settings, there is a compelling need to characterize the local spectrum of precocious puberty and its normal variants. Objective: To describe clinical patterns, demographics, aetiologies, diagnostic patterns, management, and outcomes of children presenting with precocious puberty (PP) and its normal variants at University of Calabar Teaching Hospital, Nigeria, over seven years. Materials and Methods: This was a retrospective descriptive study carried out between January 2015 and December 2021 involving children aged 0-18 years who presented with signs of early pubertal development. Data collected included demographics, clinical presentation, diagnosis (CPP, PPP, normal variants), skeletal age (Greulich–Pyle), Tanner staging, radiology, and biochemistry. GnRH analogues were used for children confirmed to have idiopathic central precocious puberty, specific treatments were given to children for the cause of peripheral precocious puberty when identified and parents were reassured for patients identified with the normal variants. Descriptive statistics used to analyse data. Results: 24 patients (12.3% of endocrine cases) had precocious puberty with a M: F ratio of 1:2.3 and median age of 5.5 years. CPP was diagnosed in 8 (33%) patients, all female and idiopathic precocious puberty including PPP: 4 (17%) with causes—virilizing adrenal tumour (1), CAH (1), McCune-Albright (1), exogenous hormone (1). Normal variants were seen in 12 (50%) children presenting as premature thelarche/adrenarche. Approximately 91.6% were from middle socioeconomic class. Most cases of normal variants required only monitoring, whereas true precocious puberty cases were managed with endocrine evaluation and treatment as indicated. Conclusion: Benign variants and female predominance were noted among children presenting with precocious puberty. CPP was most common true form of precocious puberty. Awareness and accurate diagnosis are essential for targeted management. Regional data as presented in this study will enhance understanding of epidemiology of PP in sub-Saharan-Africa.