bluuebunny/crossref_metadata_2025 huggingface dataset:
Validation Against Polysomnography of a Transthoracic Impedance Sensor for Screening of Sleep Apnea in Heart Failure Patients: A Pooled Analysis of AIRLESS and UPGRADE
Authors: Barbieri Fabian , Adukauskaite Agne , Spitaler Philipp , Senoner Thomas , Pfeifer Bernhard , Neururer Sabrina , Jacon Peggy , Venier Sandrine , Limon Sarah , Ben Messaoud Raoua , Pépin Jean-Louis , Hintringer Florian , Dichtl Wolfgang , Defaye Pascal
Published: December 2024

Background/Introduction: Cardiac implantable electronic devices and their integrated thoracic impedance sensors have been used to detect sleep apnea for over a decade now. Despite their usage in daily clinical practice, there are only limited data on their diagnostic accuracy. Methods: AIRLESS and UPGRADE were prospective investigator-driven trials meant to validate the AP scan® (Boston Scientific, Marlborough, MA, USA) in heart failure cohorts. Patients, who either fulfilled the criteria for implantation of an implantable cardioverter-defibrillator (ICD), cardiac resynchronization therapy (CRT), or upgrading to CRT according to most recent guidelines at the time of study conduction, were eligible for enrolment. Sleep apnea and its severity, measured by apnea–hypopnea index (AHI), were assessed by polysomnography. For direct comparison, the apnea sensor-derived AP scan® was used from the identical night. Results: Overall, 80 patients were analyzed. Median AHI was 21.6 events/h (7.1–34.7), while median AP scan® was 33.0 events/h (26.0–43.0). In the overall cohort, the sensor-derived AP scan® correlated significantly with the AHI (r = 0.61, p < 0.001) with a mean difference (MD) of −12.6 (95% confidence interval (CI) −38.2 to 13.0). Furthermore, the AP scan® was found to correlate well with the AHI in patients with obstructive sleep apnea r = 0.73, p = 0.011, MD −5.2, 95% CI −22.7 to 12.3), but not central sleep apnea (r = 0.28, p = 0.348, MD −10.4, 95% CI −35.4 to 14.6). Conclusions: In an exclusive heart failure cohort, the AP scan® correlated well with the PSG-derived AHI. A similar correlation was found in most subgroups except for patients suffering from central sleep apnea.
Nociplastic Pain in Endometriosis: A Scoping Review
Authors: Gentles Avonae , Goodwin Emma , Bedaiwy Yomna , Marshall Nisha , Yong Paul J.
Published: December 2024

Endometriosis is an inflammatory chronic condition associated with nociceptive, neuropathic, and nociplastic pain. Central sensitization (CS) is the primary nociplastic pain mechanism. However, there are currently no standardized methods for detecting CS or nociplastic pain. This review aims to identify available tools for characterizing CS/nociplastic pain in endometriosis-related chronic pelvic pain. Following the PRISMA-P protocol, MEDLINE, Embase, Scopus, and PsychINFO databases were searched on 23 April 2024, for the terms “endometriosis”, “central sensitization”, “nociplastic pain”, “widespread pain”, and “assessment tools”. Publications were selected if they mentioned tool(s) for detecting nociplastic pain or CS in endometriosis patients. Information was extracted on study demographics, assessment types, and the tools used for detection. Of the 379 citations retrieved, 30 papers met the inclusion criteria. When working to identify CS and nociplastic pain, fourteen studies exclusively used patient-reported questionnaires, six used quantitative sensory testing (QST), two used clinical assessments, and eight used multiple approaches combining patient-reported questionnaires and clinical assessment. This review illustrates the diversity of tools currently used to identify CS and nociplastic pain in endometriosis patients. Further research is needed to evaluate their validity and to standardize methods in order to improve the accuracy of nociplastic pain identification and guide treatment.
PAS or Not PAS? The Sonographic Assessment of Placenta Accreta Spectrum Disorders and the Clinical Validation of a New Diagnostic and Prognostic Scoring System
Authors: Vimercati Antonella , Galante Arianna , Fanelli Margherita , Cirignaco Francesca , Vitagliano Amerigo , Nicolì Pierpaolo , Tinelli Andrea , Malvasi Antonio , Dellino Miriam , Damiani Gianluca Raffaello , Crescenza Barbara , Baldini Giorgio Maria , Cicinelli Ettore , Cerbone Marco
Published: December 2024

This study aimed to evaluate our center’s experience in diagnosing and managing placenta accreta spectrum (PAS) in a high-risk population, focusing on prenatal ultrasound features associated with PAS severity and maternal outcomes. We conducted a retrospective analysis of 102 high-risk patients with confirmed placenta previa who delivered at our center between 2018 and 2023. Patients underwent transabdominal and transvaginal ultrasound scans, assessing typical sonographic features. Binary and multivariate logistic regression analyses were performed to identify sonographic markers predictive of PAS and relative complications. Key ultrasound features—retroplacental myometrial thinning (<1 mm), vascular lacunae, and retroplacental vascularization—were significantly associated with PAS and a higher risk of surgical complications. An exceedingly rare sign, the “riddled cervix” sign, was observed in only three patients with extensive cervical or parametrial involvement. Those patients had the worst surgical outcomes. This study highlights the utility of specific ultrasound features in stratifying PAS risk and guiding clinical and surgical management in high-risk pregnancies. The findings support integrating these markers into prenatal diagnostic protocols to improve patient outcomes and inform surgical planning.
Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy
Authors: Zaffino Paolo , Raggio Ciro Benito , Thummerer Adrian , Marmitt Gabriel Guterres , Langendijk Johannes Albertus , Procopio Anna , Cosentino Carlo , Seco Joao , Knopf Antje Christin , Both Stefan , Spadea Maria Francesca
Published: December 2024

In recent years, synthetic Computed Tomography (CT) images generated from Magnetic Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be comparable to real CT images in terms of dose computation for radiotherapy simulation. However, until now, there has been no independent strategy to assess the quality of each synthetic image in the absence of ground truth. In this work, we propose a Deep Learning (DL)-based framework to predict the accuracy of synthetic CT in terms of Mean Absolute Error (MAE) without the need for a ground truth (GT). The proposed algorithm generates a volumetric map as an output, informing clinicians of the predicted MAE slice-by-slice. A cascading multi-model architecture was used to deal with the complexity of the MAE prediction task. The workflow was trained and tested on two cohorts of head and neck cancer patients with different imaging modalities: 27 MR scans and 33 CBCT. The algorithm evaluation revealed an accurate HU prediction (a median absolute prediction deviation equal to 4 HU for CBCT-based synthetic CTs and 6 HU for MR-based synthetic CTs), with discrepancies that do not affect the clinical decisions made on the basis of the proposed estimation. The workflow exhibited no systematic error in MAE prediction. This work represents a proof of concept about the feasibility of synthetic CT evaluation in daily clinical practice, and it paves the way for future patient-specific quality assessment strategies.
Evaluation of Color Difference Models for Wide Color Gamut and High Dynamic Range
Authors: Basova Olga , Gladilin Sergey , Kokhan Vladislav , Kharkevich Mikhalina , Sarycheva Anastasia , Konovalenko Ivan , Chobanu Mikhail , Nikolaev Ilya
Published: December 2024

Color difference models (CDMs) are essential for accurate color reproduction in image processing. While CDMs aim to reflect perceived color differences (CDs) from psychophysical data, they remain largely untested in wide color gamut (WCG) and high dynamic range (HDR) contexts, which are underrepresented in current datasets. This gap highlights the need to validate CDMs across WCG and HDR. Moreover, the non-geodesic structure of perceptual color space necessitates datasets covering CDs of various magnitudes, while most existing datasets emphasize only small and threshold CDs. To address this, we collected a new dataset encompassing a broad range of CDs in WCG and HDR contexts and developed a novel CDM fitted to these data. Benchmarking various CDMs using STRESS and significant error fractions on both new and established datasets reveals that CAM16-UCS with power correction is the most versatile model, delivering strong average performance across WCG colors up to 1611 cd/m2. However, even the best CDM fails to achieve the desired accuracy limits and yields significant errors. CAM16-UCS, though promising, requires further refinement, particularly in its power correction component to better capture the non-geodesic structure of perceptual color space.
Managing Pharmaceutical Costs in Health Systems: A Review of Affordability, Accessibility and Sustainability Strategies
Authors: Ntais Christos , Talias Michael A. , Fanourgiakis John , Kontodimopoulos Nikolaos
Published: December 2024

Background: This paper reviews cost containment policies to control pharmaceutical expenditure either by regulating the pharmaceutical industry or targeting the demand side. Methods: The method used was the narrative literature review of studies which assessed the effect of pharmaceutical cost containment policies. Results: Governments worldwide have implemented a great variety of policy measures to manage pharmaceutical expenditure while ensuring fair access to essential medicines. Cost-sharing schemes, value-based pricing, reimbursement, reference pricing, payback mechanisms and the substitution of original drugs with generics and biosimilars are pivotal in these efforts, albeit with differing effectiveness across healthcare systems. Overall, it appears that any gains may be outweighed by the unfavorable effects of policies impacting patients. Although interventions have been created to improve physicians’ prescribing practice, they often achieve very minor benefits and at considerable cost. Policy measures pertaining to the regulation of the supply side must be supported by thorough evaluation in order to ascertain costs and effects and guarantee that unintended consequences are minimized. Conclusions: Policymakers frequently enact numerous laws and regulations to control pharmaceutical expenditure, even if there is limited evidence that they are cost-effective. The most crucial component of any policy’s success, regardless of the one selected, is its evaluation. Further research is needed to develop context-specific guidance that balances cost containment, equity and sustainability.
Discovering the Bathylithology and Bioengineering Organisms of the Punta Coles Marine Natural Reserve, Moquegua, Peru
Authors: Méndez-Ancca Sheda , Pepe-Victoriano Renzo , Gonzales Hebert Hernán Soto , Aguilar Juan Luis Ccamapaza , Meza Yesica Alvarez , Pacho Marco Antonio Quispe , Cáceres Alex Tejada , Baldarrago Centeno Danny Efraín , Aguilera Jorge González
Published: December 2024

The Punta Coles Natural Reserve (PCNR), located on the southern coast of the province of Ilo, Moquegua Region, Peru, houses an area rich in marine biodiversity which is composed of natural banks of benthic invertebrates, which constitute the potential and dynamic fishery of commercial interest for artisanal fishermen that impact the area. To mitigate this impact, all stakeholders need to know, understand, value, and balance the dependence of benthic biodiversity with the ecosystem for its preservation and the promotion of sustainable fishing. Along these lines, the components studied are bathymetry, lithology, and the tapestry of bioengineers that make up the reserve’s seabed. Mapping of the complexity of the benthic habitat of the seabed was developed on the basis of an integrated analysis of multibeam bathymetric data, geoscientific information associated with the antecedents of benthic biodiversity of the PCNR, sediment maps of the seabed, subtidal sedimentological texture, and photographic records. The characterization and granulometry of the sediments that make up the PCNR were determined according to the Udden–Wentworth classification criterion (Φ). The utility of the GPSMAP 178C multibeam echosounder for the interpretation of seabed sediments stands out. The kriging interpolation method was used to extrapolate the characteristics of the benthic habitat, which is suitable for large areas of the sea. As a result, three habitats were derived and mapped, and three structures (bioengineers), namely, “subtidal kelps” (Lessonia trabeculata), “mussels” (Aulacomya atra), and “red sea squirt” (Pyura chilensis), were associated mainly with sedimentary rock habitats and mixed substrates. In the sand substrate, these algae were found to a lesser extent. The results show that this study is a conceptual model that summarizes the understanding of ecology and prospects for the sustainability of profitable, ecoefficient fishing activities in the Moquegua Region, Peru.
Influence of Rocky Obstacle Sand Bypassing on Embayed Beach Dynamics Using a Reduced-Complexity Shoreline Model
Authors: Durand Elsa , Castelle Bruno , Idier Déborah , Marieu Vincent , Robinet Arthur , Guérin Thomas
Published: December 2024

Headland and groyne sand bypassing greatly influences embayment dynamics at medium to long timescales, but is often disregarded or partially included in reduced-complexity shoreline models. This study explores how accounting for subaqueous sediment bypassing in a shoreline model affects mean embayed beach planshape and spatial variability. We implement a generic parametrization of sand bypassing in the LX-Shore model, with simulations on a synthetic embayment in two configurations: “full bypassing” (FB) where the sediments bypass the obstacle in the surfzone and beyond, and “shoreline bypassing” (SB) where bypassing occurs only when the shoreline extends beyond the obstacle. Time-invariant wave simulations show significant differences in updrift shoreline position between FB and SB. Simulations with time-varying wave angles and fixed wave height and period reveal that FB significantly impacts the embayment mean planform and spatial variability: FB reduces beach rotation by about 1/3, particularly under slightly oblique and slightly asymmetrical wave climates, and decreases shoreline curvature, especially under highly oblique wave climates. Downdrift shoreline erosion may be overestimated by up to 20% under SB. Our simulations provide new insight into the influence of subaqueous sand bypassing on embayed beach dynamics and emphasize the importance of including this process when modelling shoreline evolution in coastal embayments.
One-Way CFD/FEM Analysis of a Fish Cage in Current Conditions
Authors: Viegas Mariana D. , Wang Shan , Guedes Soares C.
Published: December 2024

This study explores the hydrodynamic behaviour of a fish cage in a steady current by employing a fluid–structure interaction model with one-way coupling between a fluid solver and a structural model. The fluid field around the fish cage is predicted using a computational fluid dynamics solver, while the stress and deformation of the netting are calculated using finite element structural algorithm with solid elements reflecting their real geometry. The fluid velocity and hydrodynamic pressure are calculated and mapped to the structural analysis model. The fluid–structure interaction model is validated by comparing drag force results with published experimental data at different current conditions. Instead of modelling the netting of the fish cage as porous media or using lumped mass methods, the complete structural model is built in detail. The analysis of the fluid field around the nets shows that the change in the current condition has a limited impact on the flow behaviour, but the increase in the current velocity significantly enhances the magnitude of the drag force. This study reveals a reduction in flow within and downstream of the net, consistent with prior experimental findings and established research. Mechanical analysis shows that knotted nets have better performance than knotless ones, and although fluid pressure causes some structural deformation, it remains within safe limits, preventing material failure.
The Application of Machine Learning Techniques to Predict Stock Market Crises in Africa
Authors: Naeem Muhammad , Jassim Hothefa Shaker , Korsah David
Published: December 2024

This study sought to ascertain a machine learning algorithm capable of predicting crises in the African stock market with the highest accuracy. Seven different machine-learning algorithms were employed on historical stock prices of the eight stock markets, three main sentiment indicators, and the exchange rate of the respective countries’ currencies against the US dollar, each spanning from 1 May 2007 to 1 April 2023. It was revealed that extreme gradient boosting (XGBoost) emerged as the most effective way of predicting crises. Historical stock prices and exchange rates were found to be the most important features, exerting strong influences on stock market crises. Regarding the sentiment front, investors’ perceptions of possible volatility on the S&P 500 (Chicago Board Options Exchange (CBOE) VIX) and the Daily News Sentiment Index were identified as influential predictors. The study advances an understanding of market sentiment and emphasizes the importance of employing advanced computational techniques for risk management and market stability.
Phytochemical Composition of the Alien Invasive Species Amorpha fruticosa at the Northern Edge of Its Secondary Distribution Range—In the Arboretum of the Main Botanical Garden of the Russian Academy of Sciences
Authors: Shelepova Olga V. , Galkina Maria A. , Vinogradova Yulia K.
Published: December 2024

The studied introduction population of the alien North American species Amorpha fruticosa L. was formed in the Main Botanical Garden of the Russian Academy of Sciences (Moscow) 80 years ago from seeds of different geographical origin. Currently, this population consists of individuals of the second and third generations, which arose as a result of the spontaneous pollination of parental plants. It is the northernmost site of A. fruticosa growth in the secondary distribution range; in southern regions, it usually behaves like an aggressive invasive species and invades natural plant communities. A. fruticosa is known to contain a number of bioactive compounds with valuable pharmacological properties, and the aim of this study was to evaluate the biochemical composition of leaves and fruits at the northern limit of the species’ growth, since stress conditions promote active accumulation of secondary metabolites. The data on the composition of secondary metabolites, on the mineral composition, phenolic compounds, and flavonoids in the leaves and fruits of A. fruticosa, and on the amount and composition of essential oil in the extract from fruits are presented. High levels of adonitol, which is used as a sweetener in products for diabetic patients, have been reported in the fruits of A. fruticosa. α-Copaene, α-pinene, δ-cadinene, α-muurolene, and β- and α-caryophyllene predominate in the essential oil of the fruit, providing its antimicrobial activity. The phytochemical analysis of A. fruticosa from the secondary distribution range confirms the potential of this species as a valuable resource for the pharmacopoeia industry.
Nature-Based Solutions Planning for Urban Microclimate Improvement and Health: An Integrated Ecological and Economic Approach
Authors: Semeraro Teodoro , Calisi Antonio , Hang Jian , Emmanuel Rohinton , Buccolieri Riccardo
Published: December 2024

Nature-based Solutions (NbSs) play a pivotal role in mitigating the impact of microclimates on human well-being. The effectiveness of NbSs is contingent upon the synergy between natural capital, defined by the ecological structure and functions of the ecosystem, and human-derived capital, encompassing the economic investments required for implementation. This study introduces a decision-making framework designed to evaluate the impact of NbSs and advocate for optimal solutions for human health at the local scale, amalgamating ecological and economic assessments. Physiological Equivalent Temperature (PET) was chosen as a key urban parameter to assess the efficacy of NbSs in mitigating urban microclimates and enhancing human health. The PET analysis was conducted using ENVI-met 5.0.3 software across diverse urban scenarios in Gallipoli city, Italy. Integrated with a cost–benefit analysis of NbSs considering various investment scenarios, the study aimed to identify the most effective solution. Results indicated positive effects of NbSs in open spaces and around building blocks where the PET levels remained below 30 °C. Conversely, scenarios without NbSs exhibited PETs exceeding 40 °C, with peaks of 50 °C, posing potential risks to human health. Considering the social and economic benefits associated with PET mitigation, the cost–benefit analysis suggests that implementing NbSs using a mix of young and mature plants in the initial phase is advantageous compared to using only young plants. Thus, in establishing NbSs, it is crucial to consider not only the quantity of vegetation but also the strategic timing of implementation. In conclusion, our work offers an innovative framework that combines ecological and economic perspectives, providing valuable insights for decision-makers in urban planning and promoting the practical application of NbSs for enhanced human well-being.
What Drives Herbal Traditions? The Influence of Ecology and Cultural Exchanges on Wild Plant Teas in the Balkan Mountains
Authors: Alrhmoun Mousaab , Sulaiman Naji , Pieroni Andrea
Published: December 2024

This study investigates the ethnobotanical diversity of wild plant teas across various populations in the Balkan mountain regions, focusing on cultural and ecological influences. Data were collected through ethnobotanical field surveys conducted in thirteen distinct areas, encompassing approximately 900 interviews with local populations, including Albanians, Bosniaks, North Macedonians, Goranis, and Serbs. The study identifies 143 genera used in traditional teas, with species frequency and distribution patterns analysed concerning elevation and cultural factors. Statistical analyses using Principal Component Analysis (PCA) and Cluster Analysis revealed clear grouping patterns between populations based on ecological zones. The results show a clear correlation between altitude and the herbal use of plant genera, with higher elevations (above 700 m) supporting a greater variety of local species. Cultural practices, such as tea consumption during communal gatherings, also significantly shaped the selection of plants across different populations. For instance, Sideritis species were most prevalent among Albanian and North Macedonian communities, while Thymus and Mentha species were widely used across all groups. Moreover, cultural exchanges can explain the similarity of wild teas reported by Bosniaks (in Bosnia) and North Albanians, located far away from each other but showing similar traits due to possible common origins or the effect of centuries of pastoralism, which may have generated a constant exchange of local plant practices. The study concludes that ecological conditions and cultural exchanges significantly influence the choice, use, and resilience of wild plant herbal traditions in the Balkans. However, modern pressures such as urbanisation and market-driven consumption are increasingly threatening these practices. This highlights the need for conservation strategies that integrate ecological preservation and cultural heritage safeguarding.
Study on the Optimization of Carbon Sequestration in Shanghai’s Urban Artificial Wetlands: The Cases of Shanghai Fish and Dishui Lake
Authors: Wang Jin , Yu Jingren , Shen Manjuan , Che Shengquan
Published: December 2024

The study focused on optimizing carbon sequestration in urban artificial wetlands, using the Shanghai Fish and Dishui Lake as case studies. As cities like Shanghai experienced rapid urbanization, natural wetland areas diminished, making artificial wetlands essential for carbon storage and ecosystem preservation. The study investigated how various factors—such as plant species, wetland size, and landscape patterns—influenced carbon sequestration. Through field surveys and remote sensing, carbon density changes from 2018 to 2023 were analyzed using grid-based landscape pattern metrics. Results showed significant spatial variation in carbon sequestration, with larger, more fragmented wetland patches contributing more to carbon storage. Emergent plants, particularly Phragmites australis and Typha angustifolia, demonstrated the highest carbon sequestration potential. The research proposed three optimization models (point, linear, and planar) tailored for different wetland areas, focusing on expanding plant diversity, enhancing landscape complexity, and improving patch distribution. After optimization, carbon storage in the Shanghai Fish wetland was projected to increase by 2.6 times, while Dishui Lake’s carbon storage was expected to grow by 3.5 times. The study concluded that carefully planned wetland management, emphasizing plant species selection and spatial design, could significantly enhance carbon sequestration, contributing to Shanghai’s carbon neutrality goals. The research provided valuable insights for urban ecological planning, highlighting the role of artificial wetlands in climate regulation.
Polish Language Maintenance and Transmission in Finnish Diaspora: A Study of Family Dynamics and Cultural Influences
Authors: Jakubek-Głąb Izabela
Published: December 2024

The Polish diaspora is represented worldwide. Yet, little is known about Polish language and culture maintenance in Finland. However, the language practices of Polish families living there are worth being investigated, particularly in relation to language maintenance and transmission. This research aims to explore how families engage with the Polish language and what factors contribute to its preservation. The study focuses on the role of family dynamics, including interactions, communication patterns, and language choices within the family unit. The research is built on a survey of representatives of the Polish diaspora in Finland and a qualitative case study of a Polish family living in Finland. It uses content and descriptive analyses. The results identify effective strategies and interventions that can enhance the long-term vitality of the Polish language in diaspora communities. Cultural influences, such as traditions, values, and identities, are considered in relation to the sustainability of language development.
Correction: Slimani et al. Effects of Acute Long-Versus Short-Interval High-Intensity Interval Training on Attention and Psychological States in a Sample of Male and Female Adolescents: A Pilot Study. Life 2023, 13, 1846
Authors: Slimani Maamer , Issaoui Mahdi , Znazen Hela , Hammami Amri , Bragazzi Nicola Luigi
Published: December 2024

Addition of an Author [...]
Biofabrication of Silver Nanoparticles Using Pergularia tomentosa Extract and Evaluation of Their Antibacterial, Antioxidant, and Cytotoxic Properties
Authors: Aldayel Munirah F.
Published: December 2024

The biosynthesis of silver nanoparticles using plant extracts is a promising field of research because of the useful biomedical applications of metal nanoparticles. In this study, the antibacterial and antioxidant properties of silver nanoparticles biosynthesized with the aqueous leaf extract of Pergularia tomentosa were defined using a simple, eco-friendly, consistent, and cost-effective method. The leaf extract of Pergularia tomentosa (PT) served as a capping and reducing agent to biosynthesize silver nanoparticles. The effects of several parameters, such as the concentration of AgNO3, ratio of AgNO3 to extract, pH, and incubation time, were examined to optimize the synthesis process. In total, 5 mM of AgNO3, a 1:0.06 ratio of AgNO3 to Pergularia tomentosa extract, pH 9.0, and reaction mixture incubation for 24 h were found to be the ideal parameters for biosynthesizing silver nanoparticles (AgNPs). UV–visible spectroscopy, X-ray diffraction (XRD), Fourier-Transform Infrared Spectroscopy (FTIR), and scanning electron microscopy were used to characterize the biosynthesized Pergularia tomentosa silver nanoparticles (PT-AgNPs). Gram-positive bacteria (Staphylococcus aureus and Bacillus cereus) and Gram-negative bacteria (Salmonella enteritides and Escherichia coli) were used to test the PT-AgNPs’ antibacterial activity. The presence of different functional groups was determined using FTIR. The AgNPs were hexagon shaped. The nanoparticles were more toxic against S. enteritides than both B. cereus and E. coli. In antioxidant analyses, the AgNPs were found to be as strong at free radical scavenging as gallic acid (standard), with IC50 values of 0.69 and 22.30 μg/mL for DPPH and ABTS radicals, respectively. Interestingly, the PT-AgNPs displayed increased anti-inflammatory activity compared with the P. tomentosa leaf extract (79% vs. 59% at 500 µg/mL). The PT-AgNPs did not display any cytotoxicity against the MCF-7 cell line at the MIC. In conclusion, silver nanoparticles fortified with Pergularia tomentosa extract exhibited potential as effective antibacterial, anti-inflammatory, and antioxidant agents, suggesting their viability as alternatives to commercially available products.
Utilization of Magnetic Fraction Isolated from Steel Furnace Slag as a Mild Abrasive in Formulation of Cu-Free Friction Composites
Authors: Matějka Vlastimil , Jayashree Priyadarshini , Foniok Kryštof , Vlček Jozef , Matějková Petra , Straffelini Giovanni
Published: December 2024

Magnetic fraction isolated from steel furnace slag was tested as a component of Cu-free friction composites. The friction–wear performance and production of wear particles during their testing using a pin-on-disc tester against a cast iron disc were evaluated. To compare the effect of the magnetic fraction on the parameters studied, the composite with alumina and the composite with original steel furnace slag were also prepared and tested. All composites showed a comparable friction coefficient. The composite with original steel furnace slag, and the composite with a magnetic fraction showed higher wear resistance compared to the composite containing alumina. The positive effect of the magnetic fraction on the extent of the emission of wear particles was observed and explained by the decreased aggressiveness of this composite to the cast iron disc. The influence of the phase composition of the steel furnace slag and the magnetic fraction on the friction film formation was also indicated, and its effect on the production of wear particles was proposed.
Research on the Optimal Control of Working Oil Pressure of DCT Clutch Based on Linear Quadratics Form
Authors: Shi Guifa , Zhang Houzhong , Yang Xiangtian , Xu Xing , Sun Xiaoqiang
Published: December 2024

The control of the vehicle transmission system is of great significance to driving comfort. In order to design a controller for smooth shifting and comfortable driving, a dynamic model of dual-clutch transmission is established in this paper. An optimal control strategy for clutch oil pressure based on linear quadratics is proposed, which is used to optimally control the oil pressure of two clutches in the torque stage and inertia stage. The control strategy selects the slipping work and jerk as evaluation indices of shift quality and establishes an optimization objective function. On the premise of optimizing the input torque, the relative speed difference between the engine and the sliding clutch in the inertia stage is adjusted. Through the optimal trajectory control of the wet clutch oil pressure, slipping work and jerk are reduced, thereby improving driving comfort. The simulation results show that the slipping work and jerk generated by the system during the shift stage are reduced, and the shift quality is improved. Additionally, compared with the controller using the MATLAB particle swarm optimization algorithm, the response speed of the proposed controller is faster, the slipping work and jerk are better reduced, and the shift quality is improved.
Data Augmentation and Deep Learning Methods for Pressure Prediction in Ignition Process of Solid Rocket Motors
Authors: Yang Huixin , Yu Pengcheng , Cui Yan , Lou Bixuan , Li Xiang
Published: December 2024

During the ignition process of a solid rocket motor, the pressure changes dramatically and the ignition process is very complex as it includes multiple reactions. Successful completion of the ignition process is essential for the proper operation of solid rocket motors. However, the measurement of pressure becomes extremely challenging due to several issues such as the enormity and high cost of conducting tests on solid rocket motors. Therefore, it needs to be investigated using numerical calculations and other methods. Currently, the fundamental theories concerning the ignition process have not been fully developed. In addition, numerical simulations require significant simplifications. To address these issues, this study proposes a solid rocket motor pressure prediction method based on bidirectional long short-term memory (CBiLSTM) combined with adaptive Gaussian noise (AGN). The method utilizes experimental pressure data and simulated pressure data as inputs for co-training to predict pressure data under new operating conditions. By comparison, the AGN-CBiLSTM method has a higher prediction accuracy with a percentage error of 3.27% between the predicted and actual data. This method provides an effective way to evaluate the performance of solid rocket motors and has a wide range of applications in the aerospace field.
Optimization of Machining Parameters for Reducing Drum Shape Error Phenomenon in Wire Electrical Discharge Machining Processes
Authors: Wang Shih-Ming , Hsu Li-Jen , Gunawan Hariyanto , Tu Ren-Qi
Published: December 2024

Machining thicker workpieces in the process of Wire Electrical Discharge Machining (WEDM) can result in a concave phenomenon known as a “drum shape error” due to the vibration of wires and accumulation of debris, which leads to secondary discharge in the middle of the workpiece. Reducing the drum shape error typically requires a longer finishing process. Finding a balance between precision and machining time efficiency has become a challenge for modern machining shops. This study employed experimental analysis to investigate the effect of individual parameters on the shape error and machining removal rate (MRR). Key influential parameters, including open voltage (OV), pulse ON time (ON), pulse OFF time (OFF), and servo voltage (SV), were chosen for data collection using full factorial and Taguchi orthogonal arrays. Regression analysis was conducted to establish multiple regression equations. These equations were used to develop optimization rules, and subsequently, a user-friendly human–machine interface was developed using C# based on these optimization rules to create a shape error and MRR optimization system. The system can predict the optimal parameter combinations to minimize the shape error and increase the MRR. The results of the verification experiments showed that the prediction accuracy can reach 94.7% for shape error and 99.2% for MRR. Additionally, the shape error can be minimized by up to 40%.
Optimizing Districting and Seat Allocation for Enhanced Representativeness in Chile’s Chamber of Deputies
Authors: Rebolledo Rodrigo , Ulloa Ana , Cornejo Óscar , Obreque Carlos , Baesler Felipe
Published: December 2024

This paper presents a mathematical programming model to simultaneously create districts and allocate seats in Chile’s Chamber of Deputies, improving representativeness. In addition, it explicitly incorporates constraints that ensure the contiguity of the communes that form the districts while respecting natural and administrative boundaries. Implementing specific strategies and methods has resulted in significant enhancements in particular metrics used to assess the degree of representativeness. These improvements have effectively addressed certain shortcomings and resulted in more accurate and reliable representation measurements in the given context. This study proposes a novel mathematical programming model that simultaneously tackles district creation and seat allocation for Chile’s Chamber of Deputies. This integrated approach aims to achieve a more representative body. The results demonstrate a substantial decrease in malapportionment, from 11.07 in the 2015 reform to 6.55 under the proposed model. Furthermore, the sum of deviations has diminished, and the number of overrepresented districts has decreased from 17 to 13 out of 28 districts. Consequently, the malapportionment has been significantly reduced and falls within the permissible range of deviations, as outlined by the European Commission for Democracy through Law.
Analytical Valuation of Vulnerable Exchange Options with Stochastic Volatility in a Reduced-Form Model
Authors: Jeon Junkee , Kim Geonwoo
Published: December 2024

This paper investigates the valuation of vulnerable exchange options with two underlying assets that follow a two-factor volatility model. We employ a reduced-form model incorporating a Poisson process with stochastic intensity. The proposed reduced-form model depends on a stochastic intensity process that is guaranteed to remain positive and includes both systemic and idiosyncratic risks. Using measure change techniques and characteristic functions, we obtain an explicit pricing formula for vulnerable exchange options within the proposed framework. We also provide numerical examples demonstrating the sensitivity of option prices to significant parameters.
Signatures and Mechanism Analysis of Converter-Grid Subsynchronous Oscillations
Authors: Askarov Alisher , Bay Yuly , Ufa Ruslan , Radko Pavel , Ilyushin Pavel , Suvorov Aleksey
Published: December 2024

In the last decade, a rather new phenomenon related to subsynchronous oscillations (SSO) in a wide frequency range has emerged in modern power grids with power converters. The consequences of these oscillations can be severe system accidents, which have already occurred in various power systems. Taking into account the importance of studying such oscillations for effective mitigation and the existing gaps in understanding the mechanisms of SSO occurrence due to different combinations of used controllers in the inverter control system, as well as the grid parameters, a frequency analysis of simplified grid inverter models is performed. As a result, five different mechanisms of SSO occurrence, the affecting factors and the level of inverter model detail required for the adequate study of SSO are justified. The developed detailed state–space model allowed for verifying the obtained results, as well as identifying an additional sixth mechanism of SSO occurrence. The common condition for the occurrence of all the identified SSO mechanisms is a weak grid with a short-circuit ratio of less than two. The results of control hardware-in-the-loop testing confirmed the conclusions drawn. As a result, a classification of SSO occurrence mechanisms was formed, reflecting their causes and distinctive features.
Design of a Convolutional Neural Network with Type-2 Fuzzy-Based Pooling for Vehicle Recognition
Authors: Lin Cheng-Jian , Chen Bing-Hong , Lin Chun-Hui , Jhang Jyun-Yu
Published: December 2024

Convolutional neural networks typically employ convolutional layers for feature extraction and pooling layers for dimensionality reduction. However, conventional pooling methods often lead to a loss of critical feature information, particularly in images with diverse content, such as vehicle images. This study proposes a novel approach to address these problems: a convolutional neural network with type-2 fuzzy-based pooling (CNN-T2FP). This innovative pooling method utilizes type-2 fuzzy membership functions to effectively manage local imprecision in feature maps. Compared with type-1 fuzzy pooling, which only addresses uncertainty to a certain extent, type-2 fuzzy pooling exhibits improved adaptability to different image contents. The experimental results of this study revealed that the CNN-T2FP achieved average accuracies of 92.14% and 93.34% on two datasets, surpassing the performance of existing pooling techniques. In addition, t-distributed stochastic neighbor embedding plots and feature visualization maps further highlighted the potential of type-2 fuzzy-based pooling to overcome the limitations of conventional pooling methods and enhance the performance of convolutional neural networks in image analysis tasks.
Real-World Steam Powerplant Boiler Tube Leakage Detection Using Hybrid Deep Learning
Authors: Khalid Salman , Azad Muhammad Muzammil , Kim Heung Soo
Published: December 2024

The detection of boiler water-wall tube leakage in steam power plants is essential to prevent efficiency loss, unexpected shutdowns, and costly repairs. This study proposes a hybrid deep learning approach that combines convolutional neural networks (CNNs) with a support vector machine (SVM) classifier to allow early and accurate leak detection. The methodology utilizes temperature data from multiple sensors positioned at critical points in the boiler system. The data of each sensor are independently processed by a dedicated CNN model, allowing for the autonomous extraction of sensor-specific features. These features are then fused to create a comprehensive feature representation of the system’s condition, which is analyzed by an SVM classifier to accurately identify leakages. By utilizing the feature extraction capabilities of CNNs and the classification strength of an SVM, this approach effectively identifies subtle operational anomalies that are indicative of potential leaks. The model demonstrates high detection accuracy and minimizes false-positives, providing a robust solution for real-time monitoring and proactive maintenance strategies in industrial systems.
The Nonlinear Dynamic Response and Vibration Transmission Characteristics of an Unloading System with Granular Materials
Authors: Zhou Shihua , Wang Yue , Ji Kaibo , Li Xuan , Chen Yu , Ren Zhaohui
Published: December 2024

The aim of this study is to research the flow property of granular materials under nonlinear vibration, which directly affects the stability of the unloading system and the motion state of granules. According to the mechanical constitutive relation, the coupled suspension–tire model with nonlinear ordinary differential equations is established and the kinematic equations of granules are derived. Furthermore, the amplitude–frequency responses of the coupled system and force transmissibility are obtained by the incremental harmonic balance method (IHBM) with high-order approximation, and then the flow characteristics of granular materials are investigated based on the approximate analytic solution under nonlinear vibration. The theoretical analysis and numerical simulation show that the coupled suspension–tire system presents a softening nonlinear feature and the peaks are significantly smaller than that of the linear system, which further affects the motion rules of granular materials. As a result, different sliding states and flow paths are observed under the same operating conditions. This research not only shows the unloading mechanism and vibration transmission characteristics between the continuum structure and granular material but also theoretically explains the control mechanism of the coupled continuum–granular system. The research is instructive in improving the unloading efficiency of granules in practical engineering.
A New Random Coefficient Autoregressive Model Driven by an Unobservable State Variable
Authors: Pang Yuxin , Wang Dehui
Published: December 2024

A novel random coefficient autoregressive model is proposed, and a feature of the model is the non-stationarity of the state equation. The autoregressive coefficient is an unknown function with an unobservable state variable, which can be estimated by the local linear regression method. The iterative algorithm is constructed to estimate the parameters based on the ordinary least squares method. The ordinary least squares residuals are used to estimate the variances of the errors. The Kalman-smoothed estimation method is used to estimate the unobservable state variable because of its ability to deal with non-stationary stochastic processes. These methods allow deriving the analytical solutions. The performance of the estimation methods is evaluated through numerical simulation. The model is validated using actual time series data from the S&P/HKEX Large Cap Index.
Research on Small-Sample Credit Card Fraud Identification Based on Temporal Attention-Boundary-Enhanced Prototype Network
Authors: Liu Boyu , Wu Longrui , Mu Shengdong
Published: December 2024

The Nielsen Report points out that credit card fraud caused business losses of USD 28.65 billion globally in 2019, with the US accounting for more than one-third of the high share, and that insufficient identification of credit card fraud has brought about a serious loss of financial institutions’ ability to do business. In small sample data environments, traditional fraud detection methods based on prototype network models struggle with the loss of time-series features and the challenge of identifying the uncorrected sample distribution in the metric space. In this paper, we propose a credit card fraud detection method called the Time-Series Attention-Boundary-Enhanced Prototype Network (TABEP), which strengthens the temporal feature dependency between channels by incorporating a time-series attention module to achieve channel temporal fusion feature acquisition. Additionally, nearest-neighbor boundary loss is introduced after the computation of the prototype-like network model to adjust the overall distribution of features in the metric space and to clarify the representation boundaries of the prototype-like model. Experimental results show that the TABEP model achieves higher accuracy in credit card fraud detection compared to five existing baseline prototype network methods, better fits the overall data distribution, and significantly improves fraud detection performance. This study highlights the effectiveness of open innovation methods in addressing complex financial security problems, which is of great significance for promoting technological advancement in the field of credit card security.