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Search Results (33,139)

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Keywords = model monitoring

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19 pages, 1253 KB  
Article
Enhancing Electron Transfer in Cytochrome P450 Systems: Insights from CYP119–Putidaredoxin Interface Engineering
by Akbota Kakimova and Nur Basak Surmeli
Catalysts 2025, 15(10), 1000; https://doi.org/10.3390/catal15101000 (registering DOI) - 21 Oct 2025
Abstract
Cytochrome P450 enzymes (CYPs) are versatile biocatalysts capable of performing selective oxidation reactions valuable for industrial and pharmaceutical applications. However, their catalytic efficiency is often constrained by dependence on costly electron donors, the requirement for redox partners, and uncoupling reactions that divert reducing [...] Read more.
Cytochrome P450 enzymes (CYPs) are versatile biocatalysts capable of performing selective oxidation reactions valuable for industrial and pharmaceutical applications. However, their catalytic efficiency is often constrained by dependence on costly electron donors, the requirement for redox partners, and uncoupling reactions that divert reducing power toward reactive oxygen species. Improving electron transfer efficiency through optimized redox partner interactions is therefore critical for developing effective CYP-based biocatalysts. In this study, we investigated the interaction between CYP119, a thermophilic CYP from Sulfolobus acidocaldarius, and putidaredoxin (Pdx), the redox partner of P450cam. Using rational design and computational modeling with PyRosetta 3, 14 CYP119 variants were modeled and analyzed by docking simulations on the Rosie Docking Server. Structural analysis identified three key mutations (N34E, D77R, and N34E/D77R) for site-directed mutagenesis. These mutations (N34E, D77R, and N34E/D77R) enhanced Pdx binding affinity by 20-, 3-, and 12-fold, respectively, without affecting substrate binding. Catalytic assays using lauric acid and indirect assays to monitor electron transfer revealed that, despite improved complex formation, the N34E variant showed reduced electron transfer efficiency compared to D77R. These findings highlight the delicate balance between redox partner binding affinity and catalytic turnover, emphasizing that fine-tuning electron transfer interfaces are essential for engineering efficient CYP biocatalysts. Full article
(This article belongs to the Section Biocatalysis)
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20 pages, 7704 KB  
Article
Seamless User-Generated Content Processing for Smart Media: Delivering QoE-Aware Live Media with YOLO-Based Bib Number Recognition
by Alberto del Rio, Álvaro Llorente, Sofia Ortiz-Arce, Maria Belesioti, George Pappas, Alejandro Muñiz, Luis M. Contreras and Dimitris Christopoulos
Electronics 2025, 14(20), 4115; https://doi.org/10.3390/electronics14204115 (registering DOI) - 21 Oct 2025
Abstract
The increasing availability of User-Generated Content during large-scale events is transforming spectators into active co-creators of live narratives while simultaneously introducing challenges in managing heterogeneous sources, ensuring content quality, and orchestrating distributed infrastructures. A trial was conducted to evaluate automated orchestration, media enrichment, [...] Read more.
The increasing availability of User-Generated Content during large-scale events is transforming spectators into active co-creators of live narratives while simultaneously introducing challenges in managing heterogeneous sources, ensuring content quality, and orchestrating distributed infrastructures. A trial was conducted to evaluate automated orchestration, media enrichment, and real-time quality assessment in a live sporting scenario. A key innovation of this work is the use of a cloud-native architecture based on Kubernetes, enabling dynamic and scalable integration of smartphone streams and remote production tools into a unified workflow. The system also included advanced cognitive services, such as a Video Quality Probe for estimating perceived visual quality and an AI Engine based on YOLO models for detection and recognition of runners and bib numbers. Together, these components enable a fully automated workflow for live production, combining real-time analysis and quality monitoring, capabilities that previously required manual or offline processing. The results demonstrated consistently high Mean Opinion Score (MOS) values above 3 72.92% of the time, confirming acceptable perceived quality under real network conditions, while the AI Engine achieved strong performance with a Precision of 93.6% and Recall of 80.4%. Full article
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23 pages, 6319 KB  
Article
Coordinated Trajectory Planning of Discrete-Serpentine Heterogeneous Multi-Arm Space Robot for Capturing Tumbling Targets Using Manipulability Optimization
by Zhonghua Hu, Chuntao Li, Qun Sun, Jianqing Peng and Wenshuo Li
Aerospace 2025, 12(10), 944; https://doi.org/10.3390/aerospace12100944 (registering DOI) - 21 Oct 2025
Abstract
The discrete-serpentine heterogeneous multi-arm space robot (DSHMASR) has more advantages than single discrete space robots or single serpentine space robots in complex tasks of on-orbit servicing. However, the mechanical structure complexity of the DSHMASR poses challenges for modeling and motion planning. In this [...] Read more.
The discrete-serpentine heterogeneous multi-arm space robot (DSHMASR) has more advantages than single discrete space robots or single serpentine space robots in complex tasks of on-orbit servicing. However, the mechanical structure complexity of the DSHMASR poses challenges for modeling and motion planning. In this paper, a coupled kinematic model and a coordinated trajectory planning method for the DSHMASR were proposed to address these issues. Firstly, an uncontrolled satellite and the DSHMASR were modeled based on the momentum conservation law. The generalized Jacobian matrix Jg of the space robotic system was derived. Secondly, the manipulation capability of the DSHMASR was analyzed based on the null-space of Jg. Furthermore, the cooperative capturing-monitoring trajectory planning method for DSHMASR was presented through the manipulability optimization. The expected trajectory of each arm’s tip can be obtained by pose deviations and velocity deviations between the tip and the target point. Additionally, the optimized joint velocities of each arm were calculated by combining differential kinematics and manipulability optimization. Therefore, the manipulability of DSHMASR in the direction of the capture operation was enhanced simultaneously as it approached the target satellite. Finally, the proposed algorithm was demonstrated by establishing the Adams–Simulink co-simulation model. Comparisons with traditional approaches further confirm the outperformance of the proposed method in terms of manipulation capability. Full article
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34 pages, 6603 KB  
Article
Intelligent Dental Handpiece: Real-Time Motion Analysis for Skill Development
by Mohamed Sallam, Yousef Salah, Yousef Osman, Ali Hegazy, Esraa Khatab and Omar Shalash
Sensors 2025, 25(20), 6489; https://doi.org/10.3390/s25206489 (registering DOI) - 21 Oct 2025
Abstract
Modern dental education increasingly calls for smarter tools that combine precision with meaningful feedback. In response, this study presents the Intelligent Dental Handpiece (IDH), a next-generation training tool designed to support dental students and professionals by providing real-time insights into their techniques. The [...] Read more.
Modern dental education increasingly calls for smarter tools that combine precision with meaningful feedback. In response, this study presents the Intelligent Dental Handpiece (IDH), a next-generation training tool designed to support dental students and professionals by providing real-time insights into their techniques. The IDH integrates motion sensors and a lightweight machine learning system to monitor and classify hand movements during practice sessions. The system classifies three motion states: Alert (10°–15° deviation), Lever Range (0°–10°), and Stop Range (>15°), based on IMU-derived features. A dataset collected from 61 practitioners was used to train and evaluate three machine learning models: Logistic Regression, Random Forest, Support Vector Machine (Linear RBF, Polynomial kernels), and a Neural Network. Performance across models ranged from 98.52% to 100% accuracy, with Random Forest and Logistic Regression achieving perfect classification and AUC scores of 1.00. Motion features such as Deviation, Take Time, and Device type were most influential in predicting skill levels. The IDH offers a practical and scalable solution for improving dexterity, safety, and confidence in dental training environments. Full article
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10 pages, 2921 KB  
Article
Lung Ultrasound Assessment of Lung Injury Following Acute Spinal Cord Injury in Rats
by Na Ni, Ruiliang Chu, Kai Gu and Yi Zhong
Diagnostics 2025, 15(20), 2648; https://doi.org/10.3390/diagnostics15202648 (registering DOI) - 21 Oct 2025
Abstract
Background/Objectives: Acute spinal cord injury (ASCI) often leads to pulmonary complications, yet reliable, non-invasive assessment tools are limited. This study aimed to evaluate the utility of lung ultrasound (LUS) in assessing lung injury following ASCI in a rat model. Methods: Fifty-four female Sprague [...] Read more.
Background/Objectives: Acute spinal cord injury (ASCI) often leads to pulmonary complications, yet reliable, non-invasive assessment tools are limited. This study aimed to evaluate the utility of lung ultrasound (LUS) in assessing lung injury following ASCI in a rat model. Methods: Fifty-four female Sprague Dawley rats were randomized into sham (n = 27) or ASCI (n = 27) groups. LUS was performed at 12 h, 48 h, and 1 week post-injury, with lung injury quantified using a modified B-line score (BLS). Pulmonary function was assessed non-invasively, and histopathological evaluation and wet-to-dry (W/D) weight ratios were conducted post-mortem. Correlations between BLS and functional and pathological parameters were analyzed. Results: Histological analysis revealed progressive pulmonary hemorrhage, edema, and inflammatory infiltration peaking at 48 h post-injury, with residual hemorrhage and fibroplasia at 1 week. LUS findings evolved from narrow-based B-lines at 12 h to confluent B-lines with pleural abnormalities by 1 week. ASCI rats showed significant reductions in respiratory frequency, peak inspiratory and expiratory flow, and EF50 at all time points (p < 0.05). Tidal volume and minute volume decreased initially, with partial recovery at 1 week. BLS negatively correlated with all pulmonary function parameters and positively with the histological score and W/D ratio (p < 0.001). Conclusions: LUS reliably detects and tracks lung injury after ASCI, correlating well with physiological and pathological indicators. These findings support its potential as a non-invasive monitoring tool. Future refinement of ultrasound scoring may improve clinical applicability in ASCI-related pulmonary assessment. Full article
(This article belongs to the Special Issue Critical Ultrasound in Newborns/Children)
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22 pages, 4286 KB  
Article
Optimizing Helmet Use Detection in Construction Sites via Fuzzy Logic-Based State Tracking
by Xiaoxiong Zhou, Xuejun Jia, Jian Bai, Xiang Lv, Xiaodong Lv and Guangming Zhang
Sensors 2025, 25(20), 6487; https://doi.org/10.3390/s25206487 (registering DOI) - 21 Oct 2025
Abstract
Automated safety monitoring on construction sites requires precise helmet-status detection and robust multi-object tracking in long, occlusion-rich video sequences. This study proposes a two-stage framework: (i) a YOLOv5 model enhanced with self-adaptive coordinate attention (SACA), which incorporates coordinate-aware contextual information and reweights spatial–channel [...] Read more.
Automated safety monitoring on construction sites requires precise helmet-status detection and robust multi-object tracking in long, occlusion-rich video sequences. This study proposes a two-stage framework: (i) a YOLOv5 model enhanced with self-adaptive coordinate attention (SACA), which incorporates coordinate-aware contextual information and reweights spatial–channel responses to emphasize head-region cues—SACA modules are integrated into the backbone to improve small-object discrimination while maintaining computational efficiency; and (ii) a DeepSORT tracker equipped with fuzzy-logic gating and temporally consistent update rules that fuse short-term historical information to stabilize trajectories and suppress identity fragmentation. On challenging real-world video footage, the proposed detector achieved a mAP@0.5 of 0.940, surpassing YOLOv8 (0.919) and YOLOv9 (0.924). The tracker attained a MOTA of 90.5% and an IDF1 of 84.2%, with only five identity switches, outperforming YOLOv8 + StrongSORT (85.2%, 80.3%, 12) and YOLOv9 + BoT-SORT (88.1%, 83.0%, 10). Ablation experiments attribute the detection gains primarily to SACA and demonstrate that the temporal consistency rules effectively bridge short-term dropouts, reducing missed detections and identity fragmentation under severe occlusion, varied illumination, and camera motion. The proposed system thus provides accurate, low-switch helmet monitoring suitable for real-time deployment in complex construction environments. Full article
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22 pages, 5517 KB  
Article
Medical vs. Organizational Complaints: A Machine Learning Analysis Reveals Divergent Patterns in Patient Reviews Across Russian Cities
by Irina Evgenievna Kalabikhina, Anton Vasilyevich Kolotusha and Vadim Sergeevich Moshkin
Healthcare 2025, 13(20), 2641; https://doi.org/10.3390/healthcare13202641 - 20 Oct 2025
Abstract
Background: The growth of digital patient feedback presents a new opportunity for healthcare quality monitoring. This study addresses the need to automatically classify the content of patient reviews to identify primary sources of dissatisfaction. Objective: The purpose of this study is to develop [...] Read more.
Background: The growth of digital patient feedback presents a new opportunity for healthcare quality monitoring. This study addresses the need to automatically classify the content of patient reviews to identify primary sources of dissatisfaction. Objective: The purpose of this study is to develop a machine learning algorithm for classifying negative patient reviews into two core categories: medical content (M—pertaining to diagnosis, treatment, and outcomes) and organizational support (O—pertaining to logistics, cost, and communication). We aim to identify which type of concern prevails and to analyze variations across cities, patient gender, and medical specialties. Methods: A database of 18,680 negative patient reviews (rated 1 star) was compiled from the Russian aggregator infodoctor.ru for the period from July 2012 to August 2023. A training set was created using an independent annotation procedure with three experts. A logistic regression model was trained to classify reviews into M and O categories, demonstrating an accuracy of 88.5%. Results: The analysis revealed a significant structural shift in Moscow, where since 2021, medical (M) complaints began to prevail over organizational (O) ones. This trend was not observed in St. Petersburg or other major Russian cities. Notably, in St. Petersburg, M-type reviews were more common within the most represented medical specialties, whereas O-type reviews consistently dominated in other cities. Gender differences were most pronounced in St. Petersburg, where women were more frequently authors of M reviews and men of O reviews. Conclusions: The developed algorithm provides a valuable tool for the automated monitoring of patient feedback. It enables healthcare managers to distinguish between clinical and service-related issues, facilitating targeted improvements in medical service quality and patient satisfaction. Full article
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23 pages, 3840 KB  
Article
An Optical Water Type-Based Deep Learning Framework for Enhanced Turbidity Estimation in Inland Waters from Sentinel-2 Imagery
by Yue Ma, Qiuyue Chen, Kaishan Song, Qian Yang, Qiang Zheng and Yongchao Ma
Sensors 2025, 25(20), 6483; https://doi.org/10.3390/s25206483 - 20 Oct 2025
Abstract
Turbidity is a crucial and reliable indicator that is extensively utilized in water quality monitoring through remote sensing technology. The development of accurate and applicable models for turbidity estimation is essential. While many existing studies rely on uniform models based on statistical regression [...] Read more.
Turbidity is a crucial and reliable indicator that is extensively utilized in water quality monitoring through remote sensing technology. The development of accurate and applicable models for turbidity estimation is essential. While many existing studies rely on uniform models based on statistical regression or traditional machine learning techniques, the application of deep learning models for turbidity estimation remains limited. This study proposed deep learning models for turbidity estimation based on optical classification of inland waters using Sentinel-2 data. Specifically, the fuzzy c-means (FCM) clustering method was employed to classify optical water types (OWTs) based on their spectral reflectance characteristics. A weighted sum of the turbidity prediction results was generated by the OWT-based convolutional neural network-random forest (CNN-RF) model, with weights derived from the FCM membership degrees. Turbidity for four typical waters was mapped by the proposed method using Sentinel-2 images. The FCM method efficiently classified waters into three OWTs. The OWT-based weighted CNN-RF model demonstrated strong robustness and generalization performance, achieving a high prediction accuracy (R2 = 0.900, RMSE = 11.698 NTU). The turbidity maps preserved the spatial continuity of the turbidity distribution and accurately reflected water quality conditions. These findings facilitate the application of deep learning models based on optical classification in turbidity estimation and enhance the capabilities of remote sensing for water quality monitoring. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing, Analysis and Application)
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21 pages, 1205 KB  
Article
A Hybrid CNN–LSTM–Attention Mechanism Model for Anomaly Detection in Lithium-Ion Batteries of Electric Bicycles
by Zhaoyang Sun, Weiming Ye, Yuxin Mao and Yuan Sui
Batteries 2025, 11(10), 384; https://doi.org/10.3390/batteries11100384 - 20 Oct 2025
Abstract
To improve the accuracy and stability of anomaly detection in lithium-ion batteries for electric bicycles, in this study, we propose a hybrid deep learning model that integrates a convolutional neural network (CNN), long short-term memory (LSTM) network, and attention mechanism to extract local [...] Read more.
To improve the accuracy and stability of anomaly detection in lithium-ion batteries for electric bicycles, in this study, we propose a hybrid deep learning model that integrates a convolutional neural network (CNN), long short-term memory (LSTM) network, and attention mechanism to extract local temporal features, capture long-term dependencies, and adaptively focus on key time segments around anomaly occurrences, respectively, thereby achieving a balance between local and global feature modeling. In terms of data preprocessing, separate feature sets are constructed for charging and discharging conditions, and sliding windows combined with min–max normalization are applied to generate model inputs. The model was trained and validated on large-scale real-world battery operation data. The experimental results demonstrate that the proposed method achieves high detection accuracy and robustness in terms of reconstruction error distribution, alarm rate stability, and Top-K anomaly consistency. The method can effectively identify various types of abnormal operating conditions in unlabeled datasets based on unsupervised learning. This study provides a transferable deep learning solution for enhancing the safety monitoring of electric bicycle batteries. Full article
(This article belongs to the Special Issue State-of-Health Estimation of Batteries)
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24 pages, 4921 KB  
Article
YOLOv11-DCFNet: A Robust Dual-Modal Fusion Method for Infrared and Visible Road Crack Detection in Weak- or No-Light Illumination Environments
by Xinbao Chen, Yaohui Zhang, Junqi Lei, Lelin Li, Lifang Liu and Dongshui Zhang
Remote Sens. 2025, 17(20), 3488; https://doi.org/10.3390/rs17203488 (registering DOI) - 20 Oct 2025
Abstract
Road cracks represent a significant challenge that impacts the long-term performance and safety of transportation infrastructure. Early identification of these cracks is crucial for effective road maintenance management. However, traditional crack recognition methods that rely on visible light images often experience substantial performance [...] Read more.
Road cracks represent a significant challenge that impacts the long-term performance and safety of transportation infrastructure. Early identification of these cracks is crucial for effective road maintenance management. However, traditional crack recognition methods that rely on visible light images often experience substantial performance degradation in weak-light environments, such as at night or within tunnels. This degradation is characterized by blurred or deficient image textures, indistinct target edges, and reduced detection accuracy, which hinders the ability to achieve reliable all-weather target detection. To address these challenges, this study introduces a dual-modal crack detection method named YOLOv11-DCFNet. This method is based on an enhanced YOLOv11 architecture and incorporates a Cross-Modality Fusion Transformer (CFT) module. It establishes a dual-branch feature extraction structure that utilizes both infrared and visible light within the original YOLOv11 framework, effectively leveraging the high contrast capabilities of thermal infrared images to detect cracks under weak- or no-light conditions. The experimental results demonstrate that the proposed YOLOv11-DCFNet method significantly outperforms the single-modal model (YOLOv11-RGB) in both weak-light and no-light scenarios. Under weak-light conditions, the fusion model effectively utilizes the weak texture features of RGB images alongside the thermal radiation information from infrared (IR) images. This leads to an improvement in Precision from 83.8% to 95.3%, Recall from 81.5% to 90.5%, mAP@0.5 from 84.9% to 92.9%, and mAP@0.5:0.95 from 41.7% to 56.3%, thereby enhancing both detection accuracy and quality. In no-light conditions, the RGB single modality performs poorly due to the absence of visible light information, with an mAP@0.5 of only 67.5%. However, by incorporating IR thermal radiation features, the fusion model enhances Precision, Recall, and mAP@0.5 to 95.3%, 90.5%, and 92.9%, respectively, maintaining high detection accuracy and stability even in extreme no-light environments. The results of this study indicate that YOLOv11-DCFNet exhibits strong robustness and generalization ability across various low illumination conditions, providing effective technical support for night-time road maintenance and crack monitoring systems. Full article
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24 pages, 2310 KB  
Article
Optimizing Mycophenolate Therapy in Renal Transplant Patients Using Machine Learning and Population Pharmacokinetic Modeling
by Anastasia Tsyplakova, Aleksandra Catic-Djorđevic, Nikola Stefanović and Vangelis D. Karalis
Med. Sci. 2025, 13(4), 235; https://doi.org/10.3390/medsci13040235 - 20 Oct 2025
Abstract
Background/Objectives: Mycophenolic acid (MPA) is used as part of first-line combination immunosuppressive therapy for renal transplant recipients. Personalized dosing approaches are needed to balance efficacy and minimize toxicity due to the pharmacokinetic variability of the drug. In this study, population pharmacokinetic (PopPK) modeling [...] Read more.
Background/Objectives: Mycophenolic acid (MPA) is used as part of first-line combination immunosuppressive therapy for renal transplant recipients. Personalized dosing approaches are needed to balance efficacy and minimize toxicity due to the pharmacokinetic variability of the drug. In this study, population pharmacokinetic (PopPK) modeling and machine learning (ML) techniques are coupled to provide valuable insights into optimizing MPA therapy. Methods: Using data from 76 renal transplant patients, two PopPK models were developed to describe and predict MPA levels for two different formulations (enteric-coated mycophenolate sodium and mycophenolate mofetil). Covariate effects on drug clearance were assessed, and Monte Carlo simulations were used to evaluate exposure under normal and reduced clearance conditions. ML techniques, including principal component analysis (PCA) and ensemble tree models (bagging and boosting), were applied to identify predictive factors and explore associations between MPA plasma/saliva concentrations and the examined covariates. Results: Total daily dose and post-transplant time (PTP) were identified as key covariates affecting clearance. PCA highlighted MPA dose as the primary determinant of plasma levels, with urea and PTP also playing significant roles. Boosted tree analysis confirmed these findings, demonstrating strong predictive accuracy (R2 > 0.91). Incorporating saliva MPA levels improved predictive performance, suggesting that saliva may be a complementary monitoring tool, although plasma monitoring remained superior. Simulations allowed exploring potential dosing adjustments for patients with reduced clearance. Conclusions: This study demonstrates the potential of integrating machine learning with population pharmacokinetic modeling to improve the understanding of MPA variability and support individualized dosing strategies in renal transplant recipients. The developed PopPK/ML models provide a methodological foundation for future research toward more personalized immunosuppressive therapy. Full article
(This article belongs to the Section Translational Medicine)
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30 pages, 3223 KB  
Article
Injectable In Situ Thermoreversible Gel Depot System of Lidocaine Nanoemulsion for Prolonged Anesthetic Activity in Dental and Operative Procedures
by Shery Jacob, Fathima Sheik Kather, Shakta Mani Satyam, Sai H. S. Boddu, Firas Assaf, Tasnem H. Abdelfattah Allam and Anroop B. Nair
Pharmaceutics 2025, 17(10), 1355; https://doi.org/10.3390/pharmaceutics17101355 - 20 Oct 2025
Abstract
Background/Objectives: Lidocaine hydrochloride (LD-HCl) is the most commonly used local anesthetic in dentistry, often administered with epinephrine to extend its duration and reduce systemic absorption. However, its relatively short duration of action, the need for repeated injections, and the unpleasant taste may limit [...] Read more.
Background/Objectives: Lidocaine hydrochloride (LD-HCl) is the most commonly used local anesthetic in dentistry, often administered with epinephrine to extend its duration and reduce systemic absorption. However, its relatively short duration of action, the need for repeated injections, and the unpleasant taste may limit patient compliance and procedural efficiency. This study aimed to develop and evaluate a novel injectable nanoemulsion-based in situ gel depot system of LD to provide prolonged anesthetic activity. Methods: LD-loaded nanoemulsions were formulated by high-shear homogenization followed by probe sonication, employing Miglyol 812 N (oil phase), a combination of Tween 80 and soy lecithin (surfactant–co-surfactant), glycerin, and deionized water (aqueous phase). The selected nanoemulsion (S1) was dispersed in a thermoreversible poloxamer solution to form a nanoemulgel. The preparation was evaluated for globule diameter and uniformity, zeta potential, surface morphology, pH, drug content, stability, rheological behavior, injectability, and in vitro drug release. Analgesic efficacy was assessed via tail-flick and thermal paw withdrawal latency tests in Wistar rats. Cardiovascular safety was monitored using non-invasive electrocardiography and blood pressure measurements. Results: The developed nanoemulsions demonstrated a spherical shape, nanometer size (206 nm), high zeta-potential (−66.67 mV) and uniform size distribution, with a polydispersity index of approximately 0.40, while the nanoemulgel demonstrated appropriate thixotropic properties for parenteral administration. In vitro release profiles showed steady LD release (5 h), following the Higuchi model. In vivo studies showed significantly prolonged analgesic effects lasting up to 150 min (2.5 h) compared to standard LD-HCl injection (p < 0.001), with no adverse cardiovascular effects observed. Conclusions: The developed injectable LD in situ nanoemulgel offers a promising, patient-friendly alternative for prolonged anesthetic delivery in dental and operative procedures, potentially reducing the need for repeated injections and enhancing procedural comfort. Full article
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33 pages, 1014 KB  
Article
The Paradox of AI Knowledge: A Blockchain-Based Approach to Decentralized Governance in Chinese New Media Industry
by Jing Wu and Yaoyi Cai
Future Internet 2025, 17(10), 479; https://doi.org/10.3390/fi17100479 - 20 Oct 2025
Abstract
AI text-to-video systems, such as OpenAI’s Sora, promise substantial efficiency gains in media production but also pose risks of biased outputs, opaque optimization, and deceptive content. Using the Orientation–Stimulus–Orientation–Response (O-S-O-R) model, we conduct an empirical study with 209 Chinese new media professionals and [...] Read more.
AI text-to-video systems, such as OpenAI’s Sora, promise substantial efficiency gains in media production but also pose risks of biased outputs, opaque optimization, and deceptive content. Using the Orientation–Stimulus–Orientation–Response (O-S-O-R) model, we conduct an empirical study with 209 Chinese new media professionals and employ structural equation modeling to examine how information elaboration relates to AI knowledge, perceptions, and adoption intentions. Our findings reveal a knowledge paradox: higher objective AI knowledge negatively moderates elaboration, suggesting that centralized information ecosystems can misguide even well-informed practitioners. Building on these behavioral insights, we propose a blockchain-based governance framework that operationalizes five mechanisms to enhance oversight and trust while maintaining efficiency: Expert Assessment DAOs, Community Validation DAOs, real-time algorithm monitoring, professional integrity protection, and cross-border coordination. While our study focuses on China’s substantial new media market, the observed patterns and design principles generalize to global contexts. This work contributes empirical grounding for Web3-enabled AI governance, specifies implementable smart-contract patterns for multi-stakeholder validation and incentives, and outlines a research agenda spanning longitudinal, cross-cultural, and implementation studies. Full article
29 pages, 5897 KB  
Article
Incorporating Language Technologies and LLMs to Support Breast Cancer Education in Hispanic Populations: A Web-Based, Interactive Platform
by Renu Balyan, Alexa Y. Rivera and Taruna Verma
Appl. Sci. 2025, 15(20), 11231; https://doi.org/10.3390/app152011231 - 20 Oct 2025
Abstract
Breast cancer is a leading cause of mortality among women, disproportionately affecting Hispanic populations in the U.S., particularly those with limited health literacy and language access. To address these disparities, we present a bilingual, web-based educational platform tailored to low-literacy Hispanic users. The [...] Read more.
Breast cancer is a leading cause of mortality among women, disproportionately affecting Hispanic populations in the U.S., particularly those with limited health literacy and language access. To address these disparities, we present a bilingual, web-based educational platform tailored to low-literacy Hispanic users. The platform supports full navigation in English and Spanish, with seamless language switching and both written and spoken input options. It incorporates automatic speech recognition (ASR) capable of handling code-switching, enhancing accessibility for bilingual users. Educational content is delivered through culturally sensitive videos organized into four categories: prevention, detection, diagnosis, and treatment. Each video includes embedded and post-video assessment questions aligned with Bloom’s Taxonomy to foster active learning. Users can monitor their progress and quiz performance via a personalized dashboard. An integrated chatbot, powered by large language models (LLMs), allows users to ask foundational breast cancer questions in natural language. The platform also recommends relevant resources, including nearby treatment centers, and support groups. LLMs are further used for ASR, question generation, and semantic response evaluation. Combining language technologies and LLMs reduces disparities in cancer education and supports informed decision-making among underserved populations, playing a pivotal role in reducing information gaps and promoting informed healthcare decisions. Full article
19 pages, 752 KB  
Article
Four-Year Monitoring Survey of Pesticide Residues in Tomato Samples: Human Health and Environmental Risk Assessment
by Alessandro Atzei, Hamza Bouakline, Francesco Corrias and Alberto Angioni
J. Xenobiot. 2025, 15(5), 171; https://doi.org/10.3390/jox15050171 - 20 Oct 2025
Abstract
A four-year survey was conducted to monitor the presence of multiple pesticide residues contaminating tomatoes, with the aim of evaluating the potential health and environmental risks. A multiresidue liquid chromatography–triple mass spectrometry with a multiple reaction monitoring (LC-MS/MS-MRM) method was fully validated and [...] Read more.
A four-year survey was conducted to monitor the presence of multiple pesticide residues contaminating tomatoes, with the aim of evaluating the potential health and environmental risks. A multiresidue liquid chromatography–triple mass spectrometry with a multiple reaction monitoring (LC-MS/MS-MRM) method was fully validated and used to test 252 pesticides in 360 samples analysed. According to SANTE guidelines, the proposed method was considered suitable for the purpose. Dietary risk assessment was conducted using the Hazard Quotient (HQ) approach and the European Food Safety Authority (EFSA) Pesticide Residue Intake Model; meanwhile, the cumulative environmental risk assessment was conducted using the Concentration Addition (CA) and Independent Action (IA) methods. Data obtained revealed multiple contaminations in most fields examined over the years. Twenty-two pesticide residues were identified, comprising 68.2% fungicides, 27.3% insecticides, and the remaining 4.5% acaricides. Higher levels were detected for Boscalid in 2022 in three fields, with an average value of 0.42 mg/kg. Multi-residue contamination occurred each year; the lowest abundance was detected in 2023 (3.9%), and the highest in 2022 (12.3%), with 5 pesticide residues as the maximum number of compounds detected in one sample in 2022. The consumer risk assessment identified no potential health concerns for adults or toddlers, and the combined risk was considered acceptable. The environmental assessment showed maximum cumulative ratio (MCR) values that were always ≥1, indicating a contribution to the toxicity of the mixture, only slightly higher than that of the single compound with the highest toxicity. The results of this study highlight the critical need to include cumulative dietary exposure assessments in pesticide risk evaluations, especially for food products that are susceptible to contamination by multiple residues. Full article
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