bluuebunny/crossref_metadata_2025_split huggingface dataset:
A model for superimposed coalbed methane, shale gas and tight sandstone reservoirs, Taiyuan Formation, Yushe-Wuxiang Block, eastern Qinshui Basin
Authors: Xie Weidong , Gan Huajun , Chen Chongyu , Vandeginste Veerle , Chen Si , Wang Meng , Wang Jiyao , Yu Zhenghong 
Published: July 2022

AbstractSuperimposed accumulation mechanism and model of vertical source rock–reservoir system of coal-measure gas is crucial to evaluate the exploration potential, and also the basis of co-exploration and co-production of coal measure gas. This work investigates the formation mechanism of various types of reservoirs (coalbed methane, shale gas, tight sandstone) in the Taiyuan Formation (Yushe-Wuxiang Block, eastern Qinshui Basin). Source rocks (coal seams and coal-measure mudstones) in the study area are characterized by type III kerogen, organic rich and over-mature, and reach a gas generation peak during the Early to Late Cretaceous. Coalbed methane mainly adsorbs on the surface of micropores, shale gas mainly occurs in micropores, macropores and micro-factures in adsorbed and free states, and tight sandstone gas mainly occurs in macropores in a free state. The combinations of successions are identified, coalbed methane, shale gas, and tight sandstone gas horizons are divided into a mudstone-sandstone reservoir (combination I), a coal-mudstone-sandstone reservoir (combination II), and a coal-mudstone reservoir (combination III). This division occurs from top to bottom in the succession and is identified on the basis of lithology, total organic carbon content (TOC) of mudstones, gas logging, superimposition relationships, and the source rock-reservoir-caprock assemblage. The strata thickness, continuity, and gas logging results of combination III comprise the most favorable conditions for fairly good development potential, followed by combination I. The development potential of combination II is poor due to the small strata thickness and poor continuity. The identification of superimposed reservoirs can provide an engineering reference for the exploration of coal-measure gas.
Discovery, total syntheses and potent anti-inflammatory activity of pyrrolinone-fused benzoazepine alkaloids Asperazepanones A and B from Aspergillus candidus
Authors: Xu Li , Guo Feng-Wei , Zhang Xue-Qing , Zhou Tian-Yi , Wang Chao-Jie , Wei Mei-Yan , Gu Yu-Cheng , Wang Chang-Yun , Shao Chang-Lun 
Published: July 2022

AbstractNatural products are well established as an important resource and play an important role in drug discovery. Here, two pyrrolinone-fused benzoazepine alkaloids, (+)-asperazepanones A (1) and B (2) with a 6/7/5 ring system, together with the artifact (−)-asperazepanone A (1), were isolated from the coral-derivedAspergillus candidusfungus. Their structures including absolute configurations were elucidated by extensive spectroscopic methods, single crystal X-ray diffraction, and ECD calculations. Furthermore, total syntheses of (±)-1and (±)-2have been achieved starting from the commercially L-aspartic acid diethyl ester hydrochloride and monoethyl malonate in 7 and 8 steps, respectively. The key step in the syntheses was an intramolecular Friedel-Crafts reaction to build the unique tricyclic skeleton. Interestingly, (+)-2not only showed obviously inhibitory activity against NO production, but also inhibited potent LPS-induced expression of TNF-α and IL-6 at the concentration of 0.1 μM. It thus represents a potentially promising lead for anti-inflammatory drug discovery.
Alternative Designs for Testing Speech, Language, and Hearing Interventions: Cluster-Randomized Trials and Stepped-Wedge Designs
Authors: Patterson Charity G. , Leland Natalie E. , Mormer Elaine , Palmer Catherine V. 
Published: July 2022

Purpose:
Individual-randomized trials are the gold standard for testing the efficacy and effectiveness of drugs, devices, and behavioral interventions. Health care delivery, educational, and programmatic interventions are often complex, involving multiple levels of change and measurement precluding individual randomization for testing. Cluster-randomized trials and cluster-randomized stepped-wedge trials are alternatives where the intervention is allocated at the group level, such as a clinic or a school, and the outcomes are measured at the person level. These designs are introduced along with the statistical implications of similarities among individuals within the same cluster. We also illustrate the parameters that have the most impact on the likelihood of detecting intervention effects, which must be considered when planning these trials.

Conclusion:
Cluster-randomized and stepped-wedge designs should be considered by researchers as experimental alternatives to individual-randomized trials when testing speech, language, and hearing care interventions in real-world settings.
A robust and efficient method for skeleton‐based human action recognition and its application for cross‐dataset evaluation
Authors: Nguyen Tien‐Thanh , Pham Dinh‐Tan , Vu Hai , Le Thi‐Lan 
Published: July 2022

AbstractSkeleton‐based human action recognition has emerged recently thanks to its compactness and robustness to appearance variations. Although impressive results have been obtained in recent years, the performance of skeleton‐based action recognition methods has to be improved to be deployed in real‐time applications. Recently, a lightweight network structure named Double‐feature Double‐motion Network (DD‐Net) has been proposed for the skeleton‐based human action recognition. With high speed, the DD‐Net achieves state‐of‐the‐art performance on hand and body actions. The DD‐Net could not distinguish actions if they have a weak connection with the global trajectories. However, the DD‐Net is suitable for human action recognition where actions strongly correlate to the global trajectories. In this paper, the authors propose TD‐Net, an improved version of the DD‐Net in which a new branch is added. The new branch takes the normalised coordinates of joints (NCJ) to enrich the spatial information. On five datasets for skeleton‐based human activity recognition that are MSR‐Action3D, CMDFall, JHMDB, FPHAB, and NTU RGB + D, the TD‐Net consistently obtains superior performance compared with the baseline model DD‐Net. The proposed method outperforms different state‐of‐the‐art methods, including both hand‐designed and deep learning‐based methods on four datasets (MSR‐Action3D, CMDFall, JHMDB, and FPHAB). Furthermore, the generalisation of the proposed method is confirmed through cross‐dataset evaluation. To illustrate the potential use of the model for real‐time human action recognition, the authors have deployed an application on an edge device. The experimental result shows that the application can process up to 40 fps for pose estimation using MediaPipe. It takes only 0.04 ms to recognise an action from skeleton sequences.
A dual quantum image feature extraction method: PSQIFE
Authors: Su Jie , Lu Shuhan , Li Lin 
Published: July 2022

AbstractIn digital image processing, feature extraction occupies a very important position, which is related to the effect of image classification or recognition. At present, effective quantum feature extraction methods are relatively lacking. And the current feature extraction methods are mainly devoted to the extraction of basic features of images, failing to consider the global features of classical images and the global features of quantum images comprehensively. In this paper, we propose a dual quantum image feature extraction method named PSQIFE, which focuses on the global energy representation of images by constructing dual quantum image global features. The representation of the global features of the dual quantum image is obtained by quantum superposition of two parts of quantum state features. In this paper, quantum image reconstruction and quantum image fidelity tests are performed on the extracted global features by 9 classes of classical images, and the overall fidelity is above 95%. In addition, the effectiveness of PSQIFE dual quantum image feature extraction method is verified by comparing the image classification test with convolutional feature extraction method on Mnist dataset. The method has some reference significance for the research of quantum image feature extraction and classification.
Scaphoid Malunion: Incidence, Predictors, and Outcomes
Authors: Putnam Jill , Pfaff Kayla , Yao Jeffrey 
Published: July 2022

Abstract
Background Scaphoid malunion is rarely reported. Previous literature has attributed loss of carpal height and degenerative changes to scaphoid malunion, but the percentage of asymptomatic malunions remains unknown.
Purpose The authors of this study aim to define predictors of malunion and outcomes associated with scaphoid malunion.
Methods Institutional board review was obtained prior to evaluating medical records of patients 18 years and older who were treated for scaphoid fractures and/or nonunion between 2000 and 2020. The following data were collected for each patient: age, gender, fracture location, surgical technique, time to union, and whether malunion resulted. Malunion was defined using a lateral intrascaphoid angle (LISA) >45 degrees and height-to-length ratio (HLR) >60. Pain scores, range of motion (ROM), and secondary surgery were also evaluated.
Results Overall, 355 scaphoid injuries, including 196 acute fractures and 159 nonunions, were evaluated in this analysis. Of these, 55 scaphoids (15%) met the definition of malunion. Of these patients, 23% were female. The mean age at the time of injury was 29 years. Nonunion cases were more likely than acute cases to be associated with malunion. Proximal pole fractures were more likely to associated with malunion than waist fractures. When controlling for nonunion and fracture location, malunited scaphoids were not associated with any significant difference in pain score, ROM, or secondary surgery, compared with nonmalunion cases. A total of 10 patients (3.3%) without malunion and 2 patients (3.6%) with malunion went on to a secondary surgery. Final extension/flexion was 67/67 degrees and 56/59 degrees in nonmalunion and malunion groups, respectively, but these differences were not significant.
Conclusion Compared with scaphoid injuries that do not result in malunion, scaphoid injuries that heal into malunion have similar outcomes. While scaphoid malunion in a single case or series may be associated with poor outcomes, this study suggests that scaphoid malunions do not have worse functional outcomes.
Level of Evidence This is a Level IV study.
Examining the Relationship between Health Literacy, Health Numeracy, and Patient Portal Use
Authors: Di Tosto Gennaro , Walker Daniel M. , Sieck Cynthia J. , Wallace Lorraine , MacEwan Sarah R. , Gregory Megan E. , Scarborough Seth , Huerta Timothy R. , McAlearney Ann Scheck 
Published: July 2022

Abstract
Objectives The objective of this study is to investigate the relationships between health literacy and numeracy (HLN) and patient portal use, measured in inpatient and outpatient settings.
Methods Using data collected as part of a pragmatic randomized controlled trial conducted across the inpatient population of a U.S.-based academic medical center, the present study evaluated the relationships between patients' perceptions of health literacy and their skills, interpreting medical information with metrics of engagement with patient portals.
Results Self-reported levels of HLN for patients in the study sample (n = 654) were not significantly associated with inpatient portal use as measured by frequency of use or the number of different inpatient portal functions used. Use of the outpatient version of the portal over the course of 6 months following hospital discharge was also not associated with HLN. A subsequent assessment of patients after 6 months of portal use postdischarge (response rate 40%) did not reveal any differences with respect to portal use and health numeracy; however, a significant increase in self-reported levels of health literacy was found at this point.
Conclusion While previous studies have suggested that low HLN might represent a barrier to inpatient portal adoption and might limit engagement with outpatient portals, we did not find these associations to hold. Our findings, however, suggest that the inpatient setting may be effective in facilitating technology acceptance. Specifically, the introduction of an inpatient portal made available on hospital-provided tablets may have practical implications and contribute to increased adoption of patient-facing health information technology tools.
A rather wild imagination: who is and who is not a migrant in the Czech media and society?
Authors: Bartoszewicz Monika Gabriela , Eibl Otto 
Published: July 2022

AbstractThis paper focuses on migrants and migration in the context of the Czech Republic, an ethnically and nationally homogeneous country without significant migration experience. Despite this fact, the issue of migration became very prominent in 2015 and has been an integral part of Czech political and public discourse since then. Although the topic has attracted scholarly interest, but the reflection on migrant images held by citizens has been omitted. To fill this gap, first, we conducted a quantitative computer-assisted content analysis of the main Czech media (2015–2018) to investigate how important the issue of migration was and in what context migrants and migration were discussed in the media. We then conducted a series of focus groups with Czech citizens to answer not only how they perceived migrants and migration in general, but also how they perceived the (quality of) media coverage of this issue. The findings offer insight into patterns of media consumption: Our respondents were well aware that media representation of the topic is exaggerated and does not include all possible points of view. The prevailing perception was that the mostly negative media representation was fixated on the image of a migrant coming from the Middle East, most likely to be a terrorist who is not going to adapt to a “normal” life in the Czech Republic. Indeed, the very term migrant is mainly associated with someone who, according to the mental projections of the respondents, is “different” at first sight, fails to fit in and integrate into the majority society, does not look for work thus becoming dependent on the social system of the host country. In other words, for Czechs, people who come to settle and work are excluded from the socially constructed category of migrants.
Warming-induced vegetation growth cancels out soil carbon-climate feedback in the northern Asian permafrost region in the 21st century
Authors: Liu Jianzhao , Yuan Fenghui , Zuo Yunjiang , Zhou Rui , Zhu Xinhao , Li Kexin , Wang Nannan , Chen Ning , Guo Ziyu , Zhang Lihua , Sun Ying , Guo Yuedong , Song Changchun , Xu Xiaofeng 
Published: July 2022

Abstract
Permafrost soils represent an enormous carbon (C) pool that is highly vulnerable to climate warming. We used the model output ensemble of the Coupled Model Intercomparison Project Phase 6 to estimate the C storage in soil, litter, and vegetation in the current extent of northern Asian permafrost during 1900–2100. The contemporary (1995–2014) C storage was estimated to be 368.1 ± 82.5 Pg C for the full column depth of the soil, 13.3 ± 4.6 Pg C in litter, and 22.2 ± 3.2 Pg C in vegetation biomass, while these C storage levels are projected to decline by 3.9 Pg C (1.1%) in soils, increase of 0.03 Pg C (0.2%) in litter, and increase by 21.1 Pg C (95.3%) in vegetation biomass by the end of the 21st century under SSP585. The total C storage was dominated by warming-induced vegetation growth. Partial correlation analysis showed that surface air temperature (TAS), soil liquid water, and soil mineral nitrogen (SMN) dominated the soil and vegetation C pools, while SMN controlled litter C during the historical period. Under future scenarios, TAS and SMN dominated the changes of soil and litter C, while TAS determined the vegetation C increase. The growing soil C loss with warming indicates positive C-climate feedback in soils; this warming-induced acceleration of soil C loss was canceled out by the enhanced vegetation C accumulation, leading to a strong C sink in the 21st century.
Design and realization of smart energy management system for Standalone PV system
Authors: Manu D. , Shorabh S.G. , Gnana Swathika O.V. , Umashankar S. , Tejaswi P. 
Published: July 2022

Abstract
The internet connected world demands a need for integrating the renewable energy sources and loads in the microgrid with an energy management system that is capable of monitoring, controlling and conserving energy and at the same time. This paper proposes the idea of an Internet of things (IoT) based smart Energy Management System (EMS) for a Stand-Alone Photovoltaic (SAPV) System, which acts as a closed system where all the power is generated from solar energy and either stored in batteries or utilized within the system. It has various units namely: Energy generation, Energy management and IoT. A novel constant battery operating algorithm is proposed for EMS of SAPV System. All the loads are connected through IoT and hence their status maybe monitored and controlled from remote location.
A review of effect of higher-order nonlinear theories on analysis of piezoelectric laminates
Authors: Akhlaq Adnan , Dawood M.S.I Shaik , Ali J S Mohamed , Sulaeman E 
Published: July 2022

AbstractThe paper aims at reviewing the modeling of piezoelectric laminated structures for stress distribution, vibration and noise control, shape control, health monitoring, and energy harvesting. Accurate modeling of piezoelectric laminates using linear and non-linear modeling techniques is essential at the high and low electric fields. The review is done in the field application of (1) different deformation theories (2) geometric non-linear theories (3) material non-linear models for the analysis of piezoelectric laminates. The literature review emphasizes using a higher-order non-linear model to analyze piezoelectric laminates and their effect on the composite structure.
Monitoring The IIUM River Using Unmanned Aerial Vehicle And Image Classification
Authors: Nazmi Mohamad , Okasha Mohamed , Aasim Aizat , Idres Moumen 
Published: July 2022

Abstract
Prior research has shown viable methods towards identifying sources of pollution in rivers by utilizing Unmanned Aerial Vehicles (UAVs) combined with proper image classification techniques. This research attempts to develop and implement a novel approach to monitor the IIUM River whereby a Parrot Bebop 2 drone is utilized for data collection, while the Quantum Geographic Information System (QGIS) software is used for the supervised classification of the collected data. The image processing techniques of stitching or mosaicking, georeferencing and supervised classification are done using Adobe Photoshop, QGIS Georeferencing plugin, and QGIS Semi-Automatic Supervised Classification Toolbox, respectively. Results show that the classification process successfully recognized target objects, however, differing sun locations in datasets along with insufficient training data have led to some minor flaws. Despite these flaws, this research successfully achieved its objectives and will be vital for further investigations in the future.
Open ended coaxial sensors with and without cavity for non-destructive applications
Authors: Amir Mounir , Amar Hichem , Fortaki Tarek , Tebache Soufiane , Hedir Abdallah , Ghodbane Hatem 
Published: July 2022

Abstract
In this paper, a comparative study between two open ended coaxial sensors with and without cavity is presented. Those sensors are used for materials characterization (composite, dielectric, liquid) and surface defects detection. First Galerkin’s method in the Fourier transform domain is applied to the determination of the resonant frequencies of an open-ended coaxial sensor. The obtained results are used for the realization of two open ended coaxial sensors with and without cavity. The defects detection testbed includes a network analyzer, two open ended coaxial sensors and two aluminum plates with defects of various depths. The proposed method is based on the reflected electromagnetic waves from defected surface, wish are captured by a coaxial sensor. The behavior of the sensor is then studied through reflection coefficient measurements for each kind of defect. A comparative study between the realized sensors is also carried out.
DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification
Authors: Ćiprijanović Aleksandra , Kafkes Diana , Snyder Gregory , Sánchez F Javier , Perdue Gabriel Nathan , Pedro Kevin , Nord Brian , Madireddy Sandeep , Wild Stefan M 
Published: July 2022

Abstract
With increased adoption of supervised deep learning methods for work with cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: (a) increased observational noise as represented by higher levels of Poisson noise and (b) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness in the face of these perturbations. For deep learning models without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy up to 23% on data with higher observational noise. Domain adaptation also increases up to a factor of

≈

2.3

the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations. Successful development and implementation of methods that increase model robustness in astronomical survey pipelines will help pave the way for many more uses of deep learning for astronomy.