bluuebunny/crossref_metadata_2025 huggingface dataset:
Use of Cosmetic Creams and Perception of Natural and Eco-Friendly Products by Women: The Role of Sociodemographic Factors
Authors: Mitterer-Daltoé Marina Leite , Martins Vaniele B. , Parabocz Cristiane R. B. , da Cunha Mário A. A.
Published: May 2023

The present work seeks to understand the use of cosmetic creams and the perception of natural and eco-friendly products by women and to explore the impacts of their personal characteristics. The study was designed using two approaches: (i) an investigation into the role of personal characteristics on the frequency of use and amount spent on cosmetic creams; and (ii) an understanding of the perception of natural and eco-friendly cosmetic creams by the use of check-all-that-apply (CATA) questions. Results showed that scholarity has a strong influence on the use of cosmetic creams; women with a postgraduate education reported higher frequency of use and spending on cosmetic creams and showed an awareness of natural and eco-friendly cosmetics. The subject of natural and eco-friendly cosmetic creams is not something that most of the women that participated were aware of, and the CATA technique proved to be a valuable tool to discover this.
Profiling and Evaluation of the Effect of Guarana-Loaded Liposomes on Different Skin Cell Lines: An In Vitro Study
Authors: Roggia Isabel , Gomes Patrícia , Dalcin Ana Julia Figueiró , Ourique Aline Ferreira , Mânica da Cruz Ivana Beatrice , Ribeiro Euler E. , Mitjans Montserrat , Vinardell Maria Pilar
Published: May 2023

The objective of this study was to analyze the in vitro stability and toxicity of liposomes containing guarana in skin cell lines. The liposomes were produced by the reverse phase evaporation method containing 1 mg/mL guarana. The stability of the liposomes was evaluated by physical-chemical parameters for up to 90 days using three different storage conditions. The cytotoxicity of guarana (GL), liposomes (B-Lip), and guarana-loaded liposomes (G-Lip) was evaluated on spontaneously immortalized human keratinocyte cell lines (HaCaT), murine Swiss albino fibroblasts (3T3), and human fibroblasts (1BR.3.G). The evaluation was performed using cellular viability analysis. The techniques used were 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and neutral red capturing (NRU), and the analyses were conducted after 24, 48, and 72 h of exposure of these cells to the different treatments. The G-Lip exhibited physical-chemical stability for 60 days when the samples were stored in a refrigerator. The GL, B-Lip, and G-Lip demonstrated low cytotoxicity in the three different cell cultures tested since a small reduction in cell viability was only observed at the highest concentrations. In addition, greater cell damage was observed for B-Lip; however, guarana protected the cells from this damage. Thus, G-Lip structures can be considered promising systems for topical applications.
Plant and Growth Condition Interactions on the Phenolic Compound Contents and Antioxidant Activity in Salvia circinata Cav., a Medicinal Sage
Authors: Pérez-Ochoa Mónica L. , Vera-Guzmán Araceli M. , Mondragón-Chaparro Demetria M. , Sandoval-Torres Sadoth , Carrillo-Rodríguez José C. , Hernández-Delgado Sanjuana , Chávez-Servia José L.
Published: May 2023

The genus Salvia has a worldwide distribution, and its contribution to traditional medicine and as an aromatic plant has been recognized since ancient times, with few documented species having a similar phytochemical composition. In this study, the effects of natural growth conditions (in situ) and ex situ cultivation and sampling locations on the phenolic compound contents and antioxidant activity of methanolic extracts of Salvia circinata from Oaxaca, Mexico, were investigated. Stem and young leaf samples were collected from plants growing in situ in two locations in Oaxaca and later from plants propagated clonally or vegetatively from propagules obtained in situ but grown ex situ. In both sets of samples, the contents of total polyphenols and flavonoids and the antioxidant activity were evaluated by spectrophotometry, and subsequently, the contents of phenols and specific flavonoids were identified and quantified by high-performance liquid chromatography with diode-array detection (HPLC–DAD). The growth conditions and locality of origin of the samples significantly influenced the contents of total polyphenols and flavonoids and antioxidant activity, with the in situ conditions in the locality of Reforma, Oaxaca, favoring higher levels. Two phenolic acids (chlorogenic and rosmarinic) and two flavonoids (isoquercitrin and rutin) were identified, and the concentrations of these compounds were influenced by the growth conditions and localities of origin of the samples.
A Multispectral UAV Imagery Dataset of Wheat, Soybean and Barley Crops in East Kazakhstan
Authors: Maulit Almasbek , Nugumanova Aliya , Apayev Kurmash , Baiburin Yerzhan , Sutula Maxim
Published: May 2023

This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.
A Comprehensive Dataset of Spelling Errors and Users’ Corrections in Croatian Language
Authors: Gledec Gordan , Horvat Marko , Mikuc Miljenko , Blašković Bruno
Published: May 2023

This paper presents a unique and extensive dataset containing over 33 million entries with pairs in the form “spelling error → correction” from ispravi.me, the most popular Croatian online spellchecking service, collected since 2008. The dataset, compiled from the contribution of nearly 900,000 users, is a valuable resource for researchers and developers in the field of natural language processing (NLP), improving spellcheck accuracy, and language learning applications. The dataset may be used to accomplish several goals: (1) improving spellchecking accuracy by incorporating common user corrections and reducing false positives and negatives; (2) helping language learners identify common errors and learn correct spelling through targeted feedback; (3) analyzing data trends and patterns to uncover the most common spelling errors and their underlying causes; (4) identifying and evaluating factors that influence typing input; (5) improving NLP applications such as text recognition and machine translation. Tasks specific to the Croatian language include the creation of a letter-level confusion matrix and the refinement of word suggestions based on historical usage of the service. This comprehensive dataset provides researchers and practitioners with a wealth of information, opening the path for advancements in spellchecking, language learning, and NLP applications in the Croatian language.
A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women
Authors: Ziyambe Blessed , Yahya Abid , Mushiri Tawanda , Tariq Muhammad Usman , Abbas Qaisar , Babar Muhammad , Albathan Mubarak , Asim Muhammad , Hussain Ayyaz , Jabbar Sohail
Published: May 2023

Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further.
How Automated Techniques Ease Functional Assessment of the Fetal Heart: Applicability of MPI+™ for Direct Quantification of the Modified Myocardial Performance Index
Authors: Scharf Jann Lennard , Dracopoulos Christoph , Gembicki Michael , Welp Amrei , Weichert Jan
Published: May 2023

(1) Objectives: In utero functional cardiac assessments using echocardiography have become increasingly important. The myocardial performance index (MPI, Tei index) is currently used to evaluate fetal cardiac anatomy, hemodynamics and function. An ultrasound examination is highly examiner-dependent, and training is of enormous significance in terms of proper application and subsequent interpretation. Future experts will progressively be guided by applications of artificial intelligence, on whose algorithms prenatal diagnostics will rely on increasingly. The objective of this study was to demonstrate the feasibility of whether less experienced operators might benefit from an automated tool of MPI quantification in the clinical routine. (2) Methods: In this study, a total of 85 unselected, normal, singleton, second- and third-trimester fetuses with normofrequent heart rates were examined by a targeted ultrasound. The modified right ventricular MPI (RV-Mod-MPI) was measured, both by a beginner and an expert. A calculation was performed semiautomatically using a Samsung Hera W10 ultrasound system (MPI+™, Samsung Healthcare, Gangwon-do, South Korea) by taking separate recordings of the right ventricle’s in- and outflow using a conventional pulsed-wave Doppler. The measured RV-Mod-MPI values were assigned to gestational age. The data were compared between the beginner and the expert using a Bland-Altman plot to test the agreement between both operators, and the intraclass correlation was calculated. (3) Results: The mean maternal age was 32 years (19 to 42 years), and the mean maternal pre-pregnancy body mass index was 24.85 kg/m2 (ranging from 17.11 to 44.08 kg/m2). The mean gestational age was 24.44 weeks (ranging from 19.29 to 36.43 weeks). The averaged RV-Mod-MPI value of the beginner was 0.513 ± 0.09, and that of the expert was 0.501 ± 0.08. Between the beginner and the expert, the measured RV-Mod-MPI values indicated a similar distribution. The statistical analysis showed a Bland-Altman bias of 0.01136 (95% limits of agreement from −0.1674 to 0.1902). The intraclass correlation coefficient was 0.624 (95% confidence interval from 0.423 to 0.755). (4) Conclusions: For experts as well as for beginners, the RV-Mod-MPI is an excellent diagnostic tool for the assessment of fetal cardiac function. It is a time-saving procedure, offers an intuitive user interface and is easy to learn. There is no additional effort required to measure the RV-Mod-MPI. In times of reduced resources, such assisted systems of fast value acquisition represent clear added value. The establishment of the automated measurement of the RV-Mod-MPI in clinical routine should be the next level in cardiac function assessment.
Direct Anthropometry Overestimates Cranial Asymmetry—3D Digital Photography Proves to Be a Reliable Alternative
Authors: Nieberle Felix , Spoerl Steffen , Lottner Lisa-Marie , Spanier Gerrit , Schuderer Johannes G. , Fiedler Mathias , Maurer Michael , Ludwig Nils , Meier Johannes K. , Ettl Tobias , Reichert Torsten E. , Taxis Juergen
Published: May 2023

This study compared manual and digital measurements of plagiocephaly and brachycephaly in infants and evaluated whether three-dimensional (3D) digital photography measurements can be used as a superior alternative in everyday clinical practice. A total of 111 infants (103 with plagiocephalus and 8 with brachycephalus) were included in this study. Head circumference, length and width, bilateral diagonal head length, and bilateral distance from the glabella to the tragus were assessed by manual assessment (tape measure and anthropometric head calipers) and 3D photographs. Subsequently, the cranial index (CI) and cranial vault asymmetry index (CVAI) were calculated. Measured cranial parameters and CVAI were significantly more precise using 3D digital photography. Manually acquired cranial vault symmetry parameters were at least 5 mm lower than digital measurements. Differences in CI between the two measuring methods did not reach significance, whereas the calculated CVAI showed a 0.74-fold decrease using 3D digital photography and was highly significant (p < 0.001). Using the manual method, CVAI calculations overestimated asymmetry, and cranial vault symmetry parameters were measured too low, contributing to a misrepresentation of the actual anatomical situation. Considering consequential errors in therapy choices, we suggest implementing 3D photography as the primary tool for diagnosing deformational plagiocephaly and positional head deformations.
Evaluation Tools Developed for Rett Syndrome
Authors: Lotan Meir , Downs Jenny , Stahlhut Michelle , Romano Alberto
Published: May 2023

Rett syndrome (RTT) is a complex neurodevelopmental X-linked disorder associated with severe functional impairments and multiple comorbidities. There is wide variation in the clinical presentation, and because of its unique characteristics, several evaluation tools of clinical severity, behavior, and functional motor abilities have been proposed specifically for it. This opinion paper aims to present up-to date evaluation tools which have specifically been adapted for individuals with RTT often used by the authors in their clinical and research practice and to provide the reader with essential considerations and suggestions regarding their use. Due to the rarity of Rett syndrome, we found it important to present these scales in order to improve and professionalize their clinical work. The current article will review the following evaluation tools: (a) the Rett Assessment Rating Scale; (b) the Rett Syndrome Gross Motor Scale; (c) the Rett Syndrome Functional Scale; (d) the Functional Mobility Scale—Rett Syndrome; (e) the Two-Minute Walking Test modified for Rett syndrome; (f) the Rett Syndrome Hand Function Scale; (g) the StepWatch Activity Monitor; (h) the activPALTM; (i) the Modified Bouchard Activity Record; (j) the Rett Syndrome Behavioral Questionnaire; and (k) the Rett Syndrome Fear of Movement Scale. The authors recommend that service providers consider evaluation tools validated for RTT for evaluation and monitoring to guide their clinical recommendations and management. In this article, the authors suggest factors that should be considered when using these evaluation tools to assist in interpreting scores.
CCL18 as a Biomarker of Interstitial Lung Disease (ILD) and Progressive Fibrosing ILD in Patients with Idiopathic Inflammatory Myopathies
Authors: Zanatta Elisabetta , Martini Andrea , Depascale Roberto , Gamba Anna , Tonello Marta , Gatto Mariele , Giraudo Chiara , Balestro Elisabetta , Doria Andrea , Iaccarino Luca
Published: May 2023

Objectives. To assess CCL18 and OX40L as biomarkers of interstitial lung disease (ILD) and/or progressive fibrosing (PF-) ILD in idiopathic inflammatory myopathies (IIMs). Methods. Patients with IIMs seen in our center from July 2020 to March 2021 were consecutively enrolled. ILD was detected by high-resolution CT. CCL18 and OX40L serum levels were measured by validated ELISA assays in 93 patients and 35 controls. At the 2-year follow-up, PF-ILD was evaluated according to the INBUILD criteria. Results. ILD was diagnosed in 50 (53.7%) patients. CCL18 serum levels were higher in IIMs patients vs. controls (232.9 [IQR 134.7–399.07] vs. 48.4 [29.9–147.5], p < 0.0001), with no difference for OX40L. IIMs-ILD patients exhibited higher levels of CCL18 than those without ILD (306.8 [190.8–520.5] vs. 162 [75.4–255.8], p < 0.0001). High CCL18 serum levels were independently associated with IIMs-ILD diagnosis. At follow-up, 22/50 (44%) patients developed a PF-ILD. Patients who developed PF-ILD had higher CCL18 serum levels than non-progressors (511 [307–958.7] vs. 207.1 [149.3–381.7], p < 0.0001). Multivariate logistic regression analysis revealed CCL18 as the only independent predictor of PF-ILD (OR 1.006 [1.002–1.011], p = 0.005). Conclusions. Although in a relatively small sample, our data suggest that CCL18 is a useful biomarker in IIMs-ILD, particularly in the early identification of patients at risk of developing PF-ILD.
Brain Volume Changes after COVID-19 Compared to Healthy Controls by Artificial Intelligence-Based MRI Volumetry
Authors: Bendella Zeynep , Widmann Catherine Nichols , Layer Julian Philipp , Layer Yonah Lucas , Haase Robert , Sauer Malte , Bieler Luzie , Lehnen Nils Christian , Paech Daniel , Heneka Michael T. , Radbruch Alexander , Schmeel Frederic Carsten
Published: May 2023

Cohort studies that quantify volumetric brain data among individuals with different levels of COVID-19 severity are presently limited. It is still uncertain whether there exists a potential correlation between disease severity and the effects of COVID-19 on brain integrity. Our objective was to assess the potential impact of COVID-19 on measured brain volume in patients with asymptomatic/mild and severe disease after recovery from infection, compared with healthy controls, using artificial intelligence (AI)-based MRI volumetry. A total of 155 participants were prospectively enrolled in this IRB-approved analysis of three cohorts with a mild course of COVID-19 (n = 51, MILD), a severe hospitalised course (n = 48, SEV), and healthy controls (n = 56, CTL) all undergoing a standardised MRI protocol of the brain. Automated AI-based determination of various brain volumes in mL and calculation of normalised percentiles of brain volume was performed with mdbrain software, using a 3D T1-weighted magnetisation-prepared rapid gradient echo (MPRAGE) sequence. The automatically measured brain volumes and percentiles were analysed for differences between groups. The estimated influence of COVID-19 and demographic/clinical variables on brain volume was determined using multivariate analysis. There were statistically significant differences in measured brain volumes and percentiles of various brain regions among groups, even after the exclusion of patients undergoing intensive care, with significant volume reductions in COVID-19 patients, which increased with disease severity (SEV > MILD > CTL) and mainly affected the supratentorial grey matter, frontal and parietal lobes, and right thalamus. Severe COVID-19 infection, in addition to established demographic parameters such as age and sex, was a significant predictor of brain volume loss upon multivariate analysis. In conclusion, neocortical brain degeneration was detected in patients who had recovered from SARS-CoV-2 infection compared to healthy controls, worsening with greater initial COVID-19 severity and mainly affecting the fronto-parietal brain and right thalamus, regardless of ICU treatment. This suggests a direct link between COVID-19 infection and subsequent brain atrophy, which may have major implications for clinical management and future cognitive rehabilitation strategies.
CARdioimaging in Lung Cancer PatiEnts Undergoing Radical RadioTherapy: CARE-RT Trial
Authors: Nardone Valerio , Belfiore Maria Paola , De Chiara Marco , De Marco Giuseppina , Patanè Vittorio , Balestrucci Giovanni , Buono Mauro , Salvarezza Maria , Di Guida Gaetano , D’Angiolella Domenico , Grassi Roberta , D’Onofrio Ida , Cimmino Giovanni , Della Corte Carminia Maria , Gambardella Antonio , Morgillo Floriana , Ciardiello Fortunato , Reginelli Alfonso , Cappabianca Salvatore
Published: May 2023

Background: Non-small-cell lung cancer (NSCLC) is a common, steady growing lung tumour that is often discovered when a surgical approach is forbidden. For locally advanced inoperable NSCLC, the clinical approach consists of a combination of chemotherapy and radiotherapy, eventually followed by adjuvant immunotherapy, a treatment that is useful but may cause several mild and severe adverse effect. Chest radiotherapy, specifically, may affect the heart and coronary artery, impairing heart function and causing pathologic changes in myocardial tissues. The aim of this study is to evaluate the damage coming from these therapies with the aid of cardiac imaging. Methods: This is a single-centre, prospective clinical trial. Patients with NSCLC who are enrolled will undergo computed tomography (CT) and magnetic resonance imaging (MRI) before chemotherapy 3 months, 6 months, and 9–12 months after the treatment. We expect to enrol 30 patients in 2 years. Conclusions: Our clinical trial will be an opportunity not only to highlight the timing and the radiation dose needed for pathological cardiac tissue changes to happen but will also provide useful data to set new follow-up schedules and strategies, keeping in mind that, more often than not, patients affected by NSCLC may present other heart- and lung-related pathological conditions.
Automated Sagittal Skeletal Classification of Children Based on Deep Learning
Authors: Nan Lan , Tang Min , Liang Bohui , Mo Shuixue , Kang Na , Song Shaohua , Zhang Xuejun , Zeng Xiaojuan
Published: May 2023

Malocclusions are a type of cranio-maxillofacial growth and developmental deformity that occur with high incidence in children. Therefore, a simple and rapid diagnosis of malocclusions would be of great benefit to our future generation. However, the application of deep learning algorithms to the automatic detection of malocclusions in children has not been reported. Therefore, the aim of this study was to develop a deep learning-based method for automatic classification of the sagittal skeletal pattern in children and to validate its performance. This would be the first step in establishing a decision support system for early orthodontic treatment. In this study, four different state-of-the-art (SOTA) models were trained and compared by using 1613 lateral cephalograms, and the best performance model, Densenet-121, was selected was further subsequent validation. Lateral cephalograms and profile photographs were used as the input for the Densenet-121 model, respectively. The models were optimized using transfer learning and data augmentation techniques, and label distribution learning was introduced during model training to address the inevitable label ambiguity between adjacent classes. Five-fold cross-validation was conducted for a comprehensive evaluation of our method. The sensitivity, specificity, and accuracy of the CNN model based on lateral cephalometric radiographs were 83.99, 92.44, and 90.33%, respectively. The accuracy of the model with profile photographs was 83.39%. The accuracy of both CNN models was improved to 91.28 and 83.98%, respectively, while the overfitting decreased after addition of label distribution learning. Previous studies have been based on adult lateral cephalograms. Therefore, our study is novel in using deep learning network architecture with lateral cephalograms and profile photographs obtained from children in order to obtain a high-precision automatic classification of the sagittal skeletal pattern in children.
Visual Scoring of Sacroiliac Joint/Sacrum Ratios of Single-Photon Emission Computed Tomography/Computed Tomography Images Affords High Sensitivity and Negative Predictive Value in Axial Spondyloarthritis
Authors: Yoon Eun-Chong , Kim Jong-Sun , Lim Chae Hong , Park Soo Bin , Park Suyeon , Lee Kyung-Ann , Kim Hyun-Sook
Published: May 2023

Spondyloarthritis (SpA) is characterized by inflammatory back pain. Magnetic resonance imaging (MRI) was the earlier gold standard technique for detecting early inflammatory change. We reassessed the diagnostic utility of sacroiliac joint/sacrum (SIS) ratios of single-photon emission computed tomography/computed tomography (SPECT/CT) for identifying sacroiliitis. We aimed to investigate of SPECT/CT in diagnosing SpA using a rheumatologist’s visual scoring of SIS ratios assessment. We conducted a single-center, medical records review study of patients with lower back pain who underwent bone SPECT/CT from August 2016 to April 2020. We employed semiquantitative visual bone scoring methods of SIS ratio. The uptake of each sacroiliac joint was compared to that of the sacrum (0–2). A score of 2 for the sacroiliac joint of either side was considered diagnostic of sacroiliitis. Of the 443 patients assessed, 40 had axial SpA (axSpA), 24 being radiographic axSpA and 16 being nonradiographic axSpA. The sensitivity, specificity, and positive and negative predictive values of SIS ratio of SPECT/CT for axSpA were 87.5%, 56.5%, 16.6%, and 97.8%, respectively. In receiver operating curve analysis, MRI better diagnosed axSpA than did SIS ratio of SPECT/CT. Although the diagnostic utility of SIS ratio of SPECT/CT was inferior to MRI, visual scoring of SPECT/CT affords high sensitivity and negative predictive value in axSpA. When MRI is inappropriate for certain patients, SIS ratio of SPECT/CT is an alternative tool for identifying axSpA in real practice.
ABCDEG Stress Echocardiography in Aortic Stenosis
Authors: Ciampi Quirino , Cortigiani Lauro , Rivadeneira Ruiz Maria , Barbieri Andrea , Manganelli Fiore , Mori Fabio , D’Alfonso Maria Grazia , Bursi Francesca , Villari Bruno
Published: May 2023

Rest and stress echocardiography (SE) plays a pivotal role in the evaluation of valvular heart disease. The use of SE is recommended in valvular heart disease when there is a mismatch between resting transthoracic echocardiography findings and symptoms. In aortic stenosis (AS), rest echocardiographic analysis is a stepwise approach that begins with the evaluation of aortic valve morphology and proceeds to the measurement of the transvalvular aortic gradient and aortic valve area (AVA) using continuity equations or planimetry. The presence of the following three criteria suggests severe AS: AVA < 1.0 cm2, a peak velocity > 4.0 m/s, or a mean gradient > 40 mmHg. However, in approximately one in three cases, we can observe a discordant AVA < 1 cm2 with a peak velocity < 4.0 m/s or a mean gradient <40 mmHg. This is due to reduced transvalvular flow associated with LV systolic dysfunction (LVEF < 50%) defined as “classical” low-flow low-gradient (LFLG) AS or normal LVEF “paradoxical” LFLG AS. SE has an established role in evaluating LV contractile reserve (CR) patients with reduced LVEF. In classical LFLG AS, LV CR distinguished pseudo-severe AS from truly severe AS. Some observational data suggest that long-term prognosis in asymptomatic severe AS may not be as favorable as previously thought, offering a window of opportunity for intervention prior to the onset of symptoms. Therefore, guidelines recommend evaluating asymptomatic AS with exercise stress in physically active patients, particularly those younger than 70 years, and symptomatic classical LFLG severe AS with low-dose dobutamine SE. A comprehensive SE assessment includes evaluating valve function (gradients), the global systolic function of the LV, and pulmonary congestion. This assessment integrates considerations of blood pressure response, chronotropic reserve, and symptoms. StressEcho 2030 is a prospective, large-scale study that employs a comprehensive protocol (ABCDEG) to analyze the clinical and echocardiographic phenotypes of AS, capturing various vulnerability sources which support stress echo-driven treatment strategies.
Decentralized Stochastic Control with Finite-Dimensional Memories: A Memory Limitation Approach
Authors: Tottori Takehiro , Kobayashi Tetsuya J.
Published: May 2023

Decentralized stochastic control (DSC) is a stochastic optimal control problem consisting of multiple controllers. DSC assumes that each controller is unable to accurately observe the target system and the other controllers. This setup results in two difficulties in DSC; one is that each controller has to memorize the infinite-dimensional observation history, which is not practical, because the memory of the actual controllers is limited. The other is that the reduction of infinite-dimensional sequential Bayesian estimation to finite-dimensional Kalman filter is impossible in general DSC, even for linear-quadratic-Gaussian (LQG) problems. In order to address these issues, we propose an alternative theoretical framework to DSC—memory-limited DSC (ML-DSC). ML-DSC explicitly formulates the finite-dimensional memories of the controllers. Each controller is jointly optimized to compress the infinite-dimensional observation history into the prescribed finite-dimensional memory and to determine the control based on it. Therefore, ML-DSC can be a practical formulation for actual memory-limited controllers. We demonstrate how ML-DSC works in the LQG problem. The conventional DSC cannot be solved except in the special LQG problems where the information the controllers have is independent or partially nested. We show that ML-DSC can be solved in more general LQG problems where the interaction among the controllers is not restricted.
Assessing Non-University Teachers’ Self-Perception of Entrepreneurial Competence: A Study of the Teachers of the Sagrada Familia Foundation in Spain
Authors: Arruti Arantza , Paños-Castro Jessica
Published: May 2023

Different studies highlight the importance of developing entrepreneurial competence (EC) from the earliest educational levels, as well as during the teacher training stages, as they are considered to be facilitating and transforming agents in the integral development of students. The purpose of this research is to analyse the self-perceived level of knowledge and implementation of EC among active teachers from non-university centres managed by the Sagrada Familia Foundation, Spain. After designing a questionnaire based, among others sources, on EntreComp and EntreCompEdu frameworks, an expert judgement was carried out. A total of 326 participants answered the questionnaire. The result show that more than 70% of respondents had not heard about EntreComp and EntreCompEdu; more than 50% had no knowledge of entrepreneurial education (EE); and the respondents as a whole stand out in terms of perseverance, motivation, autonomy, decision-making and teamwork; only 14–18% of respondents plan subjects that pay attention to ECs. Moreover, almost 50% of respondents state that they use participatory learning methodologies, while only 13% believe that when evaluating pupils’ performance, it is necessary to support sharing achievements and progress with internal and external agents; only a further 15% involve students in the assessment process. We must advocate and work for the development of ‘teacherpreneurs’, and start developing EC in all stages of teacher training.
Plants Are Not Boring, School Botany Is
Authors: Kletečki Nataša , Hruševar Dario , Mitić Božena , Šorgo Andrej
Published: May 2023

The quality of botanical education depends on the quality and interests of the teacher. The aim of our paper was to conduct an initial study on the attitudes of primary and secondary school teachers about plants and botany. We initiated a study in which 211 biology and 168 teachers of different subjects, from primary to secondary schools in Croatia, participated on a voluntary basis. Statistical analysis was processed using jamovi 2.3 software. Teachers of all profiles (biology and others) had similar attitudes: high opinion about the importance of plants for life and education but not about school botany; botany was in the middle of their scale for potential exclusion from education. The last part of the survey referred only to biology teachers and their interests and suggestions for increasing the attractiveness of botany as a subject. The results showed that botany was the least popular, and within it, teachers were most interested in ecology and horticulture. They suggested that botany would be more interesting with active teaching methods and references to everyday contexts; however, it remains unclear why that is so, as the level of autonomy they are given does enable them to introduce such changes.
Melody Mystery: Learning Music Theory through Escape Room Puzzles
Authors: Lim Kenneth Y. T. , Truong Kim Mai , Wu Yuxuan
Published: May 2023

This paper explores the use of escape room puzzles and instructional scaffolding in teaching novice learners about basic music theory. This study used the notion of epistemic frames in games as the basis for its experiment, combined with the use of technology-based scaffolding. We examined how effective it is to engage and motivate novice learners when they are made to mimic the role of an expert in music and solve problems using their knowledge and skills in the subject. We found positive responses from participants’ attitudes toward music theory and the ability to retain knowledge of the subject, as well as evidence which proves that games can have significant effects on motivation in the learning process.
Scale Measurement of Health Primary Service Utilization among the Migrant International Population
Authors: Cruz-Riveros Consuelo , Urzúa Alfonso , Lagos Carolina
Published: May 2023

In this article, we analyze the internal structure of the scale for experience in exercising the right to health care (EERHC), based on the focus from the World Health Organization (WHO) on the right to health care, from the perspective of international migrants, in Chile. The methodology was an instrumental study (n = 563) conducted to analyze the psychometric properties of the EERHC scale. Its reliability and internal consistency were evaluated, while the exploratory structural equation modeling (ESEM) model and confirmatory factor analysis (CFA) were used to identify the structure of relationships between the variables measured. The item–dimension correlations obtained present levels of r ≥ 0.3, and the Cronbach’s α and McDonald’s ω presented ranges >0.9, considered to be acceptable on all models. Results: the model was selected for presenting a good fit index χ2 = 24,850, df = 300, p = 0.000; RMSEA = 0.07; CFI = 0.97; TLI = 0.95; and SRMR = 0.03. The evidence obtained lets us conclude that the scale has forty-five items and four dimensions. The findings demonstrate a good internal structure and are useful to measure primary health care service utilization based on the framework.
Graph Convolution Point Cloud Super-Resolution Network Based on Mixed Attention Mechanism
Authors: Chen Taoyi , Qiu Zifeng , Zhang Chunjie , Bai Huihui
Published: May 2023

In recent years, point cloud super-resolution technology has emerged as a solution to generate a denser set of points from sparse and low-quality point clouds. Traditional point cloud super-resolution methods are often optimized based on constraints such as local surface smoothing; thus, these methods are difficult to be used for complex structures. To address this problem, we proposed a novel graph convolutional point cloud super-resolution network based on a mixed attention mechanism (GCN-MA). This network consisted of two main parts, i.e., feature extraction and point upsampling. For feature extraction, we designed an improved dense connection module that integrated an attention mechanism and graph convolution, enabling the network to make good use of both global and local features of the point cloud for the super-resolution task. For point upsampling, we adopted channel attention to suppress low-frequency information that had little impact on the up-sampling results. The experimental results demonstrated that the proposed method significantly improved the point cloud super-resolution performance of the network compared to other corresponding methods.
Numerical Analysis of the Influence of Fabrication Process Uncertainty on Terahertz Metasurface Quality
Authors: Lopato Przemyslaw , Herbko Michal , Gora Paulina , Mescheder Ulrich , Kovacs Andras , Filbert Alexander
Published: May 2023

The purpose of this paper was to investigate the influence of fabrication process uncertainty on terahertz metasurface quality. The focus was on the effect of metasurface fabrication inaccuracy on resonances. To the best of our knowledge, this is the first paper to study the effect of the metasurface fabrication process on its resonant frequency. The terahertz split ring resonator-based metasurface is under consideration. Using a numerical model, the influence of the uncertainty of various geometrical parameters obtained during the fabrication process (mainly layer deposition, photolithography, and etching processes) is analyzed according to the resonance of the designed metasurface. The influence of the following parameters causes a shift of resonant frequencies of the considered metasurface: etching deviation e, metallization thickness tAl and SiO2 layer thickness tSiO2. The quality of the metasurface affected by the variations of obtained geometrical parameters was determined by the deviation of resonant frequency Δfr. The developed numerical model was verified by THz-TDS (terahertz time-domain spectroscopy) measurements of the fabricated structure.
From 5G to beyond 5G: A Comprehensive Survey of Wireless Network Evolution, Challenges, and Promising Technologies
Authors: Sufyan Ali , Khan Khan Bahadar , Khashan Osama A. , Mir Talha , Mir Usama
Published: May 2023

The histrionic growth of mobile subscribers, disruptive ecosystems such as IoT-based applications, and astounding channel capacity requirements to connect trillions of devices are massive challenges of the earlier mobile generations, 5G turned up the key solution. The prime objective of the 5G network is not only to maintain a 1000-fold capacity gain and 10 Giga Bits per second delivered to a single user, but it also assured quality-of-service, higher spectral efficiency, the ultra-reliable and improved battery lifetime of devices and massive machine-type communication (mMTC). The huge traffic load and high amount of resource consumption in 5G applications, augmented reality and virtual reality for magnificent virtual experience, and wireless body area networks will seriously affect the channel capacity of cellular cells and interrupt the admission and service of other users which makes compulsory new means of channel capacity and spectral efficiency enhancement techniques. In this research, we review several key emerging wireless technologies to increase channel capacity and spectral efficiency that will not only lead to improve network performance but also meets the ever-increasing user demands. We investigate various benefits and current research challenges of using these technologies. We analyze massive multi-input multi-output technology (mMIMO) an efficient technique and promising solution for the 5G and Beyond 5G (B5G) networks with several benefits and features. Moreover, this paper will be of vast help to the researchers who will involve advance investigation and also to the wireless network operator industry that is in the search for smooth development of state-of-the-art 5G and B5G networks.
Yolo-Papaya: A Papaya Fruit Disease Detector and Classifier Using CNNs and Convolutional Block Attention Modules
Authors: de Moraes Jairo Lucas , de Oliveira Neto Jorcy , Badue Claudine , Oliveira-Santos Thiago , de Souza Alberto F.
Published: May 2023

Agricultural losses due to post-harvest diseases can reach up to 30% of total production. Detecting diseases in fruits at an early stage is crucial to mitigate losses and ensure the quality and health of fruits. However, this task is challenging due to the different formats, sizes, shapes, and colors that the same disease can present. Convolutional neural networks have been proposed to address this issue, but most studies use self-built datasets with few samples per disease, hindering reproducibility and comparison of techniques. To address these challenges, the authors proposed a novel image dataset comprising 23,158 examples divided into nine classes of papaya fruit diseases, and a robust papaya fruit disease detector called Yolo-Papaya based on the YoloV7 detector with the implementation of a convolutional block attention module (CBAM) attention mechanism. This detector achieved an overall mAP (mean average precision) of 86.2%, with a performance of over 98% in classes such as “healthy fruits” and “Phytophthora blight”. The proposed detector and dataset can be used in practical applications for fruit quality control and are consolidated as a robust benchmark for the task of papaya fruit disease detection. The image dataset and all source code used in this study are available to the academic community on the project page, enabling reproducibility of the study and advancement of research in this domain.
Clothoid-Based Path Planning for a Formation of Fixed-Wing UAVs
Authors: Blasi Luciano , D’Amato Egidio , Notaro Immacolata , Raspaolo Gennaro
Published: May 2023

Unmanned aerial vehicles (UAVs) are playing an increasingly crucial role in many applications such as search and rescue, delivery services, and military operations. However, one of the significant challenges in this area is to plan efficient and safe trajectories for UAV formations. This paper presents an optimization procedure for trajectory planning for fixed-wing UAV formations using graph theory and clothoid curves. The proposed planning strategy consists of two main steps. Firstly, the geometric optimization of paths is carried out using graphs for each UAV, providing piece-wise linear paths whose smooth connections are made with clothoids. Secondly, the geometric paths are transformed into time-dependent trajectories, optimizing the assigned aircraft speeds to avoid collisions by solving a mixed-integer optimal control problem for each UAV of the flight formation. The proposed method is effective in achieving suboptimal paths while ensuring collision avoidance between aircraft. A sensitivity analysis of the main parameters of the algorithm was conducted in ideal conditions, highlighting the possibility of decreasing the length of the optimal path by about 4.19%, increasing the number of points used in the discretization and showing a maximum path length reduction of about 10% compared with the average solution obtained with a similar algorithm using a graph based on random directions. Furthermore, the use of clothoids, whose parameters depend on the UAV performance constraints, provides smoother connections, giving a significant improvement over traditional straight-line or circular trajectories in terms of flight dynamics compliance and trajectory tracking capabilities. The method can be applied to various UAV formation scenarios, making it a versatile and practical tool for mission planning.
GIS-Data-Driven Efficient and Safe Path Planning for Autonomous Ships in Maritime Transportation
Authors: Hu Xiao , Hu Kai , Tao Datian , Zhong Yi , Han Yi
Published: May 2023

Maritime transportation is vital to the global economy. With the increased operating and labor costs of maritime transportation, autonomous shipping has attracted much attention in both industry and academia. Autonomous shipping can not only reduce the marine accidents caused by human factors but also save labor costs. Path planning is one of the key technologies to enable the autonomy of ships. However, mainstream ship path planning focuses on searching for the shortest path and controlling the vehicle in order to track it. Such path planning methods may lead to a dynamically infeasible trajectory that fails to avoid obstacles or reduces fuel efficiency. This paper presents a data-driven, efficient, and safe path planning (ESP) method that considers ship dynamics to provide a real-time optimal trajectory generation. The optimization objectives include fuel consumption and trajectory smoothness. Furthermore, ESP is capable of fast replanning when encountering obstacles. ESP consists of three components: (1) A path search method that finds an optimal search path with the minimum number of sharp turns from the geographic data collected by the geographic information system (GIS); (2) a minimum-snap trajectory optimization formulation with dynamic ship constraints to provide a smooth and collision-free trajectory with minimal fuel consumption; (3) a local trajectory replanner based on B-spline to avoid unexpected obstacles in real time. We evaluate the performance of ESP by data-driven simulations. The geographical data have been collected and updated from GIS. The results show that ESP can plan a global trajectory with safety, minimal turning points, and minimal fuel consumption based on the maritime information provided by nautical charts. With the long-range perception of onboard radars, the ship can avoid unexpected obstacles in real time on the planned global course.
Resource Allocation and Trajectory Optimization in OTFS-Based UAV-Assisted Mobile Edge Computing
Authors: Li Wei , Guo Yan , Li Ning , Yuan Hao , Liu Cuntao
Published: May 2023

Mobile edge computing (MEC) powered by unmanned aerial vehicles (UAVs), with the advantages of flexible deployment and wide coverage, is a promising technology to solve computationally intensive communication problems. In this paper, an orthogonal time frequency space (OTFS)-based UAV-assisted MEC system is studied, in which OTFS technology is used to mitigate the Doppler effect in UAV high-speed mobile communication. The weighted total energy consumption of the system is minimized by jointly optimizing the time division, CPU frequency allocation, transmit power allocation and flight trajectory while considering Doppler compensation. Thus, the resultant problem is a challenging nonconvex problem. We propose a joint algorithm that combines the benefits of the atomic orbital search (AOS) algorithm and convex optimization. Firstly, an improved AOS algorithm is proposed to swiftly obtain the time slot allocation and high-quality solution of the UAV optimal path. Secondly, the optimal solution for the CPU frequency and transmit power allocation is found by using Lagrangian duality and the first-order Taylor formula. Finally, the optimal solution of the original problem is iteratively obtained. The simulation results show that the weighted total energy consumption of the OTFS-based system decreases by 13.6% compared with the orthogonal frequency division multiplexing (OFDM)-based system. The weighted total energy consumption of the proposed algorithm decreases by 11.7% and 26.7% compared with convex optimization and heuristic algorithms, respectively.