bluuebunny/crossref_metadata_2025 huggingface dataset:
A Prospective Observational Cohort Study Comparing High-Complexity Against Conventional Pelvic Exenteration Surgery
Authors: West Charles T. , Tiwari Abhinav , Salem Yousif , Woyton Michal , Alford Natasha , Roy Shatabdi , Russell Samantha , Ribeiro Ines S. , Smith Julian , Yano Hideaki , Cooper Keith , West Malcolm A. , Mirnezami Alex H.
Published: January 2025

Background: Conventional pelvic exenteration (PE) comprises the removal of all or most central pelvic organs and is established in clinical practise. Previously, tumours involving bone or lateral sidewall structures were deemed inoperable due to associated morbidity, mortality, and poor oncological outcomes. Recently however high-complexity PE is increasingly described and is defined as encompassing conventional PE with the additional resection of bone or pelvic sidewall structures. This observational cohort study aimed to assess surgical outcomes, health-related quality of life (HrQoL), decision regret, and costs of high-complexity PE for more advanced tumours not treatable with conventional PE. Methods: High-complexity PE data were retrieved from a prospectively maintained quaternary database. The primary outcome was overall survival. Secondary outcomes were perioperative mortality, disease control, major morbidity, HrQoL, and health resource use. For cost–utility analysis, a no-PE group was extrapolated from the literature. Results: In total, 319 cases were included, with 64 conventional and 255 high-complexity PE, and the overall survival was equivalent, with medians of 10.5 and 9.8 years (p = 0.52), respectively. Local control (p = 0.30); 90-day mortality (0.0% vs. 1.2%, p = 1.00); R0-resection rate (87% vs. 83%, p = 0.08); 12-month HrQoL (p = 0.51); and decision regret (p = 0.90) were comparable. High-complexity PE significantly increased overall major morbidity (16% vs. 31%, p = 0.02); and perioperative costs (GBP 37,271 vs. GBP 45,733, p < 0.001). When modelled against no surgery, both groups appeared cost-effective with incremental cost-effectiveness ratios of GBP 2446 and GBP 5061. Conclusions: High-complexity PE is safe and feasible, offering comparable survival outcomes and HrQoL to conventional PE, but with greater morbidity and resource use. Despite this, it appears cost-effective when compared to no surgery and palliation.
Dietary Approach in Familial Hypercholesterolemia
Authors: Popiolek-Kalisz Joanna , Salamon Klaudia , Mazur Michal , Mikolajczyk Klaudia , Kalisz Grzegorz
Published: January 2025

Introduction: Familial hypercholesterolemia (FH) is a genetic disorder that remains underdiagnosed and undertreated. It is characterized by high levels of low-density lipoprotein cholesterol (LDL-C), which leads to an increased cardiovascular disease risk. Pharmacotherapy of FH is based on high-dose statin therapy, often combined with ezetimibe and proprotein convertase subtilisin/kexin 9 inhibitors. The dietary approach is an important and supportive part of FH management. Methods: This review aimed to present the available evidence on dietary strategies in FH patients. The analyzed aspects included macronutrients such as fat and carbohydrate intake, as well as the role of dietary fiber, nutraceuticals (omega-3, beta-glucan, phytosterols, and red yeast fermented rice extract), and overall dietary models. Results and Conclusions: Based on the available data, the Mediterranean diet is a dietary model advised in cardiovascular prevention, including patients with FH. Regarding detailed recommendations, the current state of knowledge indicates dietary fat and saturated fatty acids intake limitation as an advised strategy. Supplementation of phytosterols and fiber can be also helpful in FH.
Modified Trochanteric Flip Osteotomy in Varus Intertrochanteric Osteotomy for Treatment of Legg–Calvé–Perthes Disease
Authors: Laufer Andrea , Antfang Carina , Gosheger Georg , Frommer Adrien , Toporowski Gregor , Tretow Henning , Roedl Robert , Vogt Bjoern
Published: January 2025

Background/Objectives: Legg–Calvé–Perthes disease (LCPD) presents challenges in treatment due to its varied course and unclear etiology. This study aimed to evaluate the efficacy of combining proximal femoral varus osteotomy (PFVO) with a modified trochanteric flip osteotomy to address biomechanical consequences and improve hip abductor muscle strength. Methods: We present a modified approach combining PFVO with a trochanteric flip osteotomy. In this technique, the greater trochanter in compound with its muscular insertions is separated from the femur and attached distally using a varization blade plate. Nine patients (ten hips, mean age 8 years) with LCPD were treated using this technique. Clinical examination findings and radiographic evaluations were retrospectively analyzed. The median follow-up was 33 months. Results: At the last follow-up, two patients exhibited Trendelenburg gait, but hip abduction was improved in all patients. Radiographically, consolidation at the osteotomy site was observed in all cases with no delayed union or non-union. The median CE angle improved by 7°, while the median CCD decreased by 18°. The median MPFA decreased by 13°, resulting in a median of 82°. Conclusions: Combining PFVO with a modified trochanteric flip osteotomy addresses biomechanical issues associated with PFVO, potentially improving hip containment and abductor muscle strength. This approach may offer advantages over traditional osteotomy techniques in treating LCPD, and it appears to produce a superior functional outcome in particular in regard to limping when compared to conventional PFVO. Despite satisfactory radiological outcomes in most cases, further research is needed to assess long-term effectiveness and address challenges such as femoral head enlargement and persistent gait abnormalities.
ChatGPT, Google, or PINK? Who Provides the Most Reliable Information on Side Effects of Systemic Therapy for Early Breast Cancer?
Authors: Lukac Stefan , Griewing Sebastian , Leinert Elena , Dayan Davut , Heitmeir Benedikt , Wallwiener Markus , Janni Wolfgang , Fink Visnja , Ebner Florian
Published: January 2025

Introduction: The survival in early breast cancer (BC) has been significantly improved thanks to numerous new drugs. Nevertheless, the information about the need for systemic therapy, especially chemotherapy, represents an additional stress factor for patients. A common coping strategy is searching for further information, traditionally via search engines or websites, but artificial intelligence (AI) is also increasingly being used. Who provides the most reliable information is now unclear. Material and Methods: AI in the form of ChatGPT 3.5 and 4.0, Google, and the website of PINK, a provider of a prescription-based mobile health app for patients with BC, were compared to determine the validity of the statements on the five most common side effects of nineteen approved drugs and one drug with pending approval (Ribociclib) for the systemic treatment of BC. For this purpose, the drugs were divided into three groups: chemotherapy, targeted therapy, and endocrine therapy. The reference for the comparison was the prescribing information of the respective drug. A congruence score was calculated for the information on side effects: correct information (2 points), generally appropriate information (1 point), and otherwise no point. The information sources were then compared using a Friedmann test and a Bonferroni-corrected post-hoc test. Results: In the overall comparison, ChatGPT 3.5 received the best score with a congruence of 67.5%, followed by ChatGPT 4.0 with 67.0%, PINK with 59.5%, and with Google 40.0% (p < 0.001). There were also significant differences when comparing the individual subcategories, with the best congruence achieved by PINK (73.3%, p = 0.059) in the chemotherapy category, ChatGPT 4.0 (77.5%; p < 0.001) in the targeted therapy category, and ChatGPT 3.5 (p = 0.002) in the endocrine therapy category. Conclusions: Artificial intelligence and professional online information websites provide the most reliable information on the possible side effects of the systemic treatment of early breast cancer, but congruence with prescribing information is limited. The medical consultation should still be considered the best source of information.
Effect of the Electrogalvanized and Galvannealed Zn Coatings on the Liquid Metal Embrittlement Susceptibility of High Si and Mn Advanced High-Strength Steel
Authors: Zhou Jiayi , Hu Rongxun , Sun Yu , Lei Ming , Gao Yulai
Published: January 2025

The advanced high-strength steels (AHSSs) with high Si and Mn contents are extensively applied in the automobile manufacturing industry. To improve the corrosion resistance, Zn coatings are generally applied to the steel substrate. However, heat input and tensile stress occur during the resistance spot welding (RSW) process; thus, Zn-induced liquid metal embrittlement (LME) can be produced due to the existence of liquid Zn. Unfortunately, the LME occurrence can trigger the premature failure of welded joints, seriously affecting the service life of vehicle components. In this study, the LME behaviors in high Si and Mn RSW joints with electrogalvanized (EG) and galvannealed (GA) Zn coatings were comparatively investigated. Based on the Auto/Steel Partnership (A/SP) criterion, 16 groups of different welding currents were designed. In particular, four typical groups of RSW joints were selected to reveal the characteristics of the LME behaviors. Moreover, these four typical groups of EG and GA high Si and Mn RSW joints were, respectively, etched to measure their nugget sizes. The results indicated that with the increase in the welding current, more severe LME cracks tended form. As determined during the comprehensive evaluation of the 16 groups of EG and GA welded joints, higher LME susceptibility occurred in the EG high Si and Mn steels. It was concluded that the formation of Fe-Zn intermetallic compounds (IMCs) and internal oxide layers during the annealing process could account for the lower LME susceptibility in the GA welded joints.
Evolution of Surface Integrity Characteristics and Mechanical Behavior of Diamond Burnished and Turned AISI 304 Steel Specimens After Prolonged Exposure to Natural Seawater
Authors: Argirov Yaroslav , Maximov Jordan , Duncheva Galya , Anchev Angel , Dunchev Vladimir , Mechkarova Tatyana
Published: January 2025

This article presents results on the evolution of surface integrity, microstructure, mechanical characteristics, fatigue strength, and wear behavior of AISI 304 steel specimens after prolonged exposure (up to 746 days) to a natural seawater environment, specifically near the port of Varna, Bulgaria. The samples, having different shapes and sizes according to the respective tests, were divided into two main groups based on the finishing process: fine turning (FT) and diamond burnishing (DB). Additionally, fatigue FT specimens were polished to meet the standard requirements. Some of the cylindrical samples from both groups were heat-treated to dissolve the car-bides. No significant improvement in the corrosion resistance of the heat-treated samples (FT and DB) was observed compared with untreated samples after 746 days of immersion in seawater. Overall, all types of DB specimens showed less mass loss (indicating a higher corrosion resistance), higher static and fatigue strength, greater plasticity, and greater wear resistance than the corresponding FT specimens. Notably, pitting corrosion was observed on all specimens, as well as trac-es of intergranular corrosion in some FT specimens. The hardening DB effects have a complex impact on corrosion activity. The increases in dislocation density and the surface and internal energy of the subsurface layers intensify the nucleation of corrosion centers in the surface layers; however, the refined fibrous structure and significant reduction in the roughness slow down the development of corrosion. Therefore, the implementation of DB as a smoothing process will re-duce the surface energy, and hence will lead to further increases in corrosion resistance.
Seeing the Sound: Multilingual Lip Sync for Real-Time Face-to-Face Translation
Authors: Rafiei Oskooei Amirkia , Aktaş Mehmet S. , Keleş Mustafa
Published: January 2025

Imagine a future where language is no longer a barrier to real-time conversations, enabling instant and lifelike communication across the globe. As cultural boundaries blur, the demand for seamless multilingual communication has become a critical technological challenge. This paper addresses the lack of robust solutions for real-time face-to-face translation, particularly for low-resource languages, by introducing a comprehensive framework that not only translates language but also replicates voice nuances and synchronized facial expressions. Our research tackles the primary challenge of achieving accurate lip synchronization across culturally diverse languages, filling a significant gap in the literature by evaluating the generalizability of lip sync models beyond English. Specifically, we develop a novel evaluation framework combining quantitative lip sync error metrics and qualitative assessments by human observers. This framework is applied to assess two state-of-the-art lip sync models with different architectures for Turkish, Persian, and Arabic languages, using a newly collected dataset. Based on these findings, we propose and implement a modular system that integrates language-agnostic lip sync models with neural networks to deliver a fully functional face-to-face translation experience. Inference Time Analysis shows this system achieves highly realistic, face-translated talking heads in real time, with a throughput as low as 0.381 s. This transformative framework is primed for deployment in immersive environments such as VR/AR, Metaverse ecosystems, and advanced video conferencing platforms. It offers substantial benefits to developers and businesses aiming to build next-generation multilingual communication systems for diverse applications. While this work focuses on three languages, its modular design allows scalability to additional languages. However, further testing in broader linguistic and cultural contexts is required to confirm its universal applicability, paving the way for a more interconnected and inclusive world where language ceases to hinder human connection.
The Relevance of Cognitive and Affective Factors to Explain the Acceptance of Blockchain Use: The Case of Loyalty Programmes
Authors: Souto-Romero Mar , Arias-Oliva Mario , Andrés-Sánchez Jorge de , Llorens-Marín Miguel
Published: January 2025

Blockchain technology has been highlighted as one of the most promising technologies to emerge in the 21st century. However, the expansion of blockchain applications is progressing much more slowly than initially expected, despite its promising properties. These considerations motivate this study, which evaluates the drivers that facilitate the adoption of this technology through blockchain-based loyalty programs (BBLPs). The analytical framework used is the conceptual groundwork known as the cognitive–affective–normative model. Thus, we propose to explain the behavioural intention to use BBLPs (BEHAV) with two cognitive variables, namely perceived usefulness (USEFUL) and perceived ease of use (EASE); two affective variables, namely positive emotions (PEMO) and negative emotions (NEMO); and a normative factor, namely, the subjective norm (SNORM). A partial least squares-structural equation modelling analysis suggests that, to explain the expected response of BEHAV, only the positive relationships of the cognitive constructs with the response variable are significant. The results of the quantile regression suggest that the cognitive constructs, especially USEFUL, have a consistently significant positive influence across the entire response range of the response variable. The affective variables are significant in explaining the lower quantiles of BEHAV but not across the full response range. NEMO consistently has a significant negative influence on BEHAV in the percentiles at or below the median response. PEMO has a significantly positive influence on some of the BEHAV percentiles below the median, although this impact is not consistent across the lower quantiles of the median. The normative variable appears to have a residual influence on BEHAV, which, when significant (at the 90th quantile), is, contrary to expectations, negative. The results highlight that, while cognitive variables are essential in the acceptance of BBLPs, emotions—particularly negative ones—play an especially significant role among potential users whose level of acceptance falls below the central trend.
Human Activity Recognition Using Graph Structures and Deep Neural Networks
Authors: Bsoul Abed Al Raoof K.
Published: January 2025

Human activity recognition (HAR) systems are essential in healthcare, surveillance, and sports analytics, enabling automated movement analysis. This research presents a novel HAR system combining graph structures with deep neural networks to capture both spatial and temporal patterns in activities. While CNN-based models excel at spatial feature extraction, they struggle with temporal dynamics, limiting their ability to classify complex actions. To address this, we applied the Firefly Optimization Algorithm to fine-tune the hyperparameters of both the graph-based model and a CNN baseline for comparison. The optimized graph-based system, evaluated on the UCF101 and Kinetics-400 datasets, achieved 88.9% accuracy with balanced precision, recall, and F1-scores, outperforming the baseline. It demonstrated robustness across diverse activities, including sports, household routines, and musical performances. This study highlights the potential of graph-based HAR systems for real-world applications, with future work focused on multi-modal data integration and improved handling of occlusions to enhance adaptability and performance.
In Vivo Application of the Effects of Red-to-Near-Infrared Light Spectroscopy on Skin-Brightening and Anti-Aging Properties via LED Facial Masks
Authors: Lee Sunghoon , Afandi Mohammad M. , Lee Jungah , Kim Jongsu
Published: January 2025

Most people want effective anti-aging and skin-brightening products. Although red-to-near-infrared (R/NIR) spectroscopy has recently been used in cosmetology, its practical use with high efficacy for anti-aging and skin brightening remains challenging. Herein, we aimed to determine the efficacy and improvement effects of a newly developed anti-aging and skin-brightening facial mask. A face study was conducted to assess efficacy and improvement effectiveness, with 21 female volunteers with oily, dry, and normal skin conditions applying the product under study (CF Magic Mask) to their face for 4-week periods. The dermatologist investigator evaluated the skin brightness, skin elasticity, eye wrinkles, dead skin cells on the scalp, dermal density, face lifting, scalp sebum, and global appearance. The mean skin-brightening and anti-aging parameters were improved (p < 0.05) after the use of the newly developed CF Magic Masks for 4-week periods. Significantly, the scalp sebum and dead skin cells on the scalp showed the greatest improvement, being reduced by about 26.71% and 21.96%, respectively. The global assessment by the volunteers showed moderate efficacy and preference, with no adverse effects or skin irritation indicated after the use of the test product.
Spontaneous Coronary Artery Dissection Involving the Left Main with Extension to Left Anterior Descending Artery and Left Circumflex Artery: Diagnostic and Management Challenges
Authors: Rusali Constantin Andrei , Cojocaru Lucia , Lupu Ioana Caterina , Uzea Cezar-Dan , Rusali Maria Lavinia
Published: January 2025

Spontaneous coronary artery dissection is a rare cause of unstable angina, myocardial infarction, and sudden cardiac death, particularly among young women and individuals without conventional atherosclerotic risk factors. We present the case of a 43-year-old woman who had spontaneous coronary artery dissection involving the left main with extension to left anterior descending artery and left circumflex artery. She was ultimately managed medically, with a good outcome. Spontaneous coronary artery dissection is a unique and intricate condition that demands advanced diagnostic techniques and tailored management strategies. Greater awareness and advancements in imaging technologies have enhanced the detection and understanding of spontaneous coronary artery dissection. However, continued research is crucial to resolving outstanding uncertainties and optimizing patient outcomes.
First Experiences with Fusion of PET-CT and MRI Datasets for Navigation-Assisted Percutaneous Biopsies for Primary and Metastatic Bone Tumors
Authors: Fritzsche Hagen , Pape Alexander , Schaser Klaus-Dieter , Beyer Franziska , Plodeck Verena , Hoffmann Ralf-Thorsten , Hahlbohm Patricia , Mehnert Elisabeth , Weidlich Anne
Published: January 2025

Background: The aim of this study was to compare the technique of navigation-assisted biopsy based on fused PET and MRI datasets to CT-guided biopsies in terms of the duration of the procedure, radiation dose, complication rate, and accuracy of the biopsy, particularly in anatomically complex regions. Methods: Between 2019 and 2022, retrospectively collected data included all navigated biopsies and CT-guided biopsies of suspected primary bone tumors or solitary metastases. Navigation was based on preoperative CT, PET-CT/-MRI, and MRI datasets, and tumor biopsies were performed using intraoperative 3D imaging combined with a navigation system. Results: A total of 22 navigated (main group: m/f = 10/12, mean age: 56 yrs.) and 57 CT-guided biopsies (reference group: m/f = 36/21, mean age: 63 yrs.) were performed. Patients were grouped according to anatomic sites (pelvis, spine, extremities, thorax). The duration of the procedure in the reference group was significantly shorter than in the main group, particularly in the spine. The effective radiation dose was in the same range in both groups (main/reference group: 0.579 mSv and 0.687 mSv, respectively). In the reference group, a re-biopsy had to be performed in nine patients (diagnostic yield: 84%). A total of four major and three minor complications occurred in the reference group. Conclusions: Navigation-assisted percutaneous tumor biopsy resulted in correct, histologically useable diagnoses in all patients and reached a higher accuracy and first-time success rate (diagnostic yield: 100%) in comparison to CT-guided biopsies. The fusion of PET, CT, and MRI datasets enables us to combine anatomical with metabolic information. Consequently, target selection was improved, and the rate of false negative/low-grade sampling errors was decreased. Radiation exposure could be kept at a comparable level, and the durations of both procedures were comparable to conventional methods.
Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology
Authors: Jaradat Ameera S. , Wedyan Mohammad , Alomari Saja , Barhoush Malek Mahmoud
Published: January 2025

Background/Objectives: One of the key challenges in autism is early diagnosis. Early diagnosis leads to early interventions that improve the condition and not worsen autism in the future. Currently, autism diagnoses are based on monitoring by a doctor or specialist after the child reaches a certain age exceeding three years after the parents observe the child’s abnormal behavior. Methods: The paper aims to find another way to diagnose autism that is effective and earlier than traditional methods of diagnosis. Therefore, we used the Eye Gaze fixes map dataset and Eye Tracking Scanpath dataset (ETSDS) to diagnose Autistic Spectrum Disorder (ASDs), while a subset of the ETSDS was used to recognize autism scores. Results: The experimental results showed that the higher accuracy rate reached 96.1% and 98.0% for the hybrid model on Eye Gaze fixes map datasets and ETSDS, respectively. A higher accuracy rate was reached (98.1%) on the ETSDS used to recognize autism scores. Furthermore, the results showed the outperformer for the proposed method results compared to previous works. Conclusions: This confirms the effectiveness of using artificial intelligence techniques in diagnosing diseases in general and diagnosing autism, in addition to the need to increase research in the field of diagnosing diseases using advanced techniques.
Non-Invasive Biomarkers and Breath Tests for Diagnosis and Monitoring of Chronic Liver Diseases
Authors: Boon-yasidhi Pasawat , Karnsakul Wikrom
Published: January 2025

Background: Chronic liver disease (CLD) presents a significant global health burden, demanding effective tools for diagnosis and monitoring. Traditionally, liver biopsy has been the gold standard for evaluating liver fibrosis and other chronic liver conditions. However, biopsy’s invasiveness, associated risks, and sampling variability indicate the need for reliable, noninvasive alternatives. This review examines the utility of noninvasive tests (NITs) in assessing liver disease severity, progression, and therapeutic response in patients with CLD. Result: Key modalities discussed include serum biomarker panels (e.g., FIB-4, APRI, ELF), imaging techniques like transient elastography, and magnetic resonance elastography, each offering unique benefits in fibrosis staging. Emerging biomarkers such as extracellular vesicles and circulating microRNAs show promise in early detection and personalized monitoring. Comparative studies indicate that while no single NIT matches biopsy precision, combinations of these modalities improve diagnostic accuracy and patient outcomes by reducing unnecessary biopsies. Moreover, NITs are instrumental in monitoring dynamic changes in liver health, allowing for more responsive and patient-centered care. Conclusions: Challenges remain, including standardization across tests, cost considerations, and the need for larger, diverse population studies to validate findings. Despite these limitations, NITs are increasingly integrated into clinical practice, fostering a paradigm shift toward noninvasive, accessible liver disease management. Continued advancements in NITs are essential for improved patient outcomes and will likely shape the future standard of care for CLD.
High Incidence of Isolated Tumor Cells in Sentinel Node Biopsies of Thin Melanomas: A Potential Factor in the Paradoxical Prognosis of Stage IIIA Cutaneous Melanoma?
Authors: Ronchi Andrea , D’Abbronzo Giuseppe , Carraturo Emma , Argenziano Giuseppe , Brancaccio Gabriella , Scharf Camila , Moscarella Elvira , Troiani Teresa , Iovino Francesco , Tolone Salvatore , Faenza Mario , Cazzato Gerardo , Franco Renato
Published: January 2025

Background/Objectives: This study aims to evaluate whether the presence of isolated tumor cells (ITCs) correlates with specific stages of cutaneous melanoma, potentially shedding light on their prognostic significance and the paradoxical survival outcomes in stage IIIA. Methods: This study analyzed cases of sentinel lymph node biopsies for cutaneous melanoma between 2021 and 2023. It included patients with CM diagnoses, available histological slides, and clinical information about the neoplasia stage. The correlation between the primary tumor stage and the presence of isolated tumor cells was statistically analyzed. Results: This study analyzed 462 sentinel lymph node biopsies, revealing 77.1% negative cases and 22.9% positive cases. Isolated tumor cells were observed in 24 cases (5.2%), most commonly in the early stages (e.g., pT1b and pT2a). Statistical analysis confirmed a significant correlation between ITC presence and early-stage neoplasms (p = 0.014). Conclusions: Although ITCs prompt upstaging, their prognostic impact appears limited, especially in thin melanomas, where survival aligns more closely with stage IB than stage IIIA. This aligns with findings from breast cancer studies where ITCs are not equated to metastases in staging due to their minimal impact on prognosis. Current melanoma staging practices could benefit from differentiating ITCs from larger metastatic deposits to better reflect the actual metastatic burden and guide treatment decisions.
Use of Machine Learning to Predict the Incidence of Type 2 Diabetes Among Relatively Healthy Adults: A 10-Year Longitudinal Study in Taiwan
Authors: Liu Ying-Qiang , Chang Tzu-Wei , Lee Lung-Chun , Chen Chia-Yu , Hsu Pi-Shan , Tsan Yu-Tse , Yang Chao-Tung , Chu Wei-Min
Published: January 2025

Background: The prevalence of diabetes is increasing worldwide, particularly in the Pacific Ocean island nations. Although machine learning (ML) models and data mining approaches have been applied to diabetes research, there was no study utilizing ML models to predict diabetes incidence in Taiwan. We aimed to predict the onset of diabetes in order to raise health awareness, thereby promoting any necessary lifestyle modifications and help mitigate disease burden. Methods: The research dataset used in the study was retrieved from the Clinical Data Center of Taichung Veterans General Hospital. We collected data from the available electronic health records with a total of 33 items being employed for model construction. Individuals with diabetes and those with missing data were excluded. Ultimately, 6687 adults were included in the final analysis, where we implemented three different ML algorithms, including logistic regression (LR), random forest (RF) and extreme gradient boosting (XGBoost) in order to predict diabetes. Results: The top five important factors involved in the prediction model were glycated hemoglobin (HbA1c), fasting blood glucose, weight, free thyroxine (fT4), and triglycerides (TG). Notably, random forest, logistic regression, and XGBoost reached 99%, 99%, and 98% accuracy, respectively. fT4 seems to be one of the significant features in predicting the onset of diabetes. Moreover, this would be the first study using machine learning models to predict diabetes that has demonstrated the importance of thyroid hormone. Conclusions: A total of 33 items were able to be put into the machine learning model in order to predict diabetes with promising accuracy. In comparison to prior studies on machine learning models, this study not only identified similar key factors for predicting diabetes but also highlighted the significance of thyroid hormones, a factor that was previously overlooked. Moreover, it highlighted the relevance of predicting type 2 diabetes using more affordable methods, which would be useful for clinical healthcare professionals and endocrinologists who apply the models to clinical practice.
The Morphological Stenosis Pattern of the Caroticoclinoid Foramen
Authors: Paschopoulos Ioannis , Triantafyllou George , Papadopoulos-Manolarakis Panagiotis , Luzzi Sabino , Karangeli Nektaria , Tsakotos George , Galzio Renato , Piagkou Maria
Published: January 2025

Background: The caroticoclinoid bar (CCB) or caroticoclinoid foramen (CCF) represents a well-described ossified variant of the skull base. It corresponds to an osseous bridge (resulting after homonymous ligament ossification) between the anterior and middle clinoid processes (ACPs and MCPs) surrounding the internal carotid artery (ICA)’s cavernous segment. Although extensive research has been performed on this clinically significant entity, only a few studies have been conducted on its effect on the ICA. The current study on dried skulls, using computed tomography (CT) and computed tomography angiography (CTA) scans, aimed to investigate the CCB’s presence and potential morphological stenosis patterns. Methods: One hundred (100) dried adult skulls and one hundred sixty (160) skulls from CT scans of patients were obtained (a total of 520 observations). To further calculate the ICA diameter (at the ACP-MCP region) and correlate the resulting dimeters with all potential morphological stenosis patterns of the CCB, thirty (30) CTAs of patients free of the variant were selected. Results: Concerning the osseous pattern morphology, of the total of 520 sides, the CCB was identified in 17.1%, the complete variant (creating a caroticoclinoid foramen-CCF) was calculated in 11.5%, and the incomplete one was calculated in 5.6%. No side, sex, or age impact was identified for the CCB presence. Concerning the ICA dimensions, its diameter was calculated to be between 4 and 5 mm. Thus, we observed three morphological stenosis patterns of the CCF. A low-risk pattern of stenosis (>5 mm diameter) was observed in 40 CCFs (44.9%), an intermediate risk of stenosis (4–5 mm diameter) in 38 CCFs (38.2%), and a high risk of stenosis (<4 mm diameter) was depicted in 15 CCFs (16.8%). Conclusions: In the present study, we investigated the CCF presence and potential morphological stenosis patterns by calculating and correlating the ICA diameter. In 16.8% of the current sample with CCFs (irrespective of their morphology), we observed that the ICA is probably at a high risk of compression. Radiologists and neurosurgeons intervening in the area should preoperatively diagnose the possibility of ICA compression in this area.
Spectral Differentiation of Hyperdense Non-Vascular and Vascular Renal Lesions Without Solid Components in Contrast-Enhanced Photon-Counting Detector CT Scans—A Pilot Study
Authors: Becker Judith , Feitelson Laura-Marie , Risch Franka , Canalini Luca , Kaufmann David , Wudy Ramona , Jehs Bertram , Haerting Mark , Wollny Claudia , Scheurig-Muenkler Christian , Kroencke Thomas , Schwarz Florian , Decker Josua A. , Bette Stefanie
Published: January 2025

Introduction: The number of incidental renal lesions identified in CT scans of the abdomen is increasing. Objective: The aim of this study was to determine whether hyperdense renal lesions without solid components in a portal venous CT scan can be clearly classified as vascular or non-vascular by material decomposition into iodine and water. Methods: This retrospective single-center study included 26 patients (mean age 72 years ± 9; 16 male) with 42 hyperdense renal lesions (>20 HU) in a contrast-enhanced Photon-Counting Detector CT scan (PCD-CT) between May and December 2022. Spectral decomposition into virtual non-contrast (VNC) images and iodine quantification maps was performed, and HU values were quantified within the lesions. Further imaging and histopathological reports served as reference standards. Results: Mean VNC values were 55.7 (±24.2) HU for non-vascular and 32.2 (±11.1) HU for vascular renal lesions. Mean values in the iodine maps were 5.7 (±7.8) HU for non-vascular and 33.3 (±19.0) HU for vascular renal lesions. Using a threshold of >20.3 HU in iodine maps, a total of 7/8 (87.5%) vascular lesions were correctly identified. Conclusion: This proof-of-principle study suggests that the routine use of spectral information acquired in PCD-CT scans might be able to reduce the necessary workup for hyperdense renal lesions without solid components. Further studies with larger patient cohorts are necessary to validate the results of this study and to determine the usefulness of this method in clinical routine.
An Evolutionary Federated Learning Approach to Diagnose Alzheimer’s Disease Under Uncertainty
Authors: Basnin Nanziba , Mahmud Tanjim , Islam Raihan Ul , Andersson Karl
Published: January 2025

Background: Alzheimer’s disease (AD) leads to severe cognitive impairment and functional decline in patients, and its exact cause remains unknown. Early diagnosis of AD is imperative to enable timely interventions that can slow the progression of the disease. This research tackles the complexity and uncertainty of AD by employing a multimodal approach that integrates medical imaging and demographic data. Methods: To scale this system to larger environments, such as hospital settings, and to ensure the sustainability, security, and privacy of sensitive data, this research employs both deep learning and federated learning frameworks. MRI images are pre-processed and fed into a convolutional neural network (CNN), which generates a prediction file. This prediction file is then combined with demographic data and distributed among clients for local training. Training is conducted both locally and globally using a belief rule base (BRB), which effectively integrates various data sources into a comprehensive diagnostic model. Results: The aggregated data values from local training are collected on a central server. Various aggregation methods are evaluated to assess the performance of the federated learning model, with results indicating that FedAvg outperforms other methods, achieving a global accuracy of 99.9%. Conclusions: The BRB effectively manages the uncertainty associated with AD data, providing a robust framework for integrating and analyzing diverse information. This research not only advances AD diagnostics by integrating multimodal data but also underscores the potential of federated learning for scalable, privacy-preserving healthcare solutions.
Suppressing Polarization Mode Dispersion with the Quantum Zeno Effect
Authors: Nodurft Ian , Rodriguez Perez Alejandro , Naimipour Naveed , Shaw Harry C.
Published: January 2025

Polarization mode dispersion can introduce quantum decoherence in polarization encoded information, limiting the range of quantum communications protocols. Therefore, strategies to nullify the effect would reduce quantum decoherence and potentially increase the operational range of such technology. We constructed a quantum model of polarization mode dispersion alongside a two-level absorbing material. The two-level material serves to destructively measure one of two orthogonal polarization modes, thus projecting the polarization onto the other state. The theoretical results are supported by a numerical simulation in Mathematica Documentation where we compare the evolution of the polarization state with and without the absorbing material. We find that this strategy is effective in suppressing the effects of polarization mode dispersion, and that this method produces a global phase shift related to the waveguide’s birefringent properties.
Inferential Approach for Evaluating the Association Between Land Cover and Soil Carbon in Northern Ontario
Authors: Pittman Rory , Hu Baoxin , Pittman Tyler , Webster Kara L. , Shang Jiali , Nelson Stephanie A.
Published: January 2025

Resolving the status of soil carbon with land cover is critical for addressing the impacts of climate change arising from land cover conversion in boreal regions. However, many conventional inferential approaches inadequately gauge statistical significance for this issue, due to limited sample sizes or skewness of soil properties. This study aimed to address this drawback by adopting inferential approaches suitable for smaller samples sizes, where normal distributions of soil properties were not assumed. A two-step inference process was proposed. The Kruskal–Wallis (KW) test was first employed to evaluate disparities amongst soil properties. Generalized estimating equations (GEEs) were then wielded for a more thorough analysis. The proposed method was applied to soil samples (n = 431) extracted within the southern transition zone of the boreal forest (49°–50° N, 80°40′–84° W) in northern Ontario, Canada. Sites representative of eight land cover types and seven dominant tree species were sampled, investigating the total carbon (C), carbon-to-nitrogen ratio (C:N), clay percentage, and bulk density (BD). The KW test analysis corroborated significance (p-values < 0.05) for median differences between soil properties across the cover types. GEEs supported refined robust statistical evidence of mean differences in soil C between specific tree species groupings and land covers, particularly for black spruce (Picea mariana) and wetlands. In addition to the proposed method, the results of this study provided application for the selection of appropriate predictors for C with digital soil mapping.
Boys and Girls in the Classroom—About Didactical Perspectives in Similarities and Differences
Authors: Stenberg Ingela , Boström Lena
Published: January 2025

The lower academic performance of boys in compulsory school has long been a well-known fact, but only in the last decade has it been shown in the public debate. Various explanations for boys’ underperformance mainly include background, expectations, norms, and hierarchical gender order. Few studies have focused on what can be affected, namely the teaching in the classroom. In addition, studies have not, to any significant extent, focused on the students’ perceptions. This study was conducted through eight group interviews with 2nd and 5th-grade students and explored how boys and girls perceive learning in a classroom context. Similarities and differences in perceptions emerged through a reflexive thematic analysis from a didactic theoretical perspective—the similarities in perception concerned the teacher’s leadership, interactions, learning strategies, and learning environment. The differences were about reprimands, different developed learning strategies, confidence in the teacher, and the social atmosphere. The result indicates a need for the teacher to be more aware of the processes going on in the classroom and to build both social and learning support structures with a focus on learning strategies.
Towards Intelligent Edge Computing: A Resource- and Reliability-Aware Hybrid Scheduling Method on Multi-FPGA Systems
Authors: Li Zeyu , Hao Yuchen , Gao Hongxu , Zhou Jia
Published: January 2025

Multi-FPGA systems can form larger and more powerful computing units through high-speed interconnects between chips, and are beginning to be widely used by various computing service providers, especially in edge computing. However, the new computing architecture brings new challenges to efficient and reliable task scheduling. In this context, we propose a resource- and reliability-aware hybrid scheduling method on Multi-FPGA systems. First, a set of models is established based on the resource/time requirements, communication overhead, and state conversion process of tasks to further analyze the constraints of system scheduling. On this basis, the large task is divided into subtasks based on the data dependency matrix, and the Maintenance Multiple Sequence (MMS) algorithm is used to generate execution sequences for each subtask to the Multi-FPGA systems to fully exploit resources and ensure reliable operation. Compared with state-of-the-art scheduling methods, the proposed method can achieve an average increase in resource utilization of 7%; in terms of reliability, it achieves good execution gains, with an average task completion rate of 98.3% and a mean time to failure of 15.7 years.
Using Fuzzy Logic to Analyse Weather Conditions
Authors: Małolepsza Olga , Mikołajewski Dariusz , Prokopowicz Piotr
Published: January 2025

Effective weather analysis is a very important scientific, social, and economic issue, because weather directly affects our lives and has a significant impact on various sectors, including agriculture, transport, energy, and natural disaster management. Weather analysis is therefore the basis for the operation of many decision-making support systems, especially in transport (air, sea), ensuring the continuity of supply chains for industry or the delivery of food and medicines, but also municipal economies or tourism. Its role and importance will grow with the worsening of climatic phenomena and the development of the Industry5.0 paradigm, which puts humans and their environment at the center of attention. This article presents issues related to fuzzy sets and systems and presents a weather analysis model based on them. The fuzzy system was created using Matlab, in the Fuzzy Logic Designer application, focusing on fuzzy logic. With Fuzzy Logic Designer, users can define fuzzy sets, rules, and carry out fuzzification and defuzzification processes, thereby offering great possibilities in data management.
ORNIC: A High-Performance RDMA NIC with Out-of-Order Packet Direct Write Method for Multipath Transmission
Authors: Ma Jiandong , Guo Zhichuan , Pan Yipeng , Zhang Mengting , Zhao Zhixiang , Sun Zezheng , Chang Yiwei
Published: January 2025

Remote Direct Memory Access (RDMA) technology provides a low-latency, high-bandwidth, and CPU-bypassed method for data transmission between servers. Recent works have proved that multipath transmission, especially packet spraying, can avoid network congestion, achieve load balancing, and improve overall performance in data center networks (DCNs). Multipath transmission can result in out-of-order (OOO) packet delivery. However, existing RDMA transport protocols, such as RDMA over Converged Ethernet version 2 (RoCEv2), are designed for handling sequential packets, limiting their ability to support multipath transmission. To address this issue, in this study, we propose ORNIC, a high-performance RDMA Network Interface Card (NIC) with out-of-order packet direct write method for multipath transmission. ORNIC supports both in-order and out-of-order packet reception. The payload of OOO packets is written directly to user memory without reordering. The write address is embedded in the packets only when necessary. A bitmap is used to check data integrity and detect packet loss. We redesign the bitmap structure into an array of bitmap blocks that support dynamic allocation. Once a bitmap block is full, it is marked and can be freed in advance. We implement ORNIC on a Xilinx U200 FPGA (Field-Programmable Gate Array), which consumes less than 15% of hardware resources. ORNIC can achieve 95 Gbps RDMA throughput, which is nearly 2.5 times that of MP-RDMA. When handling OOO packets, ORNIC’s performance is virtually unaffected, while the performance of Xilinx ERNIC and Mellanox CX-5 drops below 1 Gbps. Moreover, compared with MELO and LEFT, our bitmap has higher performance and lower bitmap block usage.
A Review of Multi-Fidelity Learning Approaches for Electromagnetic Problems
Authors: Sendrea Ricardo E. , Zekios Constantinos L. , Georgakopoulos Stavros V.
Published: January 2025

The demand for fast and accurate electromagnetic solutions to support current and emerging technologies has fueled the rapid development of various machine learning techniques for applications such as antenna design and optimization, microwave imaging, device diagnostics, and more. Multi-fidelity (MF) surrogate modeling methods have shown great promise in significantly reducing computational costs associated with surrogate modeling while maintaining high model accuracy. This work offers a comprehensive review of the available MF surrogate modeling methods in electromagnetics, focusing on specific methodologies, related challenges, and the generation of variable-fidelity datasets. The article is structured around the two main types of electromagnetic problems: forward and inverse. It begins by summarizing key machine learning concepts and limitations. This transitions to discussing multi-fidelity surrogate model architectures and low-fidelity data techniques for the forward problem. Subsequently, the unique challenges of the inverse problem are presented, along with traditional solutions and their limitations. Following this, the review examines MF surrogate modeling approaches tailored to the inverse problem. In conclusion, the review outlines promising future directions in MF modeling for electromagnetics, aiming to provide fundamental insights into understanding these developing methods.