bluuebunny/crossref_metadata_2025 huggingface dataset:
Crystallization Kinetics of Lithium Carbonate in a Continuous Stirred-Tank Crystallizer
Authors: Chen Pao-Chi , Jhuang Jyun-Hong , Tseng Chung-En , Lin Zhong-Yi
Published: November 2024

Lithium carbonate is an important material in the lithium battery. The materials can be obtained from a reactive crystallization process. To prepare the higher-quality crystals, such as purity, crystal size distribution, and desired morphology, it needs to be controlled effectively in the crystallization process. Therefore, a study of crystallization kinetics was required. Here, the metastable region was explored first. Subsequently, a LiCl-K2CO3-H2O reaction system in a continuous stirred-tank crystallizer with controlling pH was used to study the crystallization kinetics, such as nucleation rate (B0), agglomeration kernel (β), and crystal growth rate (G), which can be determined with measured crystal size distribution at a steady-state condition using an agglomeration population balance model. The process variables include lithium chloride solution flow rate, potassium carbonate solution flow rate, and stirring speed. The results show that B0, β, and G were in the range of 3.47 × 109–5.98 × 1012 no/m3·s, 1.78 × 10−19–1.20 × 10−12 m3-slurry/no·s, and 3.00 × 10−11–2.11 × 10−10 m/s, respectively, depending on the operating conditions. All relative supersaturations were in the range of 1.22–2.04. In addition, the crystal size observed was found to be in the range of 1.28–32.7 μm, with irregular platelet forms in most cases. In addition, more slurry density can be obtained at the feed rate of 40 mL/min. A linear regression for crystallization kinetics was also discussed in this work. Finally, this process demonstrated that the recycling of lithium was possible for a circular economy. Therefore, the result can be used as a reference for larger-scale operations in industry.
Navigating the Zooplankton Realm: Oceans of Diversity Beneath the Sea Surface
Authors: Vereshchaka Alexander
Published: November 2024

This review provides a comprehensive summary of the current knowledge on marine zooplankton diversity and distribution, highlighting key advancements and future directions in the field. While the approach used in this review is necessarily broad, focusing on overarching trends and drivers of zooplankton diversity, it emphasizes recent methodological innovations. The review begins with an introduction to zooplankton, tracing the historical development of their research. It then explores the critical roles zooplankton play in ocean ecosystems, climate change, and the global economy. Next, the review examines the environmental and anthropogenic factors influencing marine zooplankton. Finally, it presents a comparison between marine and freshwater zooplankton and concludes with a discussion of the challenges facing current zooplankton diversity research and the presentation of possible directions and implementation strategies for advancing the understanding of zooplankton biodiversity and ecology.
Syntaxonomic Diversity of Rocky Dry Grasslands of the Chrysopogono grylli-Koelerion splendentis Along the East Adriatic
Authors: Terzi Massimo , Jasprica Nenad , Pesaresi Simone
Published: November 2024

This paper presents a comprehensive revision of the dry grasslands, specifically the alliance Chrysopogono grylli-Koelerion splendentis within the order Scorzoneretalia villosae along the eastern Adriatic, aimed at developing a formalized classification of this vegetation type. The revision is based on 490 relevés, including 44 newly sampled from the loci classici of previously identified associations, alongside additional relevés from the phytosociological literature. Both hierarchical and non-hierarchical cluster analysis, as well as NMS ordination, were employed to delineate the main vegetation types. Although numerous associations have been described to date, several were found to be floristically similar and were subsequently merged. Ultimately, 15 associations and one plant community were identified, including the description of a new association, Sideritido purpureae-Asphodeletum ramosi, from Montenegro, along with four new subassociations. The associations of the Chrysopogono grylli-Koelerion splendentis predominantly occur in the sub-Mediterranean variant of the temperate bioclimate. While this study provides a comprehensive framework for the syntaxonomic diversity of this alliance, it also underscores the need for additional phytosociological data from poorly known areas within its distribution range, particularly in the southernmost regions and inland areas (e.g., Albania, Bosnia and Herzegovina).
A Global Review of Cheese Colour: Microbial Discolouration and Innovation Opportunities
Authors: Ferraz Ana Rita , Pintado Cristina Santos , Serralheiro Maria Luísa
Published: November 2024

Cheese is a biologically active food product, characterised by its colour, texture, and taste. Due to its rich matrix of fats and proteins, as well as the fact that the cheese’s surface acts as its own packaging, the cheese becomes more susceptible to contamination by microorganisms during the ripening process, particularly bacteria and fungi. The ripening of cheese involves several biochemical reactions, with the proteolytic activity of the cheese microbiota being particularly significant. Proteolysis results in the presence of free amino acids, which are precursors to various metabolic mechanisms that can cause discolouration (blue, pink, and brown) on the cheese rind. Surface defects in cheese have been documented in the literature for many years. Sporadic inconsistencies in cheese appearance can lead to product degradation and economic losses for producers. Over the past few decades, various defects have been reported in different types of cheese worldwide. This issue also presents opportunities for innovation and development in edible and bioactive coatings to prevent the appearance of colour defects. Therefore, this review provides a comprehensive analysis of cheese colour globally, identifying defects caused by microorganisms. It also explores strategies and innovation opportunities in the cheese industry to enhance the value of the final product.
Detective Gadget: Generic Iterative Entity Resolution over Dirty Data
Authors: Buoncristiano Marcello , Mecca Giansalvatore , Santoro Donatello , Veltri Enzo
Published: November 2024

In the era of Big Data, entity resolution (ER), i.e., the process of identifying which records refer to the same entity in the real world, plays a critical role in data-integration tasks, especially in mission-critical applications where accuracy is mandatory, since we want to avoid integrating different entities or missing matches. However, existing approaches struggle with the challenges posed by rapidly changing data and the presence of dirtiness, which requires an iterative refinement during the time. We present Detective Gadget, a novel system for iterative ER that seamlessly integrates data-cleaning into the ER workflow. Detective Gadgetemploys an alias-based hashing mechanism for fast and scalable matching, check functions to detect and correct mismatches, and a human-in-the-loop framework to refine results through expert feedback. The system iteratively improves data quality and matching accuracy by leveraging evidence from both automated and manual decisions. Extensive experiments across diverse real-world scenarios demonstrate its effectiveness, achieving high accuracy and efficiency while adapting to evolving datasets.
Clarification on Classical Fatigue Design for Biaxial Stress Fields
Authors: de Castro Paulo M. S. T.
Published: November 2024

Fatigue design is a compulsory topic in education for several engineering specialties, particularly mechanical engineering. Textbooks on the design of machine elements have included, since long ago, the treatment of high-cycle fatigue (HCF) according to a classical theory based on the pioneering work of many researchers such as Carl Richard Soderberg. Perusing textbooks published over a long period, as well as comparing current ones, leads to the conclusion that the classical approach is presented with some differences in many of these textbooks, and this raises the need for some clarification: what are the consequences of the differences found? The present technical note addresses this problem, starting by presenting the classical approach, emphasising the variations in presentation identified, and then making a systematic comparison in order to grade the conservatism of the several variants found.
Enhanced Positioning Strategies to Reduce Pneumothorax in CT-Guided Lung Biopsies
Authors: Brönnimann Michael P. , Manser Leonie , Maurer Martin H. , Gebauer Bernhard , Auer Timo A. , Schnapauff Dirk , Collettini Federico , Nguyen Thanh-Long , Komarek Alois , Krokidis Miltiadis E. , Heverhagen Johannes T.
Published: November 2024

Background/Objectives: This study aimed to investigate pneumothorax risk, focusing on the gravitational effect of pleural pressure caused by specific patient positioning. Methods: We retrospectively analyzed 144 percutaneous CT-guided lung biopsies performed between January 2019 and December 2023. Patients were grouped into those with or without pneumothorax. Variations in patient positioning (prone, supine, lateral, lesion in decubitus biopsy-side-down [LD BSD] and the dependent area [L DA M], and access route beginning in the dependent area [AR LD M]) were compared using the chi-square, Fisher’s exact, and Mann–Whitney U tests. Performance metrics were evaluated. Univariate and binomial logistic regression models assessed the influence of these factors and other patient-related and interventional parameters on pneumothorax occurrence. Results: Three positional variants (AR DA M, L DA M, and L LD BSD; p < 0.001), general emphysema (p = 0.009), emphysema in the access route (p = 0.025), greater needle size (18G vs. 20G; p < 0.001), and the use of a side-cut instead of a full-core system (p = 0.002) were significantly linked to lower peri-interventional pneumothorax incidence. Even after adjusting for various factors, AR DA M and general emphysema remained independently associated with a reduced pneumothorax risk (OR 0.168, p < 0.001; OR 2.72, p = 0.034). Assessing the dependent zones showed superior performance regardless of the patient’s position, with the best performance demonstrated for AR DA M (AUC 0.705; sensitivity 60%, specificity 81.8%). Conclusions: Focusing on the dependent zones of each lung and adjusting the access route accordingly can significantly reduce the risk of pneumothorax compared to conventional positioning techniques.
Segmentation-Based Measurement of Orbital Structures: Achievements in Eyeball Volume Estimation and Barriers in Optic Nerve Analysis
Authors: Lee Yong Oh , Kim Hana , Chung Yeong Woong , Cho Won-Kyung , Park Jungyul , Paik Ji-Sun
Published: November 2024

Background/Objective: Orbital diseases often require precise measurements of eyeball volume, optic nerve sheath diameter (ONSD), and apex-to-eyeball distance (AED) for accurate diagnosis and treatment planning. This study aims to automate and optimize these measurements using advanced deep learning segmentation techniques on orbital Computed Tomography (CT) scans. Methods: Orbital CT datasets from individuals of various age groups and genders were used, with annotated masks for the eyeball and optic nerve. A 2D attention U-Net architecture was employed for segmentation, enhanced with slice-level information embeddings to improve contextual understanding. After segmentation, the relevant metrics were calculated from the segmented structures and evaluated for clinical applicability. Results: The segmentation model demonstrated varying performance across orbital structures, achieving a Dice score of 0.8466 for the eyeball and 0.6387 for the optic nerve. Consequently, eyeball-related metrics, such as eyeball volume, exhibited high accuracy, with a root mean square error (RMSE) of 1.28–1.90 cm3 and a mean absolute percentage error (MAPE) of 12–21% across different genders and age groups. In contrast, the lower accuracy of optic nerve segmentation led to less reliable measurements of optic nerve sheath diameter (ONSD) and apex-to-eyeball distance (AED). Additionally, the study analyzed the automatically calculated measurements from various perspectives, revealing key insights and areas for improvement. Conclusions: Despite these challenges, the study highlights the potential of deep learning-based segmentation to automate the assessment of ocular structures, particularly in measuring eyeball volume, while leaving room for further improvement in optic nerve analysis.
Calcitonin-Gene-Related Peptide in Migraine and Tension-Type Headache in Children During Interictal Period
Authors: Sekelj Fures Jadranka , Duranovic Vlasta , Lenicek Krleza Jasna , Katusic Bojanac Ana , Loncar Lana , Dakovic Ivana , Pejic-Rosko Sanja , Vulin Katarina , Pilon-Far Andrijana , Simic Klaric Andrea
Published: November 2024

Background/Objectives: Research on calcitonin-gene-related peptide (CGRP) in adult migraine is extensive, but its role in childhood migraine remains unclear. This study aimed to evaluate serum CGRP levels in children experiencing migraine and tension-type headache (TTH) during interictal periods, comparing these levels to age-matched healthy controls. Methods: A total of 66 migraine patients, 59 with TTH, and 53 controls were recruited and stratified by headache onset age: under 7, 7–12, and over 12 years. CGRP levels were quantified using enzyme-linked immunosorbent assay (ELISA). Results: The migraine patients showed significantly higher serum CGRP levels than both the TTH patients and the controls (p < 0.001), with no significant difference between the latter two groups. Among the migraine patients, those without aura (MO) exhibited higher CGRP levels than those with aura (MA). The CGRP levels were lower in the. MA patients whose headaches began between ages 7 and 12 compared to the subjects with MO, while no significant differences were found in the patients whose headaches began after age 12. Conclusions: These findings suggest that elevated serum CGRP is indicative of pediatric migraine, with variations based on migraine type and age of onset. The difference in CGRP in preadolescent migraineurs with and without aura suggest that CGRP levels may vary depending on age and on migraine type.
Hybrid Deep Learning and Machine Learning for Detecting Hepatocyte Ballooning in Liver Ultrasound Images
Authors: Alshagathrh Fahad , Alzubaidi Mahmood , Gecík Samuel , Alswat Khalid , Aldhebaib Ali , Alahmadi Bushra , Alkubeyyer Meteb , Alosaimi Abdulaziz , Alsadoon Amani , Alkhamash Maram , Schneider Jens , Househ Mowafa
Published: November 2024

Background: Hepatocyte ballooning (HB) is a significant histological characteristic linked to the advancement of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Although clinicians now consider liver biopsy the most reliable method for identifying HB, its invasive nature and related dangers highlight the need for the development of non-invasive diagnostic options. Objective: This study aims to develop a novel methodology that combines deep learning and machine learning techniques to accurately identify and measure hepatobiliary abnormalities in liver ultrasound images. Methods: The research team expanded the dataset, consisting of ultrasound images, and used it for training deep convolutional neural networks (CNNs) such as InceptionV3, ResNet50, DenseNet121, and EfficientNetB0. A hybrid approach, combining InceptionV3 for feature extraction with a Random Forest classifier, emerged as the most accurate and stable method. An approach of dual dichotomy classification was used to categorize images into two stages: healthy vs. sick, and then mild versus severe ballooning.. Features obtained from CNNs were integrated with conventional machine learning classifiers like Random Forest and Support Vector Machines (SVM). Results: The hybrid approach achieved an accuracy of 97.40%, an area under the curve (AUC) of 0.99, and a sensitivity of 99% for the ‘Many’ class during the third phase of evaluation. The dual dichotomy classification enhanced the sensitivity in identifying severe instances of HB. The cross-validation process confirmed the strength and reliability of the suggested models. Conclusions: These results indicate that this combination method can decrease the need for invasive liver biopsies by providing a non-invasive and precise alternative for early identification and monitoring of NAFLD and NASH. Subsequent research will prioritize the validation of these models using larger datasets from multiple centers to evaluate their generalizability and incorporation into clinical practice.
Near-Infrared Spectroscopy (NIRS) to Assess Infection Complications During the Acute Phase of Acute Pancreatitis
Authors: Chiba Nobutaka , Yagi Tsukasa , Mizuochi Minori , Sato Jun , Saito Takeshi , Sakurai Atsushi , Kinoshita Kosaku
Published: November 2024

Background: Acute pancreatitis (AP) severity is correlated with systemic infection incidence in the acute phase, and it is important to assess inflammation during the disease course and to recognize infection at an early stage. As in sepsis, inflammation in AP impairs tissue oxygen metabolism and disrupts microcirculation. We performed a vascular occlusion test (VOT) via near-infrared spectroscopy (NIRS), which noninvasively monitors local oxygen in peripheral tissues, to evaluate tissue oxygen metabolism and blood circulation during the acute AP phase. Methods: Tissue oxygen metabolism was measured via an NIRS probe attached to the thenar eminence at admission and 7 days after admission. The upper arm was wrapped with a sphygmomanometer cuff while avoiding brachial artery compression for 3 min. The minimum desaturation value was defined as the minimum tissue oxygen index (TOI), the maximum reactive hyperemia value after release was defined as the maximum TOI, and the difference was defined as the ∆TOI. The time from the minimum TOI to maximum TOI was defined as the TOI interval. Results: Fifteen healthy volunteers, 13 patients with AP, and 12 patients with sepsis were included. The TOI at baseline and ∆TOI (parameter describing tissue oxygen metabolism) decreased in a stepwise manner, and the TOI interval (measure of peripheral vasodilatory capacity) was protracted in a stepwise manner among the three groups. In a subgroup analysis, no significant differences in the NIRS-derived variables between patients with AP complicated by infection and those without infection were observed at admission; however, after 7 days, the groups significantly differed. Additionally, blood lactate concentrations were significantly correlated with the ∆TOI and TOI. Conclusions: Mild tissue oxygen metabolism impairment and tissue perfusion occurred in AP compared with sepsis, and changes similar to those in sepsis occur in AP complicated by infection. Further research is needed to evaluate whether these values can be applied to treating this group of patients.
The Prognostic Significance of CD47, CD68, and CD163 Expression Levels and Their Relationship with MLR and MAR in Locally Advanced and Oligometastatic Nasopharyngeal Carcinoma
Authors: Aydin Asim Armagan , Yuceer Ramazan Oguz , Yildirim Senay , Unlu Ahmet , Kayikcioglu Erkan , Kocer Murat
Published: November 2024

Background: This study aimed to assess the prognostic and predictive implications of CD47, CD68, and CD163, biomarkers of tumor-associated macrophages (TAMs), on the treatment efficacy and clinical outcomes of nasopharyngeal carcinoma (NPC). Additionally, the prognostic value of TAM-related indices, such as the monocyte-to-lymphocyte ratio (MLR) and monocyte-to-albumin ratio (MAR), was evaluated. Methods: A retrospective cohort of 54 patients with locally advanced or oligometastatic NPC treated with concurrent chemoradiotherapy (CCRT), with or without induction chemotherapy, was analyzed. Patients were categorized based on the cumulative expression scores for CD47, CD68, and CD163: negative/low (0–3 points) and high (4–6 points). MLR and MAR were also stratified as low MLR (<0.545) vs. high MLR (≥0.545) and low MAR (<16.145) vs. high MAR (≥16.145). The primary endpoint was overall survival (OS). Results: High CD47, CD68, and CD163 expression levels were correlated with advanced clinical stage, reduced CCRT response, and elevated MLR and MAR. These TAM biomarkers were linearly correlated with each other and with established risk factors such as advanced age and elevated EBV-DNA levels. Kaplan–Meier analysis revealed that patients with low TAM expression had significantly longer OS and progression-free survival (PFS) than those with high TAM expression. Multivariate analysis identified high CD163, MLR, and MAR levels as independent adverse prognostic factors for OS. Elevated MLR is an independent risk factor for both OS and PFS in patients with NPC. Conclusions: CD47, CD68, and CD163 are significant prognostic markers in NPC, with higher levels being associated with poorer OS and PFS. Elevated MLR and MAR values also predict worse outcomes, underscoring their value as prognostic tools. CD163 and MLR are particularly strong predictors, highlighting the crucial role of TAMs in NPC management and suggesting that CD163 is a potential therapeutic target within the immune checkpoint pathway.
Could SLC26A7 Be a Promising Marker for Preoperative Diagnosis of High-Grade Papillary Thyroid Carcinoma?
Authors: Titov Sergei E. , Kozorezova Evgeniya S. , Lukyanov Sergei A. , Sergiyko Sergei V. , Demenkov Pavel S. , Veryaskina Yulia A. , Vorobyev Sergey L. , Sleptsov Ilya V. , Chernikov Roman A. , Timofeeva Natalia I. , Barashkova Svetlana V. , Lushnikova Elena L. , Uspenskaya Anna A. , Zolotoukho Anna V. , Romanova Olga V. , Zhimulev Igor F.
Published: November 2024

Background/Objectives: A modern classification distinguishes between two nosological entities posing an intermediate risk between differentiated and anaplastic carcinoma: poorly differentiated thyroid carcinoma and differentiated high-grade thyroid carcinoma. There are currently few studies searching for the preoperative molecular genetic markers of high-grade papillary thyroid carcinoma (PTC HG), primarily because of a recent WHO reclassification and singling out of a separate entity: high-grade follicular cell-derived nonanaplastic thyroid carcinoma. Therefore, this work was aimed at identifying PTC HG-specific microRNAs and mRNAs that reliably distinguish them from differentiated papillary thyroid carcinoma in preoperative cytology specimens (fine-needle aspiration biopsies). Methods: A molecular genetic profile (expression levels of 14 genes and eight microRNAs) was studied in 110 cytology specimens from patients with PTC: 13 PTCs HG and 97 PTCs without features of HG. Results: Of the examined eight microRNAs and 14 genes, significant differences in the expression levels between the PTC and PTC HG groups were revealed for genes SLC26A7, TFF3, and TPO. Only one gene (SLC26A7) proved to be crucial for detecting PTC HG. It showed the largest area under the ROC curve (0.816) in differentiation between the PTC and PTC HG groups and was the key element of the decision tree by ensuring 54% sensitivity and 87.6% specificity. Conclusions: Early preoperative diagnosis of PTC HG in patients with early stages of this cancer type will allow clinicians to modify a treatment strategy toward a larger surgery volume and lymph node dissection and may provide indications for subsequent radioactive iodine therapy.
Regression of the Flow Signal from the Neovascular Network in AMD Neovascular Membranes Treated with Faricimab
Authors: Savastano Maria Cristina , Crincoli Emanuele , Toto Lisa , Grassi Maria Oliva , Chiosi Flavia , Savastano Alfonso , Rizzo Clara , Mastropasqua Rodolfo , Boscia Francesco , Rizzo Stanislao
Published: November 2024

Objectives: To report the occurrence of the regression of the flow signal from the neovascular network in macular neovascularizations (MNVs), developing in the context of age-related macular degeneration (AMD), treated with faricimab in a treat-and-extend regimen. Methods: Eyes affected by AMD-related MNV and treated with faricimab intravitreal injections in a treat-and-extend (TE) regimen were consecutively retrospectively screened in five specialized retina centers. Changes in neovascular network characteristics during the course of the treatment were analyzed. The availability of high-quality optical coherence tomography angiography (OCTA) at the beginning of the treatment and at the regression of the MNV was necessary for inclusion. According to greatest linear diameter (GLD) changes during treatment, eyes were divided into three groups: a complete regression (CR) group, a partial remission (PR) group (a reduction of at least 50% of the GLD from baseline to last follow-up), and a stable group (stable/showing a reduction lower than 50% of the GLD from baseline to follow up). Results: One hundred and ten (110) eyes were included. The CR group was composed of 12 eyes (10.9%), while the PR group represented 60.9% of the study population. CR occurred after a mean of 6.0 ± 1.4 months, ranging from 4 to 8 months. Time to regression was significantly lower in eyes naïve to treatment before the study compared with the others (p = 0.022). A significantly lower baseline GLD was detected in the CR group (1292.2 ± 195.6 μm) compared with the PR group (1324.6 ± 135.6 μm) and the stable group (1412.5 ± 110.9 μm) (omnibus p = 0.003). Conclusions: Complete regression of the flow signal from the MNV neovascular network documented with OCTA may occur during TE regimens with faricimab. In treatment-naïve eyes, regression occurs earlier during the treatment.
A Tumor Volume Segmentation Algorithm Based on Radiomics Features in FDG-PET in Lung Cancer Patients, Validated Using Surgical Specimens
Authors: Bundschuh Lena , Buermann Jens , Toma Marieta , Schmidt Joachim , Kristiansen Glen , Essler Markus , Bundschuh Ralph Alexander , Prokic Vesna
Published: November 2024

Background: Although the integration of positron emission tomography into radiation therapy treatment planning has become part of clinical routine, the best method for tumor delineation is still a matter of debate. In this study, therefore, we analyzed a novel, radiomics-feature-based algorithm in combination with histopathological workup for patients with non-small-cell lung cancer. Methods: A total of 20 patients with biopsy-proven lung cancer who underwent [18F]fluorodeoxyglucose positron emission/computed tomography (FDG-PET/CT) examination before tumor resection were included. Tumors were segmented in positron emission tomography (PET) data using previously reported algorithms based on three different radiomics features, as well as a threshold-based algorithm. To obtain gold-standard results, lesions were measured after resection. Pathological volumes and maximal diameters were then compared with the results of the segmentation algorithms. Results: A total of 20 lesions were analyzed. For all algorithms, segmented volumes correlated well with pathological volumes. In general, the threshold-based volumes exhibited a tendency to be smaller than the radiomics-based volumes. For all lesions, conventional threshold-based segmentation produced coefficients of variation which corresponded best with pathologically based volumes; however, for lesions larger than 3 ccm, the algorithm based on Local Entropy performed best, with a significantly better coefficient of variation (p = 0.0002) than the threshold-based algorithm. Conclusions: We found that, for small lesions, results obtained using conventional threshold-based segmentation compared well with pathological volumes. For lesions larger than 3 ccm, the novel algorithm based on Local Entropy performed best. These findings confirm the results of our previous phantom studies. This algorithm is therefore worthy of inclusion in future studies for further confirmation and application.
SLA-MLP: Enhancing Sleep Stage Analysis from EEG Signals Using Multilayer Perceptron Networks
Authors: Mohammad Farah , Al Mansoor Khulood Mohammed
Published: November 2024

Background/Objectives: Sleep stage analysis is considered to be the key factor for understanding and diagnosing various sleep disorders, as it provides insights into sleep quality and overall health. Methods: Traditional methods of sleep stage classification, such as manual scoring and basic machine learning approaches, often suffer from limitations including subjective biases, limited scalability, and inadequate accuracy. Existing deep learning models have improved the accuracy of sleep stage classification but still face challenges such as overfitting, computational inefficiencies, and difficulties in handling imbalanced datasets. To address these challenges, we propose the Sleep Stage Analysis with Multilayer Perceptron (SLA-MLP) model. Results: SLA-MLP leverages advanced deep learning techniques to enhance the classification of sleep stages from EEG signals. The key steps of this approach include data collection, where diverse and high-quality EEG data are gathered; preprocessing, which involves signal cropping, spectrogram conversion, and normalization to prepare the data for analysis; data balancing, where class weights are adjusted to address any imbalances in the dataset; feature extraction, utilizing Temporal Convolutional Networks (TCNs) to extract meaningful features from the EEG signals; and final classification, applying a Multilayer Perceptron (MLP) to accurately predict sleep stages. Conclusions: SLA-MLP demonstrates superior performance compared to traditional methods by effectively addressing the limitations of existing models. Its robust preprocessing techniques, advanced feature extraction, and adaptive data balancing strategies collectively contribute to obtaining more accurate results, having an accuracy of 97.23% for the S-DSI, 96.23 for the S-DSII and 97.23% for the S-DSIII dataset. This model offers a significant advancement in the field, providing a more precise tool for sleep research and clinical applications.
A Scoping Review of Tools to Assess Digital Health Literacy Among Middle-Aged and Older Adults for Application to Dietetic Practice
Authors: Bai Jingyi Jess , Mandoh Mariam , Yu Yee Mei , Allman-Farinelli Margaret
Published: November 2024

Medical Nutrition Therapy (MNT) services have shifted from only in-person to digital consultations. This may present a challenge to older patients if they are not digitally literate. This scoping review aimed to identify and analyse tools for assessing digital health literacy to guide dietetic practice in screening patients for digital care needs and training. Publications that used or tested a digital health literacy tool with participants aged 45 years and older in clinical, community, or population settings were deemed eligible. Five scientific databases, reference lists of identified studies, and publications from key authors were searched for peer-reviewed articles published in English between 2014 and March 2024. Relevant data on participant characteristics, tool design, and validation were charted. The generalisability of studies and applicability of tools were assessed using 4-point scales. Searches yielded 1823 articles, of which 66 articles were included in the final analysis. Eight tools were identified, and the electronic Health Literacy Scale (eHEALS) was the most frequently used. After evaluating the validity and feasibility of these tools, the adapted eHEALS emerged as the most applicable for digitally delivered MNT. Future studies should explore effective educational programmes for less digitally literate populations to better connect them to online MNT healthcare services.
Pitch-Regulated Control Strategy for Coaxial Drone with Variable Rotor Space Ratio
Authors: Ke Zhifang , Zhao Molei , Hu Yu , Shu Yongjie , Liu Weibo , Meng Qingkai , Tu Jinghan , Zhang Haitao , Yao Zhaopu , Wei Wei
Published: November 2024

This study investigates the impact of rotor spacing on the aerodynamic performance of a coaxialcopter and promotes an innovative regulated control strategy for the coaxial drone. The present research introduces a coaxialcopter with variable rotor spacing, and employing finite element numerical simulations, we assess the aerodynamic behavior of this novel configuration. Through comprehensive measurements and analysis of its aerodynamic performance across varying rotor spacings from 0.1 R to 1 R, we validate the effectiveness of a rotor-spacing control strategy for enhancing takeoff maneuvers. The numerical simulation and experiment results reveal that the performance characteristics of both the upper and lower rotors converge toward that of a single rotor as the space ratio increases, along with a reduction in their thrust fluctuations and aerodynamic performance periodicity. Considering stable power consumption patterns and endurance performance, we analyzed the interrelations binding the pitch distance of the rotors, rotational speed, and pitch angle, vis à vis the thrust coefficient and power coefficient. Through the parameter optimization method, we demonstrate that adjusting rotor spacing offers a practical means to enhance payload capacity without increasing the power input, thereby improving efficiency, which validates the practicality and efficacy of the parameter optimization approach. Furthermore, optimizing rotor spacing for specific operational scenarios enhances overall aerodynamic performance, suggesting a viable flight control strategy for takeoff and landing conditions for coaxial drones.
Who Learns from Reading Texts in General Chemistry?
Authors: Buell René W. , Pazicni Samuel
Published: November 2024

Understanding how student characteristics affect learning in General Chemistry can influence the pedagogical strategies employed by instructors. Previous studies have investigated the effects of characteristics including prior knowledge, math ability, and motivations on course performance. Student characteristics can also influence study strategies employed by students. Few studies, however, have focused on the role of language and reading comprehension skill in learning in chemistry. This work explores the effects of prior knowledge and reading comprehension skill on learning from reading texts about two chemistry concepts. Linear regression analyses were utilized to establish relationships between predictors and test scores after reading to determine whether reading comprehension skill influenced learning gains after reading texts. A meta-analysis of four large-scale studies showed that prior knowledge and reading comprehension correlated with post-test scores and that an effect called expertise reversal may help low prior knowledge students close the post-test score gap if they read texts with certain readability characteristics. However, our findings also suggest that texts with similar readability characteristics can hinder the learning of those with higher prior knowledge.
Comparison of the Effect of Motivation in Medical Students with the Use of Virtual Reality and Non-Immersive Digital Resources
Authors: Toral-Murillo María Valentina , Lara-Palazuelos Nathaniel , Ossandón Rocha Cristian Daniel Exequiel , Rodríguez-Flores Elvira
Published: November 2024

The following article investigates the effect of advanced educational tools—virtual reality (Oculus) and the Sectra virtual anatomy table—on medical students’ motivation in learning anatomy. Conducted at the Universidad Autónoma de Guadalajara, the research involved 90 first-semester medical students divided into two groups, each utilizing one of the technologies during anatomy sessions. Motivation was assessed using the Reduced Instructional Materials Motivation Survey (RIMMS). Focus on aspects such as attention, accuracy, confidence, and satisfaction. Of the four variables analyzed in the RIMMS questionnaire (attention, relevance, confidence, and satisfaction), it was noted that the highest p-value of the independent variables is 0.0000, which corresponded to TOTAL SATISFACTION. The findings suggest that immersive technologies like virtual reality can substantially enhance student motivation and learning experiences in medical education.
Applying the isiNdebele Scientific Language Register in Natural Sciences to Foster Meaningful Learning
Authors: Ntuli Thuli Gladys
Published: November 2024

Conducted as a qualitative interpretative case study, this research explored the influence of the developed isiNdebele scientific language register for Natural Sciences on meaningful learning among learners. Data collection involved interviews and observations of stakeholders. The findings revealed a positive correlation between the use of indigenous languages and meaningful learning. Notably, when Natural Sciences was taught using the isiNdebele register, classrooms exhibited heightened interaction, contrasting with lessons conducted in English, where learners were predominantly passive. These outcomes underscore the importance of creating scientific registers in indigenous languages, advocating for their integration into teaching practices. The study recommends the incorporation of scientific language registers in indigenous languages to enhance meaningful learning and academic performance. By examining the impact on meaningful learning experiences, the article contributes valuable insights to the broader discourse on inclusive and culturally responsive pedagogical practices in science education. The findings underscore the potential of applying indigenous languages in science instruction to bridge linguistic gaps and promote a more accessible and enriching educational environment.
Enhanced 3D Outdoor Positioning Method Based on Adaptive Kalman Filter and Kernel Density Estimation for 6G Wireless System
Authors: Kim Kyounghun , Lee Seongwoo , Hwang Byungsun , Kim Jinwook , Seon Joonho , Kim Soohyun , Sun Youngghyu , Kim Jinyoung
Published: November 2024

The implementation of accurate positioning methods in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments has been emphasized for seamless 6G application services. In LOS environments with unobstructed paths between the transmitter and receiver, accurate tracking essential for seamless 6G services is achievable. However, accurate three-dimensional (3D) outdoor positioning has been challenging to achieve in NLOS environments where positioning accuracy may be severely degraded. In this paper, a novel 3D outdoor positioning method considering both LOS and NLOS environments is proposed. Considering the practical positioning systems, the data received from satellites often contain null values and outliers. Thus, a kernel density estimation (KDE)-based outlier removal method is used for effectively detecting the null values and outliers through temporal correlation analysis. A dilution of precision-based adaptive Kalman filter (DOP-AKF) is proposed to mitigate the effects of an NLOS environment. In the proposed method, the DOP-AKF can optimize the performance of the 3D positioning system that dynamically adapts to complex environments. Experimental results show that the proposed method can improve 3D positioning accuracy by up to 18.84% compared to conventional methods. Therefore, the proposed approach can be suggested as a promising solution for 3D outdoor positioning in 6G wireless systems.
Research on Deep Learning Model Enhancements for PCB Surface Defect Detection
Authors: Yan Hao , Zhang Hong , Gao Fengyu , Wu Huaqin , Tang Shun
Published: November 2024

With the miniaturization and increasing complexity of electronic devices, the accuracy and efficiency of printed circuit board (PCB) defect detection are crucial to ensuring product quality. To address the issues of small defect sizes and high missed detection rates in PCB surface inspection, this paper proposes an enhanced YOLOv8s model which not only improves detection performance but also achieves a lightweight design. Firstly, the Nexus Attention module is introduced, which organically integrates multiple attention mechanisms to further enhance feature extraction and fusion capabilities, improving the model’s learning and generalization performance. Secondly, an improved CGFPN network is designed to optimize multi-scale feature fusion, significantly boosting the detection of small objects. Additionally, the WaveletUnPool module is incorporated, leveraging wavelet transform technology to refine the upsampling process, accurately restoring detailed information and improving small-object detection in complex backgrounds. Lastly, the C2f-GDConv module replaces the traditional C2f module, reducing the number of model parameters and computational complexity while maintaining feature extraction efficiency. Comparative experiments on a public PCB dataset demonstrate that the enhanced model achieved a mean average precision (mAP) of 97.3% in PCB defect detection tasks, representing a 3.0% improvement over the original model, while reducing Giga Floating Point Operations (GFLOPs) by 26.8%. These enhancements make the model more practical and adaptable for industrial applications, providing a solid foundation for future research.
A Doodle-Based Control for Characters in Story Visualization
Authors: Yang Hyemin , Yang Heekyung , Min Kyungha
Published: November 2024

We propose a story visualization technique that allows users to control the arrangement, poses, and styles of characters in a scene based on user-input doodle sketches. Our method utilizes a text encoder to process scene prompts and an image encoder to handle doodle sketches, generating inputs for a predefined scene generation model. Furthermore, we achieve efficient model training by fine-tuning the backbone network by applying a small dataset and employing a LoRA-based fine-tuning technique. We demonstrate that our method can generate characters with various poses and styles from doodle sketches, and it can validate the advantages of our approach by comparing it with the results from other story visualization studies.
Wireless Remote-Monitoring Technology for Wind-Induced Galloping and Vibration of Transmission Lines
Authors: Wang Peng , Zhong Yuanchang , Chen Yu , Li Dalin
Published: November 2024

In order to achieve wireless remote monitoring of wind-induced vibrations in power-transmission lines based on MEMS sensors, it is necessary to devise a method for reconstructing the wind swing curve, enabling the device’s real-time performance to promptly acquire, restore, and analyze data. Based on existing single-axis vibration-sensitive components, a measurement array using self-powered MEMS sensors and spacers has been designed. The Orthogonal Matching Pursuit (OMP) algorithm is selected to obtain displacement data collected by sensors installed on the transmission-line spacers. Leveraging the inherent sparsity of the data, a Gaussian white noise regularization matrix is chosen to establish the observation matrix. Through the algorithm, wind data curve reconstruction is achieved, enabling the reconstruction of large-span wind-induced vibration information without distortion. The experimental results demonstrate that when applying the orthogonal tracking algorithm in transmission-line curve reconstruction, sparsity is selected based on the sampling length, that is, the number of sensors installed on the spacers is determined by the span length; a portion of the observation values are selected to generate the observation matrix; and the wind galloping data curve of the transmission line is well restored.
MMAformer: Multiscale Modality-Aware Transformer for Medical Image Segmentation
Authors: Ding Hao , Zhang Xiangfen , Lu Wenhao , Yuan Feiniu , Luo Haixia
Published: November 2024

The segmentation of medical images, particularly for brain tumors, is essential for clinical diagnosis and treatment planning. In this study, we proposed MMAformer, a Multiscale Modality-Aware Transformer model, which is designed for segmenting brain tumors by utilizing multimodality magnetic resonance imaging (MRI). Complementary information between different sequences helps the model delineate tumor boundaries and distinguish different tumor tissues. To enable the model to acquire the complementary information between related sequences, MMAformer employs a multistage encoder, which uses a cross-modal downsampling (CMD) block for learning and integrating the complementary information between sequences at different scales. In order to effectively fuse the various information extracted by the encoder, the Multimodal Gated Aggregation (MGA) block combines the dual attention mechanism and multi-gated clustering to effectively fuse the spatial, channel, and modal features of different MRI sequences. In the comparison experiments on the BraTS2020 and BraTS2021 datasets, the average Dice score of MMAformer reached 86.3% and 91.53%, respectively, indicating that MMAformer surpasses the current state-of-the-art approaches. MMAformer’s innovative architecture, which effectively captures and integrates multimodal information at various scales, offers a promising solution for tackling complex medical image segmentation challenges.
Effects of Industrial Maintenance Task Complexity on Neck and Shoulder Muscle Activity During Augmented Reality Interactions
Authors: Alhaag Mohammed H. , Alessa Faisal M. , Al-harkan Ibrahim M. , Nasr Mustafa M. , Ramadan Mohamed Z. , AlSaleem Saleem S.
Published: November 2024

Extensive studies have demonstrated the advantages of augmented reality (AR) in improving efficiency, thereby fulfilling a quality role in industry. Yet, the corresponding physical strain on individuals poses a significant challenge. This study explores the effects of task difficulty (complex versus simple maintenance activities) and multimedia guidance (e.g., paper-based versus AR via HoloLens) on physical strain, body discomfort ratings, perceived exertion, and mental effort. A 2 × 2 mixed design was employed, involving a total of 28 participants with an average age of 32.12 ± 2.45 years. Physical strain was evaluated by measuring the normalized root mean square (RMS) of electromyography (EMG) indicators, expressed as a percentage of maximum voluntary contraction (%MVC) from six muscles (i.e., right flexor carpi radialis (RFCR), right middle deltoid (RMD), right upper trapezius (RUT), right cervical extensor (RCE), and right and left splenius (RSPL and LSPL) muscles. The results indicated that AR instruction, particularly in complex tasks, led to higher physical strain in the neck and shoulder muscles (RCE and RUT) compared with paper-based methods. However, AR significantly reduced strain in the RSPL, LSPL, RMD, and RFCR muscles during both simple and complex tasks. This study highlights that while AR can lower physical strain in certain muscle groups, it also introduces increased strain in the neck and shoulders, particularly during more demanding tasks. This study highlights the need for ergonomic considerations when designing and implementing AR technologies, especially for complex tasks that inherently demand more from the user, both physically and cognitively.