bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Story Drifts in Determining the Earthquake Performance of High-Rise Buildings
Authors: Guler Mehmet Gokhan , Guler Kadir
Published: November 2024

In performance-based design and assessment, there are prescriptive limits based not only on element-based performance evaluation but also on comparing story drifts with limit values. The process of determining performance levels at the element level involves obtaining the required data through numerous calculation steps, followed by evaluation, which makes it a time-consuming process. The iterative nature of this process emphasizes the importance of selecting the structural system, element dimensions, and target performance levels during the preliminary design stage to ensure they are consistent with the final analysis results. For this purpose, the determination of story drifts, which is widely accepted in the literature, is a critical aspect of performance evaluation studies, particularly for high-rise buildings, within the framework of deformation-based calculation assumptions. The continuum model is a practical approach for the approximate analysis of high-rise buildings including moment-resisting frames and shear wall-frame systems. In the continuum model, discrete buildings are simplified such that their overall behavior is described through the contributions of flexural and shear stiffnesses at the story levels. In this study, the aim is to enhance the Miranda and Taghavi (2005) model, which is classified among the approximate methods in the literature for determining story drifts and is developed within the framework of continuum model approaches.
Research on the Embedding Behavior of Ceramic Particles on the Surface of Epoxy Resin Anti-Skid Thin Layer of Pavement
Authors: Yuan Jiaquan , Zhou Gang , Fu Chaoliang , Duan Wenhong , Zhang Yifan , Huang Haoyang , Jiang Weihong , Xiong Li , Li Huimei , Yang Xiaohua , Li Chuanqiang
Published: November 2024

Colored polymer anti-skid thin layers are widely used on urban roads to enhance driving safety, improve road aesthetics, and mitigate the urban heat island effect. However, in thin layers constructed by the spreading method, the adhesion of cementitious material to the aggregate is often weak. This leads to early-stage spalling of surface aggregates, thereby reducing the anti-skid performance of the layer. To investigate the factors contributing to spalling, this study examines the embedding behavior of ceramic particles and assesses how the fluidity of the cementitious material and aggregate shape characteristics influence the embedding depth. Using a rotational viscosity test, it is concluded that a cementitious mix ratio of adhesive/powder filler/sand filler = 1:0.5:1 or 1:0.5:1.5 facilitates effective aggregate embedding. Testing the embedding depth of aggregates with the same particle size across different cementitious materials revealed that higher cementitious viscosity results in a reduced aggregate embedding depth. Geometric parameter data for aggregate particles were extensively collected using an image acquisition device, and quantitative analysis identified the shape characteristics influencing the embedding depth. A gray correlation analysis determined that the impact of the shape characteristics on embedding depth follows the order of roundness factor > prism factor > axial coefficient.
Sampling and Comparison of Extraction Techniques Coupled with Gas Chromatography–Mass Spectrometry (GC-MS) for the Analysis of Substrates Exposed to Explosives
Authors: Upadhyaya Himanshi , Hecker Alexis J. , Goodpaster John V.
Published: November 2024

Explosive-detecting canines (EDCs) show high sensitivity in detecting explosives that they are trained to detect. The ability of canines to detect explosive residues to the parts per trillion level can sometimes result in nuisance alerts. These nuisance alerts can occur when various materials (i.e., substrates) are exposed to volatile organic compounds (VOCs) present in explosive mixtures, leading to contamination—the unintended absorption or adsorption of VOCs by the substrate. Chemical constituents such as taggant, plasticizer, and residual solvent in explosives are often composed of VOCs that canines are trained on to detect explosives. Composition C-4 (C4) is a common explosive that EDCs are trained to detect and hence is this study’s focus. Common VOCs of interest emitted from C4 include 2,3-dimethyl-2,3-dinitrobutane (DMNB), 2-ethyl-1 hexanol (2E1H), and cyclohexanone. In this study, we developed a protocol for comparing different substrates such as cotton, cardboard, wood, sheet metal, and glass that were exposed to volatiles from C4. 1-bromooctane (1-BO) was used as a single-odor compound to compare the complex odor originating from C4. Triplicates of substrates such as cotton, wood, cardboard, sheet metal, and glass were exposed to 1 g of C4 in a paint can for one week and the substrates were then extracted using various extraction methods such as liquid injection, direct SPME, and headspace analysis coupled with gas chromatography–mass spectrometry. An extraction time study was performed to determine the optimal extraction time for SPME analysis, and it was found to be 20 min. Comparison of extraction methods revealed that SPME surpassed other techniques as DMNB was found on all substrates using SPME. It was observed that porous substrates such as wood and cardboard have a higher retention capacity for volatiles in comparison to non-porous substrates such as sheet metal and glass. Finally, swabbing was evaluated as a sampling technique for the substrates of interest and the extracts were analyzed using the total vaporization–solid phase microextraction (TV-SPME) technique. No volatiles associated with C4 were identified on conducting a GC-MS analysis, suggesting that swabbing is not an ideal technique for analysis of substrates exposed to C4.
Effectiveness of the Pediatric Nursing Excellence Model on Nurses’ Knowledge and Practice in Pediatric Orthopedic Surgery Care: A Randomized Controlled Trial
Authors: Ramadan Osama Mohamed Elsayed , Hafiz Alaa Hussain , Elsharkawy Nadia Bassuoni , Katooa Nouran Essam , Abunar Areej , Abdelaziz Enas Mahrous , Baraka Samia Ibrahim Mabrouk , Shaban Mostafa , Baraka Nagwa Ibrahim Mabrouk
Published: November 2024

Background: Pediatric orthopedic nursing requires specialized competencies to optimize patient outcomes, particularly in the complex realm of pediatric surgery. This study explores the effectiveness of the Pediatric Nursing Excellence (PNE) Model in enhancing nurses’ knowledge and clinical practice in providing perioperative care for pediatric orthopedic patients in tertiary care inpatient settings. Methods: A double-blind, randomized controlled trial was conducted from February to July 2024, involving 100 nurses from two tertiary care hospitals in Tanta, Egypt. Participants were randomly assigned to receive PNE Model training (n = 50) or routine care (n = 50). Nurses’ knowledge, practical skills, and adherence to PNE principles were assessed at baseline, immediately post-intervention, and after one, three, and six months using validated tools. Results: The intervention group showed significantly greater improvements in knowledge (20.62 ± 6.7 vs. 8.16 ± 5.5, p < 0.001) and practice scores (62.28 ± 4.1 vs. 40.06 ± 14.7, p < 0.001) post-intervention, sustained over six months. Path analysis revealed that the PNE Model enhanced nursing practice directly (β = 0.25, p < 0.001) through improvements in engagement and adherence to excellence principles and indirectly (β = 0.53, p < 0.001) by significantly enhancing nurses’ knowledge (β = 0.70, p < 0.001), which in turn positively influenced their clinical practice (β = 0.75, p < 0.001). Post-intervention, 82% of nurses in the intervention group achieved high levels of professional excellence, compared to 8% at baseline (p < 0.001). Conclusions: The PNE Model demonstrates robust effectiveness in enhancing nurses’ knowledge, clinical practice, and professional excellence in pediatric orthopedic surgery care, with sustained long-term benefits. This evidence supports implementing specialized nursing education models to improve pediatric care quality in orthopedic settings.
The Role of Morphological Structure in Determining the Optimal Viewing Position During Visual Word Recognition in Beginning Readers
Authors: Ducrot Stéphanie , Casalis Séverine
Published: November 2024

Background/Objectives: The present study examines the role of morphemic units in the initial word recognition stage among beginning readers. We assess whether and to what extent sublexical units, such as morphemes, are used in processing French words and how their use varies with reading proficiency. Methods: Two experiments were conducted to investigate the perceptual and morphological effects on the recognition of words presented in central vision, using a variable-viewing-position technique. To explore changes during elementary school years, we tested children from the second and fourth grades, as well as adult readers. Results: The percentage of correct word identification was highest near the center of the word, indicating an optimal viewing position for all three participant groups. Viewing position effects were modulated by age and the properties of the stimuli (length and morphological structure). Experiment 1 demonstrated that lexical decisions are influenced by morphological structure to a decreasing extent as reading skill develops. Experiment 2 revealed that morphological processing in children primarily relies on the orthographic information provided by morphemes (surface morphology), whereas proficient readers process morphological information at a more abstract level, exhibiting a genuine morphological-facilitation effect. Conclusions: Overall, our study strongly indicates that morphemic units play a crucial role in the initial stage of word identification in early reading development. This conclusion aligns with the “word and affix” model, which posits that morphological representations become increasingly independent of orthography as reading ability and word exposure improve.
Parafoveal Microperimetric Retinal Sensitivity as a Key Parameter Associated with Vision Loss in Retinitis Pigmentosa
Authors: Hsiao Yu-Ting , Huang Hsiu-Mei , Chen Ta-Ching , Lo Jung , Chen Yung-Jen , Kuo Hsi-Kung , Lee Jong-Jer
Published: November 2024

Background: Although optical coherence tomography (OCT) is useful in determining outer retinal architecture, it may be suboptimal when monitoring subtle changes in retinitis pigmentosa (RP) patients. The aim of this study is to investigate precise microperimetric parameters for disease severity identification in RP patients. Methods: A cross-sectional and retrospective study. Thirty-nine eyes of 39 RP patients were included. Associations between logMAR visual acuity (VA), spectral-domain OCT, fundus autofluorescence imaging (FAF), and various microperimetric measures were evaluated. Microperimetric test locations were grouped into “foveal”, parafoveal “inner ring”, and perifoveal “outer ring”. Independent variables were analyzed based on logistic regression, then assessed using area under the receiver operating characteristic curve (AUROC). Results: Among all microperimetric measures, linear regression analysis indicated that mean retinal sensitivity and deep scotoma count at the parafoveal inner ring were the principal parameters associated with decreased VA. The AUROC was highest for deep scotoma count at the inner ring at a value of 0.829, with the cut-off point at 3.5. A visual function index was then established according to the number of parafoveal deep scotoma points, in order of mild (0 points), moderate (1–3 points), and severe (4 or more points). Our microperimetric visual function index also correlated significantly to logMAR VA and previously established FAF patterns. Conclusions: Our study discovered deep scotoma count at the parafoveal inner ring to be a key microperimetric parameter in evaluating vision loss in RP patients. Those with four or more deep scotoma points at the parafoveal inner ring are more likely to have functional low vision.
Enhancing Dysarthric Voice Conversion with Fuzzy Expectation Maximization in Diffusion Models for Phoneme Prediction
Authors: Hsu Wen-Shin , Lin Guang-Tao , Wang Wei-Hsun
Published: November 2024

Introduction: Dysarthria, a motor speech disorder caused by neurological damage, significantly hampers speech intelligibility, creating communication barriers for affected individuals. Voice conversion (VC) systems have been developed to address this, yet accurately predicting phonemes in dysarthric speech remains a challenge due to its variability. This study proposes a novel approach that integrates Fuzzy Expectation Maximization (FEM) with diffusion models for enhanced phoneme prediction, aiming to improve the quality of dysarthric voice conversion. Methods: The proposed method combines FEM clustering with Diffusion Probabilistic Models (DPM). Diffusion models simulate noise addition and removal to enhance the robustness of speech signals, while FEM iteratively optimizes phoneme boundaries, reducing uncertainty. The system was trained using the Saarland University Voice Disorder dataset, consisting of dysarthric and normal speech samples, with the conversion process represented in the Mel-spectrogram domain. The framework employs both subjective (Mean Opinion Score, MOS) and objective (Word Error Rate, WER) metrics for evaluation, complemented by ablation studies. Results: Experimental results showed that the proposed method significantly improved phoneme prediction accuracy and overall voice conversion quality. It achieved higher MOSs for naturalness, intelligibility, and speaker similarity compared to existing models like StarGAN-VC and CycleGAN-VC. Additionally, the proposed method demonstrated a lower WER for both mild and severe dysarthria cases, indicating better performance in producing intelligible speech. Discussion: The integration of FEM with diffusion models offers substantial improvements in handling the irregularities of dysarthric speech. The method’s robustness, as evidenced by the ablation studies, shows that it can maintain speech naturalness and intelligibility even without a speaker-encoder. These findings suggest that the proposed approach can contribute to the development of more reliable assistive communication technologies for individuals with dysarthria, providing a promising foundation for future advancements in personalized speech therapy.
Image Quality Assessment and Reliability Analysis of Artificial Intelligence-Based Tumor Classification of Stimulated Raman Histology of Tumor Biobank Samples
Authors: Meißner Anna-Katharina , Blau Tobias , Reinecke David , Fürtjes Gina , Leyer Lili , Müller Nina , von Spreckelsen Niklas , Stehle Thomas , Al Shugri Abdulkader , Büttner Reinhard , Goldbrunner Roland , Timmer Marco , Neuschmelting Volker
Published: December 2024

Background: Stimulated Raman histology (SRH) is a label-free optical imaging method for rapid intraoperative analysis of fresh tissue samples. Analysis of SRH images using Convolutional Neural Networks (CNN) has shown promising results for predicting the main histopathological classes of neurooncological tumors. Due to the relatively low number of rare tumor representations in CNN training datasets, a valid prediction of rarer entities remains limited. To develop new reliable analysis tools, larger datasets and greater tumor variety are crucial. One way to accomplish this is through research biobanks storing frozen tumor tissue samples. However, there is currently no data available regarding the pertinency of previously frozen tissue samples for SRH analysis. The aim of this study was to assess image quality and perform a comparative reliability analysis of artificial intelligence-based tumor classification using SRH in fresh and frozen tissue samples. Methods: In a monocentric prospective study, tissue samples from 25 patients undergoing brain tumor resection were obtained. SRH was acquired in fresh and defrosted samples of the same specimen after varying storage durations at −80 °C. Image quality was rated by an experienced neuropathologist, and prediction of histopathological diagnosis was performed using two established CNNs. Results: The image quality of SRH in fresh and defrosted tissue samples was high, with a mean image quality score of 1.96 (range 1–5) for both groups. CNN analysis showed high internal consistency for histo-(Cα 0.95) and molecular (Cα 0.83) pathological tumor classification. The results were confirmed using a dataset with samples from the local tumor biobank (Cα 0.91 and 0.53). Conclusions: Our results showed that SRH appears comparably reliable in fresh and frozen tissue samples, enabling the integration of tumor biobank specimens to potentially improve the diagnostic range and reliability of CNN prediction tools.
State-of-the-Art Trends in Data Compression: COMPROMISE Case Study
Authors: Podgorelec David , Strnad Damjan , Kolingerová Ivana , Žalik Borut
Published: November 2024

After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others, achieved remarkable compression ratios and encoding and decoding speeds with acceptable data quality, which has kept them in common use to this day. However, recent computing paradigms such as cloud computing, edge computing, the Internet of Things (IoT), and digital preservation have gradually posed new challenges, and, as a consequence, development trends in data compression are focusing on concepts that were not previously in the spotlight. In this article, we try to critically evaluate the most prominent of these trends and to explore their parallels, complementarities, and differences. Digital data restoration mimics the human ability to omit memorising information that is satisfactorily retrievable from the context. Feature-based data compression introduces a two-level data representation with higher-level semantic features and with residuals that correct the feature-restored (predicted) data. The integration of the advantages of individual domain-specific data compression methods into a general approach is also challenging. To the best of our knowledge, a method that addresses all these trends does not exist yet. Our methodology, COMPROMISE, has been developed exactly to make as many solutions to these challenges as possible inter-operable. It incorporates features and digital restoration. Furthermore, it is largely domain-independent (general), asymmetric, and universal. The latter refers to the ability to compress data in a common framework in a lossy, lossless, and near-lossless mode. COMPROMISE may also be considered an umbrella that links many existing domain-dependent and independent methods, supports hybrid lossless–lossy techniques, and encourages the development of new data compression algorithms.
Pedagogical Translanguaging in L2 Teaching for Adult Migrants: Assessing Feasibility and Emotional Impact
Authors: Franck Julie , Papadopoulou Despina
Published: November 2024

Pedagogical translanguaging (PTL) refers to the use of educational techniques that incorporate learners’ entire linguistic repertoire. Recent studies indicate that PTL is efficient in the teaching of morphological awareness to bilingual children. The question remains whether it can be successfully applied in the highly specific context of adult forced migrants’ classrooms. This study describes a new protocol developed within the framework of PTL to teach derivational morphology to L2 French and Greek adults. We used questionnaires to quantitatively and qualitatively assess the feasibility of the protocol, teachers’ and learners’ attitudes, and learners’ emotions in the PTL lesson. A total of 141 migrant learners (79 forced migrants) and 13 teachers were involved in 23 2 h lessons. Teachers and learners gave high overall evaluations of the feasibility of the PTL protocol and of their pleasure from teaching and learning using PTL tools. Learners’ ease of learning and learning benefits were positively influenced by their perception of the proximity between their L1 and L2. Learners reported higher positive emotions and lower negative emotions during the PTL lesson, while forced migrants showed more hope and shame overall than voluntary migrants, as well as gained more benefits from PTL due to enjoyment. These findings suggest that the use of pedagogical translanguaging in migrants’ classrooms is feasible and develops positive attitudes and emotions, which are more pronounced in forced migrants.
Integrating ICT to Adopt Online Learning in Teacher Education in Ghana
Authors: Aidoo Benjamin , Chebure Andrew
Published: November 2024

ICT has opened opportunities for innovations in pedagogical approaches. Online learning is increasingly becoming popular in education and attractive because of the diverse benefits not only to educators and students but also to institutions. However, there is a continuous rise in debates on teacher educators competence in adopting online learning. Critics of online learning argue that using ICT to transform face-to-face courses into online courses brings issues, especially to some educators. This study aimed to investigate teacher educators experiences using ICT to transform and manage a virtual face-to-face course during and after the COVID-19 pandemic. A qualitative technique with semi-structured interviews was used to collect data from six college education teachers in Ghana who had taught the same courses between the 2020 and 2022 academic years. The findings showed that ICT integration has easily facilitated online learning and positively impacted teacher educators classroom practices. However, due to insufficient technical and infrastructural support from institutions and socio-cultural perspectives, educators exhibit low competence in managing their teaching and learning activities on time. This study’s results provide policymakers with unique insights for a better, sustainable, and equitable ICT integration in Ghana’s teacher education.
ClusteredLog: Optimizing Log Structures for Efficient Data Recovery and Integrity Management in Database Systems
Authors: Ahmad Mariha Siddika , Panda Brajendra Nath
Published: November 2024

In modern database systems, efficient log management is crucial for ensuring data integrity and facilitating swift recovery from potential data corruption or system failures. Traditional log structures, which store operations sequentially as they occur, often lead to significant delays in accessing and recovering specific data objects due to their scattered nature across the log. ClusteredLog addresses the limitations of traditional logging methods by implementing a novel logical organization of log entries. Instead of simply storing operations sequentially, it groups related operations for each data item into clusters. As a result, ClusteredLog enables faster identification and recovery of damaged data items and thus reduces the need for extensive log scanning, improving overall efficiency in database recovery processes. We introduce data structures and algorithms that facilitate the creation of these clustered logs, which also track dependencies and update operations on data items. Simulation studies demonstrate that our clustered log method significantly accelerates damage assessment and recovery times compared to traditional sequential logs, particularly as the number of transactions and data items increases. This optimization is pivotal for maintaining data integrity and operational efficiency in databases, especially in scenarios involving potential malicious modifications.
InsightForge: Deriving Entrepreneurial Insights from Open-Ended and Unstructured Survey Data Using NLP Techniques
Authors: Nurullah Md. , Hodhod Rania , Friis Helle , Smith Walker , Heriot Kirk
Published: November 2024

Entrepreneurship has long been recognized as a key driver of economic development, traditionally centered on business creation and the strategic actions of individuals aiming to realize their entrepreneurial visions. Central to this process has been the business plan, often viewed as a critical blueprint that outlines the vision, strategies, and operations of new ventures. However, recent studies and observations have raised concerns regarding the continued relevance of business plans, suggesting a shift in entrepreneurial behavior. In response, this study adopts a novel approach by using artificial intelligence (AI) to explore entrepreneurial practices more rigorously. Written feedback from 150 entrepreneurs was analyzed using natural language processing (NLP) techniques, allowing us to move beyond subjective assessments and anecdotal evidence. Our study utilized a structured twenty-question survey to capture the experiences and insights of entrepreneurs. By applying AI, we were able to process large amounts of textual data, uncover nuanced patterns, and identify correlations that might otherwise go unnoticed. The proposed approach enabled a comprehensive analysis of entrepreneurial decision making and provided a robust framework for future research. The findings from this work offer valuable insights into the evolving landscape of entrepreneurship, with significant implications for education, policy, and practice in today’s dynamic business environment.
A Novel Method for Fault Migration Diagnosis of Rolling Bearings Based on MSCVIT Model
Authors: Liu Xiu-Yan , He Dong-Lin , Guo Dong-Qing , Guo Ting-Ting
Published: November 2024

The normal operation of rolling bearings is crucial to the performance and reliability of rotating machinery. However, the collected vibration signals are often mixed with complex noise, and the transformer network cannot fully extract the characteristics of the vibration signals. To solve this problem, we propose a data preprocessing method that utilizes singular value decomposition (SVD) and continuous wavelet transform (CWT) along with an improved vision transformer (ViT) model for fault diagnosis. First, the SVD is applied to identify the noise components to improve the data quality. Then, the CWT is used to convert the denoised signal into a two-dimensional (2D) time–frequency representation (TFR) to display the fault features more intuitively. Finally, an improved multi-scale convolutional block attention module (MSCBAM) is embedded into the ViT network to extract fault features. Experimental results on the classical Case Western Reserve University (CWRU) dataset show that the average diagnostic accuracy of the proposed method is 99.3%. Compared with six other fault diagnosis methods, the method proposed in this paper has also achieved good diagnostic results on three other datasets, which can be effectively applied to the timely handling of problematic equipment and reduce downtime.
Extended-Distance Capacitive Wireless Power Transfer System Based on Generalized Parity–Time Symmetry
Authors: Shu Xujian , Ou Riming , Wu Guoxin , Yang Jingjing , Jiang Yanwei
Published: November 2024

A capacitive wireless power transfer (CPT) system based on parity–time (PT) symmetry achieves constant output characteristics under distance variation without additionally increasing the system complexity of the control strategy, where the concept of PT symmetry is derived from quantum mechanics, and the systems satisfying PT symmetry are invariant under space and time inversion. However, the exact PT-symmetric region (i.e., strong coupling region) of the general system is limited by the symmetry of the structure and parameters. To overcome this limitation, a novel generalized parity–time (GPT)-symmetric CPT system is proposed in this article. According to the equivalent circuit method, the circuit model of the proposed system is built, and the transfer characteristics are analyzed. Furthermore, a prototype is implemented to verify the feasibility of the proposed CPT system. The results show that the PT-symmetric region is extended by 169.23% compared with the traditional PT-based CPT system, and a constant output power of 21.5 W is transferred with a constant transfer efficiency of 90%.
A Human Behavior Wildfire Ignition Probability Index for Application to Mainland Portugal
Authors: Almeida Pedro , Menezes Isilda Cunha , Miranda Ana Isabel
Published: November 2024

Wildfire ignitions are often linked to environmental and climatic factors, but human behavior plays a critical role, particularly in rural southern Europe. However, tools to quantify the probability of human-caused ignitions are lacking. This study addresses this by developing a human behavior wildfire ignition probability index focused on mainland Portugal, a region historically vulnerable to wildfires. Statistical analyses, including multicollinearity checks and a Generalized Linear Model, were used to analyze ignition data, while geospatial analyses estimated the ignition probabilities for 2021 and 2022. Inputs included human activity indicators, land use types, and proximity to residential roads. The resulting probability maps identified high-risk areas, particularly in forested zones and near residential roads. These maps closely aligned with documented human-caused ignitions, confirming the model’s reliability. The index is a robust tool for identifying high-risk areas and has significant potential to improve fire prevention strategies by targeting the most vulnerable regions. Future research should explore its integration into forecasting systems for real-time fire prevention and response strategies as well as its adaptation to other regions with similar wildfire risks.
Effectiveness of Sorbents in the Equipment of Firefighting Units in Practice
Authors: Betuš Miroslav , Konček Martin , Šofranko Marian , Rosová Andrea , Szücs Marek , Cvoliga Martin
Published: November 2024

The presented study deals with the effectiveness of sorbents in the equipment of firefighting units in Slovakia. Currently, there are many manufacturers of sorbents on the market and also a number of types of these products. As a result of an emergency on the road, especially in the case of traffic accidents, there can be a leakage of dangerous substances. From this point of view, it is necessary to prevent the dangerous substance escaping into the environment as quickly as possible and to choose a suitable sorption material to prevent the leakage. For the stated reasons, the aim of the submitted paper was to research the effectiveness of sorbents used by fire brigades in the Slovak Republic in traffic accidents. Part of the publication is on the specification of sorbents, and as part of the research there is an evaluation of their composition and a description, and according to the method and the successive laboratory tests, the operating fluid that is applied to the selected sorbents. After the test and the resulting values, the initial and absorbed weight of the sorbents were determined. The sorption capacity and absorbency were determined from the resulting values. The time factor and the ability to remove adsorbed sorbents from solid surfaces was evaluated after visualizing the process and the final result. The resulting values were unified and compared with other sorbents, where their suitability for the purposes of firefighting units in practice was determined.
Accurate Discrimination of Mold-Damaged Citri Reticulatae Pericarpium Using Partial Least-Squares Discriminant Analysis and Selected Wavelengths
Authors: Tan Huizhen , Liu Yang , Tang Hui , Fan Wei , Jiang Liwen , Li Pao
Published: November 2024

Unscrupulous merchants sell the mold-damaged Citri Reticulatae Pericarpium (CRP) after removing the mold. In this study, an accurate and non-destructive strategy was developed for the discrimination of mold-damaged CRPs using portable near-infrared (NIR) spectroscopy and chemometrics. The outer surface and inner surface spectra were obtained without destroying CRPs. The discrimination models were established using partial least squares-discriminant analysis (PLS-DA) and wavelength selection strategy was used to further improve the discrimination ability. The predictive ability of models was assessed using the test set and an independent test set obtained one month later. The results demonstrate that the models of the outer surface outperform those of the inner surface. With multiplicative scatter correction (MSC)-PLS-DA, 100% accuracies were obtained in test and independent test sets. Furthermore, the wavelength selection strategy simplified the models with 100% discrimination accuracy. In addition, the randomization test (RT)-PLS-DA model developed in this study combines both the benefits of high accuracy and robustness, which can be applied for the accurate discrimination of mold-damaged CRPs.
Analysis of Error-Based Switched Fractional-Order Adaptive Systems: An Error Model Approach
Authors: Aguila-Camacho Norelys , Gallegos Javier Alejandro , Chen Yangquan , Travieso-Torres Juan Carlos
Published: November 2024

Switched adaptive laws for parameter estimation have been proposed in recent years to improve the balance between control energy and system performance in adaptive schemes, which is often a big issue when using traditional integer-order or fractional-order adaptive laws in adaptive identification and control. These switched adaptive laws are represented as fractional-order differential equations whose order can switch between a number within the range (0,1) and 1. However, a general analytical framework that allows proving the boundedness of the solutions and convergence of the estimation/tracking error is not yet available, with only particular analyses for specific schemes being accessible. This paper address this issue, presenting the analysis of four error models that can appear in the field of adaptive systems when these adaptive laws are chosen. The boundedness of the solutions is proved for all cases, together with the convergence to zero of the estimation/tracking error. Additionally, sufficient conditions for parameter convergence are presented, showing that the excitation condition required for parameter convergence in the vector case is also sufficient for parameter estimation in the matrix case. A numerical example is included to show the possible advantages of using switched adaptive laws in a Model Reference Adaptive Control application. Results show that controller parameters can be found for the switched controller, enabling us to obtain an overall improvement of 7.75% with respect to the non-switched integer-order controller and 34.6% with respect to the non-switched fractional-order controller.
Safety of Thread-Embedding Acupuncture: A Multicenter, Prospective, Observational Pilot Study
Authors: Ha Seojung , Lee Suji , Goo Bonhyuk , Kim Eunseok , Kwon Ojin , Nam Sang-Soo , Kim Joo-Hee
Published: November 2024

Background/Objectives: Thread-embedding acupuncture (TEA) is widely used for cosmetic and therapeutic purposes; however, its safety profile, particularly in real-world clinical settings, remains under-researched. This study aimed to evaluate the safety profile of TEA through a prospective, observational analysis and confirm the feasibility of the study design for future studies involving larger patient populations. Methods: A multicenter, prospective observational study was conducted involving 100 patients who received TEA. Adverse events (AEs) were tracked, including incidence, severity, and duration during the 6-month post-treatment period. Bivariate analysis was used to assess factors influencing AE occurrence, including treatment site, depth, and patient-specific variables. Results: A total of 100 patients received 136 treatments during the study period. A total of 12 AEs were reported, most of which were mild and transient local reactions, including pain and bruising. More than half of the AEs occurred on the day of the procedure, with an average duration of 7 days. No serious AEs were observed, and all events resolved without any lasting effects. Patients undergoing multiple treatments showed no significantly higher AE rates than those receiving a single session. Conclusions: This study suggested that TEA generally has a favorable safety profile, with most AEs being mild and resolving without long-term effects. Further studies that evaluate the safety of TEA treatment across larger populations are recommended.
Transcriptomic and Metabolomic Analysis Reveals Improved Fruit Quality in Grafted Watermelon
Authors: Ning Kang , Cai Xiaoqi , Yan Leiyan , Zhou Weixin , Xie An , Wang Yuhong , Xu Pei
Published: November 2024

Grafting technology can improve the yield and quality of crops. In this study, we investigated the effects of grafting on watermelon using transcriptomic and metabolomic analysis. A total of 216 differentially accumulated metabolites (DAMs) were identified between pumpkin-grafted watermelon and self-grafted watermelon. KO (Kyoto Encyclopedia of Genes and Genomes Orthology) analysis revealed that the DAMs were mainly enriched in the flavone and flavonol biosynthesis pathway. In addition, high levels of phloretin and citric acid were found in pumpkin-grafted watermelon, which contributes to fruit quality. Meanwhile, compounds such as olivetol and ferulaldehyde, which confer a bitter taste, were downregulated in pumpkin-grafted watermelon. The transcriptome data indicated that the differentially expressed genes (DEGs) identified in the pulp were enriched in fructose and mannose metabolism, biosynthesis of secondary metabolites, and flavone and flavonol biosynthesis pathways. Moreover, genes related to the microtubule, cell wall, and fiber were highly expressed in the stem of pumpkin-grafted watermelon, suggesting that grafting could change the structure of the stem and improve the quality of watermelon fruit. Our study provides a comprehensive picture of the transcriptional and metabolic profile of watermelon induced by grafting, which furthers our understanding of the molecular mechanisms involved in improving watermelon fruit quality by grafting.
Population Density Prediction at Township Scale Supported by Machine Learning Method: A Case Study in Inner Mongolia
Authors: Cui Chenxi , Hu Yunfeng , Bao Yuhai , Li Hao
Published: November 2024

With the acceleration in population migration and urbanization, accurate population density prediction has become increasingly important for regional planning and resource management. This study focuses on predicting population density at the township level in Inner Mongolia. By integrating multi-source data, such as nighttime light indices and road network density, various machine learning models—including random forest, XGBoost, and LightGBM—were employed to significantly improve prediction accuracy. Interpretable machine learning techniques were utilized to quantitatively analyze the contribution of various variables to population distribution. The results indicate that nighttime light indices and road network density are key influencing factors, revealing their complex nonlinear relationships with population density. This study provides new methodological support for predicting population density in Inner Mongolia and similar regions, demonstrating the potential of machine learning in regional population research. While machine learning models effectively capture correlations between variables, they do not reveal causal relationships. Future research should introduce more detailed data and causal inference models to deepen our understanding of population distribution and its influencing factors.
Genome-Wide Identification and Expression Profiles of Nuclear Factor Y A Transcription Factors in Blueberry Under Abiotic Stress
Authors: Xu Xiuyue , Su Hong , Sun Shuwei , Sun Jing , Zhang Xiang , Yu Jiajie
Published: November 2024

Nuclear Factor Y A (NF-YA) transcription factors are widely involved in multiple plant biological processes, such as embryogenesis, abscisic acid signaling, and abiotic stress response. This study presents a comprehensive genome-wide identification and expression profiling of NF-YA transcription factors in blueberry (Vaccinium corymbosum), an important economic crop with good adaptability, under abiotic stress conditions. Given the economic significance and health benefits of blueberries, understanding their responses to environmental stresses, such as salt, drought, and temperature extremes, is crucial. A total of 24 NF-YA transcription factors were identified through bioinformatics analyses, including sequence alignment, phylogenetic tree construction, and conserved motif analysis. The expression patterns of these NF-YA genes were evaluated in various tissues (roots, stems, and leaves) and under different stress treatments (abscisic acid, salt, and cold) using quantitative real-time PCR (qRT-PCR). The results indicated that most VcNF-YA genes exhibited higher expression levels in stems and leaves compared to roots. Most VcNF-YAs were responsive to the stress treatment. Furthermore, cis-acting element analysis revealed that the promoters of VcNF-YAs were enriched with elements responsive to abiotic stress, suggesting their pivotal role in stress adaptation. This research unveils the expressional responses of NF-YA transcription factors in blueberry upon abiotic stresses and lays the groundwork for future studies on improving crop adaptation.
Lifespan Extension and Motor Function Improvement Effects of Whale Meat Extract in Caenorhabditis elegans
Authors: Shibato Junko , Takenoya Fumiko , Kimura Ai , Yamashita Michio , Rakwal Randeep , Shioda Seiji
Published: November 2024

The average life expectancy is increasing worldwide, but the same cannot be said for a healthy life expectancy (defined as the period during which a person can live a healthy and independent life). Therefore, a major challenge is how to extend healthy life expectancy. One option is to reduce age-related muscle atrophy (sarcopenia) and cognitive decline. Since there is no specific cure for frailty, the prevention of frailty based on nutrition and exercise is a new approach to achieve healthy longevity. Studies have shown that interventions combining nutritional supplements and exercise are effective in improving muscle strength, muscle mass, and walking speed. Thus, the search for drugs and functional foods to combat frailty has attracted researchers’ attention. Whale meat extract (WME) contains many imidazole dipeptides, especially the unique component balenine, which has various functional anti-fatigue and antioxidant properties, and hypermobility effects. Here, we investigated the effects of WME on the aging and health of Caenorhabditis elegans (hereafter, C. elegans). mRNA expression analysis showed that WME prolongs the lifespan of C. elegans mainly through sir-2.1, daf-2, and daf-16, and the myo-3, unc-54, unc-22, and fhod-1 genes involved in locomotor function. The results of this study showed that the expression of the antioxidant enzymes sod-2 and sod-3 was also increased. This study may provide the basis for further research on WME as a food and supplement to slow aging and improve motor function.
Potential Drug Synergy Through the ERBB2 Pathway in HER2+ Breast Tumors
Authors: Rojas-Salazar Yareli , Gómez-Montañez Emiliano , Rojas-Salazar Jorge , de Anda-Jáuregui Guillermo , Hernández-Lemus Enrique
Published: November 2024

HER2-positive (HER2+) breast cancer is characterized by the overexpression of the ERBB2 (HER2) gene, which promotes aggressive tumor growth and poor prognosis. Targeting the ERBB2 pathway with single-agent therapies has shown limited efficacy due to resistance mechanisms and the complexity of gene interactions within the tumor microenvironment. This study aims to explore potential drug synergies by analyzing gene–drug interactions and combination therapies that target the ERBB2 pathway in HER2+ breast tumors. Using gene co-expression network analysis, we identified 23 metabolic pathways with significant cross-linking of gene interactions, including those involving EGFR tyrosine kinase inhibitors, PI3K, mTOR, and others. We visualized these interactions using Cytoscape to generate individual and combined drug–gene networks, focusing on frequently used drugs such as Erlotinib, Gefitinib, Lapatinib, and Cetuximab. Individual networks highlighted the direct effects of these drugs on their target genes and neighboring genes within the ERBB2 pathway. Combined drug networks, such as those for Cetuximab with Lapatinib, Cetuximab with Erlotinib, and Erlotinib with Lapatinib, revealed potential synergies that could enhance therapeutic efficacy by simultaneously influencing multiple genes and pathways. Our findings suggest that a network-based approach to analyzing drug combinations provides valuable insights into the molecular mechanisms of HER2+ breast cancer and offers promising strategies for overcoming drug resistance and improving treatment outcomes.
Unraveling the Bone–Brain Axis: A New Frontier in Parkinson’s Disease Research
Authors: Liu Tingting , Wu Haojie , Li Jingwen , Zhu Chaoyang , Wei Jianshe
Published: November 2024

Parkinson’s disease (PD), as a widespread neurodegenerative disorder, significantly impacts patients’ quality of life. Its primary symptoms include motor disturbances, tremor, muscle stiffness, and balance disorders. In recent years, with the advancement of research, the concept of the bone–brain axis has gradually become a focal point in the field of PD research. The bone–brain axis refers to the interactions and connections between the skeletal system and the central nervous system (CNS), playing a crucial role in the pathogenesis and pathological processes of PD. The purpose of this review is to comprehensively and deeply explore the bone–brain axis in PD, covering various aspects such as the complex relationship between bone metabolism and PD, the key roles of neurotransmitters and hormones in the bone–brain axis, the role of inflammation and immunity, microRNA (miRNA) functional regulation, and potential therapeutic strategies. Through a comprehensive analysis and in-depth discussion of numerous research findings, this review aims to provide a solid theoretical foundation for a deeper understanding of the pathogenesis of PD and to offer strong support for the development of new treatment methods.
Tumor Microenvironment Drives the Cross-Talk Between Co-Stimulatory and Inhibitory Molecules in Tumor-Infiltrating Lymphocytes: Implications for Optimizing Immunotherapy Outcomes
Authors: Franzese Ornella
Published: November 2024

This review explores some of the complex mechanisms underlying antitumor T-cell response, with a specific focus on the balance and cross-talk between selected co-stimulatory and inhibitory pathways. The tumor microenvironment (TME) fosters both T-cell activation and exhaustion, a dual role influenced by the local presence of inhibitory immune checkpoints (ICs), which are exploited by cancer cells to evade immune surveillance. Recent advancements in IC blockade (ICB) therapies have transformed cancer treatment. However, only a fraction of patients respond favorably, highlighting the need for predictive biomarkers and combination therapies to overcome ICB resistance. A crucial aspect is represented by the complexity of the TME, which encompasses diverse cell types that either enhance or suppress immune responses. This review underscores the importance of identifying the most critical cross-talk between inhibitory and co-stimulatory molecules for developing approaches tailored to patient-specific molecular and immune profiles to maximize the therapeutic efficacy of IC inhibitors and enhance clinical outcomes.