bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel fluoro‐electrochemical technique for classifying diverse marine nanophytoplankton
Authors: Barton Samuel , Yang Minjun , Chen Haotian , Batchelor‐McAuley Christopher , Compton Richard G. , Bouman Heather A. , Rickaby Rosalind E. M. 
Published: September 2023

AbstractTo broaden our understanding of pelagic ecosystem responses to environmental change, it is essential that we improve the spatiotemporal resolution of in situ monitoring of phytoplankton communities. A key challenge for existing methods is in classifying and quantifying cells within the nanophytoplankton size range (2–20 μm). This is particularly difficult when there are similarities in morphology, making visual differentiation difficult for both trained taxonomists and machine learning‐based approaches. Here we present a rapid fluoro‐electrochemical technique for classifying nanophytoplankton, and using a library of 52 diverse strains of nanophytoplankton we assess the accuracy of this technique based on two measurements at the individual level: charge required to reduce per cell chlorophyll a fluorescence by 50% and cell radius. We demonstrate a high degree of accuracy overall (92%) in categorizing cells belonging to widely recognized key functional groups; however, this is reduced when we consider the broader diversity of “nano‐phytoflagellates'.” Notably, we observe that some groups, for example, calcifying Isochrysidales, have much greater resilience to electrochemically driven oxidative conditions relative to others of a similar size, making them more easily categorized by the technique. The findings of this study present a promising step forward in advancing our toolkit for monitoring phytoplankton communities. We highlight that, for improved categorization accuracy, future iterations of the method can be enhanced by measuring additional predictor variables with minimal adjustments to the set‐up. In doing so, we foresee this technique being highly applicable, and potentially invaluable, for in situ classification and enumeration of the nanophytoplankton size fraction.
Effect of Crosslinking on the Stretch‐Induced Polymorphic Transition of Trans‐1,4‐Polyisoprene
Authors: Yang Bo , Zhang Chunbo , Zhao Hui , Hu Wenxian , Liu Guoming , Wang Dujin 
Published: September 2023

AbstractTrans‐1,4‐polyisoprene (TPI) is a typical polymorphic polymer with two crystal modifications. The transition between two crystal modifications (α to β) occurs during stretching. The underlying mechanism is still under debate. In this work, the effects of crosslinking and strain rates on the crystal transition are studied. The TPI, which mainly contains α crystal (α−TPI), is crosslinked by γ‐ray radiation. The results show that crystalline morphology does not change after radiation. The melting temperature and yield stress of crosslinked α−TPI decrease with the irradiation dose. The structural evolution is studied by in situ wide‐angle X‐ray diffraction and small‐angle X‐ray scattering. During stretching, the content of β crystal (Xβ) of crosslinked α−TPI increases in the strain‐hardening region. The Xβ is lower in the sample with a higher radiation dose under the same strain. On the other hand, with increasing strain rate, the Xβ of the crosslinked sample exhibits a non‐monotonic variation. A parallel mechanical model consisting of amorphous and crystal fibrils is proposed to explain the experimental observations.
Roadmap to tackle antibiotic resistance in the environment under the One Health framework
Authors: Li Liguan , Zhang Tong 
Published: September 2023

AbstractAntibiotic resistance has been recognized as a major challenge worldwide for humans. “One Health” has been recognized as a key concept for containment of antibiotic resistance. Under the framework, the role of the environment in the development of antibiotic resistance genes (ARGs) has become increasingly obvious. Despite numerous efforts, response to antibiotic resistance is considered to be inadequate, which is probably due to the lack of a clear roadmap. Here, we propose a “One Health” roadmap to combat antibiotic resistance in the environment through (1) understanding environmental resistome. The environmental gene pool has long been recognized as the single largest reservoir of both known and novel ARGs. (2) Standardizing ARG quantification. Systematic joint efforts based on standardized quantification are urgently needed to understand the true tempospatial profiles of the environmental resistome. (3) Identifying mechanisms of resistome development. Horizontal gene transfer and co‐selection have been recognized as the two main mechanisms contributing to the environmental resistome. (4) Establishing a risk‐assessment framework. The first critical step for large‐scale cost‐effective targeted ARG management in the environment is the risk assessment to identify the priority ARGs for control. (5) Formulating regulatory standards. By correlating the environmental ARG profile with public health, we may identify the indicator ARGs that can be integrated into current environmental quality standards. (6) Developing control strategies. Systematic analysis of available control technologies is required to identify the most feasible ones to curtail the spread of ARGs in the environment. The proposed roadmap under the “One Health” framework provides a guide to tackle antibiotic resistance in the environment.
A low‐profile and broadband multibeam antenna based on modulated impedance metasurface
Authors: Zhang Zekui , Peng Yang 
Published: September 2023

AbstractA low‐profile and broadband antenna based on modulated metasurface is proposed to obtain beam switching in the azimuth plane. The antenna is composed of four ports and a metasurface. A T‐slot is etched in the squared metal patch and is designed to increase the impedance modulation range and form the metasurface. Finally, the antenna generates a linearly polarized tilted beam within a wide band in the elevation plane. And the maximum tilt angle reaches . At the azimuth plane, the beam switching is realized by feeding different ports. The experimental results show that the −10 dB relative bandwidth is 25.4% (4.47–5.74 GHz). At azimuth angles of , and , four independent beams are formed in the operating frequency band with a maximum gain of 9.43 dBi. In addition, the proposed antenna possesses the advantages of low profile () and good isolation (10 dB) between ports.
Design of Ka‐band, broadband, omnidirectional, with a top metal disk antenna
Authors: Wang Tong , Lu Fan , Wu Ting , Gao Hongwei , Jin Cheng 
Published: September 2023

AbstractIn this report, a Ka‐band, broadband, omnidirectional, with a top metal disk antenna is proposed. The antenna is fed by an air waveguide. The TE10 mode of the waveguide can efficiently be converted into an air coaxial TEM mode by a compact waveguide air coaxial transition. After matching and adjusting, the dielectric substrate is loaded and then transited to the metal radiation structure loaded with a top metal disc to obtain omnidirectional electromagnetic waves efficiently converted to free space. By optimizing the sizes of waveguide to air‐coaxial transition structure and disk, and so on, a good broadband omnidirectional antenna performance is finally achieved. The measured fractional impedance bandwidth is larger than 30% with the reference of S11 dB, which fully verifies the rationality of the design. In addition, measured patterns at the plane of different azimuth angles almost coincide with each other, which shows an excellent omnidirectional pattern.
Technical note: MR‐compatible pedal ergometer with electromechanical pedal resistance and exercise triggering enhanced by visual feedback via video display
Authors: Sedivy Petr , Dezortova Monika , Rydlo Jan , Moravec Petr , Krizek Ivan , Setinova Bara , Pajuelo Dita , Burian Martin , Hajek Milan 
Published: September 2023

AbstractBackgroundDuring and after exercise, dynamic 31P MR parameters are typically measured using an MR‐compatible ergometer. Self‐built equipment for local condition can be constructed where possible.PurposeTo develop a pedal resistance ergometer with rocker arm based on a system that combines electric weight displacement, visual self‐monitoring, and exercise triggering. The repeatability and reproducibility were tested.MethodsThe hardware and software for the ergometer were constructed from commercial components in a home laboratory. Twelve volunteers participated in the testing of the ergometer.ResultsA fully automated ergometer system was developed, allowing the pedal resistance to be adjusted during the examination. The system includes a self‐monitoring and triggering mechanism that enables both the operator and subject to monitor pedal frequency and force. The operator can modify the pedal resistance as desired during the exercise. This self‐monitoring solution is simple and cost‐effective, requiring only a commercial potentiometer, an Arduino converter, and a conventional video projector with a personal computer (PC). Additionally, all system components are located outside the magnetic resonance (MR) room, avoiding interference with the MR system. Results of several test of the reproducibility/repeatability of power at three pedal resistance values (15%, 24%, 25% maximal voluntary force) were expressed both as a coefficient of variation ranging from 6% to 3.1% and as an intraclass correlation of coefficient ranging from 0.96 to 0.99. Similar values were also found for other dynamic parameters of 31P MR spectroscopy. These findings are similar to published data obtained on different types of ergometers.ConclusionsBased on more than 1 year of usage, the ergometer proved successful in handling stationary and variable loads, and can be easily operated by a single user.
Accurate modeling and performance evaluation of a total‐body pet scanner using Monte Carlo simulations
Authors: Rezaei Hadi , Sheikhzadeh Peyman , Ghafarian Pardis , Zaidi Habib , Ay Mohammad Reza 
Published: September 2023

AbstractBackgroundThe limited axial field‐of‐view (FOV) of conventional clinical positron emission tomography (PET) scanners (∼15 to 26 cm) allows detecting only 1% of all coincidence photons, hence limiting significantly their sensitivity. To overcome this limitation, the EXPLORER consortium developed the world's first total‐body PET/CT scanner that significantly increased the sensitivity, thus enabling to decrease the scan duration or injected dose.PurposeThe purpose of this study is to perform and validate Monte Carlo simulations of the uEXPLORER PET scanner, which can be used to devise novel conceptual designs and geometrical configurations through obtaining features that are difficult to obtain experimentally.MethodsThe total‐body uEXPLORER PET scanner was modeled using GATE Monte Carlo (MC) platform. The model was validated through comparison with experimental measurements of various performance parameters, including spatial resolution, sensitivity, count rate performance, and image quality, according to NEMA‐NU2 2018 standards. Furthermore, the effects of the time coincidence window and maximum ring difference on the count rate and noise equivalent count rate (NECR) were evaluated.ResultsOverall, the validation study showed that there was a good agreement between the simulation and experimental results. The differences between the simulated and experimental total sensitivity for the NEMA and extended phantoms at the center of the FOV were 2.3% and 0.0%, respectively. The difference in peak NECR was 9.9% for the NEMA phantom and 1.0% for the extended phantom. The average bias between the simulated and experimental results of the full‐width‐at‐half maximum (FWHM) for six different positions and three directions was 0.12 mm. The simulations showed that using a variable coincidence time window based on the maximum ring difference can reduce the effect of random coincidences and improve the NECR compared to a constant time coincidence window. The NECR corresponding to 252‐ring difference was 2.11 Mcps, which is larger than the NECR corresponding to 336‐ring difference (2.04 Mcps).ConclusionThe developed MC model of the uEXPLORER PET scanner was validated against experimental measurements and can be used for further assessment and design optimization of the scanner.
An extremely lightweight CNN model for the diagnosis of chest radiographs in resource‐constrained environments
Authors: Kumar Gautam , Sharma Nirbhay , Paul Angshuman 
Published: September 2023

AbstractBackgroundIn recent years, deep learning methods have been successfully used for chest x‐ray diagnosis. However, such deep learning models often contain millions of trainable parameters and have high computation demands. As a result, providing the benefits of cutting‐edge deep learning technology to areas with low computational resources would not be easy. Computationally lightweight deep learning models may potentially alleviate this problem.PurposeWe aim to create a computationally lightweight model for the diagnosis of chest radiographs. Our model has only 0.14M parameters and 550 KB size. These make the proposed model potentially useful for deployment in resource‐constrained environments.MethodsWe fuse the concept of depthwise convolutions with squeeze and expand blocks to design the proposed architecture. The basic building block of our model is called Depthwise Convolution In Squeeze and Expand (DCISE) block. Using these DCISE blocks, we design an extremely lightweight convolutional neural network model (ExLNet), a computationally lightweight convolutional neural network (CNN) model for chest x‐ray diagnosis.ResultsWe perform rigorous experiments on three publicly available datasets, namely, National Institutes of Health (NIH), VinBig ,and Chexpert for binary and multi‐class classification tasks. We train the proposed architecture on NIH dataset and evaluate the performance on VinBig and Chexpert datasets. The proposed method outperforms several state‐of‐the‐art approaches for both binary and multi‐class classification tasks despite having a significantly less number of parameters.ConclusionsWe design a lightweight CNN architecture for the chest x‐ray classification task by introducing ExLNet which uses a novel DCISE blocks to reduce the computational burden. We show the effectiveness of the proposed architecture through various experiments performed on publicly available datasets. The proposed architecture shows consistent performance in binary as well as multi‐class classification tasks and outperforms other lightweight CNN architectures. Due to a significant reduction in the computational requirements, our method can be useful for resource‐constrained clinical environment as well.
Application of benchtop low‐field NMR spectrometers in the refining industry: A multivariate calibration approach for rapid characterization of crude oils
Authors: Sassu Lorenzo , Puligheddu Sonia , Puligheddu Cristina , Palomba Silvia , Muru Emanuela , Mattia Carmine , Allevi Claudio 
Published: September 2023

AbstractHigh‐field nuclear magnetic resonance (NMR) has proven to be a valuable tool to analyze petroleum products but has never acquired widespread acceptance in routine work in oil refinery due to the high cost. The advent of compact, reliable, and affordable cryogen‐free low‐field benchtop NMR spectrometers suggest a possible role of such instrument for a variety of uses in refining spanning from routine lab work applications to online analysis. In this work, we report the development of a novel benchtop NMR application to estimate the API gravity, sulfur content, total acidity number (TAN), and distillation yields of crude petroleum from 60 MHz 1H NMR spectra and partial least square (PLS) regression models. The results obtained on a dataset of over 170 crude oil samples and tested with external validation set show performances in terms of root mean square standard error of prediction (RMSEP) matching the analytical methods' ASTM precision. This novel benchtop NMR and multivariate analysis application can thus be used in typical refinery laboratories to provide within minutes and without physical distillation, reliable estimates of the most important properties usually measured on the crude petroleum during refining. The method proves to be a fast, cheaper alternative to the current analytical options providing readily available data for managing crude oil and has high potential to be further exploited for online application.
An optimal hybrid cascade regional convolutional network for cyberattack detection
Authors: Alqahtani Ali , Khan Surbhi Bhatia 
Published: September 2023

AbstractCyber‐physical systems (CPS) and the Internet of Things (IoT) technologies link urban systems through networks and improve the delivery of quality services to residents. To enhance municipality services, information and communication technologies (ICTs) are integrated with urban systems. However, the large number of sensors in a smart city generates a significant amount of delicate data, like medical records, credit card numerics, and location coordinates, which are transported across a network to data centers for analysis and processing. This makes smart cities vulnerable to cyberattacks because of the resource constraints of their technology infrastructure. Applications for smart cities pose many security challenges, such as zero‐day attacks resulting from exploiting weaknesses in various protocols. Therefore, this paper proposes an optimal hybrid transit search‐cascade regional convolutional neural network (hybrid TS‐Cascade R‐CNN) to detect cyberattacks. The proposed model combines the hybrid transit‐search approach with the cascade regional convolutional neural network to create an optimal solution for cyberattack detection. The cascade regional convolutional network uses a hybrid transit search algorithm to enhance the effectiveness of cyberattack detection. By integrating these two approaches, the system can leverage both global traffic patterns and local indicators to improve the accuracy of attack detection. During the training process, the proposed model recognizes and classifies malicious input even in the presence of sophisticated attacks. Finally, the experimental analysis is carried out for various attacks based on different metrics. The accuracy rate attained by the proposed approach is 99.2%, which is acceptable according to standards.
Strong approximation of stochastic semilinear subdiffusion and superdiffusion driven by fractionally integrated additive noise
Authors: Hu Ye , Yan Yubin , Sarwar Shahzad 
Published: September 2023

AbstractRecently, Kovács et al. considered a Mittag‐Leffler Euler integrator for a stochastic semilinear Volterra integral‐differential equation with additive noise and proved the strong convergence error estimates [see SIAM J. Numer. Anal. 58(1) 2020, pp. 66‐85]. In this article, we shall consider the Mittag‐Leffler integrators for more general models: stochastic semilinear subdiffusion and superdiffusion driven by fractionally integrated additive noise. The mild solutions of our models involve four different Mittag‐Leffler functions. We first consider the existence, uniqueness and the regularities of the solutions. We then introduce the full discretization schemes for solving the problems. The temporal discretization is based on the Mittag‐Leffler integrators and the spatial discretization is based on the spectral method. The optimal strong convergence error estimates are proved under the reasonable assumptions for the semilinear term and for the regularity of the noise. Numerical examples are given to show that the numerical results are consistent with the theoretical results.
Genome‐wide polygenic risk score, cardiometabolic risk factors, and type 2 diabetes mellitus in the Chinese population
Authors: Huang Ninghao , Xiao Wendi , Lv Jun , Yu Canqing , Guo Yu , Pei Pei , Yang Ling , Millwood Iona Y. , Walters Robin G. , Chen Yiping , Du Huaidong , Avery Daniel , Ou Tingting , Chen Junshi , Chen Zhengming , Huang Tao , Li Liming ,   the China Kadoorie Biobank Collaborative Group*
Published: September 2023

AbstractObjectiveType 2 diabetes (T2D) is caused by both genetic and cardiometabolic risk factors. However, the magnitude of the genetic predisposition of T2D in the Chinese population remains largely unknown.MethodsThis study included 93,488 participants from the China Kadoorie Biobank, and multiple polygenic risk scores (PRS) were calculated. A common cardiometabolic risk score (CRS) using smoking, alcohol consumption, physical activity, diet, obesity, blood pressure, and blood lipids was constructed to investigate the effects of cardiometabolic risk factors on T2D. Furthermore, an equation based on ideal PRS, CRS, and their interaction was established to explore the combined effects on T2D.ResultsAn ideally fitting PRS model (variance explained, R2 = 7.6%) was reached based on multiple PRS calculation methods. An additive interaction between PRS and CRS (coefficient = 28%, 95% CI: 0.20–0.36, p < 0.001) was found. The R2 of the T2D predictive model could increase to 8.3% when CRS and the interaction terms of PRS × CRS were considered. In the etiological composition of T2D, the ratio of genetic risk effect, cardiometabolic risk effect, and interaction between genetic and cardiometabolic factors was 67:16:17.ConclusionsThis study identified an ideally fitting PRS model for identifying and predicting the risk of T2D suitable for the Chinese population. The quantified proportional structure of genetic risk factors, cardiometabolic risk factors, and their interaction was detected, which elucidated the critical effect of genetic factors.
Effect of SiC and graphene nanoparticles on the mechanical properties of carbon fiber‐reinforced epoxy composites
Authors: Kosedag Ertan , Ekici Recep , Yildiz Nail , Caliskan Umut 
Published: September 2023

AbstractThis study experimentally investigates the mechanical properties of carbon fiber reinforced epoxy composites (CFRECs) filled with Graphene (Gr) and Silicon Carbide (SiC) nanoparticles. Gr and SiC nanoparticle filled CFREC plates at 0%, 0.5%, 1%, and 2% weight ratios were fabricated using a vacuum infusion technique from unidirectional carbon fiber fabric with both 0 and 90° fiber orientation. According to ASTM standards, tensile, compression, and three point bending tests were performed to determine the effects of additive type and weight ratios. CFRECs with Gr additive exhibit improved tensile properties compared to the unfilled composites, especially at higher filler contents and at specific fiber orientations. Also, the Gr additive showed a better improvement in the tensile behavior of the CFRECs than the SiC additive. In general, it was found that the elastic modulus values of nanoparticle additive samples were higher than that of the unfilled composite material in both fiber orientations. Except for the 0.5% SiC ratio with 0° fiber orientation, the particle added nanocomposites did not exceed the value of the unfilled composite and did not make a positive effect on the compressive strength. It has been observed that Gr additives give more positive results on the bending strength of CFRECs than SiC, especially at a 2% weight ratio.Highlights
Effects of nanoparticle types on mechanical properties of CFRECs were compared.
Weight ratio effects on the properties of nanoparticle‐filled CFRECs were studied.
The load‐bearing capacity decreased as the additive ratio increased.
Additives and ratios should be carefully selected for the intended applications.
Overall, presence Gr resulted in enhanced properties much prominently for composites.