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Recent Articles  

19 pages, 1025 KiB  
Article
Business Implications and Theoretical Integration of the Markets in Crypto-Assets (MiCA) Regulation
by Gayane Mkrtchyan and Horst Treiblmaier
FinTech 2025, 4(2), 11; https://doi.org/10.3390/fintech4020011 - 25 Mar 2025
Abstract
The Markets in Crypto-Assets Regulation (MiCA) is a comprehensive European Union regulatory framework aimed at harmonizing the crypto-asset market. The existing literature has mainly examined MiCA from a legal perspective, while empirical assessments of industry perspectives remain scarce. In this study, we examine [...] Read more.
The Markets in Crypto-Assets Regulation (MiCA) is a comprehensive European Union regulatory framework aimed at harmonizing the crypto-asset market. The existing literature has mainly examined MiCA from a legal perspective, while empirical assessments of industry perspectives remain scarce. In this study, we examine MiCA’s impact on the crypto market and its implications for both theory and practice by analyzing and integrating insights from 12 expert interviews. The findings reveal perceived benefits arising from the unified market, enhanced investor protection, and compliance clarity, alongside challenges related to the high regulatory burden, legal ambiguities, and limited innovation support. On this basis, we provide recommendations for improving the regulatory framework and its implementation. Furthermore, we integrate our findings within the technology–organization–environment (TOE) framework to provide a theory-based starting point for rigorous academic research. These findings contribute to regulatory discourse and offer practical guidance for the relevant stakeholders, including businesses, regulators, policymakers, and academics. Full article
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17 pages, 2822 KiB  
Article
Light-Emitting Diode Illumination Enhances Biomass, Pigment, and Lipid Production in Halotolerant Cyanobacterium Aphanothece halophytica
by Sitthichai Thongtha, Chokchai Kittiwongwattana, Aran Incharoensakdi and Saranya Phunpruch
Phycology 2025, 5(2), 12; https://doi.org/10.3390/phycology5020012 - 25 Mar 2025
Abstract
Light characteristics, including spectrum and intensity, significantly impact cyanobacterial biomass production, pigment biosynthesis, and cellular metabolism, influencing the composition of various biochemical compounds. This study aimed to investigate the effects of light-emitting diode (LED) illumination on biomass, pigment, and lipid production in the [...] Read more.
Light characteristics, including spectrum and intensity, significantly impact cyanobacterial biomass production, pigment biosynthesis, and cellular metabolism, influencing the composition of various biochemical compounds. This study aimed to investigate the effects of light-emitting diode (LED) illumination on biomass, pigment, and lipid production in the unicellular halotolerant cyanobacterium Aphanothece halophytica, cultivated in a suitable natural seawater (SNSW) medium. The results revealed that LED light outperformed fluorescent light, with blue LED light, particularly at an intensity of 60 μmol photons m−2 s−1, significantly enhancing growth, pigment synthesis, and lipid accumulation. This resulted in a maximum cell density of 68.96 ± 1.52 × 106 cells mL−1, a specific growth rate of 0.302 ± 0.002 day−1, and a lipid productivity of 56.81 ± 0.75 mg L−1 day−1. White LED light produced lipids suitable for biodiesel, whereas blue, green, and red LEDs promoted the accumulation of polyunsaturated fatty acids (PUFAs), beneficial for food supplements. These findings highlight the potential of LED-based cultivation strategies for optimizing biomass and biochemical compound production in A. halophytica. Full article
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26 pages, 10132 KiB  
Article
The Role of Oxytocin Neurons in the Paraventricular Nucleus in Chronic-Sleep-Deprivation-Mediated Abnormal Cardiovascular Responses
by Yifei Zhang, Yuxin Wang, Zhendong Xu, Xiangjie Kong, Hairong Wang, Zhibing Lu, Ming Chen and Linlin Bi
Curr. Issues Mol. Biol. 2025, 47(4), 220; https://doi.org/10.3390/cimb47040220 - 25 Mar 2025
Abstract
Sleep disorders increase the risk of cardiovascular diseases. However, the underlying mechanisms remain unclear. This study aims to examine the critical role of oxytocin neurons in the paraventricular nucleus (PVNOXT) in regulating the cardiovascular system and to elucidate potential mechanisms through [...] Read more.
Sleep disorders increase the risk of cardiovascular diseases. However, the underlying mechanisms remain unclear. This study aims to examine the critical role of oxytocin neurons in the paraventricular nucleus (PVNOXT) in regulating the cardiovascular system and to elucidate potential mechanisms through which sleep disturbance may contribute to cardiovascular diseases. In this study, using an automated sleep deprivation system, mice were given chronic sleep deprivation (cSD) for 7 days, 6 h per day. cSD induced blood transcriptomic alterations accompanied by lower heart rate, higher blood pressure, and elevated cardiac autophagy/apoptosis. Instant optogenetic activation of oxytocin neurons in the paraventricular nucleus (PVNOXT) provoked heart rate suppression in normal mice, whereas in cSD mice, activation precipitated intermittent cardiac arrest. On the contrary, inhibition of PVNOXT showed no influence on the cardiovascular system of normal mice, but it attenuated cSD-induced rise in blood pressure. Long-term low-frequency stimulation (LTF) of PVNOXT decreased neuronal excitability and oxytocin release, effectively reversing cSD-mediated cardiovascular responses. Mechanistically, cSD triggered the upregulation of blood-derived 3-mercaptopyruvate sulfurtransferase (mPST), and a suppression of PVNOXT postsynaptic activity to a certain extent. The quick and long-term decrease of oxytocin by LTF could lead to feedback inhibition in mPST expression and thus reverse cSD-mediated cardiovascular responses. Altogether, modulation of PVNOXT could mediate cSD-induced cardiovascular abnormalities without affecting normal mice. Our research provided potential targets and key mechanisms for cardiovascular diseases associated with sleep disorders. Full article
(This article belongs to the Special Issue Cardiovascular Disease: From Molecular Mechanisms to Therapeutic Innovations)
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18 pages, 1040 KiB  
Article
Quantitative and Qualitative Characterization of Food Waste for Circular Economy Strategies in the Restaurant Sector of Riobamba, Ecuador: A Case Study Approach
by Angélica Saeteros-Hernández, Francisco Chalen-Moreano, Ronald Zurita-Gallegos, Pedro Badillo-Arévalo, Mayra Granizo-Villacres, Carlos Cevallos-Hermida and Diego Viteri-Nuñez
Biomass 2025, 5(2), 18; https://doi.org/10.3390/biomass5020018 - 25 Mar 2025
Abstract
The aim of this study is the quantitative and qualitative characterization of food waste from the restaurant sector in Riobamba, Ecuador as part of circular economy efforts. A weekly analysis of waste generation data collected from 13 participating restaurants showed that the average [...] Read more.
The aim of this study is the quantitative and qualitative characterization of food waste from the restaurant sector in Riobamba, Ecuador as part of circular economy efforts. A weekly analysis of waste generation data collected from 13 participating restaurants showed that the average daily food waste generated was 18.48 kg/restaurant/day. The highest percentage (55%) was produced by organic waste, which was primarily composed of waste from vegetables. Plastics represented most of the recyclable waste (21%), and 24% of the waste was disposable. With a low dry matter content of 24.33 ± 5.12% and an average moisture level of 75.68 ± 5.12%, the high organic content indicates its potential for value-adding through biological recycling processes like anaerobic digestion and composting. Fruit and vegetable waste had high moisture levels (80.3 ± 2.54% and 81.2 ± 2.75%, respectively), which made them perfect for composting and biogas production. However, the moisture and dry matter contents differed greatly amongst the waste categories. The increased dry matter concentration of animal protein waste (54.5 ± 4.30%) indicated that it may be converted into products with added value, such as animal meal and oils. Plant protein waste needs to be processed quickly to avoid spoiling because of its extraordinarily high moisture content (95.7 ± 3.20%) and low dry matter (4.3 ± 3.20%). The findings underscore the necessity for focused measures, such as composting, anaerobic digestion, and enhanced recycling, to optimize resource recovery and mitigate environmental consequences. Full article
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19 pages, 2239 KiB  
Systematic Review
Resistance Mutation Profiles Associated with Current Treatments for Epidermal Growth Factor Receptor-Mutated Non-Small-Cell Lung Cancer in the United States: A Systematic Literature Review
by Pratyusha Vadagam, Dexter Waters, Anil Bhagat, Yuting Kuang, Jennifer Uyei and Julie Vanderpoel
Curr. Oncol. 2025, 32(4), 191; https://doi.org/10.3390/curroncol32040191 - 25 Mar 2025
Abstract
Treatment resistance due to gene alterations remains a challenge for patients with EGFR-mutated advanced or metastatic non-small-cell lung cancer (a/mNSCLC). A systematic literature review (SLR) was conducted to describe resistance mutation profiles and their impact on clinical outcomes in adults with a/mNSCLC in [...] Read more.
Treatment resistance due to gene alterations remains a challenge for patients with EGFR-mutated advanced or metastatic non-small-cell lung cancer (a/mNSCLC). A systematic literature review (SLR) was conducted to describe resistance mutation profiles and their impact on clinical outcomes in adults with a/mNSCLC in the United States (US). A comprehensive search of MEDLINE and Embase (2018–August 2022) identified 2986 records. Among 45 included studies, osimertinib was the most commonly reported treatment (osimertinib alone: 15 studies; as one of the treatment options: 18 studies), followed by other tyrosine kinase inhibitors (TKIs; 5 studies) and non-TKIs (1 study). For first-line (1L) and second-line (2L) osimertinib, the most frequent EGFR-dependent resistance mechanisms were T790M loss (1L: 15.4%; 2L: 20.5–49%) and C797X mutation (1L: 2.9–12.5%; 2L: 1.4–22%). EGFR-independent mechanisms included MET amplification (1L: 0.6–66%; 2L: 7.2–19%), TP53 mutation (1L: 29.2–33.3%), and CCNE1 amplification (1L: 7.9%; 2L: 10.3%). For patients receiving osimertinib, EGFR T790M mutation loss, EGFR/MET/HER2 amplification, RET fusion, and PIK3CA mutation were associated with worse progression-free survival. Resistance mechanisms resulting from current NSCLC treatments in the US are complex, underscoring the need to address such heterogeneous resistance profiles and improve outcomes for patients with EGFR-mutated a/mNSCLC. Full article
(This article belongs to the Section Thoracic Oncology)
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4 pages, 174 KiB  
Commentary
Continuous Measurement in Neurocritical Care of Cerebral Blood Flow (CBF) Calculated from ICP and Central Venous Pressure
by Erik Ryding
Neurol. Int. 2025, 17(4), 49; https://doi.org/10.3390/neurolint17040049 - 25 Mar 2025
Abstract
Background/Objectives: In neurocritical care, usually, the only continuous measurement of brain pathophysiology is intracranial pressure (ICP). The objective of this study was to find the relationship between cerebral blood flow (CBF) and parameters usually measured in neurocritical care, mainly central venous pressure and [...] Read more.
Background/Objectives: In neurocritical care, usually, the only continuous measurement of brain pathophysiology is intracranial pressure (ICP). The objective of this study was to find the relationship between cerebral blood flow (CBF) and parameters usually measured in neurocritical care, mainly central venous pressure and ICP. Methods: If the venous outflow of the CBF is considered, the CBF is controlled only by two parameters, the rICP (the ICP minus the venous blood pressure in the venous sinus at its outflow) and the Rv (the flow resistance of the soft-walled veins). For the rICP, the sinus blood pressure can be calculated from the central venous pressure (measured at the same horizontal level as the ICP) and the cervical venous flow resistance. For the Rv, the systolic ICP increase indicates the systolic arterial inflow volume, which then flows out before the diastole. The mean ICP increase divided by the mean outflow of the increased blood volume gives the Rv. This method of calculating the CBF by dividing the rICP by the Rv was named CBF(1). For validation of CBF(1), data from nine subjects in an open study were used. The data were ICP and MR blood flow measurements of arterial inflow and jugular vein outflow. Since the rICP, Rv, and CBF were unknown, an iterative method was needed to calculate these parameters. Results: The observed Rv and rICP values showed a close correlation, which indicated that CBF was dependant on the rICP only. Consequently, the comparison between the data in the study of the nine subjects, and the calculated values from CBF(1), boiled down to a comparison between the supine ICP values and the calculated rICP. The comparison showed that the rICP and supine ICP had highly significant similarity, and that the CBF(1) method was validated. Conclusions: A method for CBF measurement from ICP data in neurocritical care was found. Full article
8 pages, 176 KiB  
Article
Comparative Evaluation of Artificial Intelligence Models for Contraceptive Counseling
by Anisha V. Patel, Sona Jasani, Abdelrahman AlAshqar, Rushabh H. Doshi, Kanhai Amin, Aisvarya Panakam, Ankita Patil and Sangini S. Sheth
Digital 2025, 5(2), 10; https://doi.org/10.3390/digital5020010 - 25 Mar 2025
Abstract
Background: As digital health resources become increasingly prevalent, assessing the quality of information provided by publicly available AI tools is vital for evidence-based patient education. Objective: This study evaluates the accuracy and readability of responses from four large language models—ChatGPT 4.0, ChatGPT 3.5, [...] Read more.
Background: As digital health resources become increasingly prevalent, assessing the quality of information provided by publicly available AI tools is vital for evidence-based patient education. Objective: This study evaluates the accuracy and readability of responses from four large language models—ChatGPT 4.0, ChatGPT 3.5, Google Bard, and Microsoft Bing—in providing contraceptive counseling. Methods: A cross-sectional analysis was conducted using standardized contraception questions, established readability indices, and a panel of blinded OB/GYN physician reviewers comparing model responses to an AAFP benchmark. Results: The models varied in readability and evidence adherence; notably, ChatGPT 3.5 provided more evidence-based responses than GPT-4.0, although all outputs exceeded the recommended 6th-grade reading level. Conclusion: Our findings underscore the need for the further refinement of LLMs to balance clinical accuracy with patient-friendly language, supporting their role as a supplement to clinician counseling. Full article
20 pages, 1947 KiB  
Article
Influence of Nesting Habitat and Nest Emplacement on the Breeding Success of the Black Francolin (Francolinus francolinus, Phasianidae): A Case Study from Pakistan
by Asad Ullah, Sumaira Shams, Sultan Ayaz, Eliana Ibáñez-Arancibia, Unays Siraj, Patricio R. De los Rios-Escalante and Farhad Badshah
Birds 2025, 6(2), 16; https://doi.org/10.3390/birds6020016 - 25 Mar 2025
Abstract
Limited research exists on the breeding ecology of the black francolin (Francolinus francolinus) in northern Pakistan. This study assessed egg dimensions, clutch size, hatching, fledging, and overall breeding success across different habitats and nests (n = 25) at Totali Game [...] Read more.
Limited research exists on the breeding ecology of the black francolin (Francolinus francolinus) in northern Pakistan. This study assessed egg dimensions, clutch size, hatching, fledging, and overall breeding success across different habitats and nests (n = 25) at Totali Game Reserve, Buner. Generalized linear models (GLMs) were used to analyze the effects of nest site characteristics and nest traits on breeding parameters. Egg dimensions were consistent across sites whereas bush nests had slightly wider eggs. The average clutch size was 5.9 ± 1.7 eggs, with an average of 4.8 ± 1.0 hatchlings per nest. A total of 111 chicks fledged, averaging 4.4 ± 1.0 per nest, yielding an overall breeding success rate of 75.5%. Nests containing six eggs had higher hatching success (76.6%). GLMs results showed a significant positive relationship between clutch size and hatchling, while nest site and traits had no significant effects. However, fledgling success was positively influenced by hatchling numbers, with nests in wetland habitats yielding significantly more fledglings (4.6 ± 0.9) than those from dryland habitats (4.0 ± 1.2). These findings suggest Black Francolins prefer nesting in wetland areas in bushes, likely due to better protection and favorable conditions. Full article
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24 pages, 8640 KiB  
Article
Laboratory Modeling of the Bazhenov Formation Organic Matter Transformation in a Semi-Open System: A Comparison of Oil Generation Kinetics in Two Samples with Type II Kerogen
by Anton G. Kalmykov, Valentina V. Levkina, Margarita S. Tikhonova, Grigorii G. Savostin, Mariia L. Makhnutina, Olesya N. Vidishcheva, Dmitrii S. Volkov, Andrey V. Pirogov, Mikhail A. Proskurnin and Georgii A. Kalmykov
Fuels 2025, 6(2), 22; https://doi.org/10.3390/fuels6020022 - 25 Mar 2025
Abstract
In this study, Kerogen conversion and oil production laboratory modeling results in Bazhenov formation source rock samples (Western Siberia, Russia) are presented. Two samples from one well with a similar composition and immature type II kerogen, which were accumulated in the same deep-sea [...] Read more.
In this study, Kerogen conversion and oil production laboratory modeling results in Bazhenov formation source rock samples (Western Siberia, Russia) are presented. Two samples from one well with a similar composition and immature type II kerogen, which were accumulated in the same deep-sea conditions, were used for this investigation. Hydrous pyrolysis was performed under 300 °C, with liquid products and a sample portion collected every 12 h to study kerogen parameters via pyrolysis and the synthetic-oil composition via GC–MS. The transformation of pyrolytic parameters was similar to the natural trend previously determined for Bazhenov source rocks with different maturities. The synthetic oils’ normal alkane composition and biomarker parameters transformed with time. Sedimentary conditions and lithology biomarker parameters presumed to be constant (Pr/Ph, Ph/C18, H29/H30, and DBT/Phen) changed depending on the heating duration. The oil maturation increased slightly. Differences between the samples were detected in hydrocarbon generation endurance (5 and 8 days), n-alkane composition, and C27/C29 and DBT/Phen. A hypothesis about the influence of kerogen variability and mineral matrix on oil production was made. This paper provides the basis for more detailed and accurate investigation of the factors affecting kerogen cracking and hydrocarbon formation. Full article
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23 pages, 855 KiB  
Article
Understanding the Determinants of Adoption and Intention to Recommend AI Technology in Travel and Transportation
by Gonçalo Baptista and Antonio Pereira
Tour. Hosp. 2025, 6(2), 54; https://doi.org/10.3390/tourhosp6020054 - 25 Mar 2025
Abstract
The travel and transportation sectors continuously fight to stay up to date with new advancements in technology. Disruptive technologies, such as Artificial Intelligence (AI), are being used to develop businesses, enhance economic growth, revolutionize existing industries, create new opportunities, and increase productivity and [...] Read more.
The travel and transportation sectors continuously fight to stay up to date with new advancements in technology. Disruptive technologies, such as Artificial Intelligence (AI), are being used to develop businesses, enhance economic growth, revolutionize existing industries, create new opportunities, and increase productivity and efficiency. Notwithstanding the several advantages that this technology may bring, there is still little research on AI use in the travel and transportation sectors. This research contributes to this still understudied field to fill a gap in the literature by putting out a novel, thorough, and as far as we know not yet tested until now theoretical model, designed with the combination of the outcome of a literature meta-analysis study with Travel Experience and the Intention to Recommend technology constructs. A quantitative investigation using an online questionnaire was administered through social media and reached a total of 100 European participants. Structural equation modelling (SEM) was employed to test the suggested model empirically. The findings highlight that the user’s attitude towards AI is strongly influenced by Performance Expectancy and that the Intention to Use this technology is significantly influenced by Initial Trust and Attitude. Theoretical and practical contributions, limitations, and future areas of research are discussed. Full article
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24 pages, 857 KiB  
Article
IoT-Based Framework for Connected Municipal Public Services in a Strategic Digital City Context
by Danieli Aparecida From, Denis Alcides Rezende and Donald Francisco Quintana Sequeira
IoT 2025, 6(2), 20; https://doi.org/10.3390/iot6020020 - 25 Mar 2025
Abstract
The use of digital technology resources in public services enhances efficiency, responsiveness, and citizens’ quality of life through improved resource management, real-time monitoring, and service performance. The objective is to create and apply an IoT-based framework for connected municipal public services in a [...] Read more.
The use of digital technology resources in public services enhances efficiency, responsiveness, and citizens’ quality of life through improved resource management, real-time monitoring, and service performance. The objective is to create and apply an IoT-based framework for connected municipal public services in a strategic digital city context. The research employed a modeling process validated in a Brazilian city, identifying seven related frameworks and four themes through a bibliometric review. The original framework comprises three constructs, eight subconstructs, and 12 variables, validated through a case study inquiry. The results revealed that the researched city has yet to enlarge IoT into its municipal public services as part of a digital city project initiative. Key recommendations for IoT implementation include prioritizing the preferences of digital citizens, expanding critical services suited for IoT, and updating municipal strategies to incorporate IT resources to streamline decision-making. The conclusion reiterates that the IoT framework for municipal services is effective when actionable information supports strategic planning and decision-making and highlights the transformative potential of IoT in driving more resilient and sustainable cities aligned with citizens’ needs. This approach allows public managers to enhance citizens’ quality of life while improving the efficiency and responsiveness of urban management processes and services. Full article
(This article belongs to the Special Issue IoT-Driven Smart Cities)
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17 pages, 4960 KiB  
Article
Protective Effect of Probiotics on Cardiac Damage in Experimental Sepsis Model Induced by Lipopolysaccharide in Rats
by Necip Gökhan Taş, Osman Aktaş, Hakan Gökalp Taş, Selim Zırh, Nezahat Kurt and Hakan Uslu
Medicina 2025, 61(4), 589; https://doi.org/10.3390/medicina61040589 - 25 Mar 2025
Abstract
Background and Objective: Probiotics have been shown to be effective in controlling various adverse health conditions such as antibiotic-associated diarrhea, inflammatory bowel disease, obesity, and neurological diseases. However, to our knowledge, there is no research on the preventive effect of probiotics on [...] Read more.
Background and Objective: Probiotics have been shown to be effective in controlling various adverse health conditions such as antibiotic-associated diarrhea, inflammatory bowel disease, obesity, and neurological diseases. However, to our knowledge, there is no research on the preventive effect of probiotics on heart damage caused by infections. This study examined the preventive benefits of probiotics against sepsis-related heart injury using a rat model caused by lipopolysaccharide (LPS). Materials and Methods: Four groups of twenty-four male Wistar albino rats, each with six rats, were set up. For 14 days, Group 1 (Sham Group) was given oral normal saline, intraperitoneal Escherichia coli O111-B4 lipopolysaccharide (LPS Group) was given to Group 2, and oral probiotics were given to Group 3 (Probiotic Group). Escherichia coli O111-B4 lipopolysaccharide was injected intraperitoneally after Group 4 (Probiotic + LPS) received oral probiotics containing Lactobacillus rhamnosus GG and Bifidobacterium animalis subsp. lactis BB-12 (109 CFU/day). Blood samples were taken twenty-four hours following the administration of LPS. The animals were then euthanized by cervical dislocation, and samples of cardiac tissue were taken in order to assess any damage to the heart. The following serum values were measured: C-reactive protein (CRP), creatine kinase-myocardial band (CK-MB), cardiac troponin subunit I (cTn-I), tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and interleukin-6 (IL-6). The TNF-α, IL-1β, IL-6, glutathione (GSH), malondialdehyde (MDA), Total Oxidant Status (TOS), Total Antioxidant Status (TAS), Oxidative Stress Index (OSI), CRP, CK-MB, and cTn-I levels were assessed in tissue samples. Additionally, staining techniques were used to analyze histopathological alterations in tissues. Results: With the exception of serum IL-6 (p = 0.111), tissue and serum cytokine levels were considerably greater in the sepsis group (Group 2) than in the other groups (p < 0.05 to <0.001). The TAS, GSH, and SOD levels were significantly lower (p < 0.05 to <0.001) in septic rats, although the tissue levels of TOS, OSI, and MDA were significantly higher. With the exception of serum CRP in Group 3 (p = 0.328), the CK-MB, CRP, and cTn-I levels were considerably higher in Group 2 than in the other groups (p < 0.01 to <0.001). When compared to the other groups, histopathological examination showed significant alterations in the LPS group. Conclusions: Probiotics showed positive effects on oxidative stress markers and dramatically decreased sepsis-induced cardiac damage in the LPS-induced sepsis model. These results imply that probiotics could be used as a therapeutic approach to lessen the cardiac damage brought on by sepsis. Full article
(This article belongs to the Special Issue Infection, Inflammation and Immunity in Health and Disease)
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37 pages, 4565 KiB  
Article
On Classification of the Human Emotions from Facial Thermal Images: A Case Study Based on Machine Learning
by Marius Sorin Pavel, Simona Moldovanu and Dorel Aiordachioaie
Mach. Learn. Knowl. Extr. 2025, 7(2), 27; https://doi.org/10.3390/make7020027 - 25 Mar 2025
Abstract
(1) Background: This paper intends to accomplish a comparative study and analysis regarding the multiclass classification of facial thermal images, i.e., in three classes corresponding to predefined emotional states (neutral, happy and sad). By carrying out a comparative analysis, the main goal of [...] Read more.
(1) Background: This paper intends to accomplish a comparative study and analysis regarding the multiclass classification of facial thermal images, i.e., in three classes corresponding to predefined emotional states (neutral, happy and sad). By carrying out a comparative analysis, the main goal of the paper consists in identifying a suitable algorithm from machine learning field, which has the highest accuracy (ACC). Two categories of images were used in the process, i.e., images with Gaussian noise and images with “salt and pepper” type noise that come from two built-in special databases. An augmentation process was applied to the initial raw images that led to the development of the two databases with added noise, as well as the subsequent augmentation of all images, i.e., rotation, reflection, translation and scaling. (2) Methods: The multiclass classification process was implemented through two subsets of methods, i.e., machine learning with random forest (RF), support vector machines (SVM) and k-nearest neighbor (KNN) algorithms and deep learning with the convolutional neural network (CNN) algorithm. (3) Results: The results obtained in this paper with the two subsets of methods belonging to the field of artificial intelligence (AI), together with the two categories of facial thermal images with added noise used as input, were very good, showing a classification accuracy of over 99% for the two categories of images, and the three corresponding classes for each. (4) Discussion: The augmented databases and the additional configurations of the implemented algorithms seems to have had a positive effect on the final classification results. Full article
(This article belongs to the Section Learning)
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27 pages, 1500 KiB  
Article
An Approximate Analytical View of Fractional Physical Models in the Frame of the Caputo Operator
by Mashael M. AlBaidani, Abdul Hamid Ganie, Adnan Khan and Fahad Aljuaydi
Fractal Fract. 2025, 9(4), 199; https://doi.org/10.3390/fractalfract9040199 - 25 Mar 2025
Abstract
The development of numerical or analytical solutions for fractional mathematical models describing specific phenomena is an important subject in physics, mathematics, and engineering. This paper’s main objective is to investigate the approximation of the fractional order Caudrey–Dodd–Gibbon (CDG) nonlinear [...] Read more.
The development of numerical or analytical solutions for fractional mathematical models describing specific phenomena is an important subject in physics, mathematics, and engineering. This paper’s main objective is to investigate the approximation of the fractional order Caudrey–Dodd–Gibbon (CDG) nonlinear equation, which appears in the fields of laser optics and plasma physics. The physical issue is modeled using the Caputo derivative. Adomian and homotopy polynomials facilitate the handling of the nonlinear term. The main innovation in this paper is how the recurrence relation, which generates the series solutions after just a few iterations, is handled. We examined the assumed model in fractional form in order to demonstrate and verify the efficacy of the new methods. Moreover, the numerical simulation is used to show how the physical behavior of the suggested method’s solution has been represented in plots and tables for various fractional orders. We provide three problems of each equation to check the validity of the offered schemes. It is discovered that the outcomes derived are close to the accurate result of the problems illustrated. Additionally, we compare our results with the Laplace residual power series method (LRPSM), the natural transform decomposition method (NTDM), and the homotopy analysis shehu transform method (HASTM). From the comparison, our methods have been demonstrated to be more accurate than alternative approaches. The results demonstrate the significant benefit of the established methodologies in achieving both approximate and accurate solutions to the problems. The results show that the technique is extremely methodical, accurate, and very effective for examining the nature of nonlinear differential equations of arbitrary order that have arisen in related scientific fields. Full article
(This article belongs to the Special Issue Numerical Solutions of Caputo-Type Fractional Differential Equations and Derivatives)
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17 pages, 1528 KiB  
Technical Note
Method and System for Heart Rate Estimation Using Linear Prediction Filtering
by Vitor O. T. Souza, Fabrício G. S. Silva, José M. Araújo and Jaimilton S. Lima
Signals 2025, 6(2), 15; https://doi.org/10.3390/signals6020015 - 25 Mar 2025
Abstract
Cardiovascular diseases represent one of the major problems faced by modern society. In addition to reducing people’s quality of life, bringing high costs to the health system, and causing losses in economic productivity, they are the leading cause of death in the world. [...] Read more.
Cardiovascular diseases represent one of the major problems faced by modern society. In addition to reducing people’s quality of life, bringing high costs to the health system, and causing losses in economic productivity, they are the leading cause of death in the world. Early diagnosis and treatment are the best actions to minimize the damage and costs caused by these diseases. For this, developing techniques and technologies that have higher accuracy in the analysis of electrocardiogram (ECG) signals is necessary. Early diagnosis benefits from relevant ECG interpretation. Then, it can contribute to reducing healthcare costs by replacing interventionist responses with preventive actions. This work presents a method and system for heart rate estimation using Linear Prediction Coefficients (LPCs) centered on an ESP32 microprocessor module and an AD8232 ECG signal conditioning module. The proposal was validated with a Tektronix AFG1022 function generator that produces ECG signals and obtained measurements with accuracy above 98.87%, showing performance similar to studies presented in the literature. Also, the LPC algorithm showed good performance in rejecting low-frequency noise caused by some common artifacts, such as body movement and electrode displacement. Full article
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21 pages, 2285 KiB  
Article
Unsupervised Aerial–Ground Re-Identification from Pedestrian to Group for UAV-Based Surveillance
by Ling Mei, Yiwei Cheng, Hongxu Chen, Lvxiang Jia and Yaowen Yu
Drones 2025, 9(4), 244; https://doi.org/10.3390/drones9040244 - 25 Mar 2025
Abstract
Person re-identification (ReID) plays a crucial role in advancing UAV-based surveillance applications, enabling robust tracking and event analysis. However, existing methods in UAV scenarios primarily focus on individual pedestrians, requiring cumbersome annotation efforts and lacking seamless integration with ground-based surveillance systems. These limitations [...] Read more.
Person re-identification (ReID) plays a crucial role in advancing UAV-based surveillance applications, enabling robust tracking and event analysis. However, existing methods in UAV scenarios primarily focus on individual pedestrians, requiring cumbersome annotation efforts and lacking seamless integration with ground-based surveillance systems. These limitations hinder the broader development of UAV-based monitoring. To address these challenges, this paper proposes an Unsupervised Aerial–Ground Re-identification from Pedestrian to Group (UAGRPG) framework. Specifically, we introduce a neighbor-aware collaborative learning (NCL) and gradual graph matching (GGC) strategy to uncover the implicit associations between cross-modality groups in an unsupervised manner. Furthermore, we develop a collaborative cross-modality association learning (CCAL) module to bridge feature disparities and achieve soft alignment across modalities. To quantify the optimal group similarity between aerial and ground domains, we design a minimum pedestrian distance transformation strategy. Additionally, we introduce a new AG-GReID dataset, and extensive experiments demonstrate that our approach achieves state-of-the-art performance on both pedestrian and group re-identification tasks in aerial–ground scenarios, validating its effectiveness in integrating ground and UAV-based surveillance. Full article
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24 pages, 3728 KiB  
Article
Surfactants Adsorption onto Algerian Rock Reservoir for Enhanced Oil Recovery Applications: Prediction and Optimization Using Design of Experiments, Artificial Neural Networks, and Genetic Algorithm (GA)
by Kahina Imene Benramdane, Mohamed El Moundhir Hadji, Mohamed Khodja, Nadjib Drouiche, Bruno Grassl and Seif El Islam Lebouachera
Colloids Interfaces 2025, 9(2), 19; https://doi.org/10.3390/colloids9020019 - 25 Mar 2025
Abstract
This study investigates the adsorption of surfactants on Algerian reservoir rock from Hassi Messaoud. A new data generation method based on a design of experiments (DOE) approach has been developed to improve the accuracy of adsorption modeling using artificial neural networks (ANNs). Unlike [...] Read more.
This study investigates the adsorption of surfactants on Algerian reservoir rock from Hassi Messaoud. A new data generation method based on a design of experiments (DOE) approach has been developed to improve the accuracy of adsorption modeling using artificial neural networks (ANNs). Unlike traditional data acquisition methods, this approach enables a methodical and structured exploration of adsorption behavior while reducing the number of required experiments, leading to improved prediction accuracy, optimization, and cost-effectiveness. The modeling is based on three key parameters: surfactant type (SDS and EOR ASP 5100), concentration, and temperature. The dataset required for ANN training was generated from a polynomial model derived from a full factorial design (DOE) established in a previous study. Before training, 32 different ANN configurations were evaluated by varying learning algorithms, adaptation functions, and transfer functions. The best-performing model was a cascade-type network employing the Levenberg–Marquardt learning function, learngdm adaptation, tansig activation function for the hidden layer, and purelin for the output layer, achieving an R2 of 0.99 and an MSE of 6.84028 × 10−9. Compared to DOE-based models, ANN exhibited superior predictive accuracy, with a performance factor (PF/3) of 0.00157 and the same MSE. While DOE showed a slight advantage in relative error (9.10 × 10−5% vs. 1.88 × 10−4% for ANN), ANN proved more effective overall. Three optimization approaches—ANN-GA, DOE-GA, and DOE-DF (desirability function)—were compared, all converging to the same optimal conditions (SDS at 200 ppm and 25 °C). This similarity between the various optimization techniques confirms the strength of genetic algorithms for optimization in the field of EOR and that they can be reliably applied in practical field operations. However, ANN-GA exhibited slightly better convergence, achieving a fitness value of 2.3247. Full article
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18 pages, 2658 KiB  
Article
Explicit and Implicit Knowledge in Large-Scale Linguistic Data and Digital Footprints from Social Networks
by Maria Pilgun
Big Data Cogn. Comput. 2025, 9(4), 75; https://doi.org/10.3390/bdcc9040075 - 25 Mar 2025
Abstract
This study explores explicit and implicit knowledge in large-scale linguistic data and digital footprints from social networks. This research aims to develop and test algorithms for analyzing both explicit and implicit information in user-generated content and digital interactions. A dataset of social media [...] Read more.
This study explores explicit and implicit knowledge in large-scale linguistic data and digital footprints from social networks. This research aims to develop and test algorithms for analyzing both explicit and implicit information in user-generated content and digital interactions. A dataset of social media discussions on avian influenza in Moscow (RF) was collected and analyzed (tokens: 1,316,387; engagement: 108,430; audience: 39,454,014), with data collection conducted from 1 March 2023, 00:00 to 31 May 2023, 23:59. This study employs Brand Analytics, TextAnalyst 2.32, ChatGPT o1, o1-mini, AutoMap, and Tableau as analytical tools. The findings highlight the advantages and limitations of explicit and implicit information analysis for social media data interpretation. Explicit knowledge analysis is more predictable and suitable for tasks requiring quantitative assessments or classification of explicit data, while implicit knowledge analysis complements it by enabling a deeper understanding of subtle emotional and contextual nuances, particularly relevant for public opinion research, social well-being assessment, and predictive analytics. While explicit knowledge analysis provides structured insights, it may overlook hidden biases, whereas implicit knowledge analysis reveals underlying issues but requires complex interpretation. The research results emphasize the importance of integrating various scientific paradigms and artificial intelligence technologies, particularly large language models (LLMs), in the analysis of social networks. Full article
(This article belongs to the Special Issue Research Progress in Artificial Intelligence and Social Network Analysis)
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21 pages, 2021 KiB  
Article
A Data Mining Approach to Identify NBA Player Quarter-by-Quarter Performance Patterns
by Dimitrios Iatropoulos, Vangelis Sarlis and Christos Tjortjis
Big Data Cogn. Comput. 2025, 9(4), 74; https://doi.org/10.3390/bdcc9040074 - 25 Mar 2025
Abstract
Sports analytics is a fast-evolving domain using advanced data science methods to find useful insights. This study explores the way NBA player performance metrics evolve from quarter to quarter and affect game outcomes. Using Association Rule Mining, we identify key offensive, defensive, and [...] Read more.
Sports analytics is a fast-evolving domain using advanced data science methods to find useful insights. This study explores the way NBA player performance metrics evolve from quarter to quarter and affect game outcomes. Using Association Rule Mining, we identify key offensive, defensive, and overall impact metrics that influence success in both regular-season and playoff contexts. Defensive metrics become more critical in late-game situations, while offensive efficiency is paramount in the playoffs. Ball handling peaks in the second quarter, affecting early momentum, while overall impact metrics, such as Net Rating and Player Impact Estimate, consistently correlate with winning. In the collected dataset we performed preprocessing, applying advanced anomaly detection and discretization techniques. By segmenting performance into five categories—Offense, Defense, Ball Handling, Overall Impact, and Tempo—we uncovered strategic insights for teams, coaches, and analysts. Results emphasize the importance of managing player fatigue, optimizing lineups, and adjusting strategies based on quarter-specific trends. The analysis provides actionable recommendations for coaching decisions, roster management, and player evaluation. Future work can extend this approach to other leagues and incorporate additional contextual factors to refine evaluation and predictive models. Full article
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12 pages, 921 KiB  
Article
Novel Triterpenes and Bioactive Compounds Isolated from Smilax canariensis Brouss. ex Willd
by Jesús G. Díaz, Samuel Vega, Daniel Ganosa, Pedro Pérez de Paz and David Díaz Diaz
Separations 2025, 12(4), 74; https://doi.org/10.3390/separations12040074 - 25 Mar 2025
Abstract
The aerial parts of Smilax canariensis Brouss. ex Willd., an endemic plant species of the Canary Islands and Madeira, were chemically investigated, resulting in the isolation of multiple known and novel compounds. These include known flavonol glycosides: quercetin-3-O-rutinoside, rutin (7 [...] Read more.
The aerial parts of Smilax canariensis Brouss. ex Willd., an endemic plant species of the Canary Islands and Madeira, were chemically investigated, resulting in the isolation of multiple known and novel compounds. These include known flavonol glycosides: quercetin-3-O-rutinoside, rutin (7), quercetin-3-O-rutinoside decaacetate (7a), kaempferol-3-O-rutinoside nonaacetate, nicotiflorin acetate (8), 2-O-p-coumaroylglycerol triacetate (10), and trans-resveratrol (9). Additionally, a new sterol, 24,24-dimethy-5α-cholesta-7,25-dien-3-one (1), and two novel dammarane-type triterpenes, 24-hydroxy-24-methyl-dammara-20,25-dien-3-one (2) and 3-acetyl-25-methyl-dammara-20,24-diene (3), were identified. In addition, stigmasterol, sitosterol, and stigmast-4-en-3-one (4) were obtained. The structural elucidation of these compounds was achieved via 1D and 2D NMR spectroscopy, mass spectrometry, and comparison with literature data. This study provides the first phytochemical profile of S. canariensis and highlights its potential as a source of bioactive compounds for pharmacological applications. Full article
(This article belongs to the Section Analysis of Natural Products and Pharmaceuticals)
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25 pages, 11379 KiB  
Article
Dynamic Behaviour and Seismic Response of Scoured Bridge Piers
by Christos Antonopoulos, Enrico Tubaldi, Sandro Carbonari, Fabrizio Gara and Francesca Dezi
Infrastructures 2025, 10(4), 75; https://doi.org/10.3390/infrastructures10040075 - 25 Mar 2025
Abstract
This study explores the transverse response of bridge piers in riverbeds under a multi-hazard scenario, involving seismic actions and scoured foundations. The combined impact of scour on foundations’ stability and on the dynamic stiffness of soil–foundation systems makes bridges more susceptible to earthquake [...] Read more.
This study explores the transverse response of bridge piers in riverbeds under a multi-hazard scenario, involving seismic actions and scoured foundations. The combined impact of scour on foundations’ stability and on the dynamic stiffness of soil–foundation systems makes bridges more susceptible to earthquake damage. While previous research has extensively investigated this issue for bridges founded on piles, this work addresses the less explored but critical scenario of bridges on shallow foundations, typical of existing bridges. A comprehensive soil–foundation structure model is developed to be representative of the transverse response of multi-span and continuous girder bridges, and the effects of different scour scenarios and foundation embedment on the dynamic stiffness of the soil–foundation sub-systems are investigated through refined finite element models. Then, a parametric investigation is conducted to assess the effects of scour on the dynamic properties of the systems and, for some representative bridge prototypes, the seismic response at scoured and non-scoured conditions are compared considering real earthquakes. The research results demonstrate the significance of scour effects on the dynamic properties of the soil–foundation structure system and on the displacement demand of the bridge decks. Full article
(This article belongs to the Special Issue Bridge Modeling, Monitoring, Management and Beyond)
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11 pages, 889 KiB  
Review
Residues of 6PPD-Q in the Aquatic Environment and Toxicity to Aquatic Organisms: A Review
by Chaoju Li, Yuanqiang Yang, Zikun Tian, Zhiqiu Huang, Yi Huang and Yuhang Hong
Fishes 2025, 10(4), 146; https://doi.org/10.3390/fishes10040146 - 25 Mar 2025
Abstract
N-(1,3-dimethylbutyl)-N’-phenyl-p-benzoquinone (6PPD-Q) is an emerging environmental contaminant that is widely distributed in aquatic environments and presents significant toxicological risks to aquatic organisms. As 6PPD-Q is primarily derived from oxidative transformation of the tire antioxidant N-(1,3-dimethylbutyl)-N’-phenyl-p-phenylenediamine (6PPD), its persistence and potential for bioaccumulation in [...] Read more.
N-(1,3-dimethylbutyl)-N’-phenyl-p-benzoquinone (6PPD-Q) is an emerging environmental contaminant that is widely distributed in aquatic environments and presents significant toxicological risks to aquatic organisms. As 6PPD-Q is primarily derived from oxidative transformation of the tire antioxidant N-(1,3-dimethylbutyl)-N’-phenyl-p-phenylenediamine (6PPD), its persistence and potential for bioaccumulation in aquatic organisms have raised widespread concerns. This study reviews the environmental sources, spatial distribution, migration, and transformation behaviors of 6PPD-Q, as well as its degradation mechanisms in different environmental media. Additionally, this review systematically explores the toxicological effects of 6PPD-Q on aquatic organisms, including its physiological, biochemical, and molecular impacts on fish, crustaceans, mollusks, and algae, with a focus on potential toxicological mechanisms. Finally, we discuss the limitations of current research on 6PPD-Q and propose key directions for future studies, including long-term ecological risk assessments, mechanisms of bioaccumulation, metabolic pathway analysis, and optimization of pollution control strategies, aiming to provide a scientific basis for the ecological risk assessment and pollution management of 6PPD-Q. Full article
(This article belongs to the Special Issue Aquatic Ecotoxicology: Field and Laboratory Approaches)
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18 pages, 5498 KiB  
Article
Development and Evaluation of a Novel Upper-Limb Rehabilitation Device Integrating Piano Playing for Enhanced Motor Recovery
by Xin Zhao, Ying Zhang, Yi Zhang, Peng Zhang, Jinxu Yu and Shuai Yuan
Biomimetics 2025, 10(4), 200; https://doi.org/10.3390/biomimetics10040200 - 25 Mar 2025
Abstract
This study developed and evaluated a novel upper-limb rehabilitation device that integrates piano playing into task-oriented occupational therapy, addressing the limitations of traditional continuous passive motion (CPM) training in patient engagement and functional recovery. The system features a bi-axial sliding platform for precise [...] Read more.
This study developed and evaluated a novel upper-limb rehabilitation device that integrates piano playing into task-oriented occupational therapy, addressing the limitations of traditional continuous passive motion (CPM) training in patient engagement and functional recovery. The system features a bi-axial sliding platform for precise 61-key positioning and a ten-link, four-loop robotic hand for key striking. A hierarchical control framework incorporates MIDI-based task mapping, finger optimization using an improved Hungarian algorithm, and impedance–admittance hybrid control for adaptive force–position modulation. An 8-week randomized controlled trial demonstrated that the experimental group significantly outperformed the control group, with a 74.7% increase in Fugl–Meyer scores (50.5 ± 2.5), a 14.6-point improvement in the box and block test (BBT), a 20.2-s reduction in nine-hole peg test (NHPT) time, and a 72.6% increase in rehabilitation motivation scale (RMS) scores (55.4 ± 3.8). The results indicate that combining piano playing with robotic rehabilitation enhances neuroplasticity and engagement, significantly improving motor function, daily activity performance, and rehabilitation adherence. This mechanical-control synergy introduces a new paradigm for music-interactive rehabilitation, with potential applications in home-based remote therapy and multimodal treatment integration. Full article
(This article belongs to the Special Issue Recent Advances in Wearable Bioelectronics in Healthcare/Medical Devices)
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19 pages, 4792 KiB  
Article
Conversion of Carbon Dioxide into Solar Fuels Using MgFe2O4 Thermochemical Redox Chemistry
by Rahul R. Bhosale
C 2025, 11(2), 25; https://doi.org/10.3390/c11020025 - 25 Mar 2025
Abstract
Transforming H2O and CO2 into solar fuels like syngas is crucial for future sustainable transportation fuel production. Therefore, the MgFe2O4/CO2 splitting cycle was thermodynamically scrutinized to estimate its solar-to-fuel energy conversion efficiency in this investigation. [...] Read more.
Transforming H2O and CO2 into solar fuels like syngas is crucial for future sustainable transportation fuel production. Therefore, the MgFe2O4/CO2 splitting cycle was thermodynamically scrutinized to estimate its solar-to-fuel energy conversion efficiency in this investigation. The thermodynamic data required to solve the modeling equations were obtained using the HSC Chemistry program. The reduction non-stoichiometry was assumed to be equal to 0.1 for all computations. One of the study’s primary goals was to examine the impact of the inert sweep gas’s molar flow rate on the process parameters related to the MgFe2O4/CDS cycle. Overall, it was understood that the effect of the inert sweep gas’s molar flow rate on the thermal reduction temperature was significant when it increased from 10 to 40 mol/s compared to the rise from 40 to 100 mol/s. The energy needed to reduce MgFe2O4 increased slightly due to the surge in the inert sweep gas’s molar flow rate. In contrast, the energy penalty for heating MgFe2O4-δred from the re-oxidation to thermal reduction temperature significantly decreased. Including gas-to-gas heat exchangers with a gas-to-gas heat recovery effectiveness equal to 0.5 helped reduce the energy demand for heating the inert sweep gas. Overall, although the rise in the inert sweep gas’s molar flow rate from 10 to 100 mol/s caused a drop in the thermal reduction temperature by 180 K, the total solar energy needed to drive the cycle was increased by 85.7 kW. Accordingly, the maximum solar-to-fuel energy conversion efficiency (13.1%) was recorded at an inert sweep gas molar flow rate of 10 mol/s, which decreased by 3.7% when it was increased to 100 mol/s. Full article
(This article belongs to the Section CO2 Utilization and Conversion)
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24 pages, 3605 KiB  
Review
Solution Combustion Synthesis for Various Applications: A Review of the Mixed-Fuel Approach
by Samantha Padayatchee, Halliru Ibrahim, Holger B. Friedrich, Ezra J. Olivier and Pinkie Ntola
Fluids 2025, 10(4), 82; https://doi.org/10.3390/fluids10040082 - 25 Mar 2025
Abstract
As solution combustion synthesis (SCS) becomes a universal route to metal oxide nanomaterials, it also paves the way for mixed-fuel combustion synthesis as an advanced approach to the synthesis of materials of desirable properties for diverse applications. Major significance is attached to the [...] Read more.
As solution combustion synthesis (SCS) becomes a universal route to metal oxide nanomaterials, it also paves the way for mixed-fuel combustion synthesis as an advanced approach to the synthesis of materials of desirable properties for diverse applications. Major significance is attached to the rates of decomposition and combustion temperatures of the fuel as determinant factors of the morphology and physicochemical properties of the materials obtained. This has promoted the use of mixed-fuel systems characterized by lower decomposition temperatures of organic fuels and higher rates of combustion. The review work presented herein provides a comprehensive analysis of the applications of mixed-fuel SCS in ceramics, fuel cells, nanocomposite materials, and the recycling of lithium battery materials while taking into consideration the effects of the mixed-fuel system on the physicochemical and morphological properties of those materials, as compared to their analogues prepared via single-fuel SCS. Full article
(This article belongs to the Special Issue Turbulence and Combustion)
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21 pages, 8213 KiB  
Article
Numerical Investigation of Cylindrical Water Droplets Subjected to Air Shock Loading at a High Weber Number
by F. Edoardo Taglialatela and Giuliano De Stefano
Fluids 2025, 10(4), 81; https://doi.org/10.3390/fluids10040081 - 25 Mar 2025
Abstract
This work is devoted to the computational investigation of the deformation and breakup of cylindrical water bodies in the high-speed airflow behind incident shock waves. Both single-column and tandem-column configurations in various arrangements were simulated by reproducing the shock/droplet interaction process in a [...] Read more.
This work is devoted to the computational investigation of the deformation and breakup of cylindrical water bodies in the high-speed airflow behind incident shock waves. Both single-column and tandem-column configurations in various arrangements were simulated by reproducing the shock/droplet interaction process in a shock-tube device. The calculations were conducted by using a third-party solver recently developed for compressible two-phase flows in the framework of the open source finite volume toolbox OpenFOAM. The numerical approach is based on the use of the volume-of-fluid method to resolve the phase interface, where a particular discretization technique allows us to prevent unphysical instabilities. The numerical scheme makes use of more precise information of the local propagation speeds to maintain a high resolution and a small numerical viscosity. Qualitative and quantitative comparisons of the results with reference experimental and numerical data demonstrated good agreement for the main characteristics of the interaction process in terms of the morphology, dynamics, and breakup of the deforming water bodies. Full article
(This article belongs to the Special Issue 10th Anniversary of Fluids—Recent Advances in Fluid Mechanics)
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