bluuebunny/crossref_metadata_2025 huggingface dataset:
Non-Surgical Treatment for Hepatocellular Carcinoma: What to Expect at Follow-Up Magnetic Resonance Imaging—A Pictorial Review
Authors: Scheau Andreea-Elena , Jurca Sandra Oana , Scheau Cristian , Lupescu Ioana Gabriela
Published: October 2024

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, represents a significant global health challenge due to its rising incidence, complex management, as well as recurrence rates of up to 70% or more. Early and accurate imaging diagnosis, through modalities such as ultrasound, CT, and MRI, is crucial for effective treatment. Minimally invasive therapies, including thermal ablation methods such as radiofrequency ablation, microwave ablation, laser ablation, high-intensity focused ultrasound, and cryoablation, as well as non-thermal methods like percutaneous ethanol injection and irreversible electroporation, have shown promise in treating early and intermediate stages of HCC. Some studies have reported complete response in more than 90% of nodules and survival rates of up to 60–85% at 5 years after the procedure. These therapies are increasingly employed and induce specific morphological and physiological changes in the tumor and surrounding liver tissue, which are critical to monitor for assessing treatment efficacy and detecting recurrence. This review highlights the imaging characteristics of HCC following non-surgical treatments, focusing on the common features, challenges in post-treatment evaluation, and the importance of standardized imaging protocols such as the Liver Imaging Reporting and Data System. Understanding these imaging features is essential for radiologists to accurately assess tumor viability and guide further therapeutic decisions, ultimately improving patient outcomes.
Dynamic Response Prediction of Railway Bridges Considering Train Load Duration Using the Deep LSTM Network
Authors: Tan Sui , Ke Xiandong , Pang Zhenhao , Mao Jianxiao
Published: October 2024

Monitoring and predicting the dynamic responses of railway bridges under moving trains, including displacement and acceleration, are vital for evaluating the safety and serviceability of the train–bridge system. Traditionally, finite element analysis methods with high computational burden are used to predict the train-induced responses according to the given train loads and, hence, cannot easily be integrated as an available structural-health-monitoring strategy. Therefore, this study develops a novel framework, combining the train–bridge coupling mechanism and deep learning algorithms to efficiently predict the train-induced bridge responses while considering train load duration. Initially, the feasibility of using neural networks to calculate the train–bridge coupling vibration is demonstrated by leveraging the nonlinear relationship between train load and bridge responses. Subsequently, the instantaneous multiple moving axial loads of the moving train are regarded as the equivalent node loads that excite adjacent predefined nodes on the bridge. Afterwards, a deep long short-term memory (LSTM) network is established as a surrogate model to predict the train-induced bridge responses. Finally, the prediction accuracy is validated using a numerical case study of a simply supported railway bridge. The factors that may affect the prediction accuracy, such as network structure, training samples, the number of structural units, and noise level, are discussed. Results show that the developed framework can efficiently predict the train-induced bridge responses. The prediction accuracy of the bridge displacement is higher than that of the acceleration. In addition, the robustness of the displacement prediction is proven to be better than that of the acceleration with the variation of carriage number, riding speed, and measurement noise.
Relationship between Body Composition and Physical Performance by Sex in Professional Basketball Players
Authors: Hernandez-Martinez Jordan , Perez-Carcamo Joaquín , Coñapi-Union Bayron , Canales-Canales Sebastian , Negron-Molina Mario , Avila-Valencia Sergio , Cid-Calfucura Izham , Herrera-Valenzuela Tomas , Cisterna Diego , Branco Braulio Henrique Magnani , Valdés-Badilla Pablo
Published: October 2024

This study aimed to identify the relationship between body composition (fat-free mass and body fat percentage) and physical performance (countermovement jump, CMJ; throwing ball; maximal isometric handgrip strength, MIHS dominant and non-dominant hands; 10-m and 20-m sprints with and without ball) in Chilean professional basketball players. Its secondary aim was to analyze if there were differences in body composition and physical performance according to sex. This was a cross-sectional study that analyzed 23 professional basketball players with a mean age of 24.0 ± 4.92 years, distributed among male professional basketball players (male professional BPs, n = 12) and female professional basketball players (female professional BPs, n = 14). The main results indicate the correlation presented significant relationships between fat-free mass with CMJ (r = 0.760; p < 0.0001; ES = 1.43), MIHS dominant hand (r = 0.783; p < 0.0001; ES = 1.50) and MIHS non-dominant hand (r = 0.805; p < 0.0001; ES = 1.85), throwing ball (r = 0.586; p = 0.001; ES = 0.56), 10 m sprint with ball (r = −0.510; p = 0.007; ES = 0.35), and 20 m sprint with ball (r = −0.143; p = 0.046; ES = 0.16). As did body fat percentage with CMJ (r = −0.647; p = 0.000; ES = 0.56), throwing the ball (r = −0.657; p = 0.000; ES = 0.58), MIHS dominant hand (r = −0.745; p < 0.0001; ES = 1.17), and MIHS non-dominant hand (r = −0.820; p < 0.0001; ES = 1.50). In conclusion, body composition is related to physical performance in professional basketball players. Meanwhile, male professional BPs had better body composition and physical performance than female professional BPs.
Fall Risk Classification Using Trunk Movement Patterns from Inertial Measurement Units and Mini-BESTest in Community-Dwelling Older Adults: A Deep Learning Approach
Authors: Robles Cruz Diego , Puebla Quiñones Sebastián , Lira Belmar Andrea , Quintana Figueroa Denisse , Reyes Hidalgo María , Taramasco Toro Carla
Published: October 2024

Falls among older adults represent a critical global public health problem, as they are one of the main causes of disability in this age group. We have developed an automated approach to identifying fall risk using low-cost, accessible technology. Trunk movement patterns were collected from 181 older people, with and without a history of falls, during the execution of the Mini-BESTest. Data were captured using smartphone sensors (an accelerometer, a gyroscope, and a magnetometer) and classified based on fall history using deep learning algorithms (LSTM). The classification model achieved an overall accuracy of 88.55% a precision of 90.14%, a recall of 87.93%, and an F1 score of 89.02% by combining all signals from the Mini-BESTest tasks. The performance outperformed the metrics we obtained from individual tasks, demonstrating that aggregating all cues provides a more complete and robust assessment of fall risk in older adults. The results suggest that combining signals from multiple tasks allowed the model to better capture the complexities of postural control and dynamic gait, leading to better prediction of falls. This highlights the potential of integrating multiple assessment modalities for more effective fall risk monitoring.
Phytochemical and Functional Properties of Fruit and Vegetable Processing By-Products
Authors: Ciccoritti Roberto , Ciorba Roberto , Ceccarelli Danilo , Amoriello Monica , Amoriello Tiziana
Published: October 2024

Processing sustainability and the concept of zero waste discharge are of great interest for many industries. Every year, fruit and vegetable processing industries generate huge amounts of by-products, which are often intended for animal feed or discarded as waste, posing a problem to both environmental and economic points of view. However, to minimize the waste burden, the valorization of these residues received increased interest. In fact, fruit and vegetable by-products are an excellent source of valuable compounds, such as proteins, dietary fibers, lipids, minerals, vitamins, phenolic acids, flavonoids, anthocyanins, carotenoids, and pigments, which can be recovered and reused, creating new business prospects from a circular economy perspective. Understanding the chemical characteristics of these materials is a key concern for their valorization and the identification of their most appropriate intended use. In this study, the phytochemical and functional properties of fruit and vegetable processing by-products (peel and pomace) were investigated. Samples of different plants (i.e., apple, black and orange carrot, cucumber, kumquat, mango, parsnip, peach, black plum) were analyzed using chemical analytical methods and characterized using Fourier Transform Mid-Infrared spectroscopy (FT-MIR). The results highlighted their high nutritional composition in terms of protein, lipids, fiber, and ash, as well as bioactive and antioxidant profiles. These characteristics make these residues suitable as natural ingredients for the development of high-added-value products in food, cosmetic, and pharmaceutical industries.
Partial Path Overlapping Mitigation: An Initial Stage for Joint Detection and Decoding in Multipath Channels Using the Sum–Product Algorithm
Authors: Mirbadin Anoush , Zaraki Abolfazl
Published: October 2024

This paper addresses the problem of mitigating unknown partial path overlaps in communication systems. This study demonstrates that by utilizing the front-end insight of communication systems along with the sum–product algorithm applied to factor graphs, it is possible not only to track these overlapping components accurately, but also to detect all multipath channel impairments simultaneously. The proposed methodology involves discretizing channel parameters, such as channel paths and attenuation coefficients, to ensure the most accurate computation of means of Gaussian observations. These parameters are modeled as Bernoulli random variables with priors set to 0.5. A notable aspect of the algorithm is its integration of the received signal power into the calculation of noise variance, which is critical for its performance. To further reduce the receiver complexity, a novel implementation strategy, based on provided pre-defined look up tables (LOTs) to the reciver, is introduced. The simulation results, covering both distributed and concentrated pilot scenarios, reveal that the algorithm performs almost equally under both conditions and surpasses the established upper bound in performance.
Are There Seven Symbols for the Nucleotide-Based Genetic Code?
Authors: Kłóś Adam , Płonka Przemysław M. , Baczyński Krzysztof
Published: October 2024

The common assumption is that genetic information is built on a four-symbol alphabet, i.e., DNA nucleotides, the smallest meaningful blocks of genomes are codon triplets, and the record of genetic information does not contain any asserted symbols playing the role of the space. It is, however, well known that some nucleotides in some codons are redundant. Our study, therefore, tests the alternative scenario. As the same nucleotide may play various semiotic roles, the genomic alphabet actually contains seven semiotic symbols. Consequently, the meaningful fragments of genomes (words) can be of different sizes, and there are asserted symbols in the record of genomic information. If this is true, then, similarly to natural languages, the frequency-range of these genomic words should follow the power-law distribution. The presented hypothesis was tested, in comparison to competitive (codon-based and n-tuple) forms of tokenization, on a wide range of genomic texts.
Train-Induced Unsteady Airflow in a Metro Tunnel with a Ventilation Shaft
Authors: Wang Fei , He Xingsen , Xu Lin , Zhao Shengzhong , Weng Miaocheng
Published: October 2024

To ensure only one train operates in each ventilation section within an extra-long tunnel, a ventilation shaft was typically installed to divide the entire tunnel into multiple sections. Given the crucial role of piston wind in the metro tunnel environment and ventilation, a deeper understanding of train-induced unsteady airflow in a metro tunnel with a ventilation shaft is desirable. This study uses the unsteady flow theory of the Bernoulli equation to mathematically model piston wind in metro tunnels both with and without ventilation shafts. The influence of various shaft parameters on piston wind development is systematically analyzed. The results indicate that the shaft significantly impacts the piston wind. The maximum piston wind speed and ventilation rate in tunnels with ventilation shafts surpass those in tunnels without them. Moreover, shaft location and the cross-sectional area notably affect the maximum piston wind speed, ventilation rate, and airflow in the shaft, whereas shaft height has no significant effect. It is found that a ventilation shaft with a larger cross-sectional area positioned in the middle of the tunnel enhances the performance of piston ventilation.
Harnessing the Power of Natural Mineral Waters in Bread Formulations: Effects on Chemical, Physical, and Physicochemical Properties
Authors: Ferreira Fernanda , Guimarães Rafaela , Lemos André , Milho Catarina , da Silveira Tayse F. F. , Ueda Jonata M. , Carocho Márcio , Heleno Sandrina A. , Barros Lillian , Ferreira Isabel C. F. R. , Pintado Manuela , Alves Maria José
Published: October 2024

This study aimed to evaluate the feasibility of incorporating natural mineral waters (NMW), including thermal water (TW) and bottled mineral water (BMW), into bread (‘biju’ type) to enhance its mineral content and explore their impacts on physicochemical, technological, biochemical, and chemical composition. NMW, rich in sodium, potassium, and magnesium, resulted in bread formulations with higher contents of these minerals and greater total mineral levels, thus potentially enriching food products. Proximate composition analysis showed no significant differences in moisture, proteins, carbohydrates, and energy, except for lipids and soluble sugars. Texture analysis revealed that water type influenced textural properties, with salt content affecting hardness, springiness, and cohesiveness. Viability analysis of Saccharomyces cerevisiae showed consistent results across formulations, suggesting water pH and mineral content did not significantly affect fermentation. In addition, bread formulations without added salt were developed to assess the potential of sodium-rich NMW as a natural source of salt. For these samples, and considering the parameters assessed, except for salt and sodium content, the differences observed were slight compared to salt-added formulations, highlighting NMW’s potential to produce low-salt bread. These findings not only enhance the value of local resources but also offer an innovative and sustainable strategy for utilizing NMW springs across Europe.
Enhancing Real Estate Valuation in Kazakhstan: Integrating Machine Learning and Adaptive Neuro-Fuzzy Inference System for Improved Precision
Authors: Barlybayev Alibek , Ongalov Nurzhigit , Sharipbay Altynbek , Matkarimov Bakhyt
Published: October 2024

The concept of fair value, defined by the valuation of assets and liabilities at their current market worth, remains central to the International Financial Reporting Standards (IFRS) and has persisted despite critiques intensified by the 2008 financial crisis. This valuation method continues to be prevalent under both IFRS and the US Generally Accepted Accounting Principles (GAAP). The adoption of IFRS has notably enhanced the role of accounting in information analysis, vital for owners who prioritize both secure accounting practices and reliable data for strategic management decisions. Real estate, a significant business asset, has long been a focal point in accounting discussions, prompting extensive research into the applicability and effectiveness of various accounting standards. These investigations assess the adaptability of standards based on property type, utility, and valuation techniques. However, the challenge of accurately determining the fair value of real estate remains unresolved, signifying its importance not only in the corporate manufacturing realm but also among development companies striving to manage property values efficiently. This study addresses the challenge of accurately determining the fair market value of real estate in Kazakhstan, leveraging a multi-methodological approach that encompasses statistical models, regression analysis, data visualization, neural networks, and particularly, an Adaptive Neuro-Fuzzy Inference System (ANFIS). The integration of these diverse methodologies not only enhances the robustness of real estate valuation but also introduces new insights into effective asset management. The findings suggest that ANFIS provides superior precision in real estate pricing, demonstrating its potential as a valuable tool for strategic management and investment decision-making.
Effects of Zirconium-Based Crosslinkers with Different Zirconium Contents on Pigment Coating in Paper
Authors: Park Hyeong-Hun , Kim Chul-Hwan , Lee Tae-Gyeong , Park Ju-Hyun , Park Min-Sik , Lee Jae-Sang
Published: October 2024

This study investigates the impact of zirconium-based KZC series crosslinkers with varying zirconium contents and the polyamine-based crosslinker (PBC) on the properties of coated paper, focusing on key performance metrics such as viscosity, wet rub and pick resistance, dry pick resistance, gloss, brightness, ink set-off, and print mottle. The findings reveal that crosslinkers’ type and concentration significantly influence the coating colors’ viscosity, with PBC demonstrating lower low shear viscosity at lower concentrations. The KZC series showed stable viscosity across a zirconium content range of 7% to 20%, and both crosslinker types enhanced wet rub resistance at higher concentrations. Notably, the KZC series, particularly KZC7, exhibited superior wet pick resistance at increased concentrations, highlighting its effectiveness in enhancing the durability of the coating layer. All crosslinkers maintained excellent dry pick resistance, ensuring robust coating performance. While gloss and brightness were generally unaffected, KZC20, which had the highest zirconium content, slightly reduced these optical properties. Ink set-off tests indicated that the KZC series performed better at lower concentrations, whereas higher concentrations led to increased ink set-off, potentially due to over-crosslinking. Print mottle remained consistent across all crosslinkers, indicating uniform coating quality. Overall, the zirconium-based KZC series significantly enhances wet resistance and maintains high performance across various properties, though it may slightly reduce gloss and brightness and increase ink set-off at higher concentrations. PBC offers a balanced performance profile, emphasizing the need for careful crosslinker type and concentration optimization to achieve the desired coating properties for specific applications. This comprehensive evaluation provides valuable insights for developing and optimizing high-performance coated papers.
Study of Various Process Parameters on Bead Penetration and Porosity in Wire Arc Additive Manufacturing (WAAM) of Copper Alloy Cu1897
Authors: Shah Abid , Gupta Neel Kamal , Aliyev Rezo , Zeidler Henning
Published: October 2024

Copper-based alloys are widely known for their high thermal and electrical conductivity. Although the use of these alloys in powder-based additive manufacturing (AM) shows significant promise, applying this method in wire arc additive manufacturing (WAAM) processes poses various considerable challenges, including porosity, delamination, surface oxidation, etc. The limited research on WAAM of copper alloys, especially Cu1897, highlights the need for a more in-depth investigation. This study addresses the effects of process parameters in pulse cold metal transfer (CMT)-based WAAM of Cu1897, i.e., pulse correction (PC) and arc length correction (ALC), on bead penetration and porosity. The results showed that as PC was increased from −5 to +5, weld bead penetration increased from 2.38 mm to 3.87 mm. To further enhance penetration and reduce the porosity, the ALC was varied from +30% to −30% with a step size of 15%. The results showed that weld bead penetration increased to 4.47 mm by altering the ALC from +30% to −30%. Additionally, as the ALC varied within this range, porosity decreased significantly from 3.98% to 0.28%. Overall, it is concluded that a lower value of ALC is recommended to improve bead penetration and reduce porosity in WAAM of Cu1897.
Integration of Isotopic and Nuclear Techniques to Assess Water and Soil Resources’ Degradation: A Critical Review
Authors: Peralta Vital José L. , Calvo Gobbetti Lucas E. , Llerena Padrón Yanna , Martínez Luzardo Francisco Heriberto , Díaz Rizo Oscar , Gil Castillo Reinaldo
Published: October 2024

Isotopic and nuclear techniques are indispensable in many fields, including health, industry, food, and agriculture. The techniques discussed, collectively known as fallout radionuclide, fingerprint, and isotope hydrology, are currently being employed to characterize and assess phenomena that could potentially degrade soil and water resources. Given the intricate nature of erosion and sedimentation processes in landscapes and water reservoirs, conducting a comprehensive characterization and evaluation of these phenomena is imperative. A traditional literature review was conducted to obtain the most thorough understanding of both the current state of the art and the subject matter regarding the conception of these techniques’ application and the manner of their use (use combined/integrated or use isolated in search of particular results on a single type of degradation, whether soil or water). There is no evidence that an integrative methodology employing these isotopic and nuclear techniques has previously been utilized (as evidenced by 109 current publications), thereby impeding the analysis of the potential sequential occurrence of soil and water degradation. The findings substantiate the hypothesis that isotopic and nuclear techniques can be integrated sequentially through a synergistic convergence. This represents an emerging methodology for addressing the complex needs of the landscape’s soil and water degradation process.
Effects of In-Season Velocity-Based vs. Traditional Resistance Training in Elite Youth Male Soccer Players
Authors: Sekulović Veselin , Jezdimirović-Stojanović Tatjana , Andrić Nikola , Elizondo-Donado Andoni , Martin Diego , Mikić Mladen , Stojanović Marko D. M.
Published: October 2024

The objectives of this study were to compare the effects of two in-season velocity loss training methods (VBT) on performance outcomes and to evaluate the effects of velocity-based training compared to traditional resistance training (TRT) on performance outcomes in young elite soccer players. VBT utilized the same relative load but varied in the extent of velocity loss during the set: 15% (VL15%) and 30% (VL30%). Thirty-four players were recruited and randomly distributed into three groups: the VL15% group (n = 12; age = 18.50 ± 0.67 years; stature = 183.41 ± 4.25 cm; body mass = 75.08 ± 5.57 kg), the VL30% group (n = 11; age = 17.91 ± 0.60 years; stature = 181.21 ± 6.56 cm, body mass = 73.58 ± 6.22 kg), and the traditional strength training group TRT (n = 11; age = 18.14 ± 0.74 years; stature = 182.17 ± 5.52 cm; body mass = 74.86 ± 6.68 kg). Alongside regular soccer sessions and matches, the groups underwent a four-week (2 sesions per week) resistance training intervention with back squats involved. Changes in leg strength (SQ1RM), 20 m sprint time (SPR 20 m), countermovement jump height (CMJ), reactive strength index (RSI), and change of direction (COD) from before and after were evaluated using a 3 × 2 ANOVA. While no significant interaction was found for SQ1RM and SPR20, all of the groups showed significant pre to post improvements. Significant interactions were observed for CMJ (F = 38.24, p = 0.000), RSI (F = 8.33; p = 0.001), and change of direction agility test (COD) (F = 3.64; p = 0.038), with a post hoc analysis showing differences between the VL15 (6.0%) and TRT (1.7%) groups (p = 0.034); VL15 (12.2%) and VL30 (3.2%) groups (p = 0.004); VL15 and TRT (0.4%) (p = 0.018); VL15 (2.4%) and VL30 (1.5%) (p = 0.049); and between the VL15 and TRT (0.4%) groups (p = 0.015). Four weeks of VL15% training during the season induced similar strength increases to VL30% and TRT, superior improvements in RSI and COD compared to VL30%, and superior improvements in CMJ, RSI, and COD tests compared to TRT. Thus, incorporating the VL15% training method may be recommended to improve power-related performance metrics in elite young soccer players.
Effect of School-Based Endurance and Strength Exercise Interventions in Improving Body Composition, Physical Fitness and Cognitive Functions in Adolescents
Authors: Pérez-Ramírez José Antonio , González-Fernández Francisco Tomás , Villa-González Emilio
Published: October 2024

The aim of this study was to analyze the effects of different exercise interventions involving chronic exercise (endurance and strength groups) carried out in physical education on physical fitness and cognitive functions. A group of 72 adolescent students from the city of Melilla (Spain) aged between 13 and 17 years old (M = 15.38, SD = 0.78) participated in the current study. A D2 attention test was used in order to analyze selective attention and concentration. Physical fitness was measured through the ALPHA-Fitness battery. The analysis taken indicated a significant relationship between physical fitness level, attention and concentration. Moreover, the intervention resulted in significant reductions in body weight and waist circumference in the resistance and strength groups compared to the control group, indicating positive effects on body composition. All the groups demonstrated enhanced aerobic capacity, as evidenced by improvements in VO2 max after the intervention period. Furthermore, significant enhancements in most attentional measures (TR, TA, O, C, TOT, CON and TR−) were observed across all the groups, with an additional temporary improvement in TR+ for the strength group. Our findings suggest that an 8-week school-based exercise intervention, regardless of specific exercise type (resistance or strength), can positively impact body composition, aerobic capacity and attention in adolescents. These results emphasize the importance of integrating physical activity programs into school environments to promote holistic health and well-being in this population. Future research should focus on elucidating the underlying mechanisms of these effects and exploring the long-term benefits of such interventions.
Modelling the Uptake of 226Ra and 238U Stable Elements in Plants during Summer in the Vicinity of Tailings from an Abandoned Copper Mine
Authors: Expósito-Suárez Víctor Manuel , Suárez-Navarro José Antonio , Morales-Quijano Miguel , Gómez-Mancebo María Belén , Barragan Marta , Cortecero Miriam , Benavente José Francisco
Published: October 2024

Transfer factors (TFs) are widely used tools for assessing the uptake of radionuclides by plants. The literature contains numerous studies on TFs in tropical and temperate climates; however, the existing data on TFs in arid and semi-arid climates are very scarce. Furthermore, the current trend in nuclear energy expansion in countries with this type of climate necessitates knowledge of the mechanisms of radionuclide incorporation by plants as well as the TF values. For this reason, this work investigates the TFs of 238U and 226Ra in plants in a study area during the summer period under conditions equivalent to a semi-arid climate. The selected plants were Scolymus hispanicus L., Eryngium campestre L., Chenopodium vulvaria L., and Chenopodium album L., which were collected in the vicinity of a waste dump from an abandoned copper mine. The selected study area has radionuclide levels above the global average, in addition to heavy metals, as it is a waste dump from an abandoned copper mine. The range of transfer factors for 238U varied between 1.5 × 10−4 kg−1 kg−1 and 7.8 × 10−3 kg−1 kg−1, while for 226Ra, the range was between 1.8 × 10−4 kg−1 kg−1 and 4.0 × 10−2 kg−1 kg−1. The correlations found with PCA were (i) 238U with Fe and Al, and (ii) 226Ra with S, Ti, Ca, and Sr. A transfer model of 238U and 226Ra was created using multiple linear regression analysis. The model showed how 238U was related to the presence of Al, while 226Ra was related to Al, Fe, and Ti. The results obtained have allowed us to propose a model for the incorporation of 238U and 226Ra, taking into account the chemical composition of the soil. The results obtained indicate that both Scolymus hispanicus L. and Eryngium campestre L. could be utilized in phytoremediation for soils contaminated by natural radionuclides in semi-arid climates. The TFs, as well as the proposed model, allow us to expand the knowledge of the absorption of natural radionuclides by plants in regions with arid and semi-arid climates, which is necessary for the radiological risk assessment of future nuclear fuel cycle facilities.
Recurrence Quantification Analysis Based Methodology in Automatic Aerobic Threshold Detection: Applicability and Accuracy across Age Groups, Exercise Protocols and Health Conditions
Authors: Zimatore Giovanna , Serantoni Cassandra , Gallotta Maria Chiara , Meucci Marco , Mourot Laurent , Ferrari Dafne , Baldari Carlo , De Spirito Marco , Maulucci Giuseppe , Guidetti Laura
Published: October 2024

A new method based on the Recurrence Quantification Analysis (RQA) of the heart rate (HR) offers an objective, efficient alternative to traditional methods for Aerobic Threshold (AerT) identification that have practical limitations due to the complexity of equipment and interpretation. This study aims to validate the RQA-based method’s applicability across varied demographics, exercise protocols, and health status. Data from 123 cardiopulmonary exercise tests were analyzed, and participants were categorized into four groups: athletes, young athletes, obese individuals, and cardiac patients. Each participant’s AerT was assessed using both traditional ventilatory equivalent methods and the automatic RQA-based method. Ordinary Least Products (OLP) regression analysis revealed strong correlations (r > 0.77) between the RQA-based and traditional methods in both oxygen consumption (VO2) and HR at the AerT. Mean percentage differences in HR were below 2.5%, and the Technical Error for HR at AerT was under 8%. The study validates the RQA-based method, directly applied to HR time series, as a reliable tool for the automatic detection of the AerT, demonstrating its accuracy across diverse age groups and fitness levels. These findings suggest a versatile, cost-effective, non-invasive, and objective tool for personalized exercise prescription and health risk stratification, thereby fulfilling the study’s goal of broadening the method’s applicability.
Skeletal Muscle Energetics in Heart Failure Assessed Using 31P Magnetic Resonance Spectroscopy—A Systematic Review and Meta-Analysis
Authors: Suleman Safiyyah A. , Bilak Joanna , Puranik Amitha , McCann Gerry P. , Squire Iain B.
Published: October 2024

Introduction: Skeletal muscle (SkM) abnormalities are well-recognised in heart failure (HF). We aimed to systematically review studies of SkM energetics in patients with HF at rest and post-exercise using 31phosphorus magnetic resonance spectroscopy (31P MRS). Methods: A systematic search of cross-sectional studies used predefined search terms related to HF, SkM energetics, and 31P MRS (PROSPERO ID: CRD42023434698). Inclusion criteria for studies are as follows: 1. HF participants versus controls; and 2. SkM energetics assessed using 31P MRS reporting BOTH (i) PCr recovery time and (ii) PCr ratios (PCr/Pi and/or PCr/ATP). The primary outcome was SkM PCr recovery time following exercise, comparing patients with diagnosed HF and healthy controls and reported as standardised mean difference (SMD). Results: Of 465 identified studies, 6 met the inclusion criteria and were conducted from 1987 to 2021, comprising 162 participants (N = 86 HF, N = 76 healthy controls). HF patients (mean age 55.1 ± 4.16 years, 49 (56.9%) male) were reasonably matched to healthy controls (mean age 50 ± 8.9 years, 54 (71%) males). Two studies did not report patients’ ejection fractions (EF); the mean EF among patients from the remaining six studies was 24.8%. No studies specifically included participants with HFpEF and none characterised sarcopenia. HF patients exhibited impaired SkM energetics compared to healthy controls, which were characterised by a significantly increased PCr recovery time (SMD: −1.35, CI: −2.11, −0.59). Conclusions: PCr recovery is significantly impaired in patients with HFrEF. Females were under-represented, no HFpEF studies were identified, and no studies linking abnormal SkM energetics directly to sarcopenia were identified.
Turning Waste into Wealth: Optimization of Microwave/Ultrasound-Assisted Extraction for Maximum Recovery of Quercetin and Total Flavonoids from Red Onion (Allium cepa L.) Skin Waste
Authors: Velisdeh Zeinab Jabbari , Najafpour Darzi Ghasem , Poureini Fatemeh , Mohammadi Maedeh , Sedighi Armin , Bappy Mohammad Jabed Perves , Ebrahimifar Meysam , Mills David K.
Published: October 2024

This study optimized the extraction conditions to maximize the recovery yields of quercetin and total flavonoids from red onion skin waste using sequential microwave/ultrasound-assisted extraction. Five effective factors of quercetin extraction yield were investigated using response surface methodology. The method was successfully performed under optimal 60 s microwave irradiation conditions followed by 15 min sonication at 70 °C with 70% (v/v, water) ethanol with a solvent-to-solid ratio of 30 mL/g. The variance analysis of the model for both quercetin (Y1) and total flavonoid (Y2) recovery from DOS demonstrated that ultrasound temperature (X2) was the most highly significant and influential factor, with a p-value of <0.0001 for both responses. Additionally, three key interaction terms—X1X2, X2X4, and X2X5—were identified as highly significant, further underscoring the critical role of ultrasound temperature in optimizing the extraction process for both quercetin and total flavonoids. The maximum recovery yields of quercetin and total flavonoids from red onion skin were 10.32% and 12.52%, respectively. The predicted values for quercetin (10.05%) and total flavonoids (12.72%) were very close to the experimental results. The recovery yields obtained from different extraction methods under the identical experimental conditions mentioned earlier were ultrasound/microwave-assisted extraction (7.66% quercetin and 10.18% total flavonoids), ultrasound-assisted extraction (5.36% quercetin and 8.34% total flavonoids), and microwave-assisted extraction (5.03% quercetin and 7.91% total flavonoids). The ANOVA confirmed highly significant regression models (p-values < 0.0001), with an insignificant lack of fit (p = 0.0515 for quercetin, p = 0.1276 for total flavonoids), demonstrating the robustness and reliability of the optimization. This study provides valuable insights for improving the extraction of bioactive compounds, which is critical for developing effective cancer treatments and advancing medical research. Additionally, the model shows potential for scaling up food processing applications to recover valuable products from red onion skin waste.
A Study on Enhancing the Visual Fidelity of Aviation Simulators Using WGAN-GP for Remote Sensing Image Color Correction
Authors: Lee Chanho , Kwon Hyukjin , Choi Hanseon , Choi Jonggeun , Lee Ilkyun , Kim Byungkyoo , Jang Jisoo , Shin Dongkyoo
Published: October 2024

When implementing outside-the-window (OTW) visuals in aviation tactical simulators, maintaining terrain image color consistency is critical for enhancing pilot immersion and focus. However, due to various environmental factors, inconsistent image colors in terrain can cause visual confusion and diminish realism. To address these issues, a color correction technique based on a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is proposed. The proposed WGAN-GP model utilizes multi-scale feature extraction and Wasserstein distance to effectively measure and adjust the color distribution difference between the input image and the reference image. This approach can preserve the texture and structural characteristics of the image while maintaining color consistency. In particular, by converting Bands 2, 3, and 4 of the BigEarthNet-S2 dataset into RGB images as the reference image and preprocessing the reference image to serve as the input image, it is demonstrated that the proposed WGAN-GP model can handle large-scale remote sensing images containing various lighting conditions and color differences. The experimental results showed that the proposed WGAN-GP model outperformed traditional methods, such as histogram matching and color transfer, and was effective in reflecting the style of the reference image to the target image while maintaining the structural elements of the target image during the training process. Quantitative analysis demonstrated that the mid-stage model achieved a PSNR of 28.93 dB and an SSIM of 0.7116, which significantly outperforms traditional methods. Furthermore, the LPIPS score was reduced to 0.3978, indicating improved perceptual similarity. This approach can contribute to improving the visual elements of the simulator to enhance pilot immersion and has the potential to significantly reduce time and costs compared to the manual methods currently used by the Republic of Korea Air Force.
Assessment of the Nutritional Value and Antioxidant Properties of Plant-Based Yogurt from Chickpeas
Authors: Budryn Grażyna , Grzelczyk Joanna
Published: October 2024

Plant-based yogurts are becoming more and more popular. These are vegan products for people with food allergies or intolerances, athletes, and ordinary consumers. The use of chickpea “milk” allows one to obtain a plant-based yogurt with a high protein content, dietary fiber and antioxidant potential. This study assessed the content of polyphenols, selected nutrients, and antioxidant properties in chickpea yogurt. The vegetable chickpea yogurt was rich in protein (4.34–7.90 g/100 g) and dietary fiber (2–4%). It was also characterized by a high antioxidant activity (DPPH = 2.61 to 4.32 μmol TE/g, FRAP = 3.11 to 48.33 μmol TE/g). The use of 3-day soaking and inoculation with Vegurt or a kefir bacterial inoculum allowed us to obtain a high protein content. The addition of honey increased the polyphenol content and antioxidant potential of the plant-based yogurt (139–148 GAE mg/100 g). This research will be used in future research into optimizing the conditions for obtaining plant-based yogurts with chickpeas. Further research should include models for testing health-promoting properties.
Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
Authors: Manai Elyes , Mejri Mohamed , Fattahi Jaouhar
Published: October 2024

The number of cybersecurity vulnerabilities keeps growing every year. Each vulnerability must be reported to the MITRE Corporation and assessed by a Counting Number Authority, which generates a metrics vector that determines its severity score. This process can take up to several weeks, with higher-severity vulnerabilities taking more time. Several authors have successfully used Deep Learning to automate the score generation process and used explainable AI to build trust with the users. However, the explanations that were shown were surface label input saliency on binary classification. This is a limitation, as several metrics are multi-class and there is much more we can achieve with XAI than just visualizing saliency. In this work, we look for actionable actions CNAs can take using XAI. We achieve state-of-the-art results using an interpretable XGBoost model, generate explanations for multi-class labels using SHAP, and use the raw Shapley values to calculate cumulative word importance and generate IF rules that allow a more transparent look at how the model classified vulnerabilities. Finally, we made the code and dataset open-source for reproducibility.
Calculation of Maximum Permissible Load of Underground Power Cables–Numerical Approach for Systems with Stabilized Backfill
Authors: Szultka Seweryn , Czapp Stanislaw , Tomaszewski Adam , Tariq Hanan
Published: October 2024

The maximum permissible load of underground power cables (known in U.S. engineering as “ampacity”) is a function of many parameters, in particular, the thermal resistivity of the native soil. If this resistivity is relatively high, thermal/stabilized backfill is applied, i.e., another material is placed around the cables, providing favourable conditions for heat transfer to the environment. It has a positive impact on the reliability of the power supply and favours the operational durability of the cables. In design practice, however, there is a difficult task—correct determination of the ampacity of the cable line depending on the thermal parameters and the geometry of the backfill. Therefore, this article presents the results of a numerical analysis to determine the ampacity of cable lines in which stabilized backfill is used. A new mathematical relationship is proposed that allows the correction of the ampacity of cable lines depending on their cross-section as well as the thermal and geometric parameters of the cable surroundings.
Comparison of Different Negative-Sample Acquisition Strategies Considering Sample Representation Forms for Debris Flow Susceptibility Mapping
Authors: Gao Ruiyuan , Wu Di , Liu Hailiang , Liu Xiaoyang
Published: October 2024

The lack of reliable negative samples is an important factor limiting the quality of machine learning-based debris flow susceptibility mapping (DFSM). The purpose of this paper is to propose multiple negative-sample acquisition strategies for DFSM considering different sample representation forms. The sample representation forms mainly include a single grid, multi-grid, and watershed unit, and the negative-sample acquisition strategies are based on support vector machine (SVM), spy technique, and isolation forest (IF) methods, respectively. These three strategies can assign a value to all the samples based on different assumptions, and reliable, negative samples can be generated from samples with values below a predefined threshold. Combining different sample representation forms with negative sample acquisition strategies, nine datasets were then involved in random forest (RF) modeling. The receiver operating characteristic (ROC) curves and related statistical results were used to evaluate the models. The results show that the strategy based on the spy technique is suitable for multiple datasets, while the IF-based strategy is well-adapted to the watershed unit datasets. This study can provide more options for improving the quality of datasets in DFSM, which can further improve the performance of machine learning models.
Modified Laboratory Risk Indicator and Machine Learning in Classifying Necrotizing Fasciitis from Cellulitis Patients
Authors: Suraphee Sujitta , Busababodhin Piyapatr , Chamchong Rapeeporn , Suparatanachatpun Pinyo , Khamthong Khemmanant
Published: October 2024

Necrotizing fasciitis (NF) is a severe and life-threatening soft tissue infection that requires timely and accurate diagnosis to improve patient outcomes. The early diagnosis of NF remains challenging due to its similarity to other subcutaneous soft tissue infections like cellulitis. This study aims to employ machine learning techniques to differentiate NF from cellulitis and enhance the diagnostic accuracy of NF by developing a modified LRINEC (MLRINEC) score. These modifications aimed to improve the sensitivity and specificity of NF diagnosis. The study utilized three machine learning classifiers—Logistic Regression, decision tree, and Random Forest—to assess their effectiveness in distinguishing between NF and cellulitis cases. The MLRINEC score was developed by incorporating six key blood test parameters: creatinine, hemoglobin, platelet count, sodium, white blood cell count, and C-reactive protein using laboratory data from Maha Sarakham Hospital in Northeastern Thailand. Our findings indicate that the decision tree classifier demonstrated superior performance, achieving the highest recall, particularly in accurately identifying NF cases. A feature importance analysis revealed that hemoglobin levels and white blood cell counts were the most critical factors influencing the model’s predictions. The platelet count (PT), C-reactive protein (CRP), and creatinine (CT) also played important roles, while sodium levels (NA) were the least influential. The MLRINEC score demonstrates high accuracy in classifying NF and cellulitis patients, paving the way for improved diagnostic protocols in clinical settings.
Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
Authors: Vanhaeverbeke Jelle , Decorte Robbe , Slembrouck Maarten , Hoecke Sofie Van , Verstockt Steven
Published: October 2024

Road cycling races, such as the Tour de France, captivate millions of viewers globally, combining competitive sportsmanship with the promotion of regional landmarks. Traditionally, points of interest (POIs) are highlighted during broadcasts using manually created static overlays, a process that is both outdated and labor-intensive. This paper presents a novel, fully automated methodology for detecting and tracking POIs in live helicopter video streams, aiming to streamline the visualization workflow and enhance viewer engagement. Our approach integrates a saliency and Segment Anything-based technique to propose potential POI regions, which are then recognized using a keypoint matching method that requires only a few reference images. This system supports both automatic and semi-automatic operations, allowing video editors to intervene when necessary, thereby balancing automation with manual control. The proposed pipeline demonstrated high effectiveness, achieving over 75% precision and recall in POI detection, and offers two tracking solutions: a traditional MedianFlow tracker and an advanced SAM 2 tracker. While the former provides speed and simplicity, the latter delivers superior segmentation tracking, albeit with higher computational demands. Our findings suggest that this methodology significantly reduces manual workload and opens new possibilities for interactive visualizations, enhancing the live viewing experience of cycling races.