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15 pages, 2391 KB  
Article
Research on the Impact of Typical SCR Faults on NOx Emission Deterioration of Heavy-Duty Vehicles
by Hao Zhang, Xiaofei Cao, Fengbin Wang, Hanzhengnan Yu, Jingyuan Li and Yu Liu
Atmosphere 2025, 16(11), 1299; https://doi.org/10.3390/atmos16111299 (registering DOI) - 17 Nov 2025
Abstract
Faults of the selective catalytic reduction (SCR) significantly exacerbate nitrogen oxide (NOx) emissions from heavy-duty vehicles, thereby posing a severe hazard to atmospheric environmental quality. Currently, the paucity of systematic studies on NOx emission degradation induced by typical SCR faults has severely hindered [...] Read more.
Faults of the selective catalytic reduction (SCR) significantly exacerbate nitrogen oxide (NOx) emissions from heavy-duty vehicles, thereby posing a severe hazard to atmospheric environmental quality. Currently, the paucity of systematic studies on NOx emission degradation induced by typical SCR faults has severely hindered the advancement of precise emission regulation for heavy-duty vehicles in China. To address this critical gap, this study investigates the impact of typical SCR faults on NOx emission deterioration from heavy-duty vehicles. Initially, leveraging the China heavy-duty commercial vehicle test cycle as the benchmark, heavy-duty vehicle emission tests were designed and conducted under typical SCR faults. Emission datasets were acquired for three typical SCR faults—namely nozzle circuit disconnected fault, upstream temperature sensor inaccuracy fault, and urea-water replacement fault—as well as under normal operating conditions. Building upon these data, three representative scenarios were established by integrating vehicle operating condition, fuel consumption levels, and vehicle specific power states, enabling systematic quantification of the extent of NOx emission deterioration caused by each SCR fault. The findings reveal that the NOx emissions deterioration caused by urea-water replacement fault is the most severe, followed by nozzle circuit disconnected fault, and the impact of upstream temperature sensor inaccuracy fault is the least. This research provides crucial support for identifying key targets in emission control and enhancing the precision of heavy-duty vehicle emission regulation. Relevant authorities should prioritize cracking down on intentional non-compliant practices such as urea water substitution to safeguard a healthy and sustainable atmospheric environment. Full article
(This article belongs to the Special Issue Traffic Related Emission (3rd Edition))
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16 pages, 1065 KB  
Article
Culture Collections for Conservation Ex Situ: Characterization and Biotechnological Application Potential of Saprotrophic Fungal Strains from Brazil
by Simone Buratti, Carolina Elena Girometta, Lorenzo Goppa, Claudia Picozzi, Paola Rossi, Elena Savino, Maria Letizia Gargano, Giuseppe Venturella, Adriana de Mello Gugliotta, Vera Maria Valle Vitali and Manuela Rollini
Conservation 2025, 5(4), 70; https://doi.org/10.3390/conservation5040070 (registering DOI) - 17 Nov 2025
Abstract
Saprotrophic and lignocellulolytic fungi from tropical areas especially represent a promising yet relatively underexplored frontier for both taxonomy and applied research. This makes ex situ conservation through culture collections of paramount importance. Here, 10 lignocellulolytic strains isolated from the State of São Paulo [...] Read more.
Saprotrophic and lignocellulolytic fungi from tropical areas especially represent a promising yet relatively underexplored frontier for both taxonomy and applied research. This makes ex situ conservation through culture collections of paramount importance. Here, 10 lignocellulolytic strains isolated from the State of São Paulo (Brazil) and deposited in the Brazilian Culture Collection (now CCIBt) were identified through the ITS region. In order to prevent accidental losses, these strains have been shared with the collection of the University of Milano—Department of Food, Environmental and Nutritional Sciences (DeFENS), as well as the MicUNIPV Fungal Research Culture Collection—University of Pavia (Italy). Most of the fungal species in the examined set exhibit a neotropical distribution, while 3 out of 10 are nowadays recognized as subcosmopolitan despite their prevalence in the neotropical area. One holotropical, one cosmopolitan and one holarctic species are also present. Based on the literature, 8 out of the 10 characterized species are known to produce psilocybin (e.g., Psilocybe cubensis and Candolleomyces candolleanus) and/or enzymes with potential applications in environmental and medical biotechnology (e.g., Lentinus crinitus). All 10 strains were described for their micro- and macro-characteristics; their growth rate was evaluated and culture pictures provided. Taxonomic and nomenclatural controversies concerning Candolleomyces candolleanus, Cubamyces lactineus and Pycnoporus sanguineus are discussed. Full article
27 pages, 2656 KB  
Review
A Review of the Sampling, Analysis, and Identification Techniques of Microplastics in the Air: Insights into PM2.5 and PM10
by Leonela Anahis Solórzano, Dayana Gavilanes, Francisco Cadena, Lourdes Irusta, Alba González and Miguel Aldas
Polymers 2025, 17(22), 3045; https://doi.org/10.3390/polym17223045 (registering DOI) - 17 Nov 2025
Abstract
The massive use of plastics has raised growing environmental concerns, including microplastic (MP) pollution. While most studies have focused on MPs in aquatic environments, research on airborne microplastics has gained increasing attention in recent years. This review discusses the sampling, analytical, and identification [...] Read more.
The massive use of plastics has raised growing environmental concerns, including microplastic (MP) pollution. While most studies have focused on MPs in aquatic environments, research on airborne microplastics has gained increasing attention in recent years. This review discusses the sampling, analytical, and identification techniques used for MPs, with a particular focus on PM2.5 and PM10 fractions, which have been scarcely addressed in the literature. The main active and passive sampling methods, sample preparation protocols, MP quantification approaches, and chemical characterization techniques applied to airborne plastic particles are compiled. Attention is given to the influence of meteorological conditions on transport and deposition, as well as to the predominant sources of primary and secondary microplastics in both indoor and outdoor environments. The analysis identifies the main research challenges, particularly in the detection of microplastics and in the standardization of protocols. The review highlights the need for standardized methodologies to advance reliable quantification and to better understand the environmental implications of MPs. Full article
(This article belongs to the Topic Microplastics Across Ecosystems: Multidisciplinary Approaches to Sources, Sinks and Health Sustainable Solutions)
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27 pages, 17884 KB  
Article
Infrared–Visible Image Fusion via Cross-Modal Guided Dual-Branch Networks
by Tingyu Zhu, Jinyong Chen and Gang Wang
Appl. Sci. 2025, 15(22), 12185; https://doi.org/10.3390/app152212185 (registering DOI) - 17 Nov 2025
Abstract
In the field of low-altitude aerial drone data fusion, the fusion of infrared and visible light images remains challenging due to issues such as large modal differences, insufficient cross-modal alignment, and limited global context modeling. Traditional methods struggle to extract complementary information across [...] Read more.
In the field of low-altitude aerial drone data fusion, the fusion of infrared and visible light images remains challenging due to issues such as large modal differences, insufficient cross-modal alignment, and limited global context modeling. Traditional methods struggle to extract complementary information across modalities, while deep learning methods often lack sufficient global receptive fields (convolutional neural networks) or fail to preserve local details (standard Transformers). To address these issues, we propose a Cross-modal Guided Dual-Branch Network (CGDBN) that combines convolutional neural networks and Transformer architecture. Our framework contribution: We designed a Target-modal Feature Extraction Mechanism (TMFEM) module with specialized thermal characteristics for infrared feature extraction, which does not require processing of visible light features; we introduced Simplified Linear Attention Blocks (SLABs) into our framework to improve global context capture as a module; we designed a Cross-Modal Interaction Mechanism (CMIM) module for bidirectional feature interaction; and we designed a Density Adaptive Multimodal Fusion (DAMF) module that weights modal contributions based on content analysis. This asymmetric design recognizes that different types of images have different characteristics and require targeted processing. The experimental results on AVMS, M3FD, and TNO datasets show that the proposed model has a peak signal-to-noise ratio (PSNR) of 16.2497 on the AVMS dataset, which is 0.9971 higher than the best benchmark method YDTR (peak signal-to-noise ratio: approximately 15.2526). The peak signal-to-noise ratio on the M3FD dataset is 16.5044, which is 0.7480 higher than the best benchmark method YDTR (peak signal-to-noise ratio of approximately 15.7564). The peak signal-to-noise ratio on the TNO dataset is 17.3956, which is 0.7934 higher than the best benchmark method YDTR (peak signal-to-noise ratio: approximately 16.6022), and the overall performance on all other indicators is among the top in all comparison models. This method has broad application prospects in fields such as drone data fusion. Full article
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22 pages, 915 KB  
Article
Performance Evaluation Methodology for Federated XR Network Digital Twins in AI-Aware 6G Networks
by Xavier Calle-Heredia and Xavier Hesselbach
Future Internet 2025, 17(11), 523; https://doi.org/10.3390/fi17110523 (registering DOI) - 17 Nov 2025
Abstract
Network digital twins (NDTs) are emerging as key enablers of 6G networks integrating artificial Intelligence (AI) techniques. NDT systems offer novel features, including real-time monitoring, simulation, enhanced network planning, autonomous management, seamless integration with emerging technologies such as extended reality (XR), among others. [...] Read more.
Network digital twins (NDTs) are emerging as key enablers of 6G networks integrating artificial Intelligence (AI) techniques. NDT systems offer novel features, including real-time monitoring, simulation, enhanced network planning, autonomous management, seamless integration with emerging technologies such as extended reality (XR), among others. When NDTs converge with XR, NDTs can be customized with additional interactive services that are not available in the original network. In this work, artificial intelligence (AI) strategies are applied to a set of XR functions within federated NDTs. While existing NDT approaches follow a one-to-one (1:1) model, where a single NDT instance is deployed from an original network, the one-to-many (1:N) federation model requires the orchestration of multiple XR-tailored NDT instances. The federation of NDTs can be applied across diverse 6G use cases, including telemedicine, UAV management, Industry 4.0, and the remote driving of complex vehicles. Ensuring the optimal operation of the NDT federation requires a methodology tailored to the requirements of each use case. This paper introduces a score-based performance analysis to quantify the benefits achieved through NDT federation. Unlike existing models for the digital twin (DT) federation, this paper introduces a KPI-based rational model that quantifies the trade-off between federation benefits and the associated operational complexity. A mathematical analysis is performed to validate the consistency of the score formula both in general terms and within the context of each specific use case. Full article
(This article belongs to the Special Issue Advances in Smart Environments and Digital Twin Technologies)
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18 pages, 1331 KB  
Article
Diabetes in Pregnant Romanian Patients—Epidemiology and Prevention Strategies Proposal
by Bianca-Margareta Salmen, Teodor Salmen, Delia Reurean-Pintilei, Cristina Vaida and Roxana-Elena Bohiltea
J. Clin. Med. 2025, 14(22), 8135; https://doi.org/10.3390/jcm14228135 (registering DOI) - 17 Nov 2025
Abstract
Background/Objectives: Diabetes mellitus (DM) in pregnancy, including type 1 (T1DM), type 2 (T2DM), and gestational DM (GDM), represents an increasing health burden due to its maternal and fetal complications. Despite the increment in the global prevalence estimates of DM in pregnancy, in [...] Read more.
Background/Objectives: Diabetes mellitus (DM) in pregnancy, including type 1 (T1DM), type 2 (T2DM), and gestational DM (GDM), represents an increasing health burden due to its maternal and fetal complications. Despite the increment in the global prevalence estimates of DM in pregnancy, in Romania, it has not been comprehensively described. This study aimed to analyze the prevalence and dynamics of DM in pregnancy in Romania between 2014 and 2024, using national databases, and to identify prevention strategies for reducing maternal and fetal complications. Methods: Data were obtained from the Romanian National Public Health Institute through two distinct sources: Database 1, consisting of reports from public and medical units associated with the National Health Insurance House and Database 2, based on the reports from general practitioners. Pregnancies complicated by DM were assessed by type, age group, and environmental settlement. Additional data were extracted on pregnancies with insufficient prenatal care and those of socially vulnerable individuals. Results: From 2014 to 2024, the prevalence of DM in pregnancy in Romania was consistently lower than European and global estimates, ranging from 1.01‰ to 3.08‰ in Database 1 and from 0.84‰ to 5.88‰ in Database 2, respectively. GDM accounted for the majority of cases, accounting for 65–88% of reported DM in pregnancy. The highest incidence was observed in the 20–39 years age group, with a growing proportion in women aged ≥40 years. Urban-rural disparities decreased over the decade, with rural cases reaching parity by 2024. Vulnerable populations included adolescents, women with insufficient prenatal care, and those with social risk factors, predominantly from rural areas. Conclusions: Although the reported prevalence of DM in pregnancy in Romania is lower than international figures, the true burden is likely underestimated. GDM remains the leading type of DM in pregnancy, mirroring global trends. Strengthening the reporting system, standardizing diagnostic criteria, and targeting high-risk groups through preconceptional counselling, lifestyle interventions, advanced monitoring technologies, and improving social support through the involved authorities are crucial steps to reduce maternal and fetal morbidity. Full article
(This article belongs to the Special Issue Epidemiology of Hyperglycemia in Pregnancy and Its Maternofetal Adverse Outcomes)
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13 pages, 509 KB  
Article
What Is the Most Effective Strategy for Acute Postoperative Pain in Total Knee Arthroplasty—Retrospective Observational Study
by Jin Joo, Man Soo Kim, Jeha Lee and Hyun Jung Koh
J. Clin. Med. 2025, 14(22), 8138; https://doi.org/10.3390/jcm14228138 (registering DOI) - 17 Nov 2025
Abstract
Background and Objectives: Effective early postoperative pain control is essential for optimal recovery following total knee arthroplasty (TKA). In addition to pharmacological pain management, the choice of anesthetic agents and surgical technique can significantly impact postoperative outcomes. Remimazolam and robotic-assisted TKA (RA-TKA) have [...] Read more.
Background and Objectives: Effective early postoperative pain control is essential for optimal recovery following total knee arthroplasty (TKA). In addition to pharmacological pain management, the choice of anesthetic agents and surgical technique can significantly impact postoperative outcomes. Remimazolam and robotic-assisted TKA (RA-TKA) have recently gained attention due to their potential advantages. This study aims to evaluate the effects of remimazolam and RA-TKA on acute postoperative pain compared with conventional TKA (C-TKA) and standard anesthetic protocols. Materials and Methods: In this retrospective observational study, 460 patients undergoing elective unilateral TKA were divided in to four groups based on surgical technique and anesthetic agents; RA-TKA with remimazolam (Group RR, n = 115), C-TKA with remimazolam (Group CR, n = 134), RA-TKA with conventional anesthesia (Group RC, n = 79), and C-TKA with conventional anesthesia (Group CC, n = 152). Remimazolam was administered at 6 mg/kg/h for induction and 1 mg/kg/h for maintenance, whereas conventional anesthesia was induced with propofol (1.5 mg/kg) and maintained with sevoflurane (1.5~2.0 vol%). The primary endpoint was acute postoperative pain, assessed via patient-controlled analgesia (PCA) consumption and supplemental analgesic use on the day of surgery. Secondary endpoints included total PCA consumption and additional analgesic use during the first 72 h, recovery room stay, discharge scores, and the incidence of postoperative nausea and vomiting (PONV). Results: Group RR required significantly fewer additional analgesics on the day of surgery than the other groups. Although group RR and group CR exhibited prolonged recovery room stay and lower discharge scores, these outcomes were not correlated with PCA consumption or supplementary analgesic use. RA-TKA was associated with approximately a 31% reduction in additional analgesic use compared with C-TKA, indicating a major contribution of the surgical technique to early postoperative pain control. Remimazolam alone did not demonstrate an independent effect on acute pain management. Conclusions: RA-TKA combined with remimazolam significantly reduced the need for additional analgesics on the day of surgery, highlighting a synergistic effect of the anesthetic and surgical approach. These findings support RA-TKA with remimazolam as an effective strategy for managing early postoperative pain following TKA. Full article
(This article belongs to the Special Issue Advances in Clinical Anesthesia and Analgesia: Novel Approaches for Optimal Patient Safety and Comfort)
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20 pages, 753 KB  
Article
Transition from 24-Hour Shifts to Safer Work Schedules for Nurses in Latvian Healthcare: Policy Analysis and Recommendations
by Olga Cerela-Boltunova and Kristine Klavina
Int. J. Environ. Res. Public Health 2025, 22(11), 1736; https://doi.org/10.3390/ijerph22111736 (registering DOI) - 17 Nov 2025
Abstract
In Latvia, extended shifts, including 24 h duties, remain common inpatient care settings despite extensive international evidence on their adverse effects on staff well-being and patient safety. We conducted an evidence-informed policy analysis combining a structured review of national legislation and institutional reports [...] Read more.
In Latvia, extended shifts, including 24 h duties, remain common inpatient care settings despite extensive international evidence on their adverse effects on staff well-being and patient safety. We conducted an evidence-informed policy analysis combining a structured review of national legislation and institutional reports with comparative policy mapping across OECD/EU countries. The interpretation was guided by three theoretical frameworks: the Job Demands–Resources model, Effort–Recovery theory, and the Work–Life Interface framework. Latvian practice shows high reliance on long shifts amid workforce shortages and incomplete overtime/rest accounting. In contrast, most OECD and EU countries have implemented 8–12 h multi-shift systems with mandated rest, which are associated with lower error rates, reduced burnout, and higher staff satisfaction. We synthesised four policy options (12 h transition model; 16 h cap; modular 2 × 6 h/3 × 8 h; flexible unit-profiled schedules) and identify seven prerequisites for feasible implementation (regulatory alignment; staffing; financing; management training; digital scheduling; pilot projects; monitoring). A phased transition from 24 h shifts to structured schedules appears both feasible and desirable, with pilot implementation and monitoring aligning with WHO/ILO recommendations. Implications for policy and practice: Reform is a system-level intervention to improve staff well-being, patient safety, and workforce sustainability in Latvia. Full article
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39 pages, 2272 KB  
Review
Antimicrobial Peptides for Skin Wound Healing
by Yifan Wu, Tingting Liu, Lili Jin, Chuyuan Wang and Dianbao Zhang
Biomolecules 2025, 15(11), 1613; https://doi.org/10.3390/biom15111613 (registering DOI) - 17 Nov 2025
Abstract
Skin wound healing is a highly regulated biological process that requires the coordinated activity of multiple cell types. However, this process can be significantly impaired by factors such as metabolic diseases and infections, posing ongoing challenges for current treatment strategies. As a critical [...] Read more.
Skin wound healing is a highly regulated biological process that requires the coordinated activity of multiple cell types. However, this process can be significantly impaired by factors such as metabolic diseases and infections, posing ongoing challenges for current treatment strategies. As a critical defense mechanism for cells and organisms against external threats, antimicrobial peptides (AMPs) hold great potential to enhance both the rate and quality of healing in both acute and chronic wounds. AMPs play a crucial role in promoting skin wound healing through mechanisms such as keratinocyte migration and proliferation, collagen synthesis and tissue remodeling, promotion of angiogenesis, immunomodulatory effects and broad-spectrum antimicrobial activity. Moreover, structural modifications and optimized delivery systems have further enhanced the stability and efficacy of AMPs. This paper explores the mechanisms by which AMPs aid in the healing of damaged skin and reviews the types of AMPs in clinical trials, providing a foundation for their development and clinical application. Full article
(This article belongs to the Special Issue Advances in Antimicrobial Peptides (AMPs))
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28 pages, 5368 KB  
Article
Dynamic Estimation of Formation Wake Flow Fields Based on On-Board Sensing
by Tianhui Guo, Tielin Ma, Haiqiao Liu, Jingcheng Fu, Bingchen Cheng and Lulu Tao
Drones 2025, 9(11), 798; https://doi.org/10.3390/drones9110798 (registering DOI) - 17 Nov 2025
Abstract
Close formation flight is a practical strategy for fixed-wing unmanned aerial vehicle (UAV) swarms. Maintaining UAVs at aerodynamically optimal positions is essential for efficient formation flight. However, aerodynamic optimization methods based on computational fluid dynamics (CFD) are computationally intensive and difficult to apply [...] Read more.
Close formation flight is a practical strategy for fixed-wing unmanned aerial vehicle (UAV) swarms. Maintaining UAVs at aerodynamically optimal positions is essential for efficient formation flight. However, aerodynamic optimization methods based on computational fluid dynamics (CFD) are computationally intensive and difficult to apply in real time for large-scale formations. Inspired by bio-formation flight, this study proposes an on-board sensing-based method for wake flow field estimation, with potential for extension to complex formations. The method is based on a parameter identification-induced velocity model (PI-Model), which uses only onboard sensors, including two lateral air data systems (ADS), to sample the wake field. By minimizing the residual of the induced velocity, the model identifies key parameters of the wake and provides a dynamic estimation of the wake velocity field. Comparisons between the PI-Model and CFD simulations show that it achieves higher accuracy than the widely used single horseshoe vortex model in both wake velocity and aerodynamic effects. Applied to a two-UAV formation scenario, CFD validation confirms that the trailing UAV achieves a 15–25% drag reduction. These results verify the effectiveness of the proposed method for formation flight and demonstrate its potential for application in complex, dynamic multi-UAV formations. Full article
(This article belongs to the Special Issue Advanced Optimization Strategies for UAV Mission Planning and Operation)
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11 pages, 389 KB  
Article
Impact of Lid Wipes and Tear Substitutes on Symptoms and Signs of Ocular Surface Disease After Cataract Surgery—A Real-Life Study
by Giulia Coco, Laura Antonia Meliante, Francesca Di Stefano, Livio Vitiello and Giuseppe Giannaccare
J. Clin. Med. 2025, 14(22), 8140; https://doi.org/10.3390/jcm14228140 (registering DOI) - 17 Nov 2025
Abstract
Objective: To evaluate the efficacy of lid wipes and tear substitutes, in addition to standard postoperative treatment, in alleviating signs and symptoms of ocular surface disease (OSD) following cataract surgery. Methods: Retrospective study on patients who underwent cataract surgery and received either standard [...] Read more.
Objective: To evaluate the efficacy of lid wipes and tear substitutes, in addition to standard postoperative treatment, in alleviating signs and symptoms of ocular surface disease (OSD) following cataract surgery. Methods: Retrospective study on patients who underwent cataract surgery and received either standard postoperative treatment (topical antibiotics, corticosteroids, and nonsteroidal anti-inflammatory drugs) or the same regimen supplemented with lid wipes and tear substitutes. Preoperatively and one month postoperatively, symptoms were evaluated using the 5-item Dry Eye Questionnaire (DEQ-5) and the Ocular Surface Disease Index (OSDI), while noninvasive keratograph break-up time (NIKBUT), tear meniscus height (TMH), conjunctival hyperemia, and infrared meibography were measured by the Oculus Keratograph. Results: A total of 63 patients (mean age 75.1 ± 6.3 years) were analyzed. Patients receiving standard treatment showed no significant changes in OSDI (+2 ± 32.7; p = 0.859) or DEQ-5 scores (+1.7 ± 5.4; p = 0.204). Conversely, those receiving the adjunct of lid wipes and tear substitutes demonstrated significant improvement in OSDI scores (−19.4 ± 15.9; p < 0.0001), a trend toward improvement in DEQ-5 scores (−1.9 ± 5.5; p = 0.059), and a reduction in the meibography score of the inferior eyelid (−0.24 ± 0.60; p = 0.023). Intergroup comparisons showed significantly greater improvements in both OSDI and DEQ-5 scores in patients receiving treatment for the ocular surface. Multivariate regression analysis confirmed the association between the use of wipes and tear substitutes and improvements in OSDI (p = 0.010) and DEQ-5 scores (p = 0.015). No significant postoperative changes in objective OSD parameters were observed in either group. Conclusions: The addition of lid wipes and tear substitutes to the standard postoperative regimen significantly improved patient-reported symptoms of ocular discomfort after cataract surgery, while no significant changes were observed in objective signs of ocular surface disease. These findings support the routine use of lid wipes and tear substitutes as an effective strategy for managing postoperative ocular surface discomfort. Full article
(This article belongs to the Special Issue Clinical Challenges and Advances in Dry Eye Disease and Meibomian Gland Dysfunction)
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14 pages, 3799 KB  
Article
Slurry Aluminizing of Nickel Electroless Coated Nickel-Based Superalloy
by Thomas Kepa, Gilles Bonnet, Giulia Pedrizzetti, Virgilio Genova, Giovanni Pulci, Cecilia Bartuli and Fernando Pedraza
Coatings 2025, 15(11), 1337; https://doi.org/10.3390/coatings15111337 (registering DOI) - 17 Nov 2025
Abstract
Nickel-based superalloys require protective low-activity aluminide coatings to withstand high-temperature oxidation and corrosion in turbine applications. As opposed to conventional gas processes, this study investigates the mechanisms of formation of alternative low-activity nickel aluminide coatings on the René N5 superalloy through electroless nickel [...] Read more.
Nickel-based superalloys require protective low-activity aluminide coatings to withstand high-temperature oxidation and corrosion in turbine applications. As opposed to conventional gas processes, this study investigates the mechanisms of formation of alternative low-activity nickel aluminide coatings on the René N5 superalloy through electroless nickel pre-deposition followed by slurry aluminizing. Different thicknesses of electroless nickel layers (5, 10, 25 μm) were deposited and subsequently aluminized with varying slurry amounts (5–16 mg/cm2) under controlled heat treatments at 700–1080 °C with heating rates of 5 and 20 °C/min. Without electroless pre-deposition, high-activity coatings with refractory element precipitates formed. With electroless nickel, a precipitate-free low-activity coating developed, with thickness increasing linearly from 15 to 40 μm proportional to the initial electroless layer. An increasing slurry amount raised the overall coating thickness from 27 to 67 μm. Kirkendall porosity formed exclusively during the δ-Ni2Al3 to β-NiAl phase transformation at elevated temperature. Reducing the heating rate from 20 to 5 °C/min significantly decreased void formation by promoting more balanced Ni-Al interdiffusion. This work demonstrates that combining electroless nickel with slurry aluminizing provides an efficient route for producing low-activity coatings with controlled microstructure and minimal porosity. Full article
(This article belongs to the Section Ceramic Coatings and Engineering Technology)
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23 pages, 3479 KB  
Article
Altered Short Non-Coding RNA Landscape in the Hippocampus of a Mouse Model of CDKL5 Deficiency Disorder
by Bilal El-Mansoury, Adrian Hayes, Samuel Egan, Jordan Higgins, Stephen B. Keane, Elena Langa, Erva Ghani, Morten T. Venø, Mona Heiland, David C. Henshall and Omar Mamad
Biomolecules 2025, 15(11), 1612; https://doi.org/10.3390/biom15111612 (registering DOI) - 17 Nov 2025
Abstract
CDKL5 deficiency disorder (CDD) is a rare developmental epileptic encephalopathy (DEE) caused by mutations in cyclin-dependent kinase-like 5 (CDKL5). The clinical manifestations include early and severe epilepsy, intellectual disability, motor abnormalities, and cortical visual impairments. The pathophysiological mechanisms underlying CDD are [...] Read more.
CDKL5 deficiency disorder (CDD) is a rare developmental epileptic encephalopathy (DEE) caused by mutations in cyclin-dependent kinase-like 5 (CDKL5). The clinical manifestations include early and severe epilepsy, intellectual disability, motor abnormalities, and cortical visual impairments. The pathophysiological mechanisms underlying CDD are not fully understood, and current treatments are limited to symptomatic management and do not target the underlying cause. Characterizing the downstream molecular pathways that are disrupted by CDKL5 deficiency may provide a more complete understanding of the underlying molecular mechanisms and yield therapeutic strategies. Previous studies have focused on mapping the differential expression of protein-coding genes and post-translational modifications of CDKL5 targets, but the role of non-coding RNAs (ncRNAs) in CDD is unknown. Here we performed small RNA sequencing to define the short non-coding RNA landscape in the hippocampus of mice in the Cdkl5 exon 6 deletion mouse model (12-week-old heterozygous mice). Our findings catalog extensive bi-directional alterations in the expression of multiple ncRNA species including microRNAs, tRNAs, piwi-RNAs, snoRNAs, and snRNAs. We further validated two dysregulated miRNAs, namely, miRNA-200c-3p and miRNA-384-3p, in CDD mice. The findings reveal that the loss of this single gene has an extensive impact on the non-coding transcriptional landscape in CDD. Such dysregulated ncRNAs may hold potential as biomarkers and could provide valuable insights into underlying disease mechanisms. Full article
(This article belongs to the Section Biological Factors)
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31 pages, 5386 KB  
Article
Deep Hybrid AI Models Applied to Predict, Model, and Forecast the Next Upcoming Periods of Ozone in Craiova City
by Mihaela Tinca Udristioiu and Youness El Mghouchi
Appl. Sci. 2025, 15(22), 12187; https://doi.org/10.3390/app152212187 (registering DOI) - 17 Nov 2025
Abstract
Artificial intelligence (AI) plays an important role in analyzing air quality, providing new insights that enable informed environmental policy decisions at the local level based on air pollution modeling and forecasting. The aim of this study is to analyze various hybrid AI methods [...] Read more.
Artificial intelligence (AI) plays an important role in analyzing air quality, providing new insights that enable informed environmental policy decisions at the local level based on air pollution modeling and forecasting. The aim of this study is to analyze various hybrid AI methods to predict, model, and anticipate hourly ground-level ozone concentrations. Ground-level ozone concentrations impact human health and the environment. The data used in this study was downloaded from the website of the Romanian Agency for Environmental Protection and spans five years (2020–2024). The dataset comprises two categories of data: (i) seven meteorological parameters, including temperature (T), relative humidity, precipitation, air pressure, solar brightness, wind direction, and velocity; (ii) twenty air pollutants, including two types of particulate matter, carbon monoxide, sulfur dioxide, ground-level ozone, three types of nitrogen oxide, ammonia, six volatile organic compounds, and five toxic elements. The study follows a six-stage approach: (1) data preprocessing is conducted to identify and address anomalies, outliers, and missing values, while ozone trends are analyzed; (2) correlations between ozone concentrations and other variables are examined, considering only non-missing values; (3) data splitting is carried out in training and testing sets; (4) a total of 27 hybrid AI-based algorithms are applied to determine the optimal predictive model for ozone concentration based on related variables; (5) fifty feature selection methods are applied to find the most relevant predictors for predicting ozone concentration; (6) a novel deep NARMAX model is employed to model and anticipate hourly ozone levels in Craiova. Using a set of statistical metrics, the results of the models are assessed. This research provides a novel perspective on the robustness of the predictive performance of the proposed model. Full article
(This article belongs to the Special Issue Randomized Neural Networks and Deep Learning: Research Frontiers and Cutting-Edge Applications)
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19 pages, 4271 KB  
Review
Femoral Malunion and Its Correction: A Review
by Rahul Vaidya, Matthew Mazur, Ihunanya Agomuoh, David Abdelnour, Magd Boutany and Robert Teitge
Medicina 2025, 61(11), 2050; https://doi.org/10.3390/medicina61112050 (registering DOI) - 17 Nov 2025
Abstract
Background and Objectives: Femoral malunion, defined as healing of a femoral fracture in an anatomically incorrect position, can lead to significant biomechanical and functional impairment despite modern fixation techniques achieving union rates near 99%. The lack of a universal definition and standardized [...] Read more.
Background and Objectives: Femoral malunion, defined as healing of a femoral fracture in an anatomically incorrect position, can lead to significant biomechanical and functional impairment despite modern fixation techniques achieving union rates near 99%. The lack of a universal definition and standardized management approach continues to hinder optimal outcomes. This review aims to synthesize the literature on the causes, clinical presentation, radiologic assessment, surgical indications, corrective procedures, and outcomes of femoral malunion to guide clinical decision-making and future research. Materials and Methods: A narrative review of peer-reviewed orthopedic literature was conducted, focusing on adult femoral malunions across anatomical regions. Articles detailing deformity thresholds, imaging modalities, corrective osteotomies, and fixation strategies were included. Particular emphasis was placed on region-specific deformities—femoral head, neck, intertrochanteric, diaphyseal, and distal femur—and their corresponding surgical correction methods, including valgus intertrochanteric osteotomy, clamshell osteotomy, and lengthening with external or magnetic intramedullary devices. Results: Malunion most commonly presents as angular, rotational, or length deformity, with thresholds of >5–10° angulation, >10° rotation, or >1–2 cm shortening being clinically significant. Patients may experience pain, limp, gait asymmetry, and early-onset arthritis. Corrective techniques tailored to the anatomical site yield favorable results: valgus intertrochanteric osteotomy restores leg length and alignment; diaphyseal malunions respond well to single- or multi-plane osteotomies with internal fixation or gradual correction; distal femoral malunions often require multiplanar osteotomy to reestablish the joint line. Most series report high union rates and functional improvement, though complications such as infection and hardware failure may occur. Conclusions: Femoral malunion remains a complex but treatable condition. Successful outcomes rely on accurate deformity characterization, patient-specific surgical planning, and restoration of mechanical alignment. Standardized deformity criteria and long-term functional outcome studies are needed to refine management algorithms and improve patient care. Full article
(This article belongs to the Section Orthopedics)
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22 pages, 1524 KB  
Article
Hypergraph Neural Networks for Coalition Formation Under Uncertainty
by Gerasimos Koresis, Charilaos Akasiadis and Georgios Chalkiadakis
Algorithms 2025, 18(11), 724; https://doi.org/10.3390/a18110724 (registering DOI) - 17 Nov 2025
Abstract
Identifying effective coalitions of agents for task execution within large multiagent settings is a challenging endeavor. The problem is exacerbated by the presence of coalitional value uncertainty, which is due to uncertainty regarding the values of synergies among the different collaborating agent types. [...] Read more.
Identifying effective coalitions of agents for task execution within large multiagent settings is a challenging endeavor. The problem is exacerbated by the presence of coalitional value uncertainty, which is due to uncertainty regarding the values of synergies among the different collaborating agent types. Intuitively, in such environments, a hypergraph can be used to concisely represent coalition–task pairs in the form of hyperedges, along with their associated rewards. Therefore, this paper proposes harnessing the power of Hypergraph Neural Networks (HGNNs) that fit generic hypergraph-structured historical representations of coalitional task executions to learn the unknown values of coalitional configurations undertaking the tasks. However, the fitted model by itself cannot be used to provide suggestions on which coalitions to form; it can only be queried for the values of given coalition–task configurations. To actually provide coalitional suggestions, this work relies on informed search approaches that incorporate the output of the HGNN as an indicator of the quality of the proposed coalition configurations. The resulting approach is illustrated, via simulation results, to be able to effectively capture the uncertain values of multiagent synergies and thus suggest highly rewarding coalitional configurations. Specifically, the proposed novel hybrid approach can outperform competing baseline approaches and achieve close to 80% performance of the theoretical maximum in this setting. Full article
(This article belongs to the Special Issue Graph and Hypergraph Algorithms and Applications)
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14 pages, 4117 KB  
Article
Tool Wear Condition Monitoring Based on Improved Symmetrized Dot Pattern Enhanced Resnet18 Under Small Samples
by Xiaoqin Chen, Gonghai Wang, Yuandie Fu, Huan Zhang and Chen Gao
Lubricants 2025, 13(11), 503; https://doi.org/10.3390/lubricants13110503 (registering DOI) - 17 Nov 2025
Abstract
Timely and effective identification of the tool wear condition is crucial for ensuring the machining quality of CNC machine tools. In most industrial scenarios, the cost of sample collection is high, so only a small number of samples are available for model training, [...] Read more.
Timely and effective identification of the tool wear condition is crucial for ensuring the machining quality of CNC machine tools. In most industrial scenarios, the cost of sample collection is high, so only a small number of samples are available for model training, making it difficult for the existing tool wear condition monitoring (TCM) methods based on deep learning to achieve high performance. To address this problem, this paper proposes a TCM method based on the improved symmetric dot pattern (SDP) enhanced ResNet18. Firstly, the time series sample data is converted into grayscale matrices through SDP, the correlation coefficient between the grayscale matrices is calculated, and the optimal parameter combination of SDP is determined according to the objective of minimizing the correlation coefficient. Then, the cutting force signal is converted into a lobe diagram of the optimized SDP to enrich the sample feature information. Next, the SDP lobe diagram is input into ResNet18 for few-shot learning. The results of a series of TCM experiments demonstrate that the proposed method is significantly superior to the STFT and GAF based methods. Full article
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12 pages, 2425 KB  
Article
Direct Fabrication of Inclined-Sidewall Microgrooves with Shaped Flat-Top Beams
by Jianyong Mao, Wenqiang Chen, Kai Chen, Xun Li, Yu Tan, Ming Li and Lei Zhang
Materials 2025, 18(22), 5204; https://doi.org/10.3390/ma18225204 (registering DOI) - 17 Nov 2025
Abstract
Microgrooves have demonstrated significant application value across various fields. As a key processing technology, femtosecond laser processing exhibits notable advantages due to its high precision and minimal thermal impact. This study presents a method for fabricating microgrooves with inclined sidewalls using shaped flat-top [...] Read more.
Microgrooves have demonstrated significant application value across various fields. As a key processing technology, femtosecond laser processing exhibits notable advantages due to its high precision and minimal thermal impact. This study presents a method for fabricating microgrooves with inclined sidewalls using shaped flat-top laser beams. By controlling both the beam shape and scanning parameters, microgrooves with tailored sidewall morphology are produced in a single scan on silicon wafers. The optical performance of the fabricated structures is further evaluated through blazed grating prototypes. The experimental results aligned well with theoretical predictions. These results confirm that the proposed approach provides a viable technical pathway for efficient and high-quality fabrication of functional microstructures on comparable materials. Full article
(This article belongs to the Special Issue Laser Welding and Surface Treatment of Advanced Materials)
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14 pages, 350 KB  
Article
Assessment of Awareness, Knowledge, and Self-Reported Prevalence of Toxoplasmosis in the Tropical Zone of Saudi Arabia: A Cross-Sectional Study
by Hassan N. Moafa, Ahmad Mobarki, Sultan Moafa, Ziyad Asiri, Ahmed Hadadi, Osama M. Abualgasem, Rama M. Chandika, Jobran M Moshi, Ashwaq M Al Nazawi, Raad Shibli and Hammad Ali Fadlalmola
Trop. Med. Infect. Dis. 2025, 10(11), 323; https://doi.org/10.3390/tropicalmed10110323 (registering DOI) - 17 Nov 2025
Abstract
Background: Toxoplasmosis, caused by the parasite Toxoplasma gondii, is a zoonotic disease that poses significant health risks to immunocompromised individuals, pregnant women, and infants. Transmission occurs primarily through infected cat feces or contaminated food. Awareness of transmission routes, prevention strategies, and health [...] Read more.
Background: Toxoplasmosis, caused by the parasite Toxoplasma gondii, is a zoonotic disease that poses significant health risks to immunocompromised individuals, pregnant women, and infants. Transmission occurs primarily through infected cat feces or contaminated food. Awareness of transmission routes, prevention strategies, and health consequences remains limited in high-prevalence humid regions such as Jazan, Saudi Arabia. Methods: This cross-sectional study was conducted in Jazan, Saudi Arabia, between April and May 2025, surveying 485 adults using a five-section questionnaire covering demographics, knowledge, practices, medical history, and recommendations. The survey was distributed in both English and Arabic. Self-reported previous diagnoses were used to estimate prevalence, with risk factors presented as frequencies and percentages. Binary logistic regression analyzed categorical variables, and independent t-tests assessed continuous variables to identify predictors of awareness and knowledge regarding toxoplasmosis. Results: Participants comprised 58.6% females, 97.3% Saudis, and 69.1% individuals aged ≤30 years; 49.7% had heard of toxoplasmosis. Females (adjusted odds ratio [AOR]: 1.67, 95% CI: 1.13–2.5, p < 0.01) and those >30 years old (AOR: 1.8, 95% CI: 0.80–4.29, p > 0.05) demonstrated greater awareness and knowledge, though this was not statistically significant. No significant differences were observed based on marital status (p > 0.05). Risk behaviors included consuming unwashed fruits and vegetables (27.6%) and unpasteurized dairy products (28.2%), with 62.7% always washing hands after handling raw meat or soil. Cat ownership (20.6%) was not associated with knowledge (p = 0.97). Self-reported diagnosis prevalence was 1.9%. Conclusions: Low awareness and prevalent risky behaviors underscore the urgent need for targeted public health education interventions focusing on hygiene practices and zoonotic disease prevention in Jazan. Serological studies are recommended to obtain more accurate prevalence estimates and guide evidence-based interventions. Full article
(This article belongs to the Special Issue Advances in Toxoplasma gondii Infection Research)
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18 pages, 273 KB  
Article
Assessment of Longevity and Lifetime Productivity of Local Cattle Breeds in Relation to International Breeds
by Wioletta Sawicka-Zugaj, Witold Chabuz, Joanna Barłowska, Sebastian Mucha and Andrzej Bochniak
Animals 2025, 15(22), 3312; https://doi.org/10.3390/ani15223312 (registering DOI) - 17 Nov 2025
Abstract
This study was based on data obtained from a total of 9518 cows belonging to seven cattle breeds: local breeds—Polish White-Backed (249), Polish Red (269), Polish Black-and-White (255), and Polish Red-and-White (290)—and international breeds—Polish Holstein-Friesian (5917), Jersey (940), and Simmental (1598). The breeds [...] Read more.
This study was based on data obtained from a total of 9518 cows belonging to seven cattle breeds: local breeds—Polish White-Backed (249), Polish Red (269), Polish Black-and-White (255), and Polish Red-and-White (290)—and international breeds—Polish Holstein-Friesian (5917), Jersey (940), and Simmental (1598). The breeds were characterised in terms of the following parameters: length of life, length of productive life, milking life, longevity index, percentage share of yield in the first 305-day lactation and first complete lactation in the lifetime yield, and functional longevity. Reasons for culling and the relationship between the length of life/productive life and milk performance parameters were determined as well. The analysis of the length of life and length of productive life in seven different cattle breeds kept in Poland unequivocally demonstrates that local breeds clearly stand out in this regard in comparison to international breeds. They may in the future constitute a valuable gene reservoir for improving longevity in other breeds. The length of life of Polish White-Backed and Polish Red cows was 2817 days and 3607 days, respectively, while that of Polish Holstein-Friesian and Jersey cows was only 2131 and 1956 days, respectively, and the most common cause of culling of cows in Poland (39.07%), irrespective of breed, was reproductive problems. The favourable results of parameters related to the longevity of the local breeds of Polish Red and White-Backed show that they can become a tool for improving the longevity of international breeds. Full article
(This article belongs to the Special Issue Advancing Cattle Breeding and Production Through Genomics, Phenotyping, Bioinformatics, and Sustainable Practices)
15 pages, 1607 KB  
Article
Associations Between Th17 Cell Markers (IL-23R, CCR6, and IL-17) and Clinical Profiles in Sjögren’s Disease
by Erika Fabiola López-Villalobos, Jose Antonio Garcia-Espinoza, Mariel García-Chagollán, Jefte Felipe Uribe-Martínez, Sergio Cerpa-Cruz, José Francisco Muñoz-Valle, Claudia Azucena Palafox-Sánchez and Edith Oregon-Romero
Diagnostics 2025, 15(22), 2909; https://doi.org/10.3390/diagnostics15222909 (registering DOI) - 17 Nov 2025
Abstract
Background/objectives: Sjögren’s disease (SjD) is an autoimmune disorder characterized by lymphocytic infiltration and inflammation leading to exocrine gland dysfunction. Th17 cells play a central role in autoimmune pathology and are defined by markers such as IL-23R, CCR6, and IL-17. However, the combined characterization [...] Read more.
Background/objectives: Sjögren’s disease (SjD) is an autoimmune disorder characterized by lymphocytic infiltration and inflammation leading to exocrine gland dysfunction. Th17 cells play a central role in autoimmune pathology and are defined by markers such as IL-23R, CCR6, and IL-17. However, the combined characterization of these markers and their relevance in SjD remain poorly understood. Methods: Forty-one participants were enrolled, including twenty-two patients with SjD and nineteen control subjects (CS). Peripheral blood immunophenotyping was performed using multicolor flow cytometry, and serum cytokine concentrations were quantified within a multiplex assay. Non-parametric analyses were conducted using the Mann–Whitney U test and Spearman’s rank correlation. Results: Compared with CS, patients with SjD exhibited higher frequencies of CD3+CD4+IL-23R+ T cells and elevated IL-23 levels. The proportion of CCR6+IL-23R+ T helper cells tended to be higher in SjD than in controls, although this difference did not reach statistical significance (8.8% vs. 5.3%, p = 0.056). Within clinical subgroups, anti-Ro-negative patients showed increased frequencies of CD3+CD4+IL-23R+ cells. Patients with hypertriglyceridemia displayed reduced frequencies of CCR6+IL-23R+IFN-γ+ cells, whereas normal HDL levels were associated with CCR6 expression and IL-17A production. Conclusions: These findings highlight the heterogeneity of Th17 cells in Sjögren’s disease and reinforce the involvement of the IL-23/IL-23R axis in disease pathogenesis. Exploratory associations between Th17 subsets and lipid parameters suggest a potential immunometabolic interplay that warrants further investigation. Together, these data provide a more comprehensive view of Th17 dynamics in SjD and establish a foundation for future mechanistic studies in larger cohorts and tissue-specific contexts. Full article
(This article belongs to the Section Pathology and Molecular Diagnostics)
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11 pages, 303 KB  
Article
Induction of Labor After Fetal Demise in Third Trimester—A Retrospective Cohort Study
by Sara Vodopivec, Gorazd Kavšek, Polona Pečlin and Mirjam Druškovič
Clin. Pract. 2025, 15(11), 210; https://doi.org/10.3390/clinpract15110210 (registering DOI) - 17 Nov 2025
Abstract
Objectives: The aim of our study was to assess the efficacy and safety of two different labor induction methods in patients after fetal demise beyond 28 weeks, with an unfavorable cervix: misoprostol—prostaglandin E1 analog (PGE1) and dinoprostone—prostaglandin E2 analog (PGE2). Methods: [...] Read more.
Objectives: The aim of our study was to assess the efficacy and safety of two different labor induction methods in patients after fetal demise beyond 28 weeks, with an unfavorable cervix: misoprostol—prostaglandin E1 analog (PGE1) and dinoprostone—prostaglandin E2 analog (PGE2). Methods: This retrospective single-center cohort study included all labor cases after fetal demise (intrauterine fetal death or termination of pregnancy with feticide) from 28 to 40 weeks of gestation, where labor was induced by either PGE1 or PGE2. The primary outcome was the induction-to-delivery time interval. Secondary outcomes included the proportion of patients who delivered within 24 h, the failed induction rate, the length of labor, pain during induction, the adverse outcome rate, and the post-labor hospital stay. Results: The induction-to-delivery time interval was shorter in the PGE1 group (p = 0.048). There was no statistically significant difference in the proportion of patients who delivered within 24 h (p = 0.651) and failed inductions (p = 0.18) between groups. The duration of labor was longer in the PGE2 group (p = 0.01). Oxytocin augmentation was more common in the PGE2 group (p < 0.001). Pain during induction was greater in women in the PGE1 group (p < 0.001). There were no statistically significant differences in adverse effects between groups. There was no significant difference in induction to delivery interval between the two methods when comparing lower and higher gestational ages (28 to 34 weeks, p = 0.18; 35 to 40 weeks, p = 0.343). Conclusions: Our findings support the use of a PGE1 regimen for third-trimester labor induction after fetal demise, when no contraindications exist. This approach appears to improve the efficiency of induction and may enhance overall patient care by reducing intervention needs. Full article
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15 pages, 1573 KB  
Article
Fractional Dynamics of Information Entropy in Quantum Wire System Under Rashba Interaction
by Rabie I. Mohamed, Ramy M. Hafez, Atef F. Hashem and Mahmoud Abdel-Aty
Fractal Fract. 2025, 9(11), 741; https://doi.org/10.3390/fractalfract9110741 (registering DOI) - 17 Nov 2025
Abstract
We present a theoretical examination of the fractional dynamics of information entropy within a semiconductor nanowire system influenced by Rashba spin–orbit interaction and external magnetic fields. Moreover, we determine the fractional nanowire state through the analytical solution of the fractional Schrödinger equation, considering [...] Read more.
We present a theoretical examination of the fractional dynamics of information entropy within a semiconductor nanowire system influenced by Rashba spin–orbit interaction and external magnetic fields. Moreover, we determine the fractional nanowire state through the analytical solution of the fractional Schrödinger equation, considering various initial states of the nanowire system. Our research emphasizes the impact of the fractional order and the interaction parameters on the behavior of information entropy. Our findings reveal that the temporal behavior of information entropy is highly sensitive to any variations in the magnetic field length, the Rashba spin–orbit interaction, and the fractional order parameter. The results demonstrate that these parameters are pivotal in determining the coherence and correlation properties of the nanowire system. Therefore, precise control of these factors paves the way for enhancing entanglement performance and facilitating information transfer in spintronic and quantum communication applications. Full article
(This article belongs to the Special Issue From Fractals to Fractional Calculus: Nonlinear Dynamics and Hybrid Complex Systems)
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24 pages, 26637 KB  
Article
Numerical Analysis of a Vertical Axis Wind Turbine with Racetrack Trajectory
by Sixiong Ge, Yan Yan, Zhecheng Lou, Jie Xu, Zhehao Sheng and Jiahuan Cui
J. Mar. Sci. Eng. 2025, 13(11), 2171; https://doi.org/10.3390/jmse13112171 (registering DOI) - 17 Nov 2025
Abstract
This study presents a comprehensive numerical and theoretical analysis comparing the aerodynamic performance of a racetrack trajectory vertical axis wind turbine with a baseline VAWT. The racetrack trajectory comprises two parallel straight segments connected by semicircular arcs. However, two critical research gaps remain: [...] Read more.
This study presents a comprehensive numerical and theoretical analysis comparing the aerodynamic performance of a racetrack trajectory vertical axis wind turbine with a baseline VAWT. The racetrack trajectory comprises two parallel straight segments connected by semicircular arcs. However, two critical research gaps remain: the aerodynamic performance of this non-axisymmetric rotor, especially its sensitivity to inflow direction, is not well understood, and a computationally efficient theoretical model for its rapid design is lacking. Using unsteady Reynolds-Averaged Navier–Stokes (URANS) simulations to systematically quantify this sensitivity, and developing an adapted double multiple streamtube (DMST) model, the performance of both turbines is evaluated across tip speed ratios (TSRs) of 1.5–4 and inflow angles β = 0–90°. Results indicate that the racetrack turbine achieves a peak power coefficient of 0.49 at TSR = 2.5 and β = 90°, 16.7% higher than the baseline VAWT. Its performance is highly sensitive to inflow direction, whereas the baseline operates more uniformly across angles. Flow field and wake analyses reveal that the racetrack turbine exhibits faster wake recovery and lower turbulence intensity downstream under optimal inflow. This study demonstrates the potential of racetrack turbines for enhanced directional efficiency in marine wind conditions and validates the adapted DMST model as a reliable tool for preliminary design. Full article
(This article belongs to the Section Marine Energy)
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27 pages, 1140 KB  
Article
Flattening the Developmental Staircase: Lexical Complexity Progression in Elementary Reading Texts Across Six Decades
by Elfrieda H. Hiebert
Educ. Sci. 2025, 15(11), 1546; https://doi.org/10.3390/educsci15111546 (registering DOI) - 17 Nov 2025
Abstract
This study examined lexical complexity patterns in elementary reading textbooks across four pivotal decades (1957, 1974, 1995, 2014) to understand how educational reforms have influenced developmental progressions in reading materials. The study analyzed a corpus of 320,000 words from one continuously published core [...] Read more.
This study examined lexical complexity patterns in elementary reading textbooks across four pivotal decades (1957, 1974, 1995, 2014) to understand how educational reforms have influenced developmental progressions in reading materials. The study analyzed a corpus of 320,000 words from one continuously published core reading program across grades 1–4 for four copyrights. The corpus consisted of a 20,000-word sample for each grade and year, analyzed for type-token ratio, percentage of complex words, and percentage of single-appearing words. Results revealed three major shifts: (a) systematic within-grade complexity increases in earlier programs (1957, 1974) were replaced by flat progression in later programs (1995, 2014), (b) steep across-grade differentiation collapsed with grade-to-grade increases in lexical diversity declining from greater than 100% to under 10%, and (c) first-grade expectations accelerated dramatically, whereas third- and fourth-grade texts remained remarkably stable across all six decades. By 2014, first graders encountered lexical complexity levels that characterized fourth-grade texts in 1957. These findings challenge narratives of declining text complexity and reveal that contemporary elementary readers experience compressed developmental progressions with elevated starting points but minimal growth trajectories. The implications suggest the need for reconceptualizing text design to balance appropriate challenges with systematic scaffolding, particularly for students dependent on school-based literacy instruction. Full article
(This article belongs to the Special Issue Advances in Evidence-Based Literacy Instructional Practices)
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21 pages, 4379 KB  
Article
ReHAb Playground: A DL-Based Framework for Game-Based Hand Rehabilitation
by Samuele Rasetto, Giorgia Marullo, Ludovica Adamo, Federico Bordin, Francesca Pavesi, Chiara Innocente, Enrico Vezzetti and Luca Ulrich
Future Internet 2025, 17(11), 522; https://doi.org/10.3390/fi17110522 (registering DOI) - 17 Nov 2025
Abstract
Hand rehabilitation requires consistent, repetitive exercises that can often reduce patient motivation, especially in home-based therapy. This study introduces ReHAb Playground, a deep learning-based system that merges real-time gesture recognition with 3D hand tracking to create an engaging and adaptable rehabilitation experience built [...] Read more.
Hand rehabilitation requires consistent, repetitive exercises that can often reduce patient motivation, especially in home-based therapy. This study introduces ReHAb Playground, a deep learning-based system that merges real-time gesture recognition with 3D hand tracking to create an engaging and adaptable rehabilitation experience built in the Unity Game Engine. The system utilizes a YOLOv10n model for hand gesture classification and MediaPipe Hands for 3D hand landmark extraction. Three mini-games were developed to target specific motor and cognitive functions: Cube Grab, Coin Collection, and Simon Says. Key gameplay parameters, namely repetitions, time limits, and gestures, can be tuned according to therapeutic protocols. Experiments with healthy participants were conducted to establish reference performance ranges based on average completion times and standard deviations. The results showed a consistent decrease in both task completion and gesture times across trials, indicating learning effects and improved control of gesture-based interactions. The most pronounced improvement was observed in the more complex Coin Collection task, confirming the system’s ability to support skill acquisition and engagement in rehabilitation-oriented activities. ReHAb Playground was conceived with modularity and scalability at its core, enabling the seamless integration of additional exercises, gesture libraries, and adaptive difficulty mechanisms. While preliminary, the findings highlight its promise as an accessible, low-cost rehabilitation platform suitable for home use, capable of monitoring motor progress over time and enhancing patient adherence through engaging, game-based interactions. Future developments will focus on clinical validation with patient populations and the implementation of adaptive feedback strategies to further personalize the rehabilitation process. Full article
(This article belongs to the Special Issue Advances in Deep Learning and Next-Generation Internet Technologies)
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23 pages, 4433 KB  
Review
Autonomous Multirotor UAV Docking and Charging: A Comprehensive Review of Systems, Mechanisms, and Emerging Technologies
by Alen Šćuric, Nino Krznar, Antonia Penđer, Ivan Štedul and Denis Kotarski
Symmetry 2025, 17(11), 1988; https://doi.org/10.3390/sym17111988 (registering DOI) - 17 Nov 2025
Abstract
Multirotor Unmanned Aerial Vehicles (UAVs), characterized by their inherently symmetrical propulsion configurations, are increasingly applied across diverse domains, yet their endurance remains fundamentally constrained by the high energy demand of flight. Autonomous docking and charging systems have emerged as practical solutions, enabling UAVs [...] Read more.
Multirotor Unmanned Aerial Vehicles (UAVs), characterized by their inherently symmetrical propulsion configurations, are increasingly applied across diverse domains, yet their endurance remains fundamentally constrained by the high energy demand of flight. Autonomous docking and charging systems have emerged as practical solutions, enabling UAVs to recharge or replace batteries without human intervention. This paper provides a structured review of current approaches, offering a systematic categorization of UAV docking platforms into fixed and mobile systems, followed by an analysis of positioning and landing strategies, charging mechanisms, and modular docking concepts. Advances in vision-based guidance and sensor fusion are highlighted as key enablers of precise and reliable autonomous recovery. Contact-based charging and wireless power transfer are compared, with their benefits and limitations outlined. In addition to charging solutions, the paper presents a dedicated review of mechanisms that enable automated battery swapping, increasingly recognized as a complementary pathway to extend mission duration. By synthesizing state-of-the-art research and implementations, this study identifies key technological trends, persisting challenges, and future directions toward scalable, fully autonomous ecosystems capable of long-duration operations. Full article
(This article belongs to the Special Issue Applications Based on Symmetry in Control Systems and Robotics)
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