bluuebunny/crossref_metadata_2025 huggingface dataset:
Alzheimer’s Multiclassification Using Explainable AI Techniques
Authors: Junior Kamese Jordan , Carole Kouayep Sonia , Theodore Armand Tagne Poupi , Kim Hee-Cheol , The Alzheimer’s Disease Neuroimaging Initiative The Alzheimer’s Disease Neuroimaging Initiative
Published: September 2024

In this study, we address the early detection challenges of Alzheimer’s disease (AD) using explainable artificial intelligence (XAI) techniques. AD, characterized by amyloid plaques and tau tangles, leads to cognitive decline and remains hard to diagnose due to genetic and environmental factors. Utilizing deep learning models, we analyzed brain MRI scans from the ADNI database, categorizing them into normal cognition (NC), mild cognitive impairment (MCI), and AD. The ResNet-50 architecture was employed, enhanced by a channel-wise attention mechanism to improve feature extraction. To ensure model transparency, we integrated local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping (Grad-CAM), highlighting significant image regions contributing to predictions. Our model achieved 85% accuracy, effectively distinguishing between the classes. The LIME and Grad-CAM visualizations provided insights into the model’s decision-making process, particularly emphasizing changes near the hippocampus for MCI. These XAI methods enhance the interpretability of AI-driven AD diagnosis, fostering trust and aiding clinical decision-making. Our approach demonstrates the potential of combining deep learning with XAI for reliable and transparent medical applications.
Changes in Catalan Adolescents’ Agility over Two Decades: A Temporal Trend Study
Authors: Arboix-Alió Jordi , Peralta-Geis Mariona , Morales Jose , Moragas Marta , Buscà Bernat , Solà Josep
Published: September 2024

Physical fitness is considered an important indicator of health in adolescents. Despite the recognized benefits of regular physical activity, recent studies indicate a decline in physical fitness among adolescents, including agility, a key component of fitness often overlooked in research. This study aimed to investigate trends in agility among urban Catalan adolescents over two decades. A total of 1439 students, aged 13–15 years, participated in the 8 × 10 m shuttle run test between 2002 and 2022. The results revealed a significant decline in agility performance across both boys (F(3,839) = 392, p < 0.001, ηp2 = 0.584) and girls (F(3,593) = 414, p < 0.001, ηp2 = 0.677) over time, with notable differences observed between consecutive five-year periods. The findings underscore the urgency of addressing declining physical fitness among adolescents and advocate for enhanced physical education programs and community-based interventions to promote regular physical activity. Recognizing the importance of agility and other aspects of physical fitness in adolescent health, policymakers, educators, and healthcare professionals are urged to collaborate on initiatives to mitigate the risk of future health issues. Further research is recommended to explore the multifaceted influences on adolescent physical fitness and develop targeted interventions to improve overall health outcomes in this population.
Multi-Granularity and Multi-Interest Contrast-Enhanced Hypergraph Convolutional Networks for Session Recommendation
Authors: Mao Xingbin , Li Liang , He Jiaxing
Published: September 2024

Aiming at the problems of the incomplete recommendation and sparsity of session data in session recommendation, a new multi-granularity and multi-interest contrast-enhanced hypergraph convolutional network (MGMI-CEHCN) model for session recommendation is proposed. The session data are modeled as a heterogeneous hypergraph, the information is embedded by the hypergraph using two granularities of item price and category, and then the information between different granularities is fused, while the final item embedding is obtained through multi-layer convolution. Finally, an interest perceptron is used to detect multiple potential interests for each item, and a decentralized interest extraction network based on a gated recurrent unit (GRU) is used to integrate the user’s final interests and obtain a global session representation through a soft attention mechanism; a local session representation is generated with the help of a weighted line graph convolutional network. A further joint contrast enhancement strategy is used to maximize the mutual information between the global session representation and local session representation, to improve the recommendation performance. Experiments on several real datasets showed that the recommendation performance of the MGMI-CEHCN model outperformed the current mainstream models. On Cosmetics, the P@20 reached up to 55.25% and M@20 reached up to 38.26%, improvements of 3.06% and 3.09%, respectively; on Diginetica-buy, the P@20 reached up to 65.60% and M@20 reached up to 27.47%, improvements of 2.47% and 6.64%, respectively, which proved the validity of the model.
Enhancing Explainable Recommendations: Integrating Reason Generation and Rating Prediction through Multi-Task Learning
Authors: Zhu Xingyu , Xia Xiaona , Wu Yuheng , Zhao Wenxu
Published: September 2024

In recent years, recommender systems—which provide personalized recommendations by analyzing users’ historical behavior to infer their preferences—have become essential tools across various domains, including e-commerce, streaming media, and social platforms. Recommender systems play a crucial role in enhancing user experience by mining vast amounts of data to identify what is most relevant to users. Among these, deep learning-based recommender systems have demonstrated exceptional recommendation performance. However, these “black-box” systems lack reasonable explanations for their recommendation results, which reduces their impact and credibility. To address this situation, an effective strategy is to provide a personalized textual explanation along with the recommendation. This approach has received increasing attention from researchers because it can enhance users’ trust in recommender systems through intuitive explanations. In this context, our paper introduces a novel explainable recommendation model named GCLTE. This model integrates Graph Contrastive Learning with transformers within an Encoder–Decoder framework to perform rating prediction and reason generation simultaneously. In addition, we cleverly combine the neural network layer with the transformer using a straightforward information enhancement operation. Finally, our extensive experiments on three real-world datasets demonstrate the effectiveness of GCLTE in both recommendation and explanation. The experimental results show that our model outperforms the top existing models.
Identification of Moving Train Axle Loads for Simply Supported Composite Beam Bridges in Urban Rail Transit
Authors: Sun Huahuai , Peng Xiyang , Xu Jun , Tu Hongkai
Published: September 2024

With the rapid urbanization and expansion of rail transit systems, the axle loads of trains, which are a critical aspect of their configuration, have significantly increased. This increase poses substantial potential threats to the safety and service life of existing bridges within urban rail transit networks. Therefore, it is imperative to develop methods for monitoring and identifying train axle loads. In this study, a strain field measurement scheme was devised and implemented for an operational simply supported composite beam bridge in urban rail transit. This involved numerical modeling and validation of the bridge’s structural response, followed by the calculation of strain influence lines at specific measurement points. Subsequently, a method for identifying train axle loads, considering the dynamic amplification effect, was established. This method integrates principles from strain influence lines with mathematical optimization techniques. Specifically, the axle loads of locomotives on the forward AC03 type trains of Shanghai Metro Line 3 were found to conform to a logarithmic normal distribution model, while those of middle carriages followed a normal distribution model. Their respective mean axle loads were determined as 9.64 t and 10.77 t, with a shared variance of 0.8. Similarly, the axle loads of locomotives and middle carriages on reverse AC03-type trains also followed normal distribution models, with identical mean values around 10.5 t. The variances for axle loads of locomotives and middle carriages of reverse trains were found to be 1.36 and 0.8, respectively. The developed method effectively enables the monitoring of train axle loads and assessment of their impact on bridge structures, therefore enhancing safety and operational reliability within urban rail transit systems.
The Implementation of IoT Sensors in Fog Collector Towers and Flowmeters for the Control of Water Collection and Distribution
Authors: Carrera-Villacrés David Vinicio , Gallegos Rios Diego Fernando , Chiliquinga López Yadira Alexandra , Córdova Córdova José Javier , Arroba Giraldo Andrea Mariela
Published: September 2024

This study describes the implementation of Internet of Things (IoT) sensors in flow meters installed in drinking water systems and in fog catchers built in low-income, high-altitude communities in the Andes region of Ecuador, taking studies at the University de las Fuerzas Armadas ESPE as our reference. The influence and management of these intelligent sensors are analyzed, as well as a basic review of the materials and methods used in their implementation. The importance of validating the accuracy and reliability of IoT sensors compared to professional devices is highlighted, especially in mountain areas with difficult access. Additionally, the cost–benefit of using IoT sensors in fog catchers and drinking water distribution networks is mentioned, which depends on several factors such as the scale of the project, the objectives to be achieved concerning monitoring, and the available resources. Finally, it is highlighted that using Internet of Things (IoT) sensors in construction and water collection systems has proven beneficial in detecting possible effects on its operation and determining consumption and supply flows for a given population.
Fault Feature Extraction Using L-Kurtosis and Minimum Entropy-Based Signal Demodulation
Authors: Kumar Surinder , Chauhan Sumika , Vashishtha Govind , Kumar Sunil , Kumar Rajesh
Published: September 2024

The health of mechanical components can be assessed by analyzing the vibration and acoustic signals they produce. These signals contain valuable information about the component’s condition, often encoded within specific frequency bands. However, extracting this information is challenging due to noise contamination from various sources. Narrow-band amplitude demodulation presents a robust technique for isolating fault-related information within the signal. This work proposes a novel approach based on cluster-based segmentation for demodulating the signal and extracting the frequency band of interest. The segmentation process leverages the criteria of maximum L-kurtosis and minimum entropy. L-kurtosis maximizes impulsiveness in the signal, while minimum entropy signifies a low degree of randomness and high cyclo-stationarity, and both characteristics are crucial for identifying the desired frequency band. Simulations and experimental tests using vibration signals from different gears demonstrate the effectiveness of this technique. The processed envelope of the signal exhibits distinct improvements, highlighting the ability to accurately extract the fault-related information embedded within the complex noise-ridden signals. This approach offers a promising solution for accurate and efficient fault diagnosis in mechanical systems, contributing to enhanced reliability and reduced downtime.
Optimized Multi-Motor Power Control Strategy for Distributed Permanent Magnet Direct Drive Belt Conveyors
Authors: Zhou Qixun , Shi Keke , Xu Kaicheng , Du Guanghui , Gao Kaiying
Published: September 2024

To address the problem of energy wastage in traditional long-distance belt conveyors with low carrying capacity due to the excessive power installation of a single drive motor, a model of a distributed permanent magnet direct drive belt conveyor driven by multiple small power motors is proposed. A power regulator based on material carrying capacity is designed for energy saving. The minimum number of motors is set as the adjustment target. The number of running motors is adjusted, and the remaining motor power is distributed. The speed constraints of multiple motors are analyzed. The speed difference between adjacent motors is used as feedback to construct a ring coupling speed controller. This realizes the coordinated operation of the distributed permanent magnet direct drive belt conveyor. Co-simulations with AMESIM (version 2020.1) and MATLAB (version 2019b) are conducted to verify the system’s stability. The power regulator optimizes motor distribution under varying conditions, reducing operational losses. The experimental results confirm the effectiveness of the proposed power control strategy.
The Difference between Traditional Magnetic Stimulation and Microcoil Stimulation: Threshold and the Electric Field Gradient
Authors: Alzahrani Mohammed , Roth Bradley J.
Published: September 2024

This paper considers the threshold value of the activating function needed for stimulation in traditional magnetic stimulation and microcoil stimulation. Two analyses of excitation have been studied: spatial frequency analysis and active membrane analysis using the Hodgkin−Huxley model. The activating function depends on the spatial distribution of the electric field gradient in the active membrane analysis and the spatial frequency in the spatial frequency analysis. Both analyses show that a microcoil (tens of microns in size) has a higher threshold than a traditional coil (tens of millimeters in size) when the spatial frequency is large or the spatial extent of the activating function is small. Consequently, the stimulation threshold for a microcoil is much higher than that for a conventional coil.
Study of Millimeter-Wave Fuze Echo Characteristics under Rainfall Conditions Using the Monte Carlo Method
Authors: Yang Bing , Guo Zhe , Wu Kaiwei , Huang Zhonghua
Published: September 2024

Due to the similarity in wavelength between millimeter-wave (MMW) signals and raindrop diameters, rainfall induces significant attenuation and scattering effects that challenge the detection performance of MMW fuzes in rainy environments. To enhance the adaptability of frequency-modulated MMW fuzes in such conditions, the effects of rain on MMW signal attenuation and scattering are investigated. A mathematical model for the multipath echo signals of the fuze was developed. The Monte Carlo method was employed to simulate echo signals considering multiple scattering, and experimental validations were conducted. The results from simulations and experiments revealed that rainfall increases the bottom noise of the echo signal, with rain backscatter noise predominantly affecting the lower end of the echo signal spectrum. However, rain conditions below torrential levels did not significantly impact the detection of strong reflection targets at the high end of the spectrum. The modeling approach and findings presented offer theoretical support for designing MMW fuzes with improved environmental adaptability.
Reviewing the Phenomenon of Antimicrobial Resistance in Hospital and Municipal Wastewaters: The Crisis, the Challenges and Mitigation Methods
Authors: Hasan Nazim , Kannan Embar Prasanna , Hakami Othman , Alamri Abdullah Ali , Gopal Judy , Muthu Manikandan
Published: September 2024

Antibiotic resistance is a major crisis that the modern world is confronting. This review highlights the abundance of different types of antibiotic resistance genes (ARGs) in two major reservoirs in the environment, namely hospital and municipal wastewater, which is an unforeseen threat to human lives across the globe. The review helps understand the current state of affairs and the whereabouts on the dissemination of ARGs in both these environments. The various traditional wastewater treatment methods, such as chlorination and UV treatment, and modern methods, such as electrochemical oxidation, are discussed, and the gaps in these technologies are highlighted. The need for the development of newer techniques for wastewater treatment with enhanced efficiency is urgently underscored. Nanomaterial applications for ARG removal were observed to be less explored. This has been discussed, and prospective nanomaterials and nanocomposites for these applications are proposed.
Factors Influencing University Students’ Continuance Intentions towards Self-Directed Learning Using Artificial Intelligence Tools: Insights from Structural Equation Modeling and Fuzzy-Set Qualitative Comparative Analysis
Authors: Zhou Jinqiao , Zhang Hongfeng
Published: September 2024

This study investigates the intricate causal mechanisms of university students’ sustained use of artificial intelligence (AI) tools for self-directed learning (SDL) within the theoretical framework of self-determination theory (SDT). Employing a convenience sampling strategy, 387 university students from China were included in the study. Methodologically, we employed structural equation modeling (SEM) for the measurement and causal analysis, and we employed fuzzy-set qualitative comparative analysis (fsQCA) for the configurational analysis. The research results emphasize several important insights. Perceived usefulness, intrinsic motivation, and satisfaction play important roles in encouraging university students’ continuance intentions. Satisfaction emerges as a pivotal mediator, bridging the connection between perceived usefulness, intrinsic motivation, and continuance intention. The system quality, the information quality, and social interaction have significant positive influences on perceived usefulness. Perceived autonomy and perceived competence display strong correlations with both intrinsic motivation and satisfaction. Moreover, the results from the fsQCA show five configurations, in which the key factors collectively shape students’ continuance intentions through complex interactions through various configurations. The findings reveal diverse configurations by which university students form continuance intentions towards using AI tools for SDL, providing detailed insights into the profound and indirect impacts on forming continuance intention behaviors. This enriches and advances the current theoretical understanding.
Abutment Tooth Formation Simulator for Naked-Eye Stereoscopy
Authors: Tomita Rintaro , Nakano Akito , Kawanishi Norishige , Hoshi Noriyuki , Itamiya Tomoki , Kimoto Katsuhiko
Published: September 2024

Virtual reality is considered to be useful in improving procedural skills in dental education, but systems using wearable devices such as head-mounted displays (HMDs) have many problems in terms of long-term use and hygiene, and the accuracy of stereoscopic viewing at close ranges is inadequate. We developed an abutment tooth formation simulator that utilizes a display (spatial reality display—SRD) to precisely reproduce 3D space with naked-eye stereoscopic viewing at close range. The purpose of this was to develop and validate the usefulness of an abutment tooth formation simulator using an SRD. A 3D-CG (three-dimensional computer graphics) dental model that can be cut in real time was output to the SRD, and an automatic quantitative scoring function was also implemented by comparing the cutting results with exemplars. Dentists in the department of fixed prosthodontics performed cutting operations on both a 2D display-based simulator and an SRD-based simulator and conducted a 5-point rating feedback survey. Compared to the simulator that used a 2D display, the measurements of the simulator using an SRD were significantly more accurate. The SRD-based abutment tooth formation simulator received a positive technical evaluation and high dentist satisfaction (4.37), suggesting its usefulness and raising expectations regarding its future application in dental education.
Application of Historical Comprehensive Multimodal Transportation Data for Testing the Commuting Time Paradox: Evidence from the Portland, OR Region
Authors: Yang Huajie , Lin Jiali , Shi Jiahao , Ma Xiaobo
Published: September 2024

There have been numerous theoretical and empirical transportation studies contesting the stability of commuting time over time. The constant commuting time hypothesis posits that people adjust trip durations, shift across modes, and sort through locations, so that their average commuting time remains within a constant budget. There is a discrepancy between studies applying aggregate analysis and those using disaggregate analysis, and differences in data collection may have contributed to the varying conclusions reported in the literature. This study conducts both aggregate and disaggregate analyses with two travel surveys of the Portland region. We employ descriptive analysis and t-tests to compare the aggregate commuting times of two years and use regression models to explore factors affecting the disaggregate commuting time at the individual trip level to examine whether the stability of the commuting time remains after substantial changes in the transportation and land use systems. Our study indicates that the average commuting time, along with the average commuting distance, increased slightly, as the mode share shifted away from driving during the examined period. The growth in shares of non-driving modes, which are slower than driving, coupled with an increased travel distance, contributed to the small increase in the average commuting time. Our analysis also indicates that the average travel speed improved for transit riders as well as drivers, contradicting earlier research that claims that public transit investment has worsened the congestion in Portland.
Exploring the Association between Pro-Inflammation and the Early Diagnosis of Alzheimer’s Disease in Buccal Cells Using Immunocytochemistry and Machine Learning Techniques
Authors: Lazaros Konstantinos , Gonidi Maria , Kontara Nafsika , Krokidis Marios G. , Vrahatis Aristidis G. , Exarchos Themis , Vlamos Panagiotis
Published: September 2024

The progressive aging of the global population and the high impact of neurodegenerative diseases, such as Alzheimer’s disease (AD), underscore the urgent need for innovative diagnostic and therapeutic strategies. AD, the most prevalent neurodegenerative disorder among the elderly, is expected to affect 75 million people in developing countries by 2030. Despite extensive research, the precise etiology of AD remains elusive due to its heterogeneity and complexity. The key pathological features of AD, including amyloid-beta plaques and hyperphosphorylated tau protein, are established years before clinical symptoms appear. Recent studies highlight the pivotal role of neuroinflammation in AD pathogenesis, with the chronic activation of the brain’s immune system contributing to the disease’s progression. Pro-inflammatory cytokines, such as TNF-α, IL-1β, and IL-6, are elevated in AD and mild cognitive impairment (MCI) patients, suggesting a strong link between peripheral inflammation and CNS degeneration. There is a pressing need for minimally invasive, cost-effective diagnostic methods. Buccal mucosa cells and saliva, which share an embryological origin with the CNS, show promise for AD diagnosis and prognosis. This study integrates cellular observations with advanced data processing and machine learning to identify significant biomarkers and patterns, aiming to enhance the early diagnosis and prevention strategies for AD.
Optimizing Nigella Oil Extraction Temperature for Sustainable Production
Authors: Thani Parbat Raj , Johnson Joel B. , Bhattarai Surya , Trotter Tieneke , Walsh Kerry , Broszczak Daniel , Naiker Mani
Published: September 2024

Understanding the extraction process of Nigella oil is crucial due to its significant impact on yield, quality, and therapeutic effectiveness. This study explored the effects of various screw press temperatures (40 °C, 50 °C, 60 °C, 70 °C, and 80 °C) on the quantity and quality of Nigella oil to optimize conditions that maximize yield while maintaining its nutritional and therapeutic attributes. Our findings indicate a linear increase in oil yield as screw press temperatures rose from 40 °C to 80 °C. There were no significant differences observed in total phenolic content (TPC), cupric reducing antioxidant capacity (CUPRAC), or the composition and ratio of fatty acids across oils extracted at different temperatures. However, the ferric-reducing antioxidant power (FRAP) was highest in oils extracted at 60 °C, while the thymoquinone (TQ) content peaked between 40 °C and 60 °C. These results underscore the importance of optimizing screw press temperatures to strike a balance between maximizing oil yield and preserving its valuable therapeutic and nutritional properties
Reliability and Validity of the Orthotic Deformation Test
Authors: Sierra-Rodríguez Alejandra , Molines-Barroso Raúl J. , Martínez M. A. , Abenojar-Buendía Juana , Lázaro-Martínez José Luis , Orejana-García Angel M.
Published: September 2024

The aim of this study was to evaluate the validity of an orthotic deformation test on insole arches and the impact of researcher expertise on the test’s reproducibility. Three researchers with different levels of experience evaluated orthotic deformation by applying a vertical force with their thumb on the dorsal surface of 48 polypropylene orthoses. An electromechanical test machine was used to evaluate the stiffness of the orthoses, and the results were compared with those of the orthotic deformation tests. Fleiss’ kappa and weighted kappa and Mann–Whitney U tests were used to evaluate the interobserver reliability and the validity, respectively. There was no consensus among the three researchers (Kappa = 0.080; p-value = 0.334), although the two researchers with higher levels of experience exhibited moderate consensus (Kappa = 0.52; p-value < 0.001). Orthoses characterised by a positive test suffered from more pronounced deformation when a constant vertical force was applied by the electromechanical test machine (5.62 [4.64–7.38] vs. 4.56 [3.59–5.28] mm; p-value = 0.003). The orthotic deformation test is valid for interpreting the degree of deformation of polypropylene custom orthoses when it is compared with an axial force from a compression-device test. However, considering the high dispersion of the results, it suggests that clinical experience is required to properly interpret the tests.
Comparative Analysis of the Health Status of Heart Transplant Patients with Different Levels of Physical Activity
Authors: Salas-Reguera Itziar , Rodríguez-Barbosa José I. , Federolf Peter A. , Santos Luis
Published: September 2024

This study’s goals were to determine the health status of a group of heart transplant recipients (HTRs) and their level of physical activity and to compare the health status among them and with a group of healthy sedentary individuals. Fifty-four HTRs and eighteen sedentary individuals (S) were assigned to four groups, according to their level of physical activity (determined with the International Physical Activity Questionnaire); patients with a low, moderate, and high level of physical activity (HTRL, HTRM, and HTRH, respectively) and S participants underwent a basic blood analysis and several tests to assess their cardiovascular, neuromuscular, and functional mobility condition and their quality of life. The S and HTRH were very similar in terms of BP, HR, and blood analysis while HTRM and HTRL differed from both S and HTRH in these parameters. Regarding the cardiovascular, neuromuscular, functional mobility, and quality of life variables assessed in this study, HTRH showed the best results across all of them, followed by S, HTRM, and HTRL. It is suggested that the weekly level of physical activity of HTRs should be high, which might help them to enhance their health and quality of life.
Characterization of Mortars Incorporating Concrete Washing Fines: Impact on Mechanical Properties, Microstructure and Carbon Footprint
Authors: Haddad Bechara , Alassaad Farjallah , Affan Houssam , Mohamad Abdelrahman , Sebaibi Nassim
Published: September 2024

This study examines the potential use of wash fines, a waste product from concrete plant cleaning, as supplementary cementing materials (SCMs) in mortars. The main objective is to assess the feasibility and benefits of this incorporation in terms of technical performance and environmental impact. Extensive tests were carried out on different mortar formulations, incorporating varying rates of washing fines (0%, 10%, 20%, 30%) as a partial replacement for cement. This choice of replacement is prompted by the fineness of washing fine particles. The properties studied included compressive and flexural strength, porosity, density, water absorption, shrinkage and fire resistance. The results show that the incorporation of washing fines increases porosity and decreases mortar density. There was also a decrease in mechanical strength and fire resistance as the substitution rate increased. However, the use of washing fines enables a significant reduction in the mortar’s carbon footprint, reaching up to 29% for the formulation with 30% substitution. This study demonstrates the potential of washing fines as an alternative SCM, as part of a circular economy approach to reducing the environmental impact of the concrete industry. However, it underlines the need to optimize formulations to maintain acceptable technical performance.
Improving the Accuracy and Effectiveness of Text Classification Based on the Integration of the Bert Model and a Recurrent Neural Network (RNN_Bert_Based)
Authors: Eang Chanthol , Lee Seungjae
Published: September 2024

This paper proposes a new robust model for text classification on the Stanford Sentiment Treebank v2 (SST-2) dataset in terms of model accuracy. We developed a Recurrent Neural Network Bert based (RNN_Bert_based) model designed to improve classification accuracy on the SST-2 dataset. This dataset consists of movie review sentences, each labeled with either positive or negative sentiment, making it a binary classification task. Recurrent Neural Networks (RNNs) are effective for text classification because they capture the sequential nature of language, which is crucial for understanding context and meaning. Bert excels in text classification by providing bidirectional context, generating contextual embeddings, and leveraging pre-training on large corpora. This allows Bert to capture nuanced meanings and relationships within the text effectively. Combining Bert with RNNs can be highly effective for text classification. Bert’s bidirectional context and rich embeddings provide a deep understanding of the text, while RNNs capture sequential patterns and long-range dependencies. Together, they leverage the strengths of both architectures, leading to improved performance on complex classification tasks. Next, we also developed an integration of the Bert model and a K-Nearest Neighbor based (KNN_Bert_based) method as a comparative scheme for our proposed work. Based on the results of experimentation, our proposed model outperforms traditional text classification models as well as existing models in terms of accuracy.
Plastic Limit Pressure and Stress Intensity Factor for Cracked Elbow Containing Axial Semi-Elliptical Part-Through Crack
Authors: Damjanović Božo , Konjatić Pejo , Katinić Marko
Published: September 2024

The aim of this study is to provide a solution for the plastic limit pressure and stress intensity factor of the elbows containing a part-through axial semi-elliptical crack by considering various crack sizes. The supporting system and loading conditions of the pipeline are described. The critical part of the observed pipeline was isolated for analysis and subjected to various sizes of semi-elliptical cracks. By performing numerical analysis, results were obtained for crack dimension ratios of c/a, and depth/thickness ratios of a/t. The obtained results include plastic limit pressure and stress intensity factor. The results were analyzed with a symbolic regression algorithm, and closed-form solutions for the limit pressure and stress intensity factor were proposed. To validate pipeline integrity, the Structural Integrity Assessment Procedure (SINTAP) was applied, and the FAD (Failure Assessment Diagram) was generated for cracks below the FAD function. The failure pressure was calculated by determining the points where the loading paths intersect the FAD function.
Virtual Screening and Molecular Dynamics Simulation to Identify Inhibitors of the m6A-RNA Reader Protein YTHDC1
Authors: Aslam Memoona , Singh Nidhi , Wang Xiaowen , Li Wenjin
Published: September 2024

YTHDC1 (YTH domain containing 1), a crucial reader protein of N6-methyladenosine (m6A) mRNA, plays a critical role in various cellular functions and is considered a promising target for therapeutic intervention in acute myeloid leukemia and other cancers. In this study, we identified orthosteric small-molecule ligands for YTHDC1. Using a molecular docking approach, we screened the eMolecules database and recognized 15 top-ranked ligands. Subsequently, molecular dynamics simulations and MM/PBSA analysis were used to assess the stability and binding free energy of these potential hit compounds in complex with YTHDC1. Notably, five compounds with IDs of ZINC82121447, ZINC02170552, ZINC65274016, ZINC10763862, and ZINC02412146 exhibited high binding affinities and favorable binding free energies. The results also showed that these compounds formed strong hydrogen bonds with residues SER378, ASN363, and ASN367 and interacted with the aromatic cage of the YTHDC1 reader protein through TRP377, TRP428, and hydrophobic residue LEU439. To assess their viability as lead compounds, we conducted absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies to reveal promising features for these identified small molecules, shedding light on their pharmacokinetic and safety profiles.
Advanced Micro/Nanocapsules for Self-Healing Coatings
Authors: Kartsonakis Ioannis A. , Kontiza Artemis , Kanellopoulou Irene A.
Published: September 2024

The concept of intelligence has many applications, such as in coatings and cyber security. Smart coatings have the ability to sense and/or respond to external stimuli and generally interact with their environment. Self-healing coatings represent a significant advance in improving material durability and performance using microcapsules and nanocontainers loaded with self-healing agents, catalysts, corrosion inhibitors, and water-repellents. These smart coatings can repair damage on their own and restore mechanical properties without external intervention and are inspired by biological systems. Properties that are affected by either momentary or continuous external stimuli in smart coatings include corrosion, fouling, fungal, self-healing, piezoelectric, and microbiological properties. These coating properties can be obtained via combinations of either organic or inorganic polymer phases, additives, and pigments. In this article, a review of the advancements in micro/nanocapsules for self-healing coatings is reported from the aspect of extrinsic self-healing ability. The concept of extrinsic self-healing coatings is based on the use of capsules or multichannel vascular systems loaded with healing agents/inhibitors. The result is that self-healing coatings exhibit improved properties compared to traditional coatings. Self-healing anticorrosive coating not only enhances passive barrier function but also realizes active defense. As a result, there is a significant improvement in the service life and overall performance of the coating. Future research should be devoted to refining self-healing mechanisms and developing cost-effective solutions for a wide range of industrial applications.
Buffer with N Policy and Active Management
Authors: Chydzinski Andrzej
Published: September 2024

The N policy is a buffer and transmission management scheme proposed for nodes in wireless sensor networks to save energy. It exploits the concept that the output radio of a node is initially switched off until a critical queue of packets is built up. Then, the output transmission begins and continues until the buffer is completely flushed. The cycle then repeats. In this study, we analyze a buffer with the N policy, equipped additionally with active queue management, which allows for dropping some packets depending on the current buffer occupancy. This extension enables controlling the performance of the node to a much greater extent than in the original N policy. The main contribution is the formulae for the key performance characteristics of the extended policy: the queue size distribution, throughput, and energy efficiency. These formulae are proven for a model with a general distribution of service time and general parameterizations of active management during the energy-saving and transmission phases. Theoretical results are followed by sample numerical calculations, demonstrating how the system’s performance can be controlled using active management in the transmission phase, the energy-saving phase, or both combined. The influence of the threshold value in an actively managed buffer is then shown and compared with its passive counterpart. Finally, solutions to some optimization problems, with the cost function based on the trade-off between the queue length and throughput, are presented.
Fast and Efficient Drone Path Planning Using Riemannian Manifold in Indoor Environment
Authors: Dujari Rohit , Patel Brijesh , Patle Bhumeshwar K.
Published: September 2024

This paper introduces an innovative dual-path planning algorithm rooted in a topological three-dimensional Riemannian manifold (T3DRM) to optimize drone navigation in complex environments. It seamlessly integrates strategies for both discrete and continuous obstacles, employing spherical navigation for the former and hyperbolic paths for the latter. Serving as a transformative tool, the T3DRM facilitates efficient path planning by transitioning between discrete and continuous domains. In uncertain environments with unpredictable obstacle positions, our methodology categorizes these positions as discrete or continuous based on their distribution patterns. Discrete obstacles exhibit random distributions, while continuous obstacles display symmetrical patterns with continuity. Leveraging topological metrics, the T3DRM efficiently classifies these patterns for effective path planning. The findings of this research demonstrate the efficiency of path planning based on classified obstacle positions, enabling swift and efficient drone navigation. This research introduces a pioneering application of a T3DRM, accelerating drone navigation in uncertain environments through a dual approach that simultaneously transforms navigation in primal and dual domains. By enabling spherical and hyperbolic navigation concurrently, the T3DRM offers a comprehensive solution to discrete and continuous path planning challenges. The proposed approach can be used for various indoor applications, especially for warehouse management, surveillance and security, navigation in complex structures, indoor farming, site inspection, healthcare facilities, etc.
A Framework for I*-Statistical Convergence of Fuzzy Numbers
Authors: Tanushri  , Ahmad Ayaz , Esi Ayhan
Published: September 2024

In this study, we investigate the concept of I*-statistical convergence for sequences of fuzzy numbers. We establish several theorems that provide a comprehensive understanding of this notion, including the uniqueness of limits, the relationship between I*-statistical convergence and classical convergence, and the algebraic properties of I*-statistically convergent sequences. We also introduce the concept of I*-statistical pre-Cauchy and I*-statistical Cauchy sequences and explore its connection to I*-statistical convergence. Our results show that every I*-statistically convergent sequence is I*-statistically pre-Cauchy, but the converse is not necessarily true. Furthermore, we provide a sufficient condition for an I*-statistically pre-Cauchy sequence to be I*-statistically convergent, which involves the concept of I*−liminf.
Clonostachys rosea, a Pathogen of Brown Rot in Gastrodia elata in China
Authors: Yao Huan , Liu Kang , Peng Lei , Huang Touli , Shi Jinzhen , Sun Beilin , Zou Juan
Published: September 2024

Gastrodia elata, commonly known as Tian Ma, is a perennial mycoheterotrophic orchid. Qianyang Tian Ma (QTM), a geographical indication agricultural product from Hongjiang City, Hunan Province, China, is primarily characterized by the red variety, G. elata f. elata. A severe outbreak of tuber brown rot disease was documented in QTM during the harvesting season in Hunan. The fungal pathogen associated with the disease was isolated on potato saccharose agar (PSA) and identified through morphological and phylogenetic analyses. Pathogenicity tests were performed on healthy tubers of G. elata f. elata. The results showed that the representative isolate, named TMB, produced white hyphal colonies with a ring structure, broom-like phialides, partially curved ellipsoidal conidia, and orange–yellow spherical ascocarps on PSA. Phylogenetic analysis of the ITS, LSU, rpb2 and tub2 sequences using Bayesian and maximum-likelihood methods identified the isolate TMB as Clonostachys rosea, based on morphological and phylogenetic data. Pathogenicity tests revealed typical disease symptoms on healthy G. elata tubers 15 days post-inoculation with the isolate TMB. C. rosea is known to cause diseases in economically important crops, but there are no reports of its occurrence on G. elata f. elata in China. This study provides valuable insights into the occurrence, prevention, and control of brown rot disease in G. elata f. elata.
Potential Plasma Proteins (LGALS9, LAMP3, PRSS8 and AGRN) as Predictors of Hospitalisation Risk in COVID-19 Patients
Authors: McLarnon Thomas , McDaid Darren , Lynch Seodhna M. , Cooper Eamonn , McLaughlin Joseph , McGilligan Victoria E. , Watterson Steven , Shukla Priyank , Zhang Shu-Dong , Bucholc Magda , English Andrew , Peace Aaron , O’Kane Maurice , Kelly Martin , Bhavsar Manav , Murray Elaine K. , Gibson David S. , Walsh Colum P. , Bjourson Anthony J. , Rai Taranjit Singh
Published: September 2024

Background: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed unprecedented challenges to healthcare systems worldwide. Here, we have identified proteomic and genetic signatures for improved prognosis which is vital for COVID-19 research. Methods: We investigated the proteomic and genomic profile of COVID-19-positive patients (n = 400 for proteomics, n = 483 for genomics), focusing on differential regulation between hospitalised and non-hospitalised COVID-19 patients. Signatures had their predictive capabilities tested using independent machine learning models such as Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR). Results: This study has identified 224 differentially expressed proteins involved in various inflammatory and immunological pathways in hospitalised COVID-19 patients compared to non-hospitalised COVID-19 patients. LGALS9 (p-value < 0.001), LAMP3 (p-value < 0.001), PRSS8 (p-value < 0.001) and AGRN (p-value < 0.001) were identified as the most statistically significant proteins. Several hundred rsIDs were queried across the top 10 significant signatures, identifying three significant SNPs on the FSTL3 gene showing a correlation with hospitalisation status. Conclusions: Our study has not only identified key signatures of COVID-19 patients with worsened health but has also demonstrated their predictive capabilities as potential biomarkers, which suggests a staple role in the worsened health effects caused by COVID-19.
Schild Analysis of the Interaction between Parthenolide and Cocaine Suggests an Allosteric Relationship for Their Effects on Planarian Motility
Authors: Kakuturu Jyothi , O’Brien Mary , Pagán Oné R.
Published: September 2024

The freshwater planarian is an emerging animal model in neuroscience due to its centralized nervous system that closely parallels closely parallels the nervous system of vertebrates. Cocaine, an abused drug, is the ‘founding member’ of the local anesthetic family. Parthenolide, a sesquiterpene lactone, acts as a behavioral and physiological antagonist of cocaine in planarians and rats, respectively. Previous work from our laboratory showed that both parthenolide and cocaine reduced planarian motility and that parthenolide reversed the cocaine-induced motility decrease at concentrations where parthenolide does not affect the movement of the worms. However, the exact mechanism of the cocaine/parthenolide antagonism is unknown. Here, we report the results of a Schild analysis to explore the parthenolide/cocaine relationship in the planarian Girardia tigrina. The Schild slopes of a family of concentration–response curves of parthenolide ± a single concentration of cocaine and vice versa were −0.55 and −0.36, respectively. These slopes were not statistically different from each other. Interestingly, the slope corresponding to the parthenolide ± cocaine (but not the cocaine ± parthenolide) data set was statistically different from −1. Our data suggest an allosteric relationship between cocaine and parthenolide for their effect on planarian motility. To the best of our knowledge, this is the first study about the mechanism of action of the antagonism between cocaine and parthenolide. Further studies are needed to determine the specific nature of the parthenolide/cocaine target(s) in this organism.
Exploration into Galectin-3 Driven Endocytosis and Lattices
Authors: Shafaq-Zadah Massiullah , Dransart Estelle , Mani Satish Kailasam , Sampaio Julio Lopes , Bouidghaghen Lydia , Nilsson Ulf J. , Leffler Hakon , Johannes Ludger
Published: September 2024

Essentially all plasma membrane proteins are glycosylated, and their activity is regulated by tuning their cell surface dynamics. This is achieved by glycan-binding proteins of the galectin family that either retain glycoproteins within lattices or drive their endocytic uptake via the clathrin-independent glycolipid-lectin (GL-Lect) mechanism. Here, we have used immunofluorescence-based assays to analyze how lattice and GL-Lect mechanisms affect the internalization of the cell adhesion and migration glycoprotein α5β1 integrin. In retinal pigment epithelial (RPE-1) cells, internalized α5β1 integrin is found in small peripheral endosomes under unperturbed conditions. Pharmacological compounds were used to competitively inhibit one of the galectin family members, galectin-3 (Gal3), or to inhibit the expression of glycosphingolipids, both of which are the fabric of the GL-Lect mechanism. We found that under acute inhibition conditions, endocytic uptake of α5β1 integrin was strongly reduced, in agreement with previous studies on the GL-Lect driven internalization of the protein. In contrast, upon prolonged inhibitor treatment, the uptake of α5β1 integrin was increased, and the protein was now internalized by alternative pathways into large perinuclear endosomes. Our findings suggest that under these prolonged inhibitor treatment conditions, α5β1 integrin containing galectin lattices are dissociated, leading to an altered endocytic compartmentalization.