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The omicron BA.1 bivalent booster is used globally. Previous open-label studies of the omicron BA.1 (Moderna mRNA-1273.214) booster showed superior neutralising antibody responses against omicron BA.1 and other variants compared with the original mRNA-1273 booster. We aimed to compare the safety and immunogenicity of omicron BA.1 monovalent and bivalent boosters with the original mRNA-1273 vaccine in a large, randomised controlled trial. In this large, randomised, observer-blind, active-controlled, phase 3 trial in the UK (28 hospital and vaccination clinic sites), individuals aged 16 years or older who had previously received two injections of any authorised or approved COVID-19 vaccine, with or without an mRNA vaccine booster (third dose), were randomly allocated (1:1) using interactive response technology to receive 50 μg omicron BA.1 monovalent or bivalent vaccines or 50 μg mRNA-1273 administered as boosters via deltoid intramuscular injection. The primary outcomes were safety and immunogenicity at day 29, including prespecified non-inferiority and superiority of booster immune responses, based on the neutralising antibody geometric mean concentration (GMC) ratios of the monovalent and bivalent boosters compared with mRNA-1273. Safety was assessed in all participants who received first or second boosters, and primary immunogenicity outcomes were assessed in all participants who received the planned booster dose, had pre-booster and day 29 antibody data, had no major protocol deviations, and who were SARS-CoV-2-negative. The study is registered with EudraCT (2022-000063-51) and ClinicalTrials.gov (NCT05249829) and is ongoing. Between Feb 16 and March 24, 2022, 724 participants were randomly allocated to receive omicron BA.1 monovalent (n=366) or mRNA-1273 (n=357), and between April 2 and June 17, 2022, 2824 participants were randomly allocated to receive omicron BA.1 bivalent (n=1418) or mRNA-1273 (n=1395) vaccines as second boosters. Median durations (months) between the most recent COVID-19 vaccine and study boosters were similar for omicron BA.1 monovalent (4·0 months [IQR 3·6–4·7]) and mRNA-1273 (4·1 [3·5–4·7]), and for the omicron BA.1 bivalent (5·5 [4·8–6·2]) and mRNA-1273 (5·4 [4·8–6·2]) boosters. The omicron BA.1 monovalent and bivalent boosters elicited superior neutralising GMCs against the omicron BA.1 variant compared with mRNA-1273, with GMC ratios of 1·68 (99% CI 1·45−1·95) and 1·53 (1·41−1·67) at day 29 post-booster doses in participants without previous SARS-CoV-2 infection. Both boosters induced non-inferior ancestral SARS-CoV-2 (Asp614Gly) immune responses with GMCs that were similar for the bivalent (2987·2 [95% CI 2814·9–3169·9]) versus mRNA-1273 (2911·3 [2750·9–3081·0]) and lower for the monovalent (2699·7 [2431·3–2997·7] vs 3020·6 [2776·5–3286·2]) boosters, with respective GMC ratios of 1·05 (99% CI 0·96–1·15) and 0·82 (95% CI 0·74–0·91). Results were comparable regardless of previous SARS-CoV-2 infection status. Incidences of solicited adverse reactions with the omicron BA.1 monovalent (335 [91·3%] of 367 participants) and omicron BA.1 bivalent (1285 [90·4%] of 1421 participants) boosters were similar to those observed previously for mRNA-1273, with no new safety concerns identified and no occurrences of fatal adverse events. Omicron-containing booster vaccines generated superior immunogenicity against omicron BA.1 and comparable immunogenicity against the original strain with no new safety concerns. It remains important to continuously monitor the immune responses and real-world vaccine effectiveness as divergent SARS-CoV-2 variants emerge. Moderna. [ABSTRACT FROM AUTHOR]

• CO adsorption induces agglomeration of isolated Pd atoms. • The rate of H 2 permeation across PdCu decreases with increasing CO concentration. • CO inhibits H 2 transport by adsorbing only on Pd sites and blocking H 2 dissociation. • PdCu is more resistant to CO poisoning than Pd. Pd and PdCu alloy membranes are promising candidates for separating hydrogen from mixed gas streams due in part to their infinite selectivity to hydrogen separation. However, other gases such as CO can inhibit hydrogen transport across Pd-based membranes. In this work, the mechanism by which CO inhibits hydrogen transport across a 25 μm-thick Pd 47 Cu 53 (mol%) membrane is investigated by operando infrared-reflection absorption spectroscopy (IRAS) in the 373–533 K temperature range. In the absence of hydrogen, CO adsorbs on three different sites on the PdCu surface: (1) bridging between contiguous Pd atoms, (2) on top of isolated Pd atoms surrounded by Cu atoms, and (3) on top of oxidized Cu atoms. CO induces agglomeration of isolated Pd atoms on the PdCu surface, which is driven by the higher stability of CO adsorbed on bridging sites between Pd atoms than on isolated Pd atoms surrounded by Cu atoms. The rate of hydrogen permeation across the PdCu alloy membrane decreases with increasing CO concentration in the feed gas, and the poisoning effect of CO is more severe at lower temperatures. CO inhibits hydrogen transport across the membrane by adsorbing only on Pd sites on the PdCu surface and blocking H 2 dissociation on these sites. Due to the weaker interaction of CO with PdCu alloy surfaces than with Pd, the PdCu alloy is more resistant to CO poisoning than pure Pd. [ABSTRACT FROM AUTHOR]

Abstract Scientific teamwork collaboration is an integral element of the scientific process that often leads to significant findings. Systematic analysis of scientific teamwork collaboration continues to influence both the advance in science and knowledge production. This paper presents an overview of Science of Scientific Team Science (SSTS). SSTS explores the behaviors and attributes of teamwork and team-based collaboration specific to scientific teams from the perspective of quantitative analysis, which refers to a branch of science that analyzes and discovers scientific collaboration patterns inter- or extra-team. Aiming at assisting scientific team formation, improving collaboration environment, evaluating team performance, and fostering collaborative behaviors, this survey presents an overview in SSTS. Theoretical background of SSTS at different team development stages has been discussed. In addition, three classifications of SSTS, including interdisciplinary, multidisciplinary, and transdisciplinary research approaches have been investigated. Their associated similarities and differences, challenges and benefits, are also examined. This paper also summarizes web-based tools that enhance one's understanding and opinion of SSTS. Key technologies and open issues are then discussed. The association among scientific collaboration, scientific teamwork, SSTS, and cross-disciplinary research gives rise to critical implications for scholars who wish to employ and invest in those issues. [ABSTRACT FROM AUTHOR]

Abstract Citation prediction of scholarly papers is of great significance in guiding funding allocations, recruitment decisions, and rewards. However, little is known about how citation patterns evolve over time. By exploring the inherent involution property in scholarly paper citation, we introduce the Paper Potential Index (PPI) model based on four factors: inherent quality of scholarly paper, scholarly paper impact decaying over time, early citations, and early citers' impact. In addition, by analyzing factors that drive citation growth, we propose a multi-feature model for impact prediction. Experimental results demonstrate that the two models improve the accuracy in predicting scholarly paper citations. Compared to the multi-feature model, the PPI model yields superior predictive performance in terms of range-normalized RMSE. The PPI model better interprets the changes in citation, without the need to adjust parameters. Compared to the PPI model, the multi-feature model performs better prediction in terms of Mean Absolute Percentage Error and Accuracy; however, their predictive performance is more dependent on the parameter adjustment. [ABSTRACT FROM AUTHOR]

Transmembrane hydrogen flux of a novel 1 μm palladium membrane, supported by a nickel microstructured support grid, is characterized when exposed to carbon dioxide, carbon monoxide and water vapor over a range of temperature from 235° to 320 °C. At all temperatures, the palladium membrane is found to be primarily hydrogen transport rate limited due to surface reactions, not atomic hydrogen solution-diffusion though the bulk palladium membrane. Hydrogen flux decreases rapidly as contaminate gas concentration increases, before reaching a temperature dependent steady state, due to nearly complete surface coverage of the palladium by adsorbed contaminate. Carbon monoxide and carbon dioxide have the largest impact, while water vapor has a lesser impact. The likely source of deactivation is the blocking of surface active sites by adsorbed contaminate molecules. Independent of contaminate gas effects, this study reveals a permanent hydrogen flux decrease due to diffusion of nickel from the microstructured support grid into the palladium membrane at temperatures above 360 °C. [ABSTRACT FROM AUTHOR]

Objective To evaluate prognostic factors of patients with chordoma through a population-based analysis. Methods Surveillance, Epidemiology, and End Results (SEER) database was queried for patients with chordoma from 1973 to 2013. Kaplan-Meier univariate analysis and Cox regression multivariate analysis were performed to examine prognostic factors in overall survival (OS) and disease-specific survival (DSS). Results One thousand five hundred ninety-eight patients with chordoma are identified. Kaplan-Meier analysis showed that OS and DSS were 61% and 71% at 5 years and 41% and 57% at 10 years. Multivariate Cox regression analysis demonstrated that independent predictors of OS and DSS are age at diagnosis (hazard ratio [HR]= 2.80 [95% confidence interval {CI}, 2.12–3.70], P < 0.001; HR = 1.60 [95% CI, 1.18–2.16], P = 0.002), surgical treatment (HR = 0.62 [95% CI, 0.52–0.73], P < 0.001; HR = 0.64 [95% CI, 0.52–0.79], P < 0.001), radiation therapy (HR = 1.23 [95% CI, 1.07–1.42], P = 0.004; HR = 1.29 [95% CI, 1.09–1.54], P = 0.004), tumor size (HR = 1.53 [95% CI, 1.32–1.78], P < 0.001; HR = 1.62 [95% CI, 1.35–1.94], P < 0.001) and distant metastasis (HR = 3.40 [95% CI, 2.45–4.71], P < 0.001; HR = 3.77 [95% CI, 2.61–5.45], P < 0.001). Conclusion We report the largest study to date to evaluate prognostic factors of patients with chordoma. Multivariate analysis demonstrated that older age, greater tumor size, and distant metastasis were correlated with decreased survival, whereas surgical resection was correlated with increased survival. Patients receiving radiation therapy also showed decreased survival, likely an indication of the patients' advanced stage of disease, making them poor surgical candidates. [ABSTRACT FROM AUTHOR]

• A fine-grained collaborative team recognition method is proposed. • Collaborative teams are formulated with "core+extension" structure. • The underlying relationship between team output and collaboration intensity is found. • It is found that core members have broad collaboration relationships and fixed collaboration patterns. Scientific collaboration is a significant behavior in knowledge creation and idea exchange. To tackle large and complex research questions, a trend of team formation has been observed in recent decades. In this study, we focus on recognizing collaborative teams and exploring inner patterns using scholarly big graph data. We propose a collaborative team recognition (CORE) model with a "core + extension" team structure to recognize collaborative teams in large academic networks. In CORE, we combine an effective evaluation index called the collaboration intensity index with a series of structural features to recognize collaborative teams in which members are in close collaboration relationships. Then, CORE is used to guide the core team members to their extension members. CORE can also serve as the foundation for team-based research. The simulation results indicate that CORE reveals inner patterns of scientific collaboration: senior scholars have broad collaborative relationships and fixed collaboration patterns, which are the underlying mechanisms of team assembly. The experimental results demonstrate that CORE is promising compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper provides a review of progress in catalytic conversion of JP-8 fuel and its surrogates made over the last decade. The effect of different types of catalyst and support materials, as well as different preparation methods, is discussed in detail. The derivation of kinetic models for computational studies is also examined. Particular attention is given to the development of sulfur tolerant catalysts and the mechanisms by which catalyst poisoning occurs, as this is an important obstacle to overcome for systems using sulfur-laden fuel. Suggestions for further research are offered. [ABSTRACT FROM AUTHOR]

Oxidation behavior of dodecane and two mixtures of dodecane and m-xylene (90/10 wt.% and 80/20 wt.%) over an Rh catalyst in a meso-scale heat recirculating combustor was examined to isolate the effect of aromatic content on performance. The fuel conversion, product selectivities, and reaction kinetics were calculated, and the global combustion behavior observed. The results showed that increasing the amount of m-xylene in the fuel increased the fuel conversion from 85% (pure dodecane) to 92% (90/10) and further to 98% (80/20). The presence of xylene also significantly increased CO 2 /H 2 O selectivity and decreased CO/H 2 selectivity. Global activation energy increased linearly with increase in xylene content, supporting that addition of aromatic species to fuel lowers the overall reactivity. The non-catalytic reaction was also simulated using Chemkin software to determine the effect of the Rh catalyst on the combustor performance and to analyze the difference in chemical mechanisms. The results revealed that the catalyst promotes total oxidation over partial oxidation, and lowers the global activation energy by up to 70%. [ABSTRACT FROM AUTHOR]

The results obtained from the combustion behavior of propane over platinum and rhodium catalysts in a meso-scale heat recirculating combustor are presented. The extinction limits, conversion, product selectivity/yield, and activation energy using the two catalysts were compared in an effort to predict their performance using a liquid fuel. The extinction limits were also compared to those of non-catalytic combustion in the same combustor. The results showed that the use of a catalyst greatly expanded the range of stable operating conditions, in terms of both extinction limits and flow rates supported. The Rh catalyst was found to exhibit a higher propane conversion rate, reaching a maximum of 90.4% at stoichiometric conditions (as compared to only 61.4% offered by the Pt catalyst under lean conditions), but the Pt catalyst had superior CO 2 selectivity for most of the examined conditions, indicating more of the heat released being used for product formation as opposed to being lost to the environment. However, despite having a higher rate of heat loss, the combustion with the Rh catalyst produced an overall higher amount of enthalpy than the Pt due to its superior fuel conversion. The Pt catalyst also had a significantly smaller activation energy (13.8 kJ/mol) than the Rh catalyst (74.7 kJ/mol), except at equivalence ratios richer than Φ = 1.75 (corresponding to catalyst temperatures below 500 °C), where it abruptly changed to 211.4 kJ/mol, signifying a transition from diffusion-limited reactions to kinetically limited reactions at this point. The results reveal that Rh would be a more suitable catalyst for use in liquid-fueled meso-scale combustors, as fuel conversion has been found to be a limiting factor for combustion stability in these systems, and as its higher output energy allows for greater flexibility of use. [ABSTRACT FROM AUTHOR]

Emerging evidence suggests that chronic rhinosinusitis with nasal polyps (CRSwNP) is a highly heterogeneous disease with disparate inflammatory characteristics between different racial groups and geographies. Currently, little is known about possible underlying distinguishing factors between these inflammatory differences. Our aim was to interrogate differences in CRSwNP disease between White/non-Asian patients and Japanese patients by using whole transcriptome and single-cell RNA gene expression profiling of nasal polyps (NPs). We performed whole transcriptome RNA sequencing with endotype stratification of NPs from 8 White patients (residing in the United States) and 9 Japanese patients (residing in Japan). Reproducibility was confirmed by quantitative PCR in an independent validation set of 46 White and 31 Japanese patients. Single-cell RNA sequencing (scRNAseq) was used to stratify key cell types for contributory transcriptional signatures. Unsupervised clustering analysis identified 2 major endotypes that were present within both cohorts of patients with NPs and had previously been reported at the cytokine level: (1) type 2 endotype and (2) non–type 2 endotype. Importantly, there was a statistically significant difference in the proportion of these endotypes between these geographically distinct subgroups with NPs (P =.03). Droplet-based single-cell RNA sequencing further identified prominent type 2 inflammatory transcript expression: C-C motif chemokine ligand 13 (CCL13) and CCL18 in M2 macrophages, as well as cystatin SN (CST1) and CCL26 in basal, suprabasal, and secretory epithelial cells. NPs from both racial groups harbor the same 2 major endotypes, which we have determined to be present in differing ratios between each cohort with CRSwNP disease. Distinct inflammatory and epithelial cells contribute to the type 2 inflammatory profiles observed. [ABSTRACT FROM AUTHOR]

Emerging evidence points toward an intricate relationship between the pandemic of coronavirus disease 2019 (COVID-19) and diabetes. While preexisting diabetes is associated with severe COVID-19, it is unclear whether COVID-19 severity is a cause or consequence of diabetes. To mechanistically link COVID-19 to diabetes, we tested whether insulin-producing pancreatic β cells can be infected by SARS-CoV-2 and cause β cell depletion. We found that the SARS-CoV-2 receptor, ACE2, and related entry factors (TMPRSS2, NRP1, and TRFC) are expressed in β cells, with selectively high expression of NRP1. We discovered that SARS-CoV-2 infects human pancreatic β cells in patients who succumbed to COVID-19 and selectively infects human islet β cells in vitro. We demonstrated that SARS-CoV-2 infection attenuates pancreatic insulin levels and secretion and induces β cell apoptosis, each rescued by NRP1 inhibition. Phosphoproteomic pathway analysis of infected islets indicates apoptotic β cell signaling, similar to that observed in type 1 diabetes (T1D). In summary, our study shows SARS-CoV-2 can directly induce β cell killing. [Display omitted] • SARS-CoV-2 infects β cells in COVID-19 patients and human islets in vitro • SARS-CoV-2 infection causes β cell death and reduced GSIS in vitro • Phosphoproteomics shows SARS-CoV-2 spike protein and virus induce apoptotic kinases • High neuropilin-1 levels support β cell selectivity, and inhibitors block infection Diabetic patients are at risk for severe COVID-19, but the virus may further damage insulin-secreting β cells. Wu et al. found that patient β cells are virally infected and the highly expressed neuropilin-1 receptor is critical for viral entry, causing cell death and reduced insulin secretion, exacerbating diabetes in patients. [ABSTRACT FROM AUTHOR]

Data Mining and Information Retrieval is an emerging interdisciplinary discipline dealing with Information Retrieval and Data Mining techniques. It has undergone rapid development with the advances in mathematics, statistics, information science, and computer science. In this paper, we present an empirical analysis of publication metadata obtained from 6 top-tier journals and 9 conferences for the first 16 years of the 21st Century, and evaluate the dynamic characteristics of Data Mining and Information Retrieval. We find a steady growth both in terms of productivity and impact, evidenced by the unabated number of publications/citations over the period of study. We note that the modality for co-operation in this field is changing from independent to collaborative. Furthermore, according to the citation pattern, the field is becoming open-minded as illustrated by a gradual decline of self-citation rates, which was dropped to 10% in 2015, nearly three times lower than what it was in 2000. Finally, we explore the inner structure relying on the topics evolution from the aspects of popular keywords/topics identification and evolution. Overall, this study provides insights of Data Mining and Information Retrieval behind its demonstrated growth in the recent past, with the ultimate goal of revealing its potential of driving scientific innovation in the future. [ABSTRACT FROM AUTHOR]

Science of science has become a popular topic that attracts great attentions from the research community. The development of data analytics technologies and the readily available scholarly data enable the exploration of data-driven prediction, which plays a pivotal role in finding the trend of scientific impact. In this paper, we analyse methods and applications in data-driven prediction in the science of science, and discuss their significance. First, we introduce the background and review the current state of the science of science. Second, we review data-driven prediction based on paper citation count, and investigate research issues in this area. Then, we discuss methods to predict scholar impact, and we analyse different approaches to promote the scholarly collaboration in the collaboration network. This paper also discusses open issues and existing challenges, and suggests potential research directions. [ABSTRACT FROM AUTHOR]

In this work, a boundary layer fluid dynamics model is used to numerically investigate the heterogeneous/homogeneous combustion of n -dodecane in a platinum coated channel. An elementary lumped surface chemistry model is used to describe the oxidation of n -dodecane and its intermediates on Pt. A parallel plate microcombustor with Pt-coated walls was designed based on findings from the model. Experimental analysis of the n -dodecane/air fueled parallel plate microcombustor with Pt-coated walls shows that the combustor is capable of complete oxidation while operating at high temperatures (>1100 K) relevant to next generation thermal-to-electric power conversion. Chemiluminescence imaging in the combustor suggests that a weak flame exists, which is capable of driving oxidation to completion while avoiding extreme temperatures. This reactor shows promise as a heat source for high temperature compact thermal-to-electric power conversion. [ABSTRACT FROM AUTHOR]

Many challenges persist with traditional self-sensing cementitious composites, such as selecting conductive fillers, determining optimal aspect ratios, controlling dosage, achieving filler dispersion, designing practical electrodes, and overcoming fabrication difficulties. Therefore, this paper proposes a novel self-sensing technique for cementitious composites by incorporating a 2-dimensional (2-D) carbon-fibre textile network to address these challenges. Additionally, instead of using the entire composite volume as the sensor, an alternative approach is explored, which involves utilising the interlaminar interface by incorporating the 2-D carbon-fibre textile network. This approach provides an integrated self-sensing system, including electrical leads and conductive pathways, which can be tailored based on the design requirements. The paper introduces fundamental concepts and measurement circuit design, followed by a comprehensive study covering measurement techniques, electromechanical properties, and microstructural analysis. Furthermore, it discusses the impact of ambient conditions, such as temperature and relative humidity, on the measurements. Experimental results demonstrate a remarkable maximum fractional change in contact resistivity, reaching up to 70%. The reversibility during cyclic compression is excellent, with a maximum negative gauge factor of − 2500. These findings represent a significant step toward achieving a practical and simplified method for manufacturing self-sensing cementitious composites and open avenues for self-sensing, sustainable textile-reinforced concrete structures (TRC). [Display omitted] • A novel self-sensing system for cementitious composites has been introduced. • The system can be considered as an integrated solution, encompassing the essential components required for self-sensing. • A simplified equivalent circuit model and an equation to calculate the overall contact resistivity have been proposed. • A remarkable maximum fractional change in the overall contact resistivity, reaching up to 70%, has been achieved. • The reversibility during cyclic compression has been excellent, with a maximum negative gauge factor of − 2500. [ABSTRACT FROM AUTHOR]

• Characteristics of the coke deposits on lamellar metal (Ga or Zn)-modified zeolites were studied. • Mesoporous lamellar structure decreases the coke formation by facilitating the external coke deposition. • Metal modification decreases the coke formation by changing the acidity of zeolite. • Highest fraction of light coke is obtained for Zn-modified lamellar zeolite. • Both zeolite structure and metal-modified acidity affect the coke formation. The effects of meso-/microporous structure and metal-additive (Ga or Zn) of lamellar MFI catalysts on the characteristics of coke deposits during ethylene-to-aromatic liquids conversion were investigated. The nature, composition, and location of coke deposits in spent lamellar catalysts were analyzed and compared to those on the microporous MFI counterparts, using FTIR, UV–Vis, GC–MS, and argon adsorption-desorption. The total amount of coke and the changes in coke nature during catalyst regeneration were studied by MS/FTIR combined with temperature programmed oxidation. The lamellar meso-/microporous structure of MFI reduces the coke quantity and the heavy coke fractions. The coke preferentially deposites on external surface of lamellar zeolite due to the lower diffusion limitation for bulky coke precursors. Metal-additive changes the catalyst acidity and decreases the coke formation rate, especially when zinc is used. Therefore, the coke formation on zeolite can be tuned by modulating the textural and acidity properties of the metal-modified catalyst. [ABSTRACT FROM AUTHOR]

Volcano curves for hydrocarbon total oxidation. • The use of the volcano curve to predict catalysts for an entire homologous series is exploited. • Catalytic activity over seven monometallic and three Ag-Pd/Al 2 O 3 bimetallic catalysts is evaluated experimentally. • Hysteresis in activity, with three distinct kinetic regimes, with varying oxygen concentration is found. • Hydrocarbons with two or more carbon atoms are good surrogates of the alkane homologous series whereas methane is not. • The feed composition controls the catalyst oxidation state and potentially impacts the optimal catalyst properties. Volcano curves are typically developed and used for predicting new catalysts for a single reaction and one substrate. The concept of using the volcano curve to predict catalysts for an entire homologous series of reactions has been unexplored. Herein the catalytic activity of seven monometallic catalysts (Pt, Pd, Rh, Ag, Ni, Cu, and Co/Al 2 O 3) and three Ag-Pd/Al 2 O 3 bimetallic catalysts is evaluated in the total oxidation of small alkanes (methane, ethane, propane, and isobutane) in the 280–400 °C temperature range under fuel lean and rich conditions. We show that hysteresis in activity, with three distinct kinetic regimes, is a common phenomenon of alkane oxidation over all catalysts studied when varying the oxygen concentration, and the size of the hysteresis loop depends on the oxophilicity of the catalyst and the reducing ability of the hydrocarbon. Expectedly, the concept of the universality of the volcano curve for a homologous series is valid but only when a suitable surrogate substrate is chosen. Hydrocarbons with two or more carbon atoms can serve as surrogates of the alkane homologous series, whereas methane is not. Interestingly and consistent with the hysteresis, the feed composition controls the catalyst oxidation state and potentially impacts the optimal catalyst descriptors used to determine new catalysts. The predicted 1:3 Ag-Pd catalyst is indeed superior to single metals for the homologous series under fuel lean conditions for ethane and larger alkanes. It is inferior to Pt under fuel rich conditions and better than Pd and Pt for methane rich conditions. A method for qualitative inference of the catalyst structure, based on the volcano curve and the oxidation state of the catalyst, is proposed. [ABSTRACT FROM AUTHOR]

• Multiple particle tracking is challenging in synchrotron X-ray images. • Discriminative appearance and neighbouring topology learning are proposed. • A detection recovery method using multi-frame association is proposed. • Our method achieves tracking errors of 7.03% and association errors of 7.22%. Recent research has introduced a novel method of directly monitoring the effects of potential therapies for Cystic Fibrosis (CF) airway disease by quantifying mucociliary transit (MCT). In this method, micron-sized spherical particles are deposited into rodent airways, and synchrotron X-ray images are obtained to quantify the motion of the particles. However, the accurate tracking of these particles is challenging due to low contrast, image noise, and the presence of overlapping particles. Therefore, this paper proposes a novel method for detecting and tracking circular particles and measuring their dynamics. Accurate particle detection is achieved by applying a convolutional neural network (CNN). For robust multi-object tracking, this paper proposes a confidence model utilizing appearance and neighbouring topology learned by linear discriminant analysis. We also propose a detection recovery method using multi-frame association to restore the missed particles due to overlapping. The proposed method is tested with several different datasets and shows high levels of detection and tracking accuracy. Finally, by offering visual tracking analyses that display merging and splitting events, the proposed method can provide a better understanding of airway MCT behaviour. [ABSTRACT FROM AUTHOR]

• A new DPC algorithm for uneven density datasets is proposed. • A new local density calculation method based on fuzzy neighborhood is designed. • A new allocation strategy based on weighted shared nearest neighbor is proposed. • The new DPC algorithm has excellent clustering accuracy for different types of datasets. Uneven density data refers to data with a certain difference in sample density between clusters. The local density of density peaks clustering algorithm (DPC) does not consider the effect of sample density difference between clusters of uneven density data, which may lead to wrong selection of cluster centers; the algorithm allocation strategy makes it easy to incorrectly allocate samples originally belonging to sparse clusters to dense clusters, which reduces clustering efficiency. In this study, we proposed the density peaks clustering algorithm based on fuzzy and weighted shared neighbor for uneven density datasets (DPC-FWSN). First, a nearest neighbor fuzzy kernel function is obtained by combining K-nearest neighbor and fuzzy neighborhood. Then, local density is redefined by the nearest neighbor fuzzy kernel function. The local density can better characterize the distribution characteristics of the sample by balancing the contribution of sample density in dense and sparse areas, in order to avoid the situation that the sparse cluster does not have a cluster center. Finally, the allocation strategy for weighted shared neighbor similarity is proposed to optimize the sample allocation at the boundary of the sparse cluster. Experiments are performed on IDPC-FA, FKNN-DPC, FNDPC, DPCSA and DPC for uneven density datasets, complex morphologies datasets and real datasets. The clustering results demonstrate that DPC-FWSN effectively handles datasets with uneven density distribution. [ABSTRACT FROM AUTHOR]

Cystic Fibrosis is a genetic disease in which the production of thick sticky mucus compromises the mucociliary transit (MCT) system and causes obstruction of the conducting airways. This results in a cycle of inflammation and infection that dramatically reduces quality of life and causes an early death for most. To directly assess airway health and the effects of potential treatments, synchrotron X-ray imaging techniques have been developed to non-invasively quantify MCT, by visualizing the motion of micron-sized spherical particles deposited into the nasal airways of live mice. Since the level of contrast between the target particles and the background is quite low, and the particles often overlap, most existing methods show a low detection accuracy for the MCT tracking particles in these state-of-the-art PCXI images. This paper proposes a new way to automatically detect the circular shapes of micron-sized particles in these low-contrast X-ray images. The proposed algorithm uses a gradient-directional, sectored ring mask, combined with an edge projection into the ring boundary to identify circular shapes. This new algorithm achieves significantly improved marker particle detection rate, 92.1% precision, 93.9% recall and 92.7% F-measurement, compared to existing methods. It can detect a certain degree of overlapping particles that existing methods struggle to achieve. This algorithm provides automatic MCT particle counting, which significantly reduces the manual labelling process for MCT analysis of living animals. [ABSTRACT FROM AUTHOR]

The synthesis of hierarchical meso-/microporous zeolite materials with spatially controlled morphology, meso-/microporosity, and acidity is an expanding area of research interest for a wide range of applications. Here, we report a one-step dual template synthesis method for integration of 3-dimensional (3D) BEA nanosponge and ultra-thin 2D lamellar MFI into a new type of hierarchical meso-/microporous zeolite composite structures. Specifically, the 2D layered MFI nanosheets were laid over the surface of or interdigitated into the 3D BEA particles in the bulk BEA nanosponge-lamellar MFI (BBLM) zeolite structure, which generated a unique morphology of interconnected micropores and mesopores underneath the ‘skinning’ shell (∼3–10 nm) of MFI nanosheets. The BBLM zeolites have higher mesoporosity than either bare BEA or lamellar MFI zeolites. The micropore size decreases with increasing lamellar MFI component in the composite. The fraction of external acid sites of BBLM zeolite composite, represented by the percentage of active sites accessible to bulky organic base molecules, decreases with increasing MFI component. Additionally, the types of acid sites are diversified in the BBLM composites compared to either bare BEA or lamellar MFI zeolites. The catalysis tests using conversion of benzyl alcohol in mesitylene showed that BBLM zeolites had significant higher activity and durability than single zeolites or their physical mixture. The BBLM zeolite composite provides a good model catalyst with integrated 2D-3D structures and meso-/microporosity for studying a series of important catalytic reactions in hierarchical zeolites. The one-step dual template synthesis method described herein is versatile and facile, which may prove to be a general platform for hierarchical zeolite composite design at the unit-cell scales of zeolites and with potentially broader applicability to other porous materials. [ABSTRACT FROM AUTHOR]

Lead substituted hydroxyapatite (Pb-HAP) has been an active catalyst for oxidative coupling of methane (OCM) reactions. CO 3 2 − substituted HAP (HAP-CO 3 ) has showed enhanced oxide ion conductivity than bare HAP in high temperature solid oxide fuel cells. Substitutions for both cations and anions in HAP structure (Pb-HAP-CO 3 ) are promising to integrate the catalytic property of Pb-HAP and oxide ion conductive property of HAP-CO 3 into one apatite-based ceramic material that can be manufactured into membrane reactors for possessing CH 4 activation and O 2 permeation capabilities for efficient OCM reactions. In this work, the effects of substitutions for both cation (Pb 2+ ) and anion (CO 3 2 − ) in HAP structure on OCM reactions were studied. The composition and physicochemical properties of HAP catalysts were changed by the cation and anion substitutions, respectively, and as consequences, they influenced the catalytic performances of HAP structure in OCM reactions. The selectivity to C 2 (ethylene and ethane) products increased in the order of HAP-CO 3 < HAP < Pb-HAP-CO 3 < Pb-HAP, while Pb-HAP-CO 3 showed the best stability and comparable C 2 yield (under optimized reaction conditions) to Pb-HAP catalyst. Under different reaction temperature and/or CH 4 /O 2 ratio in the OCM reactions, the CH 4 conversion and C 2 or CO x (CO and CO 2 ) selectivity showed a strong dependence on the composition of HAP-based catalysts. The present study forms a basis for understanding of the correlations between the composition, structure, and catalytic performance of HAP and other apatite structured catalysts, which are potential membrane materials for OCM reactions in membrane reactors. [ABSTRACT FROM AUTHOR]

The current work focused on rapid and non-destructive determination of moisture content (MC), free fatty acids (FFA) and peroxide value (PV) in shelled almonds, using reflectance NIR-hyperspectral imaging (HSI). Sample set of 354 and 235 almond kernels were treated to vary the MC, rancidity (FFA and PV), respectively and used for model development. The sample set for each response was divided into calibration and testing set in the ratio of 70:30. Reference MC, FFA and PV were measured using wet analysis methods and their association with the spectral data were modelled using partial least squares regression. Superior models were obtained from the full-spectrum (900–1700 nm) data with R p 2 values of 0.957, 0.970, 0.955 and RMSE P values of 0.41%, 0.108%, 0.453 mEq for MC, FFA and PV, respectively. Competitive adaptive reweighted sampling method was used to select the feature wavelengths for rapid quantification. Multiple linear regression models were developed using the feature wavelengths having good predictability (R p 2 values: 0.941, 0.903, 0.886 and RMSE P values: 0.494%, 0.162%, 0.658 mEq for MC, FFA and PV, respectively). The current findings demonstrated great feasibility in industrial deployment of HSI technique for non-destructive estimation of moisture and rancidity indices in almonds and other nuts. [Display omitted] • Hyperspectral imaging was used to develop non-destructive method for determination of MC, FFA and PV of almond. • Full spectrum PLSR models predicted MC, FFA and PV with R p 2 values of 0.957, 0.970, 0.955, respectively. • CARS-PLS was used to select the feature wavelengths for rapid and accurate quantification. • MLR models with feature wavelengths showed R p 2 values of 0.941, 0.903, 0.886 for MC, FFA and PV, respectively. [ABSTRACT FROM AUTHOR]

The goals of this research were to search for Convolutional Neural Network (CNN) architectures, suitable for an on-device processor with limited computing resources, performing at substantially lower Network Architecture Search (NAS) costs. A new algorithm entitled an Early Exit Population Initialisation (EE-PI) for Evolutionary Algorithm (EA) was developed to achieve both goals. The EE-PI reduces the total number of parameters in the search process by filtering the models with fewer parameters than the maximum threshold. It will look for a new model to replace those models with parameters more than the threshold. Thereby, reducing the number of parameters, memory usage for model storage and processing time while maintaining the same performance or accuracy. The search time was reduced to 0.52 GPU day. This is a huge and significant achievement compared to the NAS of 4 GPU days achieved using NSGA-Net, 3,150 GPU days by the AmoebaNet model, and the 2,000 GPU days by the NASNet model. As well, Early Exit Evolutionary Algorithm networks (EEEA-Nets) yield network architectures with minimal error and computational cost suitable for a given dataset as a class of network algorithms. Using EEEA-Net on CIFAR-10, CIFAR-100, and ImageNet datasets, our experiments showed that EEEA-Net achieved the lowest error rate among state-of-the-art NAS models, with 2.46% for CIFAR-10, 15.02% for CIFAR-100, and 23.8% for ImageNet dataset. Further, we implemented this image recognition architecture for other tasks, such as object detection, semantic segmentation, and keypoint detection tasks, and, in our experiments, EEEA-Net-C2 outperformed MobileNet-V3 on all of these various tasks. (The algorithm code is available at https://github.com/chakkritte/EEEA-Net). [ABSTRACT FROM AUTHOR]

• Catalytic performance of metal (Ga or Zn)-loaded mesoporous lamellar zeolites was evaluated. • Mesoporous lamellar structure is pivotal in determining metal dispersion in zeolite catalyst. • Metal loading changes the acidity of the zeolite catalyst and effect of Zn is more pronounced compared to Ga. • Higher liquid aromatics yield and selectivity for mono-benzene alkylated aromatics was observed while less coke was formed. • Both zeolite structure (mesoporosity) and type of metal dopant affect the catalytic performance of catalyst. The effects of meso-/microporous structure and metal-modification with gallium or zinc on catalytic performances of lamellar MFI zeolites in ethylene conversion reaction to liquid aromatics were investigated. TEM, XRD, Ar adsorption-desorption, UV-Visible spectroscopy, and H 2 -TPR measurements showed that the zeolite structure is a pivotal factor for controlling the type of metal dopant species forming on zeolite, their size, and their distribution. Adding metal dopants to zeolite structure decreased the Brønsted to Lewis (B/L) acid site ratio in the zeolites and improved their catalytic performance. As a result, metal-modified lamellar MFI zeolites showed higher liquid aromatics yield and selectivity for mono-benzene alkylated aromatics and lower coke formation rate compared to their microporous commercial MFI analogies. Zinc-loaded lamellar MFI had the most efficient catalytic performance among all studied catalysts. This phenomenon can be explained by higher accessibility of reactants to active sites and facilitated transport of products from lamellar structure of this zeolite and the low B/L acid site ratio of this catalyst provided by metal-modification, which is more suitable for ethylene aromatization. A bifunctional reaction mechanism has been proposed based on the analysis results of reaction product distributions that demonstrates the effect of both zeolite acid sites and metal sites in conversion of ethylene to liquid aromatics over MFI zeolite catalysts. [ABSTRACT FROM AUTHOR]