bluuebunny/crossref_metadata_2025_split huggingface dataset:
How Do Oxygen Vacancies Influence the Catalytic Performance of Two‐Dimensional Nb2O5 in Lithium‐ and Sodium‐Oxygen Batteries?
Authors: Li Jia‐Hui , Yu Yang‐Xin 
Published: October 2021

AbstractAlkali metal‐oxygen batteries possess a higher specific capacity than alkali‐ion batteries and stand out as the most competitive next‐generation energy source. The core reaction mechanism of the battery is mainly the formation of alkali metal oxide during the discharge process and the decomposition of these oxides during the charge process. A large number of researchers have devoted themselves to seeking promising catalysts for the reaction. Two‐dimensional Nb2O5 was discovered to be a highly potential catalyst that can promote the reaction of alkali‐metal‐oxygen batteries, but few studies focus on it. In this study, the catalytic performance of both pristine Nb2O5 and oxygen‐deficiency modified Nb2O5 was investigated. Furthermore, the effect of oxygen defects on catalytic performance was analyzed from multiple angles, namely, the reaction mechanism, d‐band center theory, and the diffusion behavior of alkali metals. The exploration revealed the microscopic mechanism of oxygen deficiency affecting the alkali‐metal battery reaction and provided a theoretical basis for quantitatively changing the d‐band center of the catalyst through oxygen deficiency to ultimately change the performance of the catalyst.
DACOR: A distributed ACO‐based routing protocol for mitigating the hot spot problem in fog‐enabled WSN architecture
Authors: Moussa Noureddine , El Belrhiti El Alaoui Abdelbaki 
Published: October 2021

SummaryAnt colony optimization (ACO) and unequal clustering algorithms in wireless sensor networks (WSNs) prove their efficiency in protracting the network lifetime. However, the existing ACO and unequal clustering algorithms, respectively, do not consider jointly energy efficiency and reliability and focus only on some normal parameters to adjust the cluster radius, then neglecting the cluster head (CH) neighborhood information as it is wise to reduce the cluster radius when there are more neighbor CHs in order to balance the load and energy consumption. To resolve these problems, we propose a fault‐tolerant distributed ACO‐based routing (DACOR) protocol for mitigating the hot spot problem in fog‐enabled WSN architecture. To improve the performance of the network, we propose a multiple fog nodes (FNs) and unequal clustering‐based network model. The proposed model is energy efficient as it avoids repetitive clustering and affects CHs to FNs based on distance. Also, unlike the existing works which use either single FN/sink‐based unequal clustering or multiple FNs/sinks to mitigate hot spot problem, we propose to distribute unequal clustering to multiple FNs (partitions). Additionally, we formulate a different rule to calculate the cluster radius based on significant parameters ensuring energy efficiency and balancing. To route data from source to destination, we devise a new probabilistic formula which considers not only energy efficiency but also reliability. The performance of the proposed DACOR protocol has been investigated under different scenarios through simulations. The results show that the proposed DACOR protocol outperforms the existing protocols in terms of various main metrics.
Nitrogen addition, rather than altered precipitation, stimulates nitrous oxide emissions in an alpine steppe
Authors: Yang Yang , Xiao Yuanming , Li Changbin , Wang Bo , Gao Yongheng , Zhou Guoying 
Published: October 2021

AbstractAnthropogenic‐driven global change, including changes in atmospheric nitrogen (N) deposition and precipitation patterns, is dramatically altering N cycling in soil. How long‐term N deposition, precipitation changes, and their interaction influence nitrous oxide (N2O) emissions remains unknown, especially in the alpine steppes of the Qinghai–Tibetan Plateau (QTP). To fill this knowledge gap, a platform of N addition (10 g m−2 year−1) and altered precipitation (±50% precipitation) experiments was established in an alpine steppe of the QTP in 2013. Long‐term N addition significantly increased N2O emissions. However, neither long‐term alterations in precipitation nor the co‐occurrence of N addition and altered precipitation significantly affected N2O emissions. These unexpected findings indicate that N2O emissions are particularly susceptible to N deposition in the alpine steppes. Our results further indicated that both biotic and abiotic properties had significant effects on N2O emissions. N2O emissions occurred mainly due to nitrification, which was dominated by ammonia‐oxidizing bacteria, rather than ammonia‐oxidizing archaea. Furthermore, the alterations in belowground biomass and soil temperature induced by N addition modulated N2O emissions. Overall, this study provides pivotal insights to aid the prediction of future responses of N2O emissions to long‐term N deposition and precipitation changes in alpine ecosystems. The underlying microbial pathway and key predictors of N2O emissions identified in this study may also be used for future global‐scale model studies.
Heads in the clouds: On the carbon footprint of conference‐seeded publications in the advancement of knowledge
Authors: Seuront Laurent , Nicastro Katy R. , Zardi Gerardo I. 
Published: October 2021

AbstractThe carbon footprint of flying overseas to conferences, meetings, and workshops to share and build knowledge has been increasingly questioned over the last two decades, especially in environmental and climate sciences, due to the related colossal carbon emissions. Here, we infer the value of scientific meetings through the number of publications produced either directly or indirectly after attending a scientific conference, symposium, or workshop (i.e., the conference‐related production) and the number of publications produced per meeting (i.e., the conference‐related productivity) as proxies for the academic value of these meetings, and relate them to both the number of meetings attended and the related carbon emissions. We show that conference‐related production and productivity, respectively, increase and decay with the number of meetings attended, and noticeably that the less productive people exhibit the largest carbon footprint. Taken together, our results imply that a twofold decrease in the carbon footprint of a given scientist would result in a twofold increase in productivity through a fivefold decrease in the number of meeting attended. In light of these figures, we call for both the implementation of objective and quantitative criteria related to the optimum number of conferences to attend in an effort to maximize scientific productivity while minimizing the related carbon footprint, and the development of a rationale to minimize the carbon emission related to scientific activities.
The effect of deep feature concatenation in the classification problem: An approach on COVID‐19 disease detection
Authors: Cengil Emine , Çınar Ahmet 
Published: October 2021

AbstractIn image classification applications, the most important thing is to obtain useful features. Convolutional neural networks automatically learn the extracted features during training. The classification process is carried out with the obtained features. Therefore, obtaining successful features is critical to achieving high classification success. This article focuses on providing effective features to enhance classification performance. For this purpose, the success of the process of concatenating features in classification is taken as basis. At first, the features acquired by feature transfer method are extracted from AlexNet, Xception, NASNETLarge, and EfficientNet‐B0 architectures, which are known to be successful in classification problems. Concatenating the features results in the creation of a new feature set. The method is completed by subjecting the features to various classification algorithms. The proposed pipeline is applied to the three datasets: “COVID‐19 Image Dataset,” “COVID‐19 Pneumonia Normal Chest X‐ray (PA) Dataset,” and “COVID‐19 Radiography Database” for COVID‐19 disease detection. The whole datasets contain three classes (normal, COVID, and pneumonia). The best classification accuracies for the three datasets are 98.8%, 95.9%, and 99.6%, respectively. Performance metrics are given such as: sensitivity, precision, specificity, and F1‐score values, as well. Contribution of paper is as follows: COVID‐19 disease is similar to other lung infections. This situation makes diagnosis difficult. Furthermore, the virus's rapid spread necessitates the need to detect cases as soon as possible. There has been an increased curiosity in computer‐aided deep learning models to provide the requirements. The use of the proposed method will be beneficial as it provides high accuracy.
Hemostatic Electrospun Nanocomposite Containing Poly(lactic acid)/Halloysite Nanotube Functionalized by Poly(amidoamine) Dendrimer for Wound Healing Application: In Vitro and In Vivo Assays
Authors: Delyanee Mahsa , Solouk Atefeh , Akbari Somaye , Daliri Morteza 
Published: October 2021

AbstractThe main challenge in treating injuries is excessive bleeding whereas intervention is required if the body's hemostatic systems fail to control the bleeding. Herein, a novel nanocomposite consisting of poly(lactic acid) (PLA) and poly(amidoamine) (PAMAM) dendrimer functionalized halloysite nanotube (HNT) with a highly porous structure via electrospinning is developed. HNT is functionalized by PAMAM via divergent synthetic routes from zero to third‐generation numbers. The effect of different percentages and generation numbers of PAMAM dendrimer (G1, G2, and G3) functionalized HNT on PLA is studied using physicochemical nanocomposite characteristics. These resultant nanocomposites provide a nanofibrous structure with appropriate physicochemical characteristics such as mechanical properties, surface wettability, and water permeability. The hemostatic assays indicate that nanocomposite with PAMAM G3 functionalized HNT have the quickest blood clotting time due to the abundant amino functional group. Furthermore, the nanocomposites with 10 wt% of nanoparticles significantly promote cellular behavior in vitro. The in vivo study demonstrates that PLA/PAMAM G3 functionalized HNT promotes angiogenesis, collagen deposition, and re‐epithelialization in the wound sites of the rat model, as well as inhibiting inflammatory response. The findings indicate that nanofibrous structure and the presence of dendrimer functionalized HNT have a synergetic effect on the enhanced nanocomposite wound healing performance.
The current status of preclinical proton FLASH radiation and future directions
Authors: Diffenderfer Eric S. , Sørensen Brita S. , Mazal Alejandro , Carlson David J. 
Published: October 2021

AbstractWe review the current status of proton FLASH experimental systems, including preclinical physical and biological results. Technological limitations on preclinical investigation of FLASH biological mechanisms and determination of clinically relevant parameters are discussed. A review of the biological data reveals no reproduced proton FLASH effect in vitro and a significant in vivo FLASH sparing effect of normal tissue toxicity observed with multiple proton FLASH irradiation systems. Importantly, multiple studies suggest little or no difference in tumor growth delay for proton FLASH when compared to conventional dose rate proton radiation. A discussion follows on future areas of development with a focus on the determination of the optimal parameters for maximizing the therapeutic ratio between tumor and normal tissue response and ultimately clinical translation of proton FLASH radiation.
Analytic hierarchy process‐technique for order preference by similarity to ideal solution: A multi criteria decision‐making technique to select the best dental restorative composite materials
Authors: Yadav Ramkumar 
Published: October 2021

AbstractThe purpose of this paper is to investigate the optimal selection of a variety of dental composites using a hybrid analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) technique as a multi criteria decision‐making (MCDM) technique under a set of conflict Performance Defining Criteria (PDCs). The resinous matrix of the dental composite was fabricated by Bis‐GMA (51 wt%), TEGDMA (48 wt%), CQ (0.2 wt%), and EDMAB (0.8 wt%). Five dental composite compositions were created utilizing hybrid nTiO2‐Al2O3 particles with varying nTiO2 content (0, 2, 4, 6, 8 wt%). According to AHP, the following PDCs are listed in order: Cost (PDC‐13) > Vickers hardness (PDC‐4) > compressive strength (PDC‐1) > depth of cure (PDC‐5) > flexural modulus (PDC‐3) > flexural strength (PDC‐2) > degree of conversion (PDC‐11) > material stability (PDC‐12) > void content (PDC‐8) > water sorption (PDC‐9) > Density (PDC‐7) (PDC‐6). According to TOPSIS, the following are the rankings: nT2 > nT0 > nT4 > nT6 > nT8. The experimental results are connected to the ranking of several sets of dental formulations. As a result, the AHP‐TOPSIS method is one of the finest MCDM strategies for ranking under diverse PDCs.
Recent advances in intermixed phase of organic solar cells: Characterization, regulating strategies and device applications
Authors: Liang Qiuju , Hu Zhangbo , Yao Jianhong , Yin Yukai , Wei Puxin , Chen Zhikang , Li Wangchang , Liu Jiangang 
Published: October 2021

AbstractOrganic solar cells (OSCs) have unique advantages of low‐cost solution processing, light weight, flexibility, and semitransparency, which is a promising photovoltaic technology. The intermixed phase plays a key role in determining the power conversion efficiencies (PCE) of OSCs. The intermixed phase is an amorphous region, where the donor and acceptor mix at the molecular level. Great efforts have been devoted to optimize the content and the composition of the intermixed phase. This perspective focuses on the functions of intermixed phase and elaborates the relationship between intermixed phase behavior and photophysical process, in particular, the exciton dissociation and charge transport. Then the characterization methods, including quantitative and qualitative characterizations, for the content and composition of intermixed phases are introduced. Meanwhile, this review also introduces the strategies to control the intermixed phase behavior, such as adjusting the miscibility between donor and acceptor, changing the ratio of donor to acceptor, regulating the crystallinity and so on. Moreover, representative examples are given and discussed to understand the key parameters on tuning the intermixed phase behavior. Finally, a future controlling and development of intermixed phase behavior is briefly outlooked, which may help to achieve high PCE of OSCs.
Spiropyran‐Appended Cucurbit[6]uril Enabling Direct Generation of 2D Materials inside Living Cells
Authors: Hou Delong , Pu Liping , Zhou Shuai , Wang Rui , Xu Yong , Zhang Wenle , Wang Zhonghui , Zeng Qi , Xu Zhou , Fan Haojun , Chen Yi 
Published: October 2021

AbstractThe unique structural advantage and physicochemical properties render some 2D materials emerging platforms for intracellular bioimaging, biosensing, or disease theranostics. Despite recent advances in this field, one major challenge lies in bypassing the endocytic uptake barrier to allow internalization of very large 2D materials that have longer retention time in cells, and hence greater potency as intracellular functional platforms than small, endocytosable counterparts. Here, an engineered cucurbit[6]uril carrying at its periphery multiple spiropyran pendants that readily translocates into cytosol, and then polymerizes laterally and non‐covalently in a controlled manner, enabling direct generation of 2D materials inside living cells, is reported. The resultant 2D materials are single‐monomer‐thick and can in situ grow up to 0.8–1.2 µm in lateral size, experimentally proved too large to be endocytosed from outside the cells even after surface engineered with biorecognition entities. A Förster resonance energy transfer assay is further devised for real‐time visualization of the polymerization dynamics in vivo, clearly demonstrating the rationale in this study. With the otherwise non‐endocytosable large 2D materials gaining access to cytosol, potent intracellular signaling or theranostic platform that surpasses the intrinsic performance limit of conventional small counterparts are in sight.
Emerging Single‐Photon Detectors Based on Low‐Dimensional Materials
Authors: Wang Hailu , Guo Jiaxiang , Miao Jinshui , Luo Wenjin , Gu Yue , Xie Runzhang , Wang Fang , Zhang Lili , Wang Peng , Hu Weida 
Published: October 2021

AbstractSingle‐photon detectors (SPDs) that can sense individual photons are the most sensitive instruments for photodetection. Established SPDs such as conventional silicon or III–V compound semiconductor avalanche diodes and photomultiplier tubes have been used in a wide range of time‐correlated photon‐counting applications, including quantum information technologies, in vivo biomedical imaging, time‐of‐flight 3D scanners, and deep‐space optical communications. However, further development of these fields requires more sophisticated detectors with high detection efficiency, fast response, and photon‐number‐resolving ability, etc. Thereby, significant efforts have been made to improve the performance of conventional SPDs and to develop new photon‐counting technologies. In this review, the working mechanisms and key performance metrics of conventional SPDs are first summarized. Then emerging photon‐counting detectors (in the visible to infrared range) based on 0D quantum dots, 1D quantum nanowires, and 2D layered materials are discussed. These low‐dimensional materials exhibit many exotic properties due to the quantum confinement effect. And photodetectors built from these nD‐materials (n = 0, 1, 2) can potentially be used for ultra‐weak light detection. By reviewing the status and discussing the challenges faced by SPDs, this review aims to provide future perspectives on the research directions of emerging photon‐counting technologies.
Are the CKM anomalies induced by vector-like quarks? Limits from flavor changing and Standard Model precision tests
Authors: Belfatto B. , Berezhiani Z. 
Published: October 2021

Abstract
Recent high precision determinations of Vus and Vud indicate towards anomalies in the first row of the CKM matrix. Namely, determination of Vud from beta decays and of Vus from kaon decays imply a violation of first row unitarity at about 3σ level. Moreover, there is tension between determinations of Vus obtained from leptonic Kμ2 and semileptonic Kℓ3 kaon decays. These discrepancies can be explained if there exist extra vector-like quarks at the TeV scale, which have large enough mixings with the lighter quarks. In particular, extra vector-like weak singlets quarks can be thought as a solution to the CKM unitarity problem and an extra vector-like weak doublet can in principle resolve all tensions. The implications of this kind of mixings are examined against the flavour changing phenomena and SM precision tests. We consider separately the effects of an extra down-type isosinglet, up-type isosinglet and an isodoublet containing extra quarks of both up and down type, and determine available parameter spaces for each case. We find that the experimental constraints on flavor changing phenomena become more stringent with larger masses, so that the extra species should have masses no more than few TeV. Moreover, only one type of extra multiplet cannot entirely explain all the discrepancies, and some their combination is required, e.g. two species of isodoublet, or one isodoublet and one (up or down type) isosinglet. We show that these scenarios are testable with future experiments. Namely, if extra vector-like quarks are responsible for CKM anomalies, then at least one of them should be found at scale of few TeV, and anomalous weak isospin violating Z-boson couplings with light quarks should be detected if the experimental precision on Z hadronic decay rate is improved by a factor of 2 or so.
Iterative Metal Artifact Reduction (iMAR) of the Non-adhesive Liquid Embolic Agent Onyx in Computed Tomography
Authors: Schmitt Niclas , Weyland Charlotte S. , Wucherpfennig Lena , Herweh Christian , Bendszus Martin , Möhlenbruch Markus A. , Vollherbst Dominik F. 
Published: October 2021

Abstract
Background
A drawback of Onyx, one of the most used embolic agents for endovascular embolization of intracranial arteriovenous malformations (AVM), is the generation of imaging artifacts (IA) in computed tomography (CT). Since these artifacts can represent an obstacle for the detection of periprocedural bleeding, this study investigated the effect of artifact reduction by an iterative metal artifact reduction (iMAR) software in CT in a brain phantom.

Methods
Two different in vitro models with two-dimensional tube and three-dimensional AVM-like configuration were filled with Onyx 18. The models were inserted into a brain imaging phantom and images with (n = 5) and without (n = 10) an experimental hemorrhage adjacent were acquired. Afterwards, the iMAR algorithm was applied for artifact reduction. The IAs of the original and the post-processed images were graded quantitatively and qualitatively. Moreover, qualitative definition of the experimental hemorrhage was investigated.

Results
Comparing the IAs of the original and the post-processed CT images, quantitative and qualitative analysis showed a lower degree of IAs in the post-processed images, i.e. quantitative analysis: 2D tube model: 23.92 ± 8.02 Hounsfield units (HU; no iMAR; mean ± standard deviation) vs. 5.93 ± 0.43 HU (with iMAR; p < 0.001); qualitative analysis: 3D AVM model: 4.93 ± 0.18 vs. 3.40 ± 0.48 (p < 0.001). Furthermore, definition of the experimental hemorrhage was better in the post-processed images of both in vitro models (2D tube model: p = 0.004; 3D AVM model: p = 0.002).

Conclusion
The iMAR algorithm can significantly reduce the IAs evoked by Onyx 18 in CT. Applying iMAR could thus improve the accuracy of postprocedural CT imaging after embolization with Onyx in clinical practice.
Bayesian tests of symmetry for the generalized Von Mises distribution
Authors: Salvador Sara , Gatto Riccardo 
Published: October 2021

AbstractBayesian tests on the symmetry of the generalized von Mises model for planar directions (Gatto and Jammalamadaka in Stat Methodol 4(3):341–353, 2007) are introduced. The generalized von Mises distribution is a flexible model that can be axially symmetric or asymmetric, unimodal or bimodal. A characterization of axial symmetry is provided and taken as null hypothesis for one of the proposed Bayesian tests. The Bayesian tests are obtained by the technique of probability perturbation. The prior probability measure is perturbed so to give a positive prior probability to the null hypothesis, which would be null otherwise. This allows for the derivation of simple computational formulae for the Bayes factors. Numerical results reveal that, whenever the simulation scheme of the samples supports the null hypothesis, the null posterior probabilities appear systematically larger than their prior counterpart.