bluuebunny/crossref_metadata_2025_split huggingface dataset:
Outcomes of Three Vasovasostomy Surgical Techniques in Vasectomized Men: A Systematic Review of the Current Literature
Authors: Duijn M. , van der Zee J. A. , Bachour Y. 
Published: January 2023

AbstractTo investigate which surgical technique, macroscopic, microscopic or robot-assisted microscopic, shows highest postoperative patency- and pregnancy rates for vasectomy reversal (VR) by vasovasostomy (VV). This study is an updated version of the systematic review and meta-analysis entitled: “Outcomes of Macrosurgical Versus Microsurgical Vasovasostomy in Vasectomized Men: a Systematic Review and Meta-analysis” published in 2021. We performed a systematic review of the English-, Dutch-, and German-language literature that described postoperative outcomes of macroscopic, microscopic, or robot-assisted microscopic VV for VR. PubMed National Library of Medicine/MEDLINE, Embase, CENTRAL, The Cochrane Library, Web of Science and Scopus were searched from inception to June 2021. Two reviewers independently examined the studies and associated data for eligibility. Primary outcomes were postoperative patency and pregnancy rates. Data on interval to reversal (≤ 7 years and > 7 years) and postoperative complications were also extracted. Forty-nine studies met the inclusion criteria. We identified 46 retrospective studies and three randomized controlled trials (RCT). A total of 10,088 procedures were included. 6822 patients remained after loss to follow-up. The mean age at reversal of vasectomy was 38.0 years, and the mean interval to reversal was 6.5 years. The pooled mean postoperative patency rate was slightly higher after robot-assisted microsurgical VV (92.7%), compared to macrosurgical (81.9%) and pure microsurgical (90.1%) VV. Postoperative pregnancy rates were 42.7%, 69.7%, and 33.3% after macroscopic, pure microscopic and robot-assisted microscopic VV, respectively. Our results showed that microsurgical and robot-assisted microsurgical VV are associated with higher postoperative patency rates compared to the macrosurgical technique. VV with microsurgical assistance showed the highest post-operative pregnancy rates. However, more research is needed due to lack of RCTs and data on robot-assisted microsurgical VV.
Visual measurement method of crop height based on color feature in harvesting robot
Authors: Wang Shanqin , Wang Lihui , Xiao Huidi , Qin Chengshuai 
Published: January 2023

AbstractFocusing on the problem of inefficiency and labor waste in contact and sensing crop altimetric methods, a method is proposed for crop height measurement using machine vision. Firstly, a crops height measurement model was established based on aperture image principle, and then segmentation lines extraction of crops and background was implemented by a series of morphological operations. Secondly, the foreground information is segmented according to the H channel in the HSV color space. To help extract the complete crop area, a global scan was performed on the image using multiple images to obtain a segmentation threshold with a certain fault tolerance, and the inter-frame sum method and large-scale filter template are also adopted for image enhancement. Considering of the uneven height lines and porous holes in the crop area, the boundary line between the crop region and the background is strengthened through morphological operations such as dilation and erosion. Finally, the Sobel operator is applied to detect the horizontal line. The parameters of the height line could be calculated, and thus achieving crop height measurement. Experiment results show that the inter-frame enhanced image is filtered using the median and morphology of the large-scale window, and the complete upper boundary line of rice and wheat is obtained through hole filling to realize height measurement. Average error of crop height is less than 1.6%, and processing time per frame within 50 ms.
Unstable Shear Slip Failure and Seismic Potential Investigation Using DEM in Underground Mining
Authors: Ma Xu , Zhang Ping 
Published: January 2023

Abstract
Perturbations arising from mining operations significantly affect the stability of rock masses, and the influences aggerates with the rapid increase of mining-operation depths during recent years. The subsurface structures with major discontinuities subject to seismic hazards resulted from the shear-slip behaviors of rock masses. In order to identify the shear-slip regime of discontinuities and calculate seismic moment and seismic energy involved with shear-slip behaviors, we use discrete element modeling to study the shear slip failure along discontinuities in an underground mine. The recorded characteristic and properties of sub-contacts in DEM provide a basis for computing and visualizing the temporal and spatial distribution of seismic moment and seismic energy with mining operations. We computed the seismic energy and seismic moment using the numerical modeling method and the analytic method. We compared the result of summing seismic energy and seismic moment from the subcontacts of numerical models and the result of the analytic method. We confirmed that this tool can be used in comparative analyses. We also found that seismic moment and seismic energy, associated with shear stress drop and shear displacement increase, accumulate in the vicinity of major discontinuities. Mining operations at a greater depth cause greater changes of seismic moment and seismic energy, leading to a higher risk of inducing seismic hazards. Quantifying seismic potential using discrete element modeling can greatly facilitate the investigation of instability of geological discontinuities and thereby can help estimate the potential of seismic hazards.
Experimental and numerical study of free-falling streams of particles impacting an inclined surface
Authors: Dodds David , Sarhan Abd Alhamid Rafea , Naser Jamal 
Published: January 2023

AbstractThis paper presents a detailed experimental and numerical analysis of free-falling particle streams impacting a 45° inclined surface of differing materials. The particles used in this study were glass spheres with average diameters of 136 and 342 µm and a density of 2500 kg/m3. The three mass flow rates considered are 50, 150, and 250 grams per minute (gpm). The effect of wall material on the collision process was also analysed. Special attention was paid to the influence of wall roughness. Therefore, a plate of stainless steel with polished surface, an aluminium sheet, and a Perspex plate with similar properties to those of the rest of the wall sections were used. The experimental data were used to improve and validate a wall collision model in the frame of the Lagrangian approach. A new drag force formula that includes the effects of particle concentration as well as particle Reynolds number was implemented into commercially available codes from CFX4-4 package. It was found that the improved CFD model better predicted the experimental measurements for the particle rebound properties. The rough-wall model in these results showed greater effect on smaller particles than on larger particles. The results also showed that the improved CFD model predicted the velocity changes slightly better than the standard model, and this was confirmed by both the quantitative velocity comparisons and the qualitative concentration plots. Finally, the inclusion of the particle-particle collision was shown to be the dominant factor in providing the dispersion of the particles post collision. Without a sufficient particle-particle collision model, the standard model showed all particles behaving virtually identical, with the main particle stream continuing after the collision process.
Facile fabrication of triboelectric nanogenerators based on paper and natural rubber as low-cost bio-derived materials
Authors: Williams K. Paige , Hann-Deschaine Noah , Chamria Div , Benze Hans T. , Adhikari Ramesh Y. 
Published: January 2023

AbstractTriboelectric nanogenerators (TENGs) are devices capable of effectively harvesting electrical energy from mechanical motion prevalent around us. With the goal of developing TENGs with a small environmental footprint, herein we present the potential of using rubber and paper as biological materials for constructing triboelectric nanogenerators. We explored the performance of these TENGs with various contact material combinations, electrode sizes, and operational frequencies. The optimally configured TENG achieved a maximum open circuit output voltage of over 30 V, and a short circuit current of around 3 µA. Additionally, this optimally configured TENG was capable of charging various capacitors and achieved a maximum power output density of 21 mW/m2. This work demonstrates that biologically derived materials can be used as effective, sustainable, and low-cost contact materials for the development of triboelectric nanogenerators with minimal environmental footprint.
A Deep Reinforcement Learning Real-Time Recommendation Model Based on Long and Short-Term Preference
Authors: Hou Yan-e , Gu Wenbo , Dong WeiChuan , Dang Lanxue 
Published: January 2023

AbstractWith the development of Internet technology, the problem of information overload has increasingly attracted attention. Nowadays, the recommendation system with excellent performance in information retrieval and filtering would be widely used in the business field. However, most existing recommendation systems are considered a static process, during which recommendations for internet users are often based on pre-trained models. A major disadvantage of these static models is that they are incapable of simulating the interaction process between users and their systems. Moreover, most of these models only consider users’ real-time interests while ignoring their long-term preferences. This paper addresses the abovementioned issues and proposes a new recommendation model, DRR-Max, based on deep reinforcement learning (DRL). In the proposed framework, this paper adopted a state generation module specially designed to obtain users’ long-term and short-term preferences from user profiles and user history score item information. Next, Actor-Critical algorithm is used to simulate the real-time recommendation process.Finally, this paper uses offline and online methods to train the model. In the online mode, the network parameters were dynamically updated to simulate the interaction between the system and users in a real recommendation environment. Experimental results on the two publicly available data sets were used to demonstrate the effectiveness of our proposed model.
RivQNet: Deep Learning Based River Discharge Estimation Using Close‐Range Water Surface Imagery
Authors: Ansari S. , Rennie C. D. , Jamieson E. C. , Seidou O. , Clark S. P. 
Published: January 2023

AbstractStreamflow data is often the most critical input for hydrologic and hydraulic research, modeling, and design studies. Streamflow measurement using close range non‐contact sensing such as image velocimetry is a new technique that is yet far from maturity. Most current image‐based surface velocimetry techniques use correlation approaches that require user input to run the algorithms. This input can bias results if the operator is inexperienced. The main goal of this study is to develop a novel, accurate and fast river velocimetry scheme called RivQNet that does not require subjective user input. RivQNet processes close‐range non‐contact water surface images using artificial intelligence techniques. The algorithm is a deep‐learning optical flow estimation using a preferred available convolutional neural network architecture (i.e., FlowNet architecture). In this study the presented method is validated with common standard measurement methods and compared with conventional optical flow methodologies. The results indicate that the presented method yields accurate and dense spatial distributions of surface velocities.
Climate Impact Comparison of Electric and Gas‐Powered End‐User Appliances
Authors: Dietrich Florian , Chen Jia , Shekhar Ankit , Lober Sebastian , Krämer Konstantin , Leggett Graham , van der Veen Carina , Velzeboer Ilona , Denier van der Gon Hugo , Röckmann Thomas 
Published: January 2023

AbstractNatural gas is considered a bridging technology in the energy transition because it produces fewer carbon emissions than coal, for example. However, when leaks exist, methane is released into the atmosphere, leading to a dramatic increase in the carbon footprint of natural gas, as methane is a much stronger greenhouse gas than carbon dioxide. Therefore, we conducted a detailed study of methane emissions from gas‐powered end‐use appliances and then compared their climate impacts with those of electricity‐powered appliances. We used the Munich Oktoberfest as a case study and then extended the study to 25 major natural gas consuming countries. This showed that electricity has been the more climate‐friendly energy source at Oktoberfest since 2005, due to the extensive use of renewable electricity at the festival and the presence of methane emissions, particularly caused by the incomplete combustion and leakages of natural gas in cooking and heating appliances. By contrast, at the global level, our study shows that natural gas still produces lower carbon emissions for end‐user appliances than electricity in 18 of the 25 countries studied. However, as the share of renewable energy in the electricity mix steadily increases in most countries, the carbon footprint of electricity will be lower than that of natural gas in these countries in the near future. These findings from our comparison of the total carbon emissions of electric and gas‐powered end‐use appliances can help inform the debate on how to effectively address climate change.
Short‐Lived and Voluminous Fluid‐Flow in a Single Fracture Related to Seismic Events in the Middle Crust
Authors: Mindaleva D. , Uno M. , Tsuchiya N. 
Published: January 2023

AbstractThis study focuses on samples that underwent rapid hydration (∼10 hr) and evolved in permeability (∼10−9 to 10−8 m2) as a result of crustal fracturing. A coupled reactive transport model and thermodynamic analyses, focusing on Si alteration processes within reaction zones, are used to estimate the fluid volume required to induce fluid‐driven seismic activity. Estimated fluid volumes (101–104 m3) are used to approximate the moment magnitudes of potential seismic events. The resulting moment magnitudes (−0.6 to 3.8) and short timescales of fluid infiltration (∼10 hr) are comparable to some slow‐slip events, such as tremors and low‐frequency earthquakes. This indicates that the voluminous fluid flow in a single fracture could be a key control on the generation of crustal fracturing and the induction of seismic activity above the tremor and slow slip events source regions in the lower–middle crust.
Seismic Attenuation Extraction From Traffic Signals Recorded by a Single Seismic Station
Authors: Zhao Yumin , Nilot Enhedelihai Alex , Li Bei , Fang Gang , Luo Wei , Li Yunyue Elita 
Published: January 2023

AbstractSeismic ambient noise contains rich information about the physical properties of the critical zone. From the motor vehicle noise, we extract the daily seismic attenuation by quantifying the linear relationship between the frequency and amplitude ratio of paired instantaneous spectra. After verifying the reliability of the proposed method, we apply it to seismic ambient noise data collected at three urban sites. The estimated attenuation is compared against three environmental variables: rainfall, temperature, and traffic volume. The results show that the estimated seismic attenuation correlates most strongly with precipitation with positive time lags, suggesting a high attenuation sensitivity to changes in soil moisture and groundwater system. Furthermore, differences in attenuation‐precipitation correlations indicate that near‐surface physical properties may vary significantly due to local site conditions. Our statistical method extracts reliable near‐surface information from highly complex urban ambient noise data, achieving unprecedented spatial and temporal resolution compared to existing ambient noise imaging methods.
Global Self‐Similar Scaling of Terrestrial Carbon With Aridity
Authors: Yin Jun , Porporato Amilcare 
Published: January 2023

AbstractWhile it is well known that water availability controls vegetation growth and soil microbial activity, how aridity affects ecosystem carbon patterns is not completely understood. Toward a more quantitative assessment of terrestrial carbon stocks, here we apply dimensional analysis and scaling to the global joint distribution of terrestrial carbon stock, obtained from international survey data and harmonized global maps. The results show a remarkable self‐similar behavior of the global carbon stock with dryness index, whereby the key statistics (e.g., mean, quantiles, and standard deviation) of carbon stock tend to scale with the hydroclimatic regime (i.e., aridity) via a universal exponent. Such a scaling reflects the strong coupling between the hydrological cycle and biogeochemical process and enables robust predictions of carbon stocks as a function of aridity only. When normalized by its averages in the corresponding hydroclimatic regime, the carbon stock distributions collapse onto a single double Pareto lognormal distribution, often used in economics to describe income. The presence of this distribution in completely different physical contexts, such as biogeochemistry and economics, hints at generating mechanisms that transcend the details of the specific stock being considered.
Modelling the Apparent von Kármán Parameter in Thermally and Sedimentologically Stratified Air Flows
Authors: Li Bailiang , Yan Dongyue , He Yanyu , Liu Jianhui 
Published: January 2023

AbstractThrough a field experiment on a sandy beach at Pingtan, China, we propose the first model to characterize the variability of apparent von Kármán parameter in the thermally and sedimentologically stratified air flows. The model shows that is negatively correlated to flux Richardson number as well as sediment transport rate. The atmospheric stability can be either from thermal stratification or sedimentological stratification, neglecting either of them can lead to substantial errors in predicting shear velocity and sediment transport rate.
Comparison of TIDI Line of Sight Winds With ICON‐MIGHTI Measurements
Authors: Wu Chen , Ridley Aaron J. 
Published: January 2023

AbstractThe Thermosphere‐Ionosphere‐Mesosphere Energetics and Dynamics (TIMED) satellite has been making observations of the mesosphere and lower thermosphere (MLT) region for two decades. The TIMED Doppler Interferometer (TIDI) measures the neutral winds using four orthogonal telescopes. In this study, the line of sight (LOS) winds from individual telescopes are compared to the measurements from the Ionospheric Connection Explorer's (ICON's) Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) instrument from 90 to 100 km altitude during 2020. With the MIGHTI vector winds projected onto the LOS direction of each TIDI telescope, coincidences of the two data sets are found. The four telescopes perform differently and the performance depends on the satellite configuration and local solar zenith angle. Measurements from the coldside telescopes, Telescope 1 (Tel1) and Telescope 2 (Tel2), are better correlated with the MIGHTI winds in general with Tel2 having higher correlation coefficients across all conditions. The performance of Tel1 is comparable to that of Tel2 during backward flight while showing systematic errors larger than the average wind speeds during forward flight. The warmside LOS winds from Telescope 3 (Tel3) and Telescope 4 (Tel4) vary widely in magnitude, especially on the nightside. Compared with MIGHTI winds, the Tel4 measurements have the weakest correlation, while the Tel3 performance is comparable to that of the coldside telescopes during the ascending phase but deteriorates during the descending phase. Based on the TIDI/MIGHTI comparisons, figures of merit are generated to quantify the quality of measurements from individual telescopes in different configurations.
Altimetry‐Derived Gravity Gradients Using Spectral Method and Their Performance in Bathymetry Inversion Using Back‐Propagation Neural Network
Authors: Wan Xiaoyun , Annan Richard Fiifi , Ziggah Yao Yevenyo 
Published: January 2023

AbstractVariations in bathymetry produce gravity gradients; therefore, gravity gradients should be suitable for inverting bathymetry. However, classical bathymetry inversion methods often use gravity anomalies. Studies wherein gravity gradients are employed always use only vertical gravity gradients, ; however, the remaining five gravity gradients also contain bathymetric information. This study presents a spectral method of inverting gravity gradients from altimetry‐derived deflection of the vertical. The accuracy of derived gravity gradients is first evaluated by Laplace's equation. Results show that, except the Arctic region, the mean and standard deviation (STD) of the Laplace operator are, respectively, less than 0.001 and 0.06 E in the Atlantic, Indian and Pacific oceans. Computed was compared with from Scripps Institution of Oceanography. The differences have means and STDs smaller than 0.06 and 6.7 E, respectively, in all study areas, except the Arctic which registered an STD of 13.81 E. All six tensors were then fed into a back‐propagation neural network to predict bathymetries of each study area. The resultant bathymetries compare well with reference models from ship‐borne depths, SRTM15+V2 and GEBCO_2021. Relative to ship‐borne depths, the Atlantic, Pacific, Indian and Arctic study areas yielded mean errors, error STDs and correlation coefficients of 5.40, 3.28, 0.92, 3.07 m; 110.03, 76.23, 104.58, 132.41 m; and 0.9861, 0.9615, 0.9760, 0.9803; respectively. The bathymetries were inverted again, however, with each gravity gradient component omitted. Analysis showed that was not the most influential component across the study regions; rather, each gravity gradient component can significantly contribute to bathymetry inversion.