bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features
Authors: Wang Fen , Wang Cheng-Long , Yi Yin-Qiao , Zhang Teng , Zhong Yan , Zhu Jia-Jia , Li Hai , Yang Guang , Yu Tong-Fu , Xu Hai , Yuan Mei 
Published: June 2023

AbstractTo investigate whether the combination scheme of deep learning score (DL-score) and radiomics can improve preoperative diagnosis in the presence of micropapillary/solid (MPP/SOL) patterns in lung adenocarcinoma (ADC). A retrospective cohort of 514 confirmed pathologically lung ADC in 512 patients after surgery was enrolled. The clinicoradiographic model (model 1) and radiomics model (model 2) were developed with logistic regression. The deep learning model (model 3) was constructed based on the deep learning score (DL-score). The combine model (model 4) was based on DL-score and R-score and clinicoradiographic variables. The performance of these models was evaluated with area under the receiver operating characteristic curve (AUC) and compared using DeLong's test internally and externally. The prediction nomogram was plotted, and clinical utility depicted with decision curve. The performance of model 1, model 2, model 3 and model 4 was supported by AUCs of 0.848, 0.896, 0.906, 0.921 in the Internal validation set, that of 0.700, 0.801, 0.730, 0.827 in external validation set, respectively. These models existed statistical significance in internal validation (model 4 vs model 3, P = 0.016; model 4 vs model 1, P = 0.009, respectively) and external validation (model 4 vs model 2, P = 0.036; model 4 vs model 3, P = 0.047; model 4 vs model 1, P = 0.016, respectively). The decision curve analysis (DCA) demonstrated that model 4 predicting the lung ADC with MPP/SOL structure would be more beneficial than the model 1and model 3 but comparable with the model 2. The combined model can improve preoperative diagnosis in the presence of MPP/SOL pattern in lung ADC in clinical practice.
An improved preparation method for a CuO/CeO2-coated monolith for the CO–PrOx reaction
Authors: Meißner Jan , Ahrens Lara , Pasel Joachim , Schwedt Alexander , Wohlrab Sebastian , Mayer Joachim , Peters Ralf 
Published: June 2023

AbstractIn this study, we present a method for directly coating monoliths with a CeO2/CuO catalyst using the urea-nitrate combustion method. The catalyst was characterized by means of XRD, SEM/EDX, and EPR measurements. Experimental results are described, when this catalyst was used for the preferential oxidation of CO. The catalytic activity for the CO–PrOx-reaction was measured by recording CO conversion as a function of the reaction temperature in a hydrogen-rich gas mixture in the presence and absence of water vapor. In a long-term test of over 310 h, the catalyst’s long-term stability was demonstrated. Direct coating is shown to be a promising approach by which a larger amount of catalyst can be deposited onto the monolith in a single step than would be possible with washcoats.
Ultra-effective modified clinoptilolite adsorbent for selective thorium removal from radioactive residue
Authors: Alotaibi Abdulrahman Masoud , Ismail Aznan Fazli , Aziman Eli Syafiqah 
Published: June 2023

AbstractThis study investigated the efficacy of using phosphate-modified zeolite (PZ) as an adsorbent for removing thorium from aqueous solutions. The effects of various factors such as contact time, adsorbent mass, initial thorium concentration, and pH value of the solution on the removal efficiency were analyzed using the batch technique to obtain optimum adsorption condition. The results revealed that the optimal conditions for thorium adsorption were a contact time of 24 h, 0.03 g of PZ adsorbent, pH 3, and a temperature of 25 °C. Isotherm and kinetics parameters of the thorium adsorption on PZ were also determined, with equilibrium studies showing that the experimental data followed the Langmuir isotherm model. The maximum adsorption capacity (Qo) for thorium was found to be 17.3 mg/g with the Langmuir isotherm coefficient of 0.09 L/mg. Using phosphate anions to modify natural zeolite increased its adsorption capacity. Furthermore, adsorption kinetics studies demonstrated that the adsorption of thorium onto PZ adsorbent fitted well with the pseudo-second-order model. The applicability of the PZ adsorbent in removing thorium from real radioactive waste was also investigated, and nearly complete thorium removal was achieved (> 99%) from the leached solution obtained from cracking and leaching processes of rare earth industrial residue under optimized conditions. This study elucidates the potential of PZ adsorbent for efficient removal of thorium from rare earth residue via adsorption, leading to a reduction in waste volume for ultimate disposition.
RNA activation in ticks
Authors: Kwofie Kofi Dadzie , Hernandez Emmanuel Pacia , Anisuzzaman  , Kawada Hayato , Koike Yuki , Sasaki Sana , Inoue Takahiro , Jimbo Kei , Mikami Fusako , Ladzekpo Danielle , Umemiya-Shirafuji Rika , Yamaji Kayoko , Tanaka Tetsuya , Matsubayashi Makoto , Alim Md Abdul , Dadzie Samuel Kweku , Iwanaga Shiroh , Tsuji Naotoshi , Hatta Takeshi 
Published: June 2023

AbstractRNA activation (RNAa) is a burgeoning area of research in which double-stranded RNAs (dsRNAs) or small activating RNAs mediate the upregulation of specific genes by targeting the promoter sequence and/or AU-rich elements in the 3′- untranslated region (3’-UTR) of mRNA molecules. So far, studies on the phenomenon have been limited to mammals, plants, bacteria, Caenorhabditis elegans, and recently, Aedes aegypti. However, it is yet to be applied in other arthropods, including ticks, despite the ubiquitous presence of argonaute 2 protein, which is an indispensable requirement for the formation of RNA-induced transcriptional activation complex to enable a dsRNA-mediated gene activation. In this study, we demonstrated for the first time the possible presence of RNAa phenomenon in the tick vector, Haemaphysalis longicornis (Asian longhorned tick). We targeted the 3ʹ-UTR of a novel endochitinase-like gene (HlemCHT) identified previously in H. longicornis eggs for dsRNA-mediated gene activation. Our results showed an increased gene expression in eggs of H. longicornis endochitinase-dsRNA-injected (dsHlemCHT) ticks on day-13 post-oviposition. Furthermore, we observed that eggs of dsHlemCHT ticks exhibited relatively early egg development and hatching, suggesting a dsRNA-mediated activation of the HlemCHT gene in the eggs. This is the first attempt to provide evidence of RNAa in ticks. Although further studies are required to elucidate the detailed mechanism by which RNAa occurs in ticks, the outcome of this study provides new opportunities for the use of RNAa as a gene overexpression tool in future studies on tick biology, to reduce the global burden of ticks and tick-borne diseases.
Porphyromonas gingivalis-odontogenic infection is the potential risk for progression of nonalcoholic steatohepatitis-related neoplastic nodule formation
Authors: Sakamoto Shinnichi , Nagasaki Atsuhiro , Shrestha Madhu , Shintani Tomoaki , Watanabe Atsushi , Furusho Hisako , Chayama Kazuaki , Takata Takashi , Miyauchi Mutsumi 
Published: June 2023

AbstractPorphyromonas gingivalis (P.g.), a major periodontal pathogen is a known risk factor for various systemic diseases. However, the relationship between P.g. and nonalcoholic steatohepatitis (NASH)-related hepatocellular carcinoma (HCC) is unclear. Thus, we aimed to elucidate whether P.g.-odontogenic infection promotes NASH-related HCC development/progression and to clarify its mechanism. Using high-fat diet (HFD)-induced NASH mouse model, P.g. was infected odontogenically. After 60 weeks of infection, tumor profiles were examined. Chow diet (CD) groups were also prepared at 60 weeks. Nodule formation was only seen in HFD-mice. P.g.-odontogenic infection significantly increased the mean nodule area (P = 0.0188) and tended to promote histological progression score after 60 weeks (P = 0.0956). Interestingly, P.g. was detected in the liver. HFD-P.g. (+) showed numerous TNF-α positive hepatic crown-like structures and 8-OHdG expression in the non-neoplastic liver. In P.g.-infected hepatocytes, phosphorylation of integrin β1 signaling molecules (FAK/ERK/AKT) was upregulated in vitro. In fact, total AKT in the liver of HFD-P.g. (+) was higher than that of HFD-P.g. (−). P.g.-infected hepatocytes showed increased cell proliferation and migration, and decreased doxorubicin-mediated apoptosis. Integrin β1 knockdown inhibited these phenotypic changes. P.g.-odontogenic infection may promote the progression of neoplastic nodule formation in an HFD-induced NASH mouse model via integrin signaling and TNF-α induced oxidative DNA damage.
Design and implementation of a wireless CAN module for marine engines using ZigBee protocol
Authors: Siemuri Akpojoto , Glocker Tobias , Mekkanen Mike , Kauhaniemi Kimmo , Mantere Timo , Elmusrati Mohammed 
Published: June 2023

AbstractThis paper describes the design and implementation of a wireless control area network (CAN bus) protocol for communication between the smart NOx (nitrogen oxide) sensor on diesel engines and the engine control unit (ECU). In this research, the approach taken is based on a case study of Wärtsilä's smart NOx sensor on a W4L20 diesel engine with the objective of replacing the wired CAN protocol with a wireless CAN communication node. In the current setup, the smart NOx sensor is connected to the engine control unit (ECU) with a wired CAN bus connection. The XBee module, which uses the ZigBee (IEEE 802.15.4) technology was used in the design and implementation of the wireless CAN prototype. With the emergence of 5G networks and the era of IoT, the topic of wireless industrial automation becomes essential in the modern industry. In addition to the great advantages and opportunities that the use of wireless nodes has in automation systems, there are many real challenges. The practical design challenges have been addressed in this paper.
Green integrated cooperative spectrum sensing for cognitive satellite terrestrial networks
Authors: Yao Rugui , Yu Yongsong , Wang Peng , Fan Ye , Li Xudong , Zuo Xiaoya , Qi Nan , Miridakis Nikolaos I. , Tsiftsis Theodoros A. 
Published: June 2023

AbstractIn this paper, a two‐way relay‐aided cognitive satellite terrestrial network (TR‐CSTN) model is proposed, where primary users are located at the edge of the base station. In the TR‐CSTN, one of satellite terminal users (STUs) is selected by the fusion center as the TR to forward information between two edge primary users with power of the TR. Meanwhile, these edge primary users share the licensed frequency band with the selected TR to send information to the satellite. Then, given the limited spectrum utilization and energy efficiency (EE) of the communication system, the cooperative spectrum sensing is employed to realize green communication. Specifically, the fusion center threshold, energy detection threshold, sensing duration and number of STUs are jointly optimized to enhance EE. Furthermore, considering that the node's energy shortage results in a short network lifetime, absolute EE gets improved. In detail, a power allocation scheme named normalized power aided Lévy flight trajectory‐based whale optimization algorithm (NP‐LWOA) is provided, which fulfills effective energy compensation among STUs to prolong the network lifetime notably. Finally, numerical results confirm the theoretical analysis and show the effectiveness of the TR‐CSTN and the NP‐LWOA in efficiently achieving the concept of green communication compared with other methods.
A robust routing strategy based on deep reinforcement learning for mega satellite constellations
Authors: Chu Ke , Cheng Sixi , Zhu Lidong 
Published: June 2023

AbstractFor mega satellite constellations, it has been a great challenge to achieve global routing and guarantee the performance of inter‐satellite transmission. To address the problem, a robust routing strategy based on deep reinforcement learning is proposed in this letter. The proposed method is applicable to degraded transmission performance, which exhibits better conformity to real‐world scenarios. Moreover, the age of information (AoI) of packets are utilized as one of the multi‐optimization targets to ensure the effectiveness of message transmission throughout the network. Numerical simulations show the outstanding average AoI performance of the proposed method and its greater robustness to jamming compared to existing methods. Meanwhile, it is also more effective for utilizing resources.
Periodic average magnitude difference function for remote heart rate monitoring
Authors: Zhang Chi , Wei Shaoming , Dong Ge , Zeng Yajun 
Published: June 2023

AbstractWith the increasing attention on remote monitoring of human heart rate by radar, there is a need to develop a method that can estimate heart rate quickly and reliably. In this study, a new estimation method using a periodic average magnitude difference function (PAMDF) is proposed to estimate the heart rate from the radar signal. PAMDF advances the classical average magnitude difference function (AMDF) with the help of maximum likelihood (ML) theory. It operates in the time domain and estimates the heart rate by calculating the signal magnitude difference between all heartbeat periods. The proposed technique is more accurate than AMDF and has a similar performance to the ML estimation, while maintaining the low complexity advantage of AMDF. The algorithm was validated using radar data from two publicly available datasets.
SE‐Former: Incorporating sentence embeddings into Transformer for low‐resource NMT
Authors: Wang Dongsheng , Wang Shaoyong 
Published: June 2023

AbstractRecently, pre‐trained language models (PLM), such as Roberta and SimCSE, demonstrate strengths in many natural language understanding (NLU) tasks. However, there are few works on applying PLM to neural machine translation (NMT). Motivated by alleviating the data scarcity issue of low‐resource NMT, here incorporating sentence embeddings from SimCSE into the Transformer network is explored and SE‐Former model is proposed. In this model an embed‐fusion module is designed to utilize the output of SimCSE for NMT. Specifically, the outputs of encoder and SimCSE are fed into embed‐fusion module, attention network learns the relationship of sentence embedding and corresponding word embeddings. After addition, concatenation and linear transformation operations, the tensor fused with sentence embedding is obtained. The size of tensor output by embed‐fusion module is the same as original encoder. Finally, embed‐fusion module is connected to Transformer decoder. On IWSLT En‐Es, En‐Zh and En‐Fr tasks, SE‐Former obtains 42.13, 29.32 and 39.21 bilingual evaluation understudy (BLEU) points, respectively. Experimental results show the superiority of this method.
On‐line identification of power transformer short‐circuit parameters based on instantaneous phasor
Authors: Han Meiyu , Zhao Rende , Zhang Yunjian , Zhang Yinqing 
Published: June 2023

AbstractAmong the various faults of power transformers, winding faults are particularly common. It is known that short‐circuit parameters can reflect the winding condition, so the on‐line identification of short‐circuit parameters is of great significance for monitoring the power transformer health. An instantaneous phasor extraction method based on Second‐Order Generalised Integrator Frequency‐Locked Loop is introduced, and then an on‐line identification algorithm of power transformer short‐circuit parameters based on an instantaneous phasor is proposed. In this algorithm, multi second‐order generalised integrator frequency‐locked loop is used to extract the fundamental instantaneous phasors of the voltage and current signals on the primary and secondary sides of the transformer. After that, the short‐circuit parameters are calculated from the phasor equation of the transformer. The effectiveness of the proposed algorithm has been verified with experimental results.
Feasibility study on the integration of residential PV‐battery systems in system peak load shaving: A case study in Iran
Authors: Ashoornezhad Ali , Falaghi Hamid , Hajizadeh Amin , Ramezani Maryam 
Published: June 2023

AbstractThis paper investigates the impact of residential photovoltaic (PV) battery systems in a real test system with the goal of system peak load shaving. In order to encourage residential investors, a levelised feed‐in tariff (LFiT) scheme is introduced. Accordingly, two proposed cases and relevant suggestions are presented to reach a better performance. The profitability of the project is apprised via several economic criteria such as net present value, simple and discount payback period years (PBY and DPBY), internal rate of return (IRR), benefit to cost ratio, net cash flow, and levelised cost of energy. Moreover, different levels of peak shaving subject to customers’ participation and the size of the PV‐battery system are also obtained. An actual test system regarding 1‐year recorded data is employed to elevate the precision of results. Obtained results demonstrate that in the current situation in Iran, the PBY is about 5.83 years and the IRR is about 0.43. Meanwhile, the ratio of the LFiT should be about 3.5 to reach the same result as the current circumstances. Finally, a sensitivity analysis on systems’ parameters is performed to identify the impact of these parameters on economic indices.
An efficient analytical approach for operational reliability evaluation of integrated electricity‐heat energy systems with variable wind power
Authors: He Haojie , Shao Changzheng , Hu Bo , Xie Kaigui , Du Xiong , Xu Longxun 
Published: June 2023

AbstractThe increase in wind power penetration leads to the risk of energy supply interruption in integrated electricity‐heat energy systems (IHES). This paper presents an analytical‐based approach for the efficient operational reliability evaluation of the IHES. The wind power distribution characteristic model considering correlation is constructed based on Hidden Markov Model (HMM). To reduce computational complexity, a critical system state identification (CSSI) method is proposed that characterises both random device failures and wind power uncertainty. Under each critical system state, the analytical function relationship between the reliability index and the uncertainty factors is established based on the virtual stochastic response surface (VSRS) method. The analytical function can directly calculate the reliability index when the wind power output varies, avoiding a large number of repeated calculations of the optimal load‐shedding model of the system and reducing the time required for reliability assessment. This allows the operator to evaluate the operational reliability of the integrated energy system in real‐time. The numerical simulation of the test system combining the IEEE 33‐node distribution system and the existing 28‐node heating system proves the effectiveness of the proposed method.
Drivers’ usage of driving automation systems in different contexts: A survey in China, Germany, Spain and USA
Authors: Karlsson MariAnne , Novakazi Fjollë 
Published: June 2023

AbstractThe purpose of the study was to investigate how drivers use assisted and automated driving systems (DAS), more specifically their usage of SAE Level 1 and Level 2 systems, in different situations. An online survey was distributed to 2500 respondents in China, Germany, Spain, and the USA. The final dataset consisted of 549 respondents, all non‐professional drivers, with access to a minimum of a Level 1 system. A subset, 159 in total, had access also to a Level 2 DAS. The survey included questions on the attitude towards, access to, and usage of Level 1 and Level 2 systems in nine different situations respectively. The data was analysed on an individual and a national level. A cluster analysis showed two main groups: frequent and non‐frequent users. On an individual level, the reported usage of Level 1 and Level 2 DAS respectively differed depending on traffic situation, weather and daylight conditions and driver state. Reports by respondents with access to both Level 1 and Level 2 systems did not reveal any statistically significant differences in usage between situations. The Spanish sample was the only one that showed a consistently different usage pattern compared to samples from China, Germany, and the USA.
3D target tracking and shape reconstruction in clutter using Gaussian process and point completion network
Authors: Mao Hongge , Yang Xiaojun 
Published: June 2023

AbstractExisting problems for 3D extended target tracking and contour reconstruction under clutter environment are thoroughly investigated in this article. Due to the sparsity of available point cloud data and interference from clutter, the shape completion method, such as Point Completion Network (PCN), cannot reconstruct 3D contour directly. Moreover, the traditional Gaussian process (GP) model suffered from computing overhead and cannot handle irregular non‐convex shapes. Here, a two‐stage tracking algorithm is proposed, which firstly combines the 3D GP measurement model with a probability data association filter to jointly estimate the kinematics and 3D Gaussian basis points, which contain partial shape information. Afterwards, this method input produced Gaussian basis points to a deep learning‐based PCN to reconstruct complete geometry of the contour. The effectiveness of the proposed algorithm is verified by extended target tracking with both simple and complex geometric shapes. The simulation results show that the proposed algorithm obtains accurate kinematic state estimation and produces complete point cloud estimation for 3D contour.
Evaluation of practical edge computing CNN‐based solutions for intelligent recycling bins
Authors: Li Xueying , Grammenos Ryan 
Published: June 2023

AbstractRapid economic growth has given rise to the urgent demand for more efficient waste recycling systems. An innovative smart recycling bin is proposed that automatically separates urban waste to increase the recycling rate. Over 1800 recycling waste images were collected and combined with an existing public dataset to train neural network classification models for two embedded systems, one incorporating a Jetson Nano and the other a K210 unit. The model developed reached an accuracy of 93.99% on the Jetson Nano and 94.61% on the K210. A user interface application was also designed to collect feedback from users during their interaction with the smart bin. In terms of power consumption, the system employing the Jetson Nano consumed 4.7 W, representing a 30% reduction in power consumption compared to previous work, while the K210 required just 0.89 W of power to operate. In summary, our work demonstrated a small‐scale, fully functional prototype of an energy‐efficient, high‐accuracy smart recycling bin, with the potential of commercialisation for the purpose of improving urban waste recycling.
Neue Methoden in der Neuroradiologie: MR-Fingerprinting und synthetische Bildgebung
Authors: Keil Vera C. 
Published: June 2023

ZusammenfassungWas bedeutet „synthetische Bildgebung“? Ist es eine bestimmte Form der Akquisition oder kann man auch durch Postprocessing von Standard-MRT-Aufnahmen synthetische Bilder erzeugen? Welche Rolle spielt künstliche Intelligenz hierbei? Antworten auf die Fragen, welche synthetischen Verfahren es gibt und wofür diese bereits bei neuroradiologischen Fragestellungen genutzt werden, liefert dieser Übersichtsartikel.