bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Bi-LSTM CRF Series Models for Semantic Classification of NAVTEX Navigational Safety Messages
Authors: Lee Changui , Cho Hoyeon , Lee Seojeong
Published: September 2024

NAVTEX is a key component in the Global Maritime Distress and Safety System (GMDSS) that automatically transmits urgent maritime safety information such as navigational and meteorological warnings and forecasts to vessels. For the safe navigation of smart ships, this information from different systems should be shared harmoniously in the Common Maritime Data Structure (CMDS). To share NAVTEX messages as CMDS, words in NAVTEX messages must be semantically classified and placed within the CMDS structure. While traditional parsing methods are typically used to understand message semantics, NAVTEX requires natural language processing methods with deep learning due to its unstructured messages. This paper applies six types of Bi-LSTM CRF-based deep learning models to NAVTEX navigational safety messages and analyzes the results to find the most suitable model for understanding the semantics of each word in NAVTEX messages. This technique can be applied to accurately convey the meaning of NAVTEX navigational safety messages to equipment that requires navigational safety information on smart ships without human intervention.
Multi Autonomous Underwater Vehicle (AUV) Distributed Collaborative Search Method Based on a Fuzzy Clustering Map and Policy Iteration
Authors: Cai Kaiqian , Zhang Guocheng , Sun Yushan , Ding Guoli , Xu Fengchi
Published: September 2024

Collaborative search with multiple Autonomous Underwater Vehicles (AUVs) significantly enhances search efficiency compared to the use of a single AUV, facilitating the rapid completion of extensive search tasks. However, challenges arise in underwater environments characterized by weak communication and dynamic complexities. In large marine areas, the limited endurance of a single AUV makes it impossible to cover the entire area, necessitating a collaborative approach using multiple AUVs. Addressing the limited prior information available in uncertain marine environments, this paper proposes a map-construction method using fuzzy clustering based on regional importance. Furthermore, a collaborative search method for large marine areas has been designed using a policy-iteration-based reinforcement learning algorithm. Through continuous sensing and interaction during the marine search process, multiple AUVs constantly update the map of regional importance and iteratively optimize the collaborative search strategy to achieve higher search gains. Simulation results confirm the effective utilization of limited information in uncertain environments and demonstrate enhanced search gains in collaborative scenarios.
Clinical Characteristics of Candidemia Due to Candida parapsilosis with Serial Episodes: Insights from 5-Year Data Collection at a Tertiary Hospital in Korea
Authors: Won Eun Jeong , Sung Heungsup , Kim Mi-Na
Published: September 2024

Candida parapsilosis is a common cause of non-albicans Candida species causing candidemia, particularly invasive candidiasis. This study aimed to characterize candidemia due to the C. parapsilosis complex with serial episodes, including clinical and mycological features. Methods: Blood isolates of the C. parapsilosis complex were collected from February 2019 to January 2023 at a tertiary Korean hospital. Species identification was performed using Vitek 2 or matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, and antifungal susceptibility testing was performed using the Sensititre YeastOne® system. Clinical information was collected, and characteristics were analyzed according to single or serial isolates. Results: A total of 586 blood isolates of the C. parapsilosis complex were recovered from 68 candidemia patients during the study period. Of them, only the first isolate per patient was investigated. The only two isolates were resistant to fluconazole and no isolate was resistant to echinocandins, amphotericin B, or 5-FC. A single episode of candidemia occurred in 35 patients, while serial episodes occurred in 33 patients. Underlying liver diseases, use of vasopressors, ICU admission, severe sepsis, and CVC use were more frequent in patients with serial episodes. There was no significant difference in the median MIC values of antifungal agents or the use of azoles or amphotericin B between single and serial episodes. However, patients with serial episodes more frequently received echinocandin therapy. Overall, there was no significant difference in the 30-day mortality rate between patients with single and serial episodes. Conclusion: Our data indicate that several factors related to the underlying conditions of the patients are associated with C. parapsilosis candidemia with serial episodes, rather than the characteristics of Candida itself.
Genome Sequencing of Lentinula edodes Revealed a Genomic Variant Block Associated with a Thermo-Tolerant Trait in Fruit Body Formation
Authors: Yoo Seung-il , Moon Suyun , Hong Chang Pyo , Park Sin-Gi , Shim Donghwan , Ryu Hojin
Published: September 2024

The formation of multicellular fruiting bodies in basidiomycete mushrooms is a crucial developmental process for sexual reproduction and subsequent spore development. Temperature is one of the most critical factors influencing the phase transition for mushroom reproduction. During the domestication of mushrooms, traits related to fruiting bodies have significantly impacted agricultural adaptation and human preferences. Recent research has demonstrated that chromosomal variations, such as structural variants (SVs) and variant blocks (VBs), play crucial roles in agronomic traits and evolutionary processes. However, the lack of high-quality genomic information and important trait data have hindered comprehensive identification and characterization in Lentinula edodes breeding processes. In this study, the genomes of two monokaryotic L. edodes strains, characterized by thermo-tolerance and thermo-sensitivity during fruiting body formation, were reassembled at the chromosomal level. Comparative genomic studies of four thermo-tolerant and thermo-sensitive monokaryotic L. edodes strains identified a 0.56 Mbp variant block on chromosome 9. Genes associated with DNA repair or cellular response to DNA damage stimulus were enriched in this variant block. Finally, we developed eight CAPS markers from the variant block to discriminate the thermo-tolerant traits in L. edodes cultivars. Our findings show that the identified variant block is highly correlated with the thermo-tolerant trait for fruiting body formation and that alleles present in this block may have been artificially selected during L. edodes domestication.
Application of a Robust Maximum Diversified Portfolio to a Small Economy’s Stock Market: An Application to Fiji’s South Pacific Stock Exchange
Authors: Kumar Ronald Ravinesh , Ghanbari Hossein , Stauvermann Peter Josef
Published: September 2024

In this study, we apply a novel approach of portfolio diversification—the robust maximum diversified (RMD)—to a small and developing economy’s stock market. Using monthly returns data from August 2019 to May 2024 of 18/19 stocks listed on Fiji’s South Pacific Stock Exchange (SPX), we construct the RMD portfolio and simulate with additional constraints. To implement the RMD portfolio, we replace the covariance matrix with a matrix comprising unexplained variations. The RMD procedure diversifies weights, and not risks, hence we need to run a pairwise regression between two assets (stocks) and extract the R-square to create a P-matrix. We compute each asset’s beta using the market-weighted price index, and the CAPM to calculate market-adjusted returns. Next, together with other benchmark portfolios (1/N, minimum variance, market portfolio, semi-variance, maximum skewness, and the most diversified portfolio), we examine the expected returns against the risk-free (RF) rate. From the simulations, in terms of expected return, we note that eight portfolios perform up to the RF rate. Specifically, for returns between 4 and 5%, we find that max. RMD with positive Sharpe and Sortino (as constraints) and the most diversified portfolio offer comparable returns, although the latter has slightly lower standard deviation and downside volatility and contains 94% of all the stocks. Portfolios with returns between 5% and the RF rate are the minimum-variance, the semi-variance, and the max. RMD with positive Sharpe; the latter coincides with the RF rate and contains the most (94%) stocks compared to the other two. An investor with a diversification objective, some risk tolerance and return preference up to the RF rate can consider the max. RMD with positive Sharpe. However, depending on the level of risk-averseness, the minimum-variance or the semi-variance portfolio can be considered, with the latter having lower downside volatility. Two portfolios offer returns above the RF rate—the market portfolio (max. Sharpe) and the maximum Sortino. Although the latter has the highest return, this portfolio is the least diversified and has the largest standard deviation and downside volatility. To achieve diversification and returns above the RF rate, the market portfolio should be considered.
Perceptions of South African Accountants on Factors with a Role in the Adoption of Artificial Intelligence in Financial Reporting
Authors: Moloi Tankiso , Obeid Hassan
Published: September 2024

Purpose—The objective of this study was to conduct a detailed South African study that sought to explore and analyse the views of South African accountants regarding the factors that affect the adoption of AI in financial reporting. In other words, this study aimed to understand what accountants in South Africa think about the use of AI in their field, especially concerning its integration into financial reporting practices. Three main theories underpinned the study, namely, the diffusion of innovation, technology, organisation, and environment framework, and the institutional theory. In essence, the study sought to determine the perception of South Africa’s accountants on these factors. Design/methodology/approach—This study adopted the quantitative research method and descriptive design. In this regard, positivism as a philosophy was preferred. An online survey was developed to collect information from the participants. Participants were recruited based on their affiliation with the four IFAC-recognised accounting bodies in South Africa: SAICA, SAIPA, CIMA, and ACCA. Findings—Th study found that, overall, South African accountants believe that organisational, technological, and environmental factors play a role in adopting artificial intelligence in financial reporting. Originality/value: This study contributes by enriching the understanding of South African accountants’ perceptions of the adoption of artificial intelligence in financial reporting through the lenses of the selected theories.
Loanword Phonology of Spanish Anglicisms: New Insights from Corpus Data
Authors: Bäumler Linda
Published: September 2024

Previous research shows that several factors influence the adaptation of English phonemes in Spanish Anglicisms: speaker age, English proficiency, and geographic distance from the U.S.A, among others.Due to globalization, increased mobility, and the ubiquitous availability of English media, the question arises whether these factors are still relevant in today’s world. For the present study, 70 speakers from Mexico and Spain read a word list containing Anglicisms aloud. A generalized linear mixed effects model was applied to analyze which factors directly influence pronunciation. Results show that the realization of Spanish grapheme-phoneme correspondences plays a major role in the adaptation process. Moreover, the analysis shows that it is exposure to the English language that mainly influences the pronunciation: the more exposure speakers from both countries have to the English language, the more likely they are to imitate the English pronunciation instead of the realization of Spanish grapheme-phoneme correspondences. Finally, the analysis revealed differences not only between the phonemes and the speakers but also between the words included in the study and once more highlighted that every word has a history of its own.
Improvement in Body’s Dynamic Adaptation during Walking with Vestibular Rehabilitation Therapy in Patients with Cerebellopontine Angle Tumor Resection
Authors: Kos Natasa , Velnar Tomaz , Brcar Marusa , Brcar Marko
Published: September 2024

Background: Our study aimed to investigate the effects of vestibular rehabilitation therapy on functional gait performance in patients with balance disorders. Methods: A total of 40 post-operative patients with balance disorders were included in the study. They were divided into two groups and participated in a vestibular rehabilitation program during their hospital stay. After discharge, the intervention group performed vestibular exercises at home, while the control group did not. Balance was assessed using the Functional Gait Assessment Scale at discharge and three months after surgery. Results: The intervention group included 15 women and 5 men with an average age of 45 years, while the control group included 7 women and 13 men with an average age of 50 years. Three months after surgery, the change in Functional Gait Assessment (FGA) scores exceeded the clinically significant threshold of 5 points in 17 patients in the intervention group and 14 in the control group. There was a statistically significant difference in FGA progression between the groups (p = 0.034). After three months post-surgery, 7 patients in the intervention group experienced falls compared to 12 in the control group. Conclusion: Three months after surgery, we observed a significant improvement in the performance of balance tasks while walking and a lower risk of falls in the intervention group.
Deciphering Antibiotic-Targeted Metabolic Pathways in Acinetobacter baumannii: Insights from Transcriptomics and Genome-Scale Metabolic Modeling
Authors: Sarı Fatma Zehra , Çakır Tunahan
Published: September 2024

In the ongoing battle against antibiotic-resistant infections, Acinetobacter baumannii has emerged as a critical pathogen in healthcare settings. To understand its response to antibiotic-induced stress, we integrated transcriptomic data from various antibiotics (amikacin sulfate, ciprofloxacin, polymyxin-B, and meropenem) with metabolic modeling techniques. Key metabolic pathways, including arginine and proline metabolism, glycine–serine and threonine metabolism, glyoxylate and dicarboxylate metabolism, and propanoate metabolism, were significantly impacted by all four antibiotics across multiple strains. Specifically, biotin metabolism was consistently down-regulated under polymyxin-B treatment, while fatty acid metabolism was perturbed under amikacin sulfate. Ciprofloxacin induced up-regulation in glycerophospholipid metabolism. Validation with an independent dataset focusing on colistin treatment confirmed alterations in fatty acid degradation, elongation, and arginine metabolism. By harmonizing genetic data with metabolic modeling and a metabolite-centric approach, our findings offer insights into the intricate adaptations of A. baumannii under antibiotic pressure, suggesting more effective strategies to combat antibiotic-resistant infections.
Murine Retina Outer Plexiform Layer Development and Transcriptome Analysis of Pre-Synapses in Photoreceptors
Authors: Kim Soo-Young , Park Christine Haewon , Moon Bo-Hyun , Seabold Gail K.
Published: September 2024

Photoreceptors in the mammalian retina convert light signals into electrical and molecular signals through phototransduction and transfer the visual inputs to second-order neurons via specialized ribbon synapses. Two kinds of photoreceptors, rods and cones, possess distinct morphology and function. Currently, we have limited knowledge about rod versus (vs.) cone synapse development and the associated genes. The transcription factor neural retina leucine zipper (NRL) determines the rod vs. cone photoreceptor cell fate and is critical for rod differentiation. Nrl knockout mice fail to form rods, generating all cone or S-cone-like (SCL) photoreceptors in the retina, whereas ectopic expression of Nrl using a cone-rod homeobox (Crx) promoter (CrxpNrl) forms all rods. Here, we examined rod and cone pre-synapse development, including axonal elongation, terminal shaping, and synaptic lamination in the outer plexiform layer (OPL) in the presence or absence of Nrl. We show that NRL loss and knockdown result in delayed OPL maturation and plasticity with aberrant dendrites of bipolar neurons. The integrated analyses of the transcriptome in developing rods and SCLs with NRL CUT&RUN and synaptic gene ontology analyses identified G protein subunit beta (Gnb) 1 and p21 (RAC1) activated kinase 5 (Pak5 or Pak7) transcripts were upregulated in developing rods and down-regulated in developing SCLs. Notably, Gnb1 and Gnb5 are rod dominant, and Gnb3 is enriched in cones. NRL binds to the genes of Gnb1, Gnb3, and Gnb5. NRL also regulates pre-synapse ribbon genes, and their expression is altered in rods and SCLs. Our study of histological and gene analyses provides new insights into the morphogenesis of photoreceptor pre-synapse development and regulation of associated genes in the developing retina.
Short-Term Influence of Water Ingress on Wear in Pitch Bearings of Wind Turbines
Authors: Stammler Matthias , Ellerbrok Henry , Pasaribu Rihard , Rieper Ulf
Published: September 2024

The pitch bearings of wind turbines are slowly oscillating, grease-lubricated slewing bearings. They facilitate the pitching movements of blades which control aerodynamic loads. These bearings have diameters of several meters, their blade-side sealings can face the environment, bending moment loads can cause radial deformation of the bearing rings, and their highly variable operating temperatures can facilitate condensation of water inside them. All of this makes water ingress into the lubricant possible. There is limited public knowledge with regards to the maximum water content for safe operation in this application. This work presents the results of a series of scaled wind turbine time series tests with both ‘dry’ (no water contamination) and ‘wet’ (10 mass % demineralized water added) greases. A set of four commercially available greases were tested. The time series were scaled from wind turbine operation and represented a 13.7 h worst-case scenario of operation with small oscillation amplitudes and no longer lubrication runs in between. Three of the greases showed reduced friction and no or limited raceway damage in the wet condition, whereas one showed increased friction and raceway damages.
Advanced Defect Detection in Wrap Film Products: A Hybrid Approach with Convolutional Neural Networks and One-Class Support Vector Machines with Variational Autoencoder-Derived Covariance Vectors
Authors: Shimizu Tatsuki , Nagata Fusaomi , Habib Maki K. , Arima Koki , Otsuka Akimasa , Watanabe Keigo
Published: September 2024

This study proposes a novel approach that utilizes Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) to tackle a critical challenge: detecting defects in wrapped film products. With their delicate and reflective film wound around a core material, these products present formidable hurdles for conventional visual inspection systems. The complex task of identifying defects, such as unwound or protruding areas, remains a daunting endeavor. Despite the power of commercial image recognition systems, they struggle to capture anomalies within wrap film products. Our research methodology achieved a 90% defect detection accuracy, establishing its practical significance compared with existing methods. We introduce a pioneering methodology centered on covariance vectors extracted from latent variables, a product of a Variational Autoencoder (VAE). These covariance vectors serve as feature vectors for training a specialized One-Class SVM (OCSVM), a key component of our approach. Unlike conventional practices, our OCSVM does not require images containing defects for training; it uses defect-free images, thus circumventing the challenge of acquiring sufficient defect samples. We compare our methodology against feature vectors derived from the fully connected layers of established CNN models, AlexNet and VGG19, offering a comprehensive benchmarking perspective. Our research represents a significant advancement in defect detection technology. By harnessing the latent variable covariance vectors from a VAE encoder, our approach provides a unique solution to the challenges faced by commercial image recognition systems. These advancements in our study have the potential to revolutionize quality control mechanisms within manufacturing industries, offering a brighter future for product integrity and customer satisfaction.
Efficiency-Centered Fault Diagnosis of In-Service Induction Motors for Digital Twin Applications: A Case Study on Broken Rotor Bars
Authors: Adamou Adamou Amadou , Alaoui Chakib
Published: September 2024

The uninterrupted operation of induction motors is crucial for industries, ensuring reliability and continuous functionality. To achieve this, we propose an innovative approach that utilizes an efficiency model-based digital shadow system for in situ failure detection and diagnosis (FDD) in induction motors (IMs). The shadow model accurately estimates IM losses and efficiency across various operational conditions. Our proposed method utilizes efficiency as the primary indicator for fault detection, while losses serve as condition indicators for fault diagnosis based on real-time motor parameters and loss sources. We introduce a bond graph as a fault diagnosis network, linking loss sources, motor parameters, and faults. This interconnected approach is the key aspect of our proposed diagnostic method and aims to be used in fault diagnosis as a general method. A case study of a broken rotor bar is used to validate the proposed method using a dataset of five motors. Among these, one motor operates without failure, while the remaining four exhibit broken rotor faults categorized as 1, 2, 3, and 4. The proposed method achieves 99.99% precision in identifying one to four defective rotor bars in IMs. Comparative analysis demonstrates good performance compared to vibration-based FDD approaches. Moreover, our methodology is computationally efficient and aligned with Industry 4.0 requirements.
The Wear Behaviour of a New Eccentric Meshing Reducer with Small Teeth Difference
Authors: Yang Renqiang , Guan Zhengjun , Yang Dongdong , Zou Shuaidong , He Haifeng , Wang Guangjian
Published: September 2024

Eccentric meshing reducers are widely used in agriculture, industrial robots, and other fields due to their ability to achieve a high reduction ratio within a compact volume. However, the contact wear problem seriously affects the service performance of the eccentric meshing reducer, thereby limiting their range of applications. To effectively address this issue, this study involved a stress analysis of the contact pairs and a surface wear analysis of a new eccentric meshing reducer. The wear equation for the contact pairs was derived using Archard’s wear theory, incorporating geometric and material parameters from both the reducer gear contact pair and the spline contact pair. In parallel, a wear simulation was conducted by integrating the UMESHMOTION subprogram with ALE adaptive grids. Additionally, the effects of load amplitudes on contact pair stress and surface wear were systematically investigated. It is revealed that the contact pair stress of the reducer gear was higher than that of the spline contact pair. Furthermore, the internal spline exhibited the highest wear rate, followed by the output shaft gear, external spline, and input shaft gear, in that order. This work provides a comprehensive and in-depth understanding of the wear behaviors of general reducers with small teeth differences and offers valuable scientific references for design optimization, fault diagnosis, and maintenance strategy formulation.
Predictive Analysis of Crack Growth in Bearings via Neural Networks
Authors: Singh Manpreet , Gopaluni Dharma Teja , Shoor Sumit , Vashishtha Govind , Chauhan Sumika
Published: September 2024

Machine learning (ML) and artificial intelligence (AI) have emerged as the most advanced technologies today for solving issues as well as assessing and forecasting occurrences. The use of AI and ML in various organizations seeks to capitalize on the benefits of vast amounts of data based on scientific approaches, notably machine learning, which may identify patterns of decision-making and minimize the need for human intervention. The purpose of this research work is to develop a suitable neural network model, which is a component of AI and ML, to assess and forecast crack propagation in a bearing with a seeded crack. The bearing was continually run for many hours, and data were retrieved at time intervals that might be utilized to forecast crack growth. The variables root mean square (RMS), crest factor, signal-to-noise ratio (SNR), skewness, kurtosis, and Shannon entropy were collected from the continuously running bearing and utilized as input parameters, with the total crack area and crack width regarded as output parameters. Finally, utilizing several methodologies of the Neural Network tool in MATLAB, a realistic ANN model was trained to predict the crack area and crack width. It was observed that the ANN model performed admirably in predicting data with a better degree of accuracy. Through analysis, it was observed that the SNR was the most relevant parameter in anticipating data in bearing crack propagation, with an accuracy rate of 99.2% when evaluated as a single parameter, whereas in multiple parameter analysis, a combination of kurtosis and Shannon entropy gave a 99.39% accuracy rate.
Lubrication Characteristics of Dry-Gas Seals with Spiral Grooves
Authors: Zhang Lanxia , Ding Xuexing , Wang Shipeng , Zhang Shuai , Cui Bowen
Published: September 2024

To obtain an optimal range of structural parameters for dry-gas seals with good performance, this study employed advanced sensing technology to monitor and analyze the internal flow characteristics of dry-gas seals in real time. Additionally, the validity of the calculation program was verified through experimentation. Using steady-state performance parameters as evaluation indices, a calculation model with lubrication characteristics was developed. The results indicate that when there are 12 grooves, the gas film pressure distribution is uniform and has a high value. At pressures greater than 2 MPa, the opening force, leakage, and gas film stiffness change significantly due to enhanced dynamic pressure effects with high-pressure differences, which reduces the local contact forces and frictional forces. At a constant speed, decreasing the gas film thickness increases the pressure difference while increasing both the opening force and film stiffness; however, at higher rotational speeds where the gas flow becomes non-uniform, the stability of the gas film is affected, leading to increased frictional forces. When there are between 10 and 16 grooves with depths ranging from 5.0 to 6.0 μm, dynamic pressure effects caused by pressure gradients become apparent, resulting in good dry-gas sealing performance being achieved. This research provides a theoretical reference for optimizing the design of dry-gas seals, as well as their steady-state seal performance.
Controllable Synthesis of Magnetic Composite Derived from MIL-88D and Study on Adsorption Properties of Cu2+
Authors: Zheng Zhongyuan , Yu Jinshan , Jiang Ling , Zhang Jiacheng , Lu Min
Published: September 2024

In this paper, MIL-88D (Fe) with spindle shape was prepared by the hydrothermal synthesis method, a metal oxide with a new structure was obtained by calcination at different temperatures as a precursor, and a magnetic iron oxide composite was prepared successfully. At the same time, it was used as an adsorption material for the adsorption of heavy metal ions Cu2+. The influence of the initial pH value and adsorption time on the adsorption effect was investigated, the adsorption kinetics and adsorption isotherm were further fitted, and the adsorption mechanism was preliminarily analyzed. The results show that the magnetic iron oxide composites have a good adsorption capacity for Cu2+. The pH value is an important parameter that affects the adsorption effect. The adsorption of Cu2+ by magnetic iron oxide composites reached adsorption equilibrium in 30 min. The adsorption of Cu2+ by magnetic iron oxide composites conforms to the second-order reaction kinetics and Langmuir adsorption isotherm equation, which indicates that the adsorption process mainly occurs through chemisorption and typical single-molecular-layer adsorption.
A New Notion of Convergence Defined by The Fibonacci Sequence: A Novel Framework and Its Tauberian Conditions
Authors: Ibrahim Ibrahim S. , Listán-García María C.
Published: September 2024

The Fibonacci sequence has broad applications in mathematics, where its inherent patterns and properties are utilized to solve various problems. The sequence often emerges in areas involving growth patterns, series, and recursive relationships. It is known for its connection to the golden ratio, which appears in numerous natural phenomena and mathematical constructs. In this research paper, we introduce new concepts of convergence and summability for sequences of real and complex numbers by using Fibonacci sequences, called Δ-Fibonacci statistical convergence, strong Δ-Fibonacci summability, and Δ-Fibonacci statistical summability. And, these new concepts are supported by several significant theorems, properties, and relations in the study. Furthermore, for this type of convergence, we introduce one-sided Tauberian conditions for sequences of real numbers and two-sided Tauberian conditions for sequences of complex numbers.
Homogeneity Test of Ratios of Two Proportions in Stratified Bilateral and Unilateral Data
Authors: Wang Huipei , Ma Chang-Xing
Published: September 2024

In paired-organ studies such as ophthalmology, otolaryngology, and rheumatology, etc., various approaches take highly correlated bilateral data into account for homogeneity tests but are less likely to focus on combined bilateral and unilateral data structures. Also, it is necessary and important to adjust the effect of confounders on stratified combined bilateral and unilateral data since, in these data structures, ignoring intra-class correlation and confounding effects can cause biased statistical inference. This article derived three homogeneity tests (the likelihood ratio test, the Wald test, and the score test) concerning these cooperative structure data to detect if ratios of proportions retain consistency across strata. Simulation shows that the score test provides a robust Type I error rate and satisfactory power performance. Finally, a real example is applied to demonstrate the application of these three proposed tests.
Multiple Inputs and Mixed Data for Alzheimer’s Disease Classification Based on 3D Vision Transformer
Authors: Castro-Silva Juan A. , Moreno-García María N. , Peluffo-Ordóñez Diego H.
Published: September 2024

The current methods for diagnosing Alzheimer’s Disease using Magnetic Resonance Imaging (MRI) have significant limitations. Many previous studies used 2D Transformers to analyze individual brain slices independently, potentially losing critical 3D contextual information. Region of interest-based models often focus on only a few brain regions despite Alzheimer’s affecting multiple areas. Additionally, most classification models rely on a single test, whereas diagnosing Alzheimer’s requires a multifaceted approach integrating diverse data sources for a more accurate assessment. This study introduces a novel methodology called the Multiple Inputs and Mixed Data 3D Vision Transformer (MIMD-3DVT). This method processes consecutive slices together to capture the feature dimensions and spatial information, fuses multiple 3D ROI imaging data inputs, and integrates mixed data from demographic factors, cognitive assessments, and brain imaging. The proposed methodology was experimentally evaluated using a combined dataset that included the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), and the Open Access Series of Imaging Studies (OASIS). Our MIMD-3DVT, utilizing single or multiple ROIs, achieved an accuracy of 97.14%, outperforming the state-of-the-art methods in distinguishing between Normal Cognition and Alzheimer’s Disease.
Analysis and Controller Design for Parameter Varying T-S Fuzzy Systems with Markov Jump
Authors: Min Na , Zhang Hongyang
Published: September 2024

In this paper, we investigate a novel T-S fuzzy parameter varying system with Markov jump, in which parameters depend not only on a Markov chain but also on linear parameter varying elements that take values in convex polytopic sets. Stable conditions and the gain-scheduling controller design method for this system are obtained. Applying Lyapunov function depending on the operation mode and full block S-procedure lemma, we obtain stochastic stabilization conditions. We find that this novel system has two distinct advantages. On the one hand, it inherits the advantages of traditional T-S fuzzy systems in handling nonlinear objects under the frame of T-S fuzzy systems; on the other, it obtains the advantages of dealing with time-varying characteristics from the point of linear parameter varying (LPV) systems. Finally, the theory results are illustrated via numerical simulation.
An Optimized Object Detection Algorithm for Marine Remote Sensing Images
Authors: Ren Yougui , Li Jialu , Bao Yubin , Zhao Zhibin , Yu Ge
Published: September 2024

In order to address the challenge of the small-scale, small-target, and complex scenes often encountered in offshore remote sensing image datasets, this paper employs an interpolation method to achieve super-resolution-assisted target detection. This approach aligns with the logic of popular GANs and generative diffusion networks in terms of super-resolution but is more lightweight. Additionally, the image count is expanded fivefold by supplementing the dataset with DOTA and data augmentation techniques. Framework-wise, based on the Faster R-CNN model, the combination of a residual backbone network and pyramid balancing structure enables our model to adapt to the characteristics of small-target scenarios. Moreover, the attention mechanism, random anchor re-selection strategy, and the strategy of replacing quantization operations with bilinear interpolation further enhance the model’s detection capability at a low cost. Ablation experiments and comparative experiments show that, with a simple backbone, the algorithm in this paper achieves a mAP of 71.2% on the dataset, an improvement in accuracy of about 10% compared to the Faster R-CNN algorithm.
Stability Analysis of Innovation Collaboration between Commercial Banks and Fintech Companies
Authors: Fang Ying , Wen Guofeng
Published: September 2024

Recently, there has been an increasing trend among commercial banks to collaborate with fintech companies in order to promote corporate innovation. However, due to the uncertainty and turbulence of the business environment, it is difficult for the collaborators to generate full trust to integrate knowledge and resources. Innovation collaboration often suffers from instability. The objective of this study is to identify the factors that influence the stability of innovation collaboration and clarify the path of collaboration evolution. Based on prospect theory and evolutionary game theory, this study first establishes an evolutionary game model between commercial banks and fintech companies (fintechs), and analyses the factors influencing the stability of their collaboration. The findings indicate that the benefits and costs of innovation collaboration, the fairness of income distribution, the complementarity of knowledge and technology, the synergy of collaboration, and the perceived value of gains and losses by partners all influence the stability of innovation collaboration. This study’s findings provide valuable guidance for banks and fintech companies to balance the benefits and costs of collaboration and improve the stability of collaboration. In the future, the government will be considered as a new game player, and bilateral relations will be considered as a new mode of collaboration in studying the stability of innovation collaboration.
Analysis of Inherent Frequencies to Mitigate Liquid Sloshing in Overhead Double-Baffle Damper
Authors: Ali Ashraf , Ismail Mohamed , Kumar Madhan , Breaz Daniel , Karthikeyan Kadhavoor R.
Published: September 2024

A disco-rectangular volume-fraction-of-fluid (VOF) model tank of a prismatic size is considered here for investigating the severe effect of overhead baffles made of three different materials, nylon, polyamide, and polylactic acid. In this work, the overdamped, undamped, and nominal damped motion of baffles and their effect are studied. In this research, the behaviour of different material baffles based on the sloshing effect and natural frequency of each baffle excited in damped, undamped, and overdamped cases is studied. VOF modelling is carried out in moving Yeoh model mesh with fluid–structure interaction between the water models for various baffle plates. The results of the water volume distribution and baffle displacement operating between a sloshing time of 0 and 1 s are recorded. Also, a strong investigation is carried out for the water volume suspended on overhead baffles by three different material selections.
Optimizing Portfolio in the Evolutional Portfolio Optimization System (EPOS)
Authors: Loukeris Nikolaos , Boutalis Yiannis , Eleftheriadis Iordanis , Gikas Gregorios
Published: September 2024

A novel method of portfolio selection is provided with further higher moments, filtering with fundamentals in intelligent computing resources. The Evolutional Portfolio Optimization System (EPOS) evaluates unobtrusive relations from a vast amount of accounting and financial data, excluding hoax and noise, to select the optimal portfolio. The fundamental question of Free Will, limited in investment selection, is answered through a new philosophical approach.
Some Aspects of Teaching Improper Integrals in an Electronic Environment
Authors: Todorova-Lazarova Valentina Petrova , Todorov Todor Dimitrov
Published: September 2024

The development of computer technology and communication systems provides new opportunities for learning in an electronic environment. Remote education in virtual electronic classrooms has been widely used in recent decades, especially during the pandemic. Teaching in an electronic environment makes it possible to conduct lectures with thousands of participants. This article is devoted to a new look at teaching improper integrals at the undergraduate and postgraduate levels. A new partition of unity method is applied in a deep recursive e-learning process. A thorough list of subproblems of a real model problem is demonstrated. Additionally, other appropriate improper integrals are considered.
Differential Equations and Applications to COVID-19
Authors: Hounkonnou Tierry Mitonsou , Gouba Laure
Published: September 2024

This paper focuses on the application of the Verhulst logistic equation to model in retrospect the total COVID-19 cases in Senegal during the period from April 2022 to April 2023. Our predictions for April 2023 are compared with the real COVID-19 data for April 2023 to assess the accuracy of the model. The data analysis is conducted using Python programming language, which allows for efficient data processing and prediction generation.
Early Experiences of Serbian Surgeons Using No-Touch Technique for Vein Conduits in CABG Patients: A Follow-Up Study with Multi-Slice CT Angiography
Authors: Milutinović Aleksandar , Klajević Jelena , Živković Igor , Milošević Nemanja , Gradinac Siniša , Stanković Stefan , Antonić Želimir , Tomić Slobodan , Šljivo Armin , Perič Miodrag , Bojić Milovan , Radoičić Dragana
Published: September 2024

Background and Objectives: The saphenous vein graft (SVG) remains the most frequently used conduit worldwide, despite its common disadvantage of early graft failure. To solve the problem and reduce the SVG damage, Souza implemented a new technique where a vein is harvested with surrounding fascia and fat tissue (the so-called no-touch technique). Materials and Methods. A prospective study conducted from February 2019 to June 2024 included 23 patients who underwent myocardial revascularization using a no-touch vein, with follow-up control examinations using computed tomographic angiography to detect graft stenosis or occlusion. Results. Of the entire patient group, 17 (73.9%) were male, with a mean age of 67.39 ± 7.71 years. The mean follow-up period was 25 months. There were no major adverse cardiovascular or cerebrovascular events (MACCEs) during hospitalization, although one patient died in the hospital. Another patient died due to malignancy, but no MACCEs occurred during the follow-up period. According to multi-slice CT coronary angiography, the results were impeccable, with an astonishing 100% patency observed in all 20 IMA grafts and 58 no-touch SVGs examined. Conclusions. The excellent patency rate during the early follow-up period confirmed that the no-touch technique is a good option for surgical revascularization.
Shoulder Traction as a Possible Risk Factor for C5 Palsy in Anterior Cervical Surgery: A Cadaveric Study
Authors: Yoon Ja-Yeong , Kim Sung-Min , Moon Seong-Hwan , Kim Hak-Sun , Suk Kyung-Soo , Park Si-Young , Kwon Ji-Won , Lee Byung-Ho
Published: September 2024

Background and Objectives: Many risk factors for postoperative C5 palsy (PC5P) have been reported regarding a “cord shift” after a posterior approach. However, there are few reports about shoulder traction as a possible risk factor of anterior cervical surgery. Therefore, we assessed the stretched nerve roots when shoulder traction was applied on cadavers. Materials and Methods: Eight cadavers were employed in this study, available based on age and the presence of foramen stenosis. After dissecting the sternocleidomastoid muscle of the cadaver, the shoulder joint was pulled with a force of 2, 5, 8, 10, 15, and 20 kg. Then, the stretched length of the fifth nerve root was measured in the extra-foraminal zone. In addition, the same measurement was performed after cutting the carotid artery to accurately identify the nerve root’s origin. After an additional dissection was performed so that the superior trunk of the brachial plexus could be seen, the stretched length of the fifth and sixth nerve roots was measured again. Results: Throughout the entire experiment, the fifth nerve root stretched out for an average of 1.94 mm at 8 kg and an average of 5.03 mm at a maximum force of 20 kg. In three experiments, the elongated lengths of the C5 nerve root at 8 kg and 20 kg were 1.69/4.38 mm, 2.13/5.00 mm, and 0.75/5.31 mm, respectively, and in the third experiment, the elongated length of the C6 nerve root was 1.88/5.44 mm. Conclusions: Although this was a cadaveric experiment, it suggests that shoulder traction could be the risk factors for PC5P after anterior cervical surgery. In addition, for patients with foraminal stenosis and central stenosis, the risk would be higher. Therefore, the surgeon should be aware of this, and the patient would need sufficient explanation.
Recent Progress on Atmospheric Corrosion of Field-Exposed Magnesium Alloys
Authors: Wang Mengqi , Yang Lihui , Liu Hao , Wang Xiutong , Li Yantao , Huang Yanliang
Published: September 2024

It is well known that the poor corrosion resistance of magnesium alloys is a key factor limiting their application. Field exposure is the most reliable means to evaluate the atmospheric corrosion performance of magnesium alloys. This article reviews the field exposure corrosion behavior of magnesium alloys in typical atmospheric environments (including the marine atmosphere, industrial atmosphere, etc.) in recent years. According to the literature review, it was found that there are significant regional differences in the atmospheric corrosion behavior of magnesium alloys, which is the result of the coupling of multiple factors in the atmospheric environment. By investigating the corrosion rate and corrosion products of different types of magnesium alloys in different environments, the corrosion mechanism of magnesium alloys in different environments was summarized. Specifically, environmental parameters such as atmospheric temperature, relative humidity, CO2, and chloride ion deposition rates in the marine atmospheric environment can affect the corrosion behavior of magnesium alloys. The corrosion of magnesium alloys in different industrial atmospheric environments is mainly affected by atmospheric temperature and relative humidity, as well as atmospheric pollutants (such as SO2, CO2, NO2) and dust. This review provides assistance to the development of new corrosion-resistant magnesium alloys.
Host Factor Rab4b Promotes Japanese Encephalitis Virus Replication
Authors: Zhao Qin , Miao Chang , Chen Yi-Ting , Zhu Long-Yue , Zhang Ya-Ting , Luo Sai-Qi , Wang Yu-Luo , Zhu Zhu-Ming , Han Xinfeng , Wen Yiping , Wu Rui , Du Senyan , Yan Qi-Gui , Huang Xiaobo , Zhao Shan , Lang Yi-Fei , Wang Yiping , Zheng Yi , Zhao Fei , Cao San-Jie
Published: September 2024

Although the Japanese encephalitis virus (JEV) infects various cell types, its receptor molecules are still not clearly understood. In our laboratory’s prior research, Rab4b was identified as a potential host factor that facilitates JEV infection in PK15 cells, utilizing a genome-wide CRISPR/Cas9 knockout library (PK-15-GeCKO). To further explore the effect of Rab4b on JEV replication, we used the Rab4b knockout PK15 cell line using the CRISPR/Cas9 technology and overexpressing the Rab4b PK15 cell line, with IFA, RT–qPCR, and Western blot to study the effect of Rab4b on viral replication in the whole life cycle of the JEV. The results show that the knockout of Rab4b inhibited the replication of the JEV in PK15 cells, and the overexpression of Rab4b promoted the replication of the JEV in PK15 cell lines. Furthermore, we demonstrated for the first time that host factor Rab4b facilitates the adsorption, internalization, assembly, and release of the JEV, thereby promoting JEV replication. This study enriches the regulatory network between the JEV and host factors and lays the experimental foundation for further understanding of the function of the Rab4b protein.
Salmonella Inactivation Model by UV-C Light Treatment in Chicken Breast
Authors: García-Gimeno Rosa María , Palomo-Manzano Eva , Posada-Izquierdo Guiomar Denisse
Published: September 2024

This study aims to evaluate the effectiveness of inactivating Salmonella enteritidis in fresh chicken breast by irradiation using a combination of short-wave UV (0, 3, 6, 9, 12, and 15 J/cm2) and a natural antimicrobial such as caffeine (0, 5, 10, 15, and 20 nM/g) at 14 °C as alternative proposals to conventional techniques to reduce pathogens in food. The effect of temperature was studied in an initial phase (2 to 22 °C). The most suitable models were double Weibull in 60% of cases, with an adjustment of R2 0.9903–0.9553, and Weibull + tail in 46.67%, with an adjustment of R2 of 0.9998–0.9981. The most effective combination for the reduction in Salmonella was 12 J/cm2 of UV light and 15 nM/g of caffeine, with a reduction of 6 CFU/g and an inactivation rate of 0.72. The synergistic effect was observed by increasing caffeine and UV light. Furthermore, the physico-chemical characteristics of the food matrix were not affected by the combination of both technologies. Therefore, these results suggest that this combination can be used in the food industry to effectively inactivate Salmonella enteritidis without deteriorating product quality.