bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of a compact multiprobe system for monitoring positron-emitting tracers in plant stems
Authors: Nagao Y. , Suzui N. , Yin Y.-G. , Miyoshi Y. , Noda Y. , Enomoto K. , Tsuda M. , Yabe T. , Watabe H. , Yamaguchi M. , Kawachi N. 
Published: April 2024

Abstract
Nondestructive monitoring of positron-emitting tracers in
plant bodies at multiple points, including points separated by large
distances, has been realised with the positron multiprobe system
(PMPS) to investigate graminaceous plants, whose stems and leaves
have simple shapes. Recently, the translocation of photosynthates
into fruits has been studied intensively using a 11C tracer.
The relatively complex shapes of the stems and leaves of these
plants sometimes prevent the detector heads of the PMPS from
approaching and being fixed to the target stem properly because of
its relatively large and heavy detector heads based on
photomultiplier tubes. Owing to the compactness, lightweightness
and recent advances of silicon photomultipliers (SiPMs), fabricating
compact and lightweight detector heads has become possible. In this
study, we developed a compact PMPS (CPMPS) using SiPMs and
successfully demonstrated its capability for monitoring a 11C
tracer in strawberry stems. Moreover, we found that energy-window
filtering markedly reduced noise events without radiation shielding.
The dominant ionisation events detected by the CPMPS were Compton
scattering and subsequent photoabsorption of a single 511 keV gamma
ray, suggesting that the ionisation events of single-gamma-ray
emitters, such as 42K, 43K, 54Mn, 59Fe and
65Zn, can be detected by the CPMPS. The developed CPMPS can
also be applied to study the physiology of other plants with
intricately shaped stems and leaves, such as the tomato and
eggplant.
A miniature prototype of Time Projection Chambers for CSR External-Target Experiment
Authors: Yang Yuansheng , Qin Zhi , Li Zhijie , Wei Xianglun , Wen Xiangjie , He Zhoubo , Guo Dong , Yang Herun , Ma Peng , Lu Chengui , Hu Rongjiang , Qiu Tianli , He Zhixuan , Duan Limin 
Published: April 2024

Abstract
A miniature prototype of time projection chamber for the
cooling storage ring external-target experiment (CEE) at the Heavy
Ion Research Facility in Lanzhou (HIRFL) was designed for
three-dimensional tracking of charged particles produced in
heavy-ion collisions at the target region and for particle
identification. The prototype consists of a TPC detector chamber
with active volume of 9 cm (height), 10 cm (length) and 10 cm
(width), gas electron multiplier (GEM) detector readout and
SAMPA-based electronics. This work describes design, track
reconstruction method and performance of the prototype. The energy
resolution is 11.03% sigma using a 55Fe x-ray source. Using
heavy-ion beam of 150 MeV/u Fe, the test results show that the
two-dimensional position resolution and drift time resolution of the
prototype are better than 400 μm and 20 ns, respectively.
Double-layer hollow mesoporous silica nanoparticles for ultrasound-guided photodynamic treatment
Authors: Chen Zhihui , Liu Wei , Liu Kaiwen , Wang Siqi , Li Cuiying , Wu Feiyun , Wang Shouju , Tang Yuxia 
Published: April 2024

Abstract
Cervical carcinoma persists as a major global public health burden. While conventional therapeutic modalities inevitably cause ablation of adjacent non-tumorous tissues, photodynamic therapy (PDT) offers a targeted cytotoxic strategy through a photosensitizing agent (PS). However, the hydrophobicity and lack of selective accumulation of promising PS compounds such as zinc(II) phthalocyanine (ZnPc) impedes their clinical translation as standalone agents. The present study sought to incorporate ZnPc within double-layer hollow mesoporous silica nanoparticles (DHMSN) as nanocarriers to enhance aqueous dispersibility and tumor specificity. Owing to their compartmentalized design, the hollow mesoporous silica nanoparticles (HMSN) demonstrated enhanced ultrasonic imaging contrast. Combined with the vaporization of the perfluorocarbon perfluoropentane (PFP), the HMSN-encapsulated ZnPc enabled real-time ultrasound monitoring of PDT treatment. In vivo, the innate thermal energy induced vaporization of the DHMSN-carried PFP to significantly amplify ultrasound signals from the tumor site. Results demonstrated biocompatibility, efficient PFP microbubble generation, and robust photocatalytic activity. Collectively, this investigation establishes ultrasound-guided PDT utilizing multi-layer HMSN as a targeted therapeutic strategy for cervical malignancies with mitigated toxicity.
Comprehensive energy solution planning (CESP) framework: an evidence-based approach for sustainable energy access projects in developing countries
Authors: Colombo Emanuela , Crevani Giacomo , Stevanato Nicolò , Mereu Riccardo 
Published: April 2024

Abstract
Access to affordable, reliable, and modern energy remains a critical goal under the Agenda 2030 for Sustainable Development, especially in remote areas of developing countries. Based on traditional engineering approaches, many energy solution planning tools have been developed to identify the optimal solution in these areas to assess the competition across different technological options. Nevertheless, these approaches, based on an economic optimum, do not necessarily grant long-term sustainability of the solution in specific local contexts, since they are not able to capture the social implications within the Energy-Development nexus. Moreover, also in light of the 2030 Agenda, scientific and grey literature on energy access highlights how energy solutions planning methodologies developed in the last decades need to be complemented by a more comprehensive view, able to integrate evidence from various disciplines, especially engineering and social sciences. Based on the above considerations, this paper introduces a novel framework under the name of CESP, where three social sciences-based phases complement three engineering phases, each one characterized by specific tools, to offer an informed decision framework for the local planner. CESP encompasses a set of techno-economic and socio-technical actions to prevent potential failure as evidenced by a counterfactual analysis used to identify the reasons behind past project failures. The CESP framework presents a sequential and iterative structure that underlines the cyclic perspective of a holistic decision process where social sciences feed the engineering analysis and vice versa. Finally, CESP emerges as a practical and applicable framework for supporting energy access planning in critical areas.
Assessing the tradeoffs in emissions, air quality and health benefits from excess power generation due to climate-related policies for the transportation sector
Authors: Efstathiou Christos I , Arunachalam Saravanan , Arter Calvin A , Buonocore Jonathan 
Published: April 2024

Abstract
As the transportation sector continues to decarbonize through electrification, there is growing interest in quantifying potential tradeoffs in air pollution and health impacts due to potential excess emissions from the power sector. This study investigates air pollution and health impacts of policy-driven changes in the transportation sector and the associated power generation demand in the Northeast and Mid-Atlantic United States. Five illustrative scenarios were designed to capture the effects of different policies under the first mandatory market-based program to reduce greenhouse gases in the US power sector (Regional Greenhouse Gas Initiative—RGGI) and the Transportation and Climate Initiative (TCI). Considering future power generation with new renewable energy investments to meet demands from decarbonized transportation, the scenarios were framed using: 1. 2030 reference cases for both sectors and a hybrid TCI portfolio, 2. Departure from the reference cases defined by Pennsylvania included or not in RGGI, and 3. Power grid emissions estimated under clean energy standard (CES) policy and hybrid TCI portfolio. While the cross-sectoral policy effect on domain-wide concentrations is modest (max ΔPM2.5 ∼ 0.06 μg m3, ΔNO2 ∼ 0.3 ppbv, ΔO3 ∼ 0.15 ppbv), substantial increases in Ohio and West Virginia were attributed to Pennsylvania joining RGGI. With CES enacted and Pennsylvania in RGGI, significant reductions are seen in average concentrations (max ΔPM2.5 ∼ 1.2 μg m3, ΔNO2 ∼ 1.1 ppbv, ΔO3 ∼ 1.7 ppbv) except for Louisiana and Mississippi with corresponding disbenefits. When focusing exclusively on emissions reductions from transportation, the hybrid TCI portfolio had health benefits of 530 avoided adult deaths, and 46 000 avoided asthma exacerbations. With a ‘business as usual’ power grid, these benefits remain comparable and are mainly driven by NO2, followed by PM2.5 and O3. However, if Pennsylvania joins RGGI, total health benefits and spatial distribution change substantially, with a large portion of adverse health impacts moving from TCI states to Ohio and West Virginia. The overall monetized impact of a CES scenario can substantially exceed the estimated average range of 66–69 Billion US$, depending on the interaction with transportation decarbonization strategies and other drivers of exposure.
Quantification of static softening process and its effects on work hardening characteristics of a typical high strength steel during multi-pass deformation
Authors: Zhao Mingjie , Lu An , Jiang Lihong , Huang Liang , Li Jianjun , Guo Zhenghua 
Published: April 2024

Abstract
Heavy components of 300 M steel are usually manufactured by multi-pass forging. It is necessary to study the flow characteristics of 300 M steel during multi-pass deformation, which helps to regulate the flow behaviors during the actual forging process. In the study, multi-pass compression experiments are conducted on the Gleeble-3500 device to mimic the forging process of 300 M steel. Results show that the deformation parameters and inter-pass holding parameters can affect the work hardening rate significantly. It can be ascribed to coupling effects of dynamic softening and static softening behaviors. A unified static softening kinetics model is established to evaluate the coupling effects of static recovery, static recrystallization, and metadynamic recrystallization on the static softening behaviors. The established static softening kinetics model shows high prediction accuracy with a reliability of 0.99605. Furthermore, a new constitutive model is established to describe the effects of dynamic softening and static softening on the flow stress during multi-pass deformation. The prediction accuracy of the new constitutive model is 0.98897 with a mean absolute error of 4.075%, which demonstrates that the established constitutive model is reliable.
Surface roughness and gloss retention of bioactive resin composite after simulated toothbrush abrasion
Authors: Turkistani Alaa , Hasanain Fatin A 
Published: April 2024

Abstract

Background. Newly introduced bioactive resin composites are being used more often. Few studies have evaluated the influence of toothbrush abrasion on their surface characteristics. Methods. This study aims to assess the surface roughness (Ra) and gloss values (GU) of 3 bioactive composites and a conventional nanohybrid composite before and after simulated toothbrush abrasion. Five samples each of Filtek Z250 XT, Beautifil Flow Plus, Activa Presto and Predicta Bioactive Bulk were subjected to 10,000 cycles of simulated toothbrush abrasion. Ra and GU values were evaluated at baseline and after each 2,500 cycles. Results. Significant difference was found in both Ra and GU (P < 0.001). For each material, GU gradually decreased as the brushing cycles increased. GU values of Beautifil Flow Plus were significantly higher at the baseline, 2,500- and 5,000-cycles stages (P < 0.05). The GU values were 78.20 (7.20), 69.67 (6.17) and 63.30 (5.52) respectively. Activa Presto showed the lowest mean GU after 10,000 cycles. A significant increase in Ra at each of the measuring stages was observed in all materials compared to the baseline measurement (P < 0.001). No significant difference in Ra values of all four materials at the baseline and up to 5,000-cycles (P > 0.05). After 7,500 cycles, the mean Ra value of Activa Presto at 0.64 (0.14) was significantly higher than that of Z250 XT at 0.49 (0.03) (P < 0.001). At 10,000 cycles, Activa Presto had significantly higher Ra at 0.70 (0.10) when compared to Z250 XT at 0.52 (0.08), Beautifil Flow Plus at 0.56 (0.07) and Predicta Bioactive Bulk at 0.59 (0.10) (P < 0.001). Conclusion. All materials showed an increase in Ra and decrease in GU after simulated toothbrushing.
Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach
Authors: Benchaira Khadidja , Bitam Salim 
Published: April 2024

Abstract
Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models often struggle to balance high performance with computational efficiency. This study introduces an innovative computational framework that combines transfer learning with traditional machine learning to optimize ECG classification. We use a pre-trained Stacked Convolutional Neural Network (SCNN) to generate high-dimensional feature embeddings, which are then evaluated by an array of machine learning classifiers. Our models demonstrate exceptional performance, particularly when utilizing embeddings from SCNNs trained on diverse datasets. This underscores the importance of data diversity in improving classifier discrimination. Notably, Multilayer Perceptrons (MLPs) stand out for their ability to balance computational efficiency with strong performance, achieving test F1-scores of 0.94 and 1.00 in multi-class and binary tasks on the CinC2017 dataset, and 0.85 and 0.99 on the CPSC2018 dataset. Our approach consistently outperforms existing methods, setting new benchmarks in ECG classification. The synergy between deep learning-based feature extraction and traditional machine learning through transfer learning offers a robust, efficient, and adaptable strategy for ECG classification, addressing a critical research gap and laying the groundwork for future advancements in this crucial healthcare field.
Silk fibroin and hydroxypropyl cellulose composite injectable hydrogel-containing extracellular vesicles for myocardial infarction repair
Authors: Hua Yinjian , He Zhengfei , Ni Yunjie , Sun Linggang , Wang Rui , Li Yan , Li Xintong , Jiang Guohua 
Published: April 2024

Abstract
Extracellular vesicles (EVs) have been recognized as one of the promising specific drugs for myocardial infarction (MI) prognosis. Nevertheless, low intramyocardial retention of EVs remains a major impediment to their clinical application. In this study, we developed a silk fibroin/hydroxypropyl cellulose (SF/HPC) composite hydrogel combined with AC16 cell-derived EVs targeted modification by folic acid for the treatment of acute myocardial infarction repair. EVs were functionalized by distearoylphosphatidyl ethanolamine-polyethylene glycol (DSPE-PEG-FA) via noncovalent interaction for targeting and accelerating myocardial infarction repair. In vitro, cytocompatibility analyses revealed that the as-prepared hydrogels had excellent cell viability by MTT assay and the functionalized EVs had higher cell migration by scratch assay. In vivo, the composite hydrogels can promote myocardial tissue repair effects by delaying the process of myocardial fibrosis and promoting angiogenesis of infarct area in MI rat model.
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning
∗
Authors: Chen Qiuhao , Du Yuxuan , Jiao Yuliang , Lu Xiliang , Wu Xingyao , Zhao Qi 
Published: April 2024

Abstract
Efficient quantum compiling is essential for complex quantum algorithms realization. The Solovay–Kitaev (S–K) theorem offers a theoretical lower bound on the required operations for approaching any unitary operator. However, it is still an open question that this lower bound can be actually reached in practice. Here, we present an efficient quantum compiler which, for the first time, approaches the S–K lower bound in practical implementations, both for single-qubit and two-qubit scenarios, marking a significant milestone. Our compiler leverages deep reinforcement learning (RL) techniques to address current limitations in terms of optimality and inference time. Furthermore, we show that our compiler is versatile by demonstrating comparable performance between inverse-free basis sets, which is always the case in real quantum devices, and inverse-closed sets. Our findings also emphasize the often-neglected constant term in scaling laws, bridging the gap between theory and practice in quantum compiling. These results highlight the potential of RL-based quantum compilers, offering efficiency and practicality while contributing novel insights to quantum compiling theory.
On the relevance of weak measurements in dissipative quantum systems
Authors: Ballesteros Ferraz Lorena , Martin John , Caudano Yves 
Published: April 2024

Abstract
We investigate the impact of dissipation, including energy relaxation and decoherence, on weak measurements. While weak measurements have been successful in signal amplification, dissipation can compromise their usefulness. More precisely, we show that in systems with a unique steady state, weak values always converge to an expectation value of the measured observable as dissipation time tends to infinity, in contrast to systems with multiple steady states, where the weak values can remain anomalous, i.e. outside the range of eigenvalues of the observable, even in the limit of an infinite dissipation time. In addition, we propose a method for extracting information about the dissipative dynamics of a system using weak values at short dissipation times. Specifically, we explore the amplification of the dissipation rate in a two-level system and the use of weak values to differentiate between Markovian and non-Markovian dissipative dynamics. We also find that weak measurements operating around a weak atom-cavity coupling can probe the atom dissipation through the weak value of non-Hermitian operators within the rotating-wave approximation of the weak interaction.
A brief investigation of one-port SAW resonator for the application in communication systems
Authors: Turuk Baruna Kumar , Behera Basudeba 
Published: April 2024

Abstract
In recent years, with the rapid growth of communication technology and the necessity for Radiofrequency (RF) front-end devices, the one-port Surface Acoustic Wave Resonator (SAWR) has turned out to be a significant component in the design of a SAW device. Hence, the present study concentrates on and around one-port multi-layer SAW resonators with their applications in communication systems. A comparative and critical analysis of various parameters is explored including temperature coefficient of frequency (TCF), Quality factor (Q-factor), phase velocity, impedance ratio, bandwidth, sensitivity, metallization ratio, etc to discuss the advantages and disadvantages of conventional one-port multi-layer SAW resonator. Through the analysis, future research trends as well as the applications of one port multi-layer SAW resonator are also discussed. The study also explores that the one port multi-layer SAW resonator finds employability in various applications like multimedia and mobile communication, medical field sensing technology, broadband signal processing at high frequencies, etc. Thus, significant, and efficient parameters of the resonator can be easily identified through comparative and critical analysis. The outcome of this study will help the researchers to enhance their work in this specific field in the future.
Automatic loading method for robot guided by 3D vision for scattered and stacked shoe soles
Authors: Zhang Yuewei , Wei Honglei 
Published: April 2024

Abstract
This paper primarily introduces a method for the automated feeding of scattered stacked shoe soles using a 3D visual-guided robot. Initially, addressing issues like slow speed in pose estimation and poor robustness during the robot sorting and feeding process, we introduce an enhanced pose estimation algorithm. This algorithm combines the improved Super 4-Point Congruent Sets (Super 4PCS) with the Truncated Least Squares Semidefinite Relaxation (TEASER) algorithm, significantly boosting the speed and robustness of pose estimation during sole sorting, and achieving precise target pose estimation. Building upon this foundation, we present a sorting strategy for disordered stacked shoe soles. This strategy integrates spatial positional information of each sole, employing multi-objective decision-making and recognition algorithms to determine optimal grasping targets. Finally, an experimental platform for automated sole feeding is designed to validate the proposed method. The experimental results indicate that the pose estimation method proposed in this paper achieves an average distance error of 2.04 mm and an average angular error of 2.72°, with the robot’s average success rate in grasping reaching 97.08%. Moreover, the average processing time of the vision algorithm is only 1.34 s, demonstrating good efficiency, precision, and robustness. This method effectively meets the automated feeding needs of scattered and stacked shoe soles in the actual production processes of shoemaking enterprises.
Towards a machine-learned Poisson solver for low-temperature plasma simulations in complex geometries
Authors: Chaerony Siffa Ihda , Becker Markus M , Weltmann Klaus-Dieter , Trieschmann Jan 
Published: April 2024

Abstract
Poisson’s equation plays an important role in modeling many physical systems. In electrostatic self-consistent low-temperature plasma (LTP) simulations, Poisson’s equation is solved at each simulation time step, which can amount to a significant computational cost for the entire simulation. In this paper, we describe the development of a generic machine-learned Poisson solver specifically designed for the requirements of LTP simulations in complex 2D reactor geometries on structured Cartesian grids. Here, the reactor geometries can consist of inner electrodes and dielectric materials as often found in LTP simulations. The approach leverages a hybrid CNN-transformer network architecture in combination with a weighted multiterm loss function. We train the network using highly randomized synthetic data to ensure the generalizability of the learned solver to unseen reactor geometries. The results demonstrate that the learned solver is able to produce quantitatively and qualitatively accurate solutions. Furthermore, it generalizes well on new reactor geometries such as reference geometries found in the literature. To increase the numerical accuracy of the solutions required in LTP simulations, we employ a conventional iterative solver to refine the raw predictions, especially to recover the high-frequency features not resolved by the initial prediction. With this, the proposed learned Poisson solver provides the required accuracy and is potentially faster than a pure GPU-based conventional iterative solver. This opens up new possibilities for developing a generic and high-performing learned Poisson solver for LTP systems in complex geometries.
Can You Trust Anyone on Jihadi Internet Forums?
Authors: Hegghammer Thomas 
Published: April 2024

Abstract
The Internet is fruitful territory for deception and mimicry, partly because online communication involves a narrower range of signal types than in-person interactions. This chapter looks at how this problem affected radical Islamists trying to make use of online discussion forums in the 2000s and early 2010s. Rather than being tools for plotting and recruitment, al-Qaida-affiliated jihadi forums such as al-Falluja exhibited severe internal trust problems and a reluctance to discuss matters of operational significance due to widespread fears of infiltration by intelligence agents. The chapter looks empirically at how the distrust manifested itself, how it varied over time, which types of signs made or broke trust, and which strategies and systems online jihadists developed to counter infiltration attempts and build trust on their platforms.