bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sentiment analysis on twitter data based on spider monkey optimization and deep learning for future prediction of the brands
Authors: Kothamasu Lakshmi Anusha , Kannan E. 
Published: June 2022

SummaryIn this manuscript, a deep neural network is proposed by integrating improved adaptive‐network‐based fuzzy inference system (IANFIS) for branding online products to overcome these issues. Here, the sentiment analysis (SA) and prediction on future branding of products that are extracted from the twitter data is carried out. After the review process classifying the products as positive, negative, and neutral assessments completely concentrated in three folds, prediction of a future brand that is carried out by IANFIS for weighting the products finally classify them. This scheme helps the respective retailers/retail brands with their digital marketing team to understand their brand perception as opposed to others. The performance of the proposed method is compared with the existing methods, such as sentiment analysis on twitter data based on particle swarm optimization and genetic algorithm, sentiment analysis on twitter data based on particle swarm optimization and convolutional neural network, sentiment analysis on twitter data based on whale optimization algorithm and support vector machine, and sentiment analysis on twitter data based on convolutional neural network and long short term memory. The simulation results show that the proposed method outperforms the state of art methods.
Solvent‐Dependent Photochemistry for Diverse and Selective C−H Functionalization of 2‐tert‐Butyl‐1,4‐Benzoquinones
Authors: Hyeon Ka Cheol , Seul Lee Da , Jin Cho Eun 
Published: June 2022

AbstractControlling a reaction to selectively produce a set of distinct valuable compounds from the same substrate is a highly attractive topic in synthetic chemistry. In this work, the solvent‐dependent photochemistry of 2‐tert‐butyl‐1,4‐benzoquinones to achieve diverse C−H functionalization was investigated, which afforded three selective transformations: (1) intermolecular C−O bond formation in non‐fluorinated alcoholic solvents; (2) perfluorinated hexafluoroisopropanol solvent‐promoted intramolecular cyclization to afford coumaran derivatives; (3) 1,4‐dioxane solvent‐induced hydrogen atom transfer to afford 2‐methylallyl hydroquinones.
A Geometry Optimization of Spiral Grain Selector During Directional Solidification of Nickel‐Based Superalloy
Authors: Wang Binqiang , Zeng Long , Li Jianguo 
Published: June 2022

AbstractA spiral grain selector with excellent geometric parameters can ensure qualified single‐crystal (SX) is selected while decreasing lesser thermal gradient (G) during casting of SX nickel‐based superalloy . In this paper, three configurations of spiral grain selectors with different height of starter block (H = 18, 25, 35 mm) and same spiral passage are adopted. Based on the quantitative analysis of orientation distribution of grains in starter block, it is found that the deviation of <001> oriented grains in the projection zone of spiral passage inlet (circle with a diameter of 4 mm) are smaller than grains of overall cross‐section (circle with a diameter of 10 mm), which verify that starter block with smaller diameter have better optimization effect of grain orientation. Moreover, the average deviation of <001> oriented grains decreases to a range of 6.6–10.8° when the height of starter block is 18–27 mm, and SX selection is completed on the position of less than half turn in spiral passage regardless of the number of grains in the inlet position. Finally, a modified spiral grain selector is designed from the perspective of practical production, of which total height is reduced by at least 15 mm, and can decrease lesser G at some extent during casting .
Adiponectin in anorexia nervosa and its modifiers: A meta‐regression study
Authors: Tural Umit , Iosifescu Dan V. 
Published: June 2022

AbstractObjectiveAdiponectin, which is secreted from adipose tissue, is a protein hormone. Although a large body of studies have found that circulating adiponectin levels increase in anorexia nervosa (AN) and caloric restriction, the effect of subtypes of AN and modifiers of adiponectin in AN are not yet known.MethodsA systematic search of electronic databases was performed using the search terms “adiponectin,” “anorexia nervosa,” and “eating disorder” up to January 2021. All studies published in peer‐reviewed journals, which included cases and control groups, were selected. The main outcome was the pooled standardized mean difference (SMD) in adiponectin levels between cases and controls, using the random‐effects model. Modifiers of SMD were tested via meta‐regression. Heterogeneity and publication bias were evaluated.ResultsThirty‐four studies met all eligibility criteria. The total sample of AN participants (Hedges' g = .765, p < .0001), and specifically the binge‐eating/purging (Hedges' g = 1.211, p < .00001) and restrictive subtypes (Hedges' g = .913, p < .00001) of AN have increased adiponectin plasma levels compared with healthy controls. Meta‐regression determined that insulin, IGF‐1, BMI, triglyceride, resistin, glucose, IL‐6 levels are significant modifiers of adiponectin levels.DiscussionCompared with controls, adiponectin levels are higher in AN overall, and specifically in the binge‐eating/purging and the restrictive AN subtypes. Many of metabolic parameters of glucose metabolism and pro‐inflammatory molecules modify the relationship between AN and adiponectin levels. Adipose tissue is important to maintain metabolic stability.Public SignificanceAnorexia nervosa is a psychiatric disorder associated with a severe decrease in body weight and multiple metabolic abnormalities, including an increase in the hormone adiponectin. In this paper, we used meta‐analysis, a powerful statistical method, to aggregate data from 34 rigorously selected research reports. This enabled us to understand the value of adiponectin to differentiate clinical subtypes of anorexia nervosa and the relations between adiponectin and other important metabolic parameters.
Changes in specific and nonspecific psychopathology network structure after intensive cognitive behavior therapy in patients with anorexia nervosa
Authors: Calugi Simona , Dametti Laura , Dalle Grave Anna , Dalle Grave Riccardo 
Published: June 2022

AbstractObjectiveThis study aimed to compare eating disorder‐specific and nonspecific clinical features in patients with anorexia nervosa before and after intensive enhanced cognitive behavior therapy (CBT‐E) via network analysis.MethodsAll consecutive patients admitted to intensive CBT‐E were eligible, and the sample comprised patients aged ≥16 years who completed a 20‐week intensive CBT‐E program. Body mass index (BMI), Eating Disorder Examination Questionnaire and Brief Symptoms Inventory responses were gathered at baseline and end of treatment, and used to generate statistical networks of the connections between symptoms (nodes) and the strength and centrality thereof.ResultsA total of 214 patients were included. Most nodes had relatively similar centrality compared to other nodes in the networks. “Eating concern” and “phobic anxiety” showed the greatest bridge centrality at both time points. No differences were found between baseline and the end of treatment in either global network or individual connection strengths.ConclusionThese findings suggest that some clinical expressions not specific to eating‐disorder psychopathology remain strongly connected in the generalized network of patients with anorexia nervosa after CBT‐E. Future research should examine whether additional procedures specifically designed to target these symptoms should be integrated into this and other treatments.
A New Class of Water‐Soluble Aryl Sulfonium Decavanadates and Their Antioxidant Activity: Effects of Cluster Reduction and Counter Ion Substitution on Activity
Authors: Routh Kousik , Kaur Sandeep , Pradeep Chullikkattil P. 
Published: June 2022

AbstractThe development of biocompatible and water‐soluble inorganic antioxidants is important, given the increasing demand for antioxidants worldwide. Polyoxovanadate (POV) clusters have been extensively explored for several biological applications, but the antioxidant properties of POVs remain largely unexplored. Herein, we developed a series of water‐soluble aryl sulfonium decavanadate ([V10O28]6−) hybrids and studied their antioxidant properties. The hybrids were characterized by several analytical and spectroscopic techniques such as ESI‐MS, XPS, and single‐crystal XRD analyses. The antioxidant properties of these hybrids were explored under native and photochemically reduced conditions using standard in vitro antioxidant assays such as H2O2 scavenging assay, lipid peroxidation inhibitory assay, DPPH radical scavenging assay, and reducing power assay. These analyses showed that these aryl sulfonium POV hybrids exhibit comparable or better antioxidant activities than the parent [V10O28]6− cluster and the standard ascorbic acid in some of the assays studied. The cytotoxicity studies using MG‐63 cells revealed that these hybrids exhibit moderate cytotoxicity. Further, it was found that introducing a phenolic functional group on the counter ion moiety helps to improve the overall antioxidant properties of the POV‐based hybrids. The present study, therefore, reports a series of water‐soluble aryl sulfonium polyoxometalate hybrids‐based antioxidants for the first time. However, more toxicological studies using similar hybrids are required to fully understand the biocompatible nature of these systems before considering their potential biological applications.
Game theory based hybrid localization technique for underwater wireless sensor networks
Authors: Kumar Manni , Goyal Nitin , Qaisi Ramy Mohammed Aiesh , Najim Mohd , Gupta Sachin Kumar 
Published: June 2022

AbstractIn an underwater environment, it is necessary but tough to track the location of deployed sensor nodes because of node's mobility, nonavailability of GPS, node software or hardware failure and so forth. Due to these reasons, nodes cannot directly communicate with enough beacons in the neighbourhoods, resulting into localization error. This creates the need to develop the range‐based localization method that takes into consideration the features of the underwater environment with precise location estimation. The existing range‐based localization techniques involve huge computational and storage overhead along with localization latency. To solve these problems, a hybrid localization estimation technique of received signal strength (RSS) and angle of arrival (AoA) is presented in this article. In this hybrid method, AoA is estimated from the primary anchor (PA) while RSS is measured from the selected set of secondary anchors (SAs). Further, Game theory model is also applied to considerably increase the precision by choosing the optimum set of SAs even in the mobility assisted underwater environment. The proposed technique of identifying unknown sensor nodes is tested through NS2 simulation with multiple runs. The proposed technique achieves minimum localization error, delay, energy consumption and high packet delivery ratio in comparison to topology control for energy‐efficient localization, game theory based routing protocol, iterative mobile target localization, and RSS‐based localization.
Forecasting value at risk and expected shortfall using high‐frequency data of domestic and international stock markets
Authors: Wang Man , Cheng Yihan 
Published: June 2022

AbstractThis paper first proposes the generalized autoregressive score (GAS) factor X model for predicting Value‐at‐Risk (VaR) and expected shortfall (ES). This model is flexible for adding explanatory variables (X) that are informative for forecasting extreme risks and thus can improve forecast accuracy of the traditional GAS factor model. Then, to investigate whether high‐frequency data of domestic and international stock market are predictive of the extreme risk of Chinese stock market, X is set to be the corresponding realized volatility (RV) of these markets. Empirical studies based on 13 stock markets show that lagged RVs of domestic and influential international markets have prediction ability of extreme risks in Chinese stock market. The RV of the domestic market is more informative than that of international markets, among which the RVs of Asian and US markets are more predictive than European markets. We have also investigated compound information of the RVs of all markets, the results of which show that the mean of RVs is more informative than the first principle component; however, it is less informative than the individual RV of some markets. Additionally, there is no gain in forecast accuracy of the GAS factor RV models by forecast combination.
Mixed membership nearest neighbor model with feature difference
Authors: Cheung Simon K. C. , Cheung Tommy K. Y. 
Published: June 2022

AbstractThe nearest neighbor model has been a popular model for spatial data. This model assumes a distance‐based neighborhood structure among a set of entities that define the observations in a dataset. Values of a dependent variable are a function of the corresponding values of its neighbors and values of its own explanatory variables. In this paper, we extend the concept of “spatial neighbors” to a more general network dependency where the nearest neighbor is not necessarily distance‐based. As each entity can belong to multiple groups, observations can be related by multiple networks. By exploring the overlapping information that are exhibited by an entity and its neighbors, we modify the traditional spatial autoregressive model with autoregressive disturbances (SARAR) in three ways. First, multiple networks are allowed. Second, differences between the explanatory variables and their corresponding neighboring averages are applied. Third, apart from independent innovations, we investigate the case where the innovations are also multiple‐network dependent. Statistical inference of the model parameters is achieved via maximizing the profile log‐likelihood function. The standard errors of the profile maximum likelihood estimators are calculated by the inverse Fisher information matrix. Four variations of the multiple network autoregressive models are applied to predict changes in stock prices of 555 companies listed in the Hong Kong Stock Exchange. The model with attribute differencing and dependent innovations produces a smaller out‐of‐sample mean prediction error sum of squares than that of the other models.
Impacts of Land‐Use Change on the Hydrology of Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia
Authors: Getachew Birhan , Manjunatha Busnur Rachotappa 
Published: June 2022

AbstractHuman activities impact hydrology through changes in land use and land cover. This study examins the effects of changing land use on hydrological processes using the soil and water assessment tool (SWAT) model. The data is acquired from Landsat 4‐5 Thematic Mapper (TM) in 1989, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) in 2005, and Landsat 8 Operational Land Inventory (OLI) in 2019. Image preprocessing, which includes georeferencing, radiometric and atmospheric correction, image enhancement, band composite, mosaicking, and sub‐setting, are performed. After that, supervised classification, accuracy assessment, and change detection are carried out. The hydrological changes in 1989, 2005, and 2019 are analyzed using land‐use maps. The SWAT model's calibration, validation, and sensitivity analysis are performed using the Integrated Parameter Estimation and Uncertainty Analysis Tool in the four main rivers of the basin. Farmlands and built‐up lands are found to have steadily increased in the basin, while shrublands, grasslands, and bare lands declined. Due to an expansion of agricultural and built‐up lands and a decrease in shrublands and grasslands, the basin's mean annual water yield and surface runoff increased in 2019, while evapotranspiration and lateral flow decreased compared to 1989 and 2005. Therefore, future watershed and basin management shall consider changing land use.
Organic Electrolytes Recycling From Spent Lithium‐Ion Batteries
Authors: Zhang Ruihan , Shi Xingyi , Esan Oladapo Christopher , An Liang 
Published: June 2022

AbstractLithium‐ion batteries (LIBs) are regarded to be the most promising electrochemical energy storage device for portable electronics as well as electrical vehicles. However, due to their limited‐service life, tons of spent LIBs are expected to be produced in the recent years. Suitable recycling technology is therefore becoming more and more important as improper treatment of spent LIBs, especially the aged organic electrolyte, can cause severe environmental pollution and threats to human health. The organic solvents and high concentration of lithium salts in aged electrolytes are always sensitive toward water and air, which would easily hydrolyze and decompose into toxic fluorine‐containing compounds, leading to severe fluorine pollution of the surrounding environment. Hence, recycling aged electrolytes from spent LIBs is an efficient way to avoid this potential risk to the environment. However, several issues inhibit the realization of electrolyte recycling, including the volatile, inflammable, and toxic nature of the electrolytes, the difficulty to extract electrolytes from the electrodes and separators, and various electrolyte compositions inside LIBs from different applications and companies. Herein, the current progress in recycling methods for aged electrolytes from spent LIBs is summarized and perspectives on future development of electrolyte recycling are presented.
Proteome profiling of enzalutamide‐resistant cell lines and serum analysis identified ALCAM as marker of resistance in castration‐resistant prostate cancer
Authors: Csizmarik Anita , Keresztes Dávid , Nagy Nikolett , Bracht Thilo , Sitek Barbara , Witzke Kathrin , Puhr Martin , Tornyi Ilona , Lázár József , Takács László , Kramer Gero , Sevcenco Sabina , Maj‐Hes Agnieszka , Jurányi Zsolt , Hadaschik Boris , Nyirády Péter , Szarvas Tibor 
Published: June 2022

AbstractEnzalutamide (ENZA) is a frequently used therapy in metastatic castration‐resistant prostate cancer (mCRPC). Baseline or acquired resistance to ENZA have been observed, but the molecular mechanisms of resistance are poorly understood. We aimed to identify proteins involved in ENZA resistance and to find therapy‐predictive serum markers. We performed comparative proteome analyses on ENZA‐sensitive parental (LAPC4, DuCaP) and ‐resistant prostate cancer cell lines (LAPC4‐ENZA, DuCaP‐ENZA) using liquid chromatography tandem mass spectrometry (LC‐MS/MS). The top four most promising candidate markers were selected using bioinformatic approaches. Serum concentrations of selected markers (ALCAM, AGR2, NDRG1, IDH1) were measured in pretreatment samples of 72 ENZA‐treated mCRPC patients using ELISA. In addition, ALCAM serum levels were measured in 101 Abiraterone (ABI) and 100 Docetaxel (DOC)‐treated mCRPC patients' baseline samples. Results were correlated with clinical and follow‐up data. The functional role of ALCAM in ENZA resistance was assessed in vitro using siRNA. Our proteome analyses revealed 731 significantly differentially abundant proteins between ENZA‐sensitive and ‐resistant cells and our filtering methods identified four biomarker candidates. Serum analyses of these proteins revealed only ALCAM to be associated with poor patient survival. Furthermore, higher baseline ALCAM levels were associated with poor survival in ABI‐ but not in DOC‐treated patients. In LAPC4‐ENZA resistant cells, ALCAM silencing by siRNA knockdown resulted in significantly enhanced ENZA sensitivity. Our analyses revealed that ALCAM serum levels may help to identify ENZA‐ and ABI‐resistant patients and may thereby help to optimize future clinical decision‐making. Our functional analyses suggest the possible involvement of ALCAM in ENZA resistance.
LiteCovidNet: A lightweight deep neural network model for detection of COVID‐19 using X‐ray images
Authors: Kumar Sachin , Shastri Sourabh , Mahajan Shilpa , Singh Kuljeet , Gupta Surbhi , Rani Rajneesh , Mohan Neeraj , Mansotra Vibhakar 
Published: June 2022

AbstractThe syndrome called COVID‐19 which was firstly spread in Wuhan, China has already been declared a globally “Pandemic.” To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence‐based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network‐based “LiteCovidNet” model is proposed that detects COVID‐19 cases as the binary class (COVID‐19, Normal) and the multi‐class (COVID‐19, Normal, Pneumonia) bifurcated based on chest X‐ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi‐class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID‐19 infected patients at an early stage with convenient cost and better accuracy.
A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID‐19 lung infections
Authors: Polat Hasan 
Published: June 2022

AbstractCoronavirus disease (COVID‐19) affects the lives of billions of people worldwide and has destructive impacts on daily life routines, the global economy, and public health. Early diagnosis and quantification of COVID‐19 infection have a vital role in improving treatment outcomes and interrupting transmission. For this purpose, advances in medical imaging techniques like computed tomography (CT) scans offer great potential as an alternative to RT‐PCR assay. CT scans enable a better understanding of infection morphology and tracking of lesion boundaries. Since manual analysis of CT can be extremely tedious and time‐consuming, robust automated image segmentation is necessary for clinical diagnosis and decision support. This paper proposes an efficient segmentation framework based on the modified DeepLabV3+ using lower atrous rates in the Atrous Spatial Pyramid Pooling (ASPP) module. The lower atrous rates make receptive small to capture intricate morphological details. The encoder part of the framework utilizes a pre‐trained residual network based on dilated convolutions for optimum resolution of feature maps. In order to evaluate the robustness of the modified model, a comprehensive comparison with other state‐of‐the‐art segmentation methods was also performed. The experiments were carried out using a fivefold cross‐validation technique on a publicly available database containing 100 single‐slice CT scans from >40 patients with COVID‐19. The modified DeepLabV3+ achieved good segmentation performance using around 43.9 M parameters. The lower atrous rates in the ASPP module improved segmentation performance. After fivefold cross‐validation, the framework achieved an overall Dice similarity coefficient score of 0.881. The results demonstrate that several minor modifications to the DeepLabV3+ pipeline can provide robust solutions for improving segmentation performance and hardware implementation.