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Ultrabroadband and polarization‐sensitive imaging are essential for pioneering advancements in intelligent technology, offering a pivotal pathway to multidimensional information extraction. However, the dearth of appropriate photosensitive semiconductors in the infrared region and the lack of suitably efficient device architecture for the separation and collection of photocarriers significantly impede the development of ultra‐broadband and polarization‐sensitive photodetectors. To address these challenges, a sandwiched Ta2NiSe5/MoTe2/Graphene heterostructure device is engineered. In this device, efficient and polarization‐sensitive photocarriers are generated in the top Ta2NiSe5 layer, rapidly separated through the middle MoTe2 layer, and effectively collected by the bottom graphene layer. As a result, the developed photodetector exhibits an ultra‐broadband and polarization‐sensitive photoresponse that extends from visible light (520 nm) to short‐wave infrared (2200 nm). At 2200 nm, the device displays a notable responsivity of 5.79 A W−1, a specific detectivity of 1010 Jones, and an anisotropic ratio of 1.44. Furthermore, this device successfully demonstrates high‐resolution ultra‐broadband and polarized light imaging capabilities. This study thus presents an intriguing blueprint for the development of advanced 2D imaging platforms for future‐generation intelligent systems. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to decrease the scan time and enhance image quality in pediatric total-body PET imaging by utilizing multimodal artificial intelligence techniques. Methods: A total of 270 pediatric patients who underwent total-body PET/CT scans with a uEXPLORER at the Sun Yat-sen University Cancer Center were retrospectively enrolled. 18F-fluorodeoxyglucose (18F-FDG) was administered at a dose of 3.7 MBq/kg with an acquisition time of 600 s. Short-term scan PET images (acquired within 6, 15, 30, 60 and 150 s) were obtained by truncating the list-mode data. A three-dimensional (3D) neural network was developed with a residual network as the basic structure, fusing low-dose CT images as prior information, which were fed to the network at different scales. The short-term PET images and low-dose CT images were processed by the multimodal 3D network to generate full-length, high-dose PET images. The nonlocal means method and the same 3D network without the fused CT information were used as reference methods. The performance of the network model was evaluated by quantitative and qualitative analyses. Results: Multimodal artificial intelligence techniques can significantly improve PET image quality. When fused with prior CT information, the anatomical information of the images was enhanced, and 60 s of scan data produced images of quality comparable to that of the full-time data. Conclusion: Multimodal artificial intelligence techniques can effectively improve the quality of pediatric total-body PET/CT images acquired using ultrashort scan times. This has the potential to decrease the use of sedation, enhance guardian confidence, and reduce the probability of motion artifacts. [ABSTRACT FROM AUTHOR]

The metathesis of ethylene with 2‐butenes to propene is an established large‐scale process. However, the fundamentals behind in situ transformation of supported WOx, MoOx, or ReOx species into catalytically active metal‐carbenes and the intrinsic activity of the latter as well as the role of metathesis‐inactive cocatalysts are still unsolved. This is detrimental for catalyst development and process optimization. In this study, we provide the required essentials derived from steady‐state isotopic transient kinetic analysis. For the first time, the steady‐state concentration, the lifetime, and the intrinsic reactivity of metal carbenes were determined. The obtained results can be directly used for the design and the preparation of metathesis‐active catalysts and cocatalysts, thereby opening up possibilities for optimizing propene productivity. [ABSTRACT FROM AUTHOR]

Die Metathese von Ethen mit 2‐Butenen zu Propen ist ein etablierter großtechnischer Prozess. Allerdings sind die Grundlagen der In situ‐Umwandlung von geträgerten WOx‐, MoOx‐ oder ReOx‐Spezies in katalytisch aktive Metallcarbene und deren intrinsische Aktivität sowie die Rolle metatheseinaktiver Cokatalysatoren noch ungeklärt, was sich nachteilig auf die Katalysatorentwicklung und Prozessoptimierung auswirkt. Die Studie liefert die erforderlichen Grundlagen basierend auf steady‐state isotopic transient kinetic analysis. Zum ersten Mal wurden die stationäre Konzentration, die Lebensdauer und die intrinsische Reaktivität von Metallcarbenen bestimmt. Die erhaltenen Ergebnisse können direkt für das Design und die Herstellung von Katalysatoren für die Metathese‐Reaktion und Cokatalysatoren verwendet werden, was die Möglichkeit zur Optimierung der Propenproduktivität eröffnet. [ABSTRACT FROM AUTHOR]

The poor mixing caused by zinc powder deposition in purification process is a serious problem which can't be avoided in treatment of zinc-containing solid waste. Here, to enhance the purification efficiency of zinc-containing solid waste treatment reactor, two kinds of multi-blade combined stirring systems are compared with single layer four straight-blade and double-layer straight-blade (DFB) stirring systems which are traditionally chosen by industry. This study simulated and explored the flow field characteristics and purification effects of these four kinds of stirred-tank reactor, further proposes the unit ion purification energy (UIPE) as a criterion for purification energy consumption and effect evaluation. The results show that multi-blade combined (MBC) stirring system enhances axial flow by 12.56% in water. Meanwhile it effectively inhibits the growth of isolated mixing region which scope has decreased by 56.25%. In high viscosity Carboxymethylcellulose sodium solution, the fluid axial speed can be increased by up to 1407 times with MBC. MBC can increase the purification rate by 24.79% while the UIPE decreases by 29.45% compared with DFB which is used in industrial purification process. MBC paddle has exhibited a wide range of fluid viscosity applicability and axial velocity improvement effect. The improvement increases collisions between the impurity particles and the zinc powder particles, which increased rate of substitution reactions. The application of MBC solves the purification problem in the process of treating zinc-containing solid waste. [ABSTRACT FROM AUTHOR]

Systematic reviews are vital instruments for researchers to understand broad trends in a field and synthesize evidence on the effectiveness of interventions in addressing specific issues. The quality of a systematic review depends critically on having comprehensively surveyed all relevant literature on the review topic. In addition to database searching, handsearching is an important supplementary technique that helps increase the likelihood of identifying all relevant studies in a literature search. Traditional handsearching requires reviewers to manually browse through a curated list of field‐specific journals and conference proceedings to find articles relevant to the review topic. This manual process is not only time‐consuming, laborious, costly, and error‐prone due to human fatigue, but it also lacks replicability due to its cumbersome manual nature. To address these issues, this paper presents a free and open‐source Python package and an accompanying web‐app, Paperfetcher, to automate the retrieval of article metadata for handsearching. With Paperfetcher's assistance, researchers can retrieve article metadata from designated journals within a specified time frame in just a few clicks. In addition to handsearching, it also incorporates a beta version of citation searching in both forward and backward directions. Paperfetcher has an easy‐to‐use interface, which allows researchers to download the metadata of retrieved studies as a list of DOIs or as an RIS file to facilitate seamless import into systematic review screening software. To the best of our knowledge, Paperfetcher is the first tool to automate handsearching with high usability and a multi‐disciplinary focus. [ABSTRACT FROM AUTHOR]

Past meta-analyses in mental health interventions failed to use stringent inclusion criteria and diverse moderators, therefore, there is a need to employ more rigorous methods to provide evidence-based and updated results on this topic. This study presents an updated meta-analysis of interventions targeting anxiety or depression using more stringent inclusion criteria (e.g., baseline equivalence, no significant differential attrition) and additional moderators (e.g., sample size and program duration) than previous reviews. This meta-analysis includes 29 studies of 32 programs and 22,420 students (52% female, 79% White). Among these studies, 22 include anxiety outcomes and 24 include depression outcomes. Overall, school-based mental health interventions in grades K-12 are effective at reducing depression and anxiety (ES = 0.24, p = 0.002). Moderator analysis shows that improved outcomes for studies with anxiety outcomes, cognitive behavioral therapy, interventions delivered by clinicians, and secondary school populations. Selection modeling reveals significant publication and outcome selection bias. This meta-analysis suggests school-based mental health programs should strive to adopt cognitive behavioral therapy and deliver through clinicians at the secondary school level where possible. [ABSTRACT FROM AUTHOR]

• Strategy instruction significantly improves English achievement and learner autonomy among college students in English-as-a-Foreign-Language (EFL) classrooms in mainland China. • Effectiveness of strategy instruction on English achievement and learner autonomy is consistent across different types of training content, including cognitive, metacognitive, socioaffective, and integrated skills approach. • Effect sizes were higher when interventions assessed multiple aspects of learner autonomy (cognitive, metacognitive, motivational, and social) compared to those focusing on a single aspect, indicating a more pronounced impact when learner autonomy is evaluated comprehensively. • Short-term strategy instruction intervention are as effective as longer-term ones, indicating adaptability in implementation duration. This meta-analysis synthesized recent research on promoting learner autonomy through strategy instruction in tertiary English classrooms in China for two main purposes: (a) to estimate the effects of strategy instruction on English achievement and outcomes related to learner autonomy; and (b) to examine the moderating effects of a set of intervention and contextual characteristics. A total of 49 studies contributed to 111 effect sizes for this meta-analysis. Using a random-effects model, this study finds that the overall effect size of strategy instruction was 0.92 (p < 0.001). The type of outcome measure significantly moderated the impact of strategy instruction. Interventions that assessed multiple aspects of learner autonomy (cognitive, metacognitive, motivational, and social) had a significantly higher mean effect size than those assessing a single aspect. The results suggest strategy instruction is a viable instructional tool for promoting learner autonomy among college students in English-as-a-foreign-language classrooms. The results emphasize that fully understanding the impact of a strategy instruction intervention on learner autonomy requires an examination of its various dimensions. [ABSTRACT FROM AUTHOR]

We investigated the effect of supports based on ZrO2, TiO2, Al2O3, and SiO2 on the rate of propene formation in the metathesis of ethylene with 2-butene at 50 °C over Mo-containing catalysts possessing highly dispersed MoOx. Large improvements in this rate were achieved when using supports composed of mixed oxides (ZrO2–SiO2, ZrO2–PO4, TiO2–SiO2; Al2O3–SiO2) rather than of individual oxides (ZrO2, TiO2, Al2O3, SiO2). Although previous literature studies dealing with the metathesis reaction over Al2O3- or SiO2-suppported catalysts at higher temperatures suggest the importance of redox or acidic properties of supported MoOx species for catalyst activity, we were not able to establish any general direct correlation in this regard. Contrarily, the rate of propene formation can be significantly enhanced when promoting supports with an oxide promoter. We suggest that the created support lattice defects may facilitate the transformation of MoOx to Mo carbenes under reaction conditions or improve the intrinsic activity of the latter. [ABSTRACT FROM AUTHOR]

COVID learning loss has sparked fierce debate among stakeholders in education. There are debates among concerned educators about ways to understand learning loss reports, methods to measure learning loss, and strategies to recover from learning loss. In this article, we unpack opposing perspectives on these three controversies with the goal of extracting useful recommendations to guide post-pandemic education revival. Drawing from structural inequity theory, this article reviews the controversies through historical, current, and future lenses respectively: (1) Should we be talking about differential COVID learning loss? (2) Are we measuring COVID learning loss appropriately? and (3) Which recovery plans should we prioritize to address COVID learning loss? Within each controversy, the article makes suggestions on how practitioners may make sense of each controversy and integrate take-aways from them into their work. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic and associated societal upheavals further aggravated pre‐existing vulnerabilities of the international student population. In this article, an online mindfulness‐based group intervention—mindfulness‐based well‐being group for international students is described. A practical guide to planning, implementing, and evaluating the MBWIS group is outlined. [ABSTRACT FROM AUTHOR]

Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep representation learning and plenty of labeled data. However, machines often operate with various working conditions, or the target task has different distributions with the collected data used for training (the domain shift problem). Besides, the newly collected test data in the target domain are usually unlabeled, leading to unsupervised deep transfer learning (UDTL)-based IFD problem. Although it has achieved huge development, a standard and open source code framework and a comparative study for UDTL-based IFD are not yet established. In this article, we construct a new taxonomy and perform a comprehensive review of UDTL-based IFD according to different tasks. Comparative analysis of some typical methods and datasets reveals some open and essential issues in UDTL-based IFD, which is rarely studied, including transferability of features, the influence of backbones, negative transfer, physical priors, and so on. To emphasize the importance and reproducibility of UDTL-based IFD, the whole test framework will be released to the research community to facilitate future research. In summary, the released framework and comparative study can serve as an extended interface and basic results to carry out new studies on UDTL-based IFD. The code framework is available at https://github.com/ZhaoZhibin/UDTL. [ABSTRACT FROM AUTHOR]

Traditional deep learning assumes that the probability distributions of the test set and the training set are exactly the same. When the test samples and the training samples are from different working conditions, the distribution gap between different conditions might dramatically lower the accuracy. To address this problem, domain adaptation (DA) via joint using training and test sets is adopted to increase the final accuracy. The disadvantage of DA is that it requires test samples to participate in the training phase. However, sometimes it is impossible to collect the test samples beforehand. To solve this problem, this article proposes a conditional adversarial domain generalization with a single discriminator, which aims to extract domain-invariant features from different working conditions and generalize the features to unseen working conditions. To realize conditional adversarial training, a new conditional adversarial strategy that the feature extractor can allow the discriminator to distinguish the fault classes, but cannot distinguish the domains, is designed to better confuse the discriminator and generalize the features. Meanwhile, three loss functions, including mean square error (MSE), cross-entropy coupled with entropy, and cross-entropy combined with traditional adversarial methods, are proposed to achieve the new conditional adversarial strategy. The effectiveness of the conditional adversarial strategy is verified by training conditional generative adversarial networks (CGANs) on two image data sets, and the performance of the proposed domain generalization method for bearing fault diagnosis is also verified by bearing data sets. Compared with traditional conditional adversarial domain generalization, the proposed method can save computing resource consumption and improve accuracy that does not appear in the training phase. [ABSTRACT FROM AUTHOR]

[Display omitted] • Co/ZrO 2 catalysts with single Co2+ are efficient for C 3 H 8 dehydrogenation to C 3 H 6. • 1Co/ZrO 2 performed durable over multiple dehydrogenation/regeneration cycles. • 1Co/ZrO 2 showed higher activity than a commercial analogue of K-CrO x /Al 2 O 3. • The high activity results from a synergy effect between Co2+ and Zr cus. • Recombination of surface H species is the rate-limiting step in C 3 H 6 formation. Due to the increased production of shale gas containing propane, the share of non-oxidative propane dehydrogenation (PDH) in the large-scale production of propene is expected to continue to grow. There are, however, some ecofriendly and cost shortcomings associated with the currently applied Cr- or Pt-containing catalysts. Against this background, we present here Co/ZrO 2 alternatives and reveal the fundamentals affecting their activity, which can be used for purposeful catalyst design. Co2+ species homogeneously distributed within the lattice of ZrO 2 significantly increase the activity of coordinatively unsaturated Zr4+ for the PDH reaction. The increase is caused by accelerating both the cleavage of CH bonds in propane and the recombination of surface H species, with the latter reaction being the rate-limiting step. The best-performing catalyst outperformed an analogue of commercial K-CrO x /Al 2 O 3 in terms of the rate of propene formation and demonstrated durable performance over a series of 10 PDH/regeneration cycles under industrially relevant conditions. It also outperformed most previously developed Co-containing catalysts in terms of propene productivity. The space–time yield of propene formation achieved at 57 % equilibrium propane conversion at 550 °C was 0.71 kg·h−1·kg cat -1. [ABSTRACT FROM AUTHOR]

BACKGROUND: Radiation risk from computed tomography (CT) is always an issue for patients, especially those in clinical conditions in which repeated CT scanning is required. For patients undergoing repeated CT scanning, a low-dose protocol, such as sparse scanning, is often used, and consequently, an advanced reconstruction algorithm is also needed. OBJECTIVE: To develop a novel algorithm used for sparse-view CT reconstruction associated with the prior image. METHODS: A low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) involving a transformed model for attenuation coefficients of the object to be reconstructed and prior information application in the forward-projection process was used to reconstruct CT images from sparse-view projection data. A digital extended cardiac-torso (XCAT) ventral phantom and a diagnostic head phantom were employed to evaluate the performance of the proposed PI-NDI method. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR) and mean percent absolute error (MPAE) of the reconstructed images were measured for quantitative evaluation of the proposed PI-NDI method. RESULTS: The reconstructed images with sparse-view projection data via the proposed PI-NDI method have higher quality by visual inspection than that via the compared methods. In terms of quantitative evaluations, the RMSE measured on the images reconstructed by the PI-NDI method with sparse projection data is comparable to that by MLEM-TV, PWLS-TV and PWLS-PICCS with fully sampled projection data. When the projection data are very sparse, images reconstructed by the PI-NDI method have higher PSNR values and lower MPAE values than those from the compared algorithms. CONCLUSIONS: This study presents a new low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) for sparse-view CT image reconstruction. The experimental results validate that the new method has superior performance over other state-of-art methods. [ABSTRACT FROM AUTHOR]

The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not successful due to incomplete projection data. Moreover, model-based iterative total variation algorithms effectively reduce small streaks but do not work well at eliminating large streaks. In contrast, FBP mapping networks and deep-learning-based postprocessing networks are outstanding at removing large streak artifacts; however, these methods perform processing in separate domains, and the advantages of multiple deep learning algorithms operating in different domains have not been simultaneously explored. In this paper, we present a hybrid-domain convolutional neural network (hdNet) for the reduction of streak artifacts in limited-angle computed tomography. The network consists of three components: the first component is a convolutional neural network operating in the sinogram domain, the second is a domain transformation operation, and the last is a convolutional neural network operating in the CT image domain. After training the network, we can obtain artifact-suppressed CT images directly from the sinogram domain. Verification results based on numerical, experimental and clinical data confirm that the proposed method can significantly reduce serious artifacts. [ABSTRACT FROM AUTHOR]

PURPOSE: To reduce the cost of positron emission tomography (PET) scanning systems, image reconstruction algorithms for low-sampled data have been extensively studied. However, the current method based on total variation (TV) minimization regularization nested in the maximum likelihood-expectation maximization (MLEM) algorithm cannot distinguish true structures from noise resulting losing some fine features in the images. Thus, this work aims to recover fine features lost in the MLEM-TV algorithm from low-sampled data. METHOD: A feature refinement (FR) approach previously developed for statistical interior computed tomography (CT) reconstruction is applied to PET imaging to recover fine features in this study. The proposed method starts with a constant initial image and the FR step is performed after each MLEM-TV iteration to extract the desired structural information lost during TV minimization. A feature descriptor is specifically designed to distinguish structure from noise and artifacts. A modified steepest descent method is adopted to minimize the objective function. After evaluating the impacts of different patch sizes on the outcome of the presented method, an optimal patch size of 7×7 is selected in this study to balance structure-detection ability and computational efficiency. RESULTS: Applying MLEM-TV-FR algorithm to the simulated brain PET imaging using an emission activity phantom, a standard Shepp-Logan phantom, and mouse results in the increased peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as comparing to using the conventional MLEM-TV algorithm, as well as the substantial reduction of the used sampling numbers, which improves the computational efficiency. CONCLUSIONS: The presented algorithm can achieve image quality superior to that of the MLEM and MLEM-TV approaches in terms of the preservation of fine structure and the suppression of undesired artifacts and noise, indicating its useful potential for low-sampled data in PET imaging. [ABSTRACT FROM AUTHOR]

Frontispiece: Performance Descriptors for Catalysts Based on Molybdenum, Tungsten, or Rhenium Oxides for Metathesis of Ethylene with 2-Butenes to Propene Keywords: 2-Butene; Ethylene; Metathesis; Propene; SSITKA EN 2-Butene Ethylene Metathesis Propene SSITKA 1 1 1 09/27/23 20231002 NES 231002 B Heterogeneous Catalysis b . In their Communication (e202308872), Tatiana Otroshchenko, Evgenii V. Kondratenko et al. report performance descriptors for catalysts based on molybdenum, tungsten, or rhenium oxides for ethylene metathesis with 2-butenes to propene. [Extracted from the article]

Keywords: 2-Buten; Ethen; Metathese; Propen; SSITKA DE 2-Buten Ethen Metathese Propen SSITKA 1 1 1 09/27/23 20231002 NES 231002 B Heterogeneous Catalysis b . In their Communication (e202308872), Tatiana Otroshchenko, Evgenii V. Kondratenko et al. report performance descriptors for catalysts based on molybdenum, tungsten, or rhenium oxides for ethylene metathesis with 2-butenes to propene. [Extracted from the article]

This study examined the relationship between perceived discrimination and psychological distress among international students during the COVID-19 pandemic. A total of 188 international students from two large U.S. universities participated in the study. Perceived discrimination, psychological distress, and demographic information were assessed using self-reported questionnaires. COVID-related variables and perceived discrimination were significantly associated with international students' psychological distress. Their COVID-related anxiety mediated the relationship between perceived discrimination and psychological distress. Initiatives to mitigate the perceived discrimination experienced by international students may improve their mental health. [ABSTRACT FROM AUTHOR]

Capillary electrophoresis (CE) is a powerful separation technique with advantages over HPLC in terms of separation efficiency, speed, and cost. However, CE suffers in poor reproducibility in quantitative chemical analysis, which is one of major drawbacks preventing its widespread use in routine analytical laboratories. Here we report a novel strategy to enhance the quantitative capability of flow-gated CE. The platform integrated dual flow branches to respectively supply a sample and its standard additions that were then alternately injected into a single capillary for rapid separations (typically 20–90 s). A micro-fabricated switch was used to enable the alternate injections. It was assumed that the analytical system maintained constant conditions during neighboring injections that served as external self-standards for quantitation. This strategy was expected to reduce uncertainties caused by the fluctuation in capillary conditions and the drift of detection systems. Experimental results demonstrated that the dual-branch flow-gated CE coupled with alternate injections significantly improved the reproducibility with respect to peak height ratios under deliberate variations in injection volumes, separation voltages, optical focusing, and laser power; and thus the interday precision was ensured. To demonstrate its applicability, cyanide and amino acids in human urine were quantified rapidly with the one-point standard addition method after fluorogenic derivatization with naphthalene-2,3-dicarboxaldehyde (NDA), and the measurement accuracy was validated by determining the recovery of standard cyanide added to a urinary matrix. This strategy would be valuable to enable the quantitative capability of flow-gated CE in the measurements of a broad range of analytes, especially those lacking suited internal standards. [ABSTRACT FROM AUTHOR]

[Display omitted] • Metallic Co0 species in situ formed are suggested to be the main active sites for PDH. • CoO x but not Co0 is responsible for the formation of undesired products. • Transformation of CoO x to Co0 depends on the strength of CoO x -support interactions. • Co/Silicalite-1 showed the C 3 H 6 space time yield of 1.50 kg·h−1·kg cat −1 at 550 °C. Although Co-containing catalysts show attractive performance in the non-oxidative propane dehydrogenation to propylene (PDH), their tailored development is hindered by the ambiguity in the kind of active sites (Co0 or Co2+O x) responsible for selective and unselective pathways. Herein, we demonstrate that supported CoO x species expends its lattice oxygen through oxidation of C 3 H 8 to CO 2 /C 3 H 6 and are accordingly transformed into Co0 species. The latter are decisive for the selective dehydrogenation of propane to propylene with high activity. The ability of CoO x to in situ form Co0 is affected by metal-oxide-support interactions (MOSI). In addition to the kind of support, defective OH groups (OH nests) in ZSM-5-type zeolites seem to be relevant for the MOSI effect. In comparison with previous studies highlighting the importance of the size of CoO x species and their acidity for coke formation, we could not establish any direct correlation. Nevertheless, the presence of CoO x seems to be relevant for this undesired reaction because the formation of coke is hindered when CoO x species are transformed to Co0 species. Catalyst acidic sites are responsible for cracking reactions. Thus, the derived mechanistic insights reveal the nature of active sites both for selective and unselective pathways and provide fundamentals for catalyst development. From an industrial viewpoint, the developed Co/Silicalite-1 (Co/S-1) catalyst with the weakest MOSI and accordingly highest fraction of Co0 showed the space time yield of propylene formation of 1.50 kg·h−1·kg cat −1 at 67 % equilibrium propane conversion and propylene selectivity of 95.3 % and the Co-related TOF value of 0.145 s−1 at 550 °C. These both activity values exceed those of the state-of-the-art Co-containing catalysts. The productivity of Co/S-1 is comparable or even higher than that of industrially relevant Pt- or Cr-containing catalysts. [ABSTRACT FROM AUTHOR]

Flow-gated capillary electrophoresis (CE) coupled with microdialysis has become an important tool for in vivo bioanalytical measurements because it is capable of performing rapid and efficient separations of complex biological mixtures thus enabling high temporal resolution in chemical monitoring. However, the limit of detection (LOD) is often limited to a micro- or nano-molar range while many important target analytes have picomolar or sub-nanomolar levels in brain and other tissues. To enhance the capability of flow-gated CE for catecholamine detection, a novel and simple on-line sample preconcentration method was developed exclusively for fluorescent derivatives of catecholamines that were fluorogenically derivatized with naphthalene-2,3-dicarboxaldehyde (NDA) in the presence of cyanide. The effective preconcentration coupled with the sensitive laser-induced fluorescence (LIF) detection lowered the LOD down to 20 pM for norepinephrine (NE) and 50 pM for dopamine (DA) at 3-fold of S/N ratio, and the signal enhancement was estimated to be over 100-fold relative to normal injection when standard analytes were dissolved in artificial cerebrospinal fluid (aCSF). The basic focusing principle is novel since the sample plug contains borate while the background electrolyte (BGE) is void of borate. This strategy took advantage of the complexation between diols and borate, through which one negative charge was added to the complex entity. The sample derivatization mixture was electrokinetically injected into a capillary via the flow-gated injection, and then NE and DA derivatives were selectively focused to a narrow zone by the reversible complexation. Separation of NE and DA derivatives was executed by incoming surfactants of cholate and deoxycholate mixed in the front BGE plug. This on-line preconcentration method was finally applied to the detection of DA in rat cerebrospinal fluid (CSF) via microdialysis and on-line derivatization. It is anticipated that the method would be valuable for in vivo monitoring of DA and NE in various brain regions of live animals on flow-gated CE or microchip platforms. [ABSTRACT FROM AUTHOR]

Cyanides are poisonous chemicals that widely exist in nature and industrial processes as well as accidental fires. Rapid and accurate determination of cyanide exposure would facilitate forensic investigation, medical diagnosis, and chronic cyanide monitoring. Here, a rapid and direct method was developed for the determination of cyanide ions in urinary samples. This technique was based on an integrated capillary electrophoresis system coupled with laser-induced fluorescence (LIF) detection. Cyanide ions were derivatized with naphthalene-2,3-dicarboxaldehyde (NDA) and a primary amine (glycine) for LIF detection. Three separate reagents, NDA, glycine, and cyanide sample, were mixed online, which secured uniform conditions between samples for cyanide derivatization and reduced the risk of precipitation formation of mixtures. Conditions were optimized; the derivatization was completed in 2–4 min, and the separation was observed in 25 s. The limit of detection (LOD) was 4.0 nM at 3-fold signal-to-noise ratio for standard cyanide in buffer. The cyanide levels in urine samples from smokers and non-smokers were determined by using the method of standard addition, which demonstrated significant difference of cyanide levels in urinary samples from the two groups of people. The developed method was rapid and accurate, and is anticipated to be applicable to cyanide detection in waste water with appropriate modification. [ABSTRACT FROM AUTHOR]

Highlights: [•] Developed a rapid method for preparation of intersecting cross molds for flow gate casting. [•] Developed strategies for fabricating 4-way mixing interfaces and linear connectors of different outer diameters. [•] Used smaller inner diameter in PDMS channels for tightly accommodating capillaries or other tubing. [•] Demonstrated robust performance of a PDMS-interconnected CE system for neurotransmitter monitoring. [ABSTRACT FROM AUTHOR]

[Display omitted] • Approach for anchoring of Pt atoms on isolated Sn4+ is introduced. • Using this approach PtSn/SiO 2 (hexagonal mesoporous silica) catalysts were prepared. • Pt-Sn/atoms/sub nanoclusters were identified on the surface of catalysts. • The catalysts are active, selective, and stable in C 3 H 8 dehydrogenation to C 3 H 6. • Sn is essential to enhance H 2 desorption from Pt that is the rate-limiting step. Owing to propane availability in shale gas and the growing demand for propene, non-oxidative dehydrogenation of this alkane has attracted much attention. Although, catalysts with supported PtSn species are applied on the large scale, a further increase in their activity without loss in propene selectivity at low Pt content would certainly improve process economy. Herein, we introduce a molecularly defined strategy for controlling catalyst activity through anchoring of Pt atoms on isolated Sn4+ firmly confined in the framework of hexagonal mesoporous silica. In addition to the dispersion of Pt species, the presence of Sn4+ in close contact with Pt is essential for increasing the rate of propane dehydrogenation through enhancing H 2 desorption, which is the rate-limiting step. As the anchored Pt atoms/sub nanoclusters do not sinter under reducing conditions, the developed catalysts show high on-stream stability and propene selectivity of above 99 % under industrially relevant conditions. [ABSTRACT FROM AUTHOR]

The efficient and clean conversion of light alkanes is a research hotspot in the petrochemical industry, and the development of effective and eco-friendly non-noble metal-based catalysts is a key factor in this field. Among them, gallium is a metal component with good catalytic performance, which has been extensively used for light alkanes conversion. Herein, we critically summarize recent developments in the preparation of gallium-based catalysts and their applications in the catalytic conversion of light alkanes. First, we briefly describe the different routes of light alkane conversion. Following that, the remarkable preparation methods for gallium-based catalysts are discussed, with their state-of-the-art application in light alkane conversion. It should be noticed that the directional preparation of specific Ga species, strengthening metal-support interactions to anchor Ga species, and the application of new kinds of methods for Ga-based catalysts preparation are at the leading edge. Finally, the review provides some current limitations and future perspectives for the development of gallium-based catalysts. Recently, different kinds of Ga species were reported to be active in alkane conversion, and how to separate them with advanced in situ and ex situ characterizations is still a problem that needs to be solved. We believe that this review can provide base information for the preparation and application of Ga-based catalysts in the current stage. With these summarizations, this review can inspire new research directions of gallium-based catalysts in the catalysis conversion of light alkanes with ameliorated performances. [ABSTRACT FROM AUTHOR]

A prerequisite for the development or optimization of heterogeneous catalysts is a comprehensive understanding of the reaction mechanism and kinetics at the level as elementary as possible. In comparison with steady‐state catalytic tests typically used for this purpose, non‐steady‐state (transient) studies provide deeper insights due to higher time resolution. For several decades, temporal analysis of products (TAP) and steady‐state isotopic transient kinetic analysis (SSITKA) have been successfully applied to the analysis of various heterogeneous reactions. In this review, we highlight the contributions made by TAP and SSITKA studies between 2017 and 2023 to the understanding of reaction mechanisms and to the derivation of microkinetics. The versatility of these methods is illustrated by the examples of oxidative coupling of methane, CO/CO2 hydrogenation to hydrocarbons or methanol, metathesis of ethylene with 2‐butenes to propene, acrolein oxidation to acrylic acid, methanol conversion to olefins and non‐oxidative propane dehydrogenation to propene. We also provide our personal views on possible developments in this area. [ABSTRACT FROM AUTHOR]

With an increasing number of international students enrolled in U.S. higher education, they were reported to have severe mental health issues, especially during the coronavirus disease (COVID‐19) pandemic. It is critical to provide evidence‐based mental health services to help them cope with those issues and promote mental health and the overall well‐being of international students. In this article, we utilized a randomized controlled trial to pilot‐test the effectiveness of a mindfulness‐based well‐being group for international students (MBWIS) in improving participants' overall well‐being and mental health. The results indicated that the MBWIS not only improves international students' trait mindfulness but also increases positive mental health as well as decreases their overall psychological distress and perceived discrimination. Related findings and implications for counselors and university personnel, including how to implement MBWIS in mental health facilities, are discussed within the existing literature. [ABSTRACT FROM AUTHOR]

[Display omitted] • Highly dispersed VO x species showed high apparent TOF value but low C 3 H 6 selectivity. • VO x species in low-polyvanadate form are deemed to be more active and stable in PDH. • VO x -C 3 H 5 species was speculated to be the important intermedia during PDH. • H 2 formation is deemed as the rate-limiting step over as-prepared V-HMS. Vanadium was introduced to hexagonal mesoporous SiO 2 (HMS) to prepare supported vanadium-based catalysts (V-HMS). Their catalytic performance in the non-oxidative propane dehydrogenation to propylene (PDH) was modulated with V surface density varying from 0.12 to 15.08V atom·nm−2. VO x species with different structure were analyzed by UV–vis spectroscopy, NH 3 -TPD, H 2 -TPR and XPS. High propylene space time yield of 0.73 kg·h−1·kg cat −1 and excellent regeneration behavior were achieved. Correlating VO x polymerization degree with catalytic performance, VO x aggregates, mainly in low-polyvanadate form, were established to reveal high intrinsic activity and stability in comparison with their counterparts, including isolated or/and highly polymerized VO x as well as V 2 O 5. Temporal analysis of products (TAP) identified hydrogen formation as the rate-limiting step in the PDH reaction. In-situ DRIFTS was employed to investigate the reaction mechanism. V species containing C=C bond, i.e., VO x -C 3 H 5 , was proposed to be an intermediate of gas-phase propylene. [ABSTRACT FROM AUTHOR]

Purpose: Clinically, single radiotracer positron emission tomography (PET) imaging is a commonly used examination method; however, since each radioactive tracer reflects the information of only one kind of cell, it easily causes false negatives or false positives in disease diagnosis. Therefore, reasonably combining two or more radiotracers is recommended to improve the accuracy of diagnosis and the sensitivity and specificity of the disease when conditions permit. Methods: This paper proposes incorporating 18F‐fluorodeoxyglucose (FDG) as a higher‐quality PET image to guide the reconstruction of other lower‐count 11C‐methionine (MET) PET datasets to compensate for the lower image quality by a popular kernel algorithm. Specifically, the FDG prior is needed to extract kernel features, and these features were used to build a kernel matrix using a k‐nearest‐neighbor (kNN) search for MET image reconstruction. We created a 2‐D brain phantom to validate the proposed method by simulating sinogram data containing Poisson random noise and quantitatively compared the performance of the proposed FDG‐guided kernelized expectation maximization (KEM) method with the performance of Gaussian and non‐local means (NLM) smoothed maximum likelihood expectation maximization (MLEM), MR‐guided KEM, and multi‐guided‐S KEM algorithms. Mismatch experiments between FDG/MR and MET data were also carried out to investigate the outcomes of possible clinical situations. Results: In the simulation study, the proposed method outperformed the other algorithms by at least 3.11% in the signal‐to‐noise ratio (SNR) and 0.68% in the contrast recovery coefficient (CRC), and it reduced the mean absolute error (MAE) by 8.07%. Regarding the tumor in the reconstructed image, the proposed method contained more pathological information. Furthermore, the proposed method was still superior to the MR‐guided KEM method in the mismatch experiments. Conclusions: The proposed FDG‐guided KEM algorithm can effectively utilize and compensate for the tissue metabolism information obtained from dual‐tracer PET to maximize the advantages of PET imaging. [ABSTRACT FROM AUTHOR]

Urbanization and industrialization have resulted in an excessive intensity of land use, structural imbalances, and regional ecological deterioration. To achieve sustainable development, it is essential to optimize different land use types and enhance the ecosystem service value (ESV). However, previous studies rarely combined the optimization of land use quantitative structure with the simulation of land use spatial layout under the background of maximizing economic and ecological benefits at the same time, and this kind of research was rarely applied to state-level new areas. On the premise of maximizing economic and ecological benefits, this study develops an improved optimization method that integrates the non-dominated sorting genetic algorithm II (NSGA-II) with the patch generating land use simulation (PLUS) model. The approach optimizes land use while predicting ESV for a state-level new area-Liangjiang New Area (LJNA). The model operation takes into full account ecological spatial constraints, including ecological protection red line, prime farmland, and urban development land use suitability. Results show that LJNA encountered the most substantial increase in built-up land (392.31 km2) due to farmland occupation, leading to a continuous decrease in ESV (443.6 million yuan) from 2000 to 2020. Optimization results for 2030 indicate that the main difference between different scenarios is the area of forest and built-up land. Under ecological protection priority scenario, ESV will increase in 2030 but built-up land should be confined within 620 km2. We present multiple land use scenarios based on decision makers' preferences, offering important implications for the balance and optimization of land resources under different policy priorities using coupled spatiotemporal simulation-quantitative allocation model. This study provides valuable references for future land use optimization research. • An improved multi-objective land use optimization method is developed. • The method integrates the non-dominated sorting genetic algorithm II with the patch generating land use simulation model. • The method optimizes land use while predicting ESV for the Liangjiang New Area, China. • The ecological spatial constraints are developed based on ecological protection red line, prime farmland, and urban development land use suitability. • Taking into account both ecological and economic benefit, four development scenarios are set up. [ABSTRACT FROM AUTHOR]

Recently, low-dose computed tomography (CT) has become highly desirable due to the increasing attention paid to the potential risks of excessive radiation of the regular dose CT. However, ensuring image quality while reducing the radiation dose in the low-dose CT imaging is a major challenge. Compared to classical filtered back-projection (FBP) algorithms, statistical iterative reconstruction (SIR) methods for modeling measurement statistics and imaging geometry can significantly reduce the radiation dose, while maintaining the image quality in a variety of CT applications. To facilitate low-dose CT imaging, we in this study proposed an improved statistical iterative reconstruction scheme based on the penalized weighted least squares (PWLS) standard combined with total variation (TV) minimization and sparse dictionary learning (DL), which is named as a method of PWLS-TV-DL. To evaluate this PWLS-TV-DL method, we performed experiments on digital phantoms and physical phantoms, and analyzed the results in terms of image quality and calculation. The results show that the proposed method is better than the comparison methods, which indicates the potential of applying this PWLS-TV-DL method to reconstruct low-dose CT images. [ABSTRACT FROM AUTHOR]

As the field of education, and especially gifted education, gradually moves toward open science, our research community increasingly values transparency and openness brought by open science practices. Yet, individual researchers may be reluctant to adopt open science practices due to low incentives, barriers of extra workload, or lack of support to apply these in certain areas, such as qualitative research. We encourage and give guidelines to reviewers to champion open science practices by warmly influencing authors to consider applying open science practices to quantitative, qualitative, and mixed-methods research and providing ample support to produce higher-quality publications. Instead of imposing open science practices on authors, we advocate reviewers suggest small, non-threatening, specific steps to support authors without making them feel overwhelmed, judged, or punished. We believe that these small steps taken by reviewers will make a difference to create a more supportive environment for researchers to adopt more open science practices. [ABSTRACT FROM AUTHOR]

Together, the Yinggehai and Qiongdongnan basins have received a large amount of terrigenous sediments, but the provenance evolution of Cenozoic sediments in the two basins remains disputable. Combined with previous studies in the Yinggehai and Qiongdongnan basins, the elemental geochemistry of Oligocene to Pliocene sediment samples in the junction area of the two basins were analyzed to explore the tectonic implications, parent rock characteristics, and provenance evolution of the two basins during the Cenozoic. The results reveal that all the sediment samples were derived from continental island arc to passive continental margin settings. The light REE enrichment and stable content of heavy REE with large negative Eu anomalies indicate that they were probably derived from Hainan Island. The reconstructed provenance evolution model showed that the Red River Source (RRS) provided sedimentary materials for the Central Depression of Yinggehai Basin from the Oligocene to the Pliocene, and Hainan Island Source (HIS) was also one of the sources for sediments deposited in the Central Depression of Yinggehai Basin during the Miocene. However, most of the sediments preserved in the Yingdong Slope and Qiongdongnan Basin were derived from the HIS from the Oligocene to the Pliocene, and sediments deposited in the Yingdong Slope were also derived from the RRS during the Miocene. Furthermore, the junction area of the two basins had a mixed source of the RRS and HIS during the Cenozoic. [ABSTRACT FROM AUTHOR]

In view of the limitation that the transport of intensity equation method can only be used at a single wavelength, we propose a binocular phase retrieval algorithm based on multiwavelength illumination. First, we solve for the corresponding single-phase results at different wavelengths and then calculate the surface height of the object by combining the obtained synthetic wavelengths and synthetic phases, in order to reconstruct the initial phase results at different wavelengths. To solve the problem of amplification of traditional noise and surface profile noise in the phase diagram in the phase synthesis step, we introduce a step-by-step noise reduction method that can reduce the noise in the initial phase results to the level for a single wavelength and finally obtain the corresponding high-precision phase results at different wavelengths. The intensity images collected by the proposed dual-microscope system verify the correctness and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Background. With a high incidence and limited treatments, gastric cancer (GC) seriously threatens human health worldwide. Weikang Keli (WK) is a compound prescription summed up from clinical experience. In our previous studies, WK has been proved to exert antitumor effects. However, there are no research studies to discuss and verify its mechanism as a compound. Objective. The aim of the study is to explore the potential molecular mechanism of WK in the treatment of GC with the aid of network pharmacology and verify it through experiments. Methods. Related databases were used to obtain genes and targets of WK and gastric cancer. A protein-protein interaction (PPI) network is constructed and visualized by Cytoscape 3.7.2. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyze core targets. The cell viability of MFC and BGC-823 cells was determined by CCK8. Immunofluorescence was used to determine autophagy of GC cells. Moreover, the effect of WK on the MAPK signaling pathway in GC cells and tumor tissues of ICR mice was detected by Western blot. Results. A total of 106 cross targets of WK and GC were obtained. According to the enrichment analysis of GO and KEGG, we target the MAPK signaling pathway to discuss the mechanism of WK on GC. Cell experiments proved that WK inhibited the viability of gastric cancer cells in a dose-dependent and time-dependent manner. Autophagosome aggregation and an increase in the expression of an autophagy marker protein LC3-II can also be observed in WK groups. Further animal experiments showed that the tumor inhibition rate of WK showed a dose-effect relationship. Moreover, the expressions of p-JNK, p-p38, and p-ERR1/2 proteins in the MAPK signaling pathway in WK Group were downregulated both in the cell and animal experiments, compared with the blank control group. Conclusion. WK showed an explicit antitumor effect on gastric cancer through the MAPK signaling pathway, and the curative effect varies in different concentrations. Besides, in model mice, the antitumor effect of high-dose WK group is close to that of platinum. This study provided a theoretical basis for the application of WK in the clinical treatment of gastric cancer. [ABSTRACT FROM AUTHOR]

• We propose a co-planar optical power splitter based on cascaded hybrid-core vertical directional couplers, which serves to split arbitrary ratio power of arbitrary guide modes. • We fabricate the optical power splitter using micro-fabrication facilities. • Our fabricated chip demonstrates power splitting for the E 11 , E 21 and E 12 modes between two few-mode waveguides. • The experimental device shows that optical power splitting ranges can be larger than 11.0 dB, 12.1 dB and 11.3 dB for the E 11 , E 21 and E 12 modes, respectively, over the C + L band and beyond. • The proposed optical power splitter could be used in the mode division-multiplexing systems where power splitting for arbitrary guide modes in few-mode waveguide is required in the ultra-broadband wavelength range for mode manipulating, such as mode power distribution, mode dependent loss equalization and all-optical MIMO signal processes. We propose a co-planar optical power splitter based on cascaded hybrid-core vertical directional couplers, which serves to split arbitrary ratio power of arbitrary guide modes. We fabricate the optical power splitter using micro-fabrication facilities. Our fabricated chip demonstrates power splitting for the E 11 , E 21 and E 12 modes between two few-mode waveguides. The experimental device shows that optical power splitting ranges can be larger than 11.0 dB, 12.1 dB and 11.3 dB for the E 11 , E 21 and E 12 modes, respectively, over the C + L band and beyond. The proposed optical power splitter could be used in the mode division-multiplexing systems where power splitting for arbitrary guide modes in few-mode waveguide is required in the ultra-broadband wavelength range for mode manipulating, such as mode power distribution, mode dependent loss equalization and all-optical MIMO signal processes. [ABSTRACT FROM AUTHOR]

Positron emission tomography (PET) imaging can be used for early detection, diagnosis and postoperative patient monitoring of many diseases. Traditional PET imaging requires not only additional computed tomography (CT) imaging or magnetic resonance imaging (MR) to provide anatomical information but also attenuation correction (AC) map calculation based on CT images or MR images for accurate quantitative estimation. During a patient's treatment, PET/CT or PET/MR scans are inevitably repeated many times, leading to additional doses of ionizing radiation (CT scans) and additional economic and time costs (MR scans). To reduce adverse effects while obtaining high-quality PET/MR images in the course of a patient's treatment, especially in the stage of evaluating the effect of postoperative treatment, in this work, we propose a new method based on deep learning, which can directly obtain synthetic attenuation-corrected PET (sAC PET) and synthetic T1-weighted MR (sMR) images based only on non-attenuation-corrected PET (NAC PET) images. Our model, based on the Wasserstein generative adversarial network, first removes noise and artifacts from the NAC PET images to generate sAC PET images and then generates sMR images from the obtained sAC PET images. To evaluate the performance of this generative model, we evaluated it on paired PET/MR images from a total of eighty clinical patients. Based on qualitative and quantitative analysis, the generated sAC PET and sMR images showed a high degree of similarity to the real AC PET and real MR images. These results indicated that our proposed method can reduce the frequency of additional anatomical imaging scans during PET imaging and has great potential in improving doctors' clinical diagnosis efficiency, saving patients' economic expenditure and reducing the radiation risk brought by CT scanning. [ABSTRACT FROM AUTHOR]

• Anatomical prior information is introduced to estimate high-resolution CT images. • A united framework is applied instead of designing multiple models for individual human body sites. • An improved performance is achieved by Wasserstein GAN with an attribute loss function. Currently, many deep learning (DL)-based low-dose CT image postprocessing technologies fail to consider the anatomical differences in training data among different human body sites, such as the cranium, lung and pelvis. In addition, we can observe evident anatomical similarities at the same site among individuals. However, these anatomical differences and similarities are ignored in the current DL-based methods during the network training process. In this paper, we propose a deep network trained by introducing anatomical site labels, termed attributes for training data. Then, the network can adaptively learn to obtain the optimal weight for each anatomical site. By doing so, the proposed network can take full advantage of anatomical prior information to estimate high-resolution CT images. Furthermore, we employ a Wasserstein generative adversarial network (WGAN) augmented with attributes to preserve more structural details. Compared with the traditional networks that do not consider the anatomical prior and whose weights are consequently the same for each anatomical site, the proposed network achieves better performance by adaptively adjusting to the anatomical prior information. [ABSTRACT FROM AUTHOR]

Breast cancer is the most frequently diagnosed cancer in women worldwide. Digital breast tomosynthesis (DBT), which is based on limited-angle tomography, was developed to solve tissue overlapping problems associated with traditional breast mammography. However, due to the problems associated with tube movement during the process of data acquisition, stationary DBT (s-DBT) was developed to allow the X-ray source array to stay stationary during the DBT scanning process. In this work, we evaluate four widely used and investigated DBT image reconstruction algorithms, including the commercial Feldkamp-Davis-Kress algorithm (FBP), the simultaneous iterative reconstruction technique (SIRT), the simultaneous algebraic reconstruction technique (SART) and the total variation regularized SART (SART-TV) for an s-DBT imaging system that we set up in our own laboratory for studies using a semi-elliptical digital phantom and a rubber breast phantom to determine the most superior algorithm for s-DBT image reconstruction among the four algorithms. Several quantitative indexes for image quality assessment, including the peak signal-noise ratio (PSNR), the root mean square error (RMSE) and the structural similarity (SSIM), are used to determine the best algorithm for the imaging system that we set up. Image resolutions are measured via the calculation of the contrast-to-noise ratio (CNR) and artefact spread function (ASF). The experimental results show that the SART-TV algorithm gives reconstructed images with the highest PSNR and SSIM values and the lowest RMSE values in terms of image accuracy and similarity, along with the highest CNR values calculated for the selected features and the best ASF curves in terms of image resolution in the horizontal and vertical directions. Thus, the SART-TV algorithm is proven to be the best algorithm for use in s-DBT image reconstruction for the specific imaging task in our study. [ABSTRACT FROM AUTHOR]

Zeolite-tailored bimetallic Pt-Sn catalysts have been shown to be effective for propane dehydrogenation (PDH). Here, we have successfully prepared and compared two Sn-Beta-supported bimetallic PtSn catalysts (Pt/Sn-Beta (DG) and Pt/Sn-Beta (DA)), in which the Sn-Beta zeolite was prepared by a dry-gel (DG) synthesis method involving in situ Sn incorporation or a post-synthesis method involving dealumination and Sn incorporation (DA), respectively. The as-prepared Pt/Sn-Beta (DG), possessing a less fraction of extra-framework Sn species comparing to Pt/Sn-Beta (DA), shows a superior performance in terms of activity (TOF, 1.8 s-1), selectivity (99.0%) and stability (7360 min) at 550 °C using 40 kPa propane and 60 kPa nitrogen gas, reflecting one of the highest PDH performances for bimetallic Pt-Sn catalysts under similar conditions. By a combination of the state-of-the-art electron microscopy-based techniques and advanced spectroscopy techniques, we found that the proper Pt-Sn nano-alloying in Pt/Sn-Beta (DG), rather than Sn coverage on Pt species in Pt/Sn-Beta (DA) owing to the migration of excess extraframework Sn species, is essential for providing an enhanced PDH performance, as the active Pt species could be well isolated, accessed and stablized in Pt/Sn-Beta (DG) by alloying. We demonstrate a process that constructs highly active Pt–Sn alloy nanoparticles on Sn-Beta zeolite with a high activity (TOF of 1.8 h−1), selectivity (99%) and stability (7360 min one-pass durability) for propane dehydrogenation. [Display omitted] • Sn-Beta zeolite from the dry-gel synthesis method was served as catalyst support. • Pt–Sn nanoalloy was formed via the interaction of Pt and Sn in Sn-Beta zeolite. • Pt/Sn-Beta shows a high activity and selectivity for propane dehydrogenation. • The excellent stability of Pt/Sn-Beta is benefited from the of Pt–Sn nanoalloy. [ABSTRACT FROM AUTHOR]

X-ray radiation is harmful to human health. Thus, obtaining a better reconstructed image with few projection view constraints is a major challenge in the computed tomography (CT) field to reduce radiation dose. In this study, we proposed and tested a new algorithm that combines penalized weighted least-squares using total generalized variation (PWLS-TGV) and dictionary learning (DL), named PWLS-TGV-DL to address this challenge. We first presented and tested this new algorithm and evaluated it through both data simulation and physical experiments. We then analyzed experimental data in terms of image qualitative and quantitative measures, such as the structural similarity index (SSIM) and the root mean square error (RMSE). The experiments and data analysis indicated that applying the new algorithm to CT data recovered images more efficiently and yielded better results than the traditional CT image reconstruction approaches. [ABSTRACT FROM AUTHOR]

Purpose: In recent years, health risks concerning high‐dose x‐ray radiation have become a major concern in dental computed tomography (CT) examinations. Therefore, adopting low‐dose computed tomography (LDCT) technology has become a major focus in the CT imaging field. One of these LDCT technologies is downsampling data acquisition during low‐dose x‐ray imaging processes. However, reducing the radiation dose can adversely affect CT image quality by introducing noise and artifacts in the resultant image that can compromise diagnostic information. In this paper, we propose an artifact correction method for downsampling CT reconstruction based on deep learning. Method: We used clinical dental CT data with low‐dose artifacts reconstructed by conventional filtered back projection (FBP) as inputs to a deep neural network and corresponding high‐quality labeled normal‐dose CT data during training. We trained a generative adversarial network (GAN) with Wasserstein distance (WGAN) and mean squared error (MSE) loss, called m‐WGAN, to remove artifacts and obtain high‐quality CT dental images in a clinical dental CT examination environment. Results: The experimental results confirmed that the proposed algorithm effectively removes low‐dose artifacts from dental CT scans. In addition, we showed that the proposed method is efficient for removing noise from low‐dose CT scan images compared to existing approaches. We compared the performances of the general GAN, convolutional neural networks, and m‐WGAN. Through quantitative and qualitative analysis of the results, we concluded that the proposed m‐WGAN method resulted in better artifact correction performance preserving the texture in dental CT scanning. Conclusions: The image quality evaluation metrics indicated that the proposed method effectively improves image quality when used as a postprocessing technique for dental CT images. To the best of our knowledge, this work is the first deep learning architecture used with a commercial cone‐beam dental CT scanner. The artifact correction performance was rigorously evaluated and demonstrated to be effective. Therefore, we believe that the proposed algorithm represents a new direction in the research area of low‐dose dental CT artifact correction. [ABSTRACT FROM AUTHOR]

Low-dose CT techniques attempt to minimize the radiation exposure of patients by estimating the high-resolution normal-dose CT images to reduce the risk of radiation-induced cancer. In recent years, many deep learning methods have been proposed to solve this problem by building a mapping function between low-dose CT images and their high-dose counterparts. However, most of these methods ignore the effect of different radiation doses on the final CT images, which results in large differences in the intensity of the noise observable in CT images. What'more, the noise intensity of low-dose CT images exists significantly differences under different medical devices manufacturers. In this paper, we propose a multi-level noise-aware network (MLNAN) implemented with constrained cycle Wasserstein generative adversarial networks to recovery the low-dose CT images under uncertain noise levels. Particularly, the noise-level classification is predicted and reused as a prior pattern in generator networks. Moreover, the discriminator network introduces noise-level determination. Under two dose-reduction strategies, experiments to evaluate the performance of proposed method are conducted on two datasets, including the simulated clinical AAPM challenge datasets and commercial CT datasets from United Imaging Healthcare (UIH). The experimental results illustrate the effectiveness of our proposed method in terms of noise suppression and structural detail preservation compared with several other deep-learning based methods. Ablation studies validate the effectiveness of the individual components regarding the afforded performance improvement. Further research for practical clinical applications and other medical modalities is required in future works. • A framework is proposed to focus on the improvement of image quality under different noise intensities. • To gain better performance, we introduce several strategies for overview network framework. • The quantitative and visual results reflect the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

A mathematical derivation was conducted to illustrate that exact 3D image reconstruction could be achieved for z-homogeneous phantoms from data acquired with 2D general trajectories using the back projection filtration (BPF) algorithm. The conclusion was verified by computer simulation and experimental result with a circular scanning trajectory. Furthermore, the effect of the non-uniform degree along z-axis of the phantoms on the accuracy of the 3D reconstruction by BPF algorithm was investigated by numerical simulation with a gradual-phantom and a disk-phantom. The preliminary result showed that the performance of BPF algorithm improved with the z-axis homogeneity of the scanned object. [ABSTRACT FROM AUTHOR]

Abstract: Based upon a bench-top micro-CT system, propagation-based phase-contrast imaging has been investigated using insects and a thin plastic sheet. The system mainly includes a micro-focus source with focal spot size of 13–20μm and a cooled X-ray CCD detector with pixel size of 24μm. The edge-enhancement effect can be found clearly in the acquired images. With a 0.5mm thickness plastic edge phantom, the effects of X-ray tube voltage and imaging geometry on the phase-contrast imaging were investigated, and quantitative index, edge-enhancement index (EEI), were also calculated. In our study, an interesting phenomenon was observed that the phase-contrast effect becomes more pronounced as the tube voltage increases from 20kVp to 90kVp. Further investigation indicates that smaller focal spot size resulting from the reduction of tube current at higher tube voltage, has caused the unexpected phenomenon. Inferred from our results, phase-contrast effect is insensitive to the tube voltage in the range of 20–90kVp (widely used in medical diagnosis); however, it is sensitive to the focal spot size. In addition, for the investigation of the effect of imaging geometry, an optimal geometric magnification range of 2.5–4.5 is suggested to get a good phase-contrast imaging for a micro-CT system with source-to-detector distance of 720mm. [Copyright &y& Elsevier]

Fourier ptychographic microscopy (FPM) is a recently developed computational imaging technology with wide field-of-view (FOV) and high resolution. However, previous studies thought that the conventional FPM algorithm approximated the LED as a coherent point light source, so FPM imaging is regarded as a coherent imaging process, and the coherent transfer function(CTF)was constrained in the Fourier domain, which seemed to be problematic. At the same time, the conventional FPM data requires the acquisition of multiple data, which takes a long time and limits its real-time application. To investigate these two problems, this paper proposes the discrete cosine (DCT) coefficient Predictive searching method for FPM with Bessel function weighted apodized coherent transfer function (B-AC) constraint. The experimental results demonstrate that without adding any algorithmic complexity and computational burden, the proposed method can reduce the number of acquired images by about 60%, while retaining the accuracy of the reconstructed results. • A pre-search method of DCT coefficients under B-AC constraint is proposed. • The pre-search method of DCT coefficients can significantly improve the efficiency. • B-AC constraint is more suitable for FPM actual imaging process. • The proposed method hardly increases the computational burden. [ABSTRACT FROM AUTHOR]

Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption. [ABSTRACT FROM AUTHOR]