Search Results (101)

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

Parkinson’s disease (PD) is a disorder that causes a decrease in motor skills. Among the symptoms that have been observed, the most significant is the occurrence of Freezing of Gait (FoG), which manifests as an abrupt cessation of walking. This study investigates the impact of spatiotemporal gait parameters using wearable inertial measurement units (IMUs). Notably, 30 PD patients (15 with FoG, 15 without) and 20 healthy controls were enrolled. Gait data were acquired using two foot-mounted IMUs and key parameters such as stride time, gait phase distribution, cadence, stride length, speed, and foot clearance were extracted. Results indicated a tangible decline in motor abilities in PD patients, especially in those with FoG. Differences were observed in the segmentation of gait phases, with diminished swing phase duration observed in patients, and in the diminished spatial parameters of stride length, velocity, and foot clearance. Additionally, to validate the results, the accuracy of IMU-derived clearance measurements was validated against an optoelectronic system. While the IMUs accurately detected maximum points, the minimum clearance showed a higher measurement error. These findings support the use of wearable IMUs as a reliable and low-cost alternative to laboratory systems for the assessment of gait abnormalities in PD. Moreover, they highlight the potential for early detection and monitoring of FoG in both clinical and home settings. Full article

A total of 1.6 million tons of personal protective equipment (PPE) waste has been generated daily since 2019 and this production has not abated since that time. Within PPEs, isolation gowns make up the largest percentage by weight of landfill waste. This study aimed to evaluate the effectiveness of rapid, reproducible disinfection protocols to help facilitate safe reuse and minimize risks from microbial contamination. Disinfection of isolation gowns via fogging with hydrogen peroxide (HP) and hypochlorous acid (HC) were evaluated in the present study compared to standard ethylene oxide (EO) sterilization. This study was conducted at VCA West Coast Specialty and Emergency Animal Hospital in the United States. Ten isolation gowns (control) were cultured on tryptic soy agar contact plates in 10 predetermined areas to determine microbial load and morphology/types on non-sterile gowns before use. Following this, 10 gowns were fogged with 12% HP, and then once drying was complete, they were cultured in the predetermined areas for microbial load and morphology/types. This procedure was repeated with another set of 10 gowns fogged with 500 ppm HC. Lastly, 10 gowns were sterilized with EO using standard protocol and cultures were performed similarly. Median CFU (colony-forming unit) counts at 48 h for control, EO, HP, and HC were 4.5, 0, 0, and 0; at 72 h, they were 107, 0, 0, and 0, respectively. No significant difference was noted between the disinfection groups; post hoc pairwise analysis showed that the CFU counts for the disinfection groups were significantly lower than those for the control. The median percent reduction at 48 h for EO, HP, and HC was 100, 100, and 100; at 72 h, it was 100, 100, and 100, respectively. No significant difference was detected among the groups. The median number of microbe types for control, EO, HP, and HC was 2.5, 0, 0, and 0; there was no difference between the disinfection groups, but the number of microbe types was significantly higher for the control than for the disinfection groups. EO is environmentally toxic, expensive, and carcinogenic; it requires prolonged disinfection cycle times, expensive equipment, and trained personnel. This study suggests that HP and HC provide a cost-effective, relatively nontoxic, environmentally safe, and comparatively short disinfection time option for the disinfection and reuse of isolation gowns that does not require trained personnel or specialized equipment. Full article

Freezing of gait (FOG) is a motor symptom experienced by people with Parkinson’s Disease (PD) where they feel like they are glued to the floor. Accurate and continuous detection is needed for effective cueing to prevent or shorten FOG episodes. A convolutional neural network (CNN) was developed to detect FOG episodes in data recorded from an inertial measurement unit (IMU) on a PD patient’s ankle under semi-free living conditions. Data were split into two sets: one with all movements and another with walking and turning activities relevant to FOG detection. The CNN model was evaluated using five-fold cross-validation (5Fold-CV), leave-one-subject-out cross-validation (LOSO-CV), and performance metrics such as accuracy, sensitivity, precision, F1-score, and AUROC; Data from 24 PD participants were collected, excluding three with no FOG episodes. For walking and turning activities, the CNN model achieved AUROC = 0.9596 for 5Fold-CV and AUROC = 0.9275 for LOSO-CV. When all activities were included, AUROC dropped to 0.8888 for 5Fold-CV and 0.9017 for LOSO-CV; the model effectively detected FOG in relevant movement scenarios but struggled with distinguishing FOG from other inactive states like sitting and standing in semi-free-living environments. Full article

Inertial navigation systems (INSs) provide autonomous position estimation capabilities independent of global navigation satellite systems (GNSSs). However, the high cost of traditional sensors, such as fiber-optic gyroscopes (FOGs), limits their widespread adoption. In contrast, micro-electromechanical system (MEMS)-based inertial measurement units (IMUs) offer a low-cost alternative; however, their lower accuracy and sensor bias issues, particularly in maritime environments, remain considerable obstacles. This study proposes an improved method for bias estimation by comparing the estimated values from a trajectory generator (TG)-based acceleration and angular-velocity estimation system with actual measurements. Additionally, for X- and Y-axis accelerations, we introduce a method that leverages the correlation between altitude differences derived from an INS/GNSS/gyrocompass (IGG) and those obtained during the TG estimation process to estimate the bias. Simulation datasets from experimental voyages validate the proposed method by evaluating the mean, median, normalized cross-correlation, least squares, and fast Fourier transform (FFT). The Butterworth filter achieved the smallest angular-velocity bias estimation error. For X- and Y-axis acceleration bias, altitude-based estimation achieved differences of 1.2 × 10⁻² m/s² and 1.0 × 10⁻⁴ m/s², respectively, by comparing the input bias using 30 min data. These methods enhance the positioning and attitude estimation accuracy of low-cost IMUs, providing a cost-effective maritime navigation solution. Full article

Background/Objectives: This study investigated the prevalence of functional impairments and the effects of long COVID on long-term functional impairments in patients with severe COVID-19. Methods: We conducted a nationwide multicenter cohort study in collaboration with nine hospitals, collecting data using self-administered questionnaires from participants aged 20 years or older who were diagnosed with COVID-19, admitted to the intensive care unit (ICU) between April 2021 and September 2021, and discharged alive. Questionnaires regarding daily life, sequela, and functional impairments were mailed to patients in August 2022. The effects of long COVID on functional impairments were examined using a multivariate logistic regression analysis. Results: The survey was completed by 220 patients, with a mean of 416 days after discharge. Among respondents, 20.5% had physical impairments (n = 45), 35.0% had mental disorders (n = 77), and 42.7% had either (n = 94). Furthermore, 77.7% had long COVID (171/220), and the most common symptom was dyspnea (40.0%). The multivariate analysis showed that fatigue/malaise, upper respiratory tract symptoms, myalgia, muscle weakness, decreased concentration, sleep disorder, brain fog, and dizziness were risk factors for functional impairments at one year. Conclusions: Many patients with severe COVID-19 admitted to the ICU still suffered from post-intensive care syndrome even after one year, which manifested in combination with direct symptoms of the original disease, such as long COVID. Full article

In the context of data security, this work aims to present a novel solution that, rather than addressing the topic of endpoint security—which has already garnered significant attention within the international scientific community—offers a different perspective on the subject. In other words, the focus is not on device security but rather on the protection and security of the information contained within those devices. As we will see, the result is a next-generation decentralized infrastructure that simultaneously integrates two cognitive areas: data storage and its protection and security. In this context, an innovative Multiscale Relativistic Quantum (MuReQua) chain is considered to realize a novel decentralized and security solution for storing data. This engine is based on the principles of Quantum Mechanics, stochastic processes, and a new approach of decentralization for data storage focused on information security. The solution is broken down into four main components, considered four levels of security against attackers: (i) defocusing, (ii) fogging, (iii) puzzling, and (iv) crypto agility. The defocusing is realized thanks to a fragmentation of the contents and their distributions on different allocations, while the fogging is a component consisting of a solution of hybrid cyphering. Then, the puzzling is a unit of Information Fusion and Inverse Information Fusion, while the crypto agility component is a frontier component based on Quantum Computing, which gives a stochastic dynamic to the information and, in particular, to its data fragments. The data analytics show a very effective and robust solution, with executions time comparable with cloud technologies, but with a level of security that is a post quantum one. In the end, thanks to a specific application example, going beyond purely technical and technological aspects, this work introduces a new cognitive perspective regarding (i) the distinction between data and information, and (ii) the differentiation between the owner and the custodian of data. Full article