Search Results (190)

With the acceleration of global urbanization, airport noise pollution has emerged as a significant environmental concern that demands attention. Traditional airport noise monitoring systems are fraught with limitations, including restricted spatial coverage, inadequate real-time data acquisition capabilities, poor data correlation, and suboptimal cost-effectiveness. To address these challenges, this paper proposes an innovative airport noise perception and monitoring approach leveraging Internet of Things (IoT) technology. This method integrates multiple data streams, encompassing noise, meteorological, and ADS–B data, to achieve precise noise event tracing and deep multi-source data fusion. Furthermore, this study employs Kriging interpolation and Inverse Distance Weighting (IDW) techniques to perform spatial interpolation on data from sparse monitoring sites, thereby constructing a spatial distribution model of airport noise. The results of the practical application demonstrate that the proposed airport noise monitoring method can accurately reflect the spatiotemporal distribution patterns of airport noise and effectively correlate noise events, thereby providing robust data support for the development of airport noise control policies. Full article

Background/Objectives: The prevalence of Achromobacter xylosoxidans is increasing in people with Cystic Fibrosis (pwCF), yet its clinical pathogenicity remains controversial. The objective of this study was to chart the longitudinal prevalence and examine clinical associations before and after infection. Methods: This observational, retrospective study was conducted at a single CF center over a 14-year period. Data were collated from patient charts and clinic databases. Patients with Achromobacter sputum cultures were compared to those without the bacterium and analyzed according to whether they had single, intermittent, or chronic infections. Results: During the study period, an annual average of 124 pwCF were followed up at our clinic, with a median age of 13.6 years (IQR = 7.6–27.7). The Achromobacter detection rate increased from 0 to 6.1%. Twenty-three percent (29/124) of patients had at least one positive culture. The median age at acquisition was 17 years (IQR = 14.5–33). At the time of acquisition, the median FEV₁ was 81% (IQR = 46–94), compared to 90% (IQR = 72–99) for patients without Achromobacter, p < 0.001. Patients with Achromobacter tended to demonstrate more chronic Pseudomonas (55% vs. 27%, p = 0.06) and pancreatic insufficiency (66% vs. 47%, p = 0.07). At two years post-acquisition, the median FEV₁ for patients with intermittent and chronically infected decreased by 11.5% (IQR = −3.75–7.5), compared to 1.5% (IQR = −2.5–12.5) for those with a single positive culture, p = 0.03. Similarly, pulmonary exacerbations per year became more frequent post-acquisition in intermittent and chronically infected patients: Median (range) 2.5 (0–8) pre-, versus 3.0 (0–9) post-acquisition, p = 0.036. Conclusions: Chronic and intermittent infection with Achromobacter were associated with accelerated lung function decline and increased exacerbation frequency. Larger prospective studies are needed to confirm these findings and examine the effect of eradication on the clinical course. Full article

Conductive hydrogels, integrating high conductivity, mechanical flexibility, and biocompatibility, have emerged as crucial materials driving the evolution of next-generation wearable sensors. Their unique ability to establish seamless interfaces with biological tissues enables real-time acquisition of physiological signals, external stimuli, and even therapeutic feedback, paving the way for intelligent health monitoring and personalized medical interventions. To fully harness their potential, significant efforts have been dedicated to tailoring the conductive networks, mechanical properties, and environmental stability of these hydrogels through rational design and systematic optimization. This review comprehensively summarizes the design strategies of conductive hydrogels, categorized into metal-based, carbon-based, conductive polymer-based, ionic, and hybrid conductive systems. For each type, the review highlights structural design principles, strategies for conductivity enhancement, and approaches to simultaneously enhance mechanical robustness and long-term stability under complex environments. Furthermore, the emerging applications of conductive hydrogels in wearable sensing systems are thoroughly discussed, covering physiological signal monitoring, mechano-responsive sensing platforms, and emerging closed-loop diagnostic–therapeutic systems. Finally, this review identifies key challenges and offers future perspectives to guide the development of multifunctional, intelligent, and scalable conductive hydrogel sensors, accelerating their translation into advanced flexible electronics and smart healthcare technologies. Full article

In recent years, unmanned aerial vehicles (UAVs) have shown substantial application value in continuous target tracking tasks in complex environments. Due to the target’s movement behavior and the complexities of the surrounding environment, the UAV is prone to losing track of the target. To tackle this issue, this paper presents a reinforcement learning (RL) approach that combines UAV target search and tracking. During the target search phase, spatial information entropy is employed to guide the UAV in avoiding redundant searches, thus enhancing information acquisition efficiency. In the event of target loss, Gaussian process regression (GPR) is employed to predict the target trajectory, thereby reducing the time needed for target re-localization. In addition, to address sample efficiency limitations in conventional RL, a Kolmogorov–Arnold networks-based deep deterministic policy gradient (KbDDPG) algorithm with prior policy embedding is proposed for controller training.Simulation results demonstrate that the proposed method outperforms traditional methods in target search and tracking tasks within complex environments. It improves the UAV’s ability to re-locate the target after loss. The proposed KbDDPG efficiently leverages prior policy, leading to accelerated convergence and enhanced performance. Full article

The mining industry faces increasing challenges in maintaining high production levels while minimizing unplanned failures and operational costs. Critical assets, such as crushers, conveyor belts, mills, and ventilation systems, operate under extreme conditions, leading to accelerated wear and failure risks. Traditional maintenance strategies often fail to prevent unexpected downtimes, safety hazards, and economic losses. As a response, industries are integrating predictive monitoring technologies, including machine learning, the Internet of Things, and digital twins, to enhance early fault detection and optimize maintenance strategies. This Systematic Literature Review analyzes 166 high-impact studies from Scopus and Web of Science, identifying key trends in fault detection algorithms, hybrid AI models, and real-time monitoring techniques. The findings highlight the increasing adoption of deep learning, reinforcement learning, and digital twins for anomaly detection and process optimization. Additionally, AI-driven methods are improving sensor-based data acquisition and asset management, extending equipment lifecycles while reducing failures. Despite these advancements, challenges such as data standardization, model scalability, and system interoperability persist, requiring further research. Future work should focus on real-time AI applications, explainable models, and academia-industry collaboration to accelerate the implementation of intelligent maintenance solutions, ensuring greater reliability, efficiency, and sustainability in mining operations. Full article

This study addresses a specialized variant of the full-truckload delivery problem inspired by a Turkish logistics firm that operates in the liquid transportation sector. An exact algorithm is proposed for the relevant problem, to which no exact approach has been applied before. Multiple customer and trailer types, as well as washing operations, are introduced simultaneously during the exact solution process, bringing new aspects to the exact algorithm approach among full-truckload systems in the literature. The objective is to minimize transportation costs while addressing constraints related to multiple time windows, trailer types, customer types, product types, a heterogeneous fleet with limited capacity, multiple departure points, and various actions such as loading, unloading, and washing. Additionally, the elimination or reduction of waiting times is provided along transportation routes. In order to achieve optimal solutions, an exact algorithm based on the column generation method is proposed. A route-based insertion algorithm is also employed for initial routes/columns. Regarding the acquisition of integral solutions in the exact algorithm, both dynamic and static sets of valid inequalities are incorporated. A label-setting algorithm is used to generate columns within the exact algorithm by being accelerated through bi-directional search, ng-route relaxation, subproblem selection, and heuristic column generation. Due to the problem-dependent structure of the column generation method and acceleration techniques, a tailored version of them is included in the solution process. Performance analysis, which was conducted using artificial input sets based on the real-life operations of the logistics firm, demonstrates that optimality gaps of less than 1% can be attained within reasonable times even for large-scale instances relevant to the industry, such as 120 customers, 8 product and 8 trailer types, 4 daily time windows, and 40 departure points. Full article

The current study investigates the feasibility and performance of Fiber Bragg Grating (FBG) optical sensors in geotechnical engineering applications, aiming to demonstrate their broader applicability across different scales, from controlled laboratory experiments to real-world field implementations. More specifically, the research evaluates the sensors’ ability to monitor key parameters—strain, temperature, and acceleration—under diverse loading conditions, including static, dynamic, seismic, and centrifuge loads. Within this framework, laboratory experiments were conducted using the one-degree-of-freedom shaking table at the National Technical University of Athens to assess sensor performance during seismic loading. These tests provided insights into the behavior of geotechnical physical models under earthquake conditions and the reliability of FBG sensors in capturing dynamic responses. Additional testing was performed using the drum centrifuge at ETH Zurich, where physical models experienced gravitational accelerations up to 100 g, including impact loads. The sensors successfully captured the loading conditions, reflecting the anticipated model behavior. In the field, optical fibers were installed on the Perimeter Wall (Circuit Wall) of the Acropolis of Athens to monitor strain, temperature, and acceleration in real-time. Despite the challenges posed by the archaeological site’s constraints, the system gathered data over two years, offering insights into the structural behavior of this historic monument under environmental and loading variations. The Acropolis application serves as a key field example, illustrating the use of these sensors in a complex and historically significant site. Finally, the study details the test setups, sensor types, and data acquisition techniques, while addressing technical challenges and solutions. The results demonstrate the effectiveness of FBG sensors in geotechnical applications and highlight their potential for future projects, emphasizing their value as tools for monitoring structural integrity and advancing geotechnical engineering. Full article

Background/Objectives: Artificial intelligence (AI) is transforming neuroimaging by enhancing diagnostic precision and treatment planning. However, its applications in pediatric cancer neuroimaging remain limited. This review assesses the current state, potential applications, and challenges of AI in pediatric neuroimaging for cancer, emphasizing the unique needs of the pediatric population. Methods: A comprehensive literature review was conducted, focusing on AI’s impact on pediatric neuroimaging through accelerated image acquisition, reduced radiation, and improved tumor detection. Key methods include convolutional neural networks for tumor segmentation, radiomics for tumor characterization, and several tools for functional imaging. Challenges such as limited pediatric datasets, developmental variability, ethical concerns, and the need for explainable models were analyzed. Results: AI has shown significant potential to improve imaging quality, reduce scan times, and enhance diagnostic accuracy in pediatric neuroimaging, resulting in improved accuracy in tumor segmentation and outcome prediction for treatment. However, progress is hindered by the scarcity of pediatric datasets, issues with data sharing, and the ethical implications of applying AI in vulnerable populations. Conclusions: To overcome current limitations, future research should focus on building robust pediatric datasets, fostering multi-institutional collaborations for data sharing, and developing interpretable AI models that align with clinical practice and ethical standards. These efforts are essential in harnessing the full potential of AI in pediatric neuroimaging and improving outcomes for children with cancer. Full article

Summary Statement: A tailored image analysis workflow was applied to quantify cortical organoid health, development, morphology and cellular composition over time. The assessment of cellular composition and viability of stem cell-derived organoid models is a complex but essential approach to understanding the mechanisms of human development and disease. Aim: Our study was motivated by the need for an image-analysis workflow, including high-cell content, high-throughput methods, to measure the architectural features of developing organoids. We assessed stem cell-derived cortical organoids at 4 and 6 months post-induction using immunohistochemistry-labelled sections as the analysis testbed. The workflow leveraged fluorescence imaging tailored to classify cells as viable and dying or non-viable and assign neuronal and astrocytic perinuclear markers to count cells. Results/Outcomes: Image acquisition was accelerated by capturing the organoid slice in 3D using widefield-fluorescence microscopy. This method used computational clearing to resolve nuclear and perinuclear markers and retain their spatial information within the organoid’s heterogeneous structure. The customised workflow analysed over 1.5 million cells using DAPI-stained nuclei, filtering and quantifying viable and non-viable cells and the necrotic-core regions. Temporal analyses of neuronal cell number derived from perinuclear labelling were consistent with organoid maturation from 4 to 6 months of in vitro differentiation. Overall: We have provided a comprehensive and enhanced image analysis workflow for organoid structural evaluation, creating the ability to gather cellular-level statistics in control and disease models. Full article

Background/Objectives: The increased SNR available at 7T combined with fast readout trajectories enables accelerated spectroscopic imaging acquisitions for clinical applications. In this report, we evaluate the performance of a Hadamard slice encoding strategy with a 2D rosette trajectory for multi-slice fast spectroscopic imaging at 7T. Methods: Moderate-TE (~40 ms) spin echo and J-refocused polarization transfer sequences were acquired with simultaneous Hadamard multi-slice excitations and rosette in-plane encoding. The moderate spin echo sequence, which targets singlet compounds (i.e., N-acetyl aspartate, creatine, and choline), uses cascaded multi-slice RF excitation pulses to minimize the chemical shift dispersion error. The J-refocused sequence targets coupled spin systems (i.e., glutamate and myo-inositol) using simultaneous multi-slice excitation to maintain the same TE across all slices. A modified Hadamard slice encoding strategy was used to decrease the peak RF pulse amplitude of the simultaneous multi-slice excitation pulse for the J-refocused acquisition. Results: The accuracy of multi-slice and single-slice rosette spectroscopic imaging (RSI) is comparable to conventional Cartesian-encoded spectroscopic imaging (CSI). Spectral analyses for the J-refocused studies of glutamate and myo-inositol show that the Cramer Rao lower bounds are not significantly different between the fast RSI and conventional CSI studies. Linear regressions of creatine/N-acetyl aspartate and glutamate/N-acetyl aspartate with tissue gray matter content are consistent with literature values. Conclusions: With minimal gradient demands and fast acquisition times, the 2.2 min to 9 min for single- to four-slice RSI acquisitions are well tolerated by healthy subjects and tumor patients, and show results that are consistent with clinical outcomes. Full article

Purpose: Breath-hold T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) magnetic resonance imaging (MRI) of the upper abdomen with a slice thickness below 5 mm suffers from high image noise and blurring. The purpose of this prospective study was to improve image quality and accelerate imaging acquisition by using single-breath-hold T2-weighted HASTE with deep learning (DL) reconstruction (DL-HASTE) with a 3 mm slice thickness. Method: MRI of the upper abdomen with DL-HASTE was performed in 35 participants (5 healthy volunteers and 30 patients) at 3 Tesla. In a subgroup of five healthy participants, signal-to-noise ratio (SNR) analysis was used after DL reconstruction to identify the smallest possible layer thickness (1, 2, 3, 4, 5 mm). DL-HASTE was acquired with a 3 mm slice thickness (DL-HASTE-3 mm) in 30 patients and compared with 5 mm DL-HASTE (DL-HASTE-5 mm) and with standard HASTE (standard-HASTE-5 mm). Image quality and motion artifacts were assessed quantitatively using Laplacian variance and semi-quantitatively by two radiologists using five-point Likert scales. Results: In the five healthy participants, DL-HASTE-3 mm was identified as the optimal slice (SNR 23.227 ± 3.901). Both DL-HASTE-3 mm and DL-HASTE-5 mm were assigned significantly higher overall image quality scores than standard-HASTE-5 mm (Laplacian variance, both p < 0.001; Likert scale, p < 0.001). Compared with DL-HASTE-5 mm (1.10 × 10⁻⁵ ± 6.93 × 10⁻⁶), DL-HASTE-3 mm (1.56 × 10⁻⁵ ± 8.69 × 10⁻⁶) provided a significantly higher SNR Laplacian variance (p < 0.001) and sharpness sub-scores for the intestinal tract, adrenal glands, and small anatomic structures (bile ducts, pancreatic ducts, and vessels; p < 0.05). Lesion detectability was rated excellent for both DL-HASTE-3 mm and DL-HASTE-5 mm (both: 5 [IQR4–5]) and was assigned higher scores than standard-HASTE-5 mm (4 [IQR4–5]; p < 0.001). DL-HASTE reduced the acquisition time by 63–69% compared with standard-HASTE-5 mm (p < 0.001). Conclusions: DL-HASTE is a robust abdominal MRI technique that improves image quality while at the same time reducing acquisition time compared with the routine clinical HASTE sequence. Using ultra-thin DL-HASTE-3 mm results in an even greater improvement with a similar SNR. Full article

Background/Objectives: An on-board imager on a linear accelerator allows the acquisition of kV-2D images during irradiation. Overlaying specific structures on these images enables the visual verification of movement at regular frequencies. Our aim was to validate this tracking method for the stereotactic treatment of bone metastases. Methods: Shifts in three translational directions were simulated using an anthropomorphic phantom. For these simulated shifts, planar images were acquired at different angles of incidence, with overlaid volumes of interest. A blinded test was then administered to the 18 participants to evaluate their decisions regarding whether to stop treatment. The results considered the experience of the operators. Quantitative analyses were performed on the intra-fractional images of 29 patients. Results: Participants analyzed each image with an average (standard deviation) decision time of 3.0 s (2.3). For offsets of 0.0, 1.0, 1.5, and 2.0 mm, the results were 78%, 93%, 90%, and 100% for the expert group and 78%, 70%, 79%, and 88% for the less-experienced group. Clinical feedback confirmed this guidance technique and extended it to non-spinal bony metastases. Sudden movements exceeding the 2.0 mm threshold occurred in 3.3% of the analyzed fractions, with a detection rate of 97.8% for vertebral locations. For non-vertebral bone locations, movements exceeding a threshold of 3.0 mm occurred in 3.5% of cases and were detected in 96.5%. Conclusions: The clinical use of planar OBI and superimposed structures for visual-image guidance in bone stereotactic treatment was validated using an anthropomorphic phantom and clinical feedback. Full article

The literature on bilingual language development often reports cases of cross-linguistic interaction of the two languages being acquired. In this paper, we investigate possible cross-linguistic interaction outputs in the development of branching onsets in the bilingual acquisition of French and Portuguese. Thirty French–Portuguese bilingual children, aged between 3;6 and 6;1, participated in our study. Their elicited productions were collected using two picture naming tasks containing 29 clusters in French and 57 clusters in Portuguese. Almost all the children acquire branching onsets earlier in French than in Portuguese, independently of the quality of cluster type (Consonant + Rhotic (Cr) clusters vs. Consonant + Lateral (Cl) clusters). Epenthesis is more present in Portuguese than in French. Shared structures in both languages are not acquired at the same time. These results show that bilingual children follow separate patterns of development, close to the ones reported for monolinguals, during the acquisition of their two languages. Moreover, the bilingual children show higher rates of development of clusters in Portuguese than the ones reported for monolinguals, suggesting an accelerated acquisition of clusters in Portuguese due to a positive influence of French. Full article

The accurate estimation of flow angles is crucial for enhancing flight performance and aircraft safety. Flow angles of fixed-wing small unmanned aerial vehicles (SUAVs) are more vulnerable due to their low airspeed. Current flow angle measurement devices have not been widely implemented in SUAVs due to their substantial cost and size constraints. Moreover, there are no general estimation methods suitable for SUAVs based on their rudimentary sensor suite. This study presents a generalized optimization-assisted filter estimation (OAFE) method for estimating the relative velocity and flow angles of fixed-wing SUAVs based on a standard sensor suite. This OAFE method mainly consists of a cubature Kalman filter and an optimizer. The filter serves as the main loop with which to generate flow angles in real time by fusing the acceleration, angular rate, attitude, and airspeed. Without flow angle measurements, the optimizer generates approximate aerodynamic derivatives, which serve as pseudo-measurements with which to refine the performance of the filter. The results demonstrate that the estimated angle of attack and side slip angle displayed root mean square errors of around 0.11° and 0.24° in the simulation. The feasibility was also verified in field tests. The OAFE method does not require flow angle measurements, the prior acquisition of aerodynamic parameters, or model training, making it suitable for quick deployment on different SUAVs. Full article

The efficient classification of body position is crucial for monitoring infants’ motor development. It may fast-track the early detection of developmental issues related not only to the acquisition of motor milestones but also to postural stability and movement patterns. In turn, this may facilitate and enhance opportunities for early intervention that are crucial for promoting healthy growth and development. The manual classification of human body position based on video recordings is labour-intensive, leading to the adoption of Inertial Motion Unit (IMU) sensors. IMUs measure acceleration, angular velocity, and magnetic field intensity, enabling the automated classification of body position. Many research teams are currently employing supervised machine learning classifiers that utilise hand-crafted features for data segment classification. In this study, we used a longitudinal dataset of IMU recordings made in the lab in three different play activities of infants aged 4–12 months. The classification was conducted based on manually annotated video recordings. We found superior performance of the CatBoost Classifier over the Random Forest Classifier in the task of classifying five positions based on IMU sensor data from infants, yielding excellent classification accuracy of the Supine (97.7%), Sitting (93.5%), and Prone (89.9%) positions. Moreover, using data ablation experiments and analysing the SHAP (SHapley Additive exPlanations) values, the study assessed the importance of various groups of features from both the time and frequency domains. The results highlight that both accelerometer and magnetometer data, especially their statistical characteristics, are critical contributors to improving the accuracy of body position classification. Full article