bluuebunny/crossref_metadata_2025 huggingface dataset:
RGISQL: Integrating Refined Grammatical Information into Relational Graph Neural Network for Text-to-SQL Task
Authors: Li Shuiyan , He Yaozhen , Ao Longhao , Qi Rongzhi
Published: November 2024

The text-to-SQL task aims to convert natural language questions into corresponding SQL queries based on a given database schema. Previous models that rely on graph neural networks often struggle to accurately capture the complex grammatical relationships present in these questions, leading to poor performance when generating queries for longer requests. To address these challenges, we propose RGISQL, which integrates refined grammatical information extracted from the question and employs segmentation processing to effectively manage long queries. Additionally, RGISQL minimizes the complexity of edge embeddings by reducing the coupling within graph neural networks. By utilizing grammatical dependency trees, RGISQL is better equipped to capture the inherent structure and grammatical rules of questions. This refined grammatical information offers additional contextual and semantic cues for the model, thereby enhancing both its generalizability and interpretability. Furthermore, we dynamically assess the importance of different edges based on the graph structure, which helps reduce the coupling of edge embeddings and further improves the model’s performance. Multiple sets of experiments conducted on the Spider and Spider-Syn datasets demonstrate that RGISQL outperforms other baselines, achieving the best results in both datasets.
Self-Sovereign Identity-Based E-Portfolio Ecosystem
Authors: Hsieh Yu-Heng , Yan Jun-Yu , Liao Chia-Hung , Yuan Shyan-Ming
Published: November 2024

In Taiwan, traditional student assessments, covering academic and extracurricular achievements, have shifted from paper to electronic portfolios (e-portfolios). However, limited trust among institutions restricts students from freely sharing and using their educational data. This paper introduces a self-sovereign identity-based infrastructure aimed at enhancing personal data security within the e-portfolio ecosystem. The proposed system includes two core components: (1) a decentralized identity chain, aligning user identities across platforms and granting users full self-sovereign control; and (2) an e-portfolio application chain to manage user interactions and access permissions within the ecosystem. A trusted educational authority also audits data sources, ensuring data integrity and reliability. This infrastructure empowers users to control who can access their data, safeguarding their security, with the identity chain preventing unauthorized access and the application chain recording authorization statuses to restrict data visibility to approved parties only.
Evaluation of the Changes in the Strength of Clay Reinforced with Basalt Fiber Using Artificial Neural Network Model
Authors: Aslan Topçuoğlu Yasemin , Duranay Zeynep Bala , Gürocak Zülfü
Published: November 2024

In this research, the impact of basalt fiber reinforcement on the unconfined compressive strength of clay soils was experimentally analyzed, and the collected data were utilized in an artificial neural network (ANN) to predict the unconfined compressive strength based on the basalt fiber reinforcement ratio and length. For this purpose, two different lengths of basalt fiber (6 mm and 12 mm) were added to unreinforced bentonite clay at ratios of 0%, 1%, 2%, 3%, 4%, and 5%, and unconfined compressive tests were performed on the prepared reinforced clay samples to determine the unconfined compressive strength (qu) values. The evaluation of the obtained experimental results was carried out by creating ANN models. To validate the prediction capabilities of the ANN, a comparative analysis was performed using linear regression, support vector machines, and Gaussian process regression models. Ultimately, a five-fold cross-validation technique was employed to objectively evaluate the overall performance of the model. The evaluations revealed that the ANN model predictions using data obtained from experimental studies showed the highest accuracy and were in close agreement with the experimental results.
AFF_CGE: Combined Attention-Aware Feature Fusion and Communication Graph Embedding Learning for Detecting Encrypted Malicious Traffic
Authors: Liu Junhao , Shao Guolin , Rao Hong , Li Xiangjun , Huang Xuan
Published: November 2024

While encryption enhances data security, it also presents significant challenges for network traffic analysis, especially in detecting malicious activities. To tackle this challenge, this paper introduces combined Attention-aware Feature Fusion and Communication Graph Embedding Learning (AFF_CGE), an advanced representation learning framework designed for detecting encrypted malicious traffic. By leveraging an attention mechanism and graph neural networks, AFF_CGE extracts rich semantic information from encrypted traffic and captures complex relations between communicating nodes. Experimental results reveal that AFF_CGE substantially outperforms traditional methods, improving F1-scores by 5.3% through 22.8%. The framework achieves F1-scores ranging from 0.903 to 0.929 across various classifiers, exceeding the performance of state-of-the-art techniques. These results underscore the effectiveness and robustness of AFF_CGE in detecting encrypted malicious traffic, demonstrating its superior performance.
Surgery First and Aligners: A Case Report Combining In-House Surgical Guides and Pre-Adapted Titanium Plates
Authors: Sefidroodi Mohammedreza , Shino Inleel Lundgård , Vassis Stratos , Hammer Karen Eich , Kristensen Kasper Dahl , Pedersen Thomas Klit , Nørholt Sven Erik , Buhl Jytte
Published: November 2024

Continuous advancements in technology have made it possible to integrate clear aligner therapy (CAT) with orthognathic surgery. This case report presents a novel, individually-planned workflow, combining CAT with a surgery-first orthognathic approach (SFOA) in collaborating with engineers for an in-house production of surgical guides and customized titanium plates. The patient was evaluated subjectively, using the Oral Health-Related Quality of Life-14 (OHIP-14) questionnaire and Orthognathic Quality of Life questionnaire (OQLQ), and objectively with the Peer Assessment Rating (PAR) index. The patient displayed the planned occlusal relationship with no report of discomfort in the temporomandibular joint (TMJ) or post-surgical complications. The surgical and occlusal outcomes have remained consistent and stable after debonding. A decreased score was reported in both questionnaires and the PAR after treatment, thereby indicating improvements in both subjective and objective evaluations. This case report demonstrates that with proper individual planning, satisfactory subjective and objective outcomes can be achieved when combining SFOA with CAT.
IndoGovBERT: A Domain-Specific Language Model for Processing Indonesian Government SDG Documents
Authors: Riyadi Agus , Kovacs Mate , Serdült Uwe , Kryssanov Victor
Published: November 2024

Achieving the Sustainable Development Goals (SDGs) requires collaboration among various stakeholders, particularly governments and non-state actors (NSAs). This collaboration results in but is also based on a continually growing volume of documents that needs to be analyzed and processed in a systematic way by government officials. Artificial Intelligence and Natural Language Processing (NLP) could, thus, offer valuable support for progressing towards SDG targets, including automating the government budget tagging and classifying NSA requests and initiatives, as well as helping uncover the possibilities for matching these two categories of activities. Many non-English speaking countries, including Indonesia, however, face limited NLP resources, such as, for instance, domain-specific pre-trained language models (PTLMs). This circumstance makes it difficult to automate document processing and improve the efficacy of SDG-related government efforts. The presented study introduces IndoGovBERT, a Bidirectional Encoder Representations from Transformers (BERT)-based PTLM built with domain-specific corpora, leveraging the Indonesian government’s public and internal documents. The model is intended to automate various laborious tasks of SDG document processing by the Indonesian government. Different approaches to PTLM development known from the literature are examined in the context of typical government settings. The most effective, in terms of the resultant model performance, but also most efficient, in terms of the computational resources required, methodology is determined and deployed for the development of the IndoGovBERT model. The developed model is then scrutinized in several text classification and similarity assessment experiments, where it is compared with four Indonesian general-purpose language models, a non-transformer approach of the Multilabel Topic Model (MLTM), as well as with a Multilingual BERT model. Results obtained in all experiments highlight the superior capability of the IndoGovBERT model for Indonesian government SDG document processing. The latter suggests that the proposed PTLM development methodology could be adopted to build high-performance specialized PTLMs for governments around the globe which face SDG document processing and other NLP challenges similar to the ones dealt with in the presented study.
Harnessing the Distributed Computing Paradigm for Laser-Induced Breakdown Spectroscopy
Authors: Silva Nuno A.
Published: November 2024

Laser-induced breakdown spectroscopy allows fast and versatile elemental analysis, standing as a promising technique for a wide range of applications both at the research and industry levels. Yet, its high operation speed comes with a high throughput of data, which introduces some challenges at the level of the data processing domain, mainly due to the large computational load and data volume. In this work, we analyze and discuss opportunities of distributed computing paradigms and resources to address some of these challenges, covering most of the procedures usually employed in typical applications. We infer the possible impact of such computing resources by presenting some metrics of simple processing prototypes running in state-of-the-art computing facilities. Our results allow us to conclude that, while underexplored so far, these computing resources may allow for the development of tools for timely research and analysis in demanding applications and introduce novel solutions toward a more agile working environment.
An Enzyme-Free Impedimetric Sensor Based on Flower-like NiO/Carbon Microspheres for L-Glutamic Acid Assay
Authors: Sadri Najva , Mazloum-Ardakani Mohammad , Asadpour Farzaneh , Joseph Yvonne , Rahimi Parvaneh
Published: November 2024

This research introduces a non-enzymatic electrochemical sensor utilizing flower-like nickel oxide/carbon (fl-NiO/C) microspheres for the precise detection of L-glutamic acid (LGA), a crucial neurotransmitter in the field of healthcare and a frequently utilized food additive and flavor enhancer. The fl-NiO/C were synthesized with controllable microstructures using metal–organic frameworks (MOFs) as precursors followed by a simple calcination process. The uniformly synthesized fl-NiO/C microspheres were further characterized using Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), and field emission scanning electron microscopy (FE-SEM). The fl-NiO/C was utilized as a modifier on the surface of a glassy carbon electrode, and an impedimetric sensor based on electrochemical impedance spectroscopy (EIS) was developed for the detection of LGA. The proposed sensor demonstrated excellent catalytic activity and selectivity towards LGA across a broad concentration range of 10–800 μM with a sensitivity of 486.9 µA.mM−1.cm−2 and a detection limit of 1.28 µM (S/N = 3). The sensor was also employed to identify LGA in blood plasma samples, yielding results that align with those obtained through HPLC. This achievement highlights the potential of fl-NiO/C microspheres in advancing cutting-edge biosensing applications.
Prion-like Spreading of Disease in TDP-43 Proteinopathies
Authors: Pongrácová Emma , Buratti Emanuele , Romano Maurizio
Published: November 2024

TDP-43 is a ubiquitous nuclear protein that plays a central role in neurodegenerative disorders collectively known as TDP-43 proteinopathies. Under physiological conditions, TDP-43 is primarily localized to the nucleus, but in its pathological form it aggregates in the cytoplasm, contributing to neuronal death. Given its association with numerous diseases, particularly ALS and FTLD, the mechanisms underlying TDP-43 aggregation and its impact on neuronal function have been extensively investigated. However, little is still known about the spreading of this pathology from cell to cell. Recent research has unveiled the possibility that TDP-43 may possess prion-like properties. Specifically, misfolded TDP-43 aggregates can act as templates inducing conformational changes in native TDP-43 molecules and propagating the misfolded state across neural networks. This review summarizes the mounting and most recent evidence from in vitro and in vivo studies supporting the prion-like hypothesis and its underlying mechanisms. The prion-like behavior of TDP-43 has significant implications for diagnostics and therapeutics. Importantly, emerging strategies such as small molecule inhibitors, immunotherapies, and gene therapies targeting TDP-43 propagation offer promising avenues for developing effective treatments. By elucidating the mechanisms of TDP-43 spreading, we therefore aim to pave the way for novel therapies for TDP-43-related neurodegenerative diseases.
Automated Estimation of Building Heights with ICESat-2 and GEDI LiDAR Altimeter and Building Footprints: The Case of New York City and Los Angeles
Authors: Kaya Yunus
Published: November 2024

Accurate estimation of building height is crucial for urban aesthetics and urban planning as it enables an accurate calculation of the shadow period, the effective management of urban energy consumption, and thorough investigation of regional climatic patterns and human-environment interactions. Although three-dimensional (3D) cadastral data, ground measurements (total station, Global Positioning System (GPS), ground laser scanning) and air-based (such as Unmanned Aerial Vehicle—UAV) measurement methods are used to determine building heights, more comprehensive and advanced techniques need to be used in large-scale studies, such as in cities or countries. Although satellite-based altimetry data, such as Ice, Cloud and land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI), provide important information on building heights due to their high vertical accuracy, it is often difficult to distinguish between building photons and other objects. To overcome this challenge, a self-adaptive method with minimal data is proposed. Using building photons from ICESat-2 and GEDI data and building footprints from the New York City (NYC) and Los Angeles (LA) open data platform, the heights of 50,654 buildings in NYC and 84,045 buildings in LA were estimated. As a result of the study, root mean square error (RMSE) 8.28 m and mean absolute error (MAE) 6.24 m were obtained for NYC. In addition, 46% of the buildings had an RMSE of less than 5 m and 7% less than 1 m. In LA data, the RMSE and MAE were 6.42 m and 4.66 m, respectively. It was less than 5 m in 67% of the buildings and less than 1 m in 7%. However, ICESat-2 data had a better RMSE than GEDI data. Nevertheless, combining the two data provided the advantage of detecting more building heights. This study highlights the importance of using minimum data for determining urban-scale building heights. Moreover, continuous monitoring of urban alterations using satellite altimetry data would provide more effective energy consumption assessment and management.
Seismic Vibration Control and Multi-Objective Optimization of Transmission Tower with Tuned Mass Damper Under Near-Fault Pulse-like Ground Motions
Authors: Lin Ying , Liu Tao
Published: November 2024

Although the wind load is usually adopted as the governing lateral load in the design of transmission towers, many tall transmission towers may be damaged or even collapse in high seismic intensity areas, especially under near-fault pulse-like ground motions. To study the seismic vibration control effect of a tuned mass damper (TMD) attached to transmission tower, parametric analyses are conducted in SAP2000 through CSI OAPI programming, including TMD parameters such as the mass ratio μ from 0.5% to 10%, the frequency ratio f from 0.7 to 1.2, and the damping ratio ξ from 0.01 to 0.2. Based on the obtained analysis results, artificial neural network (ANN) is trained to predict the vibration reduction ratios of peak responses and the corresponding vibration reduction cost. Finally, the NSGA-III algorithm is adopted to perform the multi-objective optimization of a transmission tower equipped with TMD. Results show that the vibration reduction ratios first increase and then decrease with the increase of frequency ratio, but first increase and then remain stable with the increase of mass ratio and damping ratio. In addition, ANN fitting can accurately predict the nonlinear relationship between TMD parameters and objective functions. Through multi-objective optimization with the NSGA-III algorithm, TMD can simultaneously and significantly reduce different peak responses of transmission towers under near-fault pulse-like ground motions in a cost-effective manner.
Experimental and Numerical Analysis of the Impact Resistance of Polyurethane Foam Aluminum-Concrete Sandwich Structures
Authors: Zhang Yong , Yin Fenfang , Ma Junwei
Published: November 2024

The objective of this research paper is to examine the shock-absorption capabilities of sandwich structures that utilize polyurethane foam and aluminum as energy-absorbing materials. A series of drop weight impact tests were conducted on sandwich structures comprising polyurethane foam, aluminum, and concrete. The investigation encompasses variations in the thickness of the polyurethane foam-aluminum absorption layer, the impact height, and different structural combinations, coupled with numerical simulations. Results indicate that as the thickness of the polyurethane foam-aluminum energy absorption layer increases, the energy absorbed by the composite structure also increases. However, the rate of this increase tapers off as the layer thickness continues to grow. The impact height influences energy absorption within a defined range, enabling optimal utilization of the deformation and energy absorption capacities of the polyurethane foam-aluminum layer. Notably, the double-sandwich structure outperforms the single-sandwich structure in terms of impact resistance. The incorporation of the polyurethane foam-aluminum sandwich structure significantly enhances the impact resilience of concrete. Among the tested configurations, the double-sandwich structure composed of polyurethane foam, aluminum, and concrete exhibits the optimal absorption performance. Nevertheless, the layered nature of the structure significantly increases its construction complexity, potentially impacting the practical feasibility of utilizing the polyurethane foam-aluminum-concrete composite structure in real-world applications.
Nearby Real-Time Earthquake Simulation on an Urban Scale Based on Structural Monitoring
Authors: Kang Xiandong , Chen Hongyu , Zhao Guang , Lin Xuchuan , Zheng Lifu , Chen Yanan , Liu Qixuan , Zhao Zihong , Chen Xianan , Wang Fei
Published: November 2024

The real-time input of ground motions can effectively assess earthquake disasters in building structures. In this study, we proposed a nearby real-time simulation method that can be applied to assess regional earthquake disasters using structural monitoring recordings based on an urban earthquake simulation system. Real-time accelerations recorded during the 5.1 magnitude Tangshan earthquake were used to update and modify the computing models. El Centro waves were calibrated to 400 cm/s2 for structural seismic resilience analyses. The results indicated that approximately 70% of the structural functions were lost during the rare earthquake. Regional numerical models of 216 buildings were constructed and timeously updated using a geographic information system and measured data. The inputting ground motions recorded in the 5.1 magnitude Tangshan earthquake and typical El Centro waves were selected to analyze the structural seismic response, in which the damage indexes were computed, and the damage predictions of the regional 216 buildings were also simulated in different levels of earthquakes by being combined with the simplified principles of structural damage estimation. Finally, the evaluation results were visualized in three dimensions using ParaView software. The simulated results of earthquake disasters at an urban scale will promote the prediction abilities of local earthquake administration agencies and have considerable potential to provide essential information for emergency responses and regional disaster mitigation.
Adoption of Innovative Energy Facilities in the Tertiary Sector Buildings: Exploring Interdependencies and Key Drivers
Authors: Huang Ruijin , Gu Gaofeng
Published: November 2024

Innovative energy facilities, such as solar panels, heat pumps, and smart control ventilation, offer substantial opportunities to improve energy efficiency and environmental performance in the tertiary sector, aligning with green building objectives. This study aims to identify the key factors influencing the adoption of these facilities by small and medium-sized enterprise owners in the tertiary sector and to explore the interdependencies among them. To achieve this, we employed a stated choice experiment to assess preferences and decision-making by presenting respondents with multiple hypothetical scenarios, each containing alternatives described by varying attributes. A simultaneous equation model was used to analyze the key drivers of adoption and the interrelationships among these facilities. The results reveal that cost-related attributes and government incentives significantly impact the acceptance of energy facilities. Notably, while environmental responsibility is slightly associated with solar panel adoption, it shows no significant link with heat pumps or ventilation systems. Furthermore, we identified a bi-directional relationship between the adoption of solar panels and heat pumps, suggesting that acceptance of one positively influences the other. Conversely, a unidirectional relationship exists between ventilation and solar panels, where the adoption of ventilation positively influences solar panel adoption, but not the other way around. These findings contribute to a deeper understanding of decision-making processes in green building projects and provide valuable insights for policymakers and industry stakeholders aiming to promote sustainable energy solutions in the tertiary sector.
N-Acetylated Monosaccharides and Derived Glycan Structures Occurring in N- and O-Glycans During Prostate Cancer Development
Authors: Bertok Tomas , Jane Eduard , Hires Michal , Tkac Jan
Published: November 2024

Post-translational modifications of proteins play an important role in their stability, solubility and in vivo function. Also, for several reasons, such as the Golgi fragmentation during cancerogenesis, glycosylation as the most common modification is especially promising in offering high cancer specificity which, in combination with tissue-specific biomarkers available in the case of prostate diseases (PSA, PSMA, PAP), may lead to the development of novel oncodiagnostic approaches. In this review, we present the importance of subterminal glycan structures based on the N-acetylated monosaccharides GlcNAc and GalNAc in N- and also O-glycans, structures of which they are a component (LacNAc, LacdiNAc, branched structures). We also discuss the importance and clinical performance of these structures in cases of prostate cancer diagnostics using lectin-based affinity methods, which could be implemented in clinical laboratory practice in the future.
Local Endoscopic Treatment of Low-Grade Urothelial Tumors in the Upper Urinary Tract—Oncological Outcome of a Consecutive Single-Center Series of 118 Patients
Authors: Bislev Sophie , Brandt Simone Buchardt , Fuglsig Sven , Jensen Jørgen Bjerggaard
Published: November 2024

Background: Upper urinary tract urothelial tumors are mostly treated with the use of endoscopic laser ablation. The treatment is recommended when the tumor is low grade and non-invasive. Objective: The aim of the present study was to investigate the oncological outcome of patients treated endoscopically for low-grade (LG) upper tract urothelial carcinoma (UTUC). Methods: One hundred and eighteen consecutive patients with pTa LG UTUC initially treated with laser ablation from 2012–2022 at a single university hospital were included. Patient and tumor characteristics, treatment, and oncological outcomes were retrospectively registered from electronic medical journals. Survival analysis was performed using Kaplan–Meier and Nelson–Aalen plots. Results: The median number of local recurrences was 2 with a median time to first recurrence of 5 months (IQR: 2–46). The 2-year share of patients having no recurrence was 28.8%. The risk of recurrence was highest within the first 3 years following initial treatment. Two-year survival function with no progression and preserved renal unit was 67.9%. The two-year overall survival (OS) and disease-specific survival (DSS) were 84.2% and 97.1%. The 5-year OS and DSS were 59.1% and 94.1%. The median follow-up time for OS was 36 months (IQR: 20.3–58.8). Conclusions: In the patient series, we found that the risk of recurrence following laser ablation of LG UTUC was high; however, it was associated with a low risk of progression and high OS and DSS. Therefore, we conclude that treatment with laser ablation in patients with LG UTUC is safe, although frequent follow-up is needed to detect recurrence.
Immunohistochemical Characterization of Spermatogenesis in the Ascidian Ciona robusta
Authors: Sakurai Haruka , Shiba Kogiku , Takamura Katsumi , Inaba Kazuo
Published: November 2024

Animals show diverse processes of gametogenesis in the evolutionary pathway. Here, we characterized the spermatogenic cells in the testis of the marine invertebrate Ciona robusta. Ciona sperm differentiate in a non-cystic type of testis, comprising many follicles with various sizes and stages of spermatogenic cells. In the space among follicles, we observed free cells that were recognized by antibody against Müllerian inhibiting substance, a marker for vertebrate Sertoli cells. We further categorized the spermatogenic cells into four round stages (RI to RIV) and three elongated stages (EI to EIII) by morphological and immunohistochemical criteria. An antibody against a vertebrate Vasa homolog recognized a few large spermatogonium-like cells (RI) near the basal wall of a follicle. Consistent with the period of meiosis, a synaptonemal complex protein SYCP3 was recognized from early spermatocytes (RII) to early spermatids (E1). Acetylated tubulins were detected in spermatids before flagellar elongation at the RIV stage and became distributed along the flagella. Electron microscopy showed that the free cells outside the testicular follicle possessed a characteristic of vertebrate Sertoli cells. These results would provide a basis for basic and comparative studies on the mechanism of spermatogenesis.
Clinical Applications of Antisense Oligonucleotides in Cancer: A Focus on Glioblastoma
Authors: Khuu Alexandre , Verreault Maïté , Colin Philippe , Tran Helene , Idbaih Ahmed
Published: November 2024

Antisense oligonucleotides (ASOs) are promising drugs capable of modulating the protein expression of virtually any target with high specificity and high affinity through complementary base pairing. However, this requires a deep understanding of the target sequence and significant effort in designing the correct complementary drug. In addition, ASOs have been demonstrated to be well tolerated during their clinical use. Indeed, they are already used in many diseases due to pathogenic RNAs of known sequences and in several neurodegenerative diseases and metabolic diseases, for which they were given marketing authorizations (MAs) in Europe and the United States. Their use in oncology is gaining momentum with several identified targets, promising preclinical and clinical results, and recent market authorizations in the US. However, many challenges remain for their clinical use in cancer. It seems necessary to take a step back and review our knowledge of ASOs and their therapeutic uses in oncology. The objectives of this review are (i) to summarize the current state of the art of ASOs; (ii) to discuss the therapeutic use of ASOs in cancer; and (iii) to focus on ASO usage in glioblastoma, the challenges, and the perspective ahead.
Resistance to Electrical Corrosion of Au-Cu Alloy Coatings for Electronic Contacts
Authors: Chen Ting , Yu Longlin , Deng Boyi , Wang Fang , Ouyang Mingwei , Xu Xiaofeng , Qiang Xiaonong , Ma Yongfu , Wu Qiong , Ge Wen
Published: November 2024

In order to obtain electronic contacts with good performance, Au-Cu alloy coatings with different gold contents were prepared on copper substrates by direct current electrodeposition and were tested against electrochemical corrosion and arc corrosion. The experimental results showed that the hardness of the Au-Cu alloy was in the range of 115.2 HV~171.6 HV, which meets the requirements of electronic contact materials. The polarization curve (Tafel) test and electrochemical impedance spectroscopy (EIS) test results indicated that the electrochemical corrosion resistance of Au-Cu alloy plating was much better than pure copper. With the rise of gold content in the alloy coatings, the corrosion resistance of the alloy coatings enhanced gradually. Compared with pure copper, the Au-Cu alloy coatings showed more stable contact resistance. After 1000 contacts, the resistivity of the alloy with 75% gold varied from 72 mΩ to 78 mΩ, whereas under the same conditions, the resistivity of copper changed from 14 mΩ to 78 mΩ. Anode-type material transfer occurred after 1000 contacts with a reduction in the total mass of each contact element. The mass loss of Au75Cu25 and Au86Cu14 contact elements was lower than that of pure copper. The Au-Cu alloy coatings displayed excellent arc corrosion resistance when the gold content in the alloy plating was higher than 75%.
LLaMA 3 vs. State-of-the-Art Large Language Models: Performance in Detecting Nuanced Fake News
Authors: Repede Stefan Emil , Brad Remus
Published: November 2024

This study investigates the effectiveness of a proposed version of Meta’s LLaMA 3 model in detecting fake claims across bilingual (English and Romanian) datasets, focusing on a multi-class approach beyond traditional binary classifications in order to better mimic real-world scenarios. The research employs a proposed version of the LLaMA 3 model, optimized for identifying nuanced categories such as “Mostly True” and “Mostly False”, and compares its performance against leading large language models (LLMs) including Open AI’s ChatGPT versions, Google’s Gemini, and similar LLaMA models. The analysis reveals that the proposed LLaMA 3 model consistently outperforms its base version and older LLaMA models, particularly in the Romanian dataset, achieving the highest accuracy of 39% and demonstrating superior capabilities in identifying nuanced claims, over all the compared large language models. However, the model’s performance across both languages highlights some challenges, with generally low accuracy and difficulties in handling ambiguous categories by all the LLMs. The study also underscores the impact of language and cultural context on model reliability, noting that even state-of-the-art models like ChatGPT 4.o and Gemini exhibit inconsistencies when applied to Romanian text and more than a binary true/false approach.
Lightweight and Low-Parametric Network for Hardware Inference of Obstructive Sleep Apnea
Authors: Paul Tanmoy , Hassan Omiya , McCrae Christina S. , Islam Syed Kamrul , Mosa Abu Saleh Mohammad
Published: November 2024

Background: Obstructive sleep apnea is a sleep disorder that is linked to many health complications and can even be lethal in its severe form. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Artificial intelligence (AI)-embedded wearable device as a portable and less intrusive monitoring system is a highly desired alternative to polysomnography. However, AI models often require substantial storage capacity and computational power for edge inference which makes it a challenging task to implement the models in hardware with memory and power constraints. Methods: This study demonstrates the implementation of depth-wise separable convolution (DSC) as a resource-efficient alternative to spatial convolution (SC) for real-time detection of apneic activity. Single lead electrocardiogram (ECG) and oxygen saturation (SpO2) signals were acquired from the PhysioNet databank. Using each type of convolution, three different models were developed using ECG, SpO2, and model fusion. For both types of convolutions, the fusion models outperformed the models built on individual signals across all the performance metrics. Results: Although the SC-based fusion model performed the best, the DSC-based fusion model was 9.4, 1.85, and 11.3 times more energy efficient than SC-based ECG, SpO2, and fusion models, respectively. Furthermore, the accuracy, precision, and specificity yielded by the DSC-based fusion model were comparable to those of the SC-based individual models (~95%, ~94%, and ~94%, respectively). Conclusions: DSC is commonly used in mobile vision tasks, but its potential in clinical applications for 1-D signals remains unexplored. While SC-based models outperform DSC in accuracy, the DSC-based model offers a more energy-efficient solution with acceptable performance, making it suitable for AI-embedded apnea detection systems.
Clinical and Demographic Characteristics of Patients Hospitalized for Decompensated Heart Failure with Extremely High NT-proBNP Levels
Authors: Christodorescu Ruxandra Maria , Brie Daniel Miron , Brie Alina Diduța , Nistor Samuel , Tîrziu Alexandru , Dragomir Angela , Mornoș Cristian , Drăgan Simona , Duda-Seiman Daniel , Pop-Moldovan Adina , Dărăbanțiu Dan
Published: November 2024

Background: NT-proBNP levels with a wide range at admission play both a diagnostic and a prognostic role in patients with HF. The differences regarding the clinical profiles and demography in decompensated HF patients according to NT-proBNP levels at admission are not clear. Methods: This study aimed to analyze and compare clinical profiles and demographics in patients hospitalized for decompensated heart failure according to levels of NT-proBNP at admission. The study included 302 patients hospitalized for decompensated HF who were divided into three groups based on admission NT-proBNP levels: group A (n = 46, with NT-proBNP level < 3000 pg/mL), group B (n = 130, NT-proBNP level between 3000–10,000 pg/mL), and group C (n = 126, NT-proBNP level > 10,000 pg/mL). Results: Patients hospitalized with decompensated HF and very high levels of NTproBNP, above 10,000 pg/mL at admission, are older, have a lower LVEF, higher NYHA class, more renal dysfunction, and longer hospital stay, resulting in a more severe clinical profile. Conclusions: The presence of very high levels of NT-proBNP may identify a category of patients with a more severe prognosis that requires more aggressive management and closer follow-up.
Survival of Patients with Alcohol-Related Liver Disease Cirrhosis—Usefulness of the New Liver Mortality Inpatients Prognostic Score
Authors: Matovic Zaric Vera , Pantic Ivana , Lugonja Sofija , Glisic Tijana , Konjikusic Snezana , Lolic Iva , Baljosevic Nevena , Zgradic Sanja , El Mezeni Jasna , Vojnovic Marko , Brankovic Marija , Milovanovic Tamara
Published: November 2024

Background/Objectives: Alcohol can directly damage the liver, causing steatosis, steatohepatitis, cirrhosis, and hepatocellular cancer. The aim of this study was to examine 28-day survival in hospitalized patients with alcohol-related liver disease (ALD) cirrhosis, as well as to develop and validate a new survival prediction model. Methods: A total of 145 patients with ALD cirrhosis were included; 107 were diagnosed with acute decompensation (AD) and 38 with acute-on-chronic liver failure (ACLF). The new liver mortality inpatients (LIV-IN) score was calculated using the following variables: hepatic encephalopathy (HE), hepatorenal syndrome (HRS), ascites, systemic inflammatory response syndrome (SIRS), community-acquired infection (CAI), and fibrinogen. The diagnostic accuracy of the LIV-IN score was tested, along with the model for end-stage liver disease (MELD), model for end-stage liver disease-sodium (MELD-Na), albumin-bilirubin (ALBI), neutrophil-to-lymphocyte ratio (NLR), chronic liver failure consortium-C acute decompensation (CLIF-C AD), and chronic liver failure consortium-acute-on-chronic liver failure (CLIF-C ACLF). Results: Lethal outcome occurred in 46 (31.7%) patients. The mortality rate was higher in the ACLF group (n = 22, 57.9%) compared to the AD group (n = 24, 22.4%) (p < 0.01). The highest predictive power for short-term mortality was observed for the LIV-IN score (AUC 73.4%, p < 0.01). In patients with AD, the diagnostic accuracy of the CLIF-C AD score was better than for the LIV-IN score (AUC 0.699; p = 0.004, AUC 0.686; p = 0.007, respectively). In patients with ACLF, only the LIV-IN score had statistically significant discriminative power in predicting 28-day survival. Conclusions: The liver mortality inpatients prognostic score is a new, reliable prognostic model in predicting 28-day mortality.
Bladder Adenocarcinoma in a Constellation of Multiple Site Malignancies: An Unusual Case and Systematic Review
Authors: Porav-Hodade Daniel , Gherasim Raul , Loghin Andrada , Lazar Bianca , Cotoi Ovidiu Simion , Badea Mihail-Alexandru , Ilona Mártha Orsolya Katalin , Todea-Moga Ciprian , Vartolomei Mihai Dorin , Rares Georgescu , Crisan Nicolae , Feciche Ovidiu Bogdan
Published: November 2024

Background and Objectives: Multiple primary malignant tumors represent a small percentage of the total number of oncological cases and can involve either metachronous or synchronous development and represent challenges in diagnosis, staging, and treatment planning. Our purpose is to present a rare case of bladder adenocarcinoma in a female patient with multiple primary malignant tumors and to provide systematic review of the available literature. Materials and Methods: A 67-year-old female patient was admitted with altered general condition and anuria. The past medical history of the patient included malignant melanoma (2014), cervical cancer (2017), colon cancer (2021), obstructive anuria (2023), and liver metastasectomy (2023). Transurethral resection of bladder tumor was performed for bladder tumors. Results: Contrast CT highlighted multiple pulmonary metastases, a poly nodular liver conglomerate, retroperitoneal lymph node, II/III grade left ureterohydronephrosis, and no digestive tract tumor masses. The pathological result of the bladder resection showed an infiltrative adenocarcinoma. Conclusions: The difference between primary bladder adenocarcinoma tumor and metastatic colorectal adenocarcinoma is the key for the future therapeutic strategy. Identification and assessment of risk factors such as viral infection, radiotherapy, chemotherapy, smoking, and genetics are pivotal in understanding and managing multiple primary malignant tumors. Personalized prevention strategies and screening programs may facilitate the early detection of these tumors, whether synchronous or metachronous. The use of multicancer early detection (MCED) blood tests for early diagnosis appears promising. However, additional research is needed to standardize these techniques for cancer detection.
An Integrative Framework for Healthcare Recommendation Systems: Leveraging the Linear Discriminant Wolf–Convolutional Neural Network (LDW-CNN) Model
Authors: Sharma Vedna , Samant Surender Singh , Singh Tej , Fekete Gusztáv
Published: November 2024

In the evolving healthcare landscape, recommender systems have gained significant importance due to their role in predicting and anticipating a wide range of health-related data for both patients and healthcare professionals. These systems are crucial for delivering precise information while adhering to high standards of quality, reliability, and authentication. Objectives: The primary objective of this research is to address the challenge of class imbalance in healthcare recommendation systems. This is achieved by improving the prediction and diagnostic capabilities of these systems through a novel approach that integrates linear discriminant wolf (LDW) with convolutional neural networks (CNNs), forming the LDW-CNN model. Methods: The LDW-CNN model incorporates the grey wolf optimizer with linear discriminant analysis to enhance prediction accuracy. The model’s performance is evaluated using multi-disease datasets, covering heart, liver, and kidney diseases. Established error metrics are used to compare the effectiveness of the LDW-CNN model against conventional methods, such as CNNs and multi-level support vector machines (MSVMs). Results: The proposed LDW-CNN system demonstrates remarkable accuracy, achieving a rate of 98.1%, which surpasses existing deep learning approaches. In addition, the model improves specificity to 99.18% and sensitivity to 99.008%, outperforming traditional CNN and MSVM techniques in terms of predictive performance. Conclusions: The LDW-CNN model emerges as a robust solution for multidisciplinary disease prediction and recommendation, offering superior performance in healthcare recommender systems. Its high accuracy, alongside its improved specificity and sensitivity, positions it as a valuable tool for enhancing prediction and diagnosis across multiple disease domains.
Mechanical Effect of an Implant Under Denture Base in Implant-Supported Distal Free-End Removable Partial Dentures
Authors: Murashima Naomichi , Takayama Yoshiyuki , Nogawa Toshifumi , Yokoyama Atsuro , Sakaguchi Kiwamu
Published: November 2024

Background: In recent years, implant-assisted removable partial dentures (IARPDs) have been used clinically. However, the extent to which additional implants reduce the burden of supporting tissues is unclear. The aim of this study was therefore to investigate the influence of implanted IARPDs on stress sharing among supporting tissues, using finite element (FE) analysis. Methods: FE models were constructed based on the computed tomography (CT) of a patient with a unilateral defect of the mandibular premolars and molars and the surface data of an RPD with cuspids as abutments, designed using computer-aided design software. A titanium implant was placed in the area equivalent to the first premolar, second premolar, or first molar (IARPD4, IARPD5, and IARPD6, respectively). FE analysis was performed for laterally symmetrical models, i.e., bilateral distal free-end IARPDs. A vertical load of 200 N was applied to the central fossa of the artificial premolars or molars (L4, L5, or L6). Results: Equivalent stress in the alveolar mucosa and vertical displacement of the denture was smaller, with IARPDs under L5 and L6 loads, compared to RPDs. However, abutment teeth were displaced upward under an L6 load in the IARPD5 model. Conclusions: Within the limitations of this study, the area corresponding to the first molar was recommended as the location for an implant under the denture base of bilateral distal free-end IARPDs. Implants located in the area corresponding to the second premolar may apply non-physiological extrusion force on abutment teeth under the load on the artificial second molar.
A High Performance Nonlinear Longitudinal Controller for Fixed-Wing UAVs Based on Fuzzy-Guaranteed Cost Control
Authors: Li Jun , Liu Xiaobao , Wu Dawei , Pi Zhengyang , Liu Tianyi
Published: November 2024

Unmanned aerial vehicles (UAVs) have garnered more attention across various industries in recent years, leading to significant development in their design and application globally. Due to the high coupling between UAV states, model uncertainties, and various disturbances, precise longitudinal control of UAVs remains a significant research challenge. Oriented for the speed and altitude control of an electrical-powered fixed-wing UAV, this paper introduces a new control strategy based on the fuzzy guaranteed cost control (F-GCC) technique, which results in a nonlinear longitudinal state feedback control law with strict stability criterion, effectively addressing the issue of state coupling. Moreover, the strategy also includes a thrust estimation model of the electrical propulsion system to significantly reduce the nonlinearity, simplifying the controller design while effectively preserving tracking control performance. Through numerical validation, the UAV longitudinal nonlinear controller designed using the F-GCC technique offers better transient response and stronger robustness than the traditional linear and ADRC controllers.
Assessing Student Teachers’ Motivation and Learning Strategies in Digital Inquiry-Based Learning
Authors: Aidoo Benjamin , Chebure Andrew , Gyampoh Alexander Obiri , Tsyawo Johnson , Quansah Francis
Published: November 2024

Over the past two decades, teachers have adopted several teaching and learning strategies for motivating students to learn chemistry. Learning chemistry in context enables students to develop richer crosscutting learning experiences relevant to contributing to solving problems. A qualitative case study method was adopted to examine student teachers’ experiences in digital inquiry-based learning. Questionnaires with closed-ended and open-ended questions were used to evaluate student teachers’ motivational orientations and learning strategies during a general chemistry course for one month. The results show that student teachers utilized varied perspectives such as self-efficacy, task value, and intrinsic goals to elaborate their learning for knowledge construction and application when performing collaborative tasks. The approach enables students to receive maximum support and feedback from instructors who use pedagogical styles to self-direct them during class discussions, which enhances their active participation in learning with the learning materials. The findings provide a practical insight into instructional strategies in delivering chemistry concepts when students are motivated to use and adopt varied learning strategies.
Fostering SDG Content Integration in the Economics and Business Undergraduate Curriculum: A Faculty-Driven Mapping Approach
Authors: Vrečko Igor , Rožman Maja , Širec Karin
Published: November 2024

The Faculty of Economics and Business (FEB UM) initiated a project to align its undergraduate study program with sustainable development principles. This involved mapping the Sustainable Development Goals (SDGs) within the curriculum, using recommended keywords and faculty assessments. The exercise aimed to objectively showcase current sustainability integration and to identify areas for enhanced sustainable practice infusion. The method entailed both the direct and indirect consideration of SDG content. This endeavor heightened faculty awareness and enthusiasm for incorporating the SDGs, highlighting the significance of aligning academic programs with global sustainability objectives. This study has offered a framework for similar institutions aiming to boost sustainability through curriculum mapping and active faculty engagement.