Search Results (107)

Objectives: This scoping review synthesized the available evidence on bedside ultrasonography used to confirm short-term nasogastric tube (NGT) placement in adults. Methods: The review followed JBI Collaboration methodology. Searches were conducted in CINAHL, Embase, LILACS, PubMed, and Scopus, as well as in gray literature sources (Google Scholar and ProQuest Dissertation & Thesis Global). Primary studies and clinical guidelines addressing bedside ultrasonography for short-term NGT placement in adults (≥18 years) were eligible, with no limits on language or publication year. Data were extracted and narratively summarized with the I-AIM framework (Indication, Acquisition, Interpretation, and Decision-Making). Results: Twenty-nine studies met the inclusion criteria. Most were single-center observational studies performed in intensive care units or emergency departments. Ultrasound was primarily used for confirmation prior to enteral nutrition initiation, while gastric decompression was less frequently reported. Acquisition protocols varied, although supine positioning, convex abdominal probes, and linear cervical probes were most commonly described. The gastric antrum and esophagus were the principal anatomical landmarks, with interpretation based on direct tube visualization and dynamic fogging; color Doppler was occasionally used. Radiography remained the reference standard in most studies, and only a minority initiated feeding based solely on ultrasound findings. Reported facilitators included bedside feasibility, absence of radiation exposure, and timeliness. Barriers included operator dependency, limited visualization in patients with obesity or gas interposition, protocol heterogeneity, and the limited methodological robustness of available studies. Conclusions: Current evidence suggests that ultrasonography may represent a feasible, radiation-free bedside approach for confirmation of NGT placement. Evidence from selected studies suggests that, with structured training, healthcare professionals may achieve diagnostic accuracy in specific clinical settings, although further robust multicenter investigations are needed to confirm these findings. Full article

Background: Freezing of gait (FoG) is a common symptom of Parkinson’s disease, especially in its later stages of progression. Characterized by involuntary stopping during normal gait patterns, FoG greatly increases fall risk, reducing quality of life. Given the complex presentation and etiology of FoG, current treatments have proven ineffective in managing episodes. In recent years, machine learning algorithms have been leveraged to derive actionable clinical insights from biomedical datasets. As a manifestation of neuromechanical dysfunction, impending FoG episodes may be characterized through data collected by wearable devices and sensors. Objective: This scoping review evaluates the current landscape of machine and deep learning-derived biomarkers to enhance the personalized management of FoG. Methods: This scoping review was conducted using established methodological frameworks for scoping reviews and is reported in accordance using the PRISMA-ScR checklist. Three databases were queried, with screening yielding 60 studies. Results: Thirty-nine papers reported on deep learning techniques, with the most common architectures being convolutional neural networks and long short-term memory models. Conclusions: Inertial measurement units, which can be worn on various locations, may be a promising modality for practical implementation. To generate closed-loop FoG therapies, algorithms can be integrated into real-time systems like robotic exoskeletons or adaptive deep brain stimulation. Future work in generating datasets from ambulatory devices, as well as distributed computing strategies, may lead to real-time FoG management. Full article

Condensation of water vapor into discrete droplets on the surface of transparent optical devices-commonly known as fogging-severely degrades their optical performance. To address this issue, a highly transparent, self-healing, and durable polymer-based anti-fogging coating was developed via a facile one-pot copolymerization of 2-acrylamido-2-methylpropanesulfonic acid (AMPS), acrylic acid (AA), and vinyltrimethoxysilane (VTMOS). The chemical structure and composition were thoroughly characterized. The introduction of VTMOS constructs a hydrophilic-hydrophobic microphase structure through in situ formation of a Si–O–Si network, which significantly enhances the mechanical stability and water resistance. The polymer coating can maintain high transparency (>90%) under extreme conditions (85 °C steam and −40 °C freezing), exhibits long-term anti-frosting performance for 180 days, and demonstrates rapid water-assisted self-healing within 30 s. Differential scanning calorimetry (DSC) analysis reveals that each polymer unit binds approximately seven water molecules, elucidating the mechanism behind its exceptional anti-frosting capability. This work presents a practical strategy for designing high-performance, long-lasting anti-fogging coatings suitable for extreme environment applications. Full article

This paper presents PAF-Net, an image dehazing framework that integrates physical priors and adaptive feature modeling. Its aims to enhance image restoration quality in complex non-uniform haze scenes, while maintaining moderate model complexity and computational cost. Built upon the classical FFA-Net backbone, PAF-Net enhances dehazing performance from three complementary perspectives: spatial attention decision, physics-consistent feature representation in feature space, and operator-level adaptability. These perspectives are realized by three corresponding modules. Specifically, a Fog-aware Concentration Attention (FCA) module is introduced. It embeds haze-related spatial guidance into attention-weight generation, reducing attention–allocation imbalance under spatially non-uniform haze and strengthening the model’s focus on dense-haze regions. To improve physical feature consistency under non-uniform haze, a Physics-aware Feature Dehazing Unit (PFDU) is further designed to explicitly model and reorganize transmission-related and atmospheric-light-related feature components under atmospheric scattering priors. In addition, a Dynamic Convolution Module (DCM) is incorporated to adapt convolutional responses at the sample level according to global degradation patterns, enhancing robustness across diverse haze conditions. Experiments on RESIDE SOTS and real-world benchmarks (Dense-Haze and NH-Haze) demonstrate that PAF-Net achieves higher PSNR/SSIM and yields more natural visual results than representative methods. Further experiments and evaluations based on paired remote sensing datasets have verified the applicability of the designed PAF-Net algorithm in remote sensing scenes with spatially varying haze, as well as the algorithm’s generalization ability. Full article

This systematic review synthesizes passive and passive-first cooling strategies for greenhouses in hot–arid climates, organizing evidence across four domains: Airflow & Ventilation, Shading & Radiative Control, Thermal Storage & Ground Coupling, and Structural Design & Geometry. Drawing on the project corpus, we analyze 10–13 distinct techniques including ridge and side natural ventilation, windcatchers and solar chimneys, external shade nets, NIR-selective and transparent radiative-cooling films, and dynamic PV shading; earth-to-air heat exchangers (EAHE/GAHT), rock-bed sensible storage, phase-change materials (PCMs), and sunken or buried envelopes; as well as roof slope and shape, span number, and orientation. Across studies, cooling outcomes are reported as peak or daytime indoor air temperature reductions, defined relative either to outdoor conditions or to a control greenhouse, with the reference frame and temporal aggregation specified in the synthesis. Typical outcomes include ≈3–7 °C daytime reduction for optimized ventilation, ≈2–4 °C for shading and spectral covers while preserving PAR, ≈5–7 °C intake cooling for EAHE with winter pre-heating, and up to ≈14 °C peak attenuation for rock-bed storage under favorable conditions. Structural choices consistently amplify these effects by sustaining pressure head and limiting thermal heterogeneity. Performance is strongly context-dependent—governed by wind regime, diurnal amplitude, dust and UV exposure, and crop-specific light and temperature thresholds—and the most robust results arise from stacked, site-specific designs that combine skin-level radiative rejection, buoyancy-supportive geometry, and ground or latent buffering with minimal active backup. Smart controllers that modulate vents, shading, and targeted fogging or fans based on VPD or temperature differentials improve stability and reduce water and energy use by engaging actuation only when passive capacity is exceeded. We recommend standardized composite metrics encompassing temperature moderation, humidity stability, PAR availability, and water and energy use per unit yield to enable fair cross-study comparison, multi-season validation, and policy adoption. Collectively, the synthesized techniques provide a practical palette for improved greenhouse climate management under hot and arid conditions. Full article

The Intelligent Road Side Unit (RSU) is a crucial component of Intelligent Transportation Systems (ITSs), where roadside LiDAR are widely utilized for their high precision and resolution. However, water droplets and atmospheric particles in fog significantly attenuate and scatter LiDAR beams, posing a challenge to multi-target tracking and ITS safety. To enhance the accuracy and reliability of RSU-based tracking, a collaborative RSU method that integrates denoising and tracking for multi-target tracking is proposed. The proposed approach first dynamically adjusts the filtering kernel scale based on local noise levels to effectively remove noisy point clouds using a modified bilateral filter. Subsequently, a multi-RSU cooperative tracking framework is designed, which employs a particle Probability Hypothesis Density (PHD) filter to estimate target states via measurement fusion. A multi-target tracking system for intelligent RSUs in Foggy scenarios was designed and implemented. Extensive experiments were conducted using an intelligent roadside platform in real-world fog-affected traffic environments to validate the accuracy and real-time performance of the proposed algorithm. Experimental results demonstrate that the proposed method improves the target detection accuracy by 8% and 29%, respectively, compared to statistical filtering methods after removing fog noise under thin and thick fog conditions. At the same time, this method performs well in tracking multi-class targets, surpassing existing state-of-the-art methods, especially in high-order evaluation indicators such as HOTA, MOTA, and IDs. Full article

Background: Long COVID remains a challenging and heterogeneous condition, with mechanisms that are still incompletely understood. Emerging evidence suggests that patients with allergic disease may experience more persistent post-COVID symptoms, possibly due to immune dysregulation and epithelial barrier fragility. Methods: We carried out an observational, single-center study at the Allergy and Clinical Immunology Unit of Policlinico Universitario A. Gemelli IRCCS (Rome, Italy). Seventeen adults with confirmed allergic disease and long COVID were evaluated between July and December 2024. Biomarkers reflecting allergic inflammation and barrier integrity, blood eosinophil count, total immunoglobulin E (IgE), eosinophil cationic protein (ECP), and serum free light chains (FLCs), were measured and analyzed for interrelationships and symptom correlations. Results: Participants (10 men, 7 women; mean age 43.7 years) showed variable biomarker profiles, consistent with the heterogeneity of allergic inflammation. Mean eosinophil count was 179 ± 72 cells/µL, total IgE 165.4 ± 140.6 kU/L, ECP 64.2 ± 48.5 ng/mL, and the kappa/lambda FLC ratio 1.20 ± 0.69. Notably, elevated kappa FLC levels (>19.4 mg/L) were significantly associated with high ECP (>20 ng/mL) (χ² = 10.6, p = 0.001) and increased IgE (>200 kU/L) (χ² = 6.0, p = 0.015). Individuals with higher ECP and FLCs more often reported respiratory and systemic symptoms, especially fatigue, dyspnea, and cognitive fog, that persisted beyond six months. Conclusions: These findings suggest that biomarkers of allergic inflammation and barrier dysfunction, particularly ECP and FLCs, may contribute to the persistence of long-COVID symptoms in allergic patients. The observed links between humoral activation, eosinophilic activity, and prolonged symptom burden support a model of sustained inflammation and delayed epithelial recovery. Larger, longitudinal studies including non-allergic controls are warranted to confirm these associations and to explore whether restoring barrier integrity could shorten recovery trajectories in this vulnerable population. Full article

Effective communication in critical care units, such as the Cardiovascular Intensive Care Unit (CVICU), is vital for patient safety; however, clinical notes from multiple professionals are often lengthy and complex. This study evaluated the Mistral large language model for summarizing Cardiovascular Intensive Care Unit progress notes using the Illness severity, Patient summary, Action list, Situation awareness and contingency planning, and Synthesis by receiver (I-PASS) framework, a standardized mnemonic for patient handoffs in healthcare. A total of 385 patients were included in the cohort, and all the progress notes associated with each patient were combined into a single document and summarized by the model. The readability was assessed using multiple metrics, including Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning-Fog Index, Simple Measure of Gobbledygook Index (SMOG), Automated Readability Index, and Dale-Chall Score. The readability metrics showed that the summaries generated with the Mistral Large Language Model (LLM) were much more difficult to read than the original notes, requiring a higher reading level. In a small clinician review, junior residents rated the summaries overall more favorably than senior residents, who often identified missing clinical details. Although Mistral condensed the documentation, this reduced readability and some loss of context may limit its usefulness for clinical handoffs. As a preliminary study with a small clinician-reviewed sample, these findings are descriptive and will require validation in larger clinical settings. Full article

Global warming has intensified heat waves, severely threatening agricultural productivity and food security. In South Korea, heat waves have strengthened since the 1980s, often causing summer cooling demands far exceeding winter heating needs. Controlled-environment horticulture offers a vital alternative to open-field farming, yet conventional structures such as the Venlo type remain vulnerable to high-temperature stress. This study pre-evaluates the thermal performance of a high-height wide-type greenhouse, developed by the Rural Development Administration, using computational fluid dynamics and compares it with a conventional Venlo-type structure. Simulations under extreme summer conditions (35–45 °C) considered natural ventilation, fogging, fan coil units, and hybrid systems. Thermal indicators, including air and root-zone temperatures, were analyzed to assess crop-sustaining conditions. Results showed that natural ventilation alone failed to maintain suitable environments. The high-height wide-type greenhouse achieved lower and more uniform temperatures than the Venlo type. Fogging and fan coil systems provided moderate cooling, while the hybrid system achieved the greatest reductions. Overall, the high-height wide-type greenhouse, especially when integrated with hybrid cooling, effectively mitigates heat stress and enhances thermal uniformity, providing quantitative guidance for structural selection and cooling-system configuration in greenhouse design under extreme thermal conditions. Full article

The factors restricting the large-scale deployment of smart vehicular networks include application service placement/migration, mobility management, network congestion, and latency. Current vehicular networks are striving to optimize network performance through decentralized framework deployments. Specifically, the urban-level execution of current network deployments often fails to achieve the quality of service required by smart cities. To address these issues, we have proposed a vehicular edge–fog computing (VEFC)-enabled adaptive area-based traffic management (AABTM) architecture. Our design divides the urban area into multiple microzones for distributed control. These microzones are equipped with roadside units for real-time collection of vehicular information. We also propose (1) a vehicle mobility management (VMM) scheme to facilitate seamless service migration during vehicular movement; (2) a dynamic vehicular clustering (DVC) approach for the dynamic clustering of distributed network nodes to enhance service delivery; and (3) a dynamic microservice assignment (DMA) algorithm to ensure efficient resource-aware microservice placement/migration. We have evaluated the proposed schemes on different scales. The proposed schemes provide a significant improvement in vital network parameters. AABTM achieves reductions of 86.4% in latency, 53.3% in network consumption, 6.2% in energy usage, and 48.3% in execution cost, while DMA-clustering reduces network consumption by 59.2%, energy usage by 5%, and execution cost by 38.4% compared to traditional cloud-based urban traffic management frameworks. This research highlights the potential of utilizing distributed frameworks for real-time traffic management in next-generation smart vehicular networks. Full article

Urban noise is a major environmental concern that affects public health and quality of life, demanding new approaches beyond conventional noise level monitoring. This study investigates the development of an AI-driven Acoustic Event Detection and Classification (AED/C) system designed for urban sound recognition and its integration into smart city application. Using the UrbanSound8K dataset, five acoustic parameters—Mel Frequency Cepstral Coefficients (MFCC), Mel Spectrogram (MS), Spectral Contrast (SC), Tonal Centroid (TC), and Chromagram (Ch)—were mathematically modeled and applied to feature extraction. Their combinations were tested with three classical machine learning algorithms: Support Vector Machines (SVM), Random Forest (RF), Naive Bayes (NB) and a deep learning approach, i.e., Convolutional Neural Networks (CNN). A total of 52 models with the three ML algorithms were analyzed along with 4 models with CNN. The MFCC-based CNN models showed the highest accuracy, achieving up to 92.68% on test data. This achieved accuracy represents approximately +2% improvement compared to prior CNN-based approaches reported in similar studies. Additionally, the number of trained models, 56 in total, exceeds those presented in comparable research, ensuring more robust performance validation and statistical reliability. Real-time validation confirmed the applicability for IoT devices, and a low-cost wireless sensor unit (WSU) was developed with fog and cloud computing for scalable data processing. The constructed WSU demonstrates a cost reduction of at least four times compared to previously developed units, while maintaining good performance, enabling broader deployment potential in smart city applications. The findings demonstrate the potential of AI-based AED/C systems for continuous, source-specific noise classification, supporting sustainable urban planning and improved environmental management in smart cities. Full article

Road agglomerate fog seriously threatens driving safety, making real-time fog state detection crucial for implementing reliable traffic control measures. With advantages in aerial perspective and a broad field of view, UAVs have emerged as a novel solution for road agglomerate fog monitoring. This paper proposes an agglomerate fog detection method based on the fusion of SURF and optical flow characteristics. To synthesize an adequate agglomerate fog sample set, a novel network named FogGAN is presented by injecting physical cues into the generator using a limited number of field-collected fog images. Taking the region of interest (ROI) for agglomerate fog detection in the UAV image as the basic unit, SURF is employed to describe static texture features, while optical flow is employed to capture frame-to-frame motion characteristics, and a multi-feature fusion approach based on Bayesian theory is subsequently introduced. Experimental results demonstrate the effectiveness of FogGAN for its capability to generate a more realistic dataset of agglomerate fog sample images. Furthermore, the proposed SURF and optical flow fusion method performs higher precision, recall, and F1-score for UAV perspective images compared with XGBoost-based and survey-informed fusion methods. Full article

We present a deep learning model for the recognition of speech commands in the English language. The dataset is based on the Google Speech Commands Dataset by Warden P., version 0.01, and it consists of ten distinct commands (“left”, “right”, “go”, “stop”, “up”, “down”, “on”, “off”, “yes”, and “no”) along with additional “silence” and “unknown” classes. The dataset is split in a speaker-independent manner, with 70% of speakers assigned to the training set and 15% to the test set and validation set. All audio clips are sampled at 16 kHz, with a total of 46 146 clips. Audio files are converted into Mel spectrogram representations, which are then used as input to a deep learning model composed of a four-layer convolutional neural network followed by two fully connected layers. The model employs Rectified Linear Unit (ReLU) activation, the Adam optimizer, and dropout regularization to improve generalization. The achieved testing accuracy is 96.05%. Micro- and macro-averaged precision, recall, and F1-score of 95% are reported to reflect class-wise performance, and a confusion matrix is also provided. The proposed model has been deployed on a Raspberry Pi 5 as a Fog computing device for real-time speech recognition applications. Full article

Freeway weaving sections’ states under adverse weather exhibit characteristics of randomness, vulnerability, and abruption. A deep learning-based model is proposed for traffic state identification and prediction, which can be used to formulate proactive management strategies. According to traffic characteristics under adverse weather, a hybrid model combining Random Forest and an improved k-prototypes algorithm is established to redefine traffic states. Traffic state prediction is accomplished using the Weather Spatiotemporal Graph Convolution Network (WSTGCN) model. WSTGCN decomposes flows into spatiotemporal correlation and temporal variation features, which are learned using spectral graph convolutional networks (GCNs). A Time Squeeze-and-Excitation Network (TSENet) is constructed to extract the influence of weather by incorporating the weather feature matrix. The traffic states are then predicted using Gated Recurrent Unit (GRU). The proposed models were tested using data under rain, fog, and strong wind conditions from 201 weaving sections on China’s G5 and G55 freeway, and U.S. I-5 and I-80 freeway. The results indicated that the freeway weaving sections’ states under adverse weather can be classified into seven categories. Compared with other baseline models, WSTGCN achieved a 3.8–8.0% reduction in Root Mean Square Error, a 1.0–3.2% increase in Equilibrium Coefficient, and a 1.4–3.1% improvement in Accuracy Rate. Full article

To characterize changes in markers of oxidative stress for the clinical evaluation of patients with long COVID, we assessed oxidative stress and antioxidant activity based on serum samples from patients who visited our clinic between May and November 2024. Seventy-seven patients with long COVID (41 [53%] females and 36 [47%] males; median age, 44 years) were included. Median [interquartile range] serum levels of diacron-reactive oxygen metabolites (d-ROM; CARR Unit), biological antioxidant potential (BAP; μmol/L), and oxidative stress index (OSI) were 533.8 [454.9–627.6], 2385.8 [2169.2–2558.1] and 2.0 [1.7–2.5], respectively. Levels of d-ROMs (579.8 vs. 462.2) and OSI (2.3 vs. 1.8), but not BAP (2403.4 vs. 2352.6), were significantly higher in females than in males. OSI levels positively correlated with age and body mass index, whereas BAP levels negatively correlated with these parameters. d-ROM and OSI levels were significantly associated with inflammatory markers, including C-reactive protein (CRP) and fibrinogen, whereas BAP levels were inversely correlated with CRP and ferritin levels. Notably, serum free thyroxine levels were negatively correlated with d-ROMs and OSI, whereas cortisol levels were positively correlated with d-ROMs. Among long COVID symptoms, patients reporting brain fog exhibited significantly higher OSI levels (2.2 vs. 1.8), particularly among females (d-ROMs: 625.6 vs. 513.0; OSI: 2.4 vs. 2.0). The optimal OSI cut-off values were determined to be 1.32 for distinguishing long COVID from healthy controls and 1.92 for identifying brain fog among patients with long COVID. These findings suggest that oxidative stress markers may serve as indicators for the presence or prediction of psycho-neurological symptoms associated with long COVID in a gender-dependent manner. Full article