bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Mutual-Coupling Dipole Model Considering the Interactions between Particles
Authors: Kang Yongqiang , Zhang Jialin , Shi Zhipeng , Pu Xuhong , Li Shuaibing , Li Hongwei
Published: August 2022

The interactions between two or more particles and the calculation of the local electric field are widely applied in many fields, such as those of insulation, biology, medicine, and microfluidics. The dipole approximation model, which is a classical electric field calculation method, has been widely used in many fields to solve for the local electric field in a multi-particle system, but it does not consider the interactions between particles; as a result, it is easily limited by the calculation situation, and it generates a large calculation error when the distance between particles is small. Based on the physical essence of an interaction between two particles, a concept of the mutual-coupling dipole moment caused by the interactions between particles is defined for the first time. Moreover, by combining the calculation process of the dipole moment and the electric field of polarization, a novel mutual-coupling dipole model considering the interactions between particles is proposed in this paper, and analytical expressions of the local electric field that consider the interaction between two particles are obtained, thus compensating for the large error in the electric field calculation caused by the dipole approximation model when the distance between particles is small. In this paper, a mutual-coupling dipole model considering particle interactions is proposed. This model can effectively reflect the interactions between particles when the distance between particles D/R is less than 0.6 and accurately calculate the local electric fields of the particles. These results can be effectively used to investigate the interactions between particles and the control of particles in electric fields in many fields, such as in the calculation of the insulation of mixed dielectrics, the microscopic transport of medicines, the control of bio-cells and micro-fluids in electric fields, and environmental governance.
Deformation Evolution Law of Surfacing Welding on Thin Bending Plates Based on the Three-Dimensional Thermal Digital Image Correlation Method
Authors: Ma Xiqiang , Guo Nan , Yang Fang , Liu Chunyang , Guan Zhiqiang
Published: August 2022

Surfacing on the surface of thin metal bending plates will cause significant deformation, and current numerical simulation and experimental methods cannot fully and truly reflect the deformation state of the bending plate. In this paper, a non-contact detection method based on the three-dimensional (3D) thermal digital image correlation (DIC) method is proposed. The proposed method can be used for the 3D full-field dynamic measurement of metal thin bending plate surfaces. In addition, the evolution law of in-plane and out-of-plane deformation of thin bending plates during surfacing welding and cooling was studied. Moreover, the influence of curvature on the shrinkage deformation of thin bending plate weld was explored, and the correlation between the curvature of thin bending plates and the weld shrinkage was established. Results show that the proposed detection method based on the 3D thermal DIC method can rapidly and accurately detect bending deformation online. The out-of-plane deformation of the surfacing welding of the thin bending plate transits from the disk to the saddle. Furthermore, the curvature of the thin bending plate is inversely proportional to the transverse shrinkage of the weld bead. After the curvature reaches a certain value, it has little effect on the longitudinal shrinkage of the weld bead. This detection method solves the problem of welding deformation simulation verification, truly clarifies the law of welding dynamic deformation, and provides a theoretical basis for welding lightweight manufacturing.
Reactive Sputtered Ytterbium Silicate Environmental Barrier Coatings for Protection of Mo-Si-Based Alloys
Authors: Anton Ronja , Leisner Vito , Laska Nadine , Schulz Uwe
Published: August 2022

The implementation of a structural material into the hot section of a turbine engine requires the protection against oxidation and water vapour. Since Mo-Si-based alloys do not provide this protection on their own over a long period of exposure time, the necessity for protective coatings becomes inevitable. Rare earth silicates, especially Yb-silicates, are known for their great potential to protect silica against water vapour recession and also for their oxidation protective behaviour due to their low silica reactivity. Reactive pulsed DC co-sputtering was investigated and used to produce a two-layered coating of Yb2Si2O7 and Yb2SiO5. The layers were implemented into a four-layered environmental barrier coating system combined with a bond coat consisting of a graded Mo-Si interlayer and a thin Si top layer intended to form slow-growing silica. The EBCs were tested up to 100 h at 1200 °C and 800 °C isothermally in air on top of three different Mo-Si-based alloys and show promise for oxidation protection. The pesting phenomenon was completely suppressed.
Computational and Communication Infrastructure Challenges for Resilient Cloud Services
Authors: Martinez Heberth F. , Mondragon Oscar H. , Rubio Helmut A. , Marquez Jack
Published: August 2022

Fault tolerance and the availability of applications, computing infrastructure, and communications systems during unexpected events are critical in cloud environments. The microservices architecture, and the technologies that it uses, should be able to maintain acceptable service levels in the face of adverse circumstances. In this paper, we discuss the challenges faced by cloud infrastructure in relation to providing resilience to applications. Based on this analysis, we present our approach for a software platform based on a microservices architecture, as well as the resilience mechanisms to mitigate the impact of infrastructure failures on the availability of applications. We demonstrate the capacity of our platform to provide resilience to analytics applications, minimizing service interruptions and keeping acceptable response times.
Synergistic Antibacterial Activity of Green Synthesized Silver Nanomaterials with Colistin Antibiotic against Multidrug-Resistant Bacterial Pathogens
Authors: Yassin Mohamed Taha , Mostafa Ashraf Abdel-Fattah , Al-Askar Abdulaziz Abdulrahman , Al-Otibi Fatimah O.
Published: August 2022

The high frequency of nosocomial bacterial infections caused by multidrug-resistant pathogens contributes to significant morbidity and mortality worldwide. As a result, finding effective antibacterial agents is of critical importance. Hence, the aim of the present study was to greenly synthesize silver nanoparticles (AgNPs) utilizing Salvia officinalis aqueous leaf extract. The biogenic AgNPs were characterized utilizing different physicochemical techniques such as energy-dispersive X-ray spectroscopy (EDX), ultraviolet-visible spectrophotometry (UV-Vis), X-ray diffraction analysis (XRD), transmission electron microscopy (TEM), and Fourier transform infrared spectroscopy (FT-IR) analysis. Additionally, the synergistic antimicrobial effectiveness of the biosynthesized AgNPs with colistin antibiotic against multidrug-resistant bacterial strains was evaluated utilizing the standard disk diffusion assay. The bioformulated AgNPs revealed significant physicochemical features, such as a small particle size of 17.615 ± 1.24 nm and net zeta potential value of −16.2 mV. The elemental mapping of AgNPs revealed that silver was the main element, recording a relative mass percent of 83.16%, followed by carbon (9.51%), oxygen (5.80%), silicon (0.87%), and chloride (0.67%). The disc diffusion assay revealed that AgNPs showed antibacterial potency against different tested bacterial pathogens, recording the highest efficiency against the Escherichia coli strain with an inhibitory zone diameter of 37.86 ± 0.21 mm at an AgNPs concentration of 100 µg/disk. In addition, the antibacterial activity of AgNPs was significantly higher than that of colistin (p ≤ 0.05) against the multidrug resistant bacterial strain namely, Acinetobacter baumannii. The biosynthesized AgNPs revealed synergistic antibacterial activity with colistin antibiotic, demonstrating the highest synergistic percent against the A. baumannii strain (85.57%) followed by Enterobacter cloacae (53.63%), E. coli (35.76%), Klebsiella pneumoniae (35.19%), Salmonella typhimurium (33.06%), and Pseudomonas aeruginosa (13.75%). In conclusion, the biogenic AgNPs revealed unique physicochemical characteristics and significant antibacterial activities against different multidrug-resistant bacterial pathogens. Consequently, the potent synergistic effect of the AgNPs–colistin combination highlights the potential of utilizing this combination for fabrication of highly effective antibacterial coatings in intensive care units for successful control of the spread of nosocomial bacterial infections.
Optimization of Blue Photorefractive Properties and Exponential Gain of Photorefraction in Sc-Doped Ru:Fe:LiNbO3 Crystals
Authors: Xu Lei , Chen Guanying
Published: August 2022

Sc:Ru:Fe:LiNbO3 crystals were grown from congruent melt by using the Czochralski method. A series of LiNbO3 crystals (Li/Nb = 48.6/51.4) with 0.1 wt% RuO2, 0.06 wt% Fe2O3 and various concentrations of Sc203 were prepared. RF1 and RF4 refers to the samples containing 0 mol% Sc203 and 3 mol% Sc203, respectively. The photorefractive properties of RF4 were measured by Kr+ laser (λ = 476 nm blue light): ηs = 75.7%, τw = 11 s, M/# = 19.52, S = 2.85 cmJ−1, Γ = 31.8 cm−1 and ∆nmax = 6.66 × 10−5. The photorefractive properties of five systems (ηs, M/#, S, Γ and ∆nmax) under 476 nm wavelength from RF1 to RF4 continually increased the response time, while τw was continually shortened. Comparing the photorefractive properties of Sc (1 mol%):Ru (0.1 wt%):Fe (0.06 wt%): LiNbO3 measured by Kr+ laser (λ = 476 nm blue light) with Sc (1 mol%):Fe (0.06 wt%):LiNbO3 measured by He-Ne laser (633 nm red light), ηs increased by a factor of 1.9, Vw (response rate) increased by a factor of 13.9, M/# increased by a factor of 1.8 and S increased by a factor of 32. The ∆nmax improved by a factor of 1.4. A strong blue photorefraction was created by the two-center effect and the remarkable characteristic of being in phase between the two gratings recorded in shallow and deep trap centers. The photorefractive properties (ηS, τw, M/#, S, ∆nmax) were increased with an increase in Sc3+ ion concentration. Damage-resistant dopants such as Sc3+ ions were no longer resistant to damage, but they enhanced the photorefractive properties at the 476 nm wavelength. The experimental results clearly show that Sc-doped two-center Ru:Fe:LiNbO3 crystal is a promising candidate blue photorefraction material for volume holographic storage. Sc-doped LiNbO3 crystal can significantly enhance the blue photorefractive properties according to the experimental parameters. Therefore, the Sc:Ru:Fe:LiNbO3 crystal has better photorefractive properties than the Ru:Fe:LiNbO3 crystal.
Adsorption of Pesticides, Antibiotics and Microcystin-LR by Graphene and Hexagonal Boron Nitride Nano-Systems: A Semiempirical PM7 and Theoretical HSAB Study
Authors: Chi Shu-Chun , Lee Chien-Lin , Chang Chia Ming
Published: August 2022

In recent years, graphene (CC) and hexagonal boron nitride (h-BN) have been widely used in water purification and environmental remediation because of their unique physical and chemical properties. Therefore, based on the reaction enthalpy, equilibrium structure, atomic charge, molecular, orbital and electronic spectrum provided by a semiempirical PM7 method, the adsorption of pesticides, antibiotics and microcystin-LR on graphene and hexagonal boron nitride (h-BN) nano-systems was examined. For the adsorption of diazinon, parathion, oxacillin and ciprofloxacin, the results show that as the bond length decreases and the atomic partial charge increases, the adsorption energy increases. The removal efficiency for antibiotics is higher than that for pesticides. Regarding the co-adsorption of pesticides/antibiotics and microcystin-LR on nano-systems, hydrogen bonds play a crucial role in stabilizing the whole structure. In addition, the non-covalent interaction (NCI) diagrams show the adsorption strength of the nano-systems to the pesticides/antibiotics. The energy gap and HSAB global descriptors are calculated based on the energy values of HOMO and LUMO. It is proved that the graphene nano-system has excellent electron-accepting ability, and suitable sensor materials can be designed.
Review of the Nanostructuring and Doping Strategies for High-Performance ZnO Thermoelectric Materials
Authors: Sulaiman Suraya , Sudin Izman , Al-Naib Uday M. Basheer , Omar Muhammad Firdaus
Published: August 2022

Unique properties of thermoelectric materials enable the conversion of waste heat to electrical energies. Among the reported materials, Zinc oxide (ZnO) gained attention due to its superior thermoelectric performance. In this review, we attempt to oversee the approaches to improve the thermoelectric properties of ZnO, where nanostructuring and doping methods will be assessed. The outcomes of the reviewed studies are analysed and benchmarked to obtain a preliminary understanding of the parameters involved in improving the thermoelectric properties of ZnO.
Go Wild for a While? A Bibliometric Analysis of Two Themes in Tourism Demand Forecasting from 1980 to 2021: Current Status and Development
Authors: Zhang Yuruixian , Choo Wei Chong , Abdul Aziz Yuhanis , Yee Choy Leong , Ho Jen Sim
Published: August 2022

Despite the fact that the concept of forecasting has emerged in the realm of tourism, studies delving into this sector have yet to provide a comprehensive overview of the evolution of tourism forecasting visualization. This research presents an analysis of the current state-of-the-art tourism demand forecasting (TDF) and combined tourism demand forecasting (CTDF) systems. Based on the Web of Science Core Collection database, this study built a framework for bibliometric analysis from these fields in three distinct phases (1980–2021). Furthermore, the VOSviewer analysis software was employed to yield a clearer picture of the current status and developments in tourism forecasting research. Descriptive analysis and comprehensive knowledge network mappings using approaches such as co-citation analysis and cooperation networking were employed to identify trending research topics, the most important countries/regions, institutions, publications, and articles, and the most influential researchers. The results yielded demonstrate that scientific output pertaining to TDF exceeds the output pertaining to CTDF. However, there has been a substantial and exponential increase in both situations over recent years. In addition, the results indicated that tourism forecasting research has become increasingly diversified, with numerous combined methods presented. Furthermore, the most influential papers and writers were evaluated based on their citations, publications, network position, and relevance. The contemporary themes were also analyzed, and obstacles to the expansion of the literature were identified. This is the first study on two topics to demonstrate the ways in which bibliometric visualization can assist researchers in gaining perspectives in the tourism forecasting field by effectively communicating key findings, facilitating data exploration, and providing valuable data for future research.
Artificial Intelligence Based Pain Assessment Technology in Clinical Application of Real-World Neonatal Blood Sampling
Authors: Cheng Xiaoying , Zhu Huaiyu , Mei Linli , Luo Feixiang , Chen Xiaofei , Zhao Yisheng , Chen Shuohui , Pan Yun
Published: August 2022

Background: Accurate neonatal pain assessment (NPA) is the key to neonatal pain management, yet it is a challenging task for medical staff. This study aimed to analyze the clinical practicability of the artificial intelligence based NPA (AI-NPA) tool for real-world blood sampling. Method: We performed a prospective study to analyze the consistency of the NPA results given by a self-developed automated NPA system and nurses’ on-site NPAs (OS-NPAs) for 232 newborns during blood sampling in neonatal wards, where the neonatal infant pain scale (NIPS) was used for evaluation. Spearman correlation analysis and the degree of agreement of the pain score and pain grade derived by the NIPS were applied for statistical analysis. Results: Taking the OS-NPA results as the gold standard, the accuracies of the NIPS pain score and pain grade given by the automated NPA system were 88.79% and 95.25%, with kappa values of 0.92 and 0.90 (p < 0.001), respectively. Conclusion: The results of the automated NPA system for real-world neonatal blood sampling are highly consistent with the results of the OS-NPA. Considering the great advantages of automated NPA systems in repeatability, efficiency, and cost, it is worth popularizing the AI technique in NPA for precise and efficient neonatal pain management.
Improved Task Performance, Low Workload, and User-Centered Design in Medical Diagnostic Equipment Enhance Decision Confidence of Anesthesia Providers: A Meta-Analysis and a Multicenter Online Survey
Authors: Budowski Alexandra D. , Bergauer Lisa , Castellucci Clara , Braun Julia , Nöthiger Christoph B. , Spahn Donat R. , Tscholl David W. , Roche Tadzio R.
Published: August 2022

Decision confidence—the subjective belief to have made the right decision—is central in planning actions in a complex environment such as the medical field. It is unclear by which factors it is influenced. We analyzed a pooled data set of eight studies and performed a multicenter online survey assessing anesthesiologists’ opinions on decision confidence. By applying mixed models and using multiple imputation to determine the effect of missing values from the dataset on the results, we investigated how task performance, perceived workload, the utilization of user-centered medical diagnostic devices, job, work experience, and gender affected decision confidence. The odds of being confident increased with better task performance (OR: 1.27, 95% CI: 0.94 to 1.7; p = 0.12; after multiple imputation OR: 3.19, 95% CI: 2.29 to 4.45; p < 0.001) and when user-centered medical devices were used (OR: 5.01, 95% CI: 3.67 to 6.85; p < 0.001; after multiple imputation OR: 3.58, 95% CI: 2.65 to 4.85; p < 0.001). The odds of being confident decreased with higher perceived workload (OR: 0.94, 95% CI: 0.93 to 0.95; p < 0.001; after multiple imputation, OR: 0.94, 95% CI: 0.93 to 0.95; p < 0.001). Other factors, such as gender, job, or professional experience, did not affect decision confidence. Most anesthesiologists who participated in the online survey agreed that task performance (25 of 30; 83%), perceived workload (24 of 30; 80%), work experience (28 of 30; 93%), and job (21 of 30; 70%) influence decision confidence. Improved task performance, lower perceived workload, and user-centered design in medical equipment enhanced the decision confidence of anesthesia providers.
Isolated Severe Dysphonia as a Presentation of Post-COVID-19 Syndrome
Authors: Jeleniewska Joanna , Niebudek-Bogusz Ewa , Malinowski Jakub , Morawska Joanna , Miłkowska-Dymanowska Joanna , Pietruszewska Wioletta
Published: August 2022

This is the first study assessing the clinical management of severe, isolated dysphonia during post-COVID-19 syndrome. One hundred and fifty-eight subjects met the inclusion criteria for the post-COVID-19 condition as specified by the WHO. Six patients were diagnosed with isolated severe dysphonia, constituting 3.8% of the initial group. The pre- and post-examination protocol consisted of subjective voice self-assessment and routine laryngological examination, followed by an instrumental examination by means of Laryngovideostroboscopy (LVS) and High-Speed Videolaryngoscopy (HSV). The treatment included short-term systemic steroids in decreasing doses, moisturizing inhalations with hyaluronic acid, and protective agents against Laryngopharyngeal Reflux. The kinematic imaging of the glottis performed by means of HSV before treatment showed deviations in the regularity and symmetry of vocal fold vibrations, absence of mucosal wave, and incomplete glottal closure. Improvement of the structural and functional state of the larynx was observed post-treatment. Kymographic sections and Glottal Width Waveform (GWW) graphs obtained from post-treatment HSV recordings showed improvement in vocal fold vibrations. The decrease in mean Jitter and Shimmer was observed, with the following mean values of 3.16 pre-treatment and 2.97 post-treatment for Jitter and 7.16 pre-treatment and 2.77 post-treatment for Shimmer. The post-treatment self-evaluation of voice showed considerable improvement in vocal function and voice quality in all the examined patients. Severe dysphonia in patients with post-COVID-19 syndrome requires urgent ENT diagnosis using instrumental assessment with the evaluation of laryngeal phonatory function and intensive comprehensive treatment.
Pseudopterygium: An Algorithm Approach Based on the Current Evidence
Authors: Urbinati Facundo , Borroni Davide , Rodríguez-Calvo-de-Mora Marina , Sánchez-González José-María , García-Lorente María , Zamorano-Martín Francisco , Rachwani-Anil Rahul , Ortiz-Pérez Santiago , Romano Vito , Rocha-de-Lossada Carlos
Published: August 2022

Pseudopterygium is a non-progressive conjunctival adhesion to the peripheral cornea secondary to a corneal-limbus damage. According to the literature, the main etiology is a previous eye trauma. Nevertheless, this could be biased by the existence of other underdiagnosed causes of pseudopterygium, some of which may have severe consequences for the integrity of the eye and patient’s life. This comprehensive literature review was performed based on a search on the PubMed and Google Scholar databases of relevant pseudopterygium published papers according to our current knowledge and seeks to gather the existing evidence about its diverse etiologies and clinical features, as well as to propose a diagnostic algorithm to simplify its correct approach.
Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT
Authors: Kaviani Parisa , Bizzo Bernardo C. , Digumarthy Subba R. , Dasegowda Giridhar , Karout Lina , Hillis James , Neumark Nir , Kalra Mannudeep K. , Dreyer Keith J.
Published: August 2022

(1) Background: Optimal anatomic coverage is important for radiation-dose optimization. We trained and tested (R2.2.4) two (R3-2) deep learning (DL) algorithms on a machine vision tool library platform (Cognex Vision Pro Deep Learning software) to recognize anatomic landmarks and classify chest CT as those with optimum, under-scanned, or over-scanned scan length. (2) Methods: To test our hypothesis, we performed a study with 428 consecutive chest CT examinations (mean age 70 ± 14 years; male:female 190:238) performed at one of the four hospitals. CT examinations from two hospitals were used to train the DL classification algorithms to identify lung apices and bases. The developed algorithms were then tested on the data from the remaining two hospitals. For each CT, we recorded the scan lengths above and below the lung apices and bases. Model performance was assessed with receiver operating characteristics (ROC) analysis. (3) Results: The two DL models for lung apex and bases had high sensitivity, specificity, accuracy, and areas under the curve (AUC) for identifying under-scanning (100%, 99%, 99%, and 0.999 (95% CI 0.996–1.000)) and over-scanning (99%, 99%, 99%, and 0.998 (95%CI 0.992–1.000)). (4) Conclusions: Our DL models can accurately identify markers for missing anatomic coverage and over-scanning in chest CTs.
Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19
Authors: Elshennawy Nada M. , Ibrahim Dina M. , Sarhan Amany M. , Arafa Mohamed
Published: August 2022

The SARS-CoV-2 virus has proliferated around the world and caused panic to all people as it claimed many lives. Since COVID-19 is highly contagious and spreads quickly, an early diagnosis is essential. Identifying the COVID-19 patients’ mortality risk factors is essential for reducing this risk among infected individuals. For the timely examination of large datasets, new computing approaches must be created. Many machine learning (ML) techniques have been developed to predict the mortality risk factors and severity for COVID-19 patients. Contrary to expectations, deep learning approaches as well as ML algorithms have not been widely applied in predicting the mortality and severity from COVID-19. Furthermore, the accuracy achieved by ML algorithms is less than the anticipated values. In this work, three supervised deep learning predictive models are utilized to predict the mortality risk and severity for COVID-19 patients. The first one, which we refer to as CV-CNN, is built using a convolutional neural network (CNN); it is trained using a clinical dataset of 12,020 patients and is based on the 10-fold cross-validation (CV) approach for training and validation. The second predictive model, which we refer to as CV-LSTM + CNN, is developed by combining the long short-term memory (LSTM) approach with a CNN model. It is also trained using the clinical dataset based on the 10-fold CV approach for training and validation. The first two predictive models use the clinical dataset in its original CSV form. The last one, which we refer to as IMG-CNN, is a CNN model and is trained alternatively using the converted images of the clinical dataset, where each image corresponds to a data row from the original clinical dataset. The experimental results revealed that the IMG-CNN predictive model outperforms the other two with an average accuracy of 94.14%, a precision of 100%, a recall of 91.0%, a specificity of 100%, an F1-score of 95.3%, an AUC of 93.6%, and a loss of 0.22.
Extent and Distribution of Parenchymal Abnormalities in Baseline CT-Scans Do Not Predict Awake Prone Positioning Response in COVID-19 Related ARDS
Authors: Raimondi Federico , Cazzaniga Sara , Annibali Simona , Novelli Luca , Brivio Matteo , Pappacena Simone , Malandrino Luca , Bonaffini Pietro Andrea , Bianco Ilaria , Liggeri Noemi , Gritti Paolo , Lorini Ferdinando Luca , Sironi Sandro , Di Marco Fabiano
Published: August 2022

Prone positioning is frequently used for non-intubated hypoxemic patients with COVID-19, although conclusive evidence is still lacking. The aim of the present study was to investigate whether baseline CT-scans could predict the improvement in oxygenation in COVID-19 related Acute respira-tory syndrome (ARDS) patients when pronated. Methods: A retrospective study of COVID-19 patients who underwent non-invasive ventilation (NIV) and prone positioning was conducted. Results: Forty-five patients were included. On average, 50% of the overall lung volume was affected by the disease, as observed in the CT-scans, with ground glass opacities (GGOs) and consolidations accounting for 44% and 4%, respectively. The abnormalities were mainly posterior, as demonstrated by posterior/anterior distribution ratios of 1.5 and 4.4 for GGO and consolidation, respectively. The median PaO2/FiO2 ratio during NIV in a supine position (SP1) was 140 [IQR 108–169], which improved by 67% (+98) during prone positioning, on average. Once supine positioning was resumed (SP2), the improvement in oxygenation was maintained in 28 patients (62% of the overall population, categorized as “responders”). We found no significant differences between responders and non-responders in terms of the extent (p = 0.92) and the distribution of parenchymal abnormalities seen in the baseline CT (p = 0.526). Conclusion: Despite the lack of a priori estimation of the sample size, considering the absence of any trends in the differences and correlations, we can reasonably conclude that the baseline chest CT-scan does not predict a gas-exchange response in awake prone-positioned patients with COVID-19 related ARDS. Physicians dealing with this category of patients should not rely on the imaging at presentation when evaluating whether to pronate patients.
Traversing Disability: Building Social Capital through Skill Development for Employment
Authors: Ebrahim Adèle , Lorenzo Theresa , Kathard Harsha
Published: August 2022

This paper investigates the influence of social capital in the training of persons with disabilities. The expansion of social networks is regarded as a central principle of the rehabilitation agenda and thus is a central concept that may influence the training experiences of persons with disabilities. Absent from much of social capital research is its influence on skill development and employment opportunities for persons with disabilities. Social capital has the potential to play an important role in the livelihoods of persons with disabilities who are at high risk of being marginalised. The aim of this qualitative study was to explore and critically examine the experiences of persons with disabilities who participated in an auxiliary skill development programme, to determine whether and how it has strengthened and expanded their social capital to advance their opportunities for economic inclusion and/or employment. There is currently no convincing practice available, particularly in the South African employment landscape, around how social capital can be incorporated in the employment of persons with disabilities. An intrinsic case study approach was utilised as it allowed for the identification and description of an employability-related skill development programme for persons with disabilities. In this paper, we argue that the transfer of skills alone is not enough for persons with disabilities to gain employment.
Parent Support Is Related to Physical Activity among Children and Youth with Disabilities during the COVID-19 Pandemic: Findings from the National Physical Activity Measurement (NPAM) Study
Authors: James Maeghan E. , Odorico Nikoleta , Moore Sarah A. , Martin Ginis Kathleen A. , Bassett-Gunter Rebecca L. , Arbour-Nicitopoulos Kelly P.
Published: August 2022

Physical activity (PA) among children and youth with disabilities (CYD) has been negatively impacted by the COVID-19 pandemic. Parent PA support and parent PA modelling (i.e., parents engaging in PA themselves) have been shown to be associated with PA in CYD. However, parents’ influence on the PA behaviours of CYD during the pandemic remains unknown. The purpose of this study was to examine the relationship between parent PA support and parent PA modelling (i.e., parent moderate-to-vigorous PA (MVPA)) and the PA behaviours of CYD. It was hypothesized that higher levels of parent PA support and parent PA modelling would significantly relate to both child MVPA and child PA at any intensity. An online survey was sent to parents of CYD in November 2020 (i.e., during the second wave of the COVID-19 pandemic in Canada) that assessed the MVPA and total PA (any intensity), parent PA support (e.g., encouraging PA, providing transportation for PA), and parent MVPA. Separate linear regression models assessed the relationships between parent PA support and parent PA modelling with (a) child MVPA and (b) child PA at any intensity. Parent and child age, child gender and disability group, marital status, and household type were controlled for in all analyses. A total of 86 parents (Mage = 43 years, SD = 5.9; 93% mothers) of CYD (Mage = 11 years, SD = 3.3; 20% girls; 77% with a developmental disability) completed the survey. Parent PA support was significantly associated with child MVPA (β = 0.30, CI = 0.067–0.438, p = 0.008) but not child PA at any intensity. No significant relationship was shown between parent PA modelling and either child MVPA or child PA at any intensity. Findings suggest that parent PA support, but not parent PA modelling, was associated with PA in CYD, at least during the acute period of the pandemic. Greater efforts must be directed towards developing effective parent PA support interventions to reduce the detrimental effects of the COVID-19 pandemic on PA in CYD.
Unravelling the Potential of Digital Servitization in Sustainability-Oriented Organizational Performance—Does Digital Leadership Make It Different?
Authors: Pham Huy Quang , Vu Phuc Kien
Published: August 2022

This research proposed and enhanced a statistically reliable paradigm for determining the impacts of the role of digital servitization (DS) as a potential facilitator of sustainable and smart service innovation ecosystem (SSSIE) establishment and sustainability-oriented organizational performance (SOOP) improvement. Additionally, this research aimed to cast light on the role of digital leadership (DL) as a moderator of the hypothesized model. Structural equation modeling and multigroup analysis (MGA) were employed to process and analyze the data procured from a cross-sectional convenience sample of 412 respondents representing various public service sector organizations (PSSOs). The statistical outcomes corroborated that DS was positively and significantly correlated to SSSIE establishment, and at the same time, SOOP was significantly intensified by DS and SSSIE. Next, the nexus between DS and SOOP was enhanced when SSSIE was operationalized. Lastly, the MGA outcomes confirmed that all of the coefficients in the proposed model were statistically significantly different under the moderating effect of DL. These obtained findings could bring numerous valuable in-depth insights for practitioners and policymakers in digital initiatives management and governance.
Decarbonizing the Global Economy—Investigating the Role of Carbon Emission Inertia Using the Integrated Assessment Model MIND
Authors: Khabbazan Mohammad M. , Hokamp Sascha
Published: August 2022

In 2015, the 21st Conference of the Parties reaffirmed the target of keeping the global mean temperature rise below 2 °C or 1.5 °C by 2100 while finding no consensus on how to decarbonize the global economy. In this regard, the speed of decarbonization reflects the (in)flexibility of transforming the energy sector due to engineering, political, or societal constraints. Using economy–energy–climate-integrated assessment models (IAMs), the maximum absolute rate of change in carbon emission allowed from each time step to the next, so-called carbon emission inertia (CEI), governs the magnitude of emission change, affecting investment decisions and economic welfare. Employing the model of investment and endogenous technological development (MIND), we conduct a cost-effectiveness analysis and examine anthropogenic global carbon emission scenarios in line with decarbonizing the global economy while measuring the global mean temperature. We examine the role of CEI as a crucial assumption, where the CEI can vary in four scenarios from 3.7% to 12.6% p.a. We provide what-if studies on global carbon emissions, global mean temperature change, and investments in renewable energy production and show that decarbonizing the global economy might still be possible before 2100 only if the CEI is high enough. In addition, we show that climate policy scenarios with early decarbonization and without negative emissions may still comply with the 2 °C target. However, our results indicate that the 1.5 °C target is not likely to be reached without negative emission technologies. Hence, the window of opportunity is beginning to close. This work can also assist to better interpret existing publications on various climate targets when altering CEI could have played a significant role.
Machine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression Models
Authors: Geraldo-Campos Luis Alberto , Soria Juan J. , Pando-Ezcurra Tamara
Published: August 2022

COVID-19 has caused an economic crisis in the business world, leaving limitations in the continuity of the payment chain, with companies resorting to credit access. This study aimed to determine the optimal machine learning predictive model for the credit risk of companies under the Reactiva Peru Program because of COVID-19. A multivariate regression analysis was applied with four regressor variables (economic sector, granting entity, amount covered, and department) and one predictor (risk level), with a population of 501,298 companies benefiting from the program, under the CRISP-DM methodology oriented especially for data mining projects, with artificial intelligence techniques under the machine learning Lasso and Ridge regression models, with econometric algebraic mathematical verification to compare and validate the predictive models using SPSS, Jamovi, R Studio, and MATLAB software. The results revealed a better Lasso regression model (λ60 = 0.00038; RMSE = 0.3573685) that optimally predicted the level of risk compared to the Ridge regression model (λ100 = 0.00910; RMSE = 0.3573812) and the least squares model with algebraic mathematics, which corroborates that the Lasso regression model is the best predictive model to detect the level of credit risk of the Reactiva Peru Program. The best predictive model for detecting the level of corporate credit risk is the Lasso regression model.
Perspectives on the Effectiveness of Madrid’s Regional Bilingual Programme: Exploring the Correlation between English Proficiency Level and Pre-Service Teachers’ Beliefs
Authors: Mañoso-Pacheco Lidia , Sánchez-Cabrero Roberto
Published: August 2022

This study aims to examine the opinions of prospective pre-primary and primary teachers about Madrid’s regional Bilingual Programme in Spain, assessing the correlations between their self-perceived level of English and their positioning concerning the effectiveness of the regional programme. Although there is a growing body of research in the field of education on how CLIL (Content and Language Integrated Learning) provisions impact in-service teachers’ attitudes concerning bilingual education, there is a dearth of literature on the way student teachers depict the teaching reality. Thus, this paper explores pre-service teachers’ beliefs towards the Bilingual Programme via an ad hoc questionnaire, administered to a non-probabilistic sample of 170 undergraduate students at the Universidad Autónoma de Madrid. The data collected were explored using Chi-square and Somers’ D tests. The results show that the self-perceived English level, greatly determined by prior bilingual schooling, has a strong influence on their perceptions about the Bilingual Programme. The findings also indicate that, although the learning experience at the pre-primary stage is valued positively by students, the acquisition of curricular contents in primary education is seen as negatively affected due to English medium instruction.
Impact of Leaf Litter and Fine Roots in the Pool of Carbon, Nitrogen and Phosphorus Accumulated in Soil in Various Scenarios of Regeneration and Reconstruction of Forest Ecosystems
Authors: Świątek Bartłomiej , Pietrzykowski Marcin
Published: August 2022

This study determined the rate of decomposition of fine roots and leaf litter from birch, larch, and pine, and compared the impact of fine root decomposition and leaf litter on carbon, nitrogen, and phosphorus accumulation in various regenerated and reconstructed forest ecosystems. The control plots were located on podzol soils in managed forest non-degraded habitats. Over a one-year experimental season, the decomposition of birch and larch fine roots released less carbon in comparison to leaf litter. The carbon mass-loss rates were 16% for birch roots and 15% for larch roots, while for birch and larch litter, the rates were 36% and 27%, respectively. For nitrogen, mass-loss rates were 48% for birch fine roots and 60% for larch and pine fine roots, whereas for pine and birch litter the rates were 14%, and 33% for larch litter. The results of our study prove the important role of fine root input to the soil’s carbon, nitrogen, and phosphorus pool and additionally their significance for CO2 sequestration within the studied regenerated terrestrial ecosystems.
Improving a Process-Based Model to Simulate Forest Carbon Allocation under Varied Stand Density
Authors: Jiao Wenxing , Wang Weifeng , Peng Changhui , Lei Xiangdong , Ruan Honghua , Li Haikui , Yang Yanrong , Grabarnik Pavel , Shanin Vladimir
Published: August 2022

Carbon allocation is an important mechanism through which plants respond to environmental changes. To enhance our understanding of maximizing carbon uptake by controlling planting densities, the carbon allocation module of a process-based model, TRIPLEX-Management, was modified and improved by introducing light, soil water, and soil nitrogen availability factors to quantify the allocation coefficients for different plant organs. The modified TRIPLEX-Management model simulation results were verified against observations from northern Jiangsu Province, China, and then the model was used to simulate dynamic changes in forest carbon under six density scenarios (200, 400, 600, 800, 1000, and 1200 stems ha−1). The mean absolute errors between the predicted and observed variables of the mean diameter at breast height, mean height, and estimated aboveground biomass ranged from 15.0% to 26.6%, and were lower compared with the original model simulated results, which ranged from 24.4% to 60.5%. The normalized root mean square errors ranged from 0.2 to 0.3, and were lower compared with the original model simulated results, which ranged from 0.3 to 0.6. The Willmott index between the predicted and observed variables also varied from 0.5 to 0.8, indicating that the modified TRIPLEX-Management model could accurately simulate the dynamic changes in poplar (Populus spp.) plantations with different densities in northern Jiangsu Province. The density scenario results showed that the leaf and fine root allocation coefficients decreased with the increase in stand density, while the stem allocation increased. Overall, our study showed that the optimum stand density (approximately 400 stems ha−1) could reach the highest aboveground biomass for poplar stands and soil organic carbon storage, leading to higher ecological functions related to carbon sequestration without sacrificing wood production in an economical way in northern Jiangsu Province. Therefore, reasonable density control with different soil and climate conditions should be recommended to maximize carbon sequestration.
Mapping Archaeal Diversity in Soda Lakes by Coupling 16S rRNA PCR-DGGE Analysis with Remote Sensing and GIS Technology
Authors: Elshafey Naglaa , Selim Samy , Mohammed Asmaa H. , Hagagy Nashwa , Samy Mennatalla , Mostafa Ehab M. , Safhi Fatmah A. , Alshamrani Salha M. , Saddiq Amna , Alsharari Salam S. , Aseel Dalia G. , Hafiz Iram , Elkelish Amr , Pérez Leonardo M.
Published: August 2022

The haloarchaeal diversity of four hypersaline alkaline lakes from the Wadi El-Natrun depression (Northern Egypt) was investigated using culture-independent polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) of 16S rRNA gene phylotypes, which was combined with remote sensing and geographic information system (GIS) data to highlight the distribution pattern of the microbial diversity in water and sediment samples. The majority of archaeal sequences identified in all four lakes belonged to the phyla Euryarchaeota and Crenarchaeota. Sediment samples from Beida Lake and water samples from El-Hamra Lake showed the highest levels of archaeal diversity. Sequence similarities ≥ 95% were found between six of the acquired clones and uncultured Halorhabdus, Euryarchaeota, and archaeon clones. In addition, two clones shared a high level of sequence similarity (97%) with unclassified archaea, while other nine clones exhibited 96% to 99% sequence similarity with uncultured archaeon clones, and only one clone showed 97% identity with an uncultured Crenarchaeota. Likewise, 7 DGGE bands presented a sequence similarity of 90 to 98% to Halogranum sp., Halalkalicoccus tibetensis, Halalkalicoccus jeotgali, uncultured Halorubrum, Halobacteriaceae sp., or uncultured haloarchaeon. In conclusion, while the variety of alkaliphilic haloarchaea in the examined soda lakes was restricted, the possibility of uncovering novel species for biotechnological applications from these extreme habitats remains promising.
Impact of Freeze- and Spray-Drying Microencapsulation Techniques on β-Glucan Powder Biological Activity: A Comparative Study
Authors: Valková Veronika , Ďúranová Hana , Falcimaigne-Cordin Aude , Rossi Claire , Nadaud Frédéric , Nesterenko Alla , Moncada Marvin , Orel Mykola , Ivanišová Eva , Chlebová Zuzana , Gabríny Lucia , Kačániová Miroslava
Published: August 2022

The study compares the impact of freeze- and spray-drying (FD, SD) microencapsulation methods on the content of β-glucan, total polyphenols (TP), total flavonoids (TF), phenolic acids (PA), and antioxidant activity (AA) in commercially β-glucan powder (Pleurotus ostreatus) using maltodextrin as a carrier. Morphology (scanning electron microscopy- SEM), yield, moisture content (MC), and water activity (aw) were also evaluated in the samples. Our examinations revealed significant structural differences between powders microencapsulated by the drying methods. As compared to non-encapsulated powder, the SD powder with yield of 44.38 ± 0.55% exhibited more reduced (p < 0.05) values for aw (0.456 ± 0.001) and MC (8.90 ± 0.44%) than the FD one (yield: 27.97 ± 0.33%; aw: 0.506 ± 0.002; MC: 11.30 ± 0.28%). In addition, the highest values for β-glucan content (72.39 ± 0.38%), TPC (3.40 ± 0.17 mg GAE/g), and TFC (3.07 ± 0.29 mg QE/g) have been detected in the SD powder. Our results allow for the conclusion that the SD microencapsulation method using maltodextrin seems to be more powerful in terms of the β-glucan powder yield and its contents of β-glucan, TP, and TF as compared to the FD technique.
Making Full Use of Qualitative Data to Generate New Fish Product Ideas through Co-Creation with Consumers: A Methodological Approach
Authors: López-Mas Laura , Claret Anna , Stancu Violeta , Brunsø Karen , Peral Irene , Santa Cruz Elena , Krystallis Athanasios , Guerrero Luis
Published: August 2022

Co-creation is a process that directly involves different stakeholders in the idea generation phase of a new product development process. A pool of 112 new aquaculture fish product ideas was obtained by applying a combination of creative and projective techniques to the co-creation process with consumers in six focus groups conducted in three European countries (Germany, France, and Spain). The subjectivity of qualitative data analysis (e.g., focus groups) is one of its recognised disadvantages. To overcome this drawback, a combination of specialised software (i.e., Alceste), along with word frequency, co-occurrence, and context checking, was applied to provide a different approach to data analyses in qualitative studies. The method identified the most salient dimensions behind the participants’ discourse (naturalness, quality, ethics, price, and health) and inferred the importance those dimensions had for them, thus proving the existence of a correlation of 0.7 between what the participants said (frequency of mention) and what they liked the most (importance). Overall, the exploratory approach proposed is deemed useful for drawing key conclusions from qualitative research, thus offering an alternative to traditional content analysis. In future, the results obtained may be useful for selecting the co-created ideas with the greatest potential to be well received in the market.