bluuebunny/crossref_metadata_2025 huggingface dataset:
An 8-Week Very Low-Calorie Ketogenic Diet (VLCKD) Alters the Landscape of Obese-Derived Small Extracellular Vesicles (sEVs), Redefining Hepatic Cell Phenotypes
Authors: Balestra Francesco , De Luca Maria , Panzetta Giorgia , Depalo Nicoletta , Rizzi Federica , Mastrogiacomo Rita , Coletta Sergio , Serino Grazia , Piccinno Emanuele , Stabile Dolores , Pesole Pasqua Letizia , De Nunzio Valentina , Pinto Giuliano , Cerabino Nicole , Di Chito Martina , Notarnicola Maria , Shahini Endrit , De Pergola Giovanni , Scavo Maria Principia
Published: December 2024

Background. Very low-calorie ketogenic diets (VLCKD) are an effective weight-loss strategy for obese individuals, reducing risks of liver conditions such as non-alcoholic steatohepatitis and fibrosis. Small extracellular vesicles (sEVs) are implicated in liver fibrosis by influencing hepatic cell phenotypes and contributing to liver damage. This study investigates sEVs derived from serum of 60 obese adults categorized into low fibrosis risk (LR) and intermediate/high fibrosis risk (IHR) groups based on FibroScan elastography (FIB E scores, limit value 8 kPa) and all participants underwent an 8-week VLCKD intervention. Methods. The study examines the impact of these sEVs on fibrosis markers, inflammation, and autophagy in a hepatocyte cell line (HEPA-RG) using bioinformatics, RNA sequencing, lipidomics, RT-PCR, and Western blotting before (T0) and after (T1) VLCKD. Results. sEVs from LR patients post-VLCKD reduced fibrosis related gene expression (e.g., ACTA2) and enhanced proteins associated with regeneration and inflammation (e.g., HDAC6). Conversely, sEVs from IHR patients increased fibrosis and inflammation related gene expression (PIK3CB, AKT1, ACTA2) in hepatocytes, raising concerns about VLCKD suitability for IHR patients. IHR sEVs also decreased expression of HDAC10, HDAC6, HDAC3, MMP19, and MMP2, while increasing modulation of p-AKT, α-SMA, and VIM. Conclusion. These findings underscore the critical role of sEVs in regulating inflammation, remodeling, and hepatic stress responses, particularly in IHR patients, and suggest sEVs could complement instrumental evaluations like FibroScan in fibrosis assessment.
A Review of the Association Between Dietary Intake and Brain Iron Levels in Older Adults: Preliminary Findings and Future Directions
Authors: Zachariou Valentinos , Bauer Christopher E. , Pappas Colleen , Gold Brian T.
Published: December 2024

Background/Objectives: Non-heme iron is essential for critical neuronal functions such as ATP generation, synaptogenesis, neurotransmitter synthesis, and myelin formation. However, as non-heme iron accumulates with age, excessive levels can contribute to oxidative stress, potentially disrupting neuronal integrity and contributing to cognitive decline. Despite growing evidence linking high brain iron with poorer cognitive performance, there are currently no proven methods to reduce brain iron accumulation in aging or to protect cognitive function from iron’s negative effects. Recent studies suggest that nutrition may influence brain iron levels, though the evidence remains limited and mixed. Methods: In this review, we explore recent findings, including our own cross-sectional and longitudinal studies, to evaluate the potential effectiveness of healthy diets and specific nutrients in mitigating brain iron accumulation during aging. We also briefly assess the roles of age and gender as factors in the relationship between dietary factors and brain iron load. Results: The limited findings in the literature indicate that dietary choices may impact brain iron levels. In particular, nutrients such as vitamins, antioxidants, iron-chelators, and polyunsaturated fatty acids may slow brain iron accumulation in older adults. Conclusions: Our review highlights the multiple gaps in current knowledge and underscores a critical need for additional research on this important topic.
Butyrate as a Potential Modulator in Gynecological Disease Progression
Authors: Kim Nayeon , Yang Changwon
Published: December 2024

This review investigates the therapeutic potential of butyrate, a short-chain fatty acid (SCFA) produced by gut microbiota, in the prevention and treatment of various gynecological diseases, including polycystic ovary syndrome (PCOS), endometriosis, and gynecologic cancers like cervical and ovarian cancer. These conditions often pose treatment challenges, with conventional therapies offering limited and temporary relief, significant side effects, and a risk of recurrence. Emerging evidence highlights butyrate’s unique biological activities, particularly its role as a histone deacetylase (HDAC) inhibitor, which allows it to modulate gene expression, immune responses, and inflammation. In PCOS, butyrate aids in restoring hormonal balance, enhancing insulin sensitivity, and reducing chronic inflammation. For endometriosis, butyrate appears to suppress immune dysregulation and minimize lesion proliferation. Additionally, in cervical and ovarian cancers, butyrate demonstrates anticancer effects through mechanisms such as cell cycle arrest, apoptosis induction, and suppression of tumor progression. Dietary interventions, particularly high-fiber and Mediterranean diets, that increase butyrate production are proposed as complementary approaches, supporting natural microbiota modulation to enhance therapeutic outcomes. However, butyrate’s short half-life limits its clinical application, spurring interest in butyrate analogs and probiotics to maintain stable levels and extend its benefits. This review consolidates current findings on butyrate’s multifaceted impact across gynecological health, highlighting the potential for microbiota-centered therapies in advancing treatment strategies and improving women’s reproductive health.
Effects of Melissa officinalis Phytosome on Sleep Quality: Results of a Prospective, Double-Blind, Placebo-Controlled, and Cross-Over Study
Authors: Di Pierro Francesco , Sisti Davide , Rocchi Marco , Belli Annalisa , Bertuccioli Alexander , Cazzaniga Massimiliano , Palazzi Chiara Maria , Tanda Maria Laura , Zerbinati Nicola
Published: December 2024

Background: Melissa officinalis standardised extracts, characterised by the presence of hydroxycinnamic acids, have been experimentally demonstrated to be endowed with anti-anxiety and anti-insomnia pharmacological actions. These effects, probably attributable, at least in part, to the role played by rosmarinic acid on GABA-T, have not always been observed in a reproducible manner in humans, perhaps due to the poor bioavailability of these compounds. Methods: as nutraceuticals and botanicals could be an alternative option to prescription medications for alleviating symptoms of mild anxiety and insomnia, we have verified in a prospective, double-blind, placebo-controlled, and cross-over study the supporting role on sleep quality played by a Melissa officinalis highly standardised extract, formulated as Phytosome™ (MOP) to improve the oral bioavailability of its active polyphenolic components. Results: results showed a significant reduction in the ISI score in the treated group, with an average of 6.8 ± 4.1 compared to 9.7 ± 3.7 in the placebo group, indicating a significant reduction of 2.9 points (p = 0.003). The SWS phase duration increased by an average of 15%, while the REM phase decreased by 10%. Additionally, 87% of participants in the treated group reported improved sleep quality, compared to 30% in the placebo group, with significant differences measured by chi-square test (χ2(4) = 21.01, p = 0.0003), highlighting the effects due to Melissa officinalis L. No significant changes in physical activity or anxiety levels were observed. Conclusions: these findings suggest that MOP may represent a natural and safe alternative to traditional pharmacological treatments for insomnia.
Determination of Flavonoid Glycoside Isomers Using Vision Transformer and Tandem Mass Spectrometry
Authors: Park Ji In , Kim Myeong Ji , Lee Kyu Hyeong , Oh Seung Hyun , Kang Young Hoon , Kim Hyunwoo
Published: December 2024

A vision transformer (ViT)-based deep neural network was applied to classify the flavonoid glycoside isomers by analyzing electrospray ionization tandem mass spectrometry (ESI-MS/MS) spectra. Our model successfully classified the flavonoid isomers with various substitution patterns (3-O, 6-C, 7-O, 8-C, 4′-O) and multiple glycosides, achieving over 80% accuracy during training. In addition, the experimental spectra from flavonoid glycoside standards were acquired with different adducts, and our model showed robust performance regardless of the experimental conditions. As a result, the vision transformer-based computer vision model is promising for analyzing mass spectrometry data.
Are Cactus Spines Modified Leaves? Morphological and Anatomical Characterization of Saguaro Seedlings (Carnegiea gigantea) with Special Focus on Aerial Organ Primordia
Authors: Miravel-Gabriel Cristina Betzabeth , Koeth Ryan , Marsch-Martínez Nayelli , Hernández-Hernández Tania
Published: December 2024

The reduction of leaves was a key event in the evolution of the succulent syndrome in Cactaceae, evolving from large, photosynthetic leaves in Pereskia to nearly suppressed microscopic foliar buds in succulent Cactoideae. This leaf reduction was accompanied by the development of spines. Early histological studies, dating back a century, of the shoot apical meristem (SAM) in several species concluded that, in succulent cacti, axillary buds became areoles and leaves transformed into spines. However, these conclusions were based on limited observations, given the challenges of obtaining SAM samples from long-lived, often endangered species. Here, we present a complete study of early aerial organ development in seedlings of the iconic Carnegiea gigantea (saguaro), characterizing the different stages of seedling development. We focus on the SAM to track the emergence and development of primordia and aerial organs, closely following the spine development from undifferentiated structures. We demonstrate that young, few-days-old saguaro seedlings provide a valuable model for morpho-anatomical and molecular studies in Cactaceae. We also outline optimal laboratory practices for germinating saguaro seeds and conducting histological studies. Our observations confirm the absence of clear foliar structures and the presence of a distinct type of primordia, hypothesized to be foliar but lacking definitive foliar features. Based on our observations and a review of the literature, we revive the discussion on the ontogenetic origin of spines and propose saguaro seedlings as a promising model for studying the genetic identity of SAM primordia.
ESG Ratings and Real Estate Key Metrics: A Case Study
Authors: Vonlanthen Joël
Published: December 2024

This study examines whether and through which channels ESG ratings influence key metrics in the real estate industry. Focusing on Switzerland as a case study and concentrating on commercial real estate investors and their income properties, we utilize unique datasets and employ an OLS post-LASSO estimation procedure to identify and quantify the associations between ESG ratings and four key metrics: appraisal-based and transaction-based discount rates, rental incomes, and vacancy rates. Our results demonstrate that ESG ratings maintain a significant association with all four key metrics even after undergoing robustness checks. When dissecting the total ESG rating into its components, the environmental rating stands out as the most significant. While largely dependent on the specific metric being analyzed, the association of social and governance ratings tends to be less pronounced. Delving deeper into individual ESG rating levels, our findings suggest potential signaling effects, as properties with higher ESG ratings demonstrate heightened sensitivity to both types of discount rates and vacancy rates. Overall, our findings deepen the understanding of the association between ESG ratings and real estate markets, illuminating the intersection of sustainability and financial relevance.
Effectiveness of the Buddhist-Based Elderly Mental Health Counseling Training Program for Thai Health Volunteer Monks
Authors: Langgapin Saowalak , Boonchieng Waraporn , Chautrakarn Sineenart , Maneeton Narong , Senawan Sunisa
Published: December 2024

The mental health challenges faced by aging societies, particularly within the context of Thai Buddhist communities, underscore the urgent need for capacity-building initiatives to support monks in promoting the psychological well-being of the elderly. This study evaluates the effectiveness of a mental health training program for monks, rooted in Buddhist principles. The primary aim is to assess how well the program equips monks to counsel the elderly in Thai Buddhist society. A quasi-experimental design was employed, involving 32 health volunteer monks selected through purposive sampling. The study assessed the monks’ knowledge and practical counseling skills and collected participant feedback. The results indicate significant improvements in both knowledge and practical skills, with marked progress observed from pre- to post-test scores (p < 0.001) and sustained effectiveness in follow-up assessments (p = 0.005). Approximately 71.88% of participants passed the knowledge post-test and practical skills evaluation, with 62.50% demonstrating proficiency in the follow-up assessment. These findings suggest that the program is effective in preparing monks to address mental health issues among the elderly in Thai Buddhist society. Additionally, integrating indigenous practices and beliefs into mental health interventions can enhance their cultural relevance and acceptance. This approach empowers local religious leaders and fosters culturally sensitive solutions to mental health challenges across diverse populations.
Revisiting Du Guangting: Theoretical Contributions and Religious Transformations Within Daoism During the Late Tang and Five Dynasties Periods
Authors: Wang Zheng , Wu Ditao
Published: December 2024

In this study, we re-evaluate Du Guangting’s (850–933 C.E.) contributions to Daoist philosophy and religious practice during the late Tang and Five Dynasties period (755–960 C.E.), emphasizing his method of “broadly drawing upon various texts and thoroughly exploring their meanings” (廣引眾文, 窮指詳采). Through a primary analysis of Explications Expounding upon the Sage’s Commentary on True Scripture of the Dao and the De (道德真經廣聖義), and supported by the Anthology of Lost Transcendent Biographies (仙傳拾遺), this paper introduces a novel perspective on Du Guangting’s redefinition of Daoist practices, emphasizing self-cultivation and moral integrity over traditional alchemical methods. This shift not only addressed the spiritual demands of his era but also set a precedent for ethical practices in Daoism, thereby reflecting its adaptability to socio-political changes. This research underscores how Du’s work significantly contributed to both the transformation of Daoist philosophy and the evolution of its religious practices, thus influencing subsequent developments in Chinese thought and spirituality.
Automatic Detection and Classification of Natural Weld Defects Using Alternating Magneto-Optical Imaging and ResNet50
Authors: Li Yanfeng , Gao Pengyu , Luo Yongbiao , Luo Xianghan , Xu Chunmei , Chen Jiecheng , Zhang Yanxi , Lin Genxiang , Xu Wei
Published: December 2024

It is difficult to detect and identify natural defects in welded components. To solve this problem, according to the Faraday magneto-optical (MO) effect, a nondestructive testing system for MO imaging, excited by an alternating magnetic field, is established. For the acquired MO images of crack, pit, lack of penetration, gas pore, and no defect, Gaussian filtering, bilateral filtering, and median filtering are applied for image preprocessing. The effectiveness of these filtering methods is evaluated using metrics such as peak signal–noise ratio (PSNR) and mean squared error. Principal component analysis (PCA) is employed to extract column vector features from the downsampled defect MO images, which then serve as the input layer for the error backpropagation (BP) neural network model and the support vector machine (SVM) model. These two models can be used for the classification of partial defect MO images, but the recognition accuracy for cracks and gas pores is comparatively low. To further enhance the classification accuracy of natural weld defects, a convolutional neural network (CNN) classification model and a ResNet50 classification model for MO images of natural weld defects are established, and the model parameters are evaluated and optimized. The experimental results show that the overall classification accuracy of the ResNet50 model is 99%. Compared with the PCA-SVM model and CNN model, the overall classification accuracy was increased by 7.4% and 1.8%, and the classification accuracy of gas pore increased by 10% and 4%, respectively, indicating that the ResNet50 model can effectively and accurately classify natural weld defects.
Node Classification Method Based on Hierarchical Hypergraph Neural Network
Authors: Xu Feng , Xiong Wanyue , Fan Zizhu , Sun Licheng
Published: December 2024

Hypergraph neural networks have gained widespread attention due to their effectiveness in handling graph-structured data with complex relationships and multi-dimensional interactions. However, existing hypergraph neural network models mainly rely on planar message-passing mechanisms, which have limitations: (i) low efficiency in encoding long-distance information; (ii) underutilization of high-order neighborhood features, aggregating information only on the edges of the original graph. This paper proposes an innovative hierarchical hypergraph neural network (HCHG) to address these issues. The HCHG combines the high-order relationship-capturing capability of hypergraphs, uses the Louvain community detection algorithm to identify community structures within the network, and constructs hypergraphs layer by layer. In the bottom-level hypergraph, the model establishes high-order relationships through direct neighbor nodes, while in the top-level hypergraph, it captures global relationships between aggregated communities. Through three hierarchical message-passing mechanisms, the HCHG effectively integrates local and global information, enhancing the multi-resolution representation ability of node representations and significantly improving performance in node classification tasks. In addition, the model performs excellently in handling 3D multi-view datasets. Such datasets can be created by capturing 3D shapes and geometric features through sensors or by manual modeling, providing extensive application scenarios for analyzing three-dimensional shapes and complex geometric structures. Theoretical analysis and experimental results show that the HCHG outperforms traditional hypergraph neural networks in complex networks.
Data-Driven Enhancements for MPC-Based Path Tracking Controller in Autonomous Vehicles
Authors: Guo Jianhua , Xie Zhihao , Liu Ming , Hu Jincheng , Dai Zhiyuan , Guo Jinqiu
Published: December 2024

The accuracy of the control model is essential for the effectiveness of model-based control methods. However, factors such as model simplification, parameter variations, and environmental noise can introduce inaccuracies in vehicle state descriptions, thereby compromising the precision of path tracking. This study introduces data-driven enhancements for an MPC-based path tracking controller in autonomous vehicles (DD-PTC). The approach consists of two parts: firstly, Kolmogorov–Arnold Networks (KANs) are utilized to estimate tire lateral forces and correct tire cornering stiffness, thereby establishing a dynamic predictive model. Secondly, Gaussian Process Regression (GPR) is deployed to accurately capture the unmodeled dynamics of the vehicle to form a comprehensive control model. This enhanced model allows for precise path tracking through steering control. The superiority of DD-PTC is confirmed through extensive testing on the Simulink-CarSim simulation platform, where it consistently surpasses normal MPC and Linear Quadratic Regulator (LQR) strategies, especially in minimizing lateral distance errors under challenging driving conditions.
Modeling Carbon-Based Nanomaterials (CNMs) and Derived Composites and Devices
Authors: Chiminelli Agustίn , Radović Ivan , Fasano Matteo , Fantoni Alessandro , Laspalas Manuel , Kalinić Ana , Provenzano Marina , Fernandes Miguel
Published: December 2024

A review of different modeling techniques, specifically in the framework of carbon-based nanomaterials (CNMs, including nanoparticles such as graphene and carbon nanotubes—CNTs) and the composites and devices that can be derived from them, is presented. The article emphasizes that the overall performance of these materials depends on mechanisms that operate across different time and spatial scales, requiring tailored approaches based on the material type, size, internal structure/configuration, and the specific properties of interest. Far from attempting to cover the entire spectrum of models, this review examines a wide range of analysis and simulation techniques, highlighting their potential use, some of their weaknesses and strengths, and presenting the latest developments and some application examples. In this way, it is shown how modeling can provide key information for tailoring or designing new materials for specific components or systems or to obtain certain functionalities. At the same time, it is revealed to be an area constantly undergoing development and improvement, as evidenced by the progress made by various of these techniques and the new modeling approaches that have emerged in recent years.
Morningness–Eveningness Preference and Motor Wake–Sleep Inertia in Adolescents
Authors: Natale Vincenzo , Andreose Alice , Bacaro Valeria , Giovagnoli Sara , Giudetti Federica , Grimaldi Martina , Tonetti Lorenzo , Crocetti Elisabetta
Published: December 2024

The aim of the present work was to analyze possible differences in the wake–sleep and sleep–wake transition in relation to adolescents’ circadian preference using actigraphy. Overall, 729 participants were enrolled in the research and 443 of them wore actigraphs on the non-dominant wrist for at least three nights. According to the reduced Morningness–Eveningness Questionnaire for Children and Adolescents cut-off scores, 61 participants belonged to the evening-type category, while 38 participants belonged to the morning-type. We extracted the motor activity counts, minute-by-minute, during the wake–sleep and sleep–wake transitions, to depict the motor wake inertia and motor sleep inertia, respectively. We adopted the functional linear modeling statistical framework to examine the changes in both transitions according to chronotype. Overall, the results show a significantly higher motor wake inertia and lower motor sleep inertia in morning compared to evening types.
In Operando Optical Analysis of Electrolyte Colour Change and Its Correlation to Capacity Fade in Li-Ion Cells at Elevated Temperatures
Authors: Sattar Saud , Statheros Thomas , Raza Ali , Kellner Quirin , Yu Yifei , Bhagat Rohit , Roberts Alexander J. , Guo Yue
Published: December 2024

The existing body of research on battery state of health has identified various degradation modes for the electrolyte, yet very few studies have explored the role of electrolyte colour changes as a diagnostic tool for state of health (SOH). This study investigates the impact of elevated temperatures and its correlation with electrolyte colour changes and capacity fade during cycling. Specifically, the research examines whether cycling cells at elevated temperatures induces noticeable changes in electrolyte colour and whether these changes can be linked to the SOH of the cells. The methodology employs in operando optical sensors to monitor real-time colour shifts in the electrolyte, aiming to demonstrate a qualitative relationship between electrolyte colour change, degradation, thermal ageing, and capacity fade, laying the foundations for future quantitative assessment of the relationships identified. Our research builds upon these findings by offering a novel approach that integrates optical sensing to provide real-time visual evidence of electrolyte degradation and colour change during cell operation. The results demonstrate a clear relationship between elevated temperature, electrolyte colour change, and capacity fade, leading to accelerated degradation. This approach offers a new insight over traditional in exitu battery diagnostics, as it enables continuous in operando monitoring of electrolyte colour change and has the potential to unlock a detailed understanding of the chemical reactions and electrolyte breakdown during cycling.
Powering Agriculture IoT Sensors Using Natural Temperature Differences Between Air and Soil: Measurement and Evaluation
Authors: Bancik Kamil , Konecny Jaromir , Konecny Jiri , Mikus Miroslav , Choutka Jan , Hercik Radim , Koziorek Jiri , Navikas Dangirutis , Andriukaitis Darius , Prauzek Michal
Published: December 2024

As the need to monitor agriculture parameters intensifies, the development of new sensor nodes for data collection is crucial. These sensor types naturally require power for operation, but conventional battery-based power solutions have certain limitations. This study investigates the potential of harnessing the natural temperature gradient between soil and air to power wireless sensor nodes deployed in environments such as agricultural areas or remote off-grid locations where the use of batteries as a power source is impractical. We evaluated existing devices that exploit similar energy sources and applied the results to develop a state-of-the-art device for extensive testing over a 12-month period. Our main objective was to precisely measure the temperature on a thermoelectric generator (TEG) (a Peltier cell, in particular) and assess the device’s energy yield. The device harvested 7852.2 J of electrical energy during the testing period. The experiment highlights the viability of using environmental temperature differences to power wireless sensor nodes in off-grid and battery-constrained applications. The results indicate significant potential for the device as a sustainable energy solution in agricultural monitoring scenarios.
Slotted ALOHA Based Practical Byzantine Fault Tolerance (PBFT) Blockchain Networks: Performance Analysis and Optimization
Authors: Zhou Ziyi , Onireti Oluwakayode , Zhang Lei , Imran Muhammad Ali
Published: December 2024

Practical Byzantine Fault Tolerance (PBFT) is one of the most popular consensus mechanisms for the consortium and private blockchain technology. It has been recognized as a candidate consensus mechanism for the Internet of Things networks as it offers lower resource requirements and high performance when compared with other consensus mechanisms such as proof of work. In this paper, by considering the blockchain nodes are wirelessly connected, we model the network nodes distribution and transaction arrival rate as Poisson point process and we develop a framework for evaluating the performance of the wireless PBFT network. The framework utilizes slotted ALOHA as its multiple access technique. We derive the end-to-end success probability of the wireless PBFT network which serves as the basis for obtaining other key performance indicators namely, the optimal transmission interval, the transaction throughput and delay, and the viable area. The viable area represents the minimum PBFT coverage area that guarantees the liveness, safety, and resilience of the PBFT protocol while satisfying a predefined end-to-end success probability. Results show that the transmission interval required to make the wireless PBFT network viable can be reduced if either the end-to-end success probability requirement or the number of faulty nodes is lowered.
Satellite Fingerprinting Methods for GNSS Spoofing Detection
Authors: Gallardo Francisco , Pérez-Yuste Antonio , Konovaltsev Andriy
Published: December 2024

Spoofing attacks pose a significant security risk for organizations and systems relying on global navigation satellite systems (GNSS) for their operations. While the existing spoofing detection methods have shown some effectiveness, these can be vulnerable to certain attacks, such as secure code estimation and replay (SCER) attacks, among others.This paper analyzes the potential of satellite fingerprinting methods for GNSS spoofing detection and benchmarks their performance using real (in realistic scenarios by using GPS and Galileo signals generated and recorded in the advanced GNSS simulation facility of DLR) GNSS signals and scenarios. Our results show that our proposed fingerprinting methods can improve the detection accuracy of the existing methods and can be coupled with other techniques to enhance the overall performance of the detection systems, all based on relatively simple metrics. In this paper, we compare the performance of several fingerprinting methods, including those from the existing literature (based on signal Gaussian properties of the signal complex envelope, energy and in-phase symbol dispersion) and one proposed in this paper, based on the satellite instrumental delay. The innovation of this work is a new jamming and spoofing complementary detection technique, based on fingerprinting and machine learning, including a new fingerprinting metric (based on the satellite instrumental delay).
CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm
Authors: Buriboev Abror Shavkatovich , Khashimov Ahmadjon , Abduvaitov Akmal , Jeon Heung Seok
Published: December 2024

This paper presents an enhanced approach to kidney segmentation using a modified CLAHE preprocessing method, aimed at improving image clarity and CNN performance on the KiTS19 dataset. To assess the impact of the modified CLAHE method, we conducted quality evaluations using the BRISQUE metric, comparing the original, standard CLAHE and modified CLAHE versions of the dataset. The BRISQUE score decreased from 28.8 in the original dataset to 21.1 with the modified CLAHE method, indicating a significant improvement in image quality. Furthermore, CNN segmentation accuracy rose from 0.951 with the original dataset to 0.996 with the modified CLAHE method, outperforming the accuracy achieved with standard CLAHE preprocessing (0.969). These results highlight the benefits of the modified CLAHE method in refining image quality and enhancing segmentation performance. This study highlights the value of adaptive preprocessing in medical imaging workflows and shows that CNN-based kidney segmentation accuracy may be greatly increased by altering conventional CLAHE. Our method provides insightful information on optimizing preprocessing for medical imaging applications, leading to more accurate and dependable segmentation results for better clinical diagnosis.
FPGA-Based Sensors for Distributed Digital Manufacturing Systems: A State-of-the-Art Review
Authors: Khan Laraib , Isanaka Sriram Praneeth , Liou Frank
Published: December 2024

The combination of distributed digital factories (D2Fs) with sustainable practices has been proposed as a revolutionary technique in modern manufacturing. This review paper explores the convergence of D2F with innovative sensor technology, concentrating on the role of Field Programmable Gate Arrays (FPGAs) in promoting this paradigm. A D2F is defined as an integrated framework where digital twins (DTs), sensors, laser additive manufacturing (laser-AM), and subtractive manufacturing (SM) work in synchronization. Here, DTs serve as a virtual replica of physical machines, allowing accurate monitoring and control of a given manufacturing process. These DTs are supplemented by sensors, providing near-real-time data to assure the effectiveness of the manufacturing processes. FPGAs, identified for their re-programmability, reduced power usage, and enhanced processing compared to traditional processors, are increasingly being used to develop near-real-time monitoring systems within manufacturing networks. This review paper identifies the recent expansions in FPGA-based sensors and their exploration within the D2Fs operations. The primary topics incorporate the deployment of eco-efficient data management and near-real-time monitoring, targeted at lowering waste and optimizing resources. The review paper also identifies the future research directions in this field. By incorporating advanced sensors, DTs, laser-AM, and SM processes, this review emphasizes a path toward more sustainable and resilient D2Fs operations.
Using Fitness Tracker Data to Overcome Pressure Insole Wear Time Challenges for Remote Musculoskeletal Monitoring
Authors: Nurse Cameron A. , Rodzak Katherine M. , Volgyesi Peter , Noehren Brian , Zelik Karl E.
Published: December 2024

Tibia shaft fractures are common lower extremity fractures that can require surgery and rehabilitation. However, patient recovery is often poor, partly due to clinicians’ inability to monitor bone loading, which is critical to stimulating healing. We envision a future of patient care that includes at-home monitoring of tibia loading using pressure-sensing insoles. However, one issue is missing portions of daily loading due to limited insole wear time (e.g., not wearing shoes all day). Here, we introduce a method for overcoming this issue with a wrist-worn fitness tracker that can be worn all day. We developed a model to estimate tibia loading from fitness tracker data and evaluated its accuracy during 10-h remote data collections (N = 8). We found that a fitness tracker, with trained and calibrated models, could effectively supplement insole-based estimates of bone loading. Fitness tracker-based estimates of loading stimulus—the minute-by-minute weighted impulse of tibia loading—showed a strong fit relative to insole-based estimates (R2 = 0.74). However, insoles needed to be worn for a minimum amount of time for accurate estimates. We found daily loading stimulus errors less than 5% when insoles were worn at least 25% of the day. These findings suggest that a multi-sensor approach—where insoles are worn intermittently and a fitness tracker is worn continuously throughout the day—could be a viable strategy for long-term, remote monitoring of tibia loading in daily life.
Multimodal Contrastive Learning for Remote Sensing Image Feature Extraction Based on Relaxed Positive Samples
Authors: Zhang Zhenshi , Li Qiujun , Jing Wenxuan , He Guangjun , Zhu Lili , Gao Shijuan
Published: December 2024

Traditional multimodal contrastive learning brings text and its corresponding image closer together as a positive pair, where the text typically consists of fixed sentence structures or specific descriptive statements, and the image features are generally global features (with some fine-grained work using local features). Similar to unimodal self-supervised contrastive learning, this approach can be seen as enforcing a strict identity constraint in a multimodal context. However, due to the inherent complexity of remote sensing images, which cannot be easily described in a single sentence, and the fact that remote sensing images contain rich ancillary information beyond just object features, this strict identity constraint may be insufficient. To fully leverage the characteristics of remote sensing images, we propose a multimodal contrastive learning method for remote sensing image feature extraction, based on positive sample tripartite relaxation, where the model is relaxed in three aspects. The first aspect of relaxation involves both the text and image inputs. By introducing learnable parameters in the language and image branches, instead of relying on fixed sentence structures and fixed image features, the network can achieve a more flexible description of remote sensing images in text and extract ancillary information from the image features, thereby relaxing the input constraints. Second relaxation is achieved through multimodal alignment of various features. By aligning semantic information with the corresponding semantic regions in the images, the method allows for the relaxation of local image features under semantic constraints. This approach addresses the issue of selecting image patches in unimodal settings, where there is no semantic constraint. The proposed method for remote sensing image feature extraction has been validated on four datasets. On the PatternNet dataset, it achieved a 91.1% accuracy with just one-shot.
Correlation Study and Predictive Modelling of Ergonomic Parameters in Robotic-Assisted Laparoscopic Surgery
Authors: Pérez-Salazar Manuel J. , Caballero Daniel , Sánchez-Margallo Juan A. , Sánchez-Margallo Francisco M.
Published: December 2024

BACKGROUND: This study aims to continue research on the objective analysis of ergonomic conditions in robotic-assisted surgery (RAS), seeking innovative solutions for the analysis and prevention of ergonomic problems in surgical practice. METHODS: Four different robotic-assisted tasks were performed by groups of surgeons with different surgical experiences. Different wearable technologies were used to record surgeons’ posture and muscle activity during surgical practice, for which the correlation between them was analyzed. A predictive model was generated for each task based on the surgeons’ level of experience and type of surgery. Two preprocessing techniques (scaling and normalization) and two artificial intelligence techniques were tested. RESULTS: Overall, a positive correlation between prolonged maintenance of an ergonomically inadequate posture during RAS and increased accumulated muscle activation was found. Novice surgeons showed improved posture when performing RAS compared to expert surgeons. The predictive model obtained high accuracy for cutting, peg transfer, and labyrinth tasks. CONCLUSIONS: This study expands on the existing ergonomic analysis of the lead surgeon during RAS and develops predictive models for future prevention of ergonomic risk situations. Both posture and muscle loading are highly related to the surgeon’s previous experience.
Electromagnetic Imaging for Breathing Monitoring
Authors: Vassilyev Ivan , Mendakulov Zhassulan
Published: December 2024

The search for new non-invasive methods of investigating the functioning of human internal organs is an urgent task. One of these methods for assessing the functioning of the human respiratory system is electromagnetic sensing, which is based on a significant difference in the dielectric permittivity of muscle tissue and air. During breathing, when the lungs are filled with air, the dielectric permittivity of the lungs decreases, which leads to a change in the level of the electromagnetic signal passing through the body. The results of experiments on recording changes in the level of electromagnetic radiation passing through the human body performed on an experimental device consisting of eight transmitting and receiving antennas located on opposite sides of the chest have been presented in the article. The possibility of visualizing the measured “pulmonograms” in the form of dynamic two-dimensional images showing the process of filling various parts of the lungs with air has been demonstrated.
Street View Image-Based Road Marking Inspection System Using Computer Vision and Deep Learning Techniques
Authors: Wu Junjie , Liu Wen , Maruyama Yoshihisa
Published: December 2024

Road markings are vital to the infrastructure of roads, conveying extensive guidance and information to drivers and autonomous vehicles. However, road markings will inevitably wear out over time and impact traffic safety. At the same time, the inspection and maintenance of road markings is an enormous burden on human and economic resources. Considering this, we propose a road marking inspection system using computer vision and deep learning techniques with the aid of street view images captured by a regular digital camera mounted on a vehicle. The damage ratio of road markings was measured according to both the undamaged region and region of road markings using semantic segmentation, inverse perspective mapping, and image thresholding approaches. Furthermore, a road marking damage detector that uses the YOLOv11x model was developed based on the damage ratio of road markings. Finally, the mean average precision achieves 73.5%, showing that the proposed system successfully automates the inspection process for road markings. In addition, we introduce the Road Marking Damage Detection Dataset (RMDDD), which has been made publicly available to facilitate further research in this area.
Assessing Locomotive Syndrome Through Instrumented Five-Time Sit-to-Stand Test and Machine Learning
Authors: Hosseini Iman , Ghahramani Maryam
Published: December 2024

Locomotive syndrome (LS) refers to a condition where individuals face challenges in performing activities of daily living. Early detection of such deterioration is crucial to reduce the need for nursing care. The Geriatric Locomotive Function Scale (GLFS-25), a 25-question assessment, has been proposed for categorizing individuals into different stages of LS. However, its subjectivity has prompted interest in technology-based quantitative assessments. In this study, we utilized machine learning and an instrumented five-time sit-to-stand test (FTSTS) to assess LS stages. Younger and older participants were recruited, with older individuals classified into LS stages 0–2 based on their GLFS-25 scores. Equipped with a single inertial measurement unit at the pelvis level, participants performed the FTSTS. Using acceleration data, 144 features were extracted, and seven distinct machine learning models were developed using the features. Remarkably, the multilayer perceptron (MLP) model demonstrated superior performance. Following data augmentation and principal component analysis (PCA), the MLP+PCA model achieved an accuracy of 0.9, a precision of 0.92, a recall of 0.9, and an F1 score of 0.91. This underscores the efficacy of the approach for LS assessment. This study lays the foundation for the future development of a remote LS assessment system using commonplace devices like smartphones.
Theoretical and Experimental Study of Energy-Harvesting and Movement-Sensing Solutions in Pneumatic Systems
Authors: Tiboni Monica , Scassola Federico , Zanacchi Alessandro , Ghidini Marco
Published: December 2024

This paper presents an experimentally based study aimed at assessing the viability of employing a commercial energy harvester to develop a self-powered end-stroke and speed sensor for pneumatic cylinders. An energy-harvesting device was integrated into a cylinder end-cap to recover energy from the piston impact at the end of the stroke. The recovered energy powers a radio transmitter that communicates the reach of the end-stroke. This avoids the use of a dedicated end-stroke sensor, reducing the number of components in the system and also saving energy. The experiments aimed to analyze the signal characteristics generated by the module at various activation speeds, assessing whether the impact speed could be distinguished from the signal. Energy output and short-term usage effects were also investigated. The study seeks to further develop and adapt a Simulink model of the system, based on recent studies, and validate it with experimental findings at the tested activation speeds. Following confirmation of the adapted model’s validity, the authors propose using genetic algorithms to design an optimized mechanical energy harvester. This approach aims to find the parameters of an energy harvester more suitable for pneumatic cylinder applications that would enable enhanced energy extraction and overall improved performances.
Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques
Authors: Allen Janerra D. , Varanasi Sravani , Han Fei , Hong L. Elliot , Choa Fow-Sen
Published: December 2024

Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characterizing the complex abnormality patterns has been challenging. In this work, we used resting-state functional magnetic resonance imaging (fMRI) data to measure functional connectivity between 55 schizophrenia patients and 63 healthy controls across 246 regions of interest (ROIs) and extracted the disease-related connectivity patterns using energy landscape (EL) analysis. EL analysis captures the complexity of brain function in schizophrenia by focusing on functional brain state stability and region-specific dynamics. Age, sex, and smoker demographics between patients and controls were not significantly different. However, significant patient and control differences were found for the brief psychiatric rating scale (BPRS), auditory perceptual trait and state (APTS), visual perceptual trait and state (VPTS), working memory score, and processing speed score. We found that the brains of individuals with schizophrenia have abnormal energy landscape patterns between the right and left rostral lingual gyrus, and between the left lateral and orbital area in 12/47 regions. The results demonstrate the potential of the proposed imaging analysis workflow to identify potential connectivity biomarkers by indexing specific clinical features in schizophrenia patients.
Highly Sensitive T-Shaped Quartz Tuning Fork Based CH4-Light-Induced Thermoelastic Spectroscopy Sensor with Hydrogen and Helium Enhanced Technique
Authors: Wang Yuanzhi , He Ying , Qiao Shunda , Duan Xiaoming , Ma Yufei
Published: December 2024

In this paper, a highly sensitive methane (CH4) sensor based on light-induced thermoelastic spectroscopy (LITES) and a T-shaped quartz tuning fork (QTF) with hydrogen (H2) and helium (He) enhancement techniques are reported for the first time. The low resonant frequency self-designed T-shaped QTF was exploited for improving the energy accumulation time. H2 and He were utilized as surrounding gases for the T-shaped QTF to minimize energy loss, thereby enhancing the sensitivity of the LITES sensor. Additionally, a fiber-coupled multi-pass cell (FC-MPC) with a 40 m optical length was utilized to improve the optical absorption of CH4. The frequency response of the T-shaped QTF with different concentrations of H2 and He was investigated, and the Q factor in the H2 and He environment increased significantly. Compared to operating QTF in a nitrogen (N2) environment, the signal amplitude was enhanced by 2.9 times and 1.9 times in pure H2 and He environments, respectively. This enhancement corresponded to a minimum detection limit (MDL) of 80.3 ppb and 113.6 ppb. Under different CH4 concentrations, the T-shaped QTF-based H2-enhanced CH4-LITES sensor showed an excellent linear response. Furthermore, through Allan deviation analysis, the MDL of the T-shaped QTF-based H2-enhanced CH4-LITES can reach 38 ppb with an 800 s integration time.