bluuebunny/crossref_metadata_2025 huggingface dataset:
Equity, Justice, and Quality during the COVID-19 Pandemic Period: Considerations on Learning and Scholarly Performance in Brazilian Schools
Authors: Dias Ana , Scavarda Annibal , Reis Augusto , Silveira Haydee , Scavarda Ana
Published: May 2022

Due to the imperative need for change in habits caused by the COVID-19 pandemic that has plagued the world, this exploratory study plans to analyze the directions taken in teaching activities in public and private schools of the city of Rio de Janeiro (Brazil) and their consequences for learning and scholarly performance concerning elementary and middle schools. In this way, this study verifies through an email questionnaire if there was equality, justice, and quality in teaching methods during the COVID-19 pandemic. The descriptive analysis was carried out based on statistical calculations of quantitative and qualitative variables with various tests, whenever necessary, such as the chi-square, and when inconclusive, Fischer’s exact test, Kolmogorov–Smirnov and Shapiro–Wilk, non-parametric Mann–Whitney (when the comparison between two independent groups was mandatory), ANOVA, Kruskal–Wallis, and Friedman test. The results show that teachers tried to interact with students to overcome the problems faced during the COVID-19 pandemic period. Additionally, the study showed that there were differences in scholarly and learning performance, equality, and quality in the types of schools analyzed. This paper will help to fill the literature gap on the subject and will boost ongoing discussion on the inclusion of sustainable concepts in education.
Channel-Wise Attention Mechanism in the 3D Convolutional Network for Lung Nodule Detection
Authors: Zhu Xiaoyu , Wang Xiaohua , Shi Yueting , Ren Shiwei , Wang Weijiang
Published: May 2022

Pulmonary nodule detection is essential to reduce the mortality of lung cancer. One-stage detection methods have recently emerged as high-performance and lower-power alternatives to two-stage lung nodule detection methods. However, it is difficult for existing one-stage detection networks to balance sensitivity and specificity. In this paper, we propose an end-to-end detection mechanism combined with a channel-wise attention mechanism based on a 3D U-shaped residual network. First, an improved attention gate (AG) is introduced to reduce the false positive rate by employing critical feature dimensions at skip connections for feature propagation. Second, a channel interaction unit (CIU) is designed before the detection head to further improve detection sensitivity. Furthermore, the gradient harmonizing mechanism (GHM) loss function is adopted to solve the problem caused by the imbalance of positive and negative samples. We conducted experiments on the LUNA16 dataset and achieved a performance with a competition performance metric (CPM) score of 89.5% and sensitivity of 95%. The proposed method outperforms existing models in terms of sensitivity and specificity while maintaining the attractiveness of being lightweight, making it suitable for automatic lung nodule detection.
The Structure Principle and Dynamic Characteristics of Mechanical-Electric-Hydraulic Dynamic Coupling Drive System and Its Application in Electric Vehicle
Authors: Sun Yue , Zhang Hongxin , Yang Jian
Published: May 2022

To solve the problem of the low recovery rate of braking energy and the short driving range of electric vehicles, a novel mechanical-electric-hydraulic dynamic coupling drive system (MEH-DCDS) is proposed in this article. MEH-DCDS is a new power integration device that allows electric, mechanical, and hydraulic energy to be converted mutually. It comprises a swash plate plunger pump/motor and a permanent magnet synchronous motor. This article explains the structure and working principles of MEH-DCDS. We describe the dynamic characteristics of MEH-DCDS and analyze the pump and hydraulic motor in the MEH-DCDS hydraulic module. The simulation results show that the flow variation of the MEH-DCDS hydraulic module accords with the design concept of MEH-DCDS, and the pressure variation of high and low pressure accumulators also accords with the theoretical situation. The energy flow of Mechanical-Electric-Hydraulic Power Coupling Electric Vehicle (MEHPC-EV) under different working modes is expounded, and the mathematical model of its key components is established. Based on AMESim and Simulink, the article establishes a vehicle simulation dynamic model. The dynamic performance of MEHPC-EV in UDDS is analyzed by co-simulation. The simulation results show that the application of MEH-DCDS in electric vehicles is feasible. MEHPC-EV reduced battery energy consumption by 26.18% compared to EV. The research in this paper verifies the accuracy and superiority of the system, which has a significant reference value for the development and study of electric vehicles in the future.
Dimming Control Scheme of Visible Light Communication Based on Joint Multilevel Time-Shifted Coding
Authors: Li Lin , Guo Jia-Ning , Wu Qi , Zhang Jian
Published: May 2022

Dimming control is an essential objection in the signal designing of visible light communication (VLC), which requires improving the communication performance of the system as much as possible while considering the illumination quality. Here, we studied the problem of high-efficiency transmission in an indoor VLC multi-core light-emitting diode (LED) communication model while considering dimming constraints, and propose a dimming method based on joint multilevel multi-LED time-shifted coding (ML-MTSC). The scheme utilizes the code structure of time-shifted space–time codes to encode and uses pulse amplitude modulation (PAM) to expand it to achieve the dimming control function in the proposed scenario. Simulation results show that the ML-MTSC dimming control scheme proposed in this paper has improved spectral efficiency and error performance compared with the traditional scheme.
A Fine-Grained Secure Service Provisioning Platform for Hypervisor Systems
Authors: Seo Junho , Lee Seonah , Kim Ki-Il , Kim Kyong Hoon
Published: May 2022

As computing technology has been recently widely adopted, most computing devices provide security-related services as basic requirements, which is an important research issue for sustainability of computing devices. The rapid increase of software components makes it difficult to detect or prevent vulnerabilities in the large-size software. One of the prominent approaches for ensuring secure service is the isolation of service which allows the related code and data to be executed only in a particular area. In this paper, we provide a secure service provisioning platform for hypervisor systems. The main contribution of the proposed framework is to enhance the previous secure service provisioning platform in order to solve the non-preemption problem of secure services. Thus, the proposed framework improves the security isolation service in hypervisors and can be used for fine-grained secure service in secure embedded systems.
A Novel Deep Learning Architecture with Multi-Scale Guided Learning for Image Splicing Localization
Authors: Li Zhongwang , You Qi , Sun Jun
Published: May 2022

The goal of image splicing localization is to detect the tampered area in an input image. Deep learning models have shown good performance in such a task, but are generally unable to detect the boundaries of the tampered area well. In this paper, we propose a novel deep learning model for image splicing localization that not only considers local image features, but also extracts global information of images by using a multi-scale guided learning strategy. In addition, the model integrates spatial and channel self-attention mechanisms to focus on extracting important features instead of restraining unimportant or noisy features. The proposed model is trained on the CASIA v2.0 dataset, and its performance is tested on the CASIA v1.0, Columbia Uncompressed, and DSO-1 datasets. Experimental results show that, with the help of the multi-scale guided learning strategy and self-attention mechanisms, the proposed model can locate the tampered area more effectively than the state-of-the-art models.
Microwaves as Diagnostic Tool for Pituitary Tumors: Preliminary Investigations
Authors: Casula Filippo , Lodi Matteo Bruno , Curreli Nicola , Fedeli Alessandro , Scapaticci Rosa , Muntoni Giacomo , Randazzo Andrea , Djuric Nikola , Vannucci Luca , Fanti Alessandro
Published: May 2022

To date, tumors, the second cause of death worldwide, are a modern medicine plight. The development of rapid, cost-effective and reliable prevention and diagnostics tools is mandatory to support clinicians and ensure patients’ adequate intervention. Pituitary tumors are a class of neoplasm, which calls for suitable and ad hoc diagnostic tools. Recently, microwaves have gained interest as a non-ionizing, non-invasive valuable diagnostic approach for identifying pathologic tissues according to their dielectric properties. This work deals with the preliminary investigation of the feasibility of using microwaves to diagnose pituitary tumors. In particular, it focuses on benign tumors of the adenohypophysis, e.g., the pituitary adenomas. It is assumed to access the region of interest of the pituitary region by following a trans-sphenoidal approach. The problem was modeled by developing an equivalent transmission line model of the multi-layered, lossy tissues (front bone of sphenoid sinuses, air in the sinuses, posterior bone of sphenoid sinuses, the pituitary gland and the tumor). The forward problem was developed to investigate the transmission coefficient for identifying the most favorable propagation conditions. Then, it was analyzed if, by the solution of an inverse problem, it is possible to reconstruct the permittivity and electrical conductivity profiles and identify the tumor presence. The results are promising since a maximum reconstruction error of 8% is found, in the worst case, thus paving the way for the use of microwaves for the diagnosis of pituitary tumors.
Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
Authors: Gnanavel S. , Sreekrishna M. , Mani Vinodhini , Kumaran G. , Amshavalli R. S. , Alharbi Sadeen , Maashi Mashael , Khalaf Osamah Ibrahim , Abdulsahib Ghaida Muttashar , Alghamdi Ans D. , Aldhyani Theyazn H. H.
Published: May 2022

Technology evaluation in the electronics field leads to the great development of Wireless Sensor Networks (WSN) for a variety of applications. The sensor nodes are deployed in hazardous environments, and they are operated by isolated battery sources. Network connectivity is purely based on power availability, which impacts the network lifetime. Hence, power must be used wisely to prolong the network lifetime. The sensor nodes that fail due to power have to detect quickly to maintain the network. In a WSN, classifiers are used to detect the faults for checking the data generated by the sensor nodes. In this paper, six classifiers such as Support Vector Machine, Convolutional Neural Network, Multilayer Perceptron, Stochastic Gradient Descent, Random Forest and Probabilistic Neural Network have been taken for analysis. Six different faults (Offset fault, Gain fault, Stuck-at fault, Out of Bounds, Spike fault and Data loss) are injected in the data generated by the sensor nodes. The faulty data are checked by the classifiers. The simulation results show that the Random Forest detected more faults and it also outperformed all other classifiers in that category.
Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends
Authors: Varshika M. Lakshmi , Corradi Federico , Das Anup
Published: May 2022

A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence. Current trends in neuromorphic technologies address the challenges of investigating novel materials, systems, and architectures for enabling high-integration and extreme low-power brain-inspired computing. This review collects the most recent trends in exploiting the physical properties of nonvolatile memory technologies for implementing efficient in-memory and in-device computing with spike-based neuromorphic architectures.
Expressway Speed Prediction Based on Electronic Toll Collection Data
Authors: Zou Fumin , Ren Qiang , Tian Junshan , Guo Feng , Huang Shibin , Liao Lyuchao , Wu Jinshan
Published: May 2022

Expressway section speed can visually reflect the section operation condition, and accurate short time section speed prediction has a wide range of applications in path planning and traffic guidance. However, existing expressway speed prediction data have defects, such as sparse density and incomplete object challenges. Thus, this paper proposes a framework for a combined expressway traffic speed prediction model based on wavelet transform and spatial-temporal graph convolutional network (WSTGCN) of the Electronic Toll Collection (ETC) gantry transaction data. First, the framework pre-processes the ETC gantry transaction data to construct the section speeds. Then wavelet decomposition and single-branch reconstruction are performed on the section speed sequences, and the spatial features are captured by graph convolutional network (GCN) for each reconstructed single-branch sequence, and the temporal features are extracted by connecting the gated recurrent unit (GRU). The experiments use the ETC gantry transaction data of the expressway from Quanzhou to Xiamen. The results indicate that the WSTGCN model makes notable improvements compared to the model of the baseline for different prediction ranges.
A Hybrid Framework for Lung Cancer Classification
Authors: Ren Zeyu , Zhang Yudong , Wang Shuihua
Published: May 2022

Cancer is the second leading cause of death worldwide, and the death rate of lung cancer is much higher than other types of cancers. In recent years, numerous novel computer-aided diagnostic techniques with deep learning have been designed to detect lung cancer in early stages. However, deep learning models are easy to overfit, and the overfitting problem always causes lower performance. To solve this problem of lung cancer classification tasks, we proposed a hybrid framework called LCGANT. Specifically, our framework contains two main parts. The first part is a lung cancer deep convolutional GAN (LCGAN) to generate synthetic lung cancer images. The second part is a regularization enhanced transfer learning model called VGG-DF to classify lung cancer images into three classes. Our framework achieves a result of 99.84%±0.156% (accuracy), 99.84%±0.153% (precision), 99.84%±0.156% (sensitivity), and 99.84%±0.156% (F1-score). The result reaches the highest performance of the dataset for the lung cancer classification task. The proposed framework resolves the overfitting problem for lung cancer classification tasks, and it achieves better performance than other state-of-the-art methods.
Influence of the Auxiliary Air-Duct Outlet and the Brattice Location on the Methane Hazard—Numerical Simulations
Authors: Niewiadomski Adam P. , Pach Grzegorz , Różański Zenon , Wrona Paweł , Musioł Dariusz , Zapletal Pavel , Sofranko Marian
Published: May 2022

The article presents the results of research into the influence of the location of auxiliary ventilation devices on the distribution of methane concentrations at the outlet of the longwall in an underground mine. Since this area is crucial from the point of view of explosion risk, the existence of an optimal arrangement of these devices could lead to improved safety of the crew working in the area. The aim of conducted study was to examine if the impact of this devices placement is significant. The research was carried out with the use of computational fluid dynamics (CFD) modeling—Ansys Fluent. The analyses took into account the location of the two most commonly used devices: a brattice and an auxiliary air-duct. The numerical model has been prepared and validated based on in situ measurements. Thirty-two cases of device configurations were analysed. The length and position of the brattice, as well as the height and position air-duct outlet along tailgate, were modified. It has been shown that although the presented solutions are an effective risk mitigation method, contrary to the common opinion of many practitioners, the impact of their exact placement, provided it is compliant with the regulations, is not significant for the registered methane concentration distribution at a longwall outlet.
The Thermal Response of a Packed Bed Thermal Energy Storage System upon Saturated Steam Injection Using Distributed Temperature Sensing
Authors: Ahmed Zayed , Constantin Alina , Bindra Hitesh
Published: May 2022

The effectiveness of a thermal energy storage (TES) system is typically characterized with the help of thermal stratification or temperature gradients along the direction of heat injection, which is typically the flow direction of heat transfer fluid. The steepness of temperature gradients are a direct indicator of the effectiveness or efficiency of the heat storage or dispatch process. The temperature gradient evolution along the packed bed of ceramic particles upon saturated steam injection is presented in this work. Distributed temperature sensing based on optical frequency domain reflectometry was deployed in a packed bed of ceramic particles to capture the thermal front evolution in the axial direction. The physical processes accompanying steam injection in packed beds are complex due to phase change, transitioning two-phase flow, and changes in condensate accumulation. Therefore, the variation of thermal response of the TES system for various steam injection flow rates was experimentally studied using a high-resolution distributed temperature sensing system in a chemically inert alumina particle-packed bed. Distinct zones of different heat transfer modes were observed during the steam injection experiments. A distinct conduction zone, evident from diffuse thermal fronts, was observed at low flow rates, and these thermal gradients became sharper as the flow rate increased. The diffuse thermal fronts in the heat storage media suggest a low exergy efficiency of the TES system, as energy losses started initiating before a significant fraction of the bed was saturated with steam.
Assimilation Efficiencies and Gas Exchange Responses of Four Salix Species in Elevated CO2 under Soil Moisture Stress and Fertilization Treatments
Authors: Major John E. , Mosseler Alex , Malcolm John W.
Published: May 2022

Assimilation to the internal CO2 (ACi) response curve and gas exchange parameters were quantified for four North American willows ((Salix cordata (COR), S. discolor (DIS), S. eriocephala (ERI), and S. interior (INT)) grown in a 2 × 2 factorial of atmospheric CO2 and soil moisture treatments to see how they would respond to climate change factors. After the first year of greenhouse growth under said treatments, we saw no difference in the aboveground stem biomass between CO2 treatments. Thus, in the second year, a second experiment on a subset of well-watered, coppiced willows was conducted in a 2 × 2 factorial of atmospheric CO2 and soil fertilization (FERT) treatments. In both experiments, the maximum rate of carboxylation (Vcmax) significantly declined for all four species in response to elevated CO2 (eCO2). In response to a drought treatment (DRT), Vcmax declined, except for INT, which increased Vcmax. In both experiments, INT had the greatest Vcmax, maximum rate of electron transport (Jmax), and triose phosphate utilization, followed by COR and ERI, with DIS having the lowest values. FERT resulted in a strong increase in assimilation (A) and stomatal conductance (Gwv) by 92 and 119%, respectively. Gwv is the primary driver and A is a minor driver of water use efficiency (WUE) under DRT. FERT mitigated the Vcmax and A downregulation in eCO2, but eCO2 did not mitigate the DRT downregulation effect. Differences between INT and the other three willows in a number of adaptive traits and responses related to drought may reflect the evolutionary origins of INT and the taxonomic group Longifoliae in the arid southwest USA and Mexico.
Identification and Analysis of JAZ Gene Family in Ginkgo biloba Reveals Candidate Genes for Biosynthesis of Terpene Trilactones
Authors: He Xiao , Liu Xiaomeng , Zheng Jiarui , Ye Jiabao , Liao Yongling , Xu Feng
Published: May 2022

Terpene trilactones (TTLs) are the main secondary metabolites in Ginkgo biloba L. with efficacious pharmacological activity. Jasmonate ZIM-domain (JAZ) protein is a key regulatory factor of the JA signaling pathway, which regulates the biosynthesis of secondary metabolites such as terpenes, alkaloids, and flavonoids. In this study, GbJAZ01~GbJAZ11 were identified from the genome data in G. biloba, which contained TIFY-, Jas-, and weakly conserved NT-domains, and the promoters in most of them contained light, hormone, and stress-responsive elements. Phylogenetic analysis divided all JAZ proteins of Arabidopsis thaliana, Oryza sativa, Picea sitchensis, Taxus chinensis, and G. biloba into nine groups, in which GbJAZs belong to Group VI-IX. GbJAZs have similar functional motifs to A. thaliana and O. sativa, but also contain three specific motifs of gymnosperms, indicating that, although gymnosperms and angiosperms have some conservative structures and functions, their evolutionary processes are independent. Expression pattern analysis showed that the expression levels of GbJAZs were significantly up-regulated by MeJA, but the change pattern and amplitude were different, indicating that the function of GbJAZs in response to a JA signal may be different. After ABA and SA treatment, the expression of GbJAZs was up-regulated or inhibited in varying degrees, and different GbJAZs may be involved in the synergistic or antagonistic effects between JA and other hormone signals. The MeJA significantly increased the content of TTLs in G. biloba leaves, which were significantly positively correlated with the expression levels of GbJAZ01, 02, 07, and 11, and negatively correlated with the expression of GbJAZ04. They may play an important role in JA signaling pathways and the interactions between JA and other hormone signals, and participate in the regulation of the biosynthesis of TTLs. Our results provide a reference for the discovery that GbJAZs are involved in JA signaling pathways, and lay a theoretical foundation for analyzing JA signaling pathways to regulate the synthesis of secondary metabolites.
Comparison of Variable Selection Methods among Dominant Tree Species in Different Regions on Forest Stock Volume Estimation
Authors: Fang Gengsheng , Fang Luming , Yang Laibang , Wu Dasheng
Published: May 2022

The forest stock volume (FSV) is one of the crucial indicators to reflect the quality of forest resources. Variable selection methods are usually used for FSV estimated models. However, few studies have explored which variable selection methods can make the selected data set have better explanatory and robustness for the same dominant tree species in different regions after the feature variables were filtered by the feature selection methods. In this study, we chose six dominant tree species from Lin’an District, Anji County, and a part of Longquan City. The tree species include broad-leaved, coniferous, Masson pine, Chinese fir, coniferous and broad-leaved mixed forest, and all tree species which include the above five groups of tree species. The last two tree species were represented by mixed and all, respectively. Then, the satellite images, terrain factors, and forest inventory data were selected by six variable selection methods (least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), stepwise regression (Step-Reg), permutation importance (PI), mean decrease impurity (MDI), and SelectFromModel based on LightGBM (SFM)), according to different dominant tree types in different regions. The selected variables were formed into a new dataset divided by different dominant trees. Besides, extreme gradient boosting (XGBoost) was used, combined with variable selection methods to estimate the FSV. The performed results are as follows: In the feature selection of coniferous, RFE performed better both in the average and in the separate regions. In the feature selection of Chinese fir and all, PI performed better both in the average and in the separate regions. In the feature selection of Masson pine, MDI performed better both in the average and in the separate regions. In the feature selection of mixed, MDI performed better in the average while RFE performed better in the separate regions comprehensively. The results showed that not only in separate regions, but the average result two factors, RFE, MDI, and PI all performed well to select variables to estimate the FSV. Furthermore, we selected the top five high feature-importance factors of different tree types, and the results showed that tree age and canopy density were both of great importance to the estimation of FSV. Besides, in the exhibited results of feature selection methods, compared with no variable selection, the research also found that variable selection can improve the performance of the model. Additionally, from the results of different tree types in different regions, we also found that small-scale and diversity of dominant tree types may lead to the instability and unreliability of experimental results. The study provides some insight into the application the optimal variable selection methods of the same dominant tree type in different regions. This study will help the development of variable selection methods to estimate FSV.
Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks
Authors: Ogundokun Roseline Oluwaseun , Misra Sanjay , Douglas Mychal , Damaševičius Robertas , Maskeliūnas Rytis
Published: May 2022

In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN.
Actin Turnover Required for Adhesion-Independent Bleb Migration
Authors: Copos Calina , Strychalski Wanda
Published: May 2022

Cell migration is critical for many vital processes, such as wound healing, as well as harmful processes, such as cancer metastasis. Experiments have highlighted the diversity in migration strategies employed by cells in physiologically relevant environments. In 3D fibrous matrices and confinement between two surfaces, some cells migrate using round membrane protrusions, called blebs. In bleb-based migration, the role of substrate adhesion is thought to be minimal, and it remains unclear if a cell can migrate without any adhesion complexes. We present a 2D computational fluid-structure model of a cell using cycles of bleb expansion and retraction in a channel with several geometries. The cell model consists of a plasma membrane, an underlying actin cortex, and viscous cytoplasm. Cellular structures are immersed in viscous fluid which permeates them, and the fluid equations are solved using the method of regularized Stokeslets. Simulations show that the cell cannot effectively migrate when the actin cortex is modeled as a purely elastic material. We find that cells do migrate in rigid channels if actin turnover is included with a viscoelastic description for the cortex. Our study highlights the non-trivial relationship between cell rheology and its external environment during migration with cytoplasmic streaming.
Quantifying Uniform Droplet Formation in Microfluidics Using Variational Mode Decomposition
Authors: Izaguirre Michael , Nearhood Luke , Parsa Shima
Published: May 2022

Using variational mode decomposition, we analyze the signal from velocities at the center of the channel of a microfluidics drop-maker. We simulate the formation of water in oil droplets in a microfluidic device. To compare signals from different drop-makers, we choose the length of the water inlet in one drop-maker to be slightly shorter than the other. This small difference in length leads to the formation of satellite droplets and uncertainty in droplet uniformity in one of the drop-makers. By decomposing the velocity signal into only five intrinsic modes, we can fully separate the oscillatory and noisy parts of the velocity from an underlying average flow at the center of the channel. We show that the fifth intrinsic mode is solely sufficient to identify the uniform droplet formation while the other modes encompass the oscillations and noise. Mono-disperse droplets are formed consistently and as long as the fifth mode is a plateau with a local standard deviation of less than 0.02 for a normalized signal at the channel inlet. Spikes in the fifth mode appear, coinciding with fluctuations in the sizes of droplets. Interestingly, the spikes in the fifth mode indicate non-uniform droplet formation even for the velocities measured upstream in the water inlet in a region far before where droplets form. These results are not sensitive to the spatial resolution of the signal, as we decompose a velocity signal averaged over an area as wide as 40% of the channel width.
A Special Family of m-Fold Symmetric Bi-Univalent Functions Satisfying Subordination Condition
Authors: Aldawish Ibtisam , Swamy Sondekola Rudra , Frasin Basem Aref
Published: May 2022

In this paper, we introduce a special family Mσm(τ,ν,η,φ) of the function family σm of m-fold symmetric bi-univalent functions defined in the open unit disc D and obtain estimates of the first two Taylor–Maclaurin coefficients for functions in the special family. Further, the Fekete–Szegö functional for functions in this special family is also estimated. The results presented in this paper not only generalize and improve some recent works, but also give new results as special cases.
Nuclear Magnetic Resonance (NMR) Outputs Generation for Clastic Rocks Using Multi Regression Analysis, Examples from Offshore Western Australia
Authors: Rezaee Reza
Published: May 2022

A large database of nuclear magnetic resonance (NMR) logging data from clastic rocks of offshore oil and gas fields of Western Australia was used to assess the performance of multi regression analysis (MRA) to calculate NMR log outputs from conventional well logs. This short paper introduces a set of MRA equations for the calculation of the NMR log outputs using conventional well logs as inputs. This study shows that unlike machine learning methods the MRA approach fails to predict most of the NMR log outputs with acceptable accuracy but can provide Coates and SDR permeabilities with R2 of more than 0.75.
The Emergence and Development of the Coat of Arms of Macedonia in Illyrian Heraldry
Authors: Nacevski Ivan
Published: May 2022

Macedonia is a region in the Balkans with traditional boundaries at the lower Néstos (Mesta in Bulgaria) River and the Rhodope Mountains to the east; the Skopska Crna Gora and Shar mountains, bordering Southern Serbia, in the north; the Korab range and Ohrid and Prespa Lakes in the west; and the Pindus Mountains and the Aliákmon River in the south. Illyrian heraldry consists of manuscript collections with coats of arms—armorials that appeared on the Dalmatian coast, and in Italy, Spain, and Austria, in the late sixteenth and early seventeenth centuries. The two Stematographias of Pavle Ritter Vitezovich and Hristofor Zhefarovich are traditionally added to this group, as well as a number of other documents directly or indirectly related to the armorials. There is a possibility of a third: two different sources with relatively similar blazons, resulting in the simplification and inverse coloring of the both coats of arms. This would mean that it is quite possible that the Macedonian coat of arms was taken over by Capaccio, who took it from another older source. First of all, the coats of arms with a lion attributed to Alexander the Great should be taken into consideration.
Dreaming for Our Daughters: Un/Learning Monoracialism on Our Journey of Multiracial Motherhood
Authors: Ashlee Aeriel A. , Combs Lisa Delacruz
Published: May 2022

How is monoracialism un/learned from generation to generation? In this duoethnography, the co-authors engage in reflexive letter writing to purposefully connect their personal relationships with Aeriel’s daughter, Azaelea, to their academic ideas as poststructural scholars. In doing so, they practice letting go of inherited dichotomies (such as mother versus scholar), lean into expansive ontoepistemological possibilities informed by their both/and positionalities as mama-scholar and auntie-scholar, in order to dream of expansive, healing, and liberatory futures that can emerge from connecting across difference, listening with raw openness, and pursuing radical interrelatedness.
The Skin We Live in: Pigmentation Traits and Tanning Behaviour in British Young Adults, an Observational and Genetically-Informed Study
Authors: Bonilla Carolina , Mejia-Lancheros Cilia
Published: May 2022

Skin cancer incidence has been increasing worldwide, representing a particularly high burden for populations of European ancestry. Outdoor and indoor tanning using ultraviolet (UV) radiation devices are major risk factors for skin cancer. While tanning behaviours can be modified by targeted interventions to reduce skin cancer rates, there is insufficient evidence on the motivations for tanning preferences and their relationship with pigmentation phenotypes. The present observational and genetically-informed study investigates motives for tanning and the role that pigmentation phenotypes play on outdoor and indoor tanning behaviour in British young adults. This study included 3722 participants from the Avon Longitudinal Study of Parents and Children in South West England, with data on pigmentation features, tanning ability and preferences, and SNP genotypes. Liking to tan and outdoor tanning were strongly influenced by pigmentary traits and tanning ability. However, the association of these phenotypes with UV indoor tanning was weaker. Our results provide evidence to support the implementation of skin cancer preventative interventions that consider individual biological characteristics and motives for undergoing outdoor and indoor tanning.
How and Why Chromosomes Interact with the Cytoskeleton during Meiosis
Authors: Kim Hyung Jun , Liu Chenshu , Dernburg Abby F.
Published: May 2022

During the early meiotic prophase, connections are established between chromosomes and cytoplasmic motors via a nuclear envelope bridge, known as a LINC (linker of nucleoskeleton and cytoskeleton) complex. These widely conserved links can promote both chromosome and nuclear motions. Studies in diverse organisms have illuminated the molecular architecture of these connections, but important questions remain regarding how they contribute to meiotic processes. Here, we summarize the current knowledge in the field, outline the challenges in studying these chromosome dynamics, and highlight distinctive features that have been characterized in major model systems.
The Energy Cost of Extracting Critical Raw Materials from Tailings: The Case of Coltan
Authors: Magdalena Ricardo , Calvo Guiomar , Valero Alicia
Published: May 2022

Niobium and tantalum are mainly produced from columbite–tantalite ores, and 60% of their production is nowadays located in the Democratic Republic of Congo and Rwanda. The concentration of supply, the scarcity, the wide range of use in all electronic devices, and the expected future demand boosted by the clean and digital transition means that Nb and Ta have high supply risks. In this context, extraction from rich Ta and Nb tailings from abandoned mines could partly offset such risks. This study analyzes the energy cost that the reprocessing of both elements from tailings would have. To that end, we simulate with HSC Chemistry software the different processes needed to beneficiate and refine both metals from zinc tailings as a function of Nb and Ta concentration. At current energy and metal prices, tantalum recovery from rich Ta-Nb tailings would be cost-effective if ore-handling costs were allocated to a paying metal. By way of contrast, niobium recovery would not be favored unless market prices increase.
Determinants of the Limpopo Province of South Africa’s Response to COVID-19: A Mixed Methods Protocol to Analyze and Share Lessons Learned
Authors: Matlala Sogo F. , Tshitangano Takalani G. , Setati Musa E. , Ramalivhana Naledzani J. , Mphekgwana Peter M.
Published: May 2022

COVID-19 is a priority health research agenda item in South Africa. The World Health Organization declared the COVID-19 outbreak as a Public Health Emergency of International Concern, which requires all countries to respond and share data with others. Responses included the implementation of measures to reduce the spread of COVID-19 and protect health systems from being overwhelmed by seriously ill patients. Each country was mandated to assess its own risk and rapidly implement the necessary measures to reduce COVID-19 transmission and minimize its impact. Countries were further encouraged to share their experiences of responding to the COVID-19 pandemic. Media reports in South Africa suggest that the Limpopo Department of Health implemented a successful COVID-19 response. This study aims to analyze, document and publish those successes to make them accessible to other researchers and public health practitioners. The study will also allow for the participation of public health students to meet the requirements of their postgraduate degrees. This convergent parallel mixed method study will collect secondary data of responses to COVID-19 by the Limpopo Department of Health from the records the department keeps. Quantitative and qualitative data detailing activities and statistics describing facilitators and barriers to implementation of COVID-19 response from March 2020 will be extracted from records.
Health-Related Quality of Life in Relation to Fruit and Vegetable Intake among Polish Pharmacists
Authors: Kurnik-Łucka Magdalena , Grońska Dominika , Wojnarski Marcin , Pasieka Paweł , Rząsa-Duran Elżbieta , Gil Krzysztof
Published: May 2022

A direct association between health-related quality of life (HQoL) and nutrition remains unclear, although lifestyle habits are known to impact on human health. Thus, the aim of this study was to estimate an association between dietary habits, such as vegetable and fruit consumption, in particular, and HQoL. SF-36 (RAND-SF-36) and the Dietary Habits and Nutrition Beliefs Questionnaire (KomPAN) were addressed to Polish pharmacists with a valid professional license. After the preliminary verification, 667 pharmacists were included into the study, and altogether, 11 questions from the KomPAN questionnaire and all RAND-SF-36 multiple choice questions were processed for statistical analysis. All HQoL scores, excluding physical functioning and role-physical, were significantly higher in the group declaring fruit consumption ≥1 time per day (p < 0.005), while physical functioning (p = 0.008) and general health (p = 0.03) were significantly higher in the group declaring vegetable consumption ≥1 time per day. Thus, there is indeed a positive association between vegetable and fruit consumption and HQoL. Increased fruit intake could certainly impact on the health-related quality of life of Polish pharmacists, primarily in terms of mental functioning, while increased vegetable intake could impact in terms of physical functioning.
The Efficacy of Expiratory Muscle Training during Inspiratory Load in Healthy Adult Males: A Randomized Controlled Trial
Authors: Tsukamoto Toshiya , Kato Michitaka , Kurita Yasunari , Uchida Masaki , Kubo Akira , Maruyama Hitoshi
Published: May 2022

This study aimed to evaluate the effects of expiratory muscle training (EMT) on respiratory muscle strength and respiratory distress during inspiratory load. Thirty-one healthy adult males were randomly divided into an EMT group who underwent EMT (n = 15) and a control group who did not undergo EMT (n = 16). The EMT group underwent EMT with a 50% load of maximum expiratory mouth pressure (PEmax) for 15 min, twice a day, every day, for 4 weeks. The parameter of respiratory muscle fatigue was a decrease in maximum inspiratory mouth pressure (PImax) and PEmax during 20 min of inspiratory load; thus, PImax and PEmax during inspiratory load were measured. Respiratory distress during inspiratory load was assessed using the Borg scale. These assessments were performed on the same subjects in each group before and after the 4 week study. In the EMT group, the PEmax values after the study were significantly higher than those before the study (p < 0.01). Furthermore, before the study, the PImax and PEmax values for the EMT group during inspiratory load were significantly lower than those before inspiratory load (p < 0.01). However, after the study, there was no difference in these values between during and before inspiratory load. In the EMT group, the Borg scale value during inspiratory load from 6 to 20 min was significantly lower after the study than before the study (p < 0.05). EMT increases expiratory muscle strength, thereby attenuating decreased respiratory muscle strength (PImax and PEmax) and respiratory distress during inspiratory load in healthy subjects.
Therapeutic Patient Education after Anterior Cruciate Ligament Reconstruction: Evaluation of the Knowledge and Certitudes with a Self-Report Questionnaire
Authors: Fouasson-Chailloux Alban , Crenn Vincent , Louguet Bastien , Grondin Jérôme , Menu Pierre , Dauty Marc
Published: May 2022

Therapeutic education aims to help patients acquire skills and knowledge, and to improve psychosocial aspects to manage chronic disease. After anterior cruciate ligament reconstruction (ACLR), only 35 to 60% of the patients are able to go back to their previous sport. Return to sport depends on the motivation of the patient. No therapeutic education has already been proposed. We aimed to evaluate the effect of therapeutic education sessions on knowledge improvement during inpatient rehabilitation after ACLR, compared to patients operated with the same surgical technic, but who had no therapeutic education because of outpatient rehabilitation. Sessions were performed by a multidisciplinary team. The evaluation of the knowledge was performed with a true or false 12-items self-report questionnaire. Fifty-four patients were studied and compared to 54 patients with no therapeutic education. The educated and the non-educated groups were comparable. The number of correct answers increased from 73% before therapeutic education to 95% at the end of the hospitalization (p < 0.001). This improvement persisted over time with 91.5% of correct answers at four months (p = 0.94). The non-educated group had 70% of correct answers. This was significantly lower than the results obtained from the educated group at four months (p < 0.001). It was comparable to the result obtained before therapeutic education (p = 0.91). Therapeutic patient education performed during hospitalization for rehabilitation enables patients to have a better knowledge of the stages from rehabilitation to return to sport and the risks of complication after ACLR.
Supporting the Delivery of Infection Prevention and Control Training to Healthcare Workers: Insights from the Sector
Authors: Qureshi Mohammed , Chughtai Abrar , Seale Holly
Published: May 2022

Infection prevention and control (IPC) cannot be implemented without healthcare workers (HCWs) being properly trained and competent. The provision of training is essential, yet there is a gap in our understanding of the factors impacting the implementation of IPC training. This paper reports the results from in-depth interviews that explored the current landscape around IPC training delivered across low-, middle-, and high-income countries. Semi-structured interviews were conducted with the key stakeholders involved in policymaking or IPC implementation in Saudi Arabia, Pakistan, India, Indonesia, the Philippines, and Australia. Although the training was mandated for many HCWs, participants indicated that only some training elements were mandatory. Participants spoke about covering various topics, but those in low-resource settings spoke about the challenges of delivering training. Classroom-based training dominated, but online delivery modes were also used in some locations. Whilst HCW’s training was postulated to have improved during the COVID-19 pandemic, the capacity to deliver training did not improve in some settings. More research is needed to establish the essential elements that could underpin the development of training packages.
Conveying Intangible Cultural Heritage in Museums with Interactive Storytelling and Projection Mapping: The Case of the Mastic Villages
Authors: Nikolakopoulou Vasiliki , Printezis Petros , Maniatis Vassilis , Kontizas Dimitris , Vosinakis Spyros , Chatzigrigoriou Pavlos , Koutsabasis Panayiotis
Published: May 2022

Spatial Augmented Reality (SAR), as implemented with projection mapping, is part of mixed-reality technology with numerous applications in the cultural domain. In museums, interactive projection mapping has been exploited to superimpose virtual content on exhibited artefacts, offering users various hybrid ways to interact with the artefacts’ physical and digital content. For this reason, it has been widely used in the context of architectural heritage to promote culture and raise awareness about historical buildings or landscapes by visualizing significant elements they convey. This paper presents the design, development, and iterative user evaluation of an interactive projection mapping installation for the Mastic Museum on Chios island in Greece that promotes UNESCO’s intangible cultural heritage. The installation affords tangible interaction to activate the video projections presented in a storytelling manner on a 3D-printed scale model of a representative historic settlement exhibited inside the museum. The concept of this installation aims to connect the tangible and intangible cultural heritage of mastic and the related villages with narration and vivid illustrations. Three evaluation phases took place during the development at the lab and the museum, informing UX, learning, and design considerations.