bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel Woodbury solution method for nonlinear seismic response analysis of large‐scale structures
Authors: Yu Ding‐Hao , Li Gang 
Published: September 2023

AbstractThe Woodbury formula is an efficient tool in mathematics to calculate low‐rank perturbation problems and has been applied to improve the computational efficiency of nonlinear seismic response analysis (NSRA) of structures with local nonlinearity. Using the Woodbury formula for NSRA can avoid the time‐consuming recalculation and factorization of the large‐dimensional global stiffness matrix of a structure by only solving a small‐dimensional Schur complement system representing local nonlinearity per iteration. Because the dimension of the Schur complement matrix is determined by the inelastic degree of freedom (IDOF) number, which represents the scale of local nonlinear regions, a small IDOF number is helpful for achieving the high‐efficiency advantage of the Woodbury formula. However, when performing NSRA for large‐scale structures, the IDOF number is usually relatively large, which contradicts the efficiency requirement of the Woodbury formula. To solve this problem and extend the advantage of the Woodbury formula to the NSRA of large‐scale structures, this paper first proposes a two‐stage IDOF number reduction method by eliminating the IDOFs that have insignificant effects on the results, and consequently, a variant Woodbury formula is derived. Because only the principal component in the Schur complement matrix is retained, the dimension of this matrix and the cost for factorizing it can be reduced significantly without losing accuracy, thus greatly improving the efficiency of the proposed method. Moreover, to reduce the additional computational time introduced by the IDOF number reduction procedure and to further improve the computational performance of the proposed method, an OpenMP parallel computational strategy is incorporated. Finally, the validity of the proposed method is verified by implementing incremental dynamic analysis for a large‐scale reinforced concrete structure.
A critical analysis of India's pre‐pack regime for MSMEs
Authors: Tripathi Urmika 
Published: September 2023

AbstractHybrid restructuring procedures, such as pre‐packs, have been encouraged to deal with an expected increase in insolvent firms in the aftermath of the COVID‐19 pandemic. Pre‐packs have been gaining popularity as a restructuring mechanism across the world. India too introduced a pre‐packaged insolvency resolution process for micro, small and medium enterprises (MSMEs) in April 2021. This article first provides a brief overview of the pre‐pack models employed in certain other jurisdictions such as the United States of America, United Kingdom and Singapore with a view to understanding the key features of pre‐pack models and how they have been operationalised in these jurisdictions. It then briefly discusses other restructuring avenues, that were available to a corporate debtor in India, and the circumstances that led up to the introduction of pre‐packs in India. Finally, it provides a detailed overview of the Indian pre‐pack regime and evaluates its effectiveness for MSMEs, potential issues that may cause delays in the process and how the current pre‐pack regime can be further streamlined. It argues that some of the procedural requirements and features of the Indian pre‐pack regime may not be suitable for MSME insolvencies.
DeepHistoNet: A robust deep‐learning model for the classification of hepatocellular, lung, and colon carcinoma
Authors: Kadirappa Ravindranath , S. Deivalakshmi , R. Pandeeswari , Ko Seok‐Bum 
Published: September 2023

AbstractIn recent days, non‐communicable diseases (NCDs) require more attention since they require specialized infrastructure for treatment. As per the cancer population registry estimate, nearly 800,000 new cancer cases will be detected yearly. The statistics alarm the need for early cancer detection and diagnosis. Cancer identification can be made either through manual efforts or by computer‐aided algorithms. Manual efforts‐based cancer detection is labor intensive and also offers more time complexity. In contrast, computer‐aided algorithms offer feasibility in reducing time and manual efforts. With the motivation to develop a computer‐aided diagnosis system for NCD, we developed a cancer detection methodology. In the present article, a deep learning (DL)‐based cancer identification model is developed. In DL‐based architectures, the features are generally extracted using convolutional neural networks. The proposed attention‐guided, densely connected residual, and dilated convolution deep neural network called DeepHistoNet acquire precise patterns for classification. Experimentation has been carried out on Kasturba Medical College (KMC), TCGA‐LIHC, and LC25000 datasets to prove the robustness of the model. Performance evaluation metrics like F1‐score, sensitivity, specificity, recall, and accuracy validate the experimentation. Experimental results demonstrate that the proposed DeepHistoNet model outperforms the other state‐of‐the‐art methods. The proposed model has been able to classify the KMC liver dataset with 97.1% accuracy and 0.9867 value of area under the curve–receiver operating characteristic curve (AUC‐ROC), which is the best result obtained compared to the state‐of‐the‐art techniques. The performance of the DeepHistoNet has been even better on the LC25000 dataset. On the LC25000 dataset, the proposed model achieved 99.8% classification accuracy. To our knowledge, DeepHistoNet is a novel approach for multiple histopathological image classification.Research Highlights
A novel robust DL model is proposed for histopathological image carcinoma classification.
The precise patterns for accurate classification are extracted using dense cross‐connected residual blocks.
Spatial attention is provided to the network so that the spatial information is not lost during the feature extraction.
DeepHistoNet is trained and evaluated on the liver, lung, and colon histopathology datasets to demonstrate its resilience. The results are promising and outperform state‐of‐the‐art techniques.
The proposed methodology has obtained the AUC‐ROC value of 0.9867 with a classification accuracy of 97.1% on the KMC dataset.
The proposed DeepHistoNet has classified the LC25000 dataset with 99.8% accuracy. The results are the best obtained till date.
Raman spectroscopy for crystallochemical analysis of Mg‐rich layered silicates: Serpentine and talc
Authors: Aspiotis Stylianos , Schlüter Jochen , Hildebrandt Frank , Mihailova Boriana 
Published: September 2023

AbstractA series of 10 talc (nominally MMg3TSi4O10X(OH)2) and 22 serpentine (nominally MMg3TSi2O5X(OH)4) minerals have been studied by Raman spectroscopy and electron microprobe analysis (EMPA) to establish quantitative relationships between the content of octahedrally coordinated M‐site cations and Raman peak parameters, which would allow for noninvasive material profiling. We show that in the case of talc, the position of the strong and well‐resolved peak near 360 cm−1, related to MO6 vibrations, as well as the integrated intensities of the OH‐stretching Raman signals (3600–3750 cm−1) can be used to determine the amount of MMg and M(Fe2+ + Mn2+) with a precision similar to that of EMPA. In the case of serpentine, the overall shape of the Raman scattering arising from OH‐stretching modes (3600–3750 cm−1) can be initially used to distinguish between the different polymorphs: antigorite, chrysotile, and lizardite, as well as the lizardite‐/chrysotile‐rich polygonal/polyhedral serpentine varieties and then, the full width at half maximum (FWHM) of the strongest MO6 peak at ~380 cm−1 can be used to estimate MMg content in each of the analyzed polymorphs/varieties and a relative error of 5%. In addition, MFe2+ amount in chrysotile and chrysotile‐rich polygonal serpentine can be quantified by FWHM of the peak near 230 cm−1 with a relative error of 5%, whereas MFe2+ amount in antigorite can be derived from FWHM of the peak near 130 cm−1 with similar relative uncertainties. We demonstrate the effectiveness of our approach on two types of rock‐based cultural‐heritage objects, cylinder seals and inscribed gems, where sampling is prohibitive.
Ascorbic acid nanoencapsulation using polyelectrolyte complex formation and optimization using hybrid artificial neural network‐genetic algorithm
Authors: Khuntia Anjali , Mitra Jayeeta 
Published: September 2023

AbstractBackgroundsThe polyelectrolyte complex (PEC) formation using chitosan (Cs) and alginate (Alg) has been employed to carry out Vitamin C (VC) nanoencapsulation because of its instability in adverse conditions. This method includes VC nanoencapsulation using physical cross‐linking. The effect of the mixing order as well as the different mass ratios of both the polymer and the VC content, were investigated on the size, pdi, zeta potential, encapsulation efficiency (EE%), and yield%. Hence, considering different mass ratios of both polymer (4:1–1:4) and VC content (10%–30% w/w of Cs) as independent variables, PECs were formed using 0.1% (w/v) of Cs and Alg solution.ResultThe result showed that, for equal mass ratio (1:1) of Cs and Alg, the addition of Alg solution to Cs solution led to lower particle size, while higher particle size was observed with reverse order of addition. However, no significant effect of polymer mass ratios was observed on particle size and NPs stability, while EE varied with VC concentration. Artificial neural network (ANN) was applied to train the experimental parameters Cs: Alg ratios (X1:X2) and VC content (X3) for the output variables. Moreover, process optimization was carried out using multi‐objective genetic algorithm (MOGA) with the goal of lowering particle size and increasing EE while increasing nanoparticle yield.ConclusionThe PEC method effectively encapsulated VC as obtained from higher EE. The Cs: Alg ratio of 4:2.1 and 18% VC content (w/w of Cs) was found optimum for nanoencapsulation. The final ANN‐GA model showed an acceptable agreement with experimental data.
Antecedents of memorable tourism experience related to behavioral intentions
Authors: Coudounaris Dafnis N. , Sthapit Erose 
Published: September 2023

AbstractThis study investigates the antecedent factors of memorable tourism experience (MTE) and their relationship to visitors’ behavioral intentions (BIs); Kim, Ritchie, & McCormick, ). Based on a survey of 314 structured interviews among visitors to the zoo and museum in Rovaniemi, Finland, the study reveals different MTE dimensions than in Kim et al.’s study. Only four factors—hedonism, local culture, involvement, and knowledge—influence visitors’ BIs. The results suggest the need to rebuild the scale of MTE by incorporating other relevant dimensions.
Environmental regulation, public environmental concern, and pollution reduction
Authors: Liu Liwen , Zhou Sisi 
Published: September 2023

AbstractSelecting the national water ecological civilization city (NWECC) policy as the starting point, this paper investigates whether environmental regulation might mitigate pollution discharge by the channel of public environmental concern. The results indicate that the NWECC policy has the effect of reducing pollution while significantly enhancing public environmental concern. The mediating effect analysis indicates that the NWECC policy reduces urban pollution via public environmental concern. The heterogeneity study shows that the pollution reduction effect of the NWECC policy is more apparent in cities with low economic development levels, low administrative levels, and low water resource endowments.
Quantitative hemodynamic assessment of stenotic below‐the‐knee arteries using spatio‐temporal bolus tracking on 4D‐CT angiography
Authors: Boonen Pieter Thomas , Buls Nico , van Gompel Gert , Devos Hannes , de Brucker Yannick , Leiner Tim , Aerden Dimitri , de Mey Johan , Vandemeulebroucke Jef 
Published: September 2023

AbstractBackgroundPeripheral arterial disease (PAD) is a chronic occlusive disease that restricts blood flow in the lower limbs, causing partial or complete blockages of the blood flow. While digital subtraction angiography (DSA) has traditionally been the preferred method for assessing blood flow in the lower limbs, advancements in wide beam Computed Tomography (CT), allowing successive acquisition at high frame rate, might enable hemodynamic measurements.PurposeTo quantify the arterial blood flow in stenotic below‐the‐knee (BTK) arteries. To this end, we propose a novel method for contrast bolus tracking and assessment of quantitative hemodynamic parameters in stenotic arteries using 4D‐CT.MethodsFifty patients with suspected PAD underwent 4D‐CT angiography in addition to the clinical run‐off computed tomography angiography (CTA). From these dynamic acquisitions, the BTK arteries were segmented and the region of maximum blood flow was extracted. Time attenuation curves (TAC) were estimated using 2D spatio‐temporal B‐spline regression, enforcing both spatial and temporal smoothness. From these curves, quantitative hemodynamic parameters, describing the shape of the propagating contrast bolus were automatically extracted. We evaluated the robustness of the proposed TAC fitting method with respect to interphase delay and imaging noise and compared it to commonly used approaches. Finally, to illustrate the potential value of 4D‐CT, we assessed the correlation between the obtained hemodynamic parameters and the presence of PAD.Results280 out of 292 arteries were successfully segmented, with failures mainly due to a delayed contrast arrival. The proposed method led to physiologically plausible hemodynamic parameters and was significantly more robust compared to 1D temporal regression. A significant correlation between the presence of proximal stenoses and several hemodynamic parameters was found.ConclusionsThe proposed method based on spatio‐temporal bolus tracking was shown to lead to stable and physiologically plausible estimation of quantitative hemodynamic parameters, even in the case of stenotic arteries. These parameters may provide valuable information in the evaluation of PAD and contribute to its diagnosis.
Stellate Ganglion Block for the Treatment of COVID‐19‐Induced Olfactory Dysfunction: A Prospective Pilot Study
Authors: Peterson Andrew M. , Miller Brevin J. , Kallogjeri Dorina , Piccirillo Jay F. , Kukuljan Sara , Roland Lauren T. , Schneider John S. , Crock Lara W. , Farrell Nyssa F. 
Published: September 2023

AbstractObjectiveThe objective of this study was to explore the safety and feasibility of stellate ganglion blocks (SGBs) to treat persistent COVID‐19‐induced olfactory dysfunction (OD). Secondarily, the goal was to determine effect sizes to plan a future randomized clinical trial.Study DesignProspective case series.SettingQuaternary Care Academic Medical Center.MethodsIn this single‐arm pilot trial, adult participants with a COVID‐19 diagnosis ≥ 12 months prior to enrollment with OD underwent bilateral SGBs. Subjects were followed for 1 month after completion of SGB. The primary outcome measure was the change in the Clinical Global Impression‐Improvement Scale for smell loss. Secondary outcome measures included changes in the University of Pennsylvania Smell Identification Test (UPSIT) and Olfactory Dysfunction Outcomes Rating (ODOR).ResultsTwenty participants were enrolled with a mean (SD) age of 46 (11) years and a mean (SD) duration of OD of 21 (5) months. At 1 month, 10 (50%) participants experienced at least slight subjective improvement in their OD, 11 (55%) attained a clinically meaningful improvement in smell identification using the UPSIT, and 7 (35%) achieved a clinically meaningful improvement in olfactory‐specific quality of life (QoL) measured by the ODOR. The median difference between UPSIT scores at baseline and 1 month was 6 (95% confidence interval: 3‐11), exceeding the minimal clinically important difference of 4. There were no serious adverse events.ConclusionSequential SGBs for COVID‐19‐associated OD were safe and associated with modest improvements in subjective olfaction, odor identification, and olfactory‐specific QoL. A placebo‐controlled trial is warranted to determine the efficacy of SGBs for COVID‐19‐associated OD.
Synthesis and characterization of UV and thermal curable polybenzoxazine/silica nanocomposites
Authors: Akkaya Büşra , Şen Ferhat 
Published: September 2023

AbstractIn this study, it was aimed to synthesize and characterize UV and thermally curable polybenzoxazine/silica nanocomposites as high performance coating materials. For this purpose, synthesis and characterization of allyl‐functional benzoxazine monomers and thiol‐functional silica nanoparticles were performed to carry out UV polymerization. Allyl functional benzoxazine monomers were characterized by FT‐IR (Fourier Transform Infrared) and 1H NMR (Nuclear Magnetic Resonance) spectroscopy techniques. Using the obtained benzoxazine monomers and silica nanoparticles, polybenzoxazine/silica nanocomposites in various formulations were prepared by UV and thermal curing. Structural characterization of prepared nanocomposites were analyzed by FT‐IR spectroscopy technique. The thermal and mechanical properties of the nanocomposite films were analyzed by TGA/DTA (Thermogravimetric Analysis/Differential Thermal Analysis) and DMA (Dynamic Mechanical Analysis). Morphological structures were investigated by SEM (Scanning Electron Microscopy) and particle distributions were investigated by EDS (Energy Dissipative X‐Ray Spectroscopy). In addition, gel contents and water absorption capacities of the samples were determined. It was determined that thiol functional nanosilica reinforcement increased the thermal and mechanical strength of nanocomposite films and the optimum amount of thiol functional nanosilica to be added at room temperature was 1 mmol. When all the results obtained were evaluated, it was concluded that nanocomposite films with superior thermal and mechanical properties that could be used in the coating industry were produced.
Reliability modeling and optimization of K/N phased mission system with backup missions and global redundancy strategy
Authors: Li Xiaopeng , Qin Sha , Liu Kanglun , Li Yizhao 
Published: September 2023

AbstractThe mission success probability (MSP) is a critical indicator for phased mission systems (PMSs). In the modern aerospace industry, redundancy techniques, including component/phase redundancy, are commonly seen to increase the MSP of the whole system. These component/phase redundancies make the reliability analysis more complex. Meanwhile, one or more components are required for normal working for different subsystems, called the K/N structure. In this article, a Markov‐process method is proposed for PMS with K/N subsystems and different redundancy strategies. Then, a universal system optimization model is proposed to optimize system structure and redundancy strategies for all subsystems at the same time. Then, an improved genetic algorithm (GA) is used to resolve the optimization problem. At last, a propulsion system is used as an engineering case, showing the proposed binary decision diagram‐based method.
Multiple case study exploring the impact of visitor restrictions in hospitals on childbirth support during the COVID‐19 pandemic
Authors: Hicks Lauren E. 
Published: September 2023

AbstractObjectiveChildbirth is a life‐changing experience for new mothers, birth partners, and families. The coronavirus disease 2019 (COVID‐19) pandemic restricted labouring mothers' access to support in hospitals through visitor restrictions. The goal of this research was to describe childbirth support in hospitals during the COVID‐19 pandemic.MethodsA qualitative study using a multiple case study design describes childbirth support in hospitals during the COVID‐19 pandemic from the perspectives of the new mother, birth partner, and obstetric hospital staff, including doulas, nurses, and obstetric providers. The setting was a large metropolitan area in South Texas. Sixteen participants were interviewed, including four new mothers, three birth partners, three doulas, three obstetric nurses, and three obstetric providers.ResultsThemes that emerged from the data include: under a spotlight, unmet expectations, discovering serenity, and quality childbirth support.ConclusionDuring the pandemic, new mothers and birth partners experienced fewer distractions, better communication with the hospital staff, and had better opportunities to bond with the newborn during labour and after birth, but did report missing the presence of their family and friends. Doulas, obstetric nurses, and obstetric providers found they were able to provide better quality and safer care to labouring mothers and birth partners.
Differences in digital prenatal service utilization by self‐reported race
Authors: Peahl Alex , Jahnke Hannah R. , Rubin‐Miller Lily , Henrich Natalie , Shah Neel 
Published: September 2023

AbstractObjectiveThe objective of this study was to understand the use of supplemental digital prenatal services by race and ethnicity among pregnant people utilizing a comprehensive, employer‐sponsored digital health platform.Materials and MethodsWe conducted a retrospective cohort study of pregnant people enrolled in a comprehensive, employer‐sponsored digital health platform, assessing utilization of app‐based supplemental digital prenatal services by race and ethnicity. Care utilization included three categories: asynchronous utilization (e.g., article reads, videos); interactions with a care advocate, a trained care navigator; and provider interactions (e.g., messages and visits with a medical provider). Differences in service utilization by race and ethnicity were assessed using basic descriptive statistics with between‐group comparisons.ResultsOf 5662 users, 2362 (41.7%) were White, 266 (4.7%) were Black, and 1411 (24.9%) reported ‘prefer not to say’ for their race. More Black and Asian users sent messages to a provider and had provider appointments than White and Hispanic users (messages: White: 33.7%, Hispanic: 33.6%, Asian: 44.9%, Black: 45.1%, p < 0.001; provider appointments: White: 49.8%, Hispanic: 43.3%, Asian: 52.4%, Black: 46.2%, p < 0.001). More Black users accessed doula and mental health appointments than other racial groups.ConclusionsOur study demonstrates that pregnant people of historically marginalized groups use digital supplemental prenatal services as much or more than White patients. Digital prenatal services may be a promising avenue for improving care access and experience for patient groups that face the biggest barriers to care and worst maternity care outcomes.
Melatonin enhances the sensitivity of colorectal cancer cells to 5‐fluorouracil through the regulation of the miR‐532‐3p/β‐catenin pathway
Authors: Gao Jun , Hou Yi , Yang Xiaorui , Liu Jia , Zhang Ying 
Published: September 2023

AbstractThis research aimed to investigate whether melatonin affected sensitivity to 5‐fluorouracil (5‐FU) in colorectal cancer (CRC) as well as to show the underlying molecular mechanism. Melatonin and 5‐FU were added to CRC cells at varying doses. The effect of melatonin on sensitivity to 5‐FU was investigated by measuring cell activity and apoptosis, and the potential underlying mechanism was further explored by detecting miR‐532‐3p expression and the associated pathway proteins. Melatonin could suppress cell malignancy in SW480 and HCT116 cells. Melatonin also significantly promoted sensitivity to 5‐FU in CRC cells. miR‐532‐3p expression was downregulated in CRC and was also markedly enhanced when treated with 1 mmol/L melatonin. The inhibitory ability of the co‐cultured melatonin, 5‐FU, and miR‐532‐3p inhibitor on SW480 and HCT116 cells was markedly diminished, and the IC50 value was significantly enhanced. Relative to the melatonin group, melatonin+miR‐532‐3p inhibitor markedly declined apoptosis rate. Bioinformatics analysis predicted the target of miR‐532‐3p. β‐catenin level presented obvious downregulation in the melatonin group, while it was notably upregulated in the co‐culture group in relative to with that in the melatonin group. Overall, melatonin promotes sensitivity to 5‐FU in CRC cells by regulating the miR‐532‐3p/β‐catenin pathway.