bluuebunny/crossref_metadata_2025_split huggingface dataset:
A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms
Authors: Yu Wenke , Lu Yanwei , Shou Huafeng , Xu Hong’en , Shi Lei , Geng Xiaolu , Song Tao 
Published: December 2022

AbstractBackgroundPrediction models with high accuracy rates for nonmetastatic cervical cancer (CC) patients are limited. This study aimed to construct and compare predictive models on the basis of machine learning (ML) algorithms for predicting the 5‐year survival status of CC patients through using the Surveillance, Epidemiology, and End Results public database of the National Cancer Institute.MethodsThe data registered from 2004 to 2016 were extracted and randomly divided into training and validation cohorts (8:2). The least absolute shrinkage and selection operator (LASSO) regression was employed to identify significant factors. Then, four predictive models were constructed, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). The predictive models were evaluated and compared using Receiver‐operating characteristics with areas under the curves (AUCs) and decision curve analysis (DCA), respectively.ResultsA total of 13,802 patients were involved and classified into training (N = 11,041) and validation (N = 2761) cohorts. By using the LASSO regression method, seven factors were identified. In the training cohort, the XGBoost model showed the best performance (AUC = 0.8400) compared to the other three models (all p < 0.05 by Delong's test). In the validation cohort, the XGBoost model also demonstrated a superior prediction ability (AUC = 0.8365) than LR and SVM models (both p < 0.05 by Delong's test), although the difference was not statistically significant between the XGBoost and the RF models (p = 0.4251 by Delong's test). Based on the DCA results, the XGBoost model was also superior, and feature importance analysis indicated that the tumor stage was the most important variable among the seven factors.ConclusionsThe XGBoost model proved to be an effective algorithm with better prediction abilities. This model is proposed to support better decision‐making for nonmetastatic CC patients in the future.
Identification of abnormal conditions for gold flotation process based on multivariate information fusion and double‐channel convolutional neural network
Authors: Wang Shu , Zhang Lin , Lu Di , Fu Yu 
Published: December 2022

AbstractAccurate and in‐time working condition identification plays a great role in industrial processes. However, most of the current flotation process identification models only use the characteristics of flotation froth as an identification basis, which often causes identification errors due to the instability of the froth, and a large amount of process data is not fully utilized. In this paper, an abnormal condition identification method based on multivariate information fusion and double‐channel convolutional neural network (double‐channel CNN) is proposed to achieve higher accuracy. First, a double‐channel CNN is used to extract depth features from different distributions of froth images and process data in parallel. Then, double normalized attention mechanism (double normalized AM) and multivariate information fusion methods are used to attach weights to the features and fuse them so as to ensure a higher response of key features and increase the reliability of the identification model. The method shows better performance than existing methods in offline simulations and has been validated online at a mineral processing plant in Shandong.
Machine learning modelling and evaluation of jet fires from natural gas processing, storage, and transport
Authors: Mashhadimoslem Hossein , Ghaemi Ahad , Palacios Adriana , Almansoori Ali , Elkamel Ali 
Published: December 2022

AbstractJet fires and their repercussions play a significant role in catastrophic incidents that typically have a cascading impact in process industries. Several hydrocarbon experiments from 19 papers were incorporated into the current endeavour to develop simulations of jet flames using machine learning (ML) models. Dimensionless characteristics have been used as output and input variables, including mass flow rates, fuel density, jet flame length, and heat release fluxes. When training three layers of the multi‐layer feedforward neural network (MLFFNN) method, a Bayesian regularization backpropagation approach was adopted and evaluated with the radial based functions (RBF) algorithm. Through an optimization procedure, the first and second hidden layers of the MLFFNN have been optimized to include 10 and five neurons, respectively. The RBF algorithm with 40 neurons in a single layer has been computed using the same method. The best mean square error (MSE) validation results for RBF and MLFFNN were 0.006 and 0.0002, respectively, for 40 and 100 epochs. The MLFFNN and RBF models' respective regression statistical analysis outputs were 0.9949 and 0.9645. The ML method has been identified as a potentially useful technique for precisely predicting the geometrical and radiative characteristics of jet flames.
Disparities in eating disorder symptoms and mental healthcare engagement prior to and following the onset of the COVID‐19 pandemic: Findings from a national study of US college students
Authors: Romano Kelly A. , Lipson Sarah K. , Beccia Ariel L. , Quatromoni Paula A. , Murgueitio Jose 
Published: December 2022

AbstractObjectiveEarly COVID‐19 eating disorders (EDs) research used regionally restricted samples with little sociodemographic diversity. The present study aimed to address these research gaps by examining whether pandemic‐related changes in ED symptoms and mental healthcare prevalence differed for historically marginalized groups within a national sample of US college students.MethodParticipants included 242,906 US college students (Mage = 23.45, SD = 7.04; MBMI = 25.28, SD = 5.91) who completed the repeated cross‐sectional multi‐institute Healthy Minds Study between January 2019 and May 2021. Moderated logistic regressions examined whether pandemic‐related changes in individuals' likelihoods of exhibiting current probable ED, reporting lifetime ED diagnoses, and—among individuals with current probable ED—mental healthcare engagement differed for diverse gender, sexual, and racial/ethnic identity groups, and by body mass index (BMI) and financial stress.ResultsThere were increases of 5% and 12% in individuals' likelihoods of exhibiting current probable ED and symptomatic individuals' mental healthcare engagement, respectively, pre‐ to post‐COVID‐19 onset, but no pandemic‐related changes in lifetime ED diagnosis prevalence. There were also important variations in these time‐trends for different marginalized groups. For example, individuals identifying as genderqueer/gender nonconforming and lesbian exhibited increasing ED symptoms pre‐ to post‐COVID‐19 onset, and individuals with current probable ED and higher BMIs were increasingly likely to receive mental healthcare. Associations between financial stress, and the ED and mental healthcare outcomes did not change over time.DiscussionThese findings provide insight into groups of US college students that experienced disproportionate ED burden during the pandemic at the population level, and directions for research and interventions that warrant consideration.
Supported Metal Sulfate Catalysts FexZny/SiO2 for Selective Dimerization of Isobutene in Mixed C4 to Isooctenes
Authors: Yu Yue , Lv Zhongwu , Zhong Haijun , Bai Wei , Ji Min 
Published: December 2022

AbstractIsobutene dimerization is an important route to properly utilize mixed C4 and further produce isooctane with a high‐octane number to replace MTBE in gasoline. A highly selective catalyst, which is effective for isobutene dimerization but ineffective for other olefins in the C4 mixture, is necessary for industrial implementation. In this work, a series of supported metal sulfate catalysts FexZny/SiO2 were prepared and characterized by XRD, SEM, N2 physical adsorption‐desorption, NH3‐TPD, Py‐FTIR, and XPS. Fe0.2Zn1.8/SiO2 can achieve isobutene conversion up to 89 % with 0 % conversion of n‐butene, and the selectivity of isooctenes (C8=) product is 57 % (10 h on stream). Furthermore, isobutene conversion can sustain above 80 % after 50 h. It is found that these supported catalysts contain Zn2+, Zn+, Fe3+, and Fe2+ species, and there is a synergetic effect between Zn2+ and Fe2+. Zn2+ is beneficial to improve the conversion of isobutene, and Fe2+ facilitates the formation of C8=, resulting in a high C8= yield.
Efficient Synthesis of Bicyclic 2‐Pyridinones and 6‐Amino‐2‐pyridinones by the Regioselective Cycloaddition of Nitro Ketene Aminal with 3‐Hydroxy‐2‐aryl‐acrylate
Authors: Wang Si‐Yu , Zhang Zhong‐Wei , Yuan Liu , Jin Yi , Zhang Cong‐Hai , Lin Jun 
Published: December 2022

AbstractAn efficient and simple procedure for the preparation of 2‐pyridinone is reported. The obtained products contained various bicyclic 2‐pyridinones and multi‐substituted 6‐amino‐2‐pyridinones. This approach employed 3‐hydroxy‐2‐aryl acrylate and nitro ketene aminal as substrates, and the corresponding 2‐pyridinones were obtained in good to excellent yields. This method may be used for the synthesis of bicyclic 2‐pyridinones, as well as multi‐substituted complex 2‐pyridinones.
Dual‐channel capsule generation adversarial network based blockchain technology for a secured dynamic optimal routing in mobile ad hoc network
Authors: M Vanitha , Anand Sujatha Jamuna , V Radhamani , Rajalakshmi N. R. 
Published: December 2022

AbstractMobile ad hoc network (MANET) routing is allocated packets to the base station. It preserves the routing buttressing and the efficiency of MANET. None of effective approaches presented so far averting spiteful node occurrences. To overcome this issue, in this article, dual‐channel capsule generation adversarial network (DCCGAN) optimized with arithmetic optimization algorithm is proposed for blockchain based trusted distributed routing scheme in MANET (DCCGAN‐AOA‐BC‐MANET). The proposed approach offers the distributed trust module for routing nodes in MANETs that is proof‐of‐authority through blockchain. Here, the token is engendered for the packet stream admittances with the secret key transmitted to every routing mobile node, also it offers the optimum path for data transmission. The trusted distributed optimum routing information is distributed based upon proposed blockchain based MANET. The proposed approach is simulated utilizing NS‐3 tool. The performance metrics, such as delay performance, average delay, average latency, average energy consume, BC token transactions throughput. The simulation of proposed DCCGAN‐AOA‐BC‐MANET method provides 76.26%, 65.57%, 80.32%, and 42.9% lower transaction latency, 73.06%, 63.82%, 85.56%, and 38.84% lower energy consumption compared to the existing methods.
Decades of Global Sturgeon Conservation Efforts Are Threatened by an Expanding Captive Culture Industry
Authors: White Shannon L , Fox Dewayne A , Beridze Tamar , Bolden Stephania K , Johnson Robin L , Savoy Thomas F , Scheele Fleur , Schreier Andrea D , Kazyak David C 
Published: December 2022

Abstract
After centuries of overexploitation and habitat loss, many of the world's sturgeon (Acipenseridae) populations are at the brink of extinction. Although significant resources are invested into the conservation and restoration of imperiled sturgeons, the burgeoning commercial culture industry poses an imminent threat to the persistence of many populations. In the past decade, the number and distribution of captive sturgeon facilities has grown exponentially and now encompasses diverse interest groups ranging from hobby aquarists to industrial-scale commercial facilities. Expansion of sturgeon captive culture has largely fallen outside the purview of existing regulatory frameworks, raising concerns that continued growth of this industry has real potential to jeopardize conservation of global sturgeon populations. Here, we highlight some of the most significant threats commercial culture poses to wild populations, with particular emphasis on how releases can accelerate wild population declines through mechanisms such as hybridization, introgression, competition, and disease transmission. We also note that in some circumstances, commercial captive culture has continued to motivate harvest of wild populations, potentially accelerating species' declines. Given the prevalence and trajectory of sturgeon captive culture programs, we comment on modifications to regulatory frameworks that could improve the ability of captive culture to support wild sturgeon conservation.
Plant‐based diets could ameliorate the risk factors of cardiovascular diseases in adults with chronic diseases
Authors: Lotfi Mostafa , Nouri Mehran , Turki Jalil Abduladheem , Rezaianzadeh Abbas , Babajafari Siavash , Ghoddusi Johari Masoumeh , Faghih Shiva 
Published: December 2022

AbstractAdherence to plant‐based diets is recommended to prevent and control chronic diseases. However, not all plant‐based foods are healthy for this purpose. This study investigated the relationship between plant‐based diets and risk factors for cardiovascular diseases (CVDs) in adults with chronic diseases. This cross‐sectional study was performed on 3678 males and females (age range: 40–70 years) with chronic diseases who participated in the Kharameh cohort study. A validated semiquantitative food‐frequency questionnaire was used to calculate the plant‐based diet index (PDI), healthy plant‐based diet index (hPDI), and unhealthy plant‐based diet index (uPDI). Lipid profile, fasting blood sugar (FBS), blood pressure, and anthropometric indices were measured. Multivariable‐adjusted logistic regression analysis was performed to determine the association between plant‐based diets and CVDs risk factors. Higher adherence to the PDI was inversely associated with the level of FBS (odds ratio [OR] = 0.42; 95% confidence interval [CI]: 0.33–0.53; p < .001). A significant decrease was observed for total cholesterol in those with higher adherence to hPDI (OR = 0.80; 95% CI: 0.65–0.98; p = .035). Additionally, the score of uPDI was positively related to FBS (OR = 1.23; 95% CI: 1.00–1.53; p = .01), total cholesterol (OR = 1.23; 95% CI: 1.01–1.49; p = .061), and low‐density lipoprotein (OR = 1.39; 95% CI: 1.13–1.71; p = .009). It was concluded that adherence to PDI and hPDI was related to a lower level of FBS and total cholesterol, respectively. Moreover, the findings suggested that regular intake of the uPDI was correlated with some risk factors for CVDs in adults with chronic diseases.
Nutritional, functional, and sensorial properties of oat milk produced by single and combined acid, alkaline, α‐amylase, and sprouting treatments
Authors: Babolanimogadam Nima , Gandomi Hassan , Akhondzadeh Basti Afshin , Taherzadeh Mohammad J. 
Published: December 2022

AbstractIn this study, the effects of different treatments of the oat slurry on the nutritional, functional, and sensorial properties of oat milk were evaluated. The sprouting and sprouting–acidic treatments have the highest oat milk yield (91.70%) and protein extraction yield (82.74%), respectively. The protein concentrations of alkali, sprouting–acidic, and α‐amylase–alkali treatments were significantly (p < .05) higher than other treatments. The alkali treatments showed higher fat content (0.66%). In addition, acidic and alkali treatments in single or combined with other treatments showed the highest dry matter and energy value. The carbohydrate content of α‐amylase–alkali treatment (4.35%) was higher than other treatments and also, all acidic treatments showed higher ash content (>1) compared to the other treatments. Furthermore, the sprouting–α‐amylase and acidic–α‐amylase showed the lowest starch (0.28%) and the highest reducing sugar content (3.15%) compared to the other treatments, respectively. Moreover, the α‐amylase–alkali treatment showed the highest total phenolic content and antioxidant activity (342.67 mg GAE/L and 183.08 mg BHT eq/L, respectively). Furthermore, sensory evaluation of most treatments showed acceptable scores (≥7) for consumers, especially in the case of α‐amylase, sprouting, and α‐amylase–sprouting treatments. Results show that the different treatments had different effects on the nutritional, functional, and sensorial properties of oat milk. In conclusion, from the nutritional and functional point of view, the two‐stage treatments were more effective than singular treatments on investigated factors proposing their application in functional plant milk preparation.
Risk of COVID‐19 infection among mobile extracorporeal membrane oxygenation team
Authors: Tawel Rabee , Altawil Lubna , Hassan Koya Sunil , Jaouni Hani , Alinier Guillaume , Nashwan Abdulqadir J. , Labib Ahmed 
Published: December 2022

AbstractBackground and AimThe transport of coronavirus‐2019 (COVID‐19) patients on extracorporeal membrane oxygenation (ECMO) is a challenging situation, especially for healthcare workers (HCWs), due to the risk of cross‐infection. Hence, certain precautions are needed for their safety. The study aims to evaluate the risk of COVID‐19 transmission to HCWs who transport COVID‐19 patients on ECMO device.MethodsA retrospective review of adult patients with COVID‐19 infection supported with ECMO and transported by ground route to the Medical Intensive Care Unit (MICU) at Hamad General Hospital (HGH) and a survey of HCWs involved in those cases.ResultsA total of 63 HCWs of the mobile ECMO team were exposed to COVID‐19‐positive patients on 199 occasions. HCWs exposure time was nearly 110 h, and the total transport distance was 1018 km. During the study period, only two of the mobile ECMO HCWs tested positive for COVID‐19. There was zero incidence of transfer‐associated injuries or accidents to HCWs.ConclusionsThe risk of COVID‐19 cross‐infection to the mobile ECMO team seems to be very low, provided that strict infection prevention and control measures are applied.
Variable chemical process and radiative nonlinear impact on magnetohydrodynamics Cross nanofluid: An approach toward controlling global warming
Authors: Darvesh Adil , Altamirano Gilder C. , Sánchez‐Chero Manuel , Zamora William R. M. , Campos Franklin G. , Sajid Tanveer , Ayub Assad 
Published: December 2022

AbstractSolar energy is the basic source of renewable energy, and it is being used for controlling global pollution/warming. As the Cross nanofluid is very useful for cooling solar devices, in this paper analysis of the global warming effect is investigated by incorporating the nonlinear thermal radiation over the exponentially extendable surface because it plays a major role related to solar energy absorption of nanofluid. Furthermore, the mathematical modeling of Cross nanofluid involving magnetic effect and diffusion is discussed by using the fact of chemical reaction. Chemical reaction finds astonishing applications in pollution studies, chemical processing equipment, and polymer production. As a result of this study, it is noticed that more magnetized conducting fluid controls the motion of fluids for both cases of shear thinning and shear thickening. Brownian motion parameter Nb affects the rate of the random motion of nanoparticles. Increased Nb temperature also increases due to these random movements of nanoparticles. That is the reason why pollutant nanoparticles spread in air as a result of global warming increase.
Performance analysis of various denoising filters on intravascular ultrasound coronary artery images
Authors: Arora Priyanka , Singh Parminder , Girdhar Akshay , Vijayvergiya Rajesh 
Published: December 2022

AbstractAccurate diagnosis of atherosclerotic coronary artery stenosis largely depends on intravascular ultrasound (IVUS) image quality. Coherent sources present during IVUS acquisition generates speckle noise and obstructs the clear view of the artery. Denoising aims to remove speckle noise while preserving edges and other important features. Accordingly, speckle noise suppression serves as an important preprocessing step for IVUS image analysis. This paper presents a performance analysis of various despeckling filters such as Median, Wiener, Speckle Reducing Anisotropic Diffusion (SRAD) filter, Wavelet, Non‐local means (NLM) and curvelet based non‐local means (CNLM) for IVUS images. To evaluate the efficacy, each filter is evaluated for three categories of cross‐sectional IVUS images, that is, healthy, mild calcification, and dense calcification of real pullbacks from four patients. As it is challenging to denoise the IVUS images with complex lesions, that is, calcified arc >180°, in this performance analysis, the denoising performance for different denoising filters is considered not only for the healthy IVUS but also for complex lesions. Various measures such as peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), and expert evaluation are used to compare these approaches. The results depict that the CNLM denoising filter outperforms others in terms of PSNR and FSIM for speckle noise suppression for IVUS images while preserving the essential edge and feature details for better image visualization and diagnosis.
Apt and actionable possible identities matter: The case of academic outcomes
Authors: O'Donnell S. Casey , Oyserman Daphna 
Published: December 2022

AbstractIntroductionWe review the longitudinal evidence documenting that middle and high school students with school‐focused possible future identities subsequently attain better school outcomes. Consistent results across operationalizations of possible identities and academic outcomes imply that results are robust. However, variability in study designs means that the existing literature cannot explain the process from possible identity to academic outcomes. We draw on identity‐based motivation theory to address this gap. We predict that imagining a possible school‐focused future drives school engagement to the extent that students repeatedly experience their school‐focused future identities as apt (relevant) and actionable (linked to strategies they can use now).MethodsWe operationalize aptness as having pairs of positive and negative school‐focused possible identities (balance) and actionability as having a roadmap of concrete, linked strategies for school‐focused possible selves (plausibility). We use machine learning to capture features of possible identities that predict academic outcomes and network analyses to examine these features (training sample USA 47% female,Mage = 14, N1 = 602,N2 = 540. Test sample USA 55% female,Mage = 13,N = 247).ResultsWe report regression analyses showing that balance, plausibility, and our machine algorithm predict better end‐of‐school‐year grades (grade point average). We use network analysis to show that our machine algorithm is associated with structural features of possible identities and balance and plausibility scores.ConclusionsOur results support the inference that student academic outcomes are improved when students experience their school‐focused possible identities as apt and actionable.