bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploration of 2D Ti3C2 MXene for all solution processed piezoelectric nanogenerator applications
Authors: Auliya Rahmat Zaki , Ooi Poh Choon , Sadri Rad , Talik Noor Azrina , Yau Zhi Yong , Mohammad Haniff Muhammad Aniq Shazni , Goh Boon Tong , Dee Chang Fu , Aslfattahi Navid , Al-Bati Sameer , Ibtehaj Khatatbeh , Hj Jumali Mohammad Hafizuddin , Mohd Razip Wee M. F. , Mohamed Mohd Ambri , Othman Masuri 
Published: August 2021

AbstractA new 2D titanium carbide (Ti3C2), a low dimensional material of the MXene family has attracted remarkable interest in several electronic applications, but its unique structure and novel properties are still less explored in piezoelectric energy harvesters. Herein, a systematic study has been conducted to examine the role of Ti3C2 multilayers when it is incorporated in the piezoelectric polymer host. The 0.03 g/L of Ti3C2 has been identified as the most appropriate concentration to ensure the optimum performance of the fabricated device with a generated output voltage of about 6.0 V. The probable reasons might be due to the uniformity of nanofiller distribution in the polyvinylidene difluoride (PVDF) and the incorporation of Ti3C2 in a polymer matrix is found to enhance the β-phase of PVDF and diminish the undesired α-phase configuration. Low tapping frequency and force were demonstrated to scavenge electrical energy from abundant mechanical energy resources particularly human motion and environmental stimuli. The fabricated device attained a power density of 14 µW.cm−2 at 10.8 MΩ of load resistor which is considerably high among 2D material-based piezoelectric nanogenerators. The device has also shown stable electrical performance for up to 4 weeks and is practically able to store energy in a capacitor and light up a LED. Hence, the Ti3C2-based piezoelectric nanogenerator suggests the potential to realize the energy harvesting application for low-power electronic devices.
Role of prostate health index to predict Gleason score upgrading and high-risk prostate cancer in radical prostatectomy specimens
Authors: Kim Hwanik , Jung Gyoohwan , Kim Jin Hyuck , Byun Seok-Soo , Hong Sung Kyu 
Published: August 2021

AbstractWe evaluated the role of prostate health index (PHI) in predicting Gleason score (GS) upgrading in International Society of Urological Pathology Grade Group (ISUP GG) 1 & 2 prostate cancer (PCa) or adverse pathologic outcomes at radical prostatectomy (RP). A total of 300 patients with prostate specific antigen ≥ 3 ng/mL, PHI and prostate biopsy (71 patients with RP included) were retrospectively included in the study. The primary study outcomes are PCa and clinically significant PCa (csPCa, defined as ISUP GG ≥ 2) diagnostic rate of PHI, and GS upgrading rate at RP specimen. The secondary outcomes are the comparison between GS upgrading and non-upgrading group, GS upgrading and high-risk PCa (ISUP GG ≥ 3 or ≥ pT3a) predictability of preoperative clinical factors. Overall, 139 (46.3%) and 92 (30.7%) were diagnosed with PCa and csPCa, respectively. GS upgrading rate was 34.3% in all patients with RP. Significant differences were shown in the total prostate volume (p = 0.047), the distribution of ISUP GG at biopsy (p = 0.001) and RP (p = 0.032), respectively. PHI values ≥ 55 [Odds ratio (OR): 3.64 (95% confidence interval (CI) = 1.05–12.68, p = 0.042] and presence of PI-RADS lesion ≥ 4 (OR: 7.03, 95% CI = 1.68–29.51, p = 0.018) were the significant predictors of GS upgrading in RP specimens (AUC = 0.737). PHI values ≥ 55 (OR: 9.05, 5% CI = 1.04–78.52, p = 0.046) is a significant factor for predicting adverse pathologic features in RP specimens (AUC = 0.781). PHI could predict GS upgrading in combination with PIRADS lesions ≥ 4 in ISUP GG 1 & 2. PHI alone could evaluate the possibility of high-risk PCa after surgery as well.
Automated versus manual urine output monitoring in the intensive care unit
Authors: Minor Joni , Smith Ali , Deutsch Frederic , Kellum John A. 
Published: August 2021

AbstractAcute kidney injury (AKI) is defined by changes in serum creatinine and urine output (UO). Significant limitations exist regarding accurate ascertainment of urine output even within the intensive care unit. We sought to evaluate an automated urine output collections system and compare it to nursing measurements. We prospectively collected urine output using an electronic urine monitoring system and compared it to charted hourly UO in 44 patients after cardiac surgery at a single university hospital ICU. We calculated UO and oliguria rates and compared them to data from the sensor and from nursing charting. A total of 187 hourly UO measurements were obtained and on average, UO was reported 47 min late, with a median of 18 min, and a maximum of almost 6 h. Patients had a mean hourly UO of 76.3 ml over the observation period. Compared to manual measurements by study personnel, nurses significantly overestimated hourly UO by 19.9 ml (95% CI: 10.3; 29.5; p =  < 0.001). By contrast, the mean difference between the UO measured with the sensor and by study personnel was 2.29 ml (95% CI: − 6.7; 11.3), p = 0.61. Electronic UO monitoring is significantly more accurate than nurse-performed manual measurements in actual intensive care patients. Furthermore, timely ascertainment of UO is difficult to achieve with manual technique, resulting in important delays in detecting oliguria perhaps leading to missed cases of AKI.
Machine learning-based approach for disease severity classification of carpal tunnel syndrome
Authors: Park Dougho , Kim Byung Hee , Lee Sang-Eok , Kim Dong Young , Kim Mansu , Kwon Heum Dai , Kim Mun-Chul , Kim Ae Ryoung , Kim Hyoung Seop , Lee Jang Woo 
Published: August 2021

AbstractIdentifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables each were retrospectively analyzed. CTS was confirmed using electrodiagnosis, and its severity was classified into three grades: mild, moderate, and severe. The dataset was randomly split into a training (70%) and test (30%) set. A total of 507 mild, 276 moderate, and 254 severe CTS hands were included. Extreme gradient boosting (XGB) showed the highest external validation accuracy in the multi-class classification at 76.6% (95% confidence interval [CI] 71.2–81.5). XGB also had an optimal model training accuracy of 76.1%. Random forest (RF) and k-nearest neighbors had the second-highest external validation accuracy of 75.6% (95% CI 70.0–80.5). For the RF and XGB models, the numeric rating scale of pain was the most important variable, and body mass index was the second most important. The one-versus-rest classification yielded improved external validation accuracies for each severity grade compared with the multi-class classification (mild, 83.6%; moderate, 78.8%; severe, 90.9%). The CTS severity classification based on the ML model was validated and is readily applicable to aiding clinical evaluations.
Positive relationship between seasonal Indo-Pacific Ocean wave power and SST
Authors: Kaur Sukhwinder , Kumar Prashant , Weller Evan , Young Ian R. 
Published: August 2021

AbstractThe influence of increasing sea surface temperatures (SSTs), in response to greenhouse warming, on wave power (WP) remains uncertain. Here, seasonal relationships between SST anomalies and mean and extreme WP over the Indo-Pacific Ocean are examined. Overall, seasonal WP has significantly increased over much of the Pacific, Indian, and Southern Ocean by 1.21–3.10 kW/m dec−1 over 1979–2019. Contributions from wave characteristics, namely significant wave height (SWH) and peak wave period (PWP), to changes in WP show that SWH contributes most in extra-tropical regions, and PWP most in tropical regions. Further, seasonal relationships between SST anomalies and WP indicate that increases in WP are also seen during strong El Niño years in December–February, and in-phase combinations of El Niño and positive Indian Ocean Dipole (IOD) events during June–August and September–November. Results highlight both long-term increasing SSTs and climate variability roles for inducing large-scale seasonal WP changes throughout the Indo-Pacific.
Characterizing rhizosphere microbiota of peanut (Arachis hypogaea L.) from pre-sowing to post-harvest of crop under field conditions
Authors: Hinsu Ankit T. , Panchal Ketankumar J. , Pandit Ramesh J. , Koringa Prakash G. , Kothari Ramesh K. 
Published: August 2021

AbstractThe rhizosphere, a narrow zone of soil near plant roots, is a hot spot for microbial activity. Rhizosphere microbiota directly or indirectly benefit plants by supplementing nutrients, producing beneficial chemicals, or suppressing pathogens. Plants attract and modulate bacteria within the rhizosphere by releasing exudates. Plants also tend to select the rhizosphere microbiota based on their needs; a phenomenon termed as “rhizosphere effect”. In this study, we characterized the rhizosphere microbiota of peanut plants across the crop development cycle from pre-sowing of seeds to post-harvest of crop under field conditions. The rhizosphere and bulk soil samples from different crop developmental stages were also compared. The composition of bulk soil microbiota resembled microbiota of pre-sowing and post-harvest soil and was markedly different from rhizosphere soil samples. Rhizosphere samples were enriched with multiple organisms mostly from the Proteobacteria, Firmicutes and Bacteroidota phyla. Differences in diversity were observed among the rhizosphere samples but not in bulk soil across different crop development stages.Pseudomonas_M indicawas highly enriched during the germination of seeds. Furthermore, Plant Growth Promoting (PGP) bacteria likeBacilluswere enriched during the middle stages of crop development but there was a decline in PGP organisms in the matured crop stage. We also observed a significant association of pH and Electrical Conductivity (EC) with the profiles of microbial community. Overall, this study portrayed the changes in rhizosphere microbiota of peanut during different developmental stages of crop and may help to design stage specific bio-strategies such as bio-fertilizer to improve crop yield.
Limited resilience of the soil microbiome to mechanical compaction within four growing seasons of agricultural management
Authors: Longepierre Manon , Widmer Franco , Keller Thomas , Weisskopf Peter , Colombi Tino , Six Johan , Hartmann Martin 
Published: August 2021

Abstract
Soil compaction affects many soil functions, but we have little information on the resistance and resilience of soil microorganisms to this disturbance. Here, we present data on the response of soil microbial diversity to a single compaction event and its temporal evolution under different agricultural management systems during four growing seasons. Crop yield was reduced (up to −90%) in the first two seasons after compaction, but mostly recovered in subsequent seasons. Soil compaction increased soil bulk density (+15%), and decreased air permeability (−94%) and gas diffusion (−59%), and those properties did not fully recover within four growing seasons. Soil compaction induced cropping system-dependent shifts in microbial community structures with little resilience over the four growing seasons. Microbial taxa sensitive to soil compaction were detected in all major phyla. Overall, anaerobic prokaryotes and saprotrophic fungi increased in compacted soils, whereas aerobic prokaryotes and plant-associated fungi were mostly negatively affected. Most measured properties showed large spatial variability across the replicated blocks, demonstrating the dependence of compaction effects on initial conditions. This study demonstrates that soil compaction is a disturbance that can have long-lasting effects on soil properties and soil microorganisms, but those effects are not necessarily aligned with changes in crop yield.
Fabrication, microstructural evolution and excellent EMW absorbing properties of SiC fibers with high iron content
Authors: Chen Shaohong , Dong Xichao , Yao Xiaoji , Wu Pengfei , Liu Anhua , Yu Zhaoju 
Published: August 2021

Continuous Fe-SiC fibers with the highest Fe content of 7.70 wt% were successfully fabricated and exhibit excellent EMW absorbing performance. The microstructure of Fe-SiC fibers possesses SiC crystallites, turbostratic carbon, and FexSiy@C.
Key role for EphB2 receptor in kidney fibrosis
Authors: Huang Zhimin , Liu Simeng , Tang Anna , Al-Rabadi Laith , Henkemeyer Mark , Mimche Patrice N. , Huang Yufeng 
Published: August 2021

Abstract
Erythropoietin producing hepatocellular (Eph)–Eph receptor interacting (Ephrin) receptor–ligand signaling has been implicated in the development of tissue fibrosis, though it has not been well defined in the kidney. We detected substantial up-regulation of expression and phosphorylation of the EphB2 receptor tyrosine kinase in fibrotic kidney tissue obtained both from mice subjected to the unilateral renal ischemia–reperfusion (IR) model at 14 days and in patients suffering from chronic kidney disease (CKD). Knockout (KO) mice lacking EphB2 expression exhibited a normal renal structure and function, indicating no major role for this receptor in kidney development or action. Although IR injury is well-known to cause tissue damage, fibrosis, and renal dysfunction, we found that kidneys from EphB2KO mice showed much less renal tubular injury and retained a more preserved renal function. IR-injured kidneys from EphB2 KOs exhibited greatly reduced fibrosis and inflammation compared with injured wildtype (WT) littermates, and this correlated with a significant reduction in renal expression of profibrotic molecules, inflammatory cytokines, NADPH oxidases, and markers for cell proliferation, tubular epithelial-to-mesenchymal transition (EMT), myofibroblast activation, and apoptosis. A panel of 760 fibrosis-associated genes were further assessed, revealing that 506 genes in WT mouse kidney following IR injury changed their expression. However, 70.9% of those genes were back to or close to normal in expression when EphB2 was deleted. These data indicate that endogenous EphB2 expression and signaling are abnormally activated after kidney injury and subsequently contribute to the development of renal fibrosis via regulation of multiple profibrotic pathways.
The Influence of Linguistic Bias Upon Speech-Language Pathologists’ Attitudes Toward Clinical Scenarios Involving Nonstandard Dialects of English
Authors: Easton Catherine , Verdon Sarah 
Published: August 2021

PurposeVariation within languages, including dialects, takes on an indexical function, marking belonging and connection. Meanwhile, attitudes toward these speech varieties become marked by linguistic bias. Within the speech-language pathology profession, research evidence, assessment tools, and intervention programs have largely been designed for and by the White, English-speaking middle class. As such, linguistic bias with a preference for standardized dialects is prevalent in the training and practice of the speech-language pathology profession, resulting in discriminatory and racialized practices.MethodTo investigate the influence of linguistic bias upon speech-language pathologists' (SLPs') clinical decision making, data were collected from 129 Australian SLPs via an online survey. Inferential statistics were used to investigate the relationship between clinical decision making and SLPs' attitudes toward nonstandard dialects as well as personal and professional factors. A content analysis of extended responses was conducted to identify themes in clinical decision making.ResultsSLPs with more years of experience and those who had received professional development were significantly more likely to seek out more information before making a diagnosis, while those with more negative attitudes toward linguistic diversity were significantly more likely to identify a disorder than a difference. SLPs provided a range of justifications for their clinical decision making, but few acknowledged the influence of their own attitudes and bias upon their decision making.ConclusionsSLPs' linguistic bias towards speakers of nonstandard dialects has the potential to impact upon their clinical judgment of difference versus disorder and lead to inequality of service provision for speakers who do not express themselves in standardized forms. Before the profession can truly move toward an antiracist approach of equitable service provision for all, SLPs must engage in critical self-reflection to disrupt the adherence of the speech-language pathology profession to standardized “White” norms of communication.
Eye Gaze and Perceptual Adaptation to Audiovisual Degraded Speech
Authors: Banks Briony , Gowen Emma , Munro Kevin J. , Adank Patti 
Published: August 2021

Purpose
Visual cues from a speaker's face may benefit perceptual adaptation to degraded speech, but current evidence is limited. We aimed to replicate results from previous studies to establish the extent to which visual speech cues can lead to greater adaptation over time, extending existing results to a real-time adaptation paradigm (i.e., without a separate training period). A second aim was to investigate whether eye gaze patterns toward the speaker's mouth were related to better perception, hypothesizing that listeners who looked more at the speaker's mouth would show greater adaptation.

Method

A group of listeners (
n
= 30) was presented with 90 noise-vocoded sentences in audiovisual format, whereas a control group (
n
= 29) was presented with the audio signal only. Recognition accuracy was measured throughout and eye tracking was used to measure fixations toward the speaker's eyes and mouth in the audiovisual group.

Results
Previous studies were partially replicated: The audiovisual group had better recognition throughout and adapted slightly more rapidly, but both groups showed an equal amount of improvement overall. Longer fixations on the speaker's mouth in the audiovisual group were related to better overall accuracy. An exploratory analysis further demonstrated that the duration of fixations to the speaker's mouth decreased over time.

Conclusions

The results suggest that visual cues may not benefit adaptation to degraded speech as much as previously thought. Longer fixations on a speaker's mouth may play a role in successfully decoding visual speech cues; however, this will need to be confirmed in future research to fully understand how patterns of eye gaze are related to audiovisual speech recognition. All materials, data, and code are available at
https://osf.io/2wqkf/
.
3D convolutional neural networks based automatic modulation classification in the presence of channel noise
Authors: Khan Rahim , Yang Qiang , Ullah Inam , Rehman Ateeq Ur , Tufail Ahsan Bin , Noor Alam , Rehman Abdul , Cengiz Korhan 
Published: August 2021

AbstractAutomatic modulation classification is a task that is essentially required in many intelligent communication systems such as fibre‐optic, next‐generation 5G or 6G systems, cognitive radio as well as multimedia internet‐of‐things networks etc. Deep learning (DL) is a representation learning method that takes raw data and finds representations for different tasks such as classification and detection. DL techniques like Convolutional Neural Networks (CNNs) have a strong potential to process and analyse large chunks of data. In this work, we considered the problem of multiclass (eight classes) classification of modulated signals, which are, Binary Phase Shift Keying, Quadrature Phase Shift Keying, 16 and 64 Quadrature Amplitude Modulation corrupted by Additive White Gaussian Noise, Rician and Rayleigh fading channels using 3D‐CNN architectures in both frequency and spatial domains while deploying three approaches for data augmentation, which are, random zoomed in/out, random shift and random weak Gaussian blurring augmentation techniques with a cross‐validation (CV) based hyperparameter selection statistical approach. Simulation results testify the performance of 10‐fold CV without augmentation in the spatial domain to be the best while the worst performing method happens to be 10‐fold CV without augmentation in the frequency domain and we found learning in the spatial domain to be better than learning in the frequency domain.
A 3D controllable diffraction‐limited spot array generated by means of a spaced‐dipole array
Authors: Yu Yanzhong , Huang Han , Lin Shunda , Zeng Yongxi , Chen Musheng , Zhan Qiwen 
Published: August 2021

AbstractThe creation of an array of three‐dimensional (3D) multifocal spots in the focal region has attracted interest due to potential applications in parallel or simultaneous process areas. Based on the theory of pattern synthesis of antenna array and the electromagnetic time reversal technique, an optimisation‐free approach is reported to construct a 3D controllable diffraction‐limited spot array in the focal volume of a 4pi focussing system formed by two high‐numerical‐aperture (NA) objectives. The proposed method can be implemented readily by inversely focussing the field radiated from a virtual spaced‐dipole antenna array mounted at the focus of the 4pi configuration. By solving the inverse problem, the required illumination in the pupil plane for producing the 3D spot array can be found. It is demonstrated that the 3D diffraction‐limited focal spot array owns the properties of controllable polarisation, scheduled number, tunable location, and adjustable interval. This array may find applications in 3D simultaneous optical manipulation and trapping, 3D parallel fabrication, 3D optical data storage, and so on.
Siamese recurrent neural networks for the robust classification of grid disturbances in transmission power systems considering unknown events
Authors: Kummerow André , Monsalve Cristian , Bretschneider Peter 
Published: August 2021

AbstractThe automated identification and localisation of grid disturbances is a major research area and key technology for the monitoring and control of future power systems. Current recognition systems rely on sufficient training data and are very error‐prone to disturbance events, which are unseen during training. This study introduces a robust Siamese recurrent neural network using attention‐based embedding functions to simultaneously identify and locate disturbances from synchrophasor data. Additionally, a novel double‐sigmoid classifier is introduced for reliable differentiation between known and unknown disturbance types and locations. Different models are evaluated within an open‐set classification problem for a generic power transmission system considering different unknown disturbance events. A detailed analysis of the results is provided and classification results are compared with a state‐of‐the‐art open‐set classifier.