bluuebunny/crossref_metadata_2025 huggingface dataset:
Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
Authors: Della Monica Umberto , Munjal Kimberly-Annalena , Tamas Mark Paul , Boi Biagio , Esposito Christian , Khondoker Rahamatullah
Published: January 2025

To ensure security and stable quality, deeper cybersecurity evaluations are essential for the development of safety features and functionalities in vehicles. Among these, the AEB system is the most relevant. This research presents a comprehensive TARA of the AEB system, emphasizing the identification, validation, and mitigation of major cybersecurity threats and risks. We systematically examine potential attack vectors by utilizing the STRIDE threat model. This approach enables a detailed analysis of each security threat associated with AEB systems, providing insights into how malicious actors could use the attack paths. The assessment aligns with ISO/SAE 21434, which offers a robust framework for risk management in automotive cybersecurity and IT security, ensuring a thorough evaluation of a system’s architecture. By assessing the AEB system’s architecture against these standards, we identify key components and communication pathways that may be particularly prone to cyberattacks. The results of this analysis highlight critical flaws within the AEB framework and propose corrective measures to enhance cybersecurity resilience. This article provides a structured methodology for assessing and mitigating automotive cybersecurity risks in compliance with industry standards, aiming to facilitate the safe implementation of AEB technology and ultimately improve overall vehicle security and safety.
The Fluid Ionosphere
Authors: Materassi Massimo
Published: January 2025

In this review paper, the equations of motion describing the fluid dynamics of the ionosphere are constructed step by step, so that any master or post-graduate student may get familiar with the general theory of the “traditional” approach to Ionospheric Physics, in which chemicals forming the Earth’s upper atmosphere are represented as fluids in mutual interaction. The hypotheses on which the smooth-field fluid representation is based are discussed in terms of microscopic dynamics of the gas particles; this discussion is oriented to prepare the reader for the post-fluid approaches to the physics of turbulence. The fluid-dynamical picture of the ionosphere is the most classical and conceptually simple one, and this makes it extremely widespread in terms of theoretical models and applications. The advantages and achievements of this theory are highlighted, its limits are discussed, and perspectives to go beyond it sketched.
Spatial Analysis of Carbon Metabolism in Different Economic Divisions Based on Land Use and Cover Change (LUCC) in China
Authors: Yuan Cui , Liu Yaju , Lu Jingzhao , Guo Chengyi , Quan Tingting , Su Wei
Published: January 2025

Urbanization has greatly altered Earth’s surface form, and land use changes can lead to significant changes in carbon emissions. However, how these changes affect ecosystems remains unclear. Therefore, this study calculated the carbon absorption and emissions in 31 Chinese provinces using high-resolution (300 m) land use data. Subsequently, a carbon flow model was used to evaluate the carbon transfer that occurred from the changes in land use in every province between 2000 and 2020. The standard deviation ellipse analytic techniques were also employed to research the spatiotemporal evolution features of carbon flow in various economic zones. Furthermore, the flux and utility analysis approaches in ecological network analysis were used to quantitatively examine the interaction relationship between two carbon metabolism land uses. The results revealed that the continuous expansion of China’s construction land has reduced the area of agricultural land, resulting in industrial land (53.14%) and urban land (39.38%) being the main contributors to the total carbon emissions. Among them, the five eastern provinces of Hebei, Jiangsu, Zhejiang, Shandong, and Guangdong had carbon emissions of more than 100 million tons. From 2000 to 2020, the center of gravity of the carbon flow in construction land had shifted significantly from Henan Province to Gansu Province. The ecological relationship of exploitation and control dominated the two land use types. It is mostly found in Xinjiang, Qinghai, Gansu, Inner Mongolia, and Ningxia provinces. The findings could provide relevant policy implications for the Chinese government to mitigate carbon metabolism on land.
The Influence of Multisensory Perception on Student Outdoor Comfort in University Campus Design
Authors: Touhami Hichem , Berkouk Djihed , Bouzir Tallal Abdel Karim , Khelil Sara , Gomaa Mohammed M.
Published: January 2025

The user’s experience is critical in spatial design, particularly in outdoor spaces like university campuses, where the physical environment significantly influences students’ relaxation and stress relief. This study investigates the combined impact of thermal, luminous, and auditory environments on students’ perceptions within recreational areas at Bordj Bou Arreridj University Campus. A mixed-method approach combined field surveys and on-site measurements across eleven locations within three distinct spatial configurations. The findings from this study indicate that the auditory environment had the most substantial influence on overall perceptions, surpassing luminous and thermal factors. The open courtyard (Area 1) was perceived as less comfortable due to excessive heat and noise exposure. The shaded zone (Area 2) was identified as the most vulnerable, experiencing significant thermal stress and noise disturbances. In contrast, the secluded patio (Area 3) achieved the highest comfort rating and was perceived as the most cheerful and suitable space. Correlation analysis revealed significant interrelationships between physical and perceptual dimensions, highlighting the critical role of factors such as wind velocity, sky view factor, and illuminance in shaping thermal, luminous, and acoustic perceptions. A fuzzy logic model was developed to predict user perceptions of comfort, suitability, and mood based on measured environmental parameters to address the complexity of multisensory interactions. This study highlights the importance of integrating multisensory evaluations into spatial design to optimize the quality of outdoor environments.
In Search of L-Fuzzy Contexts Adaptable to Variable Information: A Tool for Time-Varying Data Analysis
Authors: Alcalde Cristina , Burusco Ana
Published: January 2025

This paper sheds light on the study of a new structure, called L-fuzzy hypercontext, which provides an extension of the range of applications of fuzzy concept analysis. The advantage of working with L-fuzzy hypercontexts lies in the fact that we can establish relation among elements such that their values are, in turn, other relations. Thus, they are easily adaptable to different situations that vary over time. The usefulness of the developed theory is illustrated by a practical case in which the valuation made by the different clients of a hotel company is analyzed.
Lightweight GAN-Assisted Class Imbalance Mitigation for Apple Flower Bud Detection
Authors: Yuan Wenan , Li Peng
Published: January 2025

Multi-class object detectors often suffer from the class imbalance issue, where substantial model performance discrepancies exist between classes. Generative adversarial networks (GANs), an emerging deep learning research topic, are able to learn from existing data distributions and generate similar synthetic data, which might serve as valid training data for improving object detectors. The current study investigated the utility of lightweight unconditional GAN in addressing weak object detector class performance by incorporating synthetic data into real data for model retraining, under an agricultural context. AriAplBud, a multi-growth stage aerial apple flower bud dataset was deployed in the study. A baseline YOLO11n detector was first developed based on training, validation, and test datasets derived from AriAplBud. Six FastGAN models were developed based on dedicated subsets of the same YOLO training and validation datasets for different apple flower bud growth stages. Positive sample rates and average instance number per image of synthetic data generated by each of the FastGAN models were investigated based on 1000 synthetic images and the baseline detector at various confidence thresholds. In total, 13 new YOLO11n detectors were retrained specifically for the two weak growth stages, tip and half-inch green, by including synthetic data in training datasets to increase total instance number to 1000, 2000, 4000, and 8000, respectively, pseudo-labeled by the baseline detector. FastGAN showed its resilience in successfully generating positive samples, despite apple flower bud instances being generally small and randomly distributed in the images. Positive sample rates of the synthetic datasets were negatively correlated with the detector confidence thresholds as expected, which ranged from 0 to 1. Higher overall positive sample rates were observed for the growth stages with higher detector performance. The synthetic images generally contained fewer detector-detectable instances per image than the corresponding real training images. The best achieved YOLO11n AP improvements in the retrained detectors for tip and half-inch green were 30.13% and 14.02% respectively, while the best achieved YOLO11n mAP improvement was 2.83%. However, the relationship between synthetic training instance quantity and detector class performances had yet to be determined. GAN was concluded to be beneficial in retraining object detectors and improving their performances. Further studies are still in need to investigate the influence of synthetic training data quantity and quality on retrained object detector performance.
After Extraction, Upper Premolars Undergo Programmed Socket Collapse with Development of Cavitations Rather than Complete Socket Healing: A Radiological Study
Authors: Ghanaati Shahram , Śmieszek-Wilczewska Joanna , Al-Maawi Sarah , Heselich Anja , Sader Robert
Published: January 2025

The alveolar ridge undergoes a loss in volume and atrophy after tooth extraction. Understanding the wound healing and bone regeneration process after tooth extraction is a key factor in the insertion of dental implants. Therefore, the aim of the present study was to analyze the socket healing process after the extraction of upper premolars based on cone beam computed tomography (CBCT) over six months. Special focus was placed on the morphological changes in the alveolar crest and within the socket. A retrospective analysis of patients in need of tooth extraction in the upper premolar region was performed in this study. All patients received flapless tooth extraction under local anesthesia and CBCT immediately after tooth extraction. Further CBCT analysis was performed after three months for the first group (n = 18) and after six months for the second group (n = 18). The results showed that all sockets underwent an inward movement of the defect walls towards the defect center, resulting in reduced total alveolar ridge volume and defect volume. This result was observed after three months and persisted after six months. The inward movement was quantified as a vertical socket collapse of up to 30.1 ± 9.0% after three months and 34.3 ± 6.7% after six months. The horizontal inward movement was quantified as a buccal socket collapse of 47.7 ± 12.3% after three months and 55.7 ± 29.1% after six months. New bone formation within the socket was evident, especially in the occlusal part of the socket. Additionally, bone formation was primarily observed as bone apposition along the socket walls and did not reach the defect center in most cases. The combination of socket collapse and bone apposition led to the formation of cavitations inside the socket that were mostly localized under the occlusal part. These novel findings with respect to socket collapse and formation of cavitation represent a paradigm shift and call for reconsidering the current understanding of socket healing. Based on the data, socket healing should be understood as a patient-specific process that requires 3D radiographic analysis for planning dental implants.
Two Demosponges as Promising Bioremediators of a Potential Pathogenic Vibrio
Authors: Aguilo-Arce Joseba , Scrascia Maria , Trani Roberta , Pazzani Carlo , Ferriol Pere , Longo Caterina
Published: January 2025

With more than 9600 valid species worldwide [...]
ECO-SCORE: Development of a New Ultrasound Score for the Study of Cystic and Solid-Cystic Adnexal Masses Based on Imaging Characteristics
Authors: Rodríguez-Rubio Carmen , Vegas-Viedma Sara , del Olmo-Reillo Malena , Quintana-Zapata Paula , Sancho-Sauco Javier , Pablos-Antona Mª Jesús , Alcázar Juan Luis , Pelayo-Delgado Irene
Published: January 2025

The accurate diagnosis of adnexal masses is a critical challenge in gynecological practice. Current ultrasound-based models, such as the ADNEX model, IOTA Simple Rules, and O-RADS, have demonstrated good diagnostic performance but are limited by the inclusion of demographic factors and solid confounding lesions. This study aimed to develop and validate a novel ultrasound score (ECO-SCORE) for cystic and solid-cystic lesions based solely on imaging characteristics to improve diagnostic accuracy and applicability in clinical practice. Methods: We conducted a retrospective study on 330 women diagnosed with adnexal masses, including 251 benign and 79 malignant cases. Ultrasound features were analyzed using logistic regression to identify key predictors of malignancy. A new scoring model was developed, excluding demographic or tumor-marker data. Diagnostic performance metrics, including sensitivity, specificity, AUC, and odds ratios, were calculated and compared to existing models using a testing set (20% of the data). Results: The ECO-SCORE achieved an AUC of 97.08%, outperforming ADNEX model (87.5%), IOTA Simple Rules (85.7%), and O-RADS (87.5%). Sensitivity and specificity were 92.98% and 88.88%, respectively, with an odds ratio of 106. Key predictors included irregular contour, absence of acoustic shadows, vascularization within solid areas, and vascularization of papillae. Conclusions: The ECO-SCORE demonstrated superior diagnostic accuracy compared to established models, highlighting its potential as a reliable tool for assessing adnexal masses using ultrasound features exclusively. Further multicenter validation is needed to confirm its robustness across different clinical settings.
Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms
Authors: Garcia-Junior Marcelo Augusto , Andrade Bruno Silva , Lima Ana Paula , Soares Iara Pereira , Notário Ana Flávia Oliveira , Bernardino Sttephany Silva , Guevara-Vega Marco Fidel , Honório-Silva Ghabriel , Munoz Rodrigo Alejandro Abarza , Jardim Ana Carolina Gomes , Martins Mário Machado , Goulart Luiz Ricardo , Cunha Thulio Marquez , Carneiro Murillo Guimarães , Sabino-Silva Robinson
Published: January 2025

Developing affordable, rapid, and accurate biosensors is essential for SARS-CoV-2 surveillance and early detection. We created a bio-inspired peptide, using the SAGAPEP AI platform, for COVID-19 salivary diagnostics via a portable electrochemical device coupled to Machine Learning algorithms. SAGAPEP enabled molecular docking simulations against the SARS-CoV-2 Spike protein’s RBD, leading to the synthesis of Bio-Inspired Artificial Intelligence Peptide 1 (BIAI1). Molecular docking was used to confirm interactions between BIAI1 and SARS-CoV-2, and BIAI1 was functionalized on rhodamine-modified electrodes. Cyclic voltammetry (CV) using a [Fe(CN)6]3−/4 solution detected virus levels in saliva samples with and without SARS-CoV-2. Support vector machine (SVM)-based machine learning analyzed electrochemical data, enhancing sensitivity and specificity. Molecular docking revealed stable hydrogen bonds and electrostatic interactions with RBD, showing an average affinity of −250 kcal/mol. Our biosensor achieved 100% sensitivity, 80% specificity, and 90% accuracy for 1.8 × 10⁴ focus-forming units in infected saliva. Validation with COVID-19-positive and -negative samples using a neural network showed 90% sensitivity, specificity, and accuracy. This BIAI1-based electrochemical biosensor, integrated with machine learning, demonstrates a promising non-invasive, portable solution for COVID-19 screening and detection in saliva.
Future Self-Continuity and Grit in College Students: The Role of Future Time Perspective
Authors: Wei Dandan , Ouyang Yuting , Zeng Xueyun
Published: January 2025

The present study examines the relationship between future self-continuity (FSC) and grit in college students, focusing on the mediating role of future time perspective (FTP) and its dimensions. A survey was conducted among 389 college students from universities in Beijing and Guizhou Province using relevant questionnaires. The results show the following: (1) FSC positively predicts grit (r = 0.25, p < 0.01); (2) a significant mediating role of FTP was found between FSC and grit; (3) and among the dimensions of FTP, behavioral commitment and future purpose consciousness significantly mediated the relationship between FSC and grit. This study demonstrates that FSC shapes college students’ grit through the mediation of future time perspective.
Mechanistic Understanding of the Non-Uniform Corrosion of Steel Bridges: From the Perspective of Micro-Environments
Authors: Wang Zhuang , Liu Yongjian , Ye Kecheng , Chen Sha , Yang Guang
Published: January 2025

The prevention of non-uniform corrosion in steel bridges remains a global challenge. Addressing this gap, the present study investigates atmospheric corrosion in steel bridges and introduces a non-uniform corrosion rate prediction process based on micro-environments, including the temperature and relative humidity of the air near the steel bridge surface (TS and RHS) and the deposition of pollutants on the steel bridge (DP). Using a CFST arch bridge as a case study, this research examines the causes of non-uniform corrosion and explores effective protective measures. The findings reveal a maximum of 1.48 °C for the TS and 15.7% for the RHs in different parts of the arch ribs. Significant disparities of the DP are observed in different parts of the arch ribs. The largest DP at the upper chord is up to 44.3 mg/m2/day (mdd), and the smallest amount of deposition at the lower chord is close to 0 mdd. Under the influence of the aforementioned factors, significant non-uniform corrosion of the ribs was found, with the fastest corrosion rate being up to 18.5 μm/year and the slowest corrosion rate being close to 0 μm/year. Uneven pollutant deposition is the primary cause of the non-uniform corrosion of the CFST arch bridge. By conducting the regular cleaning of the ribs, non-uniform corrosion could be mitigated.
Immune Resistance in Glioblastoma: Understanding the Barriers to ICI and CAR-T Cell Therapy
Authors: Eckert Thomas , Zobaer M. S. , Boulos Jessie , Alexander-Bryant Angela , Baker Tiffany G. , Rivers Charlotte , Das Arabinda , Vandergrift William A. , Martinez Jaime , Zukas Alicia , Lindhorst Scott M. , Patel Sunil , Strickland Ben , Rowland Nathan C.
Published: January 2025

Background: Glioblastoma (GBM) is the most common primary malignant brain tumor, with fewer than 5% of patients surviving five years after diagnosis. The introduction of immune checkpoint inhibitors (ICIs), followed by chimeric antigen receptor (CAR) T-cell therapy, marked major advancements in oncology. Despite demonstrating efficacy in other blood and solid cancers, these therapies have yielded limited success in clinical trials for both newly diagnosed and recurrent GBM. A deeper understanding of GBM’s resistance to immunotherapy is essential for enhancing treatment responses and translating results seen in other cancer models. Objectives: In this review, we examine clinical trial outcomes involving ICIs and CAR-T for GBM patients and explore the evasive mechanisms of GBM and the tumor microenvironment. Findings and Discussion: Multiple clinical trials investigating ICIs in GBM have shown poor outcomes, with no significant improvement in progression-free survival (PFS) or overall survival (OS). Results from smaller case studies with CAR-T therapy have warranted further investigation. However, no large-scale trials or robust studies have yet established these immunotherapeutic approaches as definitive treatment strategies. Future research should shift focus from addressing the scarcity of functional T cells to exploiting the abundant myeloid-derived cells within the tumor microenvironment. Conclusions: Translating these therapies into effective treatments for glioblastoma in humans remains a significant challenge. The highly immunosuppressive nature of GBM and its tumor microenvironment continue to hinder the success of these innovative immunotherapeutic approaches. Targeting the myeloid-derived compartment may lead to more robust and sustained immune responses.
Methanol-Tolerant Pd-Co Alloy Nanoparticles on Reduced Graphene Oxide as Cathode Catalyst for Oxygen Reduction in Fuel Cells
Authors: Yellatur Chandra Sekhar , Vinothkumar Venkatachalam , Kuppam Poshan Kumar Reddy , Oh Juwon , Kim Tae Hyun
Published: January 2025

The design of efficient and cost-effective electrocatalysts to replace Pt in an oxygen reduction reaction (ORR) is crucial for advancing proton exchange membrane fuel cell (PEMFC) technologies. This study synthesized Pd-Co bimetallic alloy nanoparticles supported on reduced graphene oxide (rGO) through a simple chemical-reduction method, making it suitable for low-cost, large-scale fabrication and significantly reducing the need for Pt. The nanostructures were systematically characterized using various analytical techniques, including X-ray diffraction (XRD), high-resolution transmission electron microscopy (HR-TEM), energy-dispersive X-ray spectroscopy (EDX), and cyclic voltammetry (CV). Electrochemical investigations revealed that the Pd-Co/rGO catalyst exhibits remarkable ORR performance in an alkaline environment, with an electrode-area-normalized activity rivaling that of the commercial Pt/C catalyst. Remarkably, Pd-Co/rGO demonstrated an onset potential (Eonset) of 0.944 V (vs. RHE) and a half-wave potential (E1/2) of 0.782 V (vs. RHE), highlighting its excellent ORR activity. Furthermore, the Pd-Co/rGO catalyst displayed superior methanol-tolerant ORR activity, outperforming Pt/C and monometallic Pd/rGO and Co/rGO systems. The enhanced electrocatalytic performance is attributed to the smallest size, consistent shape, and good dispersion of the alloy structure on the RGO surface. These findings establish Pd-Co/rGO as a promising alternative to Pt-based catalysts, addressing key challenges such as methanol crossover while advancing PEMFC technology in alkaline media.
Multidisciplinary Management of Acute Tetraparesis in an Infant with Achondroplasia, with a Focus on Anesthetic Strategies: A Case Report
Authors: Nedomová Barbora , Chrenko Robert , Jakešová Salome , Zahradníková Petra , Hanko Martin , Tichá Ľubica
Published: January 2025

Background/Objectives: This report details a rare instance of an infant with achondroplasia developing acute tetraparesis after a cervical whiplash injury, highlighting key multidisciplinary management considerations and specific anesthetic strategies to mitigate potential risks. Case presentation: A 1-year-old boy with achondroplasia presented with acute tetraparesis after a whiplash injury. Initial craniocervical computed tomography demonstrated a reduced volume of the posterior fossa, foramen magnum stenosis, and ventriculomegaly, without any fractures or dislocations. Moreover, magnetic resonance imaging (MRI) revealed pathological signal changes in the medulla oblongata, cervical spinal cord in segments C1 and C2, and the posterior atlantoaxial ligament. After initial conservative therapy and head immobilization using a soft cervical collar, partial remission of the tetraparesis was achieved. Two weeks post-injury, microsurgical posterior fossa decompression extending to the foramen magnum and C1 laminectomy was performed under general anesthesia with intraoperative neuromonitoring. Following an unsuccessful intubation attempt using a fiberoptic bronchoscope, successful airway management was achieved using a combined technique incorporating video laryngoscopy. Venous access was secured under ultrasound guidance. The patient exhibited complete remission of neurological symptoms by the third postoperative month during follow-up. Conclusions: This case report underscores the crucial need for a multidisciplinary approach in managing children with achondroplasia, especially with foramen magnum stenosis and complex cervical spine injuries. Anesthetic management required meticulously planned airway strategies using advanced techniques like video laryngoscopy and fiberoptic bronchoscopy to reduce airway risks. It also highlights the importance of conservative therapy paired with timely neurosurgical intervention, resulting in the patient’s full recovery.
Climate Change Worry in German University Students: Determinants and Associations with Health-Related Outcomes
Authors: Söder Andrea , Herr Raphael M. , Görig Tatiana , Diehl Katharina
Published: January 2025

Climate change is known to have an impact on human health, including mental health. To better understand this phenomenon, the Climate Change Worry Scale (CCWS), a 10-item questionnaire, was developed to assess climate change worry as a psychological response to climate change. The aim of this study was to validate a German version of the CCWS among university students and to explore potential associations with health outcomes. The CCWS was translated into German and used in an online survey of 1105 university students. We tested the scale’s psychometric properties and assessed its associations with sociodemographic characteristics and health outcomes. These included the Somatic Symptom Scale-8, Jenkins Sleep Scale, WHO-5 Well-being Index, and Patient Health Questionnaire 8. All CCWS items loaded on one factor and the items showed high internal consistency. Positive associations were observed between climate change worry and self-reported somatic symptoms, sleep difficulties, mental well-being, and depressive symptoms in multivariate regression models. The German version of the CCWS is a valid tool to measure climate change worry and can be used in future studies. The association between the CCWS and mental health underscores the need to recognize that students perceive climate change as a serious threat.
On the Effectiveness of a Common Attack to Chebyshev Chaotic Encryption Scheme
Authors: Liu Xiaoqi , Morales-Luna Guillermo
Published: January 2025

Chebyshev polynomials define a rather canonical chaotic cryptosystem and some strong attacks have been designed to that cryptosystem. We report the numerical experiments performed with multiple precision arithmetic using conventional software as gmp and mpfr to test the Chebyshev cryptosystem and Bergamo’s attack. As a conclusion, we point out the relevance in the cryptosystem robustness of the number of significant digits (length) of plaintexts and the number of correct digits (precision) of the arithmetical calculations. Furthermore, after the observed experimental results, we use techniques of Numerical Analysis to explain the occurrences of the observed results and to reinforce the importance of the above-mentioned parameters ℓ and m.
Spectroscopic Ellipsometry and Wave Optics: A Dual Approach to Characterizing TiN/AlN Composite Dielectrics
Authors: El Hachemi Mohamed , Khanna Nikhar , Barborini Emanuele
Published: January 2025

In this paper, we present a method for retrieving the optical properties of a nano-designed TiN/AlN composite dielectric, using spectroscopic ellipsometry for experimental measurements and wave optics simulations for numerical analysis. Composite cermets have gained attention for solar–thermal energy conversion, but their fundamental optical properties are not well understood. While characterizing uniformly deposited layers is generally straightforward, the process becomes more complex for nanoparticulate composites. The refractive index is essential for investigating and tuning the optical characteristics of the composite. Our method employs COMSOL Multiphysics software, validated by experimental spectroscopic ellipsometry studies. The strong agreement between experimental and numerical results supports this approach as a rational way to design material models for optical property studies across a broad spectrum.
Mesopelagic Fish Biodiversity in the Ligurian Cetacean Sanctuary
Authors: Lanteri Luca , Pinto Cecilia , Palandri Giovanni , Cima Chantal , Dei Matteo , Relini Giulio , Orsi Relini Lidia , Garibaldi Fulvio
Published: January 2025

Mesopelagic fish belonging to the families Gonostomatidae, Phosichthyidae, Sternoptychidae, Stomiidae, Paralepididae, Myctophidae, Evermannellidae, Microstomatidae, and Nemichthyidae were sampled using an Isaacs Kidd Midwater Trawl (IKMT) in the Ligurian Sea, Northwestern Mediterranean, the core of the Cetacean Sanctuary established in 2001. In September–October 2002 and September 2003, 31 species of mesopelagic fish were captured in oblique hauls from 800 m depth to the surface. The mesopelagic fish fraction represented about 22% of the collected biomass, dominated by two main species of the genus Cyclothone, representing 93.8% of total abundance, followed by Argyropelecus hemigymnus and Lampanyctus crocodilus. The presence of Valenciennellus tripunctulatus was a new record for the study area. The family Myctophidae was the most represented in terms of number of species (n = 13). The abundance and biodiversity of mesopelagic fishes were influenced by the sampling period and oceanographic conditions, specifically temperature. While no diel vertical migrations could be observed, day and night samples revealed different compositions of juveniles and adults of Benthosema glaciale and Ceratoscopelus maderensis. The broad size range of specimens sampled confirmed that adult individuals of Chauliodus sloani and Lampanyctus crocodilus can inhabit and forage within the water column beyond the slope rather than exclusively near the bottom.
Pearl Mussel Population Dynamics in Estonia: A Study on the Interplay of Geology, Geomorphology, and Watershed Land Use
Authors: Kaldma Katrin , Kohv Marko , Hang Tiit , Aia Joosep , Jõeleht Argo , Paat Raul , Kesler Martin
Published: January 2025

Freshwater pearl mussels (Margaritifera margaritifera) are vital bioindicators in river ecosystems, with their population health reflecting broader environmental conditions. Mussel population declines are well documented across Europe, and in Estonia, Pudisoo River supports the last remaining population. Long-term monitoring also indicates a significant decline in Pudisoo River, but the causes remain underexplored. This study aimed to assess how geology, geomorphology, and land use changes could impact pearl mussel population trends and distribution. We conducted field surveys, bioindication tests, and hydrological and morphometric analyses to evaluate physicochemical and environmental parameters in the Pudisoo River. The study revealed a declining but stabilizing population, concentrated in river sections with higher gradients and coarser riverbed substrates. Sediment transport and accumulation, influenced by both natural and human-induced factors, significantly affect habitat suitability, especially for juvenile mussels. Positive correlations were found between mussel populations and river sinuosity, suggesting that the varied microhabitats created by sinuosity, such as gradients in flow velocity and sediment deposition, play an important role in supporting the species. This finding highlights the importance of directing in-channel restoration efforts towards enhancing channel morphology to create more diverse habitats. Research on sediment dynamics and hydrological connectivity will be crucial for developing effective habitat management strategies for the species, especially considering the impacts of land use and climate change.
Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest
Authors: Ozlu Ucan Gulfem , Gwassi Omar Abboosh Hussein , Apaydin Burak Kerem , Ucan Bahadir
Published: January 2025

Background/Objectives: Dental age estimation is a vital component of forensic science, helping to determine the identity and actual age of an individual. However, its effectiveness is challenged by methodological variability and biological differences between individuals. Therefore, to overcome the drawbacks such as the dependence on manual measurements, requiring a lot of time and effort, and the difficulty of routine clinical application due to large sample sizes, we aimed to automatically estimate tooth age from panoramic radiographs (OPGs) using artificial intelligence (AI) algorithms. Methods: Two-Dimensional Deep Convolutional Neural Network (2D-DCNN) and One-Dimensional Deep Convolutional Neural Network (1D-DCNN) techniques were used to extract features from panoramic radiographs and patient records. To perform age estimation using feature information, Genetic algorithm (GA) and Random Forest algorithm (RF) were modified, combined, and defined as Modified Genetic–Random Forest Algorithm (MG-RF). The performance of the system used in our study was analyzed based on the MSE, MAE, RMSE, and R2 values calculated during the implementation of the code. Results: As a result of the applied algorithms, the MSE value was 0.00027, MAE value was 0.0079, RMSE was 0.0888, and R2 score was 0.999. Conclusions: The findings of our study indicate that the AI-based system employed herein is an effective tool for age detection. Consequently, we propose that this technology could be utilized in forensic sciences in the future.
Predicting COPD Readmission: An Intelligent Clinical Decision Support System
Authors: López-Canay Julia , Casal-Guisande Manuel , Pinheira Alberto , Golpe Rafael , Comesaña-Campos Alberto , Fernández-García Alberto , Represas-Represas Cristina , Fernández-Villar Alberto
Published: January 2025

Background: COPD is a chronic disease characterized by frequent exacerbations that require hospitalization, significantly increasing the care burden. In recent years, the use of artificial intelligence-based tools to improve the management of patients with COPD has progressed, but the prediction of readmission has been less explored. In fact, in the state of the art, no models specifically designed to make medium-term readmission predictions (2–3 months after admission) have been found. This work presents a new intelligent clinical decision support system to predict the risk of hospital readmission in 90 days in patients with COPD after an episode of acute exacerbation. Methods: The system is structured in two levels: the first one consists of three machine learning algorithms —Random Forest, Naïve Bayes, and Multilayer Perceptron—that operate concurrently to predict the risk of readmission; the second level, an expert system based on a fuzzy inference engine that combines the generated risks, determining the final prediction. The employed database includes more than five hundred patients with demographic, clinical, and social variables. Prior to building the model, the initial dataset was divided into training and test subsets. In order to reduce the high dimensionality of the problem, filter-based feature selection techniques were employed, followed by recursive feature selection supported by the use of the Random Forest algorithm, guaranteeing the usability of the system and its potential integration into the clinical environment. After training the models in the first level, the knowledge base of the expert system was determined on the training data subset using the Wang–Mendel automatic rule generation algorithm. Results: Preliminary results obtained on the test set are promising, with an AUC of approximately 0.8. At the selected cutoff point, a sensitivity of 0.67 and a specificity of 0.75 were achieved. Conclusions: This highlights the system’s future potential for the early identification of patients at risk of readmission. For future implementation in clinical practice, an extensive clinical validation process will be required, along with the expansion of the database, which will likely contribute to improving the system’s robustness and generalization capacity.
Pathophysiological Responses of Oral Keratinocytes After Exposure to Flavored E-Cigarette Liquids
Authors: Shamim Abrar , Herzog Hannah , Shah Raivat , Pecorelli Sara , Nisbet Virginia , George Ann , Cuadra Giancarlo A. , Palazzolo Dominic L.
Published: January 2025

Background: Electronic cigarettes (ECIGs) have grown in popularity, particularly among adolescents and young adults. Flavored ECIG-liquids (E-liquids) are aerosolized by these ECIGs and inhaled into the respiratory system. Several studies have shown detrimental effects of E-liquids in airway tissues, revealing that flavoring agents may be the most irritating component. However, research on the effects of E-liquids on biological processes of the oral cavity, which is the first site of aerosol contact, is limited. Hence, this study focuses on the effects of E-liquid flavors on oral epithelial cells using the OKF6/TERT-2 cell line model. Methodology: E-liquid was prepared with and without flavors (tobacco, menthol, cinnamon, and strawberry). OKF6/TERT-2 oral epithelial cells, cultured at 37 °C and 5% CO2, were exposed to 1% E-liquid ± flavors for 24 h. Outcomes determined include cell morphology, media pH, wound healing capability, oxidative stress, expression of mucin and tight junction genes, glycoprotein release, and levels of inflammatory cytokines (TNFα, IL-6, and IL-8). Results: Exposure to 1% flavored E-liquids negatively affect cellular confluency, adherence, and morphology. E-liquids ± flavors, particularly cinnamon, increase oxidative stress and production of IL-8, curtail wound healing recovery, and decrease glycoprotein release. Gene expression of muc5b is downregulated after exposure to E-liquids. In contrast, E-liquids upregulate occludin and claudin-1. Conclusions: This study suggests that ECIG use is not without risk. Flavored E-liquids, particularly cinnamon, result in pathophysiological responses of OKF6/TERT-2 cells. The dysregulation of inflammatory responses and cellular biology induced by E-liquids may contribute to various oral pathologies.
A PFC Control Management to Improve the Efficiency of DC-DC Converters
Authors: Cacciotto Fabio , Torrisi Salvatore , Aiello Giovanni , Rizzo Santi Agatino
Published: January 2025

This paper presents a novel technique for controlling the Power Factor Correction (PFC) of a two-stage converter. The proposed solution operates the PFC in a special intermittent mode at a medium or light load. As a result, the flyback converter stage can be optimized to operate within a tight input voltage range, thus obtaining better efficiency and more compactness compared to a traditionally controlled two-stage converter. Circuit models of the converter have been developed to test the goodness of the proposed solution.
Fault Diagnosis of Gas Insulated Switchgear Isolation Switch Based on Improved Support Vector Data Description Method
Authors: Zhang Nan , Wu Tianchi , Zhang Yunpeng , Yin Bo , Yang Xuebin , Liu Chengliang , Lu Senxiang
Published: January 2025

To improve the efficiency and precision of fault diagnosis for isolation switches within Gas-insulated switchgear (GIS), this study introduces an advanced technique utilizing an enhanced support vector data description (SVDD) algorithm. Initially, various operational states of the GIS isolation switch are simulated, and the corresponding vibration signals are captured. Subsequently, both the entropy and time-domain features of these signals are extracted to construct a multi-dimensional feature space. High-dimensional feature datasets are then reduced in dimensionality using the kernel principal component analysis (KPCA) method. Furthermore, the conventional SVDD algorithm is modified by incorporating a penalty factor, which allows for a more adaptable classification boundary. This adaptation not only focuses on positive samples but also considers the influence of selected negative samples on the classification hypersphere. Finally, the collected experimental data are classified and predicted. The results indicate that this GIS fault-diagnosis approach effectively overcomes the limitations of traditional methods, which are heavily dependent on training sample data and demonstrate poor algorithm generalization performance. This method is proven to be applicable for the fault diagnosis of isolation switches in GIS.
Automated Scattering Media Estimation in Peplography Using SVD and DCT
Authors: Song Seungwoo , Kim Hyun-Woo , Cho Myungjin , Lee Min-Chul
Published: January 2025

In this paper, we propose automation of estimating scattering media information in peplography using singular value decomposition (SVD) and discrete cosine transform (DCT). Conventional scattering media-removal methods reduce light scattering in images utilizing a variety of image-processing techniques and machine learning algorithms. However, under conditions of heavy scattering media, they may not clearly visualize the object information. Peplography has been proposed as a solution to this problem. Peplography is capable of visualizing the object information by estimating the scattering media information and detecting the ballistic photons from heavy scattering media. Following that, 3D information can be obtained by integral imaging. However, it is difficult to apply this method to real-world situations since the process of scattering media estimation in peplography is not automated. To overcome this problem, we use automatic scattering media-estimation methods using SVD and DCT. They can estimate the scattering media information automatically by truncating the singular value matrix and Gaussian low-pass filter in the frequency domain. To evaluate our proposed method, we implement the experiment with two different conditions and compare the result image with the conventional method using metrics such as structural similarity (SSIM), feature similarity (FSIMc), gradient magnitude similarity deviation (GMSD), and learned perceptual image path similarity (LPIPS).
Real-Time High Dynamic Equalization Industrial Imaging Enhancement Based on Fully Convolutional Network
Authors: Shi Chenbo , Ren Xiangqun , Mo Yuanzheng , Zhang Guodong , Yan Shaojia , Wang Yu , Zhu Changsheng
Published: January 2025

Severe reflections on the surfaces of smooth objects can result in low dynamic range and uneven illumination in images, which negatively impacts downstream tasks such as defect detection and QR code recognition on images of smooth workpieces. Consequently, this paper proposes a novel approach to real-time high dynamic equalization imaging based on a fully convolutional network, termed Multi-exposure Image Fusion with Multi-dimensional Attention Mechanism and Training Storage Units (MEF-AT). Specifically, this paper innovatively proposes using training storage units, which utilize intermediate results during network training as auxiliary images, to remove uneven illumination and enhance image dynamic range effectively. Furthermore, by integrating a multi-dimensional attention mechanism into the backbone network, the model can more efficiently extract and utilize critical image information. Additionally, this paper introduces a Deep Guided Filter (DGF) with learnable parameters, which upsample the weight maps generated by the network, thus better adapting to complex industrial scenarios and producing higher quality fused images. An image evaluation metric assessing the lighting uniformity is introduced to thoroughly evaluate the proposed method’s performance. Given the lack of an MEF dataset for smooth workpieces, this paper collects a new dataset for multi-exposure fusion tasks on metallic workpieces. Our method takes less than 4 ms to run four 2K images on a GPU 3090. Both qualitative and quantitative experimental results demonstrate our method’s superior comprehensive performance in proprietary industrial and public datasets.
The Gasification of Marine and Coastal Resources for Syngas Production: A Review
Authors: Vonk Gwendal , Boy Virginie , Lanoisellé Jean-Louis , Lendormi Thomas
Published: January 2025

Coasts are home to one-third of the human population. In the process of energy transition, local biomass and waste resources represent a renewable fuel that can substitute fossil fuels in order to reduce greenhouse gas emissions, hence including marine resources as part of the eligible feedstock for renewable energy production. Gasification regroups different technologies that aim to convert a solid fuel into a useful gas, and has several applications, such as heat production, power generation, and chemical synthesis. Gasification technologies regroup the traditional “dry” processes that use relatively dry fuels, but recent developments have been made with “wet” processes such as hydrothermal gasification, in sub- or supercritical conditions for the water, which can accept wet fuel. This review focuses on scientific articles that performed gasification of marine resources in order to produce a syngas. First, a definition of marine resources is made, followed by the presentation of marine resources studied in the literature. Secondly, this review presents the different types of gasification reactors and their operating conditions, followed by a summary of the different syngas produced with their composition as a performance indicator. Finally, this review exposes the limitations of the current literature and concludes with perspective propositions.
The Future of Nuclear Energy: Key Chemical Aspects of Systems for Developing Generation III+, Generation IV, and Small Modular Reactors
Authors: Kiegiel Katarzyna , Chmielewska-Śmietanko Dagmara , Herdzik-Koniecko Irena , Miśkiewicz Agnieszka , Smoliński Tomasz , Rogowski Marcin , Ntang Albert , Rotich Nelson Kiprono , Madaj Krzysztof , Chmielewski Andrzej G.
Published: January 2025

Nuclear power plants have the lowest life-cycle greenhouse gas emissions intensity and produce more electricity with less land use compared to any other low-carbon-emission-based energy source. There is growing global interest in Generation IV reactors and, at the same time, there is great interest in using small modular reactors. However, the development of new reactors introduces new engineering and chemical challenges critical to advancing nuclear energy safety, efficiency, and sustainability. For Generation III+ reactors, water chemistry control is essential to mitigate corrosion processes and manage radiolysis in the reactor’s primary circuit. Generation IV reactors, such as molten salt reactors (MSRs), face the challenge of handling and processing chemically aggressive coolants. Small modular reactor (SMR) technologies will have to address several drawbacks before the technology can reach technology readiness level 9 (TRL9). Issues related to the management of irradiated graphite from high-temperature reactors (HTR) must be addressed. Additionally, spent fuel processing, along with the disposal and storage of radioactive waste, should be integral to the development of new reactors. This paper presents the key chemical and engineering aspects related to the development of next-generation nuclear reactors and SMRs along with the challenges associated with them.