bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diet selection and asocial learning: Natal habitat influence on lifelong foraging strategies in solitary large mammals
Authors: Jimbo Mina , Ishinazaka Tsuyoshi , Shirane Yuri , Umemura Yoshihiro , Yamanaka Masami , Uno Hiroyuki , Sashika Mariko , Tsubota Toshio , Shimozuru Michito 
Published: July 2022

AbstractExploring the process of diet selection will contribute to improvement in our understanding of animal foraging strategies. The overwhelming majority of ecological research on animal learning and foraging concentrates on how social learning influences the feeding styles of animals living in groups. In solitary animals that live long after independence from their mothers, foraging experience after independence is expected to have a significant influence on diet selection, but few studies have addressed this point. We used brown bears (Ursus arctos), which spend 1–2 years with their mothers before foraging alone, as a model species and investigated how their diet changed later in life. We estimated the diets of bears at the individual level by using stable isotope analysis of guard hairs and examined the factors that drove dietary variation. We also quantified the extent to which the diets of bears shifted by comparing the diets of bears at the time of capture with the average diet in their natal habitat. Our results indicated that females retained the average diet of their natal habitat, whereas the diets of males significantly changed more than 6 years after becoming independent from their mothers, when they reached physical maturity. Males were dependent on energy‐rich marine animals at older ages regardless of their natal habitats, which we attribute to several factors, including habitat exploration, acquisition of foraging experience, and social dominance. Our results provide the first evidence, suggesting that foraging experience after independence influences diet selection later in life in solitary large mammals.
Removing flowers of a generalist plant changes pollinator visitation, composition, and interaction network structure
Authors: Bain Justin A. , Dickson Rachel G. , Gruver Andrea M. , CaraDonna Paul J. 
Published: July 2022

AbstractPollination is essential for ecosystem functioning, yet our understanding of the empirical consequences of species loss for plant–pollinator interactions remains limited. It is hypothesized that the loss of abundant and generalized (well‐connected) species from a pollination network will have a large effect on the remaining species and their interactions. However, to date, relatively few studies have experimentally removed species from their natural setting to address this hypothesis. We investigated the consequences of losing an abundant, generalist native species from a series of plant–pollinator networks by experimentally removing the flowers of Helianthella quinquenervis (Asteraceae) from half of a series of 10 paired plots (15 m diameter) within a subalpine ecosystem. We then asked how the localized loss of this species influenced patterns of pollinator visitation, floral visitor composition, and interaction network structure. The experimental removal of Helianthella flowers led to an overall decline in plot‐level pollinator visitation rates and shifts in pollinator composition. Species‐level responses to floral removal differed between the two other abundant, co‐flowering plants in our experiment: Potentilla pulcherrima received higher visitation rates, whereas Erigeron speciosus visitation rates did not change. Experimental floral removal altered the structural properties of the localized plant–pollinator networks such that they were more specialized, less nested, and less robust to further species loss. Such changes to interaction network structure were consistently driven more by species turnover than by interaction rewiring. Our findings suggest that the local loss of an abundant, well‐linked, generalist plant can bring about diverse responses within intact pollination networks, including potential competitive and facilitative effects for individual species, changes to network structure that may render them more sensitive to future change, but also numerous changes to interactions that may also suggest flexibility in response to species loss.
Sensitivity of ventricular systolic function to afterload during veno‐arterial extracorporeal membrane oxygenation
Authors: Ng Pauline Yeung , Ma Tammy Sin Kwan , Ip April , Lee Man Kei , Ng Andrew Kei‐Yan , Ngai Chun Wai , Chan Wai Ming , Siu Chung Wah , Sin Wai Ching 
Published: July 2022

AbstractAimsVeno‐arterial extracorporeal membrane oxygenation (V‐A ECMO) increases afterload to the injured heart and may hinder myocardial recovery. We aimed to compare the sensitivity of left ventricular (LV) systolic function to the afterload effects of peripheral V‐A ECMO during the acute and delayed stages of acute myocardial dysfunction.Methods and resultsA total of 46 adult patients who were supported by peripheral V‐A ECMO between April 2019 and June 2021 were analysed. Serial cardiac performance parameters were measured by transthoracic echocardiography (TTE) on mean day 1 ± 1 of V‐A ECMO initiation (n = 45, ‘acute phase’) and mean day 4 ± 2 of V‐A ECMO initiation (n = 36, ‘delayed phase’). Measurements were obtained at 100%, 120%, and 50% of ECMO target blood flow (TBF). LV global longitudinal strain (GLS) significantly improved from −6.1 (−8.9 to −4.0)% during 120% TBF to −8.8 (−11.5 to −6.0)% during 50% TBF (P < 0.001). The sensitivity of LV GLS to changes in ECMO flow was significantly greater in the acute phase of myocardial injury compared with the delayed phase [median (IQR) percentage change: 72.7 (26.8–100.0)% vs. 22.5 (14.9–43.8)%, P < 0.001]. Findings from other echocardiographic parameters including LV ejection fraction [43.0 (29.1–56.8)% vs. 22.8 (9.2–42.2)%, P = 0.012] and LV outflow tract velocity‐time integral [45.8 (18.6–58.7)% vs. 24.2 (12.6–34.0)%, P = 0.001] were similar. A total of 24 (52.2%) patients were weaned off ECMO successfully.ConclusionsWe demonstrated that LV systolic function was significantly more sensitive to the afterload effects of V‐A ECMO during the acute stage of myocardial dysfunction compared with the delayed phase. Understanding the evolution of the heart–ECMO interaction over the course of acute myocardial dysfunction informs the clinical utility of echocardiographic assessment in patients on V‐A ECMO.
Migration Mechanism of the B−H Activation of Carboranes
Authors: Tang Wanyong , He Yuhan , Li Hui 
Published: July 2022

AbstractDensity functional theory was used to investigate the possible migration mechanism of the B−H activation on carboranes. The effects of inducing groups, mediating atoms and ligands were clarified, and the specific reaction process and driving force of transition metal migration in carboranes were explored. The study of thermodynamics, dynamics, wave function analysis and bond order showed that hydrogen atoms and ligands had remarkable effects on metal migration in carboranes. Results indicated that the transition metal complexes can migrate between B/B or C/B mediated by H‐atom, which contributes to the regioselective B−H activation of carboranes.
Intermediates in Friedel‐Crafts Acylation of Fumaryl Halides
Authors: Bayer Marie C. , Greither Nikolaus , Jessen Christoph , Nitzer Alexander , Kornath Andreas J. 
Published: July 2022

AbstractFumaryl chloride and fluoride were reacted with different Lewis acids to synthesize the intermediates of the Friedel‐Crafts acylation. The salt of the monoacyl cation [C4H2FO2]+[Sb3F16]− was obtained from the reaction of fumaryl fluoride with SbF5 in SO2ClF solutions. The reaction was repeated using fumaryl chloride as starting material, which reacted under halogen exchange to obtain the salt of the monoacyl cation [C4H2FO2]+[SbCl2F4]−. In addition, the reaction of fumaryl chloride with SbCl5 in SO2ClF was studied. The covalent donor‐acceptor complex C4H2Cl2O2 ⋅ 2 SbCl5 was formed, containing oxygen‐bonded Lewis acids. The compounds were characterized by low‐temperature vibrational spectroscopy. Single‐crystal X‐ray structure analyses were conducted for [C4H2FO2]+[Sb3F16]− as well as for C4H2Cl2O2 ⋅ 2 SbCl5. In the solid state of [C4H2FO2]+[Sb3F16]− C⋅⋅⋅O and C⋅⋅⋅F contacts are observed and the origin of these interactions is discussed by means of ESP maps and NBO analysis. The monoacyl cation is stabilized by electrostatic attraction and electron back‐donation from oxygen and fluorine ligands to the positive ring‐structured π‐hole at the oxocarbenium center. Besides, the formation of the diacyl cation is not observed, which is based on small distances between the positive charges involving charge‐charge repulsion. The great advantage of using fumaryl halides in Friedel‐Crafts acylation is featured by the possibility to synthesize ketones keeping an acyl fluoride moiety.
A novel high gain dual band ear bud shaped patch antenna for under water communications
Authors: Pramod Kumar Aylapogu , Kiran Kumar Gurrala 
Published: July 2022

AbstractThis article elucidates a high gain dual band ear bud shaped patch antenna with mm wave technology. The pivotal characteristic of this design is to provide higher gain and less interference at higher frequencies. It operates at a frequency of 40.59 and 52.002 GHz. The major advantage of this design is that it produces the circular polarization in entire band of frequency; moreover this antenna is useful for under water communication. The proposed design introduces two splits, imprinted at the walls of patch to get a wide band. For an optimal design, a future antenna parametric analysis was carried out. The proposed design is developed with the aid of HFSS software; we measured major parametersS11, VSWR, gain and radiation patterns in free space and underground environment. In the results, the predicted antenna radiation pattern is seen for E‐plane and H‐plane at various frequency bands. The gain 3D polar plots and current distribution plots are presented, including a comparison of previous work to the current work. We assure that the suggested design is applicable for under water communication.
Experimental and computational study on the glowing ignition of wood
Authors: Yang Jiuling , Hamins Anthony , Dubrulle Laura , Zammarano Mauro 
Published: July 2022

AbstractWildland Urban Interface (WUI) fires are a rapidly increasing hazard. Cellulosic materials play a key role in the spread of WUI fires. The physics controlling the glowing ignition of cellulosic materials is not well understood. In this work, an experimental and computational study of the glowing ignition of wood was performed. Eight wood specimens (Balsa, Red Cedar, Pine, Douglas Fir, Cypress, Redwood, Purpleheart, and Ipe) were tested under a cone calorimeter in the vertical configuration. Non‐uniform glowing propagation due to sample‐specific surface cracking was observed for all wood samples. The average temperature at the center of each sample was measured using an optical pyrometer. The inflection point in the pyrometer temperature‐versus‐time curve, which was identified by the maximum peak of the first temperature derivative, was used to identify the onset of glowing ignition. Additionally, a comprehensive model was applied to predict the glowing ignition. The comparisons between model predictions and experimental results showed that the surface temperature, the time to glowing ignition, and the glowing ignition temperature of medium‐density wood (ρ = 300–500 kg m−3) could be reasonably predicted. For lighter (e.g., Balsa, ρ = 150 kg m−3) and denser wood (e.g., Ipe ρ = 960 kg m−3), however, significant discrepancies were observed. Hence, further work is needed to understand the influence of wood density on the thermo‐physical and thermodynamic properties that can significantly affect glowing.
Sowing kernels for food safety: Importance of rapid on‐site detction of pesticide residues in agricultural foods
Authors: Umapathi Reddicherla , Rani Gokana Mohana , Kim Eunsu , Park So‐Young , Cho Youngjin , Huh Yun Suk 
Published: July 2022

AbstractSustainable cultivation and safe agricultural production are needed to ensure the safety of all living organisms and ecosystems. Most agricultural foods currently contain unacceptable levels of pesticide residues. In an attempt to ensure pest control and high crop yields, farmers spray a large number of pesticides in quantities that exceed the safety limits for agricultural crops. Pesticide residues are highly toxic to humans, causing severe and even deadly diseases. Traditional analytical strategies for laboratory pesticide detection are often limited because they are time‐consuming and require trained personnel, which makes them very costly. Hence, accurate, rapid, and on‐site analysis of pesticides is drawing increasing attention for food safety reasons. Therefore, on‐site or point‐of‐care (POC) detection of pesticide residues has become an approach of paramount importance. Recently, various portable detection technologies, such as colorimetric, fluorescence, electrochemical, surface plasmon resonance, chemiluminescence, phosphorescence, microfluidic, and surface‐enhanced Raman scattering techniques, have been designed for on‐site monitoring of pesticide residues in vegetables and fruits. These devices have demonstrated great power of detection in preclinical agricultural settings. This review highlights the emerging insights and novel advances in portable devices, POC technologies, and on‐site sensing approaches for the detection of pesticide residues in agricultural foods. However, greater rigor in the design of portable pesticide‐detection devices is essential. The increasing demand for rapid detection of hazardous pesticide components has prompted a timely opportunity to summarize recent developments and progress in portable devices and on‐site sensing strategies to evaluate food safety.
Cryptocurrencies trading algorithms: A review
Authors: Ruiz Roque da Silva Isabela , Junior Eli Hadad , Balbi Pedro Paulo 
Published: July 2022

AbstractThis study conducts a bibliometric analysis and systematic review of cryptocurrency trading algorithms to identify existing gaps in the area. From our standpoint, this is the first study to carry out a deep analysis of price forecasts and portfolio management in cryptocurrencies in addition to analyzing the most relevant studies and authors, trend topics of the area, and identifying countries with the most published studies. During our research, we identified some gaps that can be used for further research. Currently, there are approximately 16,000 cryptocurrencies; however, in majority of the papers, the authors have only used the top 10 ranking market capitalization cryptocurrencies, leaving aside potential minor cryptocurrencies. Thus, trading strategies using Big Data can be a potential research topic, considering the greater number of emerging cryptocurrencies.
High‐frequency data and stock–bond investing
Authors: Lai Yu‐Sheng 
Published: July 2022

AbstractUnderstanding the comovements between stock and bond returns is crucial in asset allocation. This paper employs a new class of multivariate covariance models with realized covariance measures for modeling the joint distribution of returns. Estimation results indicate that high‐frequency data not only enhance explanatory power but also sort out the features of heteroskedasticity with a short response time and short‐run momentum effects in describing the covariance dynamics. To confirm the efficiency of the models in covariance predictions, multiperiod volatility‐timing strategies are implemented to evaluate the benefits of incorporating the distinguishing features into the modeling. Out‐of‐sample forecasting results indicate that the models outperform conventional models in finding optimal portfolio shares and thus considerably reduce the conditional volatility in a portfolio. Consequently, investors with high risk aversions are willing to pay pronounced performance fees to obtain the economic value of volatility timing.
Investigation of rice performance characteristics: A comparative study of LR, ANN, and RSM
Authors: Mahfeli Mandana , Zarein Mohammad , Zomorodian Aliasghar , Khafajeh Hamid 
Published: July 2022

AbstractParboiling is a type of heat pretreatment used in rice processing to reach higher head rice yield and improve the nutrition properties of raw rice. In this research, the goals were prediction and determination of optimum conditions for parboiled rice processing using the response surface method (RSM) as well as modeling the output values by linear regression (LR) and artificial neural networks (ANN). The parameters including steaming time (0, 5, 10, and 15 min), dryer type (solar and continuous dryers), and drying air temperature (35, 40, and 45°C) were employed as input values. In addition, the breakage resistance (BR) and head rice yield (HRY) were selected as output values. The ANN‐based nonlinear regression, the multi‐layer perceptron (MLP), and the radial basis function (RBF) have been developed to model the process parameters, as well as the central composite design (CCD) was conducted for optimization of BR and HRY values. The outputs of RBF network have been successfully applied to predict higher coefficient of determination of BR and HRY as 0.989 and 0.986, respectively, indicating the appropriateness of the model equation in predicting head rice yield and breakage resistance when the three processing variables (steaming time, dryer type, and drying air temperature) are mathematically combined. Also, the lower root mean square error (RMSE) was obtained for each one as 0.043 and 0.041. The optimum values of BR and HRY were obtained as 12.80 N and 67.3%, respectively, at 9.62 min and 36.9°C for a solar dryer with a desirability of 0.941. In addition, the same values were obtained as 14.50 N and 72.1%, respectively, at 8.77 min and 37.0°C for a continuous dryer with a desirability of 0.971.
Lateralized overgrowth with vascular malformation caused by a somaticPTPN11pathogenic variant: Another piece added to the puzzle of mosaicRASopathies
Authors: Mussa Alessandro , Turchiano Antonella , Cardaropoli Simona , Coppo Paola , Pantaleo Antonino , Bagnulo Rosanna , Ranieri Carlotta , Iacoviello Matteo , Garganese Antonella , Stella Alessandro , Vallero Stefano Gabriele , Bertin Daniele , Santoro Federica , Carli Diana , Ferrero Giovanni Battista , Resta Nicoletta 
Published: July 2022

AbstractLateralized/segmental overgrowth disorders (LOs) encompass a heterogeneous group of congenital conditions with excessive body tissue growth. Documented molecular alterations in LOs mostly consist of somatic variants in genes of the PI3KCA/AKT/mTOR pathway or of chromosome band 11p15.5 imprinted region anomalies. In some cases, somatic pathogenic variants in genes of the RAS/MAPK pathway have been reported. We present the first case of a somatic pathogenic variant (T507K) inPTPN11causing a LO phenotype characterized by severe lateralized overgrowth, vascular proliferation, and cerebral astrocytoma. The T507K variant was detected in DNA from overgrown tissue in a leg with capillary malformation. The astrocytoma tissue showed a higherPTPN11variant allele frequency. A pathogenic variant inFGFR1was also found in tumor tissue, representing a second hit on the RAS/MAPK pathway. These findings indicate that RAS/MAPK cascade overactivation can cause mosaic overgrowth phenotypes resembling PIK3CA‐related overgrowth disorders (PROS) with cancer predisposition and are consistent with the hypothesis that RAS/MAPK hyperactivation can be involved in the pathogenesis of astrocytoma. This observation raises the issue of cancer predisposition in patients with RAS/MAPK pathway gene variants and expands genotype spectrum of LOs and the treatment options for similar cases through inhibition of the RAS/MAPK oversignaling.
Macroeconomic factors and financing strategies in working capital: Evidence from China
Authors: Mahmood Faisal , Ahmed Zahoor , Hussain Nazim , Zaied Younes Ben 
Published: July 2022

AbstractChanging economic situation of a country affects working capital by influencing lenders' financing abilities, firms' business activities, and managers' decisions. Hence, this paper explores the influence of macroeconomic factors in the relationship between working capital financing and firm performance over the period of 2000–2018. Applying the two‐step generalized methods of moments, we found a non‐linear and inverted U‐shaped relationship between working capital finance (WCF) and firm performance, significantly influenced by the macroeconomic indicators that is, gross domestic product (GDP) growth, Inflation rate, and interest rate. We segregated the data regarding the global financial recession period 2008–2010 and disclosed that the non‐linear and inverted U‐shaped relationship between WCF and firm performance turns to linear and negative during this recession period. We also evaluated the role of GDP growth, inflation, and interest rate in WCF and firm performance relationship during the global recession and found that these factors do not influence the nexus during this period. Firm managers, lending agencies, and researchers may use these results in choosing the best financing options in working capital under the changing situation of macroeconomic factors. The WCF and firm performance analysis during the global recession period (2008–2010) may be the best prediction model for the current financial crisis caused by COVID‐19.
An effective deep learning model for grading abnormalities in retinal fundus images using variational auto‐encoders
Authors: Sundar Sumod , Sumathy Subramanian 
Published: July 2022

AbstractDiabetic retinopathy (DR) and Diabetic Macular Edema (DME) are severe diseases that affect the eyes due to damage in blood vessels. Computer‐aided automated grading will help clinicians conduct disease diagnoses at ease. Experiments of automated image processing with deep learning techniques using CNN produce promising results, especially in the medical imaging domain. However, the disease grading tasks in retinal images using CNN struggle to retain high‐quality information at the output. A novel deep learning model based on variational auto‐encoder to grade DR and DME abnormalities in retinal images is proposed. The objective of the proposed model is to extract the most relevant retinal image features efficiently. It focuses on addressing less relevant candidate region generation and translational invariance present in images. The experiments are conducted in IDRID dataset and evaluated using accuracy, U‐kappa, sensitivity, specificity and precision metrics. The results outperform compared with other state‐of‐art techniques.