Search Results (186)

Accurate behavioral segmentation of vessel trajectories from Automatic Identification System (AIS) is essential for maritime safety and traffic management. Existing methods often rely on predefined thresholds or emphasize geometric criteria and offer limited behavioral interpretability for mobility analysis. This paper introduces an unsupervised behavioral segmentation framework that integrates clustering with matheuristic optimization. Trajectories are cleaned with a forward sliding window, and three smoothed movement features, namely speed, acceleration, and turning rate, are computed for each point. Each feature is discretized by the Jenks Natural Breaks algorithm to extract key feature points and pointwise feature labels. Segment boundaries are near-optimally chosen from these key feature points using a Matheuristic Fixed Set Search (MFSS) that minimizes a Minimum Description Length (MDL) objective. This ensures behavioral consistency within each segment and clear separation between adjacent segments. Experiments on an AIS dataset from the Qiongzhou Strait, China, demonstrate that our proposed method yields more compact, distinctly differentiated segments than baseline methods, while preserving intra-segment behavioral continuity. These segments exhibit strong semantic coherence, making them well-suited for downstream tasks such as traffic risk assessment and route planning. Full article

In this paper, we discuss the structural and magnetic properties of Ta(d)/Co₄₀Fe₄₀B₂₀/MgO/Ta multilayers. The CoFeB wedge layer was deposited on three buffers differing in Ta layer thickness: d = 5, 10, and 15 nm. A structural analysis showed that the Ta seed-buffer of 5 nm was amorphous, whereas thicker Ta grew in a β-tetragonal disordered structure. X-ray reflectivity measurements revealed that the Ta/CoFeB interface roughness for annealed samples ranged from 0.55 to 0.67 nm for a sample with a 0.85 nm CoFeB layer and decreased to approximately 0.47 nm for thicker CoFeB layers, while the average interface CoFeB/MgO thickness was about 0.2–0.3 nm. The morphological roughness of the amorphous single 5 nm Ta layer was the lowest, whereas crystalline grains in thicker Ta buffers induced higher roughness. The 5 nm thick MgO layer exhibited a strong (001)-oriented texture, which was the highest for the smoothest 5 nm Ta buffer. The magnetic dead layer thickness for the annealed sample with a 15 nm Ta buffer was 0.39 nm and increased with the decrease in the Ta buffer thickness. Temperature-dependent measurements offered further insight into the diffusion processes and the formation of the magnetic dead layer (MDL) at the Ta/CoFeB interface. Full article

Background/Objectives: Clear aligners (CAs) are a popular alternative to classical fixed appliances (FAs) for orthodontic treatment. This systematic review aimed to compare the external apical root resorption (EARR) in patients undergoing orthodontic therapy with either FAs or removable CAs. Methods: An electronic search was conducted to identify comparative studies. Risk of bias was assessed using the Cochrane RoB 2.0 tool for randomized controlled trials (RCTs) and the ROBINS-I tool for non-RCTs. EARR at the following incisors was considered: maxillary central (MxC), maxillary lateral (MxL), mandibular central (MdC), and mandibular lateral (MdL). A random-effects meta-analysis was performed, and mean differences were estimated. Results: Ten studies (one RCT, two prospective, and seven retrospective studies) were included. Four had a low risk of bias, four had a moderate risk, and two had a serious concern. In total, 286 patients (1476 incisors) and 289 patients (1487 incisors) in the CA and FA groups were considered, respectively. The mean follow-up was 22.7 ± 9.9 (standard deviation) in the CA group and 22.5 ± 8.2 months in the FA group. The meta-analysis found that CAs caused significantly less EARR than FAs for all tooth types except for MdL. On a patient basis, the mean difference (MD) in favour of CAs ranged from −0.64 mm (95% CI (confidence interval): −0.90, −0.38 mm) for MxC to −0.26 mm (95% CI: −0.43, −0.09 mm) in MdC. Heterogeneity across studies was generally high, except for MdC cases. Conclusions: EARR at incisor teeth is generally lower using CAs compared to FAs. Further evidence-based studies are needed to confirm these results and understand the clinical relevance of such a difference. Full article

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants that tend to accumulate in solid matrices such as sewage sludge, raising concerns regarding their fate and potential ecological risks. This study aimed to develop and validate a robust analytical method for the accurate determination of PFAS in dehydrated sludge. A liquid chromatography–tandem mass spectrometry (LC–MS/MS) method was optimized for 28 PFAS, including perfluoroalkyl carboxylic acids (PFCAs) and sulfonic acids (PFSAs). Solid–liquid extraction with basic methanol was followed by cleanup using a cartridge packed with ferrite and sodium sulfate to remove moisture and particulate interferences. Chromatographic separation was performed with an Avantor^® ACE^® PFAS Delay column coupled to an Agilent triple quadrupole MS operating in negative electrospray ionization mode. The method achieved excellent sensitivity (MDL < 0.02 µg/g dry weight for most compounds), satisfactory precision (RSD < 15%), and recoveries between 80–118%. Optimization of mobile phase additives, gradient conditions, and MS parameters enhanced chromatographic resolution and signal-to-noise ratio. The validated method demonstrates high reliability for PFAS determination in complex solid matrices and can be applied as a valuable tool for environmental monitoring and risk assessment of sludge management practices. Full article

The satellite Internet of Things (SatIoT) enables real-time acquisition and large-scale coverage of hyperspectral imagery, providing essential data support for decision-making in domains such as geological exploration, environmental monitoring, and urban management. Hyperspectral remote sensing classification constitutes a critical component of intelligent applications driven by the SatIoT, yet it faces two major challenges: the massive data volume imposes heavy storage and processing burdens on conventional satellite systems, while dimensionality reduction often compromises classification accuracy; furthermore, mainstream neural network models are constrained by insufficient labeled data and spectral shifts, frequently leading to misclassification of unknown categories and degradation of cross-regional performance. To address these issues, this study proposes an open-set hyperspectral classification method with dual branches of reconstruction and prototype-based classification. Specifically, we build upon an autoencoder. We design a spectral–spatial attention module and an information residual connection module. These modules accurately capture spectral–spatial features. This improves the reconstruction accuracy of known classes. It also adapts to the high-dimensional characteristics of satellite data. Prototype representations of unknown classes are constructed by incorporating classification confidence, enabling effective separation in the feature space and targeted recognition of unknown categories in complex scenarios. By jointly leveraging prototype distance and reconstruction error, the proposed method achieves synergistic improvement in both accurate classification of known classes and reliable detection of unknown ones. Comparative experiments and visualization analyses on three publicly available datasets: Salinas-A, PaviaU, and Dioni-demonstrate that the proposed approach significantly outperforms baseline methods such as MDL4OW and IADMRN in terms of unknown detection rate (UDR), open-set overall accuracy (OpenOA), and open-set F1 score, while on the Salinas-A dataset, the performance gap between closed-set and open-set classification is as small as 1.82%, highlighting superior robustness. Full article

Glacial lakes in the Upper Indus Basin (UIB) are rapidly evolving due to accelerated glacier retreat driven by climate change. Here we present a comprehensive inventory of glacial lakes using Sentinel-1 SAR data with adaptive backscatter thresholding, enabling consistent detection under challenging conditions and improving delineation accuracy. In August 2023, we identified 6019 glacial lakes at scales from 0.001 to 5.80 km², covering a cumulative area of 266 km² (~0.06% of the basin). Although more than 90% of the lakes are smaller than 0.1 km², large lakes (>0.1 km²) account for over 57% of the total lake area. Most lakes are concentrated between 4000 and 4600 m, coinciding with the main glacierized zone. Regional patterns reveal that the Hindu Kush and Himalayas are dominated by glacier erosion lakes (GELs) and moraine-dammed lakes (MDLs), reflecting widespread glacier retreat, whereas the Karakoram is characterized by numerous supraglacial lakes (SGLs) associated with extensive debris-covered glaciers. Compared to previous optical-based inventories, our SAR-based approach captures more lakes and better represents small and transient features such as SGLs. These findings provide a more accurate baseline for assessing cryospheric change and glacial lake hazards in one of the world’s most heavily glacierized basins. Full article

Clustering of aircraft trajectories in terminal airspace is essential for procedure evaluation, flow monitoring, and anomaly detection, yet it is challenged by dense traffic, irregular sampling, and diverse maneuvering behaviors. This study proposes a unified framework that integrates dynamics-aware segmentation, Transformer–Variational Autoencoder (Transformer–VAE)-based representation learning, and density-aware clustering with joint optimization. A dynamic-feature Minimum Description Length (DFE-MDL) algorithm is introduced to preserve maneuver boundaries and reduce reconstruction errors, while the Transformer–VAE encoder captures nonlinear spatiotemporal dependencies and generates compact latent embeddings. Clusters are initialized using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and further refined through Kullback–Leibler (KL) divergence minimization to improve consistency and separability. Experiments on large-scale ADS-B data from Guangzhou Baiyun International Airport, comprising over 27,000 trajectories, demonstrate that the framework outperforms conventional geometric and deep learning baselines. Results show higher reconstruction fidelity, clearer cluster separation, and reduced computation time, enabling interpretable flow structures that reflect operational practices. Overall, the framework provides a data-driven and scalable approach for terminal-area trajectory analysis, offering practical value for STAR/SID compliance monitoring, anomaly detection, and airspace management. Full article

The increasing use of reclaimed wastewater in agriculture raises growing concerns about the accumulation of priority organic micropollutants in edible crops. In this study, we developed and validated a novel QuEChERS–SPME–GC/MS method for the simultaneous determination of 15 polycyclic aromatic hydrocarbons (PAHs), 3 nitro-PAHs, and 14 polychlorinated biphenyls congeners in Eruca vesicaria (rocket) leaves. The method was optimized to address the matrix complexity of leafy vegetables and included a two-step dispersive solid-phase extraction (d-SPE) cleanup and aqueous dilution prior to SPME. Validation showed excellent performance, with MDLs between 0.1 and 6.7 µg/kg, recoveries generally between 70 and 120%, and precision (RSD%) below 20%. The greenness of the protocol was assessed using the AGREE metric, yielding a score of 0.60. Application to rocket samples irrigated with treated wastewater revealed no significant accumulation of target pollutants compared to commercial samples. All PCB and N-PAH congeners were below detection limits, and PAH concentrations were low and mostly limited to lighter compounds. Human health risk assessment based on toxic equivalent concentrations confirmed that estimated cancer risk (CR) values 10⁻⁹–10⁻⁸ were well below accepted safety thresholds. These findings support the safe use of reclaimed water for leafy crop irrigation under proper treatment conditions and highlight the suitability of the method for trace-level food safety monitoring. Full article

Wavelength modulation-tunable diode laser absorption spectroscopy (WM-TDLAS) is a critical tool for gas detection. However, noise in second harmonic signals degrades detection performance. This study presents a hybrid denoising algorithm combining Empirical Mode Decomposition (EMD) and wavelet adaptive thresholding to enhance WM-TDLAS performance. The algorithm decomposes raw signals into intrinsic mode functions (IMFs) via EMD, selectively denoises high-frequency IMFs using wavelet thresholding, and reconstructs the signal while preserving spectral features. Simulation and experimental validation using the CH₄ absorption spectrum at 1654 nm demonstrate that the system achieves a threefold improvement in detection precision (0.1181 ppm). Allan variance analysis revealed that the detection capability of the system was significantly enhanced, with the minimum detection limit (MDL) drastically reduced from 2.31 ppb to 0.53 ppb at 230 s integration time. This approach enhances WM-TDLAS performance without hardware modification, offering significant potential for environmental monitoring and industrial safety applications. Full article

With the expansion of muskmelon cultivation, manual pollination is increasingly inadequate for sustaining industry development. Therefore, the development of automatic pollination robots holds significant importance in improving pollination efficiency and reducing labor dependency. Accurate flower detection and localization is a key technology for enabling automated pollination robots. In this study, the YOLO-MDL model was developed as an enhancement of YOLOv7 to achieve real-time detection and localization of muskmelon flowers. This approach adds a Coordinate Attention (CA) module to focus on relevant channel information and a Contextual Transformer (CoT) module to leverage contextual relationships among input tokens, enhancing the model’s visual representation. The pollination robot converts the 2D coordinates into 3D coordinates using a depth camera and conducts experiments on real-time detection and localization of muskmelon flowers in a greenhouse. The YOLO-MDL model was deployed in ROS to control a robotic arm for automatic pollination, verifying the accuracy of flower detection and measurement localization errors. The results indicate that the YOLO-MDL model enhances AP and F1 scores by 3.3% and 1.8%, respectively, compared to the original model. It achieves AP and F1 scores of 91.2% and 85.1%, demonstrating a clear advantage in accuracy over other models. In the localization experiments, smaller errors were revealed in all three directions. The RMSE values were 0.36 mm for the X-axis, 1.26 mm for the Y-axis, and 3.87 mm for the Z-axis. The YOLO-MDL model proposed in this study demonstrates strong performance in detecting and localizing muskmelon flowers. Based on this model, the robot can execute more precise automatic pollination and provide technical support for the future deployment of automatic pollination robots in muskmelon cultivation. Full article

Rotator cuff tears are common in individuals over 40, and physical therapy is often prescribed post-surgery. However, access can be limited by cost, convenience, and insurance coverage. CuffLink is a telehealth rehabilitation system that integrates the Strengthening and Stabilization System mechanical exerciser with the interACTION mobile health platform. The system includes a triple-axis accelerometer (LSM6DSOX + LIS3MDL FeatherWing), a rotary encoder, a VL530X time-of-flight sensor, and two wearable BioMech Health IMUs to capture upper-limb motion. CuffLink is designed to facilitate controlled, home-based exercise while enabling clinicians to remotely monitor joint function. Concurrent validity and test–retest reliability were used to assess device accuracy and repeatability. The results showed moderate to good validity for shoulder rotation (ICC = 0.81), device rotation (ICC = 0.94), and linear tracking (from zero: ICC = 0.75 and RMSE = 2.41; from start: ICC = 0.88 and RMSE = 2.02) and good reliability (e.g., RMSEs as low as 1.66 cm), with greater consistency in linear tracking compared to angular measures. Shoulder rotation and abduction exhibited higher variability in both validity and reliability measures. Future improvements will focus on manufacturability, signal stability, and force sensing. CuffLink supports accessible, data-driven rehabilitation and holds promise for advancing digital health in orthopedic recovery. Full article

Methane hydrate is a crystalline compound in which methane is trapped within a water lattice under high-pressure, low-temperature conditions. Its presence in oceanic and permafrost sediments makes it a promising alternative energy source, but also a potential contributor to climate change. Accurate in situ detection remains a major challenge due to hydrate’s dispersed occurrence and the limitations of conventional geophysical methods. This study investigates the feasibility of using the associated alpha particle (AAP) technique for the direct detection of methane hydrate. A series of laboratory measurements was conducted on sand-based samples with varying levels of methane hydrate simulant. Using a 14 MeV neutron generator and a LaBr₃ gamma detector, the 4.44 MeV carbon peak was monitored and calibrated against volumetric hydrate saturation. The minimum detection limit (MDL) was experimentally determined to be $(67\pm 25)\%$. Although the result is subject to high uncertainty, it provides a preliminary benchmark for evaluating the method’s sensitivity and highlights the potential of AAP-based gamma spectroscopy for in situ detection, especially when supported by higher neutron flux in future applications. Full article

A methane (CH₄) sensor based on off-axis integrated cavity output spectroscopy (OA-ICOS) was developed, equipped with two measurement schemes: direct absorption spectroscopy (DAS) and wavelength modulation spectroscopy (WMS). The sensor used an optical resonant cavity composed of two high reflection mirrors (reflectivity > 99%). With a cavity length of 7 cm, an effective optical path length of 10.8 m and a cavity volume of 8.9 mL were achieved. A distributed feedback laser was used to precisely target the CH₄ absorption line near 1.6537 µm. Compared with the original system, the cavity mode noise of the CH₄ sensor was further reduced by adding white noise perturbations. The white noise perturbations were generated by the broadband random noise from the signal generator. The special customized narrowband RF noise source was not required. The system complexity and cost could be reduced. In DAS mode, the signal-to-noise ratio (SNR) of the OA-ICOS was 16.2 and the minimum detection limit (MDL) was 2.2 ppm at 117 s. In WMS mode, the SNR of the OA-ICOS was 113.9 and the MDL was 1.2 ppm at 106 s. Compared with the results obtained from the WMS mode and DAS mode, the SNR and MDL was improved 7.0 times and 1.8 times, respectively. The proposed sensor system not only enabled high-accuracy trace gas measurement, but also demonstrated strong potential for applications due to its compact design and low cost. Full article

Hundreds of veterinary drugs are widely used in agricultural activities and continuously enter aquatic environments through various pathways, posing potential risks to ecosystem. Considering that sediments function both as sinks and sources of these contaminants, it is crucial to promptly and accurately acquire veterinary drug residue level in sediments. In this study, a highly effective analytical method for simultaneous determination of 103 veterinary drugs from 16 classes in sediments was developed using high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS). The extraction procedure was performed twice by ultrasound-assisted extraction with an acetonitrile-buffer mixture consisting of Na₂EDTA, Na₃PO₄·12H₂O, and Na₃C₆H₅O₇·2H₂O. The supernatant was cleaned using 500 mg/6 mL Oasis HLB solid-phase cartridges. The elution solutions were concentrated and redissolved in formic acid–methanol–water (0.1/20/79.9, v/v, FA-MeOH-H₂O) for detection. Results showed that all 103 target drugs exhibited good linearity with R² > 0.990 over a concentration range of 0.010 to 1000 μg·L⁻¹, and method detection limits (MDLs) ranged from 0.025 to 5 μg·kg⁻¹. The recoveries at three spiking levels (2, 5, and 10 times of the method quantification limits, MQLs) varied from 33% to 150%, 32% to 140%, and 40% to 140%, respectively, with relative standard deviations (RSDs, n = 3) of 0.7%~29%, 0.8%~23%, and 0.5%~20%. The matrix effects for all compounds ranged from –85% to 84% with 32 targets negligible, 51 moderate, and 20 significant. An isotope-labeled surrogate method was proposed for quantitation to effectively overcome matrix effects and improve accuracy with better recoveries of 60%~120% for 93 target drugs and RSDs (n = 3) all below 20%. This method was applied to determine 12 sediment samples collected from the Jiulong River, and 16 target drugs were detected in the concentrations range of 0.1~7.6 μg·kg⁻¹. The method is accurate, sensitive, and efficient, providing a powerful analytical tool for behavior and effect studies of multi-classed veterinary drug residue in sediment environments. Full article

A carbon dioxide (CO₂) sensor based on light-induced thermoelastic spectroscopy (LITES) using a 2 μm diode laser and a self-designed low-frequency trapezoidal-head QTF is reported for the first time in this invited paper. The self-designed trapezoidal-head QTF with a low resonant frequency of 9464.18 Hz and a high quality factor (Q) of 12,133.56 can significantly increase the accumulation time and signal level of the CO₂-LITES sensor. A continuous-wave (CW) distributed-feedback (DFB) diode laser is used as the light source, and the strongest absorption line of CO₂ located at 2004.01 nm is chosen. A comparison between the standard commercial QTF with the resonant frequency of 32.768 kHz and the self-designed trapezoidal-head QTF is performed. The experimental results show that the CO₂-LITES sensor with the self-designed trapezoidal-head QTF has an excellent linear response to CO₂ concentration, and its minimum detection limit (MDL) can reach 46.08 ppm (parts per million). When the average time is increased to 100 s based on the Allan variance analysis, the MDL of the sensor can be improved to 3.59 ppm. Compared with the 16.85 ppm of the CO₂-LITES sensor with the commercial QTF, the performance is improved by 4.7 times, demonstrating the superiority of the self-designed trapezoidal-head QTF. Full article