bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metasurface absorber for millimeter waves: a deep learning-optimized approach for enhancing the isolation of wideband dual-port MIMO antennas
Authors: Narayanaswamy Nagesh Kallollu , Satheesha T. Y. , Alzahrani Yazeed , Pandey Ashish , Dwivedi Ajay Kumar , Singh Vivek , Tolani Manoj 
Published: December 2024

AbstractIn this communication, the concept of metasurface absorber is utilized to enhance the isolation in the dual port multiple-input multiple-output (MIMO) antenna specially designed for a wideband millimeter wave operation. The frequency of operation of the designed module is 32.5–42.5 GHz with sufficient gain attributes. The designed metasurface array consists of two circular rings on two different layers. The concept of deep learning is utilized to optimize the dimensional configuration of the metasurface to achieve the maximum absorption of electromagnetic waves in the band of interest. The suppression of mutual coupling by double-ring metasurface is analyzed with the help of the wave theory concept. In contrast to previous decoupling methods using metasurfaces, the suggested metasurface is intended to be in the same plane as the array. The findings demonstrate the capability of effectively separating antenna elements in wideband MIMO antenna without compromising the geometrical complexity. Diversity metrics such as envelope correlation coefficient (ECC), mean effective gain (MEG), channel capacity loss (CCL), and total active reflection coefficient (TARC) for the proposed frequency range are also used to evaluate the performance of the constructed MIMO antenna. The wideband characteristics with a compact configuration make the design MIMO module a suitable candidate for mm-wave applications. The congruence between simulation and measurement confirms the validity of the suggested design.
Free vibration of nanobeams with surface and dynamic flexoelectric effects
Authors: Wang Peng , Xu JiaWei , Zhang XiWen , Lv YingHui 
Published: December 2024

AbstractIn this paper, the free vibration of piezoelectric nanobeams considering static flexoelectric, dynamic flexoelectric, and surface effects is studied. Based on the theories of the Timoshenko beam and Euler-Bernoulli beam, a theoretical model of flexoelectric nanobeams is established and the governing equations and boundary conditions of this model are derived using the variational principle. Then, the analytical solution of the frequency equation is obtained by using the Navier method. Numerical results indicate that the size dependence of the dynamic flexoelectric effect is stronger than the surface effect. The surface and dynamic flexoelectric effects exhibit a competitive mechanism on the natural frequency.
The effects of episodic context on memory integration
Authors: Liu Zhenghao , Johansson Mikael , Johansson Roger , Bramão Inês 
Published: December 2024

AbstractInformation encountered in different events, such as people and objects, can be interlinked in memory. Such memory integration supports novel inferences about the world. This study investigates the role of episodic context on memory integration in two experiments using an associative inference task. Participants encoded events with overlapping (AB and BC) and non-overlapping associations (XY) presented in the same or different episodic contexts. Inference performance across events (AC) was tested in the absence (Experiment 1) and in the presence (Experiment 2) of the encoding context. Our data show that inferences across events encoded in the same context were more accurate, faster, and made with greater confidence compared to those encoded in different contexts. However, this effect was observed only when the context was presented during testing, suggesting that context enhances associative inferences by facilitating retrieval of events associated with that context. These findings demonstrate that revisiting the encoding context promotes memory integration by providing privileged access to contextually associated memory traces and facilitating their flexible recombination to form novel inferences.
How well do climate modes explain precipitation variability?
Authors: Hobeichi Sanaa , Abramowitz Gab , Sen Gupta Alex , Taschetto Andréa S. , Richardson Doug , Rampal Neelesh , Ayat Hooman , Alexander Lisa V. , Pitman Andrew J. 
Published: December 2024

AbstractLarge-scale modes of climate variability, such as the El Niño-Southern Oscillation, North Atlantic Oscillation, and Indian Ocean Dipole, show significant regional correlations with seasonal weather conditions, and are routinely forecast by meteorological agencies attempting to anticipate seasonal precipitation patterns. Here, we use machine learning together with more traditional approaches to quantify how much precipitation variability can be explained by large-scale modes of variability, and to understand the degree to which these modes interact non-linearly. We find that the relationship between climate modes and precipitation is predominantly non-linear. In some regions and seasons climate modes can explain up to 80% of precipitation variability. However, variability explained is below 10% for more than half of the land surface, and only 1% of the land shows values above 50%. This outcome provides a clear rationale to limit expectations of predictability from modes of variability in all but a few select regions and seasons.
City–company collaboration towards aligned science-based target setting
Authors: Kılkış Şiir , Bjørn Anders , Bai Xuemei , Liu Jianguo , Whiteman Gail , Crona Beatrice , Andersen Lauren Seaby , Hasan Syezlin , Vijay Varsha , Sabag Oscar 
Published: December 2024

Abstract
Cities and companies have great potential to reduce pressures on Earth system boundaries. Science-based target setting has emerged as a powerful tool to help achieve the potential, but its uptake has been limited. Moreover, cities and companies usually develop their targets separately, even though many are co-located. Focusing on the top 200 cities and 500 companies by greenhouse gas emissions, we analyse the current state and potential of adopting science-based targets for climate. Of these key actors, 110 cities with existing net-zero targets and 22 companies with existing science-based targets could together eliminate up to 3.41 GtCO2e of annual emissions. We argue that this reduction potential could increase by as much as 67% (to 5.70 GtCO2e) if the cities and companies that already have targets bring their co-located counterparts on board to keep abreast of their ambitions. Using freshwater as another example, we discuss entry points for addressing interrelated Earth system boundaries through city–company collaborations. Our findings elucidate previously untapped potentials that could accelerate transformations for operating within Earth system boundaries.
No paleoclimatic anomalies are associated with the late Eocene extraterrestrial impact events
Authors: Wade Bridget S. , Cheng Natalie K. Y. 
Published: December 2024

AbstractTwo distinct extraterrestrial impacts events struck the Earth less than 25,000 years apart in the late Eocene, approximately 35.65 million years ago. These resulted in the Popigai (northern Siberia) and Chesapeake Bay (eastern North America) impacts structures, the largest of the Cenozoic era. To examine the paleoclimatic consequences attributed to the late Eocene Chesapeake and Popigai extraterrestrial impact events, we present multispecies planktonic and benthic foraminiferal oxygen (δ18O) and carbon (δ13C) isotope records. Here we generate data from the Gulf of Mexico, Deep Sea Drilling Project Site 94 covering 35.85 to 35.49 million years ago. No isotopic anomalies or excursions were recorded across the impact horizons. However, ~100,000 years before the impacts, a negative 0.75‰ δ18O shift occurs in planktonic foraminifera, coincident with a 0.25‰ positive change in benthic foraminifera. We interpret this as a warming of ~2 °C in the surface ocean, accompanied by 1 °C deep water cooling, but these modifications are before and not coeval with the impact horizons. Despite the close succession of two or more large extraterrestrial impact events within a short space of time (less than 25,000 years), our study from the Gulf of Mexico indicates no detectable paleoclimatic response.
A 24-month National Cohort Study examining long-term effects of COVID-19 in children and young people
Authors: Stephenson Terence , Pinto Pereira Snehal M. , Nugawela Manjula D. , Dalrymple Emma , Harnden Anthony , Whittaker Elizabeth , Heyman Isobel , Ford Tamsin , Segal Terry , Chalder Trudie , Ladhani Shamez N. , McOwat Kelsey , Simmons Ruth , Xu Laila , Fox-Smith Lana ,   CLoCk Consortium, Buszewicz Marta , Crawley Esther , De Stavola Bianca , Garg Shruti , Hargreaves Dougal , Levin Michael , Poustie Vanessa , Semple Malcolm , Sharma Kishan , Swann Olivia , Shafran Roz 
Published: December 2024

Abstract
Background
Some children and young people (CYP) infected with SARS-COV-2 experience impairing symptoms post-infection, known as post-COVID-19 condition (PCC). Using data from the National Long COVID in Children and Young People (CloCk) study, we report symptoms and their impact up to 24-months post-infection.

Methods
CloCk is a cohort of CYP in England aged 11-to-17-years when they had a SARS-CoV-2 PCR-test (between September 2020 and March 2021). Of 31,012 eligible CYP 24-months post-PCR test, 12,632 participated (response = 40.7%). CYP were grouped by infection status: ‘initial test-negatives; no subsequent positive-test’ (NN); ‘initial test-negatives; subsequent positive-test’ (NP); ‘initial test-positives; no reported re-infection’ (PN); and ‘initial test-positives; reported re-infection’ (PP). The Delphi research definition of PCC in CYP was operationalised; symptom severity/impact and validated scales (e.g., Chalder Fatigue Scale) were recorded. We examine symptom profiles 24-month post-index-test by infection status.

Results
7.2% of CYP consistently fulfil the PCC definition at 3-, 6-, 12- and 24-months. These CYPs have a median of 5-to-6 symptoms at each time-point. Between 20% and 25% of all infection status groups report 3+ symptoms 24-months post-testing; 10–25% experience 5+ symptoms. The reinfected group has more symptoms than the other positive groups; the NN group has the lowest symptom burden (p < 0.001). PCC is more common in older CYPs and in the most deprived. Symptom severity/impact is higher in those fulfilling the PCC definition.

Conclusions
The discrepancy in the proportion of CYP fulfilling the Delphi PCC definition at 24-months and those consistently fulfilling the definition across time, highlights the importance of longitudinal studies and the need to consider clinical impairment and range of symptoms.
Development of an artificial intelligence model for wire electrical discharge machining of Inconel 625 in biomedical applications
Authors: Thejasree Pasupuleti , Manikandan Natarajan , Sunheriya Neeraj , Giri Jayant , Chadge Rajkumar , Sathish T. , Kumar Ajay , Ammarullah Muhammad Imam 
Published: December 2024

AbstractSuperalloys, particularly nickel alloys such as Inconel 625, are increasingly used in biomedical engineering for manufacturing critical components such as implants and surgical instruments due to their exceptional mechanical properties and corrosion resistance. However, traditional machining methods often struggle with these materials due to their high strength and thermal conductivity. This study investigates the application of Wire Electrical Discharge Machining (WEDM) as an advanced method for processing Inconel 625 in biomedical contexts. The authors develop an Adaptive Neuro‐Fuzzy Inference System for forecasting WEDM parameters using grey‐based data. The model's variable inputs are analysed through analysis of variance (ANOVA) and Taguchi design, aiming to optimise process performance attributes relevant to biomedical applications. Comparative studies between predicted and experimental data demonstrate a high degree of accuracy, indicating that the proposed model effectively enhances the machining process. The results suggest that this intelligent system supports decision‐making in the production of high‐quality biomedical devices and components.
Network traffic anomaly detection model based on feature grouping and multi‐autoencoders integration
Authors: Zhou Yang , Zeng Haoyang , Zheng Zhourong , Zhang Wei 
Published: December 2024

AbstractThis paper presents a network traffic anomaly detection model based on feature grouping and multiple autoencoders (multi‐AEs) integration. This model comprises four modules: feature grouping module, feature learning module, AUC and optimal threshold calculation module, and anomaly detection application module. In the feature grouping module, multiple group features are constructed by selecting the different features according to their attributes and variances. In the feature learning module, the group features of normal traffic are learned based on multi‐AEs. In the AUC and optimal threshold calculation module, the AUC of each AE is calculated according to the ROC curve of the verification data, and the optimal thresholds for each AE are determined using the Youden index. In the anomaly detection application module, the AEs that participated in fusion are selected and their weights are calculated by analysing AUC value, and the scores of unknown traffic in each AE are evaluated considering both the reconstruction error distribution and the optimal threshold. Finally, the anomaly detection result can be obtained by the fusion of these multiple scores. Through validation on the UNSW‐NB15 and CICIDS2017 datasets, the accuracy of the proposed model is improved by 12.04% and 10.52%, respectively, compared to the baseline model.
A fine‐grained image classification method based on information interaction
Authors: Zhu Shuo , Zhang Xukang , Wang Yu , Wang Zongyang , Sun Jiahao 
Published: December 2024

AbstractTo enhance the accuracy of fine‐grained image classification and address challenges such as excessive interference factors within the dataset, inadequate extraction of local key features, and insufficient channel semantic association, a dual‐branch information interaction model that integrates convolutional neural networks (CNN) with Vision Transformers is proposed. This model leverages the Vision Transformer branch to extract global features, which are subsequently combined with the CNN branch to further augment the model's capability for local information extraction. In order to enhance the ability of the CNN branch to extract global information and reduce the loss of feature information, a feature enhancement module is added to the CNN branch. Since the Vision Transformer branch directly convolves with the convolution kernel will result in the inability to learn the underlying features of the image, a shallow feature extraction module is proposed, and the CNN and Vision Transformer branches interact with the information of the dual branches through the down‐sampling Down module and the up‐sampling UP module. The accuracy of the improved method on CUB‐200‐2011, Stanford Cars and FGVC‐Aircraft fine‐grained image classification datasets are 95.2%, 97.1% and 96.9%, respectively. The experimental results show that the method has good generalization on different datasets.
Compact broadband high‐gain dielectric rod antenna with air–dielectric hybrid design for millimetre‐wave applications
Authors: Zhang Yuwei , Lin Shu , Xu Zhenyu , Zhang Xingqi , Zhang Xinyue 
Published: December 2024

AbstractThis paper presents a compact, broadband, high‐gain dielectric rod antenna. By utilising an air–dielectric hybrid method for designing the radiating and matching sections, gradual variations in the average equivalent permittivity along the rod are achieved, and the mechanism of the method is analysed. This approach facilitates a smooth impedance transition from the feed to the air, resulting in good impedance matching characteristics. Additionally, the antenna is fed through a tapered double‐ridged waveguide (DRW) with a coaxial line to tapered DRW transition. The proposed antenna achieves an impressive impedance bandwidth of 90.2%, covering the entire K and Ka bands. The unique structure of the proposed dielectric rod significantly enhances the antenna's gain, reaching a maximum value of 17.86 dBi. With its small aperture and compact longitudinal dimensions, this antenna is suitable for applications in settings like the Space Environment Simulation Research Infrastructure (SESRI).
FRB 20121102A monitoring: Updated periodicity in the L band
Authors: Braga C. A. , Cruces M. , Cassanelli T. , Espinoza-Dupouy M. C. , Rodriguez L. , Spitler L. G. , Vera-Casanova J. , Limaye P. 
Published: December 2024

Context. FRB 20121102A was the first fast radio burst to be observed to repeat. Since then, thousands of bursts have been detected by multiple radio telescopes around the world. Previous work has shown an indication of a cyclic activity level with a periodicity of around 160 days. Knowing when the source repeats is essential for planning multi-wavelength monitoring to constrain the emission extent and progenitor source.
Aims. We report the monitoring of FRB 20121102A using the 100-m Effelsberg radio telescope in the L band and update the periodicity of the cyclic activity level.
Methods. We used the Lomb-Scargle periodogram on a sample of 284 observing epochs, of which 42% correspond to detections and 58% to non-detections. Our dataset is composed of the seven epochs of our monitoring plus publicly available data. We investigated two methods: i) a binary model, describing the observing epochs with 1 if there are detections and with 0 for non-detections, and ii) a normalised rates model that considers the inferred detection rates.
Results. We report no detections in 12.5-hour observations down to a fluence of 0.29 Jy ms. The best period we find for the cyclic activity window is 159.3 ± 0.8 days for the binary model and 159.3 ± 0.2 days for the normalised rates model. We show the activity phase to be 53%. The normalised rates show clear Gaussian-like behaviour for the activity level, in that the number of detections peaks at the centre of the activity window.
Conclusions. The periodicity found through both methods is consistent for the L- and S-band datasets, implying it is intrinsic to the source. The activity phase in the S band however, shows an indication of it ending before the L-band activity phase, supporting the idea of a chromatic dependence of the activity window. The sample in the C band is not large enough to further confirm this result.
Homöopathie und das vegetative Nervensystem – Grundlagen und Erfahrungen
Authors: Riker Ulf 
Published: December 2024

ZusammenfassungVegetative Störungen lassen sich homöopathisch sehr gut positiv beeinflussen, zumal es sich um funktionelle Zusammenhänge ohne eigentliche Organschädigungen handelt. In der Homöopathie wird versucht, diese Störungen ganzheitlich zu behandeln. Durch die Auswahl spezifischer homöopathischer Mittel, die auf die individuellen Symptome und die Konstitution des Patienten abgestimmt sind, soll das vegetative Nervensystem wieder ins Gleichgewicht gebracht werden. Ziel ist es, nicht nur die Symptome zu lindern, sondern auch die zugrunde liegenden Ursachen anzugehen und die Selbstheilungskräfte des Körpers zu aktivieren. Das Vegetativum ist dabei oft Vermittler zwischen der emotionalen und der körperlichen Ebene. Erwartungseffekte (Placebo) sind in diesem Zusammenhang denkbar, ebenso wie in allen Bereichen der konventionellen Medizin auch. In diesem Kontext gewinnt die Homöopathie zunehmend an Bedeutung als ergänzende Therapieform zur Unterstützung der Gesundheit und des Wohlbefindens bei vegetativen Störungen.
CFD-based simulation of the hydrodynamics of steady-state gas–liquid in ultrasonically oscillating microreactors
Authors: Huang Guangyong , Wei Xiangfu , Lin Lin , Sun Cuimin , Liu Xuejun , Chen Jiapeng , Dang Haojie , Zhu Rongxing , You Hui 
Published: December 2024

Ultrasound-driven microbubble oscillation in a microchannel is commonly used in mixing and mass transfer, and its steady-state mechanisms are complex. This study proposes an acoustic streaming steady-state model for the oscillation between two microbubbles driven by ultrasound in a square microchannel (150 × 150 μm2). The breathing mode and shape oscillation mode of the bubble cap were experimentally analyzed at an ultrasound frequency of 97.5 kHz, and the flow patterns of liquid slugs were found to be completely opposite. A two-dimensional numerical model of the two microbubble oscillations was developed from a new perspective of steady state. The correctness of the model was verified by comparing the 2 μm diameter polystyrene tracer flow field with the simulation results. The steady-state flow field was analyzed in terms of ultrasound amplitude, shape, size, and liquid property parameters. The streaming velocity was found to be directly proportional to the square of the microbubble amplitude. When the bubble cap became more hydrophilic and sharper, the mixing effect improved. In particular, it was found that the microbubbles had the most intense disturbance to the fluid when the distance between the two microbubbles was twice the channel width.
Energy Recovery from Municipal Waste using Machine Learning Algorithm to Produce Biogas
Authors: Mojtahed Ali , Massulli Axel Riccardo 
Published: December 2024

Abstract
The application of machine learning in the field of renewable energy witnessed progressive growth in recent years. Historically, its role in improving efficiency in energy systems, optimization of demand and production and renewable generation and/or price forecasting, has been well established in the literature.
This paper aims to apply the AI approach in the field of energy recovery from municipal waste collection to produce biogas. Generally, the correlation between the collective waste and the amount of energy that can be extracted is not straightforward. Several aerobic and anaerobic digestion processes are performed inside the collection. On the other hand, climate condition and the quality of the ground itself has a determining role to accelerate or limit the chemical transformation. As a result, the available models in this field are mostly fit to real data obtained from one specific case study that cannot be applied to other cases.
The methodology conducted in this paper contains municipal waste database in Italy from 2017 to 2022 that is used to train the Machine learning models. After creating the models, the new sets of data are imported to verify model accuracy. In this step, six algorithms are introduced: simple tree, linear regression, support vector regression, gaussian process, kernel and artificial neural networks. The final results are compared with the available models in this field. Three-step optimization is applied to improve the accuracy of the machine learning model including manual filtration, principal component analysis and Auto minimal dependency detection. Eventually, the final results show accuracy in the range of 92-99 % depending on the used algorithm. On the other hand, using the available mathematical mode with the same input, the maximum accuracy of 90% is achieved. The simulation is carried out using MATLAB ML toolbox.