bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of Hardness and Microanalysis of Sintered Aluminum-Based Supplemented Metal Matrix Machined Composites
Authors: Bhutta Muhammad Raheel , Gillani Fouzia , Zahid Taiba , Bibi Saira , Ghafoor Usman
Published: September 2023

Aluminum metal matrix composites (AMMCs) have become increasingly ubiquitous in the fields of aerospace and automobile businesses due to their lightweight properties. Their machining is a challenging task because of the presence of supplemented particles, also called reinforcements. As the wt% of the supplemented particles changes, the morphological and machining behaviors of the AMMCs change. The present work is focused on exploring the thermo-mechanical properties of AMMCs which would help in AMMC applications in the aerospace industry with a new collection of composites containing silicon carbide (SiC) and zircon/zirconium silicate (ZrSiO4) as supplements in wt% of 5%, 20%, 30%, and 40%. Uniform binary and hybrid sample pallets are prepared by powder metallurgy (PM). The said samples are sintered and then machined using wire electric discharge machining (WEDM) employing brass wire with a feed rate of 2 to 3 mm/min. Also, analysis of porosity and recast layer formation is performed via the microstructure, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS). Some interesting and useful findings are obtained which can extend the applications of AMMCs in automobiles and the aerospace industry. The results reveal that temperature and wt% are playing their significant roles in the changes in the thermo-mechanical properties of AMMCs. Mathematical equations via regression analysis using Minitab 17 and Excel are developed for the congruence of experimental data. Analysis of Variance (ANOVA) is also performed. Hence, the most optimized relationships for the best machining output are established and presented in this proposed study.
Impact of Cancer Type and Treatment Protocol on Cardiac Function in Pediatric Oncology Patients: An Analysis Utilizing Speckle Tracking, Global Longitudinal Strain, and Myocardial Performance Index
Authors: Ardelean Andrada Mara , Olariu Ioana Cristina , Isac Raluca , Nalla Akhila , Jurac Ruxandra , Stolojanu Cristiana , Murariu Mircea , Fericean Roxana Manuela , Braescu Laurentiu , Mavrea Adelina , Dumitru Catalin , Doros Gabriela
Published: September 2023

Pediatric hemato-oncology patients undergoing anthracycline therapy are at risk of cardiotoxicity, with disease type and treatment intensity potentially affecting cardiac function. Novel echocardiographic measures like speckle tracking echocardiography (STE), global longitudinal strain (GLS), and the myocardial performance index (MPI) may predict early changes in cardiac function not detected by traditional methods. This study aimed to assess the impact of cancer type and treatment protocol on these parameters and their potential in predicting long-term cardiac complications. We conducted a single-center, retrospective cohort study of 99 pediatric oncology patients and 46 controls that were assessed at 3, 6, and 12 months. The median age was 10.7 ± 4.4 years for cases and 10.2 ± 3.6 years for controls. STE, GLS, and MPI were measured, and statistical analyses were performed to determine any significant correlations with cardiotoxicity. Significant variations were observed in traditional cardiac function measurements between the patient and control groups, with a lower average ejection fraction (EF) of 62.8 ± 5.7% in patients vs. 66.4 ± 6.1% in controls (p < 0.001), poorer GLS of −16.3 ± 5.1 in patients compared to −19.0 ± 5.4 in controls (p = 0.004), and higher MPI values of 0.37 ± 0.06 in patients compared to 0.55 ± 0.10 in controls, indicating worse overall cardiac function (p < 0.001). However, differences in cardiac function measurements by cancer histology or treatment protocol were not statistically significant. Regression analyses showed that the combination of GLS, SMOD, and MPI increased the odds of cardiac toxicity with an odds ratio of 7.30 (95% CI: 2.65–12.81, p < 0.001). The study underscores the predictive value of the combined GLS, SMOD, and MPI measurements in pediatric oncology patients undergoing anthracycline treatment for cardiotoxicity. Although variations across cancer types and treatment protocols were not significant, the study emphasizes the potential utility of these novel echocardiographic measures in early detection and long-term prediction of anthracycline-induced cardiotoxicity. Further studies in larger, multi-center cohorts are required for validation.
Reliability of Cone Beam Computed Tomography in Predicting Implant Treatment Outcomes in Edentulous Patients
Authors: Alhossan Abdulaziz , Chang Yu-Cheng , Wang Tun-Jan , Wang Yu-Bo , Fiorellini Joseph P.
Published: September 2023

Since the development of CBCT has been utilized in dentistry, the images of the CBCT can assist the surgeon to evaluate the anatomy carefully. Despite the value of radiology evaluation, implant procedures may require additional consideration rather than only evaluating the anatomical factors. The purpose of this study is to evaluate the predictability of using CBCT alone to plan for implant placement in edentulous patients digitally. CBCT images were analyzed by clinicians, measuring the maxillary and mandibular ridge heights and widths digitally of four predetermined implant sites in the maxillary and two selected implant sites in the mandibular arches of 91 patients planning for implant-supported overdenture. A total of 47 patients out of the 91 had completed implant placement on the edentulous ridge, contributing to 55 upper and/or lower arches (136 dental implants). Both predictabilities are low, implying that CBCT planning for implant placement on the edentulous ridge is not a good index and is insufficient to predict the surgical procedures as a solo method. The findings of this study indicate that digital planning by CBCT is insufficient to serve as an individual tool to predict implant procedures. Further information and evaluation must be considered for implant placement in the edentulous ridge.
Genotypic Frequencies of Mutations Associated with Alpha-1 Antitrypsin Deficiency in Unrelated Bone Marrow Donors from the Murcia Region Donor Registry in the Southeast of Spain
Authors: Cuenca Irene , Botella Carmen , Moya-Quiles María Rosa , Jimenez-Coll Víctor , Galian José Antonio , Martinez-Banaclocha Helios , Muro-Pérez Manuel , Minguela Alfredo , Legaz Isabel , Muro Manuel
Published: September 2023

Alpha-1 antitrypsin (AAT1) deficiency (AAT1D) is an inherited disease with an increased risk of chronic obstructive pulmonary disease (COPD), liver disease, and skin and blood vessel problems. AAT1D is caused by mutations in the SERPINE1 gene (Serine Protease Inhibitor, group A, member 1). Numerous variants of this gene, the Pi system, have been identified. The most frequent allelic variants are Pi*M, Pi*S, and Pi*Z. The development of COPD requires both a genetic predisposition and the contribution of an environmental factor, smoking being the most important. Studies on this deficiency worldwide are very scarce, and it is currently considered a rare disease because it is underdiagnosed. The aim of this study was to analyze the genotypic frequencies of mutations associated with AAT1 deficiency in unrelated bone marrow donors from the donor registry of the Region of Murcia in southeastern Spain due to the high risk of presenting with different pathologies and underdiagnosis in the population. A total of 112 DNA-healthy voluntary unrelated bone marrow donors from different parts of the Region of Murcia were analyzed retrospectively. AAT1 deficiency patient testing involved an automated biochemical screening routine. The three main variants, Pi*M, Pi*Z, and Pi*S, were analyzed in the SERPINE1 gene. Our results showed a frequency of 3.12% of the Pi*Z (K342) mutation in over 224 alleles tested in the healthy population. The frequency of Pi*S (V264) was 11.1%. The frequency of the haplotype with the most dangerous mutation, EK342 EE264, was 4.46%, and the frequency of EK342 EV264 was 1.78% in the healthy population. Frequencies of other EE342 EV264-mutated haplotypes accounted for 18.7%. As for the EE342 VV264 haplotype, 0.89% of the total healthy population presented heterozygous for the EV264 mutation and one individual presented homozygous for the VV264 mutation. In conclusion, the frequencies of Pi mutations in the healthy population of the Region of Murcia were not remarkably different from the few studies reported in Spain. The genotype and haplotype frequencies followed the usual pattern. Health authorities should be aware of this high prevalence of the Pi*S allelic variant and pathological genotypes such as Pi*MZ and Pi*SZ in the healthy population if they consider screening the smoking population.
Retro-Mode in NIDEK Mirante: A Comparative Analysis with Other Imaging Modalities for AMD and CSR
Authors: Wylęgała Adam , Wozniak Przemysław , Sędziak-Marcinek Bogumiła , Bolek Bartłomiej , Szkodny Dominika , Wylęgała Edward
Published: September 2023

Background: Retro-mode is a novel technique capable of creating pseudo-3D images of the retina. However, its clinical utility remains unknown. This study aimed to evaluate the Nidek Mirante multimodal imaging platform for ocular assessment in patients with various retinal conditions. Methods: A total of 115 participants with central serous chorioretinopathy (CSR) and age-related macular degeneration (AMD) were included. Two experienced graders independently evaluated the images, and statistical analysis was performed to assess interclass correlation coefficients (ICC) between graders and modalities; Results: For CSR detection, retro-mode demonstrated exceptionally high ICC rates (ICC = 1; 100%), while color and autofluorescence (FAF) showed moderate coefficients (0.69 and 0.78, respectively). The detection of pigment epithelial detachment was high across all methods, with only retro-mode deviated right (DR) allowing detection in 69% of cases, while retro-mode DR and deviated left (DL) achieved 100% detection. FAF-green achieved a 95% detection rate. In detecting retinal atrophy, most modalities demonstrated high detection rates, with the lowest detection rates offered by retro-mode DL (ICC = 0.85) and DR (ICC = 0.89), while retro-mode ring aperture offered 0.97. Infra-red and fluorescein angiography imaging offered the highest detection rates among the tested modalities, with 97% and 100%, respectively. Conclusion: Retro-mode showed promise for comprehensive ocular evaluation and diagnosis, with certain imaging modalities demonstrating higher accuracy in detecting specific retinal features.
Predicting Future Incidences of Cardiac Arrhythmias Using Discrete Heartbeats from Normal Sinus Rhythm ECG Signals via Deep Learning Methods
Authors: Kim Yehyun , Lee Myeonggyu , Yoon Jaeung , Kim Yeji , Min Hyunseok , Cho Hyungjoo , Park Junbeom , Shin Taeyoung
Published: September 2023

This study aims to compare the effectiveness of using discrete heartbeats versus an entire 12-lead electrocardiogram (ECG) as the input for predicting future occurrences of arrhythmia and atrial fibrillation using deep learning models. Experiments were conducted using two types of inputs: a combination of discrete heartbeats extracted from 12-lead ECG and an entire 12-lead ECG signal of 10 s. This study utilized 326,904 ECG signals from 134,447 patients and categorized them into three groups: true–normal sinus rhythm (T-NSR), atrial fibrillation–normal sinus rhythm (AF-NSR), and clinically important arrhythmia–normal sinus rhythm (CIA-NSR). The T-NSR group comprised patients with at least three normal rhythms in a year and no atrial fibrillation or arrhythmias history. Clinically important arrhythmia included atrial fibrillation, atrial flutter, atrial premature contraction, atrial tachycardia, ventricular premature contraction, ventricular tachycardia, right and left bundle branch block, and atrioventricular block over the second degree. The AF-NSR group included normal sinus rhythm paired with atrial fibrillation or atrial flutter within 14 days, and the CIA-NSR group comprised normal sinus rhythm paired with CIA occurring within 14 days. Three deep learning models, ResNet-18, LSTM, and Transformer-based models, were utilized to distinguish T-NSR from AF-NSR and T-NSR from CIA-NSR. The experiments demonstrated the potential of using discrete heartbeats in predicting future arrhythmia and atrial fibrillation incidences extracted from 12-lead electrocardiogram (ECG) signals alone, without any additional patient information. The analysis reveals that these discrete heartbeats contain subtle patterns that deep learning models can identify. Focusing on discrete heartbeats may lead to more timely and accurate diagnoses of these conditions, improving patient outcomes and enabling automated diagnosis using ECG signals as a biomarker.
Preoperative Assessment of Perianal Fistulas with Combined Magnetic Resonance and Tridimensional Endoanal Ultrasound: A Prospective Study
Authors: Varsamis Nikolaos , Kosmidis Christoforos , Chatzimavroudis Grigorios , Apostolidou Kiouti Fani , Efthymiadis Christoforos , Lalas Vasilis , Mystakidou Chrysi Maria , Sevva Christina , Papadopoulos Konstantinos , Anthimidis George , Koulouris Charilaos , Karakousis Alexandros Vasileios , Sapalidis Konstantinos , Kesisoglou Isaak
Published: September 2023

Background: we designed a prospective study of diagnostic accuracy that compared pelvic MRI and 3D-EAUS with pelvic MRI alone in the preoperative evaluation and postoperative outcomes of patients with perianal fistulas. Methods: the sample size was 72 patients and this was divided into two imaging groups. MRI alone was performed on the first group. Both MRI and 3D-EAUS were performed in parallel on the second group. Surgical exploration took place after two weeks and was the standard reference. Park’s classification, the presence of a concomitant abscess or a secondary tract, and the location of the internal opening were recorded. All patients were re-evaluated for complete fistula healing and fecal incontinence six months postoperatively. All of the collected data were subjected to statistical analysis. Results: the MRI group included 36 patients with 42 fistulas. The MRI + 3D-EAUS group included 36 patients with 46 fistulas. The adjusted sensitivity and negative predictive value were 1.00 for most fistula types in the group that underwent combined imaging. The adjusted specificity improved for intersphincteric fistulas in the same group. The adjusted balanced accuracy improved for all fistula types except rectovaginal. The combination of imaging methods showed improved diagnostic accuracy only in the detection of a secondary tract. The healing rate at six months was 100%. Fecal incontinence at six months did not present a statistically significant difference between the two groups (Fisher’s exact test p-value > 0.9). Patients with complex perianal fistulas had a statistically significant higher probability of undergoing a second surgery (x2 test p-value = 0.019). Conclusions: the combination of pelvic MRI and 3D-EAUS showed improved metrics of diagnostic accuracy and should be used in the preoperative evaluation of all patients with perianal fistulas, especially those with complex types.
Brain–Computer Interface: The HOL–SSA Decomposition and Two-Phase Classification on the HGD EEG Data
Authors: Antony Mary Judith , Sankaralingam Baghavathi Priya , Khan Shakir , Almjally Abrar , Almujally Nouf Abdullah , Mahendran Rakesh Kumar
Published: September 2023

An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain–Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL–SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method’s ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.
AI Evaluation of Imaging Factors in the Evolution of Stage-Treated Metastases Using Gamma Knife
Authors: Buzea Calin G. , Buga Razvan , Paun Maria-Alexandra , Albu Madalina , Iancu Dragos T. , Dobrovat Bogdan , Agop Maricel , Paun Viorel-Puiu , Eva Lucian
Published: September 2023

Background: The study investigated whether three deep-learning models, namely, the CNN_model (trained from scratch), the TL_model (transfer learning), and the FT_model (fine-tuning), could predict the early response of brain metastases (BM) to radiosurgery using a minimal pre-processing of the MRI images. The dataset consisted of 19 BM patients who underwent stereotactic-radiosurgery (SRS) within 3 months. The images used included axial fluid-attenuated inversion recovery (FLAIR) sequences and high-resolution contrast-enhanced T1-weighted (CE T1w) sequences from the tumor center. The patients were classified as responders (complete or partial response) or non-responders (stable or progressive disease). Methods: A total of 2320 images from the regression class and 874 from the progression class were randomly assigned to training, testing, and validation groups. The DL models were trained using the training-group images and labels, and the validation dataset was used to select the best model for classifying the evaluation images as showing regression or progression. Results: Among the 19 patients, 15 were classified as “responders” and 4 as “non-responders”. The CNN_model achieved good performance for both classes, showing high precision, recall, and F1-scores. The overall accuracy was 0.98, with an AUC of 0.989. The TL_model performed well in identifying the “progression” class, but could benefit from improved precision, while the “regression” class exhibited high precision, but lower recall. The overall accuracy of the TL_model was 0.92, and the AUC was 0.936. The FT_model showed high recall for “progression”, but low precision, and for the “regression” class, it exhibited a high precision, but lower recall. The overall accuracy for the FT_model was 0.83, with an AUC of 0.885. Conclusions: Among the three models analyzed, the CNN_model, trained from scratch, provided the most accurate predictions of SRS responses for unlearned BM images. This suggests that CNN models could potentially predict SRS prognoses from small datasets. However, further analysis is needed, especially in cases where class imbalances exist.
Impact of Phacoemulsification Parameters on Central Retinal Thickness Change Following Cataract Surgery
Authors: Mackenbrock Lars H. B. , Baur Isabella D. , Łabuz Grzegorz , Auffarth Gerd U. , Khoramnia Ramin
Published: September 2023

Cataract surgery can lead to inflammatory processes in the retina due to its invasive nature, resulting in prolonged recovery times and reduced functional outcomes. The aim of the current study is to explore the impact that phacoemulsification parameters have on macular thickness following surgery. This prospective single-center study enrolled 46 healthy patients (46 eyes) who underwent uneventful cataract surgery. Retinal thickness was assessed using optical coherence tomography (OCT) preoperatively, as well as 1, 4, and 12 weeks after surgery. The macula was divided into a central (CMT), inner (IMT), and outer ring (OMT). Cataract density was automatically determined using an anterior segment OCT and a custom MATLAB script. Corrected distance visual acuity (CDVA), intraocular pressure (IOP) as well as cumulative dissipated energy (CDE), ultrasound time (UT), and fluids used during phacoemulsification were recorded. Retinal thickness and volume increased significantly following cataract surgery, reaching its maximum 4 weeks post-operatively. Statistically significant correlations were found between the CDE and IMT, OMT and retinal volume change (rIMT = 0.356, rOMT = 0.298, rvolume = 0.357 with p < 0.05) as well as between the ultrasound time and IMT, OMT, and retinal volume change (rIMT = 0.369, rOMT = 0.293 and rvolume = 0.409 with p < 0.05). Changes in CMT did not correlate with any surgical metrics. Additionally, no correlation was found to the amount of fluid used, whether CDVA or IOP. However, a link between nuclear cataract density and changes in OMT (r = 0.310, p < 0.05) was established. How ultrasound energy impacts the choroidea, and to what extent retinal metabolism changes after surgery, needs to be explored in future studies.
A Deep Learning Model Based on Capsule Networks for COVID Diagnostics through X-ray Images
Authors: Rangel Gabriela , Cuevas-Tello Juan C. , Rivera Mariano , Renteria Octavio
Published: September 2023

X-ray diagnostics are widely used to detect various diseases, such as bone fracture, pneumonia, or intracranial hemorrhage. This method is simple and accessible in most hospitals, but requires an expert who is sometimes unavailable. Today, some diagnoses are made with the help of deep learning algorithms based on Convolutional Neural Networks (CNN), but these algorithms show limitations. Recently, Capsule Networks (CapsNet) have been proposed to overcome these problems. In our work, CapsNet is used to detect whether a chest X-ray image has disease (COVID or pneumonia) or is healthy. An improved model called DRCaps is proposed, which combines the advantage of CapsNet and the dilation rate (dr) parameter to manage images with 226 × 226 resolution. We performed experiments with 16,669 chest images, in which our model achieved an accuracy of 90%. Furthermore, the model size is 11M with a reconstruction stage, which helps to avoid overfitting. Experiments show how the reconstruction stage works and how we can avoid the max-pooling operation for networks with a stride and dilation rate to downsampling the convolution layers. In this paper, DRCaps is superior to other comparable models in terms of accuracy, parameters, and image size handling. The main idea is to keep the model as simple as possible without using data augmentation or a complex preprocessing stage.
Evaluating the Efficiency of the Cobas 6800 System for BK Virus Detection in Plasma and Urine Samples
Authors: Song Junhyup , Kim Sinyoung , Kwak Eunmin , Park Younhee
Published: September 2023

We evaluated the overall performance of the Cobas 6800 BKV test in detecting BK virus (BKV). We examined the imprecision of the Cobas 6800 BKV test and compared the qualitative and quantitative results obtained from the Cobas 6800 BKV test and the Real-Q BKV quantification assay. We assessed 88 plasma and 26 urine samples collected between September and November 2022 from patients with BKV infection using the Real-Q BKV quantitative assay. The lognormal coefficient of variation indicated that the inter-assay precision of the Cobas 6800 BKV test ranged from 13.86 to 33.83%. A strong correlation was observed between the quantitative results obtained using the Cobas 6800 BKV test and the Real-Q BKV quantification assay for plasma samples. The Spearman’s rank correlation coefficients (ρ) for plasma, polymerase chain reaction (PCR) media-stabilized urine, and raw urine samples were 0.939, 0.874, and 0.888, respectively. Our analyses suggest that the Cobas 6800 BKV test is suitable for clinical applications owing to the strong correlation between the results obtained using this test and the Real-Q BKV quantification assay in plasma and urine samples. Furthermore, utilizing fresh raw urine samples can be a viable approach for the Cobas 6800 BKV test as it is less labor- and time-intensive.
Selective IgM Deficiency: Evidence, Controversies, and Gaps
Authors: Taietti Ivan , Votto Martina , De Filippo Maria , Naso Matteo , Montagna Lorenza , Montagna Daniela , Licari Amelia , Marseglia Gian Luigi , Castagnoli Riccardo
Published: September 2023

Selective Immunoglobulin M deficiency (SIgMD) has been recently included in the inborn errors of immunity (IEI) classification by the International Union of Immunological Societies Expert Committee. The understanding of SIgMD is still extremely limited, especially so in cases of SIgMD in the pediatric population. The epidemiology of SIgMD in the pediatric population is still unknown. The pathogenesis of SIgMD remains elusive, and thus far no genetic nor molecular basis has been clearly established as a definitive cause of this primary immunodeficiency. Recurrent respiratory infections represent the main clinical manifestations in children, followed by allergic and autoimmune diseases. No conclusive data on the correct therapeutic management of SIgMD are available. Although, for most SIgMD patients, Ig replacement therapy is not required, it may be recommended for patients with significantly associated antibody deficiency and recurrent or severe infections. Prophylactic antibiotics and the prompt treatment of febrile illness are crucial. There is insufficient evidence on the prognosis of this condition. Therefore, further studies are required to define the disease trajectories and to increase our understanding of the molecular mechanisms underlying SIgMD in order to facilitate a better clinical, immunological, and prognostic characterization of the condition and develop tailored therapeutic management strategies.
Clinical and Psychosocial Impact of Communication about Oral Potentially Malignant Disorders: A Scoping Review
Authors: Arboleda Lady P. A. , Pereira Thaís C. E. , Epstein Joel B. , Migliorati Cesar A. , Warnakulasuriya Saman , Diniz-Freitas Márcio , Lopes Marcio A. , Santos-Silva Alan R.
Published: September 2023

Delivering bad news has been widely studied in cancer, thus, this scoping review aims to identify the available evidence concerning the communication of oral potentially malignant disorders (OPMDs) and their clinical and psychosocial impacts. A search was performed using electronic databases (Medline/PubMed, Scopus, Embase, and Web of Science) and one grey literature database (Google Scholar). Studies focused on communicating the diagnosis of OPMDs and the patients’ perceptions were included. Study selection and data extraction were performed by two authors in a two-phase process. Five publications were included in the qualitative analysis. Differences regarding the study design, population, OPMDs assessed, and outcomes of professional–patient communication were found in each study. Protocols for OPMD communication have not yet been reported and there is a need to standardize strategies as communication skills may provide better clinical outcomes for patients diagnosed with potentially malignant disorders. Although future studies are needed, a brief list recommending the aspects that must be communicated is proposed.
Effect of Dentin Conditioning with EDTA and Diode Lasers on Expression of Odontoblast-like Cell Markers of Dental Pulp Stem Cells
Authors: Martín Gabriela , Preve Valentín , Hargreaves Kenneth , Diogenes Anibal , Inostroza Carolina , Saint-Jean Nicole , Brizuela Claudia
Published: September 2023

Regenerative endodontic procedures rely on the delivery of mesenchymal stem cells into the root canal and on the effect of local growth factors from the dentin and blood clot. The aim of this study was to assess the effect of dentin conditioning with ethylenediamine tetraacetic acid (EDTA) and diode lasers with different wavelengths (808 nm and 980 nm) on the expression of odontoblast-like cell markers. Forty dentin cylinders were divided into four groups according to the irrigation protocol: EDTA, EDTA + 808 nm diode laser, EDTA + 980 nm diode laser, and phosphate-buffered saline as the control group. Dental pulp stem cells were seeded into the previously conditioned cylinders and incubated for 14 days. The quantitative real-time polymerase chain reaction was used to evaluate the expression of dentin sialophosphoprotein (DSPP), dentin morphoprotein-1 (DMP-1), and transforming growth factor-beta 1 (TGF-β1). Data analysis was performed using the Kruskal–Wallis test. The activation of EDTA with 980 nm and 808 nm diode lasers resulted in lower DSPP and DMP-1 expression than that for EDTA alone (p < 0.05 and p < 0.01, respectively). The expression of TGF was similar among all groups. The highest level of expression of odontoblast-like differentiation markers was observed with EDTA alone. However, the use of an 808 nm diode laser during EDTA irrigation reduced the expression of odontoblastic differentiation markers.
Research on the Distributed Propeller Slipstream Effect of UAV Wing Based on the Actuator Disk Method
Authors: Cao Mingzhi , Liu Kun , Wang Chunqiang , Wei Jingbo , Qin Zijie
Published: September 2023

Distributed electric propulsion technology has great potential and advantages in the development of drones. In this paper, to study the slipstream effect of distributed propellers, the actuator disk method was used to verify a single propeller, and the calculated thrust was in good agreement with the test results. Then, based on the actuator disk method, the influence of different installation positions on the slipstream effect was studied, and the distributed propeller layout was optimized by a genetic algorithm to improve the low-speed performance of the unmanned aerial vehicle (UAV) during the take-off phase and increase the cruise duration. The analysis results showed that the lift of the wing will be larger when the propellers are higher than the wing. The wing lift and drag of the counter-rotating are less than those of the co-rotating. Compared with the original layout, the lift coefficient of the optimized distributed propeller layout is significantly increased by 30.97%, while the lift/drag ratio is increased by 7.34%. Finally, we designed the test platform and qualitatively verified the calculated results without quantitative verification.
Pollination Ecology of Rocket (Eruca vesicaria (L.) Cav. ssp. sativa (Mill.) Thell) in the Semi-Arid Environments of Northwest India: Native Bees Are the Major Pollinators
Authors: Sihag Ram Chander
Published: September 2023

Several insect species visit the flowers of a plant to obtain floral rewards in the form of pollen and/or nectar. In return, we would anticipate that those visitors would contribute to the reproductive success of the plant. Do these visitors contribute equally towards the reproductive success of the plant? This issue has been the interest of many pollination ecologists. To find a solution to this problem, I investigated the pollination ecology of rocket (Eruca vesicaria (L.) Cav. ssp. sativa (Mill.) Thell), an important leafy vegetable used as salad. I captured the flower visitors with a hand net from the experimental field and had these identified. I also recorded the number of loose pollen grains carried on the body of the visitors of different species and deposited on the stigmas. Effects of single and multiple visits of visitors on the seed set of rocket flowers were also determined. Abundances and foraging rates of the flower visitors of this species were recorded and their values were used to calculate their respective contributions towards the reproductive success of this species. Five species of Hymenoptera, three of Diptera, one of Lepidoptera, and one of Coleoptera visited the flowers of the rocket. Apis florea was the most abundant among the flower visitors, followed by the dipterous flies, Apis mellifera, Apis dorsata, Andrena savignyi, and Andrena leaena in descending order. The number of loose pollen grains carried and deposited, foraging behaviors, foraging rates, and abundances did not provide conclusive measures to differentiate the contributions of different flower visitors towards the reproductive success of the rocket. However, the data recorded on abundances, foraging behaviors, and foraging rates together could do so. Accordingly, Andrena savignyi was the most efficient pollinator of rocket, followed by Andrena leaena, Apis dorsata, Apis mellifera, and Apis florea; dipterous flies were the least efficient pollinators of this plant species. In rocket, 28.84% of pollination was brought by Andrena savignyi, 24.69% by Andrena leaena, 20.34% by Apis dorsata, 18.37% by Apis mellifera, and 7.7% by Apis florea; dipterous flies caused only 0.06% pollination. Butterflies were very rare and Coccinella sp. was not a pollinator of this plant. Therefore, not all the pollinators of rocket contributed equally towards its reproductive success (seed production). Bees brought about 99.94% of total pollination and melittophily distinctly predominated over other pollination modes. However, among the bees, native bees together are the major pollinators in the flowers of rockets and accomplished more than 81.5% pollination. Therefore, the conservation of native bees is most important for the pollination of crops such as rockets.
Adoption of Fintech Services in Young Students: Empirical Approach from a Developing Country
Authors: Bermeo-Giraldo María Camila , Valencia-Arias Alejandro , Palacios-Moya Lucia , Valencia Jackeline
Published: September 2023

This work aimed to identify the main variables that determine the adoption of Fintech services in young students in the Colombian context through a model with five factors proposed to explain this behavior in 124 Colombian university students. The methodological design followed a quantitative approach and an exploratory–descriptive scope. For data processing, the statistical techniques exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to extract the relevant factors and evaluate the measurement model. To test the hypotheses about the relationships of the conceptual model constructs, Cramer’s V coefficient was used. The results showed that financial education and social influence have a positive effect on perceived benefit; in turn, low regulation is not strongly related to perceived benefit and is not dependent on social influence. However, digital literacy is affected by financial education and social influence. It is concluded that the number of mobile users in Colombia is increasing rapidly; however, the adoption of Fintech is slow. In addition, most of the university students in this study do not know what Fintech is, but they recognize that they use it frequently.
Exploring the Attitudes of School Staff towards the Role of Autism Classes in Inclusive Education for Autistic Students: A Qualitative Study in Irish Primary Schools
Authors: Rice Catherine , Kenny Neil , Connolly Leanne
Published: September 2023

The use of the designated special class model for autistic pupils in mainstream schools within the Irish education system has dramatically increased in recent years, as has the scrutiny regarding its alignment with increased policy focus on inclusive education. This qualitative study used semi-structured interviews to explore the views of twelve staff members, four special class teachers, four mainstream class teachers, and four principals of primary schools with special classes regarding the role of the special class model in supporting educational inclusion for autistic pupils. Results showed that participants felt specialist provision was an important aspect of appropriate provision for autistic pupils in Irish mainstream primary schools. Findings highlight the need for a tailored, child-centred approach to inclusion, given the challenges of mainstream class environments, with participants advocating for the safe space and support system of the special class for autistic pupils. Participants also identified challenges to inclusion, such as a lack of specialist training or CPD for teachers regarding inclusive practices and the tensions surrounding the inclusion of autistic children in mainstream settings. Principals have a critical role in developing inclusive school environments and supporting the special class. However, there is a need for greater support, training, and resources to help principals effectively fulfil their responsibilities. This underscores the need for departmental consideration of specific training, criteria, and continuous professional development to ensure effective support and inclusion of autistic students within the mainstream school environment.
Introducing Computer Science Unplugged in Pakistan: A Machine Learning Approach
Authors: Jehan Seema , Akram Pakeeza
Published: September 2023

Introducing computational thinking at elementary school can develop students’ capabilities and interest in Computing skills. In this study, we introduced the Computer Science unplugged (CS-unplugged) technique in Pakistan. We use paper-based activities to equip students with basic Computer Science skills without introducing any programming language. This study contributes twofold: First, we report the impact of CS-Unplugged training on more than 350 elementary students. The empirical study reveals that the students perform better in solving problems after unplugged training. Improved results in the post-training activity support this impact. Second, we applied machine learning to predict students’ performance. We employed different supervised machine learning algorithms to predict the students’ performance. Our results indicate that the Logistic regression-based model can predict the positive response of the student with a 0.91 receiver operating characteristic curve (ROC curve). This pilot study results encourage introducing unplugged techniques at elementary schools in third-world countries. The goal is to have minimal changes in infrastructure and focus on better student learning. In the future, we plan to introduce more unplugged problem-solving techniques to elementary students by providing little training to the science or math teacher.
Editorial for the Special Issue on Advances in Augmented and Mixed Reality in Education
Authors: Meletiou-Mavrotheris Maria , Katzis Konstantinos , Dimopoulos Christos , Lasica Ilona-Elefteryja
Published: September 2023

The Education Sciences Special Issue, Advances in Augmented and Mixed Reality in Education (Available online: https://www [...]
Entrepreneurship Education: Examining Long-Term Effects of a Practical Program Implemented in Children
Authors: Reyes-Aceves Fanny-Yumayra , Ramos-Lopez Leonardo , Mungaray-Lagarda Alejandro
Published: September 2023

Entrepreneurship education (EE) plays a crucial role in equipping individuals with the necessary competences to thrive in an increasingly dynamic and competitive world. By fostering entrepreneurial competencies, recognizing individual abilities, and promoting innovation and creativity, EE contributes to personal growth, employability, and economic development. This document presents an analysis conducted on the entrepreneurial competencies (EC) of students in San Quintin, Baja California, Mexico, who have participated in a practical EE program over the past 5 years. To conduct the analysis, a questionnaire was administered to both a control group and an experimental group, and the data were captured in the SPSS V23 software for interpretation. The results demonstrate a significant correlation between EE programs and EC developed by the students in the experimental group. Therefore, it is recommended to promote and sustain such initiatives, aiming for their long-term continuity, strengthening, and growth.
Motivation among Teenage Football Players: A Longitudinal Investigation throughout a Competitive Season
Authors: Rodrigues Filipe , Monteiro Diogo , Matos Rui , Jacinto Miguel , Antunes Raúl , Amaro Nuno
Published: September 2023

The present study aimed to investigate the dynamic changes in behavioral regulations among teenage football players throughout a competitive season, with three measurement points (T1, T2, and T3). The total sample size for the study comprised 108 participants (78 male; 30 female). The participants’ mean age was 14.31 (SD = 1.48). A MANOVA repeated measures analysis was performed within the group for all six behavioral regulations based on self-determination theory. The results of the study revealed noteworthy patterns in the behavioral regulations and intrinsic motivation scores over the competitive season. Identified regulation demonstrated a significant increase from T1 to T3 (p < 0.01, η2p = 0.13). Intrinsic motivation displayed a significant decrease over the same period from T1 to T3 (p < 0.01, η2p = 0.12). On the other hand, amotivation, external regulation, introjected regulation, and integrated regulation did not show significant changes between the three time points. The findings have practical implications for coaches and sports practitioners, as they highlight the need to create a supportive and autonomy-promoting environment to sustain athletes’ intrinsic motivation throughout a competitive season. Also, recognizing the increase in identified regulation can inform strategies that align team goals and training sessions with players’ individual values, thereby enhancing their commitment and dedication to the team’s success.
Synthesis of Wideband All-Frequency Absorptive Filtering Power Divider with High Selectivity and Flat Output Port Distributions
Authors: Zhang Siran , Liu Hongmei , Chen Shuyi , Wang Zhongbao , Fang Shaojun
Published: September 2023

In this paper, a wideband absorptive filtering power divider (AFPD) which features the characteristics of high selectivity and flat output distributions is proposed. It is composed of one unequal width three-coupled line (TCL), two coupled lines (CLs), two stepped open-circuited stubs, two kinds of isolation resistors, and two types of absorptive branches. The design equations of the proposed AFPD are derived using an even-odd decomposition method, and parametric investigations are also performed. It is found that the passband bandwidth can be adjusted by the stepped open-circuited stub which generates two transmission zeros (TZs). By combining the TCL with the CLs, the passband bandwidth is effectively enlarged. In addition, two isolated resistors are utilized for achieving good isolation and output-port matching performance. Without affecting the passband responses, the input port absorptive feature within the whole frequency band can be obtained by loading the absorptive branches both on the input and output ports. For validation, an example operating at the center frequency of 2 GHz was modeled and tested. Results exhibit that the passband FBW reaches 72% under 1 dB criterion, which illustrates flat output port distributions. In addition, for 10 dB return loss, the input and output impedance matching bandwidths are 250% and 78%, respectively. The features of good filtering responses are demonstrated by realizing the rectangle coefficient of 1.24 and the out-of-band suppression of more than 20 dB.
Co-Simulation Platform with Hardware-in-the-Loop Using RTDS and EXata for Smart Grid
Authors: Gong Peng , Yang Haowei , Wu Haiqiao , Li Huibo , Liu Yu , Qi Zhenheng , Wang Weidong , Wu Dapeng , Gao Xiang
Published: September 2023

The modern smart grid is a vital component of national development and is a complex coupled network composed of power and communication networks. The faults or attacks of either network may cause the performance of a power grid to decline or result in a large-scale power outage, leading to significant economic losses. To assess the impact of grid faults or attacks, hardware-in-the-loop (HIL) simulation tools that integrate real grid networks and software virtual networks (SVNs) are used. However, scheduling faults and modifying model parameters using most existing simulators can be challenging, and traditional HIL interfaces only support a single device. To address these limitations, we designed and implemented a grid co-simulation platform that could dynamically simulate grid faults and evaluate grid sub-nets. This platform used RTDS and EXata as power and communication simulators, respectively, integrated using a protocol conversion module to synchronize and convert protocol formats. Additionally, the platform had a programmable fault configuration interface (PFCI) to modify model parameters and a real sub-net access interface (RSAI) to access physical grid devices or sub-nets in the SVN, improving simulation accuracy. We also conducted several tests to demonstrate the effectiveness of the proposed platform.
Adaptive Quantization Mechanism for Federated Learning Models Based on DAG Blockchain
Authors: Li Tong , Yang Chao , Wang Lei , Li Tingting , Zhao Hai , Chen Jiewei
Published: September 2023

With the development of the power internet of things, the traditional centralized computing pattern has been difficult to apply to many power business scenarios, including power load forecasting, substation defect detection, and demand-side response. How to perform efficient and reliable machine learning tasks while ensuring that user data privacy is not violated has attracted the attention of the industry. Blockchain-based federated learning (FL), proposed as a new decentralized and distributed learning framework for building privacy-enhanced IoT systems, is receiving more and more attention from scholars. The framework provides some advantages, including decentralization, scalability, and data privacy, but at the same time its consensus mechanism consumes a significant amount of computational resources. Moreover, the number of model parameters has increased dramatically, leading to an increasing amount of transmitted data and a vast communication overhead. To reduce the communication overhead, we propose an FL framework in the directed acyclic graph (DAG)-based blockchain system, which achieves efficient and trusted sharing of FL networks. We design an adaptive model compression method based on k-means to compress the FL model and reduce the communication overhead of each round in FL training. Meanwhile, the original accuracy-based tips selection algorithm is optimized, and a tips selection algorithm considering multi-factor evaluation is proposed. Simulation experimental results show that the method proposed in this paper reduces the total bytes of communication of the blockchain-based federated learning system while balancing the accuracy of the FL model compared to previous work.
A Genetic Algorithm for Residential Virtual Power Plants with Electric Vehicle Management Providing Ancillary Services
Authors: González-Romera Eva , Romero-Cadaval Enrique , Roncero-Clemente Carlos , Milanés-Montero María-Isabel , Barrero-González Fermín , Alvi Anas-Abdullah
Published: September 2023

Virtual power plants are a useful tool for integrating distributed resources such as renewable generation, electric vehicles, manageable loads, and energy storage systems under a coordinated management system to obtain economic advantages and provide ancillary services to the grid. This study proposes a management system for a residential virtual power plant that includes household loads, photovoltaic generation, energy storage systems, and electric vehicles. With the proposed management system, the virtual power plant is economically optimized (as in commercial virtual power plants) while providing ancillary services (as in technical virtual power plants) to the distribution grid. A genetic algorithm with appropriate constraints is designed and tested to manage the energy storage system and the charge/discharge of electric vehicles, with several economic and technical objectives. Single-objective optimization techniques are compared to multi-objective ones to show that the former perform better in the studied scenarios. A deterministic gradient-based optimization method is also used to validate the performance of the genetic algorithm. The results show that these technical targets (usually reserved for larger virtual power plants) and economic targets can be easily managed in restricted-sized virtual power plants.