bluuebunny/crossref_metadata_2025 huggingface dataset:
Photobacterium damselae subsp. damselae in Stranded Cetaceans: A 6-Year Monitoring of the Ligurian Sea in Italy
Authors: Battistini Roberta , Masotti Chiara , Giorda Federica , Grattarola Carla , Peletto Simone , Testori Camilla , Zoppi Simona , Berio Enrica , Crescio Maria Ines , Pussini Nicola , Serracca Laura , Casalone Cristina
Published: September 2024

Photobacterium damselae subsp. damselae (Pdd) is an increasingly common bacterium in post-mortem diagnostics of beached marine mammals, but little is known about its precise etiological responsibility. To estimate the prevalence of Pdd in stranded cetaceans from 2017 to 2022 on the Ligurian coast (Pelagos Sanctuary), we tested tissues from 53 stranded individuals belonging to four cetacean species. DNA extracts from cetacean tissue were screened using a polymerase chain reaction (PCR) assay targeting the Pdd ureC gene. Positive samples were screened by PCR for dly, hlyApl and hlyAch hemolysin genes, which were confirmed by sequencing. Twenty-two out of 53 (41.5%) cetaceans analyzed by PCR were confirmed for Pdd DNA in at least one tissue among those analyzed. Five of these cetaceans were positive for at least one of the hemolysin genes tested. In all Pdd-positive cetaceans, other pathogens that were considered responsible for the causa mortis of the animals were also found. The results provide new information on the spread of Pdd in cetaceans and support the thesis that Pdd might be an opportunistic agent that could contribute to worsening health conditions in subjects already compromised by other pathogens. However, further studies are needed to investigate and deepen this hypothesis.
Effects of Ferulic Acid on Lipopolysaccharide-Induced Oxidative Stress and Gut Microbiota Imbalance in Linwu Ducks
Authors: Liu Yang , Huang Xuan , Li Chuang , Deng Ping , Zhang Xu , Hu Yan , Dai Qiuzhong
Published: September 2024

Oxidative stress is a major factor that limits the development of the poultry industry. Ferulic acid (FA) has an antioxidant effect in birds, but the mechanism is not fully understood. In this study, we stimulated oxidative stress in 28-day-old female Linwu ducks by lipopolysaccharide (LPS) and fed them a diet supplemented with FA for 28 days. Results showed that FA alleviated LPS-induced growth performance regression, oxidative stress, and microbiota imbalance in ducks. An integrated metagenomics and metabolomics analysis revealed that s_Blautia_obeum, s_Faecalibacterium_prausnitzii, s_gemmiger_formicilis, and s_Ruminococcaceae_bacterium could be the biomarkers in the antioxidant effect of FA, which interacted with dihydro-3-coumaric acid, L-phenylalanine, and 13(S)-HODE, and regulated the phenylalanine metabolism and PPAR signaling pathway. This study revealed the mechanism of the antioxidant effect of FA, which provided evidence of applying FA as a new antioxidant in commercial duck production.
Local Application of a New Chalconic Derivative (Chalcone T4) Reduces Inflammation and Oxidative Stress in a Periodontitis Model in Rats
Authors: Camilli Angelo Constantino , de Godoi Mariely Araújo , Costa Vitória Bonan , Fernandes Natalie Aparecida Rodrigues , Cirelli Giovani , da Silva Larissa Kely Faustino , Assis Letícia Ribeiro , Regasini Luis Octavio , Guimarães-Stabili Morgana Rodrigues
Published: September 2024

Chalcones are phenolic compounds with biological properties. This study had the aim to evaluate the effects of topical administration of a new synthetic chalcone, Chalcone T4, in an animal model of periodontitis induced by ligature. Forty rats were distributed in the following experimental groups: negative control (without periodontitis and topical application of distilled water), positive control (periodontitis and topical application of distilled water), chalcone I and II (periodontitis and topical application of 0.6 mg/mL and 1.8 mg/mL, respectively). Chalcone or distilled water was administered into the gingival sulcus of the first molars daily for 10 days, starting with the ligature installation. The following outcomes were evaluated: alveolar bone loss (µCT and methylene blue dye staining), quantification of osteoclasts (histomorphometry), cell infiltrate and collagen content (stereometry), gene expression of mediators (Nfact11, Tnf-α, Mmp-13, iNos, Sod and Nrf2) by (RT-qPCR); expression of BCL-2 and Caspase-1 (immunohistochemistry). Chalcone T4 inhibited bone resorption and prevented collagen matrix degradation. Reduction in the expression of inflammatory markers (Nfact11, Tnf-α, Mmp-13, and Caspase-1), attenuation of oxidative stress (iNOS reduction, and increase in Sod), and pro-apoptotic effect of the compound (BCL-2 reduction), were associated its effects on periodontal tissues. Topical application of Chalcone T4 prevented bone resorption and inflammation, demonstrating potential in the adjunctive treatment of periodontitis.
Subcrestal versus Bone-Level One-Stage Implants: Early Bone and Soft Tissues Modification: One-Year Randomized Clinical Trial
Authors: Mensi Magda , Scotti Eleonora , Calza Stefano , Salgarello Stefano , Sordillo Annamaria , Zola Matteo , Lops Diego
Published: September 2024

Reducing marginal bone resorption is a challenge in implant dentistry. Sub-gingival implant placement has been suggested as a suitable strategy to avoid long-term esthetic and biological complications. A total of 38 healthy patients received bone-level (BLG-Control) or 2 mm sub-crestal (SCG-Test) conical connection, platform-switched implants. The test group received an immediate tissue-level abutment, following the one-time abutment (OTA) concept. Marginal bone modification (MBM) was calculated on standardized radiographs at surgery (T0), loading (T1), and 6 (T2) and 12 (T3) months after loading and classified as bone loss (BL) if it occurred below the implant neck and bone remodeling (BR) if above. Pocket-probing depth (PPD), Bleeding on probing (BoP), and Plaque Index (PI) were collected. At 12 months, the mean MBM was 0.61 mm in the test group and 0.52 mm in the control group. In all the cases of the test group (SCG), MBM occurred only above the implant neck, therefore being classified as BR, and no BL was observed. In the control group (BLG), MBM occurred below the implant neck, thus corresponding entirely to BL. The test group had an average PPD of 2.38 mm compared to 3.04 mm in the control group, with BoP at 50% and 43%, and PI at 33% and 19.44%, respectively. At one year after loading, sub-crestal conical connection, platform-switched implants show comparable MBM to bone level implants; however, no bone loss was observed.
An Evaluation of the Maternal Patient Experience through Natural Language Processing Techniques: The Case of Twitter Data in the United States during COVID-19
Authors: Banik Debapriya , Chalil Madathil Sreenath , Lopes Amit Joe , Luna Fong Sergio A. , Mukka Santosh K.
Published: September 2024

The healthcare sector constantly investigates ways to improve patient outcomes and provide more patient-centered care. Delivering quality medical care involves ensuring that patients have a positive experience. Most healthcare organizations use patient survey feedback to measure patients’ experiences. However, the power of social media can be harnessed using artificial intelligence and machine learning techniques to provide researchers with valuable insights into understanding patient experience and care. Our primary research objective is to develop a social media analytics model to evaluate the maternal patient experience during the COVID-19 pandemic. We used the “COVID-19 Tweets” Dataset, which has over 28 million tweets, and extracted tweets from the US with words relevant to maternal patients. The maternal patient cohort was selected because the United States has the highest percentage of maternal mortality and morbidity rate among the developed countries in the world. We evaluated patient experience using natural language processing (NLP) techniques such as word clouds, word clustering, frequency analysis, and network analysis of words that relate to “pains” and “gains” regarding the maternal patient experience, which are expressed through social media. The pandemic showcased the worries of mothers and providers on the risks of COVID-19. However, many people also shared how they survived the pandemic. Both providers and maternal patients had concerns regarding the pregnancy risks due to COVID-19. This model will help process improvement experts without domain expertise to understand the various domain challenges efficiently. Such insights can help decision-makers improve the patient care system.
Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter
Authors: Kim Dongmyoung , Jeon Taesu , Paek Insu , Roynarin Wirachai
Published: September 2024

To compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5MW model. The first method for wind speed estimation involves a three-dimensional (3D) look-up table-based approach, constructed using drive train differential equations. The second method involves applying a continuous–discrete extended Kalman filter. To verify and compare the performance of the algorithms designed using these different methods, feed-forward control algorithms, available power estimation algorithms, and a linear quadratic regulator, based on fuzzy logic (LQRF) control algorithms, were selected and applied as verification means, using the estimated wind speed as the input. Based on the simulation results, the performance of the two methods was compared. The method using drive train differential equations demonstrated superior performance in terms of reductions in the standard deviations of rotor speed and electrical power, as well as in its prediction accuracy for the available power.
Subsidence Prediction Method Based on Elastic Foundation Beam and Equivalent Mining Height Theory and Its Application
Authors: Meng Fanfei , Liu Wang , Ni Hongyang , Jiao Shijun
Published: September 2024

Grouting technology in overburden separation is recognized as an effective method to prevent surface subsidence and reuse solid waste. This study used mechanical analysis to explore deflection characteristics of key strata and accurately predict and control surface subsidence. Conceptualizing the coal–rock mass beneath the key strata as an elastic foundation, we developed a method to calculate the elastic foundation coefficients for various regions and established an equation for key strata deflection, validated through discrete element numerical simulations. This simulation also examined subsidence behavior under different grout injection–extraction ratios. Additionally, combining the equivalent mining height theory with the probability integral method, we formulated a predictive model for surface subsidence during grouting. Applied to the 8006 working face of the Wuyang Coal Mine, this model was supported by numerical simulations and field data, which showed a maximum surface subsidence of 546 mm at a 33% injection–extraction ratio, closely matching the theoretical value of 557 mm and demonstrating a nominal error of 2%. Post-grouting, the surface tilt was reduced to below 3 mm/m, meeting regulatory standards and eliminating the need for ongoing surface structure maintenance. These results confirm the model’s effectiveness in forecasting and controlling surface subsidence with grouting. The study can provide a basis for determining the grouting injection–extraction ratios and evaluating the effectiveness of surface subsidence control in grouting into overburden separation projects.
Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain
Authors: Bhattacharjee Pratixita , Augustyniak Piotr
Published: September 2024

Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and interference. In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. It adaptively modifies time points, which improves the alignment between both signals. In contrast, the Temporal Convolutional Network model trains on the original signal, and gradient descent optimization is utilized to improve the selection of time points. Evaluation of both strategies’ efficacy is performed by calculating signal distances at lower and higher sampling rates. First, a collection of synthetic data points that resembled the P-QRS-T wave was used to train the model. Then, the ECG-ID database for real data analysis was used. Filtering to remove baseline wander followed by evaluation and testing were carried out in the real patient data. The results, in particular MSE = 0.0005, RMSE = 0.0216, and Pearson’s CC = 0.9904 for 120 sps in the case of the transformer patient data model, provide viable paths for maintaining the precision and dependability of ECG-based diagnostic systems at much lower sampling rate. Outcomes indicate that both techniques are effective at improving the fidelity between the original and modified ECG signals.
Characterization of Seismic Signal Patterns and Dynamic Pore Pressure Fluctuations Due to Wave-Induced Erosion on Non-Cohesive Slopes
Authors: Feng Zheng-Yi , Wu Wei-Ting , Chen Su-Chin
Published: September 2024

Wave erosion of slopes can easily trigger landslides into marine environments and pose severe threats to both the ecological environment and human activities. Therefore, near-shore slope monitoring becomes crucial for preventing and alerting people to these potential disasters. To achieve a comprehensive understanding, it is imperative to conduct a detailed investigation into the dynamics of wave erosion processes acting on slopes. This research is conducted through flume tests, using a wave maker to create waves of various heights and frequencies to erode the slope models. During the tests, seismic signals, acoustic signals, and pore pressure generated by wave erosion and slope failure are recorded. Seismic and acoustic signals are analyzed, and time-frequency spectra are calculated using the Hilbert–Huang Transform to identify the erosion events and signal frequency ranges. Arias Intensity is used to assess seismic energy and explore the relationship between the amount of erosion and energy. The results show that wave height has a more decisive influence on erosion behavior and retreat than wave frequency. Rapid drawdown may potentially cause the slope to slide during cyclic swash and backwash wave action. As wave erosion changes from swash to impact, there is a significant increase in the spectral magnitude and Power Spectral Density (PSD) of both seismic and acoustic signals. An increase in pore pressure is observed due to the rise in the run-up height of waves. The amplitude of pore pressure will increase as the slope undergoes further erosion. Understanding the results of this study can aid in predicting erosion and in planning effective management strategies for slopes subject to wave action.
Ancient Painting Inpainting with Regional Attention-Style Transfer and Global Context Perception
Authors: Liu Xiaotong , Wan Jin , Wang Nan
Published: September 2024

Ancient paintings, as a vital component of cultural heritage, encapsulate a profound depth of cultural significance. Over time, they often suffer from different degradation conditions, leading to damage. Existing ancient painting inpainting methods struggle with semantic discontinuities, blurred textures, and details in missing areas. To address these issues, this paper proposes a generative adversarial network (GAN)-based ancient painting inpainting method named RG-GAN. Firstly, to address the inconsistency between the styles of missing and non-missing areas, this paper proposes a Regional Attention-Style Transfer Module (RASTM) to achieve complex style transfer while maintaining the authenticity of the content. Meanwhile, a multi-scale fusion generator (MFG) is proposed to use the multi-scale residual downsampling module to reduce the size of the feature map and effectively extract and integrate the features of different scales. Secondly, a multi-scale fusion mechanism leverages the Multi-scale Cross-layer Perception Module (MCPM) to enhance feature representation of filled areas to solve the semantic incoherence of the missing region of the image. Finally, the Global Context Perception Discriminator (GCPD) is proposed for the deficiencies in capturing detailed information, which enhances the information interaction across dimensions and improves the discriminator’s ability to identify specific spatial areas and extract critical detail information. Experiments on the ancient painting and ancient Huaniao++ datasets demonstrate that our method achieves the highest PSNR values of 34.62 and 23.46 and the lowest LPIPS values of 0.0507 and 0.0938, respectively.
Game Difficulty Prediction Based on Facial Cues and Game Performance
Authors: Yin Lu , Zhang He , He Renke
Published: September 2024

Current research on game difficulty prediction mainly uses heuristic functions or physiological signals. The former does not consider user data, while the latter easily causes interference to the user. This paper proposes a difficulty prediction method based on multiple facial cues and game performance. Specifically, we first utilize various computer vision methods to detect players’ facial expressions, gaze directions, and head poses. Then, we build a dataset by combining these three kinds of data and game performance as inputs, with the subjective difficulty ratings as labels. Finally, we compare the performance of several machine learning methods on this dataset using two classification tasks. The experimental results showed that the multilayer perceptron classifier (abbreviated as MLP) achieved the highest performance on these tasks, and its accuracy increased with the increase in input feature dimensions. These results demonstrate the effectiveness of our method. The proposed method could assist in improving game design and user experience.
Theoretical Notions with Practical Applications Regarding the Statistical Calculation of Dental Bur Wear
Authors: Ilie Filip
Published: September 2024

This paper presents theoretical notions with practical applications for establishing the lifetime of a dental bur (durability and reliability) based on the wear of the active part and considering a series of statistical indicators. To justify applying theoretical notions in practice, a conical–cylindrical dental bur is studied experimentally to obtain a series of data of the work process necessary for statistical calculation. The parameter taken into consideration in this paper is mass lost through the wear of the dental bur active part, based on testing. This is useful for dental bur lifetime establishment and its extension or even optimization in operation. The loss of mass of the dental bur active part is analyzed in the work process using the results and experimental data obtained and validated by statistical–mathematical numerical calculation. The numerical calculation approximates the mass lost through wear at different rotation speeds and operation times, and based on a comparison with the experimentally determined ones, the lifetime was established. The results show that the dental bur works with high yield in the first 20 h of work, after which it should be replaced with a new one. Theoretically, the studied dental bur can work over 21 h. Practically, it can work up to 18 h without major risk of failure, but the lifetime can be extended up to 20 h, at which point the failure risk can reach 10% and it is recommended to replace the bur.
Depth of Cure, Surface Characteristics, Hardness, and Brushing Wear of 4 Direct Restorative Materials in Paediatric Dentistry
Authors: Ludovichetti Francesco Saverio , Guariso Angela , Parcianello Roberta Gaia , Pezzato Luca , Bertolini Rachele , Lucchi Patrizia , Mazzoleni Sergio
Published: September 2024

Aim: The study aimed to compare the depth of cure, hardness, surface roughness, and wear resistance of four restorative materials used in pediatric dentistry: FUJI IX GP FAST, RivaSilver, SDR flow+, and Vertise Flow. Materials and Methods: The depth of cure was measured per ISO 4049 standards using a digital caliper, with 15 samples of each material. Hardness was evaluated using a Vickers indenter under a 10 N load for 20 s. Surface roughness was assessed before and after acid exposure using an optical profilometer according to ISO 4288. Brushing wear resistance was analyzed by subjecting samples to 20 and 60 min of brushing, followed by roughness measurements. Statistical analysis was performed using independent sample t-tests to determine the significance of differences between the materials, with p-values < 0.05 considered significant. Results: SDR flow+ exhibited the highest depth of cure with an average of 3.5 mm (±0.2 mm), significantly higher than Vertise Flow at 2.8 mm (±0.3 mm) (p < 0.001). Hardness testing revealed SDR flow+ had the highest average hardness (85 HV ± 4 HV), while Vertise Flow had the lowest (72 HV ± 5 HV) (p < 0.001). Surface roughness increased significantly after acid exposure for RivaSilver (from 1.2 μm ± 0.12 μm to 1.6 μm ± 0.15 μm, p = 0.007) and for SDR flow+ (from 0.85 μm ± 0.08 μm to 1.3 μm ± 0.14 μm, p = 0.001). Brushing wear resistance was highest in RivaSilver (Ra increase from 1.2 μm to 1.4 μm ± 0.11 μm) and lowest in FUJI IX GP FAST (Ra increase from 1.5 μm to 1.9 μm ± 0.15 μm, p < 0.001). Conclusions: The study demonstrates significant differences in performance among the tested materials. SDR flow+ showed a superior depth of cure and hardness, making it suitable for high-stress applications. However, all materials displayed increased surface roughness following acid exposure and brushing, with FUJI IX GP FAST showing the highest wear. These findings highlight the need to select restorative materials based on the specific clinical demands of pediatric patients, considering both their mechanical properties and resistance to wear and acid.
The Photoionization Processes of Deep Trap Levels in n-GaN Films Grown by MOVPE Technique on Ammono-GaN Substrates
Authors: Kruszewski Piotr , Sakowski Konrad , Gościński Krzysztof , Prystawko Paweł
Published: September 2024

In this paper, we present various theoretical models that accurately approximate the spectral density of the optical capture cross-section (σe0) obtained through the analysis of photo-capacitance transients using the deep-level optical spectroscopy (DLOS) technique applied to semi-transparent Ni/Au Schottky barrier diodes (SBDs) fabricated on n-GaN films. The theoretical models examined in this study involved a variety of approaches, from the Lucovsky model that assumes no lattice relaxation to more sophisticated models such as the Chantre–Bois and the Pässler models, which consider the electron–phonon coupling phenomenon. By applying theoretical models to the experimentally determined data, we were able to estimate the photoionization (E0), trap level position (ET), and Franck–Condon (dFC) energy, respectively. In addition, the results of our analysis confirm that the photoionization processes of deep traps in n-GaN grown by the metal–organic vapor-phase epitaxy technique (MOVPE) are strongly coupled to the lattice. Moreover, it was shown that the Pässler model is preferred for the accurate determination of the individual trap parameters of defects present in n-GaN films grown on an Ammono-GaN substrate. Finally, a new trap level, Ec-1.99 eV with dFC = 0.15, that has not been previously reported in n-GaN films grown by MOVPE was found.
A Class-Incremental Learning Method for Interactive Event Detection via Interaction, Contrast and Distillation
Authors: Duan Jiashun , Zhang Xin
Published: September 2024

Event detection is a crucial task in information extraction. Existing research primarily focuses on machine automatic detection tasks, which often perform poorly in certain practical applications. To address this, an interactive event-detection mode of “machine recommendation-human review–machine incremental learning” was proposed. In this mode, we study a few-shot continual class-incremental learning scenario, where the challenge is to learn new-class events with limited samples while preserving memory of old class events. To tackle these challenges, we propose a class-incremental learning method for interactive event detection via Interaction, Contrast and Distillation (ICD). We design a replay strategy based on representative and confusable samples to retain the most valuable samples under limited conditions; we introduce semantic-boundary-smoothness contrastive learning for effective learning of new-class events with few samples; and we employ hierarchical distillation to mitigate catastrophic forgetting. These methods complement each other and show strong performance. Experimental results demonstrate that, in the 5-shot 5-round class incremental-learning settings on two Chinese event-detection datasets ACE and DuEE, our method achieves final recall rates of 71.48% and 90.39%, respectively, improving by 6.86% and 3.90% over the best baseline methods.
Damage Status and Failure Precursors of Different Coal Impact Types Based on Comprehensive Monitoring of Infrared Radiation and Acoustic Emission
Authors: Yin Shan , Li Zhonghui , Wang Enyuan , Liu Yubing , Niu Yue , Yang Hengze
Published: September 2024

Different coal failure impact types exhibit different damage statuses and failure modes, resulting in distinct signal characteristics of infrared radiation (IR) and acoustic emission (AE). This paper combines IR and AE monitoring methods to innovatively establish coal damage and failure precursor warning models and obtains the IR and AE precursor characteristics for different coal failure impact types. This research shows that there is a good correspondence between IR and AE timing and spatial distribution of different coal impact types. As the impact tendency increases, the intensity of IR and AE signals increases with coal failure, and the AE positioning points and IR high-temperature areas tend to concentrate. The coal body gradually changes from tensile failure to shear failure. The shear cracks in the failure stage of coal with no, weak, and strong impact are 39.9%, 50.9%, and 53.7%, respectively. The IR and AE instability precursor point of coal with no, weak, and strong impact occurred at 55.2%, 66.3%, and 93.4% of coal failure, respectively. After the IR and AE combined instability precursor point, the dissipated energy and combined damage variable increase rapidly, and the coal body will undergo instability and failure. The research results provide a theoretical basis for comprehensive monitoring of coal body failure and rock burst.
Thermal Buckling and Postbuckling Analysis of Cracked FG-GPL RC Plates Using a Phase-Field Crack Model
Authors: Cho Jin-Rae
Published: September 2024

A phase-field crack model is developed for numerical analysis of thermal buckling and postbuckling behavior of a functionally graded (FG) graphene platelet-reinforced composite (FG-GPLRC) plate with a central crack. The inclined central crack is represented according to a stable, effective phase-field formulation (PFF) by introducing a virtual crack rotation. The problem is formulated using first-order shear deformation theory (SDT) incorporated with von Kármán geometric nonlinearity. And it is approximated by combining regular Laplace interpolation functions and crack-tip singular functions in the framework of the 2D extended natural element method (XNEM). Troublesome shear locking is suppressed by applying the concept of the MITC (mixed-interpolated tensorial components)3+ shell element to the present numerical method. The results demonstrate the effectiveness of this method in accurately predicting the critical buckling temperature rise (CBTR) and the thermal postbuckling path. In addition, the parametric results reveal that the CBTR and postbuckling path of the FG-GPLRC plate are distinct from those of the FG carbon nanotube-reinforced composite (FG-CNTRC) plate and remarkably affected by the parameters associated with the crack and graphene platelet (GPL).
Perspectives on Adhesive–Bolted Hybrid Connection between Fe Shape Memory Alloys and Concrete Structures for Active Reinforcements
Authors: Qiang Xuhong , Zhang Delin , Wu Yapeng , Jiang Xu
Published: September 2024

The prestressed active reinforcement of concrete structures using iron-based shape memory alloys (Fe-SMAs) is investigated in this experimental study through three connecting methods: adhesive–bolted hybrid connection, bolted connection, and adhesively bonded connection by activating at elevated temperatures (heating and cooling) and constraining deformation to generate prestress inside Fe-SMAs, through which compressive stress is generated in the parent concrete structures. In tests, the Fe-SMA is activated at 250 °C using a hot air gun, generating a prestress of 184.6–246 MPa. The experimental results show that local stress concentration in the concrete specimen and Fe-SMA plate around the hole is caused by the bolted connection. The adhesively bonded connection is prone to softening and slip of the structural adhesive during the activation process, thereby reducing the overall recovery force of Fe-SMAs. The adhesive–bolted hybrid connection effectively mitigates the local stress concentration problem of concrete and Fe-SMAs at anchor holes, while avoiding the prestress loss caused by the softening and slip of structural adhesive during elevated-temperature activation, achieving good reinforcement effect. This study on the connection methods of an Fe-SMA for reinforcing concrete structures provides both experimental support and practical guidance for its engineering application, offering new perspectives for future research.
In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
Authors: Mirzaei S. Morteza , Radmehr Ahmad , Holton Carvel , Ahmadian Mehdi
Published: September 2024

This study aims to develop a robust and efficient system to identify ties and ballasts in motion using a variety of non-contact sensors mounted on a robotic rail cart. The sensors include distance LiDAR sensors and inductive proximity sensors for ferrous materials to collect data while traversing railroad tracks. Many existing tie/ballast health monitoring devices cannot be mounted on Hyrail vehicles for in-motion inspection due to their inability to filter out unwanted targets (i.e., ties or ballasts). The system studied here addresses that limitation by exploring several approaches based on distance LiDAR sensors. The first approach is based on calculating the running standard deviation of the measured distance from LiDAR sensors to tie or ballast surfaces. The second approach uses machine learning (ML) methods that combine two primary algorithms (Logistic Regression and Decision Tree) and three preprocessing methods (six models in total). The results indicate that the optimal configuration for non-contact, in-motion differentiation of ties and ballasts is integrating two distance LiDAR sensors with a Decision Tree model. This configuration provides rapid, accurate, and robust tie/ballast differentiation. The study also facilitates further sensor and inspection research and development in railroad track maintenance.
Upgrading Biomass Wastes to Graphene Quantum Dots with White-Light-Emitting Features in the Solid State
Authors: Magri Pierre , Franchetti Pascal , Gaumet Jean-Jacques , Maxit Benoit , Diliberto Sébastien , Pierrat Philippe
Published: September 2024

The emergence of bio-based carbonaceous materials for various applications has attracted significant attention during the last few years. Here, we report a rapid, efficient, and reproducible microwave-assisted synthesis of graphene quantum dots (GQDs) with identical features irrespective of the nature of biomass waste investigated. The synthesized GQDs were fully characterized by X-ray photoelectron spectroscopy, Fourier-transform infrared spectroscopy, transmission electron microscopy, and dynamic light scattering. The nanoparticles displayed narrow sizes of 1–2 nm and high solubility in polar solvents such as water and ethanol. The protocol described herein is advantageous in comparison to dealing with the synthesis of GQDs from biomass waste previously reported since our protocol is faster owing to the use of microwave heating and the avoidance of dialysis for the purification step. Furthermore, in solution, the water-soluble particles showed excitation-dependent photoluminescence ranging from blue to orange emission wavelengths. Interestingly, thin films displayed white-light emission under 325 nm UV-light excitation, while aggregation-induced quenching was usually observed, opening the way for their potential use as a phosphor in white-light-emitting diodes.
Toward a New Era of Smart and Secure Healthcare Information Exchange Systems: Combining Blockchain and Artificial Intelligence
Authors: Merhej Joseph , Harb Hassan , Abouaissa Abdelhafid , Idoumghar Lhassane
Published: September 2024

Healthcare Information Exchange (HIE) is becoming a fundamental operation in current healthcare systems. In such systems, electronic health records (EHRs) are digitally stored inside each medical centers and, sometimes, are required to be shared between various healthcare facilities (HCFs). Indeed, sharing patient information is crucial and might be vulnerable to power outages, data misuse, privacy or security violations, and an audit trail. Hence, researchers have focused recently on cutting-edge technologies to develop secure HIE systems and ensure data privacy during transactions. Among such technologies, blockchain and artificial intelligence (AI) occupy a vital role in researchers’ focuses and efforts to detect risky transactions in HIE systems, thus enhancing their security and privacy. While the blockchain allows HCFs to link to each other without requiring a central authority, AI models offer an additional security layer when sharing patient data between HCFs. This paper presents a survey about HIE systems, and the aim is two-fold: we first present the architecture of HIE systems along with their challenges; then, we categorize and classify the current state-the-art-techniques that show the potential of using blockchain and AI technologies in such systems.
A Hybrid GAN-Inception Deep Learning Approach for Enhanced Coordinate-Based Acoustic Emission Source Localization
Authors: Huang Xuhui , Han Ming , Deng Yiming
Published: September 2024

In this paper, we propose a novel approach to coordinate-based acoustic emission (AE) source localization to address the challenges of limited and imbalanced datasets from fiber-optic AE sensors used for structural health monitoring (SHM). We have developed a hybrid deep learning model combining four generative adversarial network (GAN) variants for data augmentation with an adapted inception neural network for regression-based prediction. The experimental setup features a single fiber-optic AE sensor based on a tightly coiled fiber-optic Fabry-Perot interferometer formed by two identical fiber Bragg gratings. AE signals were generated using the Hsu-Nielsen pencil lead break test on a grid-marked thin aluminum plate with 35 distinct locations, simulating real-world structural monitoring conditions in bounded isotropic plate-like structures. It is demonstrated that the single-sensor configuration can achieve precise localization, avoiding the need for a multiple sensor array. The GAN-based signal augmentation expanded the dataset from 900 to 4500 samples, with the Wasserstein distance between the original and synthetic datasets decreasing by 83% after 2000 training epochs, demonstrating the high fidelity of the synthetic data. Among the GAN variants, the standard GAN architecture proved the most effective, outperforming other variants in this specific application. The hybrid model exhibits superior performance compared to non-augmented deep learning approaches, with the median error distribution comparisons revealing a significant 50% reduction in prediction errors, accompanied by substantially improved consistency across various AE source locations. Overall, this developed hybrid approach offers a promising solution for enhancing AE-based SHM in complex infrastructures, improving damage detection accuracy and reliability for more efficient predictive maintenance strategies.
Improved Performance of the Permanent Magnet Synchronous Motor Sensorless Control System Based on Direct Torque Control Strategy and Sliding Mode Control Using Fractional Order and Fractal Dimension Calculus
Authors: Nicola Marcel , Nicola Claudiu-Ionel , Selișteanu Dan , Ionete Cosmin , Șendrescu Dorin
Published: September 2024

This article starts from the premise that one of the global control strategies of the Permanent Magnet Synchronous Motor (PMSM), namely the Direct Torque Control (DTC) control strategy, is characterized by the fact that the internal flux and torque control loop usually uses ON–OFF controllers with hysteresis, which offer easy implementation and very short response times, but the oscillations introduced by them must be cancelled by the external speed loop controller. Typically, this is a PI speed controller, whose performance is good around global operating points and for relatively small variations in external parameters and disturbances, caused in particular by load torque variation. Exploiting the advantages of the DTC strategy, this article presents a way to improve the performance of the sensorless control system (SCS) of the PMSM using the Proportional Integrator (PI), PI Equilibrium Optimizer Algorithm (EOA), Fractional Order (FO) PI, Tilt Integral Derivative (TID) and FO Lead–Lag under constant flux conditions. Sliding Mode Control (SMC) and FOSMC are proposed under conditions where the flux is variable. The performance indicators of the control system are the usual ones: response time, settling time, overshoot, steady-state error and speed ripple, plus another one given by the fractal dimension (FD) of the PMSM rotor speed signal, and the hypothesis that the FD of the controlled signal is higher when the control system performs better is verified. The article also presents the basic equations of the PMSM, based on which the synthesis of integer and fractional controllers, the synthesis of an observer for estimating the PMSM rotor speed, electromagnetic torque and stator flux are presented. The comparison of the performance for the proposed control systems and the demonstration of the parametric robustness are performed by numerical simulations in Matlab/Simulink using Simscape Electrical and Fractional-Order Modelling and Control (FOMCON). Real-time control based on an embedded system using a TMS320F28379D controller demonstrates the good performance of the PMSM-SCS based on the DTC strategy in a complete Hardware-In-the-Loop (HIL) implementation.
The One-Dimensional Flow Pressure Loss Correction Model Based on the Particle Flow through Concrete Bend
Authors: Gao Guoqiang , Wei Lixiang , Zhao Xuan , Wan Minshun , Li Hongzhou
Published: September 2024

The rheological behavior of concrete pumping in the bend section is complex. The existing conversion calculation for pumping pressure loss based on the straight pipe section often fails to meet engineering precision requirements. This paper develops a pressure loss correction model for a concrete pumping bend section based on a new one-dimensional flow model for the straight pipe section simulation of particle flow; the study analyzes the impact of parameters such as elbow shape and pumping flow rate on pressure loss. An equivalent conversion relationship between the bend section and the straight section is established. The comparison with the measured pressure values from the project shows that the relative error between the pressure loss values calculated by the correction model and the actual measurements is 15.8%.
Study of Systems of Active Vibration Protection of Navigation Instrument Equipment
Authors: Korobiichuk Igor , Mel’nick Viktorij , Kosova Vera , Pavlenko Viktoriia , Bursacov Kirilo
Published: September 2024

Assessment of the influence of vibration isolator parameters on the distribution of the system’s natural frequencies is a significant task in the design of vibration isolation systems. The root method was used to determine the natural frequencies of the controlled vibration isolator. For a certain feedback structure of a controlled electrodynamic type vibration isolator, the need for a consistent selection of parameters has been justified. A mathematical solution has been proposed for the approximate determination of the roots of the characteristic equation of the controlled vibration isolator, which enables the analytical assessment of the influence of the vibration isolator parameters on the distribution of its natural frequencies. The research has been conducted in relative parameters, which makes it possible to generalize the results. The specificity of the inertial dynamic vibration isolator, which in some cases is associated with the implementation of anti-resonance conditions, can lead to the fact that resonant frequencies can occur on both sides of the tuning frequency of the vibration isolator. The use of an elastic suspension on flat springs to protect navigation equipment from vibration allows reduction in the intensity of translational vibration, while not changing the orientation of the device relative to the Earth. The implementation of an elastic suspension according to the scheme of the inverted pendulum allows an increase in the effectiveness of vibration isolation, under the conditions of a controlled change of the vibration isolator parameters and due to the use of feedback. The results of this research can be used in precision systems, such as vibration isolators, laser processing equipment, ultraprecision measurements or medical devices.
Assessing Downburst Kinematics Using Video Footage Analysis
Authors: Romanic Djordje , Vavatsikos Lalita Allard
Published: September 2024

Measurements of downburst outflows using standard meteorological instruments (e.g., anemometers) are rare due to their transient and localized nature. However, video recordings of such events are becoming more frequent. This short communication (Technical Note) study presents a new approach to estimating the kinematics of a downburst event using video footage recordings of the event. The main geometric dimensions of the event, such as downdraft diameter, cloud base height, outflow depth, and the radius of the outflow at a given moment in time, are estimated by sizing them against reference structures of known dimensions that are present in the video footage. From this analysis, and knowing the frame rate of the video recording, one can estimate the characteristic velocities in the downburst event, such as the mean downdraft velocity and the mean velocity of the radial outflow propagation. The proposed method is tested on an August 2015 downburst event that hit Tucson, Arizona, United States. The diameter of the downburst outflow increased with the time from approximately 1.10 km to 3.35 km. This range of values indicates that the event was a microburst. The mean descending velocity of downburst downdraft was 8.9 m s−1 and the horizontal velocity of outflow propagation was 17.7 m s−1. The latter velocity is similar to the measured wind gust at the nearby weather station and Doppler radar. The outflow depth is estimated at 160 m, and the cloud base height was approximately 1.24 km. Estimating the kinematics of downbursts using video footage, while subject to certain limitations, does yield a useful estimation of the main downburst kinematics that contribute to a better quantification of these localized windstorms.
Influence of Climate Change on Carbon Sequestration in Pine Forests of Central Spain
Authors: Enríquez-de-Salamanca Álvaro
Published: September 2024

Climate change influences carbon sequestration by forests, either positively or negatively. In the Mediterranean region, this effect is predominantly negative, although it seems to be compensated by the elevation. This study aims to analyse the impact of climate change on carbon sequestration in five pine species—Pinus halepensis, P. nigra, P. pinaster, P. pinea, and P. sylvestris—growing across an altitudinal gradient from 573 to 1743 m a.s.l. in central Spain. Data from 300 forest inventory plots in ten forests were used to first calculate carbon sequestration in 2024. To estimate future carbon sequestration, the expected values of precipitation and temperature for 2100 were determined, based on regionalised climate scenarios for RCP4.5 and RCP8.5. Values from 13 meteorological stations located around the forests, at different elevations, were analysed, conducting a statistical analysis to determine whether variations were significant. A statistically significant variation was detected for temperature and precipitation changes only under the RCP8.5 scenario. Using temperature and precipitation data for 2024 and 2100, net potential productivity in both years was established, considering its variation ratio equivalent as equivalent to that of growth and carbon sequestration. An inflection point was detected in 2100 at 1646 m a.s.l., with a decrease in productivity below and an increase above that elevation. Results reflect a decline in carbon sequestration in all the species, ranging from 6% in P. sylvestris to 28% in P. halepensis, conditioned by the elevation. Regionally, the average decrease would be 16.4%. In temperate and boreal regions, forest growth is expected to increase due to climate change, but the Mediterranean region will experience a significant decrease, except in mountain areas. To maintain current levels of carbon sequestration, it would be necessary to increase the existing carbon sinks through new plantations and the restoration of degraded forests.
Dementia Classification Approach Based on Non-Singleton General Type-2 Fuzzy Reasoning
Authors: Gonzalez Claudia I.
Published: September 2024

Dementia is the most critical neurodegenerative disease that gradually destroys memory and other cognitive functions. Therefore, early detection is essential, and to build an effective detection model, it is required to understand its type, symptoms, stages and causes, and diagnosis methodologies. This paper presents a novel approach to classify dementia based on a data set with some relevant patient features. The classification methodology employs non-singleton general type-2 fuzzy sets, non-singleton interval type-2 fuzzy sets, and non-singleton type 1 fuzzy sets. These advanced fuzzy sets are compared with traditional singleton fuzzy sets to evaluate their performance. The Takagi–Sugeno–Kang TSK inference method is used to handle fuzzy reasoning. In the process, the parameters of the membership functions (MFs) and rules are obtained using ANFIS, and non-singleton MFs are optimized with PSO. The results demonstrate that non-singleton general type-2 fuzzy sets improve classification accuracy compared to singleton fuzzy sets, demonstrating their ability to model the uncertainties inherent in the diagnosis of dementia. This improvement suggests that non-singleton fuzzy systems offer a more robust framework for developing effective diagnostic tools in the medical domain. Accurate classification of dementia is of utmost importance to improve patient care and advance medical research.