bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fracture law of different overlying strata in mining of protective seam under close distance coal seam
Authors: Wang Yingwei , Yuan Honghu , Gao Mingzhong , Li Minghui , Sun Jingge 
Published: January 2023

AbstractTo study the fracture law of different overlying strata during mining of the lower protective seam under a close distance coal seam at a depth of 1000 m, roof bed‐separation and fissure imaging monitoring were carried out in the inlet roadway ahead of the working face of the JI15‐31040 lower protective seam in the Pingdingshan XII coal mine. The results show that with the overlying strata of the lower protective seam being affected by mining, the displacement of the overlying strata in the roof of 0–4 m was relatively large, and the fissure development was very significant. The displacement of the overlying strata in the roof of 4–12 m was relatively small, the fissures in this range were mostly randomly closed fissures, and this area was stable. Bed separation occurred in the roof of 12–16 m, and the bed‐separating fissures were mostly located at the coal–rock junction. During the mining process, the fractal dimension of the roof gradually increased, and the fissure network developed continuously. The changing process of the fractal dimension of the roof can be roughly divided into three typical change areas: less influence area (distance from the working face > 70 m), rapid increase area (15 m < distance from the working face < 70 m), and stable area (distance from the working face < 15 m), and the area within 70 m in front of the working face was the key supporting area. Under the influence of mining, the roof fissures showed an overall change trend of “from zero to some, from short to long, from small to large, and from narrow to wide.” This study can provide a reference for mining of the protective seam in deep coal seams.
Detecting bearing failures in wind energy parks: A main bearing early damage detection method using SCADA data and a convolutional autoencoder
Authors: Tutivén Christian , Encalada‐Dávila Ángel , Vidal Yolanda , Benalcázar‐Parra Carlos 
Published: January 2023

AbstractWind energy maintenance and operation costs can total millions of dollars each year in an average industrial‐size wind park. Therefore, moving from preventive and corrective maintenance to predictive maintenance is imperative in the wind energy sector. This paper contributes to this challenge by providing a main bearing early damage detection technique that exclusively uses standard supervisory control and data acquisition (SCADA) data (10‐min average) and a convolutional autoencoder with the following contributions. (i) Entirely semisupervised (not requiring the labeling of data through work order logs and avoiding the problem of data imbalance between classes) based only on healthy data, thus expanding its range of application (even when the failure of interest has never occurred in the park before). (ii) Validated using real‐world SCADA data and shown to be resistant to seasonality, and operational and environmental conditions. (iii) Reliable predictions with minimum false alarms thanks to specially designed fault prognosis indicators based on the image mean square error metric. (vi) The early warning is achieved months in advance, thus providing adequate time for plant operators to plan properly. (v) The main use of exogenous variables in the model (variables that are not affected by other variables, e.g., wind speed, wind turbulence, and ambient temperature) guarantees the detection of damage directly related only to the low‐speed shaft temperature (the only nonexogenous variable used by the stated model). (vi) Finally, the proposed strategy is validated in a wind park made up of 12 wind turbines.
Evolution of symmetry index in minerals
Authors: Bermanec Marko , Vidović Noa , Gavryliv Liubomyr , Morrison Shaunna M. , Hazen Robert M. 
Published: January 2023

AbstractCrystal structures of minerals are defined by a specific atomic arrangement within the unit‐cell, which follows the laws of symmetry specific to each crystal system. The causes for a mineral to crystallize in a given crystal system have been the subject of many studies showing their dependency on different formation conditions, such as the presence of aqueous fluids, biotic activity and many others. Different attempts have been made to quantify and interpret the information that we can gather from studying crystal symmetry and its distribution in the mineral kingdom. However, these methods are mostly outdated or at least not compatible for use on large datasets available today. Therefore, a revision of symmetry index calculation has been made in accordance with the growing understanding of mineral species and their characteristics. In the gathered data, we observe a gradual but significant decrease in crystal symmetry through the stages of mineral evolution, from the formation of the solar system to modern day. However, this decrease is neither uniform nor linear, which provides further implications for mineral evolution from the viewpoint of crystal symmetry. The temporal distribution of minerals based on the number of essential elements in their chemical formulae and their symmetry index has been calculated and compared to explore their behaviour. Minerals with four to eight essential elements have the lowest average symmetry index, while being the most abundant throughout all stages of mineral evolution. There are many open questions, including those pertaining to whether or not biological activity on Earth has influenced the observed decrease in mineral symmetry through time and whether or not the trajectory of planetary evolution of a geologically active body is one of decreasing mineral symmetry/increasing complexity.
The effect of pharmacist‐led interventions on the management and outcomes in chronic kidney disease (CKD): A systematic review and meta‐analysis protocol
Authors: Ardavani Ashkon , Curtis Ffion , Khunti Kamlesh , Wilkinson Thomas J. 
Published: January 2023

AbstractBackground and AimsChronic kidney disease (CKD) is a progressive condition that results in a decline in kidney function over time. There are several conditions that increase the likelihood of developing CKD, particularly diabetes and hypertension. CKD increases the risk of mortality and has a detrimental impact on quality of life (QoL). Strategies for managing CKD include controlling cardiovascular risk factors and treating complications of CKD. There is an ever‐increasing role of pharmacists in managing CKD, from the optimization of risk factors to patient education. However, currently, there is a lack of data on the effect pharmacist‐led interventions have on the clinical, economic, and humanistic outcomes.MethodsThis protocol, in adherence to PRISMA‐P (Preferred Reporting Items for Systematic review and Meta‐Analysis Protocols) standards, describes a prospective systematic review and meta‐analysis of randomized controlled trials, where any intervention led by a pharmacist in CKD is used. Comparison groups will consist of usual care or non‐pharmacist‐led interventions. Literature searches will be conducted in the following databases: MEDLINE, Scopus, and Web of Science. Data pertaining to clinical (e.g., mortality), economic (e.g., healthcare‐associated costs), and humanistic (e.g., QoL) outcomes will be extracted. Risk of bias will be assessed using the United States National Heart Lung and Blood Institute quality assessment tool for controlled intervention studies. A meta‐analysis will be conducted to synthesize appropriate comparable outcomes.ResultsThe findings of this review will be published in a peer‐reviewed journal, where the results will be presented in lay language with appropriate infographics online and via social media.ConclusionThe findings of this review can identify gaps in the literature concerning optimizing pharmacist‐led interventions in improving outcomes. In addition, this review will establish the importance of pharmacists in managing CKD patients, and whether this may result in their increased incorporation in multidisciplinary teams.
Association of lifespan reproductive duration with depression in Swedish twins: The role of hormone replacement therapy
Authors: Wang Shuqi , Yang Wenzhe , Li Xuerui , Wang Zhiyu , Zhang Lulu , Wang Jiao , Qi Xiuying , Dove Abigail , Xu Weili 
Published: January 2023

AbstractObjectiveTo examine the association between reproductive duration and postmenopausal depression (taking the use of hormone replacement therapy [HRT] into account).MethodsIn this population‐based cohort study, 11 320 postmenopausal women (mean age 63.6 years) were followed for up to 18 years. Reproductive duration was categorized into three groups: short (≤34 years), average (35–39 years), and long (≥40 years). Depression was ascertained from the Sweden National Patient Registry.ResultsDuring the follow up, 593 (5.24%) women developed depression. In the multi‐adjusted generalized estimating equation model, the odds ratios (ORs) of depression were 1.28 (95% confidence interval [CI] 1.05–1.55) and 1.25 (95% CI 1.01–1.55) for women with short and long reproductive durations, respectively, compared with those women with average reproductive duration. Women with a non‐typical reproductive duration (≤34 or ≥40 years) who received HRT were at a higher risk of depression (OR 1.82, 95% CI 1.42–2.33). There was a significant additive interaction between non‐typical reproductive duration and the use of HRT on depression (attributable proportion 0.26, 95% CI 0.03–0.50).ConclusionWomen with a short or long reproductive duration, especially those with a history of HRT use, have a higher risk of depression after menopause compared with those with an average reproductive duration.
Bell's palsy during rechallenge of immune checkpoint inhibitor
Authors: Takemura Kohji , Yamanaka Taro , Hayashida Michikata , Kizawa Rika , Yamaguchi Takeshi , Tanabe Yuko , Sakaguchi Kazushige , Suyama Koichi , Urakami Shinji , Miura Yuji 
Published: January 2023

IntroductionThe peripheral nervous system is one of the target organs of immune‐related adverse events. Peripheral facial nerve palsy, also called Bell's palsy, which is induced by immune checkpoint inhibitors, is quite rare, and its clinical features are not well known.Case presentationA man with renal cell carcinoma who received rechallenging immune checkpoint inhibitor therapy developed unilateral facial palsy and was diagnosed with Bell's palsy. He did not have any severe immune‐related adverse events during his previous immune checkpoint inhibitor treatment. Corticosteroid therapy was immediately initiated, and his facial palsy symptoms promptly improved.ConclusionPhysicians should be aware that Bell's palsy can occur as an immune‐related adverse event. Additionally, careful observation is necessary during rechallenge with immune checkpoint inhibitors, even in patients who did not have previous immune‐related adverse events.
Effects of multiwavelength photobiomodulation for the treatment of traumatic soft tissue injuries associated with bone fractures: A double‐blind, randomized controlled clinical trial
Authors: Jana Neto Frederico Carlos , Martimbianco Ana Luiza Cabrera , Mesquita‐Ferrari Raquel Agnelli , Bussadori Sandra Kalil , Alves Gustavo Porto , Almeida Paulo Victor Dias , Delgado Felipe Guimaraes , Fonseca Lucas Resende , Gama Maiara Zeni Graiff , Jorge Mariana Djrdjrjan , Hamblin Michael R. , Fernandes Kristianne Porta Santos 
Published: January 2023

AbstractThis study evaluated the efficacy and safety of multiwavelength photobiomodulation (MPBM) in healing soft tissue injuries associated with tibial and/or ankle fractures. Participants were randomized into the MPBM or control group. Primary outcome was wound healing, measured by the Bates‐Jensen scale. Assessments were performed daily. Twenty‐seven hospitalized adults were included. MPBM showed an improvement in the daily mean Bates‐Jensen scale (MPBM 32.1 vs. control 34.2;p = 0.029), daily mean pain score change (MPBM 0.5 vs. control 0.2;p = 0.04) and occurrence of infection at the site of the external fixator pins (MPBM 15.3% vs. control 57.1%;p = 0.02). MPBM group also showed faster‐wound resolution (MPBM 13.1 vs. control 23.1 days). Subgroup analysis showed improvement in the MPBM group among less severe patients on the Bates‐Jensen scale (MPBM 27.4 vs. control 34.7;p = 0.0081) and mean time for wound resolution (MPBM 7.0 vs. control 14.6 days;p = 0.03). MPBM appears safe and effective in reducing wound resolution time, infection in the surgical pin sites, reported pain and time before definitive surgery.
Cover Image
Authors: Cheng Chenyaqiong , Liu Pei , Zhao Pengtao , Du Guorong , Wang Shengnan , Liu Hui , Cao Xiaomeng , Zhao Qinghao , Wang Xiaoyu 
Published: January 2023

The cover image is based on the Research Article Developing novel oenological tannins from 44 plants sources by assessing astringency and color in model wine by Chenyaqiong Cheng et al., https://doi.org/10.1002/jsfa.12247.image
Enrichment of keystone fungal taxa after flue gas desulphurization gypsum application drives reclamation of the saline‐sodic soil
Authors: Zhang Lu , Ge An‐Hui , Tóth Tibor , Yang Fan , Wang Zhichun , An Fenghua 
Published: January 2023

AbstractSoil salinization is a major threat to sustainable agriculture in the western part of Songnen Plain, Northeast China. Flue gas desulphurization gypsum (FGDG) is commonly used for reclaiming of saline soils due to its high efficiency and cost‐effectiveness. However, the responses of soil and root‐associated fungi after FGDG amendment and the keystone fungal taxa in determining soil reclamation effect remain elusive. In this study, a long‐term field experiment was conducted to explore the effect of different levels of one‐time FGDG application on the soil reclamation and the herbage (Leymus chinensis) growth. Our results showed that the FGDG application greatly reduced soil salinity level, increased soil fertility and fungal diversity, and promoted aboveground biomass of L. chinensis, whereas reduced FGDG dose to 60% of gypsum requirement (GR) exhibited non‐significant difference on these characters compared with 100% GR treatment. We further discovered that both soil and root fungal communities were greatly changed after FGDG application, with root fungal community more sensitive than soil fungi. Through applying the Random Forest model, the arbuscular mycorrhizal fungi Glomeromycota was identified as the keystone taxa in predicting the soil salinity, soil fertility, and plant growth trait. Intriguingly, some Glomeromycota‐affiliated ZOTUs were consistently enriched in the FGDG application treatments, and acted as keystone hubs in the co‐occurrence network of fungal communities. Together, our study provides new insights that the enrichment of keystone Glomeromycota after FGDG application determines the reclamation of the saline‐sodic soil, which would guide the microbial‐based strategy for the development of degraded ecosystems.
Abandoned terrace recognition based on deep learning and change detection on the Loess Plateau in China
Authors: Guo Huili , Sun Liquan , Yao Ailing , Chen Ziyu , Feng Hao , Wu Shufang , Siddique Kadambot H. M. 
Published: January 2023

AbstractTerraces are an important cultivated land resource. Terrace abandonment affects the soil quality, soil and water conservation benefits, and biodiversity of terraces. Therefore, it is important to quantify the number and spatial distribution of abandoned terraces to protect cultivated land and food security. However, the traditional remote sensing method cannot identify small plots and make accurate assessment of abandoned farmland quickly in mountainous areas. To accurately identifying abandoned terraces, this study used semantic segmentation based on deep learning and change detection to identify abandoned terraces and their spatial distribution in a small watershed on the Loess Plateau in 2021. A comparative analysis of the accuracy of three deep learning models revealed that RefineNet is superior to DeepLabv3+ and DeepLabv3 for identifying abandoned terraces. The user's accuracy, producer's accuracy, overall accuracy, and appa values for RefineNet were 0.817, 0.894, 0.800, and 0.539, respectively. For change detection, the corresponding values were 0.821, 0.753, 0.731, and 0.426, respectively. In addition, semantic segmentation produced better recognition results than change detection in complex terrain and geomorphological areas. The abandoned terraces in the study area were mainly distributed in mountainous areas far from residential areas and more likely at high elevations with large slopes. This study provides a new method for recognizing abandoned terraces and spatial distribution information for managing and utilizing abandoned terraces.
Fabrication of PLCL Block Polymer with Tunable Structure and Properties for Biomedical Application
Authors: Luo Chenmin , Liu Shengyang , Luo Wei , Wang Jing , He Hongyan , Chen Can , Xiao Lan , Liu Changsheng , Li Yulin 
Published: January 2023

AbstractBiodegradable materials are pivotal in the biomedical field, where how to precisely control their structure and performance is critical for their translational application. In this study, poly(L‐lactide‐b‐ε‐caprolactone) block copolymers (bPLCL) with well‐defined segment structure are obtained by a first synthesis of poly(ε‐caprolactone) soft block, followed by ring opening polymerization of lactide to form poly(L‐lactide acid) hard block. The pre‐polymerization allows for fabrication of bPLCL with the definite compositions of soft/hard segment while preserving the individual segment of their special soft or hard segment. These priorities make the bPLCL afford biodegradable polymer with better mechanical and biodegradable controllability than the random poly(L‐lactide‐co‐ε‐caprolactone) (rPLCL) synthesized via traditional one‐pot polymerization. 10 mol% ε‐caprolactone introduction can result in a formation of an elastic polymer with elongation at break of 286.15% ± 55.23%. Also, bPLCL preserves the unique crystalline structure of the soft and hard segments to present a more sustainable biodegradability than the rPLCL. The combinative merits make the pre‐polymerization technique a promising strategy for a scalable production of PLCL materials for potential biomedical application.
Fabrication and DOE Optimization of Electrospun Chitosan/Gelatin/PVA Nanofibers for Skin Tissue Engineering
Authors: Hadipour‐Goudarzi Elmira , Hemmatinejad Nahid , Shokrgozar Mohammad Ali 
Published: January 2023

AbstractChitosan/gelatin‐based nanofibers display excellent biological performance in tissue engineering because of their biocompatible composition and nanofibrous structure with a high surface‐to‐volume ratio mimicking the native extracellular matrix. In this study, to save time and cost of experiments, a response surface methodology based on Box–Behnken design (BBD) is developed to predict the mean diameter of (chitosan:gelatin)/poly(vinyl alcohol) (PVA) nanofibers in three volume ratios of chitosan:gelatin by considering PVA percentage, applied voltage, and flow rate as input variables. The morphology and chemical composition of nanofibers are investigated through scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR), respectively. The optimum conditions to yield the minimum diameter of nanofibers with chitosan:gelatin ratios of 25:75, 50:50, and 75:25 are found and result in 165, 121, and 92 nm, respectively, which show good accordance with BBD estimated results. The tensile testing indicates that nanofibers containing higher ratio of chitosan:gelatin result in higher tensile stress and lower toughness and tensile strain. The water contact angle analysis (WCA) shows the appropriate hydrophilicity of crosslinked nanofibers. The MTT assay shows excellent cell viability and cell attachment of nanofibers for mouse fibroblast (L929) cells. The results indicate that optimum nanofibers are potent candidates for wound healing applications.
3D multi‐view squeeze‐and‐excitation convolutional neural network for lung nodule classification
Authors: Yang Yang , Li Xiaoqin , Fu Jipeng , Han Zhenbo , Gao Bin 
Published: January 2023

AbstractPurposeEarly screening is crucial to improve the survival rate and recovery rate of lung cancer patients. Computer‐aided diagnosis system (CAD) is a powerful tool to assist clinicians in early diagnosis. Lung nodules are characterized by spatial heterogeneity. However, many attempts use the two‐dimensional multi‐view (MV) framework to learn and simply integrate multiple view features. These methods suffer from the problems of not capturing the spatial characteristics effectively and ignoring the variability of multiple views. In this paper, we propose a three‐dimensional MV convolutional neural network (3D MVCNN) framework and embed the squeeze‐and‐excitation (SE) module in it to further address the variability of each view in the MV framework.MethodsFirst, the 3D multiple view samples of lung nodules are extracted by the spatial sampling method, and a 3D CNN is established to extract 3D abstract features. Second, build a 3D MVCNN framework according to the 3D multiple view samples and 3D CNN. This framework can learn more features of different views of lung nodules, taking into account the characteristics of spatial heterogeneity of lung nodules. Finally, to further address the variability of each view in the MV framework, a 3D MVSECNN model is constructed by introducing a SE module in the feature fusion stage. For training and testing purposes we used independent subsets of the public LIDC‐IDRI dataset.ResultsFor the LIDC‐IDRI dataset, this study achieved 96.04% accuracy and 98.59% sensitivity in the binary classification, and 87.76% accuracy in the ternary classification, which was higher than other state‐of‐the‐art studies. The consistency score of 0.948 between the model predictions and pathological diagnosis was significantly higher than that between the clinician's annotations and pathological diagnosis.ConclusionsThe results show that our proposed method can effectively learn the spatial heterogeneity of nodules and solve the problem of multiple view variability. Moreover, the consistency analysis indicates that our method can provide clinicians with more accurate results of benign‐malignant lung nodule classification for auxiliary diagnosis, which is important for assisting clinicians in clinical diagnosis.
Geomechanical parameters assessment and geological characterization using fuzzy C means clustering of electrical resistivity and seismic data
Authors: Benjumea Beatriz , Gabàs Anna , Macau Albert , Bellmunt Fabian , Ledo Juanjo , Ripoll Jordi , Figueras Sara 
Published: January 2023

AbstractA broad understanding of the subsoil characteristics is required to deal with uncertainty in the infrastructure planning phase or even in the diagnosis of infrastructure damage. In this work, electrical resistivity and seismic methods have been employed for lithological characterization and geomechanical parameters assessment. The study area is characterized by high variability in geotechnical characteristics due to complex lithology. For lithological discrimination, we have used a soft clustering method to combine the independently electrical‐ and seismic‐derived models. The outputs of this integration process are zonal models that help to decrease interpretation uncertainties. From single seismic profiles, both Vp and Vs information are extracted allowing geomechanical parameters to be estimated. All this information is critical for assessing the variance and inhomogeneity of the subsoil characteristics. The methodology shown in this work can be used in the infrastructure planning phase as well as to ascertain the causes of detected damage on built infrastructure.