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18 pages, 4740 KB

Open AccessArticle

Functionalized Siloxane Coating as Protection of the Surface of Cement Composites Against Phototropic Colonization

by Joanna Karasiewicz, Marta Thomas, Paulina Nowicka-Krawczyk, Rafał M. Olszyński, Piotr K. Zakrzewski and Agnieszka Ślosarczyk

Int. J. Mol. Sci. 2026, 27(3), 1586; https://doi.org/10.3390/ijms27031586 (registering DOI) - 5 Feb 2026

Abstract

This article presents the concept of using a functionalised siloxane compound HOL9 with amphiphilic properties as a coating for cement composites to enhance their antifouling properties against algae. The biological properties of the compound were assessed based on its ability to inhibit chlorophyll [...] Read more.

This article presents the concept of using a functionalised siloxane compound HOL9 with amphiphilic properties as a coating for cement composites to enhance their antifouling properties against algae. The biological properties of the compound were assessed based on its ability to inhibit chlorophyll fluorescence intensity, which is used as an indicator of photosynthetic activity and biofilm development. The greatest decrease in algal photosynthetic activity was observed for a 10% aqueous solution of HOL9 applied by painting. In these conditions, the maximum ^chlFI value decreased by 97.6%. In addition, the impact of the protective coating containing HOL9 on the fundamental physical and mechanical characteristics of the cement composite, along with its resilience to frost cycling, was thoroughly investigated. The coating applied by immersion demonstrated a 50.7% strength loss after 150 freeze–thaw cycles, while the coating applied by painting exhibited a 43.8% loss. In comparison, the control samples experienced a 42.8% strength reduction. It has been demonstrated that the method of application, the modifier concentration, and the type of solvent can have a substantial impact on the protective properties of concrete. The most marked inhibition of algae photosynthetic activity was observed with a 10% aqueous solution applied by painting. Full article

(This article belongs to the Special Issue Molecular Advancements in Functional Materials)

25 pages, 634 KB

Open AccessArticle

Branding Seoul: Multi-Celebrity Participation in Destination Branding

by Riela Provi Drianda, Nadia Ayu Rahma Lestari and Meyriana Kesuma

Tour. Hosp. 2026, 7(2), 39; https://doi.org/10.3390/tourhosp7020039 (registering DOI) - 5 Feb 2026

Abstract

This study examines multi-celebrity deployment as a destination branding practice, using Seoul as an empirical case. The analysis draws on 172 official tourism promotional videos released by the Seoul Tourism Organization between 2011 and 2025, featuring 67 identifiable celebrities and 438 destination references. [...] Read more.

This study examines multi-celebrity deployment as a destination branding practice, using Seoul as an empirical case. The analysis draws on 172 official tourism promotional videos released by the Seoul Tourism Organization between 2011 and 2025, featuring 67 identifiable celebrities and 438 destination references. A qualitative content analysis examines how celebrity endorsement is organized as a branding mechanism, focusing on who appears, what is represented, and how representations are communicated across media formats over time. The findings show that Seoul’s tourism promotion operates through a structured multi-celebrity branding system in which multiple endorsers are coordinated across campaigns and periods. Endorser selection is anchored in Hallyu-affiliated celebrities who function as primary carriers of destination meaning, while emerging, non-Hallyu, and heritage-linked figures occupy complementary roles that broaden representational scope and reduce reliance on individual figures. Celebrity endorsement continues to emphasize major and symbolically dense attractions, while also extending visibility to everyday neighborhoods and locally oriented urban landscapes. Long-term ambassador-led campaigns coexist with travel vlogs and other creative video formats, enabling variation in narrative tone and experiential framing. Theoretically, the study extends celebrity endorsement research by conceptualizing multi-celebrity deployment as a coordinated branding system. Practically, the findings show how destination marketing organizations can mobilize a broad pool of celebrity resources to structure endorsement portfolios over time. Coordinated use of celebrities with different levels of familiarity supports wider spatial representation, enables ongoing narrative renewal, and maintains promotional continuity across changing media environments. This configuration is most applicable to destinations with strong cultural visibility and an established celebrity ecosystem, and may be less transferable to destinations with limited access to influential figures. Full article

39 pages, 2591 KB

Open AccessReview

Prostate Cancer-Associated Fibroblasts: A Review on CAF Functions, Heterogeneity, Resistance Mechanisms, and Future in a Chip

by Nikolett Lupsa, Erika Heninger, Adeline B. Ding, Cristina Sanchez De Diego, Katherine Vietor, Shannon R. Reese, Aaron M. LeBeau, David Kosoff, David J. Beebe, Sheena C. Kerr and Joshua M. Lang

Int. J. Mol. Sci. 2026, 27(3), 1585; https://doi.org/10.3390/ijms27031585 (registering DOI) - 5 Feb 2026

Abstract

Cancer-associated fibroblasts (CAFs) are key regulators of the prostate tumor microenvironment (TME) with influence on disease progression and therapeutic response. CAFs originate from multiple precursors and retain remarkable plasticity while tumors evolve. Therefore, the CAF pool displays considerable functional heterogeneity, which is well-reflected [...] Read more.

Cancer-associated fibroblasts (CAFs) are key regulators of the prostate tumor microenvironment (TME) with influence on disease progression and therapeutic response. CAFs originate from multiple precursors and retain remarkable plasticity while tumors evolve. Therefore, the CAF pool displays considerable functional heterogeneity, which is well-reflected in complex molecular signatures. However, overlapping biomarker patterns with other stromal subsets make it challenging to identify and assess the role of specific CAF subpopulations. Through reciprocal tumor–stroma interactions, CAFs promote extracellular matrix (ECM) remodeling, angiogenesis, metabolic reprogramming, and immune evasion, collectively fostering an adaptive niche that supports tumor survival, though some CAF subsets have been shown to support anti-tumor response. In prostate cancer (PCa), CAFs promote resistance to androgen receptor pathway inhibitor therapy, chemotherapy, and radiotherapy, emphasizing their potential value as therapeutic targets. However, CAF targeting has shown limited clinical benefit in PCa, due to complex, context-dependent CAF functions that make it challenging to exploit this unique stromal population for therapeutic gain. Recent advances in organ-on-a-chip (OOC) models offer new opportunities to investigate the mechanisms behind TME interactions and evaluate CAF-targeted strategies in physiologically relevant fully humanized environments. This review provides current insights into CAF heterogeneity and therapy resistance in PCa and highlights emerging translational OOC models to guide the development of more effective therapies to disrupt the TME. Full article

(This article belongs to the Special Issue Cancer-Associated Stromal Cells)

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28 pages, 1384 KB

Open AccessReview

Artificial Intelligence for Exosomal Biomarker Discovery for Cardiovascular Diseases: Multi-omics Integration, Reproducibility, and Translational Prospects

by Rasit Dinc and Nurittin Ardic

Cells 2026, 15(3), 304; https://doi.org/10.3390/cells15030304 (registering DOI) - 5 Feb 2026

Abstract

Exosomes and other extracellular vesicles (EVs) carry microRNAs, proteins, and lipids that reflect cardiovascular pathophysiology and can enable minimally invasive biomarker discovery. However, EV datasets are highly dimensional and heterogeneous, strongly influenced by pre-analytic variables and non-standardized isolation/characterization workflows, limiting reproducibility across studies. [...] Read more.

Exosomes and other extracellular vesicles (EVs) carry microRNAs, proteins, and lipids that reflect cardiovascular pathophysiology and can enable minimally invasive biomarker discovery. However, EV datasets are highly dimensional and heterogeneous, strongly influenced by pre-analytic variables and non-standardized isolation/characterization workflows, limiting reproducibility across studies. Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and network-based approaches, can support EV biomarker development by integrating multi-omics profiles with clinical metadata. These approaches enable feature selection, disease subtyping, and interpretable model development. Among the AI approaches evaluated, ensemble methods (Random Forest, gradient boosting) demonstrate the most consistent performance for EV biomarker classification (AUC 0.80–0.92), while graph neural networks (GNNs) are particularly promising for path integration but require larger validation cohorts. Evolutionary neural networks applied to EV morphological features yield comparable discrimination but face interpretability challenges for clinical use. Current studies report promising discrimination performance for selected EV-derived panels in acute myocardial infarction and heart failure. However, most evidence remains exploratory, based on small cohorts (n < 50) and limited external validation. For clinical implementation, EV biomarkers need direct comparison against established standards (high-sensitivity troponin and natriuretic peptides), supported by locked-in assay plans, and validation in multicenter cohorts using MISEV-aligned protocols and transparent AI reporting practices. Through a comprehensive, integrative, and comparative analysis of AI methodologies for EV biomarker discovery, together with explicit criteria for reproducibility and translational readiness, this review establishes a practical framework to advance exosomal diagnostics from exploratory research toward clinical implementation. Full article

18 pages, 15522 KB

Open AccessArticle

Fabrication of Copper Nanowires Highly Conductive and Flexible Circuits by Direct Ink Writing

by Hui Guo, Haoting Huang, Shijian Shi, Qinghua Sun, Jinping Sun, Kang Liu, Qiang Zhu and Peng Zhang

Materials 2026, 19(3), 618; https://doi.org/10.3390/ma19030618 (registering DOI) - 5 Feb 2026

Abstract

Direct ink writing (DIW) has emerged as a promising method for fabricating flexible electronics. Copper nanowires are a key material for the conductive inks required for this technology. However, copper nanowires suffer from significant challenges, including low aspect ratios, poor oxidation resistance, and [...] Read more.

Direct ink writing (DIW) has emerged as a promising method for fabricating flexible electronics. Copper nanowires are a key material for the conductive inks required for this technology. However, copper nanowires suffer from significant challenges, including low aspect ratios, poor oxidation resistance, and difficulty in printing. In this study, a liquid-phase reduction method was used to synthesize copper nanowires with a high aspect ratio (up to 2884) and excellent oxidation resistance. The conductive ink was prepared using ethylene glycol, isopropanolamine (MIPA), and ethanol as solvents. Rheological dynamics simulations were used to investigate the influence of printing parameters on ink printing accuracy, ultimately achieving precise control of the printing process. High-precision copper nanowire flexible circuits with a low resistivity of 2.11 μΩ·cm were fabricated under thermal sintering conditions using the DIW method. These circuits exhibited excellent adhesion, flexural behavior, and water resistance, demonstrating significant practical significance for the low-cost fabrication of high-precision flexible electronic devices. Full article

(This article belongs to the Special Issue Advancements in Plastic Forming and Metal Processing: From Fundamentals to Applications)

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25 pages, 3065 KB

Open AccessArticle

From Elderflower to Bioactive Extracts: Phytochemical Characterization and Anti-Inflammatory Activity

by Maryna Koval, Sławomir Dresler, Sandra Kowalik, Benedikt Schwarz, Anna Wawruszak, Anna Filipek, Małgorzata Komar, Thomas Jakschitz, Stavros Beteinakis, Günther Bonn, Wojciech Koch and Wirginia Kukula-Koch

Molecules 2026, 31(3), 561; https://doi.org/10.3390/molecules31030561 (registering DOI) - 5 Feb 2026

Abstract

This study provides a phytochemical characterization of Sambucus nigra L. (elderflower) and correlates its chemical profile with anti-inflammatory bioactivity, establishing an optimized extraction methodology. A comparative analysis of ultrasound-assisted extraction (UAE), accelerated solvent extraction (ASE), and shaking maceration was conducted using solvents of [...] Read more.

This study provides a phytochemical characterization of Sambucus nigra L. (elderflower) and correlates its chemical profile with anti-inflammatory bioactivity, establishing an optimized extraction methodology. A comparative analysis of ultrasound-assisted extraction (UAE), accelerated solvent extraction (ASE), and shaking maceration was conducted using solvents of varying polarity (ethanol, ethanol–water mixture (1:1, v/v), and water). High-resolution fingerprinting via HPLC-ESI-QTOF-MS/MS confirmed a rich polyphenolic profile, dominated by flavonoids such as rutin, naringenin, and phenolic acids, notably chlorogenic acid. Quantitative analysis revealed that UAE with ethanol–water mixture (1:1, v/v) for 20 min yielded the highest recovery of rutin (4.87%) and chlorogenic acid (8.22%). The anti-inflammatory potential was evaluated in TNFα-stimulated HaCaT NF-κB Luc reporter keratinocytes. Anhydrous ethanolic extracts demonstrated superior efficacy, significantly inhibiting NF-κB pathway activation at non-cytotoxic concentrations. Chemometric analysis, specifically PLS-DA, identified naringenin as a principal contributor to this observed anti-inflammatory effect. These findings underscore the critical role of solvent selection in modulating the phytochemical composition and resultant bioefficacy of elderflower extracts. The potent, naringenin-driven inhibition of NF-κB in keratinocytes highlights the significant therapeutic potential of optimized S. nigra extracts for applications in dermatological and cosmetic formulations aimed at managing inflammatory skin disorders. Full article

(This article belongs to the Special Issue Anti-Aging and Skin Rejuvenation Ingredients: Design and Research)

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15 pages, 920 KB

Open AccessArticle

Modeling of Delivery Infrastructure for Solving Problems by Type of Goods

by Jamshid Barotov, Ziyoda Mukhamedova, Jamshid Kobulov, Rashida Tursunkhodjaeva, Shuxrat Saidivaliyev, Rustam Abdullayev and Diyor Boboyev

Systems 2026, 14(2), 184; https://doi.org/10.3390/systems14020184 (registering DOI) - 5 Feb 2026

Abstract

The paper introduces a novel intelligent modeling system of a railway cargo delivery which combines queuing theory and station-level technological activities to model the manner in which re-handling and waiting processes produce delivery delays. The proposed model is in contrast to the available [...] Read more.

The paper introduces a novel intelligent modeling system of a railway cargo delivery which combines queuing theory and station-level technological activities to model the manner in which re-handling and waiting processes produce delivery delays. The proposed model is in contrast to the available literature, which focuses more on routing or time management; it clearly connects processing of the stations, queue behavior, and reliability of the delivery in one decision system. When applied to a real-life railway route, the optimization of technological sequences is demonstrated to decrease delivery time and congestion rates significantly, as well as decrease the possibility of punishment in case of late deliveries. These findings show that the study is original in terms of the presentation of a data-driven and operationally based approach on the enhancement of railway freight performance. This study introduces a shipment-type-specific intelligent delivery model that integrates queuing theory with real station technological processes. Unlike existing approaches focused mainly on routing or average travel time, the proposed framework explicitly accounts for wagon processing sequences, re-handling operations, and delay-risk assessment. Validation on the Khamza–Bukhara corridor demonstrates a reduction in intermediate re-handlings from four to two and total delivery time from 68 h to 54 h, confirming the operational and economic effectiveness of the model. Full article

(This article belongs to the Special Issue Optimization-Based Decision-Making Models in Rail Systems Engineering)

18 pages, 298 KB

Open AccessReview

A Survey on Quantum Machine Learning Applications in Medicine and Healthcare

by Radosław Idzikowski, Mateusz A. Kucharski, Konrad Pempera and Michał Jaroszczuk

Appl. Sci. 2026, 16(3), 1630; https://doi.org/10.3390/app16031630 (registering DOI) - 5 Feb 2026

Abstract

Quantum machine learning (QML) is an emerging field combining quantum computing and artificial intelligence, with promising applications in medicine and healthcare. This survey reviews more than 60 studies published between 2018 and 2025, highlighting a sharp increase in research activity, especially in the [...] Read more.

Quantum machine learning (QML) is an emerging field combining quantum computing and artificial intelligence, with promising applications in medicine and healthcare. This survey reviews more than 60 studies published between 2018 and 2025, highlighting a sharp increase in research activity, especially in the last three years. We address seven core research questions related to publication trends, the use of real quantum hardware versus simulators, quantum architectures overview, dataset types, medical domains, algorithmic frameworks, and reported results. Our analysis shows that most QML research in healthcare is conducted on simulators due to limited hardware access, and it relies on small datasets. Quantum convolutional neural network (QCNN) architectures dominate image-based medical tasks such as tumor detection, pneumonia diagnosis, and ECG interpretation, while feature-based datasets are mainly analyzed with variational quantum classifiers and quantum support vector machines. Despite hardware constraints, QML models often match or surpass classical machine learning approaches in accuracy, frequently reaching 95–99%. However, these performance statements should be qualified to recognize experimental limitations and avoid excessive optimism and should not be interpreted as definitive proof of quantum superiority at this stage. Additionally, issues with reproducibility and reporting of hardware details persist, which is a significant research gap. This review emphasizes the need for standardized benchmarks, more real hardware testing, and architecture-aware algorithm design. With the potential for accelerated diagnostics and personalized healthcare, QML represents a strategic direction for future medical research. Full article

(This article belongs to the Section Quantum Science and Technology)

17 pages, 2085 KB

Open AccessArticle

Performance Evaluation of Nano Ag/Co Modified Hydroxyapatite Catalyst Synthesized via Dielectric Barrier Discharge for Highly Efficient Toluene Oxidation

by Shu-Yao Zhang, Xue-Min Wang, En-Peng Deng, Ya-Ni Zhang, Hui Zhu, Qiang Chen, Si-Wen Pan and Yu-Xin Miao

ChemEngineering 2026, 10(2), 26; https://doi.org/10.3390/chemengineering10020026 (registering DOI) - 5 Feb 2026

Abstract

In this study, a series of Ag/Co-HA catalysts were synthesized using a plasma-assisted method. Plasma is a partially ionized gas composed of electrons, ions, neutral molecules, free radicals, photons, and excited-state substances, which can serve as a highly reactive medium for catalyst modification. [...] Read more.

In this study, a series of Ag/Co-HA catalysts were synthesized using a plasma-assisted method. Plasma is a partially ionized gas composed of electrons, ions, neutral molecules, free radicals, photons, and excited-state substances, which can serve as a highly reactive medium for catalyst modification. Its unique discharge characteristics can effectively regulate the dispersion of active sites, electronic structure, and metal–support interactions. The study compared the performance of catalysts prepared by the traditional high-temperature calcination method with those treated by rapid plasma in the toluene oxidation removal reaction. The results showed that the catalyst treated by dielectric barrier discharge (DBD) plasma exhibited excellent low-temperature catalytic activity, achieving 100% toluene conversion and approximately 75% CO₂ selectivity at 275 °C, while the catalyst prepared by traditional calcination only achieved 73% toluene conversion and approximately 50% CO₂ selectivity at 285 °C. This study provides a simple preparation method for the Ag/5Co-HA-P catalyst. Due to the plasma treatment’s ability to precisely control the catalyst structure, along with advantages such as low energy consumption, short processing time, and environmental friendliness, it holds significant application prospects in the field of VOCs treatment. Full article

(This article belongs to the Special Issue Advanced Functional Materials and Interfaces for Electrochemical Energy Storage and Environmental Catalysis)

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16 pages, 3059 KB

Open AccessArticle

Comparative Evaluation of YOLO- and Transformer-Based Models for Photovoltaic Fault Detection Using Thermal Imagery

by Mahdi Shamisavi, Isaac Segovia Ramirez and Carlos Quiterio Gómez Muñoz

Energies 2026, 19(3), 845; https://doi.org/10.3390/en19030845 (registering DOI) - 5 Feb 2026

Abstract

Photovoltaic systems represent one of the most reliable and widely used technologies for electricity generation from renewable energy sources, although their performance is affected by the occurrence of faults and defects that lead to energy losses and efficiency reduction. Therefore, detecting and localizing [...] Read more.

Photovoltaic systems represent one of the most reliable and widely used technologies for electricity generation from renewable energy sources, although their performance is affected by the occurrence of faults and defects that lead to energy losses and efficiency reduction. Therefore, detecting and localizing defects in photovoltaic panels is essential. A wide variety of image analysis techniques based on aerial thermal imagery acquired by drones have been widely implemented for proper maintenance operations, requiring a comprehensive comparison among these approaches to assess their relative performance and suitability for different scenarios. This study presents a comparative evaluation of several vision-based approaches using artificial intelligence for photovoltaic defect detection. YOLO- and Transformer-based models are analyzed and benchmarked in terms of accuracy, inference time, per-class performance, and sensitivity to object size. Experimental results demonstrate that both YOLO- and Transformer-based models are computationally lightweight and suitable for real-time implementation. However, Transformer-based architectures exhibit higher detection accuracy and stronger generalization capabilities, while YOLOv5 achieves superior inference speed. The RF-DETR-Small model provides the best balance between accuracy, computational efficiency, and robustness across different defect types and object scales. These findings highlight the potential of Transformer-based vision models as a highly effective alternative for real-time, on-site photovoltaic fault detection and predictive maintenance applications. Full article

(This article belongs to the Special Issue Renewable Energy System Forecasting and Maintenance Management)

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32 pages, 2652 KB

Open AccessArticle

Risk Factor Analysis of Single Motorcycle Accidents in Road Traffic

by Edward Kozłowski, Mateusz Traczyński, Przemysław Skoczyński, Piotr Jaskowski and Radovan Madlenak

Appl. Sci. 2026, 16(3), 1629; https://doi.org/10.3390/app16031629 (registering DOI) - 5 Feb 2026

Abstract

This research examines the risk factors that influence injury severity in individual motorcycle accidents, utilising a dataset of 5253 incidents. Five machine learning algorithms—multinomial logistic regression, classification trees, random forests, XGBoost, and neural networks—were used to classify the results into three groups: Death [...] Read more.

This research examines the risk factors that influence injury severity in individual motorcycle accidents, utilising a dataset of 5253 incidents. Five machine learning algorithms—multinomial logistic regression, classification trees, random forests, XGBoost, and neural networks—were used to classify the results into three groups: Death (13.48%), Injury (80.14%), and No injury (6.38%). In all models, passenger presence was the most important predictor of injury. Motorcycle accidents involving passengers do not always have more serious consequences for several overlapping reasons. On the one hand, a motorcycle with a passenger has a significantly higher mass, which increases the braking distance and kinetic energy at the moment of collision, hindering quick defensive manoeuvres, cornering, and reactions to sudden hazards. Often, the rider also refrains from sudden movements to prevent the passenger from losing their balance. In the case of single-rider motorcycle accidents on roadways, approximately 5% of those involved with a passenger were fatalities, while approximately 48% were uninjured; in the case of those without a passenger, no one was uninjured. It follows from the above that the presence of a passenger increases the rider’s sense of responsibility. Other factors that significantly increased risk were single-lane carriageways, vehicle overturning, contaminated road surfaces, and collisions with complex objects, e.g., like trees. The multinomial logistic regression model had an overall accuracy of 69.2% on the test set. The Recurrent Neural Network achieved the best overall accuracy of 79.56%. Balanced accuracy, as the average between sensitivity and specificity of the RNN model for the “death” class was 68.15%, for the “injury” class—72.6%, and for the “no injury” class—96.61%. The Area Under the ROC Curve of the Recurrent Neural Networks model for “no injury” was 0.97, indicating it was very good at distinguishing between this class and the other classes. Even though it was easy to tell which cases did not involve injuries, it was still hard to tell the difference between fatal and non-fatal injuries in all models. The results support interventions tailored to specific situations, such as improved road lighting and speed control in rural areas, as well as helmet enforcement and safety measures at intersections in cities. Full article

(This article belongs to the Special Issue New Challenges in Vehicle Dynamics and Road Traffic Safety)

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24 pages, 5185 KB

Open AccessArticle

An Evaluation Study of PET Image Quality Factors in Brain Tumor Diagnosis

by Ali Albweady

Tomography 2026, 12(2), 20; https://doi.org/10.3390/tomography12020020 (registering DOI) - 5 Feb 2026

Abstract

Objectives: This retrospective, multi-center study analyzed pre-existing anonymized clinical data from electronic health records and imaging archives. The analysis utilized real-world clinical data from 200 patients across four tertiary care centers, without additional patient recruitment or interventions. This study aims to investigate [...] Read more.

Objectives: This retrospective, multi-center study analyzed pre-existing anonymized clinical data from electronic health records and imaging archives. The analysis utilized real-world clinical data from 200 patients across four tertiary care centers, without additional patient recruitment or interventions. This study aims to investigate the impact of metabolic and physiological factors—specifically blood glucose levels, cortisol concentrations, fasting duration, and tumor histology—on the quality and diagnostic reliability of ¹⁸F-FDG PET/CT imaging in patients with primary brain tumors and inflammatory lesions. Methods: A total of 200 patients with primary brain tumors (including astrocytoma, glioblastoma, meningioma, and oligodendroglioma) were evaluated across four institutions using standardized protocols. The study examined the effects of prolonged fasting (>12 h), hyperglycemia (>150 mg/dL), and strict fasting (4–6 h) on tumor-to-background contrast and visual analog scale (DQS) scores. Results: Prolonged fasting was associated with elevated cortisol levels (correlation +0.54, p < 0.001), while hyperglycemia significantly reduced tumor SUVmax by up to 20% (r = −0.35, p = 0.012). Strict fasting and glucose control resulted in improved tumor-to-background contrast and DQS scores (r = +0.83, p < 0.001). Glioblastomas exhibited the highest SUVmax (9.1 ± 3.5), indicating aggressive metabolic activity, whereas meningiomas showed elevated cortisol levels (20.5 ± 6.8 µg/dL) linked to disruption of the hypothalamic–pituitary axis. Regression analysis confirmed that both cortisol and glucose levels independently degraded image quality (β = −0.25 and −0.18, respectively; p < 0.05). Conclusions: The findings highlight the necessity for harmonized patient preparation protocols. Recommendations are in alignment with the SNMMI Procedure Standard/EANM Practice Guideline for Brain [¹⁸F] FDG PET imaging. Full article

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20 pages, 435 KB

Open AccessArticle

How Healthcare Professionals Perceive Emergency Pediatric Care Provision in Two Public Hospitals in Greece: A Cross-Sectional Study

by Eleni Vathi, Konstantinos Petsios, Evangelos Dousis, Ioannis Koutelekos, Despoina Koumpagioti, Eirini Anastasopoulou, Anastasia Ntikoudi, Eugenia Vlachou and Eleni Evangelou

Pediatr. Rep. 2026, 18(1), 27; https://doi.org/10.3390/pediatric18010027 (registering DOI) - 5 Feb 2026

Abstract

Background/Objectives: High-quality pediatric emergency care requires timely access, effective communication, privacy, pain management, comfort, and child- and family-centered practices; however, implementation may be constrained by several barriers. The aim of the study was to evaluate the quality of pediatric emergency care as [...] Read more.

Background/Objectives: High-quality pediatric emergency care requires timely access, effective communication, privacy, pain management, comfort, and child- and family-centered practices; however, implementation may be constrained by several barriers. The aim of the study was to evaluate the quality of pediatric emergency care as perceived by healthcare professionals, with emphasis on child-centered care and variations based on workplace and professional characteristics. Methods: A cross-sectional survey was performed in the emergency departments in two tertiary public pediatric hospitals in Athens, Greece. A study-developed 14-item Quality of Care Assessment Scale with paired ratings of agreement with quality principles and implementation in practice was completed by 162 professionals (122 doctors, 24 nurses, 16 assistant nurses). Independent items evaluated perceived barriers, overall assessments (0–100), and information provided to parents/children (5-point Likert scale). Inferential tests and descriptive statistics were also used (p < 0.05). Results: There was a significant degree of agreement with quality principles, but there was a constant lack of implementation (principle–practice gap). The primary perceived weakness was waiting times; child-friendly settings and privacy during examinations and information-giving were also lacking. Internal consistency ranged from good to acceptable (implementation α = 0.800; agreement α = 0.711). Children were most frequently rated as “moderately informed” (48.1%), while parents were most frequently rated as “quite informed” (50.0%). Compared to the organization of care (mean 60.85), perceived safety was higher (mean 73.27). Perceptions varied by age, educational level, profession, department, shift rotations, and hospital. The main barriers were workload (30.2%), poor coordination (34.0%), and lack of resources (46.9%). Conclusions: Health professionals seem to perceive that consistent delivery of child-centered care is impaired by organizational and structural limitations. Reducing the standards-to-practice gap requires targeted system-level interventions that focus on staffing, care organization, environment, and professional support. Full article

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2 pages, 237 KB

Open AccessArticle

Impact of Uneven Lighting Environments on Guide Sign Visibility in Interchange Areas of Road Tunnels: A Study of Bright-to-Dark Transitions in Diverging Area

by Yuping Zhang, Jiangbi Hu, Zechao Zhang, Ronghua Wang, Shousong Dong and Yansong Zhang

Appl. Sci. 2026, 16(3), 1624; https://doi.org/10.3390/app16031624 (registering DOI) - 5 Feb 2026

Abstract

To investigate the impact of “bright to dark” uneven lighting conditions on driver recognition of retroreflective guide signs in the diverging zones of underwater ramp interchanges, this study adjusts the luminance contrast between adjacent lighting segments in a tunnel to explore its effect [...] Read more.

To investigate the impact of “bright to dark” uneven lighting conditions on driver recognition of retroreflective guide signs in the diverging zones of underwater ramp interchanges, this study adjusts the luminance contrast between adjacent lighting segments in a tunnel to explore its effect on the recognition distance of retroreflective guide signs, thereby providing a basis for the rational design of lighting conditions in this area. Based on the driver’s safe recognition process, the minimum sight distance requirements for retroreflective guide signs were first determined under three representative operating speeds. Following the tunnel lighting design principles specified in Chinese standards, eight dark-environment luminance levels and nineteen bright-environment luminance levels were established. A total of 124 non-uniform “bright–dark” lighting combinations were then generated by varying the luminance difference in 1 cd/m² increments. Subsequently, 24 passenger car drivers were selected to conduct dynamic recognition experiments of guide signs under the “bright–dark” lighting transition conditions in the tunnel ramp diverging zone. Recognition distances were measured using a non-contact speedometer to capture the driver’s distance to the sign. A regression model was developed to quantify the relationship between sign recognition distance, luminance difference, and dark-environment luminance. The model’s accuracy is reflected in the fact that 92.7% of the predicted values had an absolute error of less than 10 m compared to the observed values. The results show that luminance difference has a significant impact on recognition distance, which increases initially and then stabilizes as luminance difference grows. When the dark-environment luminance is below 3.5 cd/m², the effect of luminance difference on recognition distance is more pronounced than when it exceeds 3.5 cd/m². Based on these findings, threshold values of bright-environment luminance ensuring safe recognition distances under varying dark-environment luminance conditions are proposed. It is further recommended that, for design speeds of 100 km/h or higher, the dark-environment luminance should not be lower than 2.5 cd/m² to maintain safe visibility of retroreflective guide signs. Full article

11 pages, 713 KB

Open AccessArticle

Distinct Coagulation Phenotypes and Long-Term Neurological Outcomes in Post-Cardiac Arrest Syndrome: A Latent Class Analysis of a 9-Year Single-Center Cohort

by Sin Young Park, Sang Hoon Oh, Hyo Joon Kim, Han Joon Kim and Jee Yong Lim

J. Clin. Med. 2026, 15(3), 1287; https://doi.org/10.3390/jcm15031287 (registering DOI) - 5 Feb 2026

Abstract

Background/Objectives: Post-cardiac arrest syndrome (PCAS) induces systemic ischemia–reperfusion injury accompanied by sepsis-like coagulopathy. This coagulopathy presents heterogeneously, yet distinct coagulation phenotypes and their impact on hypoxic–ischemic brain injury (HIBI) remain poorly defined. We aimed to identify coagulation phenotypes using latent class analysis (LCA) [...] Read more.

Background/Objectives: Post-cardiac arrest syndrome (PCAS) induces systemic ischemia–reperfusion injury accompanied by sepsis-like coagulopathy. This coagulopathy presents heterogeneously, yet distinct coagulation phenotypes and their impact on hypoxic–ischemic brain injury (HIBI) remain poorly defined. We aimed to identify coagulation phenotypes using latent class analysis (LCA) and assess their association with 6-month neurological outcomes. Methods: We retrospectively analyzed adult out-of-hospital cardiac arrest (OHCA) patients treated with targeted temperature management (TTM) between 2011 and 2019 from a prospective registry at a tertiary academic center. LCA was performed using coagulation biomarkers measured at admission and 24 h post-return of spontaneous circulation: D-dimer, fibrinogen, antithrombin III (ATIII), platelet count, and PT-INR. The primary outcome was poor neurological outcome (Cerebral Performance Category 3–5) at 6 months. Secondary outcomes included in-hospital mortality and cerebral edema severity assessed by gray-to-white matter ratio (GWR) on brain CT. Results: Among 325 patients, LCA identified three phenotypes: Class 1 (Preserved Coagulation, 36.9%), Class 2 (Hypercoagulable State, 41.5%) characterized by elevated D-dimer with preserved fibrinogen and ATIII, and Class 3 (Consumptive Coagulopathy, 21.5%) marked by profound D-dimer elevation with fibrinogen <150 mg/dL and ATIII <60%. Class 3 exhibited the lowest GWR and highest neuron-specific enolase levels. In multivariable analysis adjusting for age, low-flow time, initial rhythm, and lactate, Class 3 independently predicted poor neurological outcome (adjusted OR 4.52; 95% CI 2.15–9.48), whereas Class 2 did not. Conclusions: PCAS-related coagulopathy is heterogeneous. A consumptive coagulopathy phenotype identifies a high-risk subgroup associated with severe brain injury and poor long-term neurological outcomes. Early identification of this phenotype may enable targeted prognostication and guide future phenotype-specific interventional strategies.: Full article

(This article belongs to the Special Issue Cardiac Arrest and Postcardiac Arrest Care: State of the Art and Future Directions)

19 pages, 2197 KB

Open AccessFeature PaperArticle

Rumen Microbial Composition and Fermentation Variables Associated with Methane Production in Italian Simmental Dairy Cows

by Cristina Pavanello, Marcello Franchini, Alberto Romanzin, Lara Tat, Stefano Bovolenta and Mirco Corazzin

Animals 2026, 16(3), 510; https://doi.org/10.3390/ani16030510 (registering DOI) - 5 Feb 2026

Abstract

The study investigated differences in ruminal and fecal microbiota composition, fermentation traits, and volatile organic compounds (VOC) in Simmental dairy cows classified as high (HME) or low (LME) methane emitters. Methane emissions from 48 cows were quantified using the Laser Methane Smart portable [...] Read more.

The study investigated differences in ruminal and fecal microbiota composition, fermentation traits, and volatile organic compounds (VOC) in Simmental dairy cows classified as high (HME) or low (LME) methane emitters. Methane emissions from 48 cows were quantified using the Laser Methane Smart portable gas detector. The 12 animals with the highest and lowest emissions were selected and assigned to the HME and LME groups, respectively, balanced for body weight, days in milk, and body condition score. Rumen fluid and fecal samples were analyzed for pH, ammonia, volatile fatty acids (VFA), VOC, and microbiota composition. As expected, CH₄ emissions were significantly higher in HME than in LME cows (22.5 vs. 13.2 g/kg DMI; 16.9 vs. 8.4 g/kg FCM). The neutral detergent fiber digestibility was higher in HME cows (51.4% vs. 47.9%). The valeric acid concentration and the acetate-to-propionate ratio were significantly higher in HME cows (3.53 vs. 3.31). The VOC profiles significantly differed between groups in both feces and rumen fluid. The microbiota analysis revealed a significant difference between groups at the order and genus levels (Bray–Curtis dissimilarity). The Shannon index was higher in LME cows (2.08 vs. 1.95). HME cows exhibited a higher abundance of Methanosphaera and Methanobacteriales. Overall, the results indicate that re-shaping the rumen microbial community can play a key role in reducing methane emissions, strengthening the case for microbiome-driven approaches and offering insights that can support mitigation strategies across dairy production systems. Full article

(This article belongs to the Section Cattle)

18 pages, 2396 KB

Open AccessArticle

Microbiological and Physicochemical Parameters Related to Legionella spp. Colonisation in Hot Water Distribution Systems in Madrid: Case Study in Sports Centres

by María Concepción Almonacid Garrido, Alejandra García-Alonso, María José Villanueva-Suárez and María Dolores Tenorio-Sanz

Appl. Sci. 2026, 16(3), 1628; https://doi.org/10.3390/app16031628 (registering DOI) - 5 Feb 2026

Abstract

Domestic hot water systems are one of the most important reservoirs of Legionella. It is thought that physicochemical and microbiological water quality influences bacterial development. Accordingly, the objective of this study was to evaluate this relationship in domestic hot water in public [...] Read more.

Domestic hot water systems are one of the most important reservoirs of Legionella. It is thought that physicochemical and microbiological water quality influences bacterial development. Accordingly, the objective of this study was to evaluate this relationship in domestic hot water in public buildings in Madrid for potential health risks and to assess the parameters that could be associated with Legionella contamination, which would assist in developing control strategies to prevent legionellosis. A total of 1695 DHW samples were evaluated over a 14-year period (2007–2020). Legionella was analysed using culture plates and qPCR. The influencing parameters (pH, electrical conductivity, colour, turbidity, Fe nitrites, and coliforms) were analysed following official methods. Furthermore, sport centre risk assessment was carried out. Legionella was isolated in 64 samples. Non-compliance levels for turbidity, colour, iron, nitrites and coliforms were found primarily in samples containing Legionella. Nitrites > 0.5 mg/L, turbidity > 1 NFU, colour ≥ 1 Pt/Co, and building type were good parameters to test Legionella colonisation. The selected influencing factors may be a useful tool for ensuring water supply quality. Full article

(This article belongs to the Special Issue New Approaches to Water Treatment: Challenges and Trends, 2nd Edition)

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15 pages, 475 KB

Open AccessArticle

On the Problem of Forming Sustainable Production Schedules in the Context of Conflicting Objective Functions of Management Agents

by Zhanna V. Burlutskaya, Irina V. Vatamaniuk, Aleksei M. Gintciak, Daria A. Ablavatskaia and Kapiton N. Pospelov

Sustainability 2026, 18(3), 1655; https://doi.org/10.3390/su18031655 (registering DOI) - 5 Feb 2026

Abstract

This study addresses the foundational step of developing a classification and taxonomy of agent objective functions as a prerequisite for analyzing stability and forming robust production schedules in distributed manufacturing systems. The research is based on the premise that instability or insufficient robustness [...] Read more.

This study addresses the foundational step of developing a classification and taxonomy of agent objective functions as a prerequisite for analyzing stability and forming robust production schedules in distributed manufacturing systems. The research is based on the premise that instability or insufficient robustness in scheduling solutions often arises from the neglect of the inherent multi-agent nature of real-world distributed production systems. These systems are characterized by the presence of multiple decision-making entities, each pursuing its own objectives or performance indicators. Since strategic management in such systems is typically oriented toward achieving global system-level goals, it often overlooks the interests of individual agents. As a result, the implemented decisions may encounter resistance from specific agents and lead to deterioration in the performance of their individual objective functions. These features underline the need to develop tools for identifying robust solutions, in which both the system as a whole and its constituent agents can achieve sustainably high performance across their respective objectives. The aim of this study is to analyze the divergent objective functions of management agents in distributed manufacturing systems in the context of forming robust production schedules. The research explores typical objective functions of structural units within the production system and presents their classification in terms of constraints, nature, granularity, behavioral orientation, and inter-agent dependency. The outcomes of the study include a comprehensive taxonomy of agent objective functions, along with the selection of relevant game-theoretic models for each pair of agents based on their interaction strategies. The findings contribute to the development of methodological and technological tools for decision support in sustainable manufacturing, extending current research on intelligent agent modeling and coordination in complex production environments. Full article

25 pages, 943 KB

Open AccessArticle

Environmental Policy Stringency, Low-Carbon Innovation, and GVC Functional Specialization: The Moderating Roles of National Innovation Systems and Entrepreneurship

by Jiumei Lin, Yaya Fan and Zhi Xu

Systems 2026, 14(2), 183; https://doi.org/10.3390/systems14020183 (registering DOI) - 5 Feb 2026

Abstract

This study investigates the impact of Environmental Policy Stringency (EPS) on GVC functional specialization. We find that EPS promotes high value-added, low-carbon upstream and downstream specialization—supporting the “Porter Hypothesis (PH)”—while simultaneously driving carbon-intensive production to regions with lax regulations, validating the “Pollution Haven [...] Read more.

This study investigates the impact of Environmental Policy Stringency (EPS) on GVC functional specialization. We find that EPS promotes high value-added, low-carbon upstream and downstream specialization—supporting the “Porter Hypothesis (PH)”—while simultaneously driving carbon-intensive production to regions with lax regulations, validating the “Pollution Haven Hypothesis (PHH)”. These findings demonstrate that both effects coexist across distinct GVC stages. Heterogeneity analysis reveal that policy impacts vary different instruments: market-based instruments drive upstream functional specialization, whereas non-market measures drive downstream functional specialization. In terms of temporal dynamics, the Paris Agreement intensified the PHH in production activities while catalyzing medium-to-long-term incentives for upstream and downstream specialization. The influence of EPS on GVC structural adjustments has strengthened notably since the Paris Agreement, reflecting a significant temporal lag and long-term efficacy. Mechanistically, low-carbon innovation serves as the primary channel for functional upgrading, an effect significantly amplified by robust national innovation systems (NIS) and entrepreneurship. Meanwhile, NIS and entrepreneurship partly amplify the positive effect of EPS on high-end functional specialization. From a GVC functional perspective, this study offers new evidence reconciling the PH and the PHH. Full article

(This article belongs to the Section Systems Practice in Social Science)

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22 pages, 12628 KB

Open AccessArticle

Research on the Evolution of Human–Land Patterns and Influencing Factors in the Mountainous Regions of Southwest China

by Qingsong Ni, Zongfeng Chen, Chenlin Wang and Xueqi Liu

Land 2026, 15(2), 269; https://doi.org/10.3390/land15020269 (registering DOI) - 5 Feb 2026

Abstract

Against the backdrop of rapid urbanization, the human–land relationship in the mountainous regions of Southwest China (Sichuan, Yunnan, Guangxi, Chongqing, and Guizhou) confronts dual pressures from terrain constraints and development demands, shaping a uniquely complex evolutionary pattern. To clarify the evolutionary laws of [...] Read more.

Against the backdrop of rapid urbanization, the human–land relationship in the mountainous regions of Southwest China (Sichuan, Yunnan, Guangxi, Chongqing, and Guizhou) confronts dual pressures from terrain constraints and development demands, shaping a uniquely complex evolutionary pattern. To clarify the evolutionary laws of the regional human–land system, this study focuses on the period of 2000–2020, integrating land use, socioeconomic, and topographic data to construct a comprehensive analytical framework of “Human Activity Intensity (HAI)–Land Use Dynamic Degree (LUDD)–decoupling model–geographic detector.” This framework is employed to explore the spatio-temporal evolution characteristics of the human–land pattern, the differentiation of decoupling modes, and the underlying driving mechanisms. The key findings are as follows: Human Activity Intensity (HAI) presents a stable spatial pattern of “agglomeration in low-altitude areas and dispersion in high-altitude areas,” undergoing a three-stage temporal evolution of “terrain anchoring–policy constraint–all-round expansion.” Land use dynamics are predominantly governed by terrain: low-altitude river valley plains exhibit significant changes, while high-altitude karst regions remain relatively stable, with an overall policy-responsive fluctuation of “rise–fall–rebound.” Human–land decoupling forms a continuous spectrum encompassing four modes: “collaborative optimization–extensive transition–rigid stagnation–advantageous aggregation,” with strong negative decoupling dominating low-altitude favorable areas and recessive decoupling prevailing in high-altitude mountainous areas. In terms of driving mechanisms, terrain factors serve as the rigid foundation of the human–land relationship, while the urban–rural population structure, urbanization level, and land use intensity act as core human drivers. Additionally, the interaction of factors such as “terrain–economy–transportation” plays a crucial role in the differentiation of decoupling modes. This study clarifies the evolutionary logic of “terrain laying the foundation and human factors shaping the pattern” for the human–land relationship in Southwest China’s mountainous regions, providing scientific support for the coordinated advancement of regional economic development and ecological protection, as well as a Chinese case study for global research on human–land coordination in ecologically fragile mountainous areas. Full article

(This article belongs to the Special Issue Coupled Man-Land Relationship for Regional Sustainability)

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13 pages, 1250 KB

Open AccessArticle

Tritium and Plutonium Time Series from the Puruogangri Ice Field, Tibetan Plateau, China

by László Palcsu, Miruna Cotan, Lide Tian, Cheng Wang, Liu Feng, Xu Chenhao, Yu Songlin, Magdolna Szilágyi, Loránd Zákány, Zoltán Dezső, Danny Vargas and Marjan Temovski

Water 2026, 18(3), 425; https://doi.org/10.3390/w18030425 (registering DOI) - 5 Feb 2026

Abstract

Ice cores retrieved from the Third Pole provide invaluable information about past and present environmental changes. Here we present, for the first time, a continuous tritium and plutonium isotope profile of the Puruogangri ice field, Tibetan Plateau, China, for the last 70 years. [...] Read more.

Ice cores retrieved from the Third Pole provide invaluable information about past and present environmental changes. Here we present, for the first time, a continuous tritium and plutonium isotope profile of the Puruogangri ice field, Tibetan Plateau, China, for the last 70 years. The age-depth profile has been composed by different time anchors such as the onset of thermonuclear weapon tests, the so-called bomb peak of tritium, the Chernobyl event, and the time of ice coring. The accumulation rate of ice calculated from the age-depth relation shows a decrease after 1963. It was 57, 15, and 22 cm/year in the periods of 1954–1963, 1963–1986, and 1986–2023, respectively. The concentrations of plutonium isotopes (²³⁹Pu: up to 2.7 fg/g) are slightly lower than those of the Belukha ice core, Siberian Altai, Russia, and almost the same as the Miaoergou glacier, eastern Tien Shan, China. Contrary to this latter ice core profile, the Puruogangri plutonium profile reflects that the Chinese weapon test started in 1966. This is confirmed by the tritium time series as well. ²⁴⁰Pu/²³⁹Pu atomic ratios vary between 0.14 and 0.23, with an average of 0.177 ± 0.024. The overall obtained local fallout of ²³⁹Pu and ²⁴⁰Pu is 13.2 and 9.0 Bq/m² (4.0 and 1.1 ng/m²), respectively. Full article

(This article belongs to the Section Water and Climate Change)

19 pages, 2190 KB

Open AccessArticle

Spectral Characterization of Prospidium Chloride Using Complementary Analytical Techniques

by Antoniy A. Kladiev, Elena V. Uspenskaya, Mikhail G. Baryshev, Vasilii A. Ivlev, Vasilii G. Vasil’ev, Samvel S. Barsegyan and Ainaz Safdari

Sci. Pharm. 2026, 94(1), 15; https://doi.org/10.3390/scipharm94010015 (registering DOI) - 5 Feb 2026

Abstract

The clinical efficacy of chemotherapy against rapidly proliferating cells stimulates both the development of new agents and the reassessment of established drugs. Spectroscopic methods (UV, FT-IR, and ¹H NMR) were applied to characterize prospidium chloride and related substances. The FT-IR spectrum of [...] Read more.

The clinical efficacy of chemotherapy against rapidly proliferating cells stimulates both the development of new agents and the reassessment of established drugs. Spectroscopic methods (UV, FT-IR, and ¹H NMR) were applied to characterize prospidium chloride and related substances. The FT-IR spectrum of prospidium chloride, arising from vibrational transitions within the alkyl fragments of the dispirotripiperazinium cation, is reported with band assignments. Electronic transitions between molecular orbitals are analyzed using quantum–mechanical selection rules (Laporte and spin selection rules). The n→σ* transition (ΔS = 0) corresponds to the absorption maximum at λmax = 282 ± 0.40 nm (ε = 3.89 ± 0.08 L·mol⁻¹·cm⁻¹). A ¹H NMR spectrum (700 MHz) was used to assign chemical shifts δ (ppm), J-coupling constants (Hz), and gauche conformational features of prospidium chloride and its dihydroxy and epoxy impurities. Quantitative ¹H NMR (qNMR) was applied to determine the content of the active pharmaceutical ingredient and related substances. The methods provide complementary structural information for the characterization of prospidium chloride. Full article

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27 pages, 3345 KB

Open AccessArticle

Distributive Disturbances: Examining Community Exposure to Drinking Water Contaminants Amidst the Jackson, Mississippi (USA) Water Crisis

by Ambria N. McDonald, Yolanda J. McDonald, Andrea Chow, Julia Kosinski and Dorceta E. Taylor

Water 2026, 18(3), 424; https://doi.org/10.3390/w18030424 (registering DOI) - 5 Feb 2026

Abstract

Community water systems in the United States provide drinking water to more than 300 million people annually, making their reliability fundamental to public health. In regions with long histories of racial segregation and unequal infrastructure maintenance, water system failures can deepen existing environmental [...] Read more.

Community water systems in the United States provide drinking water to more than 300 million people annually, making their reliability fundamental to public health. In regions with long histories of racial segregation and unequal infrastructure maintenance, water system failures can deepen existing environmental injustices. This study examines water quality conditions in the Jackson, Mississippi, metropolitan area following the 2022 distribution system collapse and a decade of repeated noncompliance with the Safe Drinking Water Act’s Lead and Copper Rule (LCR). Using the U.S. Environmental Protection Agency’s 2024 updated LCR tap sampling protocol, water samples from 29 sites were collected. Samples were analyzed for lead, copper, iron, zinc, chlorine, sulfate, pH, and total dissolved solids concentrations. Chlorine-to-sulfate mass ratios (CSMR) were also calculated to evaluate corrosion potential. Demographic surveys, statistical analyses, and geospatial visualizations were used to interpret neighborhood-level patterns. Our findings show that all sites met primary drinking water standards and complied with LCR action levels but exceeded secondary drinking water standards at 100% of study sites. Seven sites exhibited CSMR values above the threshold, indicating increased susceptibility to corrosion. These results highlight the need for targeted corrosion control, treatment optimization, and ongoing monitoring, particularly in historically marginalized communities. Full article

(This article belongs to the Special Issue Spatial Linkages Between Geographical Environments and Health Indicators: From Water Quality Parameters to Regional Health Status)

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4 pages, 563 KB

Open AccessInteresting Images

Palatal Mucosal Inflammation Caused by an Unusual Foreign Body in an Infant

by Shunya Ikeda, Yuko Iwamoto, Masashi Ogawa, Tatsuya Akitomo and Ryota Nomura

Diagnostics 2026, 16(3), 493; https://doi.org/10.3390/diagnostics16030493 (registering DOI) - 5 Feb 2026

Abstract

Infants may place various objects in their mouths during the developmental process, which can sometimes involve life-threatening risks, such as choking. We describe the case of a 1-year 3-month-old female with a foreign body in the oral cavity. She was referred to our [...] Read more.

Infants may place various objects in their mouths during the developmental process, which can sometimes involve life-threatening risks, such as choking. We describe the case of a 1-year 3-month-old female with a foreign body in the oral cavity. She was referred to our hospital with chief complaints of suspected supernumerary teeth and blisters, and the initial examination revealed blister-like swelling and a white swelling on the hard palate. Intraoral photographs were obtained and examined from multiple angles, revealing findings that resembled a character. Careful re-examination showed that a three-dimensional sticker was attached to the hard palate, which could be removed in one piece. It is important for dental professionals to conduct intraoral examinations of pediatric patients with the understanding that unexpected findings may be present, and think about a foreign body in palatal lesions. In addition, this report highlights a new risk for caregivers supervising infants, as seemingly harmless stickers can remain in the mouth for extended periods. Full article

(This article belongs to the Section Clinical Diagnosis and Prognosis)

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18 pages, 4473 KB

Open AccessArticle

A Cooperative Optimization Method for Speed Planning and Energy Management of Fuel Cell Buses at Multi-Signalized Intersections

by Wei Guo, Fengyan Yi, Jiaming Zhou, Jinming Zhang, Shuo Wang, Hongtao Gong, Shuaihua Wang, Zongjing Huang and Chunrui Liu

World Electr. Veh. J. 2026, 17(2), 79; https://doi.org/10.3390/wevj17020079 (registering DOI) - 5 Feb 2026

Abstract

Urban bus operations under signalized traffic conditions are characterized by frequent stop-and-start behaviors which significantly degrade fuel economy, especially for fuel cell buses (FCB). In this paper, a collaborative optimization method is proposed that combines speed planning and energy management for FCB in [...] Read more.

Urban bus operations under signalized traffic conditions are characterized by frequent stop-and-start behaviors which significantly degrade fuel economy, especially for fuel cell buses (FCB). In this paper, a collaborative optimization method is proposed that combines speed planning and energy management for FCB in this situation. The method calculates the target speed of FCB using traffic light phase information and the remaining signal time. With an intelligent driving model, the vehicle can adjust its speed in advance when approaching intersections so it can pass through intersections without stopping. At the same time, a learning-based energy management strategy is used to reasonably share power between the fuel cell and the battery. The results indicate that the method proposed in this paper reduces hydrogen consumption by approximately 11.3% compared to the standard method. Full article

(This article belongs to the Section Vehicle Management)

30 pages, 2539 KB

Open AccessArticle

Machine Learning–Driven MPPT Control of PEM Fuel Cells with DC–DC Boost Converter Integration

by Ayşe Kocalmış Bilhan, Cem Haydaroğlu, Heybet Kılıç and Mahmut Temel Özdemir

Electronics 2026, 15(3), 701; https://doi.org/10.3390/electronics15030701 (registering DOI) - 5 Feb 2026

Abstract

Proton exchange membrane fuel cells (PEMFCs) are attractive energy sources for clean and efficient power generation; however, their nonlinear characteristics and sensitivity to operating condition variations make maximum power point tracking (MPPT) a challenging control problem. Conventional MPPT techniques often exhibit slow convergence, [...] Read more.

Proton exchange membrane fuel cells (PEMFCs) are attractive energy sources for clean and efficient power generation; however, their nonlinear characteristics and sensitivity to operating condition variations make maximum power point tracking (MPPT) a challenging control problem. Conventional MPPT techniques often exhibit slow convergence, steady-state oscillations, and degraded performance under dynamic fuel flow variations. This paper proposes a machine learning–driven MPPT control strategy for a PEMFC system integrated with a DC–DC boost converter. The MPPT problem is formulated as a supervised classification task, where machine learning classifiers generate duty-cycle commands to regulate the converter and ensure operation at the maximum power point. A detailed PEMFC–converter model is developed in MATLAB/Simulink-2025b, and a dataset of 3000 labeled samples is generated under varying fuel flow conditions. Several classification algorithms, including decision trees, support vector machines (SVM), k-nearest neighbors (kNN), and ensemble learning methods, are systematically evaluated within an identical simulation framework. Simulation results show that the proposed machine learning-based MPPT controller significantly improves dynamic and steady-state performance. Ensemble Boosted Trees achieve the best overall response with a settling time of approximately 32 ms, peak power overshoot below 4.5%, and steady-state power ripple limited to 1.5%. Quadratic SVM and weighted kNN classifiers also demonstrate stable tracking behavior with power ripple below 2.1%, while overly complex models such as Cubic SVM suffer from large oscillations and reduced accuracy. These results confirm that classification-based machine learning offers an effective, fast, and robust MPPT solution for PEMFC systems under dynamic operating conditions. Full article

(This article belongs to the Special Issue Artificial Intelligence and Advanced Signal Processing Techniques and Their Applications)

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