Abstract This study addresses the challenge of predicting magnetic fields within magnetically shielded room for magnetocardiography (MCG) and magnetoencephalography (MEG). Traditional dense sensor arrays are prohibitively expensive, while single-point scanning introduces systematic errors. We propose a method utilizing a second-order spherical harmonic function to reconstruct the magnetic field. Six fluxgate sensors are symmetrically positioned around the target area, enabling the computation of spherical harmonic coefficients via regularized least-squares fitting to capture dipole and quadrupole modes. Experimental results demonstrate average prediction errors of 0.49 nT (5.23%), 0.33 nT (6.17%), and 0.15 nT (4.89%) for the X-, Y -, and Z -axis components, respectively. Compared to traditional methods, this method reduces the required sensor count by 77% and measurement time by 96% when predicting the magnetic field of 27 points in the target area. The key innovation lies in enabling high-precision field prediction with sparse measurements, thereby overcoming the limitations of point measurements. This technique surpasses prior art by achieving sub-nano Tesla accuracy while significantly reducing both cost and measurement time, providing an efficient solution for MCG and MEG systems.

Abstract Offshore fish farms have been developed for providing large quantities of improved-quality aquaculture products. Critical components such as nets/tendons of the fish cages’ net systems in offshore farms may become damaged due to severe environmental conditions. Fish escape through the damaged nets with dire economic/biological consequences. Thus, early detection of these damages is important. Currently, structural health monitoring (SHM) in the net systems is costly, time-consuming, and sporadic as it is conducted via divers and remote operating vehicles. SHM in the tendons can also be achieved by checking the force signals acquired via load sensors, with only failures such as broken tendons successfully detected and not incipient damages such as degradation. The present case study investigates the detection of a single damaged vertical tendon in a cage’s net system via an automated vibration-based SHM method. The method’s novelty lies in integrating vector autoregressive models identified based on simulated displacement data from two spatial measurement points on the fish cage under changing wave and current conditions, thus allowing the accurate detection of degraded tendons and enabling a remote, continuous, cost-effective monitoring with a constant stream of integrity data. Test cases for the healthy and damaged cage are examined, with degradation (fatigue damage) considered along the whole tendon and at specific points in the tendon. The degradation is simulated by stiffness reduction, and the method successfully detects all 184 test cases for the damaged cage and 34 of 36 test cases for the healthy cage.

This paper mainly conducts research on the electrode distribution of the multi-electrode electromagnetic flow measurement system. Through simulation work, the weight function of the area to which the electrodes on the pipeline cross-section belong with respect to the potential difference is roughly obtained. Moreover, by comparing the simulation data with the actual experimental data, the correctness of the simulation work is verified. Tikhonov regularization is utilized to inversely solve the average velocity of the electrode area, and the TR-CNN algorithm is established to refine the velocity field of the pipeline cross-section in question. It mainly introduces the influence of different electrode placement methods on the potential difference. The results show that it has a relatively small impact on the velocity distribution of the fluid cross-section before flowing through the elbow, and the potential difference is highly sensitive to the velocity in the area where the magnetic induction coil and the electrodes are relatively close. The Pitot tube is used to conduct verification measurements on the fluid velocity field in the pipeline. The results indicate that as the measurement points are farther away from the elbow, the “skewing” phenomenon of the fluid flow velocity gradually weakens. In terms of prediction performance, the mean square error (MSE) of the cross-section error is approximately 0.015, and the mean absolute error (MAE) is about 0.095. These error indicators jointly demonstrate that the system has a relatively high measurement accuracy in practical applications.

To evaluate the dosimetric impact of depth-dependent ion recombination and empirical effective point of measurement (EPOM) positioning in megavoltage photon beams, with particular focus on flattening filter-free (FFF) beams. Ion recombination correction factors ([Formula: see text]) were characterised as a function of depth and field size for three ionisation chambers (Roos, SNC125, CC13) using the two-voltage method under reference conditions (SSD 100cm and 10cm field size) and for additional MLC-defined 5cm and 2cm square field sizes, on a point-by-point basis across multiple beam energies. Empirical EPOMs were derived by aligning percentage depth ionisation (PDI) curves to a reference plane-parallel chamber. The dosimetric consequences of using generic [Formula: see text] and EPOM assumptions were assessed, and scan-derived [Formula: see text] values were validated against point dose measurements. A marked depth dependence in [Formula: see text] was observed for all chambers, most notably in FFF beams. The CC13 exhibited the greatest depth-related variation, resulting in recombination-related PDD deviations of up to 1.3% at extended depths. Empirically determined EPOMs were consistently smaller than the conventional 0.6[Formula: see text] shift, with normalised values of 0.42 and 0.38 for SNC125 and CC13, respectively. Using the conventional shift would introduce a residual dose deviation of approximately - 0.5%. The combined influence of uncorrected ion recombination and the generic EPOM produced a PDD bias of 0.8% at 10cm depth for the 10 FFF beam, which is relevant both for reference dosimetry and for depth-sensitive treatment sites. This study demonstrates that empirical, chamber-specific EPOM and [Formula: see text] correction factors improve dosimetric accuracy and PDD measurements, and consequently, reference dosimetry and TPS beam modelling for which PDD10cm is a key parameter. With the increasing adoption of FFF beams, reliance on generic assumptions for [Formula: see text] and EPOM introduces clinically relevant systematic deviations, approaching 1.0% at the calibration depth and becoming larger at greater depths. These corrections should be considered an essential component of linac and chamber commissioning to ensure robust reference dosimetry and accurate beam modelling. Given their measurable impact, such practices warrant inclusion in ACPSEM guidelines, in alignment with emerging best-practice frameworks and the evolving precision requirements of modern radiotherapy.

Syndesmotic injuries can lead to ankle instability. They are treated with syndesmotic screws or suture device. The necessity for screw removal remains a topic of debate. When removal is performed, it usually occurs 2 to 3 months post-operation. In such cases, the screw is typically visualized flouroscopically before removal. We describe an ultrasound-guided procedure for screw removal that avoids radiation exposure and has minimal infrastructural requirements. In this prospective cohort study, Cohort 1 included 26 screws (18 patients) that were removed under both fluoroscopic and ultrasound guidance. This allowed for the assessment of the accuracy of ultrasound compared to fluoroscopic visualization. In Cohort 2, 22 screws (17 patients) were removed solely under ultrasound guidance to evaluate the practical applicability of the method. A total of 35 patients were included into our study, eighteen in the first and seventeen in the second cohort respectively. In Cohort 1, the mean distance between the fluoroscopic and ultrasound measurement points of the screw heads was 2.9mm. The mean radiation dose was 1.4cGy/cm², and the mean operation duration was 15.2min. Cohort 2 had a mean operative duration of 10.3min. There were no significant differences in baseline characteristics between the groups. Radiation exposure was not present in Cohort 2. In Cohort 1, the ultrasound-guided screw localization revealed high accuracy, with the threshold < 5mm (p = 0.040). We did not identify any significant predictors for the screw localization accuracy. The duration of the surgical procedure was similar in both groups. In the entire pooled cohort, a longer distance from skin to screw was associated with a longer surgery duration (ρ = 0.419, p = 0.012). Skin-to-screw distance was found to be the only independent predictor of surgery duration (p = 0.009). Ultrasound-guided localisation and removal of syndesmotic screws demonstrate comparable accuracy compared to fluoroscopy, with the additional benefits of lower infrastructural requirements and associated costs. Furthermore, this method has a similar operative time to fluoroscopy and eliminates radiation exposure, supporting its feasibility as an efficient and safe alternative for syndesmotic screw removal. In accordance with the Declaration of Helsinki the study protocol was approved by the Ethics Review Board of the University of Regensburg, Protocol number 21-2204-101.

Introduction: Previous research investigated the interplay between impairments in personality functioning and perceived negative interactions with parents. Those studies mostly utilized cross-sectional methodologies in community samples. This study aimed to clarify the direction of associations between impaired personality functioning (i.e., difficulties in identity formation and less capacity of empathy and intimacy) and perceived negative interactions with parents (e.g., conflicts with parents) in treatment-referred youth. Methods: The sample consisted of 285 youth (79.3% self-identified women; M age = 19.1, SD = 2.58, range = 12-23 years) who completed questionnaires at three measurement points within one year. Data were analyzed with a Random-Intercept Cross-Lagged Panel Model. Results: Results showed that impaired personality functioning is correlated with perceived negative interactions. However, an absence of reciprocal relationships between impaired personality functioning and perceived negative interactions with parents over time is found. Youth's impaired personality functioning is not related to youth's perceived negative interactions with parents 6 months later. Also, youth's negative interactions with parents are not associated with the personality functioning 6 months later. Discussion: Future research is needed to investigate other interpersonal factors (e.g., peer relationships, social support) that play a role in the development of personality functioning.

Point air absolute temperature measurements by high-spectral-resolution lidar

ABSTRACT Manual measurement of cattle body size presents challenges, such as inducing stress responses in animals and inefficiencies. For large livestock like cattle, measurement based on full point clouds involves extensive computations and interference between different cloud sections. To address this, we propose MMLG‐Point, a novel deep learning model for cattle point cloud segmentation and body size measurement, which introduces a Multilevel Geometric Perception Encoder and a Transformer‐based decoder architecture. The encoder integrates Kernel Point Convolution (KPConv) and Separable Structure‐Aware Learning (SSAL) with residual multiscale fusion to capture local geometric structures of large livestock point clouds, while the decoder employs CrossNorm and SelfNorm (CNSN) modules to enhance generalization under limited labeled data. Furthermore, an unsupervised pretraining strategy based on masked point reconstruction is proposed, enabling the model to learn structural and semantic representations from unlabeled cattle point clouds. Experimental results demonstrate that MMLG‐Point achieves outstanding segmentation accuracy with minimal supervision, obtaining an overall accuracy (OA) of 94.3% and a mean Intersection over Union (mIoU) of 89.4% on the Simmental cattle dataset using only 12 labeled samples. The model also exhibits strong cross‐species generalization, achieving 92.3% OA and 86.7% mIoU on pig datasets. Based on segmentation results, an automatic cattle body measurement algorithm is developed, incorporating density analysis, curvature detection, and contour extraction to compute parameters such as withers height, hip height, body length, chest girth, and abdominal circumference, achieving a mean absolute percentage error (MAPE) below 6%. These results confirm that the proposed MMLG‐Point framework provides an effective and generalizable approach for high‐precision segmentation and measurement of large livestock point clouds.

Liquid density and bubble point measurements of R-290+POE ISO 32 lubricating oil mixtures between 283.15 and 343.15 K

Noise is one of the physical environmental factors that can have negative impacts on comfort and occupational health. The Zamhur Batoh Gas Station (SPBU Zamhur Batoh) in Banda Aceh, located on the city’s main traffic route, has a high potential for noise exposure due to vehicle traffic and fuel dispensing operations. This study aims to analyze the effects of noise intensity, types of noise, and duration of noise exposure on work comfort among employees at SPBU Zamhur Batoh Banda Aceh in 2025 Methods: This study employed a descriptive analytic method with a cross-sectional approach. The population consisted of all 20 employees of SPBU Zamhur Batoh Banda Aceh, who were also taken as the study sample. Noise intensity data were collected through measurements using a Sound Level Meter (SLM) at five measurement points during five different time sessions on both workdays and holidays. Data on types of noise, duration of exposure, and work comfort were obtained through questionnaires. Results: The results showed that 45% of respondents worked under noise intensity levels exceeding the Threshold Limit Value (TLV) of &gt;85 dBA, and all respondents in this category reported experiencing discomfort at work. The dominant types of noise were intermittent noise (45%), impulsive noise (20%), repetitive impulsive noise (10%), and continuous noise (25%). A total of 80% of respondents were exposed to noise for more than 40 hours per week, and 87.5% of them reported discomfort during work. Conclusion: Statistical analysis indicated a significant relationship between noise intensity (p=0.038) and duration of exposure (p=0.032) with work comfort, while no significant relationship was found between types of noise and work comfort (p=0.551).

Current research on the effectiveness of mindfulness-based interventions in schools generally shows positive effects on various teacher outcomes. However, previous research has not examined the effects of teacher training independently from those of classroom training. Our study evaluated the impact of the MoMento teacher and classroom training (cf. combined mindfulness-based training) on mindfulness skills and well-being of primary school teachers (N=27) using a block-randomized waitlist control group design across three measurement points. Self-report questionnaires assessing mindfulness skills (intra- and interpersonal mindfulness, self-compassion, and self-criticism) as well as well-being (emotional exhaustion, job satisfaction, life satisfaction, psychological well-being, positive and negative affect) were completed before and after the 8-week teacher training, as well as after the subsequent implementation of the 10-week classroom training. Significant improvements were found in mindfulness skills (intrapersonal mindfulness, self-criticism), but no improvements were observed in well-being. The improvements in mindfulness skills mostly occurred during the teacher training and were maintained through the subsequent implementation of the classroom training. Our study provides a foundation for a detailed examination of the effects of combined mindfulness-based programs. In the context of teacher professional development, disentangling training effects may provide valuable insights for the design of future interventions.

Despite existing protection limits established by different health agencies and regulatory bodies, chronic exposure to non-ionising electromagnetic field radiation (NIR) has raised concerns about its potential biological effects and its impact on human health. Exposure to NIR in urban environments is almost inevitable due to the density of devices and communication systems that emit these waves. Correctly measuring exposure levels among city residents is key to determining whether there is a relationship between these levels and potential health problems associated with NIR. Several factors, including the ubiquity of electromagnetic fields (EMFs) and people’s unawareness of their exposure, make the NIR assessment challenging. This paper proposes a standardised procedure for NIR testing and measurement for frequencies from 100 kHz to 3 GHz, designed explicitly for outdoor urban environments. The measurement procedure is intended for populated urban areas, a complex environment for signal propagation. The complete procedure, techniques, and equipment used for wideband and narrowband measurements are detailed, along with their corresponding overall uncertainty budgets. The data collected by this procedure are suitable and valuable for comparative epidemiological studies due to a systematic measurement protocol and rigorous control of measurement uncertainty. The proposed measurement procedure has been tested in two cities in central Spain, with a total population of 262,000. A total of 534 measurement points have been performed. The results can be used to verify compliance with exposure limits and to demonstrate levels below the applicable regulatory limits. Furthermore, it has been possible to test the validity of the hypothesis that urban environments can be characterised by NIR exposure, which was postulated in this work based on an ITU-R-inspired simplification that classifies urban outdoor areas into representative exposure categories.

Evaluation of exposure characteristics and radiological risks for cyclotron-based 18F radiopharmaceutical production workers in China.

ABSTRACT Deep eutectic solvents (DES) are emerging as promising and sustainable alternatives to traditional solvents in various industrial processes. They offer distinct advantages, including low toxicity, biodegradability, cost‐effectiveness, and the ability to be tailored to specific applications. This study investigated the fabrication of polysulfone (PSf) membranes using a lignin‐derived hydrophobic DES composed of thymol and 2,6‐dimethoxyphenol (Thy:Dmp) in a 1:1 M ratio via non‐solvent induced phase separation (NIPS). The Thy:Dmp DES exhibited a viscosity of 34.9 ± 0.26 mPa·s at 25°C. Hansen solubility parameter (HSP) calculations confirmed thermodynamic compatibility between PSf and the DES (RED = 0.6), while cloud point measurements established the ternary phase behavior for the PSf/Thy:Dmp/ethanol system. Fourier Transform Infrared spectroscopy (FTIR) confirmed complete solvent removal and polymer structure preservation after membrane formation. The resulting membranes exhibited asymmetric morphology with finger‐like macrovoids and 81.2% ± 1.80% porosity. Contact angle measurements indicated moderately hydrophilic membrane surfaces (64.0° ± 1.20°). Membrane performance evaluation displayed water permeability of 58.3 LMH/bar and bovine serum albumin (BSA, 66.5 kDa) rejection of 94.4%. These results demonstrate that lignin‐derived hydrophobic DES can serve as a functional alternative to conventional solvents for polysulfone membrane fabrication.

This study investigated the fault ride through capability of inverter-based resources in weak distribution networks and proposes a fault-oriented reactive power compensation strategy using only point of common coupling voltage measurements. The proposed strategy determines the reactive power command based on the minimum phase voltage, which represents the most severely depressed phase during unbalanced faults, without fault type detection or sequence component analysis. As a result, the same control framework can be applied to single-line-to-ground, double-line-to-ground, and three-phase faults. A detailed MATLAB/Simulink model of a Korean distribution feeder was developed using actual system parameters. The proposed strategy was compared with a no control case and a conservative fixed capacity reactive power injection scheme derived from commonly adopted power factor limits. Simulation results show that the no control case provides no voltage support, while the fixed capacity approach yields limited improvement in weak grids. In contrast, the proposed strategy maintains stable inverter operation and improves voltage recovery. At locations with an extremely low weighted short circuit ratio of 0.303, the proposed strategy prevents inverter tripping during temporary faults and satisfies low voltage ride through requirements, demonstrating its practical effectiveness.

This study conducts a landslide deformation assessment in Tianshui, Gansu Province, on the Chinese Loess Plateau, utilizing the Small Baseline Subset InSAR (SBAS-InSAR) method integrated with velocity direction conversion and Z-score clustering. The Chinese Loess Plateau is one of the most landslide-prone regions in China due to frequent rains, strong topographical gradients and severe soil erosion. By constructing subsets of interferograms, SBAS-InSAR can mitigate the influence of decorrelation to a certain extent, making it a highly effective technique for monitoring regional surface deformation and identifying landslides. To overcome the limitations of the satellite’s one-dimensional Line-of-Sight (LOS) measurements and the challenge of distinguishing true landslide signals from noise, two optimization strategies were implemented. First, LOS velocities were projected onto the local steepest slope direction, assuming translational movement parallel to the slope. Second, a Z-score clustering algorithm was employed to aggregate measurement points with consistent kinematic signatures, enhancing identification robustness, with a slight trade-off in spatial completeness. Based on 205 Sentinel-1 Single-Look Complex (SLC) images acquired from 2014 to 2024, the integrated workflow identified 69 “active, very slow” and 63 “active, extremely slow” landslides. These results were validated through high-resolution historical optical imagery. Time series analysis reveals that creep deformation in this region is highly sensitive to seasonal rainfall patterns. This study demonstrates that the SBAS-InSAR post-processing framework provides a cost-effective, millimeter-scale solution for updating landslide inventories and supporting regional risk management and early warning systems in loess-covered terrains, with the exception of densely forested areas.

Anterior palatal bone thickness: A retrospective cross-sectional cone-beam computed tomography pilot study in children and adolescents.

Adequacy of trial registration and consistency in outcome reporting in rheumatology RCTs: A meta-research study.

Extensive research has been conducted in recent years to mitigate damages caused by undesired vibrations in motor vehicles used for transportation. Vibrations occurring in highly active moving components of vehicles not only lead to discomfort in driving comfort but also reduce the material’s service life, causing fatigue and structural damage. Rotating elements in vehicles, such as the shaft (cardan shaft), crankshaft, and gears, can be sources of problems. In this study, vibrations resulting from an imbalance problem in the vehicle shaft were experimentally investigated, and the effects of balancing on the vehicle shaft were evaluated. For this purpose, vibration data were measured at three different points on a vehicle. Vibrations generated by the shaft under different road conditions and vehicle speeds were recorded and compared before and after balancing. The results of the study demonstrated a one-third reduction in vibration amplitude at the point beneath the driver's seat, which is directly associated with the comfort of both driver and passengers. Specifically, under the most demanding conditions (80 km·h⁻¹ speed and an uneven road surface), the vibration amplitude induced by the unbalanced driveshaft reached a high value of 2198 µm; after balancing, a reduction of approximately 65–70% in this amplitude was achieved at the measurement point under the driver's seat. In contrast, no significant difference was observed in the vibrations measured at the engine block and the luggage compartment. The findings indicate that vibrations originating from the vehicle driveshaft can be substantially mitigated through proper balancing.

Sensor degradation poses a significant challenge in autonomous driving. During heavy rainfall, interference from raindrops can adversely affect the quality of LiDAR point clouds, resulting in, for instance, inaccurate point measurements. This, in turn, can potentially lead to safety concerns if autonomous driving systems are not weather-aware, i.e., if they are unable to discern such changes. In this study, we release a new, large-scale, multi-modal emulated rain dataset, REHEARSE-3D, to promote research advancements in 3D point cloud de-raining. Distinct from the most relevant competitors, our dataset is unique in several respects. First, it is the largest point-wise annotated dataset (9.2 billion annotated points), and second, it is the only one with high-resolution LiDAR data (LiDAR-256) enriched with 4D RADAR point clouds logged in both daytime and nighttime conditions in a controlled weather environment. Furthermore, REHEARSE-3D involves rain-characteristic information, which is of significant value not only for sensor noise modeling but also for analyzing the impact of weather at the point level. Leveraging REHEARSE-3D, we benchmark raindrop detection and removal in fused LiDAR and 4D RADAR point clouds. Our comprehensive study further evaluates the performance of various statistical and deep learning models, where SalsaNext and 3D-OutDet achieve above 94% IoU for raindrop detection.