bluuebunny/crossref_metadata_2025_split huggingface dataset:
Activated Neuron Group Based Extract-Remember Model for Humanlike Object Recognition
Authors: Duan Tong , Yi Peng 
Published: August 2022

Abstract
Convolutional neural networks (CNNs) exhibit similarities to the human visual cortex, such as in structure and response to images. However, when it comes to the learning process, CNN and visual neural network are quite different: CNNs are generally trained by complex mathematical analysis based gradient descent algorithms, while the human brain seems to learn how to recognize objects in a more intuitive way: extract and remember the features of the object. This difference raises an interesting question --- could CNN recognize objects in a more humanlike way? In this work, a straightforward but novel ''extract-remember'' model (ERM) is proposed: first, we observed that different image classes can stably trigger the activation of different neuron groups even in a randomly weighted CNN, which means that the activated neurons jointly represent the core features of the stimuli; then, the simple remember operation is involved: try to identify the commonly activated neuron group (CANG) related to a specific object during the training process, instead of adjusting the huge amount of values of the weights. After mapping different object classes into different CANGs, the CNN can directly recognize the object in a new image by determining whether its response contains the remembered CANGs, and recognition results on different image datasets show the significant efficiency of ERM. The proposed ERM has high interpretability, suggesting a new direction for artificial neuron network design, which may in turn, also help to understand the object recognition process of human brain.
Neonatal intensive care unit occupancy rate and probability of discharge of very preterm infants
Authors: Beltempo Marc , Sargi Elyonora , Patel Sharina , Lacroix Guy , Lapointe Anie , Taylor-Ducharme Sharon , Morin Stéphane , Bizgu Victoria , Piedboeuf Bruno 
Published: August 2022

Abstract
Objective
To assess the association of NICU occupancy with probability of discharge and length of stay (LOS) among infants born < 33 weeks gestational age (GA).
Study design:
Retrospective study of 3388 infants born 23–32 weeks GA, admitted to five Level 3/4 NICUs (2014–2018) and discharged alive. Standardized ratios of observed-to-expected number of discharges were calculated for each quintile of unit occupancy. Multivariable linear regression models were used to assess the association between LOS and occupancy.
Results
At the lowest unit occupancy quintiles (Q1 and Q2), infants were 12% and 11% less likely to be discharged compared to the expected number. At the highest unit occupancy quintile (Q5), infants were 20% more likely to be discharged. Highest occupancy (Q5) was also associated with a 4.7-day (95% CI 1.7, 7.7) reduction in LOS compared Q1.
Conclusion
NICU occupancy was associated with likelihood of discharge and LOS among infants born < 33 weeks GA.
Multi-Scenario Radiative Human Body Cooling with a Selective Emission-Transmission Textile
Authors: Wu Xueke , Li Jinlei , Jiang Qinyuan , Zhang Wenshuo , Wang Baoshun , Li Run , Zhao Siming , Wang Fei , Huang Ya , Lyu Pei , Zhao Yanlong , Zhu Jia , Zhang Rufan 
Published: August 2022

Abstract
Radiative cooling (RC) is a promising sustainable human body cooling technology. However, there still lacks a RC material that can simultaneously enable efficient human body cooling in both outdoor and indoor hot scenarios. Here, we propose a selective emission-transmission (SET) RC model, which is featured with selective high emission in the atmospheric window waveband (8-13 μm) and high transmission in the remaining mid-infrared wavebands, to guide the design of multi-scenario RC materials. Assisted with designs at molecular and nano-scale, we demonstrated a polyoxymethylene (POM) nano-textile, which selectively exhibits high emittance of 75.7% in the 8-13 μm waveband, high human radiation transmittance of 48.5% (4-25 μm), and also a high solar reflectance of 94.7% (0.3-2.5 μm). As a result, the POM nano-textile exhibits superior human body cooling performance in various scenarios including sunny outdoor (2.6-8.8 ℃ cooling), cloudy outdoor (0.7-3.6 ℃ cooling) and indoor (0.5-1.2 ℃ cooling), compared to existing RC materials and commercial cotton. In addition, the POM nano-textile also possesses good wearability. When the POM nano-textile is tailored onto a protective clothing, it provides better cooling performance than its commercial counterpart. This work provides an alternative pathway towards multi-scenario personal thermal management.
Attention-based Bi-Prediction Network with Knowledge Distillation for Versatile Video Coding (VVC)
Authors: Choi Young-Ju , Lee Young-Woon , Kim Jong-Ho , Jeong Se-Yoon , Choi Jin-Soo , Kim Byung-Gyu 
Published: August 2022

Abstract
In video coding, inter bi-prediction contributes to improve the coding efficiency significantly by producing precise fused prediction block. Although block-wise bi-prediction methods, such as bi-prediction with CU-level weight (BCW), are applied in Versatile Video Coding (VVC), linear fusion-based strategy is still difficult to represent diverse pixel variations inside a block. In addition, a pixel-wise bi-prediction method called bi-directional optical flow (BDOF) has been proposed to refine bi-prediction block. However, non-linear optical flow equation in BDOF mode is applied under assumptions so this method is yet unable to accurately compensate various kind of bi-prediction block. In this paper, we propose an attention-based bi-prediction network (ABPN) to substitute the whole existing bi-prediction methods. The proposed ABPN is designed to learn efficient representations of the fused features by utilizing attention mechanism. Furthermore, the knowledge distillation (KD)-based approach is employed to compress the size of the proposed network while keeping comparable output as the large model. The proposed ABPN is integrated into the VTM-11.0 NNVC-1.0 standard reference software. When compared with VTM anchor, it is verified that the BD-rate reduction of the lightweighted ABPN can be up to 5.89\% and 4.91\% on Y component under random access (RA) and low delay B (LDB), respectively.
A Comparative Study of Particle Filter Resampling Techniques for Power Systems State Estimation
Authors: Abolmasoumi Amir Hossein , Heydari Somayeh , Farahani Ali , Ghadimi Ali , Bayat Mohammad 
Published: August 2022

Abstract
The effect of the resampling stage on the overall performance of the particle filter state estimation for power systems is investigated. As each general particle filter algorithm includes three steps of the prediction, update and resampling, the resampling step is essential to avoid the sample degeneracy problem. In this paper, the standard particle filter is applied to the problem of the decentralized state estimation in power systems. Various existing resampling techniques are described, tested, and evaluated in different power systems with different fault scenarios. The resampling methods are compared based on their mean square error, their needed computation time and their performance against various data quality issues such as delays, missing measurements, etc. More efficient resampling methods are then determined for three cases of the power system cases: a single-machine-infinite-bus system, the medium size two-area four-machine system and the large-scale New-England system considering different fault scenarios.
Integrated hydrobulging of prolate ellipsoids from preforms with multiple thicknesses
Authors: Zhang Jian , Tang Yinhui , Zhan Ming , Wang Fang , Zhao Xilu 
Published: August 2022

Abstract
The integrated hydrobulging of stainless-steel prolate ellipsoids from preforms with two thicknesses was investigated. The produced ellipsoids were closed with two 16-mm-thick closures and had nominal semiminor and semimajor axes of 89 and 125 mm, respectively. The ellipsoidal preforms comprised eight conical segments inscribed inside the target perfect ellipsoid. The four end and middle segments of the preforms had nominal thicknesses of 0.67 and 0.83 mm, respectively. The hydrobulging of these preforms was explored analytically and numerically and was compared with that of prolate ellipsoids with constant thickness. Two nominally identical ellipsoidal preforms were fabricated, measured, and hydrobulged to confirm the theoretical predictions. The results indicated that varying the preform thicknesses is an efficient method of overcoming insufficient hydrobulging of the ends of prolate ellipsoids in other methods. Moreover, hydrobulging instability can be effectively monitored by measuring geometric dimensions, such as axial height.
Proof-of-concept study for a long-acting formulation of ivermectin injected to cattle as a complementary malaria vector control tool
Authors: Pooda Sié Hermann , Moiroux Nicolas , Porciani Angélique , Courjaud Anne-Laure , Roberge Christophe , Gaudriault Georges , Sidibé Issa , Belem Adrien Marie Gaston , Rayaissé Jean-Baptiste , Dabiré Roch K. , Mouline Karine 
Published: August 2022

Abstract
Background: Peridomestic animals play a role in maintaining residual transmission of Plasmodium, being alternative blood meal sources for malaria vectors. The blood of animals treated with veterinary formulations of ivermectin show a mosquitocidal effect on malaria vectors, but for a too short period of time to impact Plasmodium transmission. This study proposes to treat cattle with innovative long-acting (six months) injectable formulations of ivermectin as a complementary vector control measure.Methods: The local breeds of cattle were used in two treatment arms (Four cattle injected with two long-acting formulations of ivermectin with the BEPO® technology at the therapeutic dose of 1.2 mg/kg, and two control arms (Four cattle injected with the vehicles of the formulations). The lethality of the formulations was evaluated on 3-5 days old Anopheles coluzzii mosquitoes through direct-skin feeding assays, from one to 210 days after treatments. Formulations’ efficacy was evaluated and compared using Cox proportional hazards survival models, Kaplan-Meier estimates and 4 parameters log-logistic regression on cumulative mortalities. These results were extrapolated to field scenarii using entomological and transmission models, considering different mosquito feeding preferences, hosts abundances, and bed nets uses ratios. Results: Both formulations released mosquitocidal concentrations of ivermectin until 210 days post-treatment (Hazard ratios > 1). The treatments significantly reduced mosquito survival, with the average median survival times of 4-5 days post-feeding. The Lethal Concentrations to kill 50% of the Anopheles (LC50) before they become infectious (10 days after infectious blood meal) were reached until 210 days post injection for both formulations. Modeling showed that administering long-acting ivermectin formulations to calves would significantly decrease Anopheles populations in the fields. The population of infectious vectors may decrease from 35% to 75% depending mostly on the mosquito feeding preference and on the hosts species relative availability.Conclusions: This study shows that, in complement to bed nets, a long-lasting BEPO® formulation of ivermectin injected to cattle could help to decrease field populations of malaria mosquitoes and residual transmission, the impact of this approach being dependant on the field epidemiological context.
Blockchain-assisted authentication and key agreement scheme for IoT based Healthcare System
Authors: RANI DIVYA , Tripathi Sachin , Tomar Ashish 
Published: August 2022

Abstract
Nowadays, the research work on integration of Blockchain in IoT based Healthcare System is highly focused in order to make the system decentralised, transparent and immutable. However, there are very few works that have integrated Blockchain for authentication and key establishment in IoT based Healthcare System. The patient’s medical data collected through IoT devices are vulnerable to attack by unauthorised user. Therefore, this paper consists of blockchain based authentication and key establishment scheme. Unlike the existing work, this paper uses Consortium Blockchain in order to remove public access and reduce third-party dependency. In addition, a secret key is established between Hospital Server and Smart Device for secure communication between them. A detailed Informal security analysis shows that the proposed protocol is secure and satisfy the predefined security goals. The Blockchain concept is implemented using Hyperledger Fabric platform. This provide multi-channel support where the transactions are executed privately by creating separate channel making the proposed system more confidential. Finally, the performance analysis and comparative study shows that the proposed protocol is computationally effective and have some additional features that makes it different from the existing work.
Long non-coding RNA SLC25A21-AS1 inhibits the development of epithelial ovarian cancer by specifically inducing PTBP3 degradation
Authors: Li Sihui , Shen Shizhen , Ge Wanzhong , Cen Yixuan , Zhang Songfa , Cheng Xiaodong , Wang Xinyu , Xie Xing , Lu Weiguo 
Published: August 2022

Abstract
Background
Epithelial ovarian cancer (EOC) is a highly prevalent disease that rapidly metastasizes and has poor prognosis. Most women are in the middle or late stages when diagnosed and have low survival rates. Recently, long non-coding RNAs (lncRNAs) were recognized to play pivotal roles in the development of EOC.
Methods
The expression of SLC25A21-AS1 and PTBP3 in EOC cells was assessed via qPCR. The proliferation activity of these cells was detected by EdU and CCK8 assays, while the death rate of apoptotic cells and the cell cycle were detected by flow cytometry. Detection of cell transfer rate by Transwell assay. Protein expression was measured through Western immunoblotting. Interactions between SLC25A21-AS1 and PTBP3 were detected through RNA immunoprecipitation (RIP), IF-FISH co-localization experiments and Electrophoretic mobility shift assay (EMSA).The in vivo importance of SLC25A21-AS1 as a tumor suppressor modulator was assessed using murine xenograft models.
Results
The lncRNA SLC25A21-AS1 has negligible expression in ovarian cancer tissues compared with that in normal ovarian tissues. A series of functional tests revealed that the upregulation of SLC25A21-AS1 markedly blocked the proliferation and metastasis of EOC cells in vitro, while its downregulation had the opposite effect. Overexpression of SLC25A21-AS1 in a nude mouse model of EOC in vivo resulted in slower tumor growth and weakened metastatic potential. Moreover, SLC25A21-AS1 reduced the protein stability of PTBP3 and promoted its degradation. Subsequent ubiquitination experiments confirmed that SLC25A21-AS1 acts on PTBP3 through the ubiquitin–proteasome pathway and binds to PTBP3 to exert its proteolytic effect, thereby inhibiting EOC cell proliferation and metastasis.
Conclusions
Our research reveals the effect of SLC25A21-AS1 in EOC development and suggests it can serve as a prognostic target by promoting the degradation of PTBP3 to improve patient survival.
MLACNN: an attention mechanism based CNN architecture for predicting genome-wide DNA methylation
Authors: Bai JianGuo , Yang Hai , Wu ChangDe 
Published: August 2022

Abstract
Methylation, as an important epigenetic regulation of methylation genes, plays an important role in the regulation of some biological processes. Although the traditional methods of detecting methylation in biological experiments are constantly improving, with the development of artificial intelligence, using deep learning and machine learning methods to detect methylation has gradually become a new trend. Traditional machine learning based methods rely too much on manual feature extraction. Although there are some deep learning methods to study methylation, most network architectures extract fewer features due to the simple network structure. We propose a bottleneck network based on attention mechanism, and use some new methods to ensure that the deep network can learn more effective features and minimize overfitting to get more significant prediction results. The model uses three coding methods to encode the original DNA sequence, and then uses the feature fusion based on the attention mechanism to get the best fusion method. The results show that MLACNN is superior to the previous methods and can obtain more satisfactory performance.
Exploring young people’s experiences of equity in relation to everyday challenges: a focus group study
Authors: Gard Helena , Enskär Karin , Ingvarsdotter Karin , Isma Gabriella E. , Mangrio Elisabeth 
Published: August 2022

Abstract
Background
Reports indicate a decrease in youth mental health in Sweden but at the same time research suggests that what is interpreted as mental ill-health could be considered everyday challenges by young people themselves. The distribution of mental health and illness among young people is also uneven, due to structural inequities related to factors such as race, gender, and socioeconomics. Sweden in particular is a country with large socioeconomic inequities in youth mental health and in school results, compared to other European countries. The aim of the study was to explore young people’s experiences of equity in relation to everyday challenges, with a focus on race, gender, and socioeconomics.
Methods
Sixty-five young people aged 13–15 years old were recruited by student health services and participated in focus group discussions at schools in the southernmost part of Sweden. Data was analyzed by secondary analysis with deductive qualitative content analysis, using the ecosocial theory of disease distribution as theoretical framework.
Results
The analysis resulted in one main theme: Navigating equity factors to gain and keep status, with three underlying themes: Guided by social norms, Negative impact in everyday life, and Importance of family influence. Participants were aware and critical of norms and expectations related to race, gender, and socioeconomics. Experiences of prejudice and unfairness were both the participants’ own lived experiences and based on observations of friends and school mates.
Conclusions
Young people identify equity issues related to race, gender, and socioeconomics in their everyday life. Passing norms and expectations related to these issues is a way to lose and gain status in a school setting. Many of the inequities discussed are related to socioeconomics and the direct consequences of having or not having money. Young people’s everyday experiences of equity issues are important to consider in youth mental health promotion aiming to tackle health inequities. Further research is needed on those experiences and how they affect mental health.
Effects of gestational diseases on the distribution of trace elements in the maternal-fetal system
Authors: Ding Ailing , Wan Huimin , Peng Juan , Wang Huizi , Zhu Shaodan , Dong Xudong 
Published: August 2022

Abstract
Purpose
To evaluate the levels and differences of trace elements (Zn, Cu, Fe, Mn, Ni, Cr) in maternal-fetal systems (maternal blood, placenta, umbilical cord blood) of different healthy states. To analyze the influence of pregnancy disease on the distribution of trace elements between the mother and fetus.
Methods
Maternal, placental and umbilical blood samples were collected from 140 parturients (60 in the normal group, 40 in the HDCP group and 40 in the GDM group). The contents of trace elements in paired samples were determined by inductively coupled plasma–mass spectrometry (ICP–MS). SPSS software was used to analyze the differences in trace element levels in matched samples of each group.
Results
The distribution of Fe content in the normal group was as follows: umbilical cord blood > maternal blood > placenta (p < 0.001). Although the distribution characteristics of Fe in the HDCP group and GDM group were the same as those in the normal group, there was no significant difference in Fe content between maternal and umbilical cord blood (p > 0.05). In addition, the levels of Fe in maternal blood, placental blood and umbilical cord blood in the HDCP group were higher than those in the normal control group (p < 0.05). The distribution characteristics of Mn content in the HDCP group and GDM group were placenta > umbilical cord blood > maternal blood. There were significant differences in Mn content between the placenta and maternal and umbilical cord blood (p < 0.001). The distribution characteristics of Mn content in the HDCP group and GDM group were placenta > umbilical cord blood > maternal blood. There were significant differences in the Mn content between the placenta, maternal blood and umbilical blood in the HDCP group (p < 0.05). Only the Mn content between the placenta and maternal blood was significantly different in the GDM group (p < 0.001). In addition, the content of Cr in the placenta and umbilical blood of the HDCP group was higher than that of the normal group (p < 0.05). The content of Ni in the cord blood of the HDCP group and GDM group was higher than that of the normal group (p < 0.05).
Conclusion
HDCP and GDM affect the transport of Fe, Ni and Cr from the placenta to the fetus. In addition, the occurrence of GDM also changes the distribution of Fe and Mn in the maternal-fetal system (maternal blood, placenta and umbilical cord blood).
Generating flavors using Scientific Machine Learning
Authors: Queiroz Luana P. , Rebello Carine M. , Costa Erbet A. , Santana Vinícius V. , Rodrigues Bruno C. L. , Rodrigues Alírio E. , Ribeiro Ana M. , Nogueira Idelfonso B. R. 
Published: August 2022

Abstract
The flavor is an essential component in developing numerous products in the market. The increasing consumption of processed and fast food and healthy packages has upraised the investment in new flavoring agents and, consequently, molecules with flavoring properties. In this context, this work brings a Scientific Machine Learning approach to address this product engineering need. Scientific Machine Learning in computational chemistry has opened paths in predicting a compound's properties without requiring synthesis. This work proposes a novel framework of deep generative models within this context to design new flavor molecules.
Enhanced Gaussian noise model based probabilistic constellation shaping to mitigate non-linearity at increased SNR
Authors: Khaki Zahid G , Qazi Gausia 
Published: August 2022

Abstract
In long-haul unrepeated optical transmission systems, the different input distributions employing constellation shaping, have led to increased achievable information rate (AIR) at the expense of reduced signal-to-noise ratio (SNR). In this paper, at optimal launch power, enhanced Gaussian noise (EGN) model based probabilistic shaping (PS) is implemented to inhibit the fiber non-linearities. The method exploits the inherent system and input conditions to result into maximum mutual information (MI) without using digital signal processors. Using sequential quadratic programming (SQP), a robust 16-QAM coherent optical system is implemented in back-to-back configuration employing probabilistic shaping. The shaping is optimized for minimum values of the objective function, second and fourth order moments of input. The higher order noise coefficients are thereby reduced, resulting in minimal non-linear interference noise (NLIN) at increased values of SNR. The optimized EGN model returns values of 1.41 and 2.24 for μ^4 and μ^6 respectively, having highest SNR value of 14.52 dBm and the least SNR penalty of 0.15 dBm towards the uniform probability distribution.