bluuebunny/crossref_metadata_2025_split huggingface dataset:
An efficient linear and elliptical antenna array design using sail fish optimization
Authors: Saha Rajrup , Das Avishek , Mandal Durbadal , Kar Rajib 
Published: December 2023

AbstractThis article introduces an innovative approach to antenna array design, focusing on synthesizing the optimal radiation pattern for fifth-generation (5G) communication. The authors have designed a reliable linear and elliptical antenna array (EEA) of dipole elements by employing sailfish optimization (SFO). 5G technology promises transformative improvements in wireless communication with high data rates, expanded capacity, minimal latency, and exceptional service quality. The crux of 5G lies in the precision of antenna array design, aiming for an emission pattern with minimal side lobe levels (SLLs) and a narrow half-power beam width (HPBW). A narrower HPBW is essential for efficient long-range communication, whereas reducing the SLLs enhances signal clarity. The SFO optimizes the current excitation of each antenna element for reducing the mutual coupling effects and lowering the SLL and HPBW values in linear and EEAs. This paper uses the exact excitation to each element to show the linear antenna arrays (LAA) (10-, 16-element) design examples and EAA (8-, 12-, 20-element) structures. The LAA and EAA design examples obtained with the SFO algorithm establish the advancement in SLL suppression over the uniform antenna array and the methods proclaimed in the recent article.
Robust adaptive beamforming via residual convolutional neural network
Authors: Liu Fulai , Qin Dongbao , Li Xubin , Du Yufeng , Dou Xiuquan , Du Ruiyan 
Published: December 2023

AbstractAiming at the problem that the covariance matrix includes the desired signal and the signal steer vector mismatches will degrade the beamforming performance, an effective robust adaptive beamforming (RAB) approach is presented in this paper based on a residual convolutional neural network (RAB-RCNN). In the presented method, the RAB-RCNN model is designed by introducing a residual unit, which can extract the deeper features from the signal sample covariance matrix. Residual noise elimination and interferences power estimation are utilized to reconstruct the desired signal covariance matrix, and correct the mismatched steering vector (SV) by the eigenvalue decomposition of the reconstructed desired signal covariance matrix. The projection method is utilized to redesign the signal interference-plus-noise covariance matrix. Furthermore, the beamforming weight vector is calculated with the two parameters obtained before and used as the label of the RAB-RCNN model, The trained model can rapidly and precisely output the predicted beamforming weight vector without complex matrix operations, including the matrix inversion of the signal covariance matrix, so that the calculation time can be reduced for beamforming. Simulations demonstrate the robustness of the presented approach against SV mismatches due to the direction-of-arrival estimation error, sensor position error, and local scattering interference.
Dispersed Relativistic Electron Precipitation Patterns Between the Ion and Electron Isotropy Boundaries
Authors: Artemyev A. V. , Angelopoulos V. , Zhang X.‐J. , Chen L. , Runov A. 
Published: December 2023

AbstractRelativistic electron precipitation to the Earth's atmosphere is an important loss mechanism of inner magnetosphere electrons, contributing significantly to the dynamics of the radiation belts. Such precipitation may be driven by electron resonant scattering by middle‐latitude whistler‐mode waves at dawn to noon; by electromagnetic ion cyclotron (EMIC) waves at dusk; or by curvature scattering at the isotropy boundary (at the inner edge of the electron plasma sheet anywhere on the nightside, from dusk to dawn). Using low‐altitude ELFIN and near‐equatorial THEMIS measurements, we report on a new type of relativistic electron precipitation that shares some properties with the traditional curvature scattering mechanism (occurring on the nightside and often having a clear energy/L‐shell dispersion). However, it is less common than the typical electron isotropy boundary and it is observed most often during substorms. It is seen equatorward of (and well separated from) the electron isotropy boundary and around or poleward of the ion isotropy boundary (the inner edge of the ion plasma sheet). It may be due to one or more of the following mechanisms: EMIC waves in the presence of a specific radial profile of the cold plasma density; a regional suppression of the magnetic field enhancing curvature scattering locally; and/or electron resonant scattering by kinetic Alfvén waves.
Application of a Three‐Dimensional Coupled Hydrodynamic‐Ice Model to Assess Spatiotemporal Variations in Ice Cover and Underlying Mechanisms in Lake Nam Co, Tibetan Plateau, 2007–2017
Authors: Wu Yang , Huang Anning , Lu Youyu , Fujisaki‐Manome Ayumi , Zhang Zhiqi , Dai Xianglin , Wang Yu 
Published: December 2023

AbstractA three‐dimensional lake‐ice coupled model is used to investigate the space‐time variations of ice and underlying mechanisms in Lake Nam Co (LNC), the third largest lake over Tibetan Plateau (TP), during 2007–2017. The model reasonably reproduces the in situ measured ice thickness and water temperature profile, and satellite retrieved ice coverage and lake surface temperature. Seasonally, the lake ice first forms in the eastern basin during early January, expands from east to west during January and February, covers nearly the entire LNC in March, starts melting from west to east in April, and eventually disappears in May. The eastward drift of thin ice throughout the ice‐covered phase and the eastward water heat transport during the ice melting phase are key factors to determine the spatial variation of ice and freeze‐thaw processes. A multiple linear regression analysis confirms that the eastward drift of thin ice can be mostly attributed to the prevailing westerly. During 2007–2017, ice volume, duration, ice‐on and ice‐off dates show significant interannual variations, and they are highly correlated with the surface air temperature (T2m) averaged over January‐March, from the preceding December to May, in December and over March–May, respectively, suggesting the “cumulative effects” of T2m. Seasonal and interannual variations of ice drift are attributed to the combined effects of wind and ice volume variations. Sensitivity analysis further points out the important impacts of ice on the lake temperature and circulation structure in winter and spring, hence the necessity of hydrodynamic‐ice coupled models in large TP lakes.
Puckered and JNK signaling in pioneer neurons coordinates the motor activity of the Drosophila embryo
Authors: Karkali Katerina , Vernon Samuel W. , Baines Richard A. , Panayotou George , Martín-Blanco Enrique 
Published: December 2023

AbstractCentral nervous system organogenesis is a complex process that obeys precise architectural rules. The impact that nervous system architecture may have on its functionality remains, however, relatively unexplored. To clarify this problem, we analyze the development of the Drosophila embryonic Ventral Nerve Cord (VNC). VNC morphogenesis requires the tight control of Jun kinase (JNK) signaling in a subset of pioneer neurons, exerted in part via a negative feedback loop mediated by the dual specificity phosphatase Puckered. Here we show that the JNK pathway autonomously regulates neuronal electrophysiological properties without affecting synaptic vesicle transport. Manipulating JNK signaling activity in pioneer neurons during early embryogenesis directly influences their function as organizers of VNC architecture and, moreover, uncovers a role in the coordination of the embryonic motor circuitry that is required for hatching. Together, our data reveal critical links, mediated by the control of the JNK signaling cascade by Puckered, between the structural organization of the VNC and its functional optimization.
Micro- and nanofabrication of dynamic hydrogels with multichannel information
Authors: Zhang Mingchao , Lee Yohan , Zheng Zhiqiang , Khan Muhammad Turab Ali , Lyu Xianglong , Byun Junghwan , Giessen Harald , Sitti Metin 
Published: December 2023

AbstractCreating micro/nanostructures containing multi-channel information within responsive hydrogels presents exciting opportunities for dynamically changing functionalities. However, fabricating these structures is immensely challenging due to the soft and dynamic nature of hydrogels, often resulting in unintended structural deformations or destruction. Here, we demonstrate that dehydrated hydrogels, treated by a programmable femtosecond laser, can allow for a robust fabrication of micro/nanostructures. The dehydration enhances the rigidity of the hydrogels and temporarily locks the dynamic behaviours, significantly promoting their structural integrity during the fabrication process. By utilizing versatile dosage domains of the femtosecond laser, we create micro-grooves on the hydrogel surface through the use of a high-dosage mode, while also altering the fluorescent intensity within the rest of the non-ablated areas via a low-dosage laser. In this way, we rationally design a pixel unit containing three-channel information: structural color, polarization state, and fluorescent intensity, and encode three complex image information sets into these channels. Distinct images at the same location were simultaneously printed onto the hydrogel, which can be observed individually under different imaging modes without cross-talk. Notably, the recovered dynamic responsiveness of the hydrogel enables a multi-information-encoded surface that can sequentially display different information as the temperature changes.
High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data
Authors: Zang Chengxi , Zhang Hao , Xu Jie , Zhang Hansi , Fouladvand Sajjad , Havaldar Shreyas , Cheng Feixiong , Chen Kun , Chen Yong , Glicksberg Benjamin S. , Chen Jin , Bian Jiang , Wang Fei 
Published: December 2023

AbstractTarget trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer’s disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer’s patients.
Real-time photonic blind interference cancellation
Authors: Lederman Joshua C. , Zhang Weipeng , de Lima Thomas Ferreira , Blow Eric C. , Bilodeau Simon , Shastri Bhavin J. , Prucnal Paul R. 
Published: December 2023

AbstractmmWave devices can broadcast multiple spatially-separated data streams simultaneously in order to increase data transfer rates. Data transfer can, however, be compromised by interference. Photonic blind interference cancellation systems offer a power-efficient means of mitigating interference, but previous demonstrations of such systems have been limited by high latencies and the need for regular calibration. Here, we demonstrate real-time photonic blind interference cancellation using an FPGA-photonic system executing a zero-calibration control algorithm. Our system offers a greater than 200-fold reduction in latency compared to previous work, enabling sub-second cancellation weight identification. We further investigate key trade-offs between system latency, power consumption, and success rate, and we validate sub-Nyquist sampling for blind interference cancellation. We estimate that photonic interference cancellation can reduce the power required for digitization and signal recovery by greater than 74 times compared to the digital electronic alternative.
Dual activation and C-C coupling on single atom catalyst for CO2 photoreduction
Authors: Sun Fu-li , Lin Cun-biao , Zhang Wei , Chen Qing , Chen Wen-xian , Li Xiao-nian , Zhuang Gui-lin 
Published: December 2023

AbstractAn excellent single-atomic photocatalyst, Ti@C4N3, is theoretically found to effectively convert CO2 to C2H6 by density functional theory (DFT) calculations and non-adiabatic molecular dynamics (NAMD) simulations. The Ti@C4N3 photocatalyst has remarkable stability both thermally, chemically, and mechanically. Electronically, it has strong absorption properties (λ = 327.77 and 529.61 nm), suitable band positions, and a long photogenerated electron lifetime (τe = 38.21 ps), allowing photogenerated electrons to migrate to the surface. Notably, the high-valence active site effectively activates two CO2 through dual activation: Under light irradiation, the weakly adsorbed CO2 undergoes photo-induced activation by the photoelectron of conduction band minimum (CBM); without light, the high Lewis acidity of the Ti site induces CO2 activation through back-donating π-bond. Contrast simulation results uncovered that dual activation of CO2 is attributed to the thermal and photonic synergy. Furthermore, two activated CO2 species under light easily couple to form oxalate with the barrier of 0.19 eV, and further reduced to C2H6 with a low activation energy of 1.09 eV.
Multi-variate model of T cell clonotype competition and homeostasis
Authors: Luque Duque Daniel , Gaevert Jessica A. , Thomas Paul G. , López-García Martín , Lythe Grant , Molina-París Carmen 
Published: December 2023

AbstractDiversity of the naive T cell repertoire is maintained by competition for stimuli provided by self-peptides bound to major histocompatibility complexes (self-pMHCs). We extend an existing bi-variate competition model to a multi-variate model of the dynamics of multiple T cell clonotypes which share stimuli. In order to understand the late-time behaviour of the system, we analyse: (i) the dynamics until the extinction of the first clonotype, (ii) the time to the first extinction event, (iii) the probability of extinction of each clonotype, and (iv) the size of the surviving clonotypes when the first extinction event takes place. We also find the probability distribution of the number of cell divisions per clonotype before its extinction. The mean size of a new clonotype at quasi-steady state is an increasing function of the stimulus available to it, and a decreasing function of the fraction of stimuli it shares with other clonotypes. Thus, the probability of, and time to, extinction of a new clonotype entering the pool of T cell clonotypes is determined by the extent of competition for stimuli it experiences and by its initial number of cells.
Eco-friendly orange peel extract as corrosion resistant for carbon steel's deterioration in petroleum formation water
Authors: Elazabawy Olfat E. , Attia Enass M. , Shawky N. H. , Hyba Amira M. 
Published: December 2023

AbstractThe goal of the ongoing study is to determine how orange peel extract (OPE), an environmentally benign additive, affects the corrosion resistance of carbon steel in formation water (FW). The study utilized diverse techniques to investigate the effects of different peel extract concentrations, ranging from 0.5 to 2.5 percent (v/v), and concentrations of 100 to 500 ppm at room temperature (25 °C). Subsequently, the optimal concentration of 2.5 percent (v/v) was identified, and the temperature range was expanded to 25–55 °C for further examination. These techniques include dielectric spectroscopy (EIS), potentiodynamic polarisation, open circuit potential, and weight loss quantification. The inhibitory efficiency was assessed using the aforementioned techniques, and the results were further verified through the utilization of energy-dispersive radiation (EDS) and FTIR analyses. The outcomes of electrochemical testing demonstrated that orange peel extract (OPE) displayed significant effectiveness in preventing corrosion, with an inhibition rate of 90.13% when used at a concentration of 2.5% and a temperature of 25 °C. The findings suggested that orange peel extract (OPE) acts as a corrosion inhibitor with both inhibitory mechanisms. Its performance improves as the concentration of the inhibitor increases, conforming to the Langmuir adsorption isotherm model, and it adsorbed to the steel surface through physical adsorption. The findings revealed that orange peel extract (OPE) effectively served as a corrosion inhibitor for carbon steel by adsorbing its active components onto the surface of the steel. This adsorption process was primarily physical and followed the Langmuir isotherm.
Identifying primary aldosteronism patients who require adrenal venous sampling: a multi-center study﻿
Authors: Kitamoto Takumi , Idé Tsuyoshi , Tezuka Yuta , Wada Norio , Shibayama Yui , Tsurutani Yuya , Takiguchi Tomoko , Inoue Kosuke , Suematsu Sachiko , Omata Kei , Ono Yoshikiyo , Morimoto Ryo , Yamazaki Yuto , Saito Jun , Sasano Hironobu , Satoh Fumitoshi , Nishikawa Tetsuo 
Published: December 2023

AbstractAdrenal venous sampling (AVS) is crucial for subtyping primary aldosteronism (PA) to explore the possibility of curing hypertension. Because AVS availability is limited, efforts have been made to develop strategies to bypass it. However, it has so far proven unsuccessful in applying clinical practice, partly due to heterogeneity and missing values of the cohorts. For this purpose, we retrospectively assessed 210 PA cases from three institutions where segment-selective AVS, which is more accurate and sensitive for detecting PA cases with surgical indications, was available. A machine learning-based classification model featuring a new cross-center domain adaptation capability was developed. The model identified 102 patients with PA who benefited from surgery in the present cohort. A new data imputation technique was used to address cross-center heterogeneity, making a common prediction model applicable across multiple cohorts. Logistic regression demonstrated higher accuracy than Random Forest and Deep Learning [(0.89, 0.86) vs. (0.84, 0.84), (0.82, 0.84) for surgical or medical indications in terms of f-score]. A derived integrated flowchart revealed that 35.2% of PA cases required AVS with 94.1% accuracy. The present model enabled us to reduce the burden of AVS on patients who would benefit the most.
Short-term variations in trabecular bone texture parameters associated to radio-clinical biomarkers improve the prediction of radiographic knee osteoarthritis progression
Authors: Almhdie-Imjabbar Ahmad , Toumi Hechmi , Lespessailles Eric 
Published: December 2023

AbstractThe present study aims to examine whether the short-term variations in trabecular bone texture (TBT) parameters, combined with a targeted set of clinical and radiographic data, would improve the prediction of long-term radiographic knee osteoarthritis (KOA) progression. Longitudinal (baseline, 24 and 48-month) data, obtained from the Osteoarthritis Initiative cohort, were available for 1352 individuals, with preexisting OA (1 < Kellgren–Lawrence < 4) at baseline. KOA progression was defined as an increase in the medial joint space narrowing score from the 24-months to the 48-months control point. 16 regions of interest were automatically selected from each radiographic knee and analyzed using fractal dimension. Variations from baseline to 24 months in TBT descriptors as well as selected radiographic and clinical readings were calculated. Different logistic regression models were developed to evaluate the progression prediction performance when associating TBT variations with the selected clinical and radiographic readings. The most predictive model was mainly determined using the area under the receiver operating characteristic curve (AUC). The proposed prediction model including short-term variations in TBT parameters, associated with clinical covariates and radiographic scores, improved the capacity of predicting long-term radiographic KOA progression (AUC of 0.739), compared to models based solely on baseline values (AUC of 0.676, p-value < 0.008).
Improved Retinex algorithm for low illumination image enhancement in the chemical plant area
Authors: Wang Xin , Hu Shaolin , Li Jichao 
Published: December 2023

AbstractDue to the complexity of the chemical plant area at night and the harsh lighting environment, the images obtained by monitoring equipment have issues such as blurred details and insufficient contrast, which is not conducive to the subsequent target detection work. A low illumination image enhancement model based on an improved Retinex algorithm is proposed to address the above issues. The model consists of a decomposition network, an adjustment network, and a reconstruction network. In the decomposition network, a new decomposition network USD-Net is established based on U-Net, which decomposes the original image into illumination and reflection maps, enhancing the extraction of image details and low-frequency information; Using an adjustment network to enhance the decomposed lighting image, and introducing a Mobilenetv3 lightweight network and residual structure to simplify the network model and improve the contrast of the image; In the reconstruction network, the BM3D method is used for image denoising to enhance the ability to restore image detail features; The enhanced illumination and reflection images were fused based on the Retinex algorithm to achieve low illumination image enhancement in the chemical plant area. This article uses five image quality evaluation indicators, namely Peak Signal-to-Noise Ratio, Structural Similarity Index, Natural Image Quality Evaluator, Interpolation Error, and Level of Effort, to compare eight traditional or modern algorithms and evaluate three different types of datasets. The experimental results show that the improved algorithm enhances the texture details of the image, improves the contrast and saturation of the image, and has good stability and robustness, which can effectively meet the needs of low illumination image enhancement in chemical plant areas.
A CNN based m5c RNA methylation predictor
Authors: Aslam Irum , Shah Sajid , Jabeen Saima , ELAffendi Mohammed , A. Abdel Latif Asmaa , Ul Haq Nuhman , Ali Gauhar 
Published: December 2023

AbstractPost-transcriptional modifications of RNA play a key role in performing a variety of biological processes, such as stability and immune tolerance, RNA splicing, protein translation and RNA degradation. One of these RNA modifications is m5c which participates in various cellular functions like RNA structural stability and translation efficiency, got popularity among biologists. By applying biological experiments to detect RNA m5c methylation sites would require much more efforts, time and money. Most of the researchers are using pre-processed RNA sequences of 41 nucleotides where the methylated cytosine is in the center. Therefore, it is possible that some of the information around these motif may have lost. The conventional methods are unable to process the RNA sequence directly due to high dimensionality and thus need optimized techniques for better features extraction. To handle the above challenges the goal of this study is to employ an end-to-end, 1D CNN based model to classify and interpret m5c methylated data sites. Moreover, our aim is to analyze the sequence in its full length where the methylated cytosine may not be in the center. The evaluation of the proposed architecture showed a promising results by outperforming state-of-the-art techniques in terms of sensitivity and accuracy. Our model achieve 96.70% sensitivity and 96.21% accuracy for 41 nucleotides sequences while 96.10% accuracy for full length sequences.