Mapping Technique Research Articles

Cross-domain autonomous driving visual segmentation based on enhanced target data learning

Within the broader context of Information and Communications Technology (ICT), the quest for reliable and scalable visual segmentation methods poses significant challenges, particularly in autonomous driving, where real-world scene complexity requires advanced solutions. To address data scarcity and improve segmentation performance, we propose a novel unsupervised domain adaptation (UDA) approach that enhances target domain learning. Our method introduces multiple perturbations consistency, leveraging spatial context within the target domain to improve recognition. By applying perturbations at input and feature levels and using a consistency loss, we enhance contextual learning. Additionally, a weight mapping technique reduces the impact of detrimental source domain information. Experimental results demonstrate that our approach outperforms baseline methods on the GTAV→Cityscapes and SYNTHIA→Cityscapes datasets.

Nanoscale Os isotopic quantification of Wadi Tayin dunite platinum group minerals by atom probe tomography

Serpentinites record processes that redistribute major and trace elements between mantle and crust. Platinum Group Elements (PGEs) are trace elements in serpentinites hosted in sulphides and alloys. Alloys are challenging to find, and most analytical techniques lack the spatial resolution to analyse them; hence, this research adopts automatic mineral mapping technique to detect PGM grains in a sample from the Wadi Tayin (Oman) peridotite and uses atom probe tomography, a nanoscale quantitative analytical technique, to analyse the grain. This work applies and assesses the applicability of atom probe tomography to measure the 187Os/188Os isotopic ratio of natural alloys and uses the ratio to constrain the source of Os. A novel algorithm is used to automatically determine the number of counts of the 187Os and 188Os peaks, to calculate the isotopic 187Os/188Os ratio and the analytical uncertainty. The 187Os/188Os ratio is 0.126 ± 0.003, consistent with the isotopic composition reported by literature in the dunite of the Main Mantle Section of the Wadi Tayin ophiolite.The analytical uncertainty is one order of magnitude higher than conventional bulk rock techniques, such as negative-thermal ionisation mass spectrometry (N-TIMS) and inductively coupled plasma mass spectrometry (ICP-MS). However, the precision is sufficient to conclude that the non-radiogenic 187Os/188Os ratio is compatible with a mantle origin for the alloy. Decreasing whole-rock Re with increasing LOI and the overprinting of magmatic pentlandite by magnetite demonstrate that progressive serpentinisation may have modified the Re budget. The results indicate that atom probe tomography can analyse 187Os/188Os ratio quantitatively in micron-sized natural alloys and provide insights into natural processes.

Accelerated Cartesian cardiac T2 mapping based on a calibrationless locally low-rank tensor constraint.

Cardiac T2 mapping is a valuable tool for diagnosing myocardial edema, inflammation, and infiltration, yet its spatial resolution is limited by the single-shot balanced steady-state free precession acquisition and duration of the cardiac quiescent period, which may reduce sensitivity in detecting focal lesions in the myocardium. To improve spatial resolution without extending the acquisition window, this study examined a novel accelerated Cartesian cardiac T2 mapping technique. We introduce a novel improved-resolution cardiac T2 mapping approach leveraging a calibrationless space-contrast-coil locally low-rank tensor (SCC-LLRT)-constrained reconstruction algorithm in conjunction with Cartesian undersampling trajectory. The method was validated with phantom imaging and in vivo imaging that involved 13 healthy participants and 20 patients. The SCC-LLRT algorithm was compared with a conventional locally low-rank (LLR)-constrained algorithm and a nonlinear inversion (NLINV) reconstruction algorithm. The improved-resolution T2 mapping (1.4 mm × 1.4 mm) was compared globally and regionally with the regular-resolution T2 mapping (2.3 mm × 1.9 mm) according to the 16-segment model of the American Heart Association. The agreement between the improved-resolution and regular-resolution T2 mappings was evaluated by linear regression and Bland-Altman analyses. Image quality was scored by two experienced reviewers on a five-point scale (1, worst; 5, best). In healthy participants, SCC-LLRT significantly reduced artifacts (4.50±0.39) compared with LLR (2.31±0.60; P<0.001) and NLINV (3.65±0.56; P<0.01), suppressed noise (4.12±0.35) compared with NLINV (2.65±0.50; P<0.001), and improved the overall image quality (4.38±0.40) compared with LLR (2.54±0.41; P<0.001) and NLINV (3.04±0.50; P<0.001). Compared with the regular-resolution T2 mapping, the proposed method significantly improved the sharpness of myocardial boundaries (4.46±0.60 vs. 3.04±0.50; P<0.001) and the conspicuity of papillary muscles and fine structures (4.46±0.63 vs. 2.65±0.30; P<0.001). Myocardial T2 values obtained with the proposed method correlated significantly with those from regular-resolution T2 mapping in both healthy participants (r=0.79; P<0.01) and patients (r=0.94; P<0.001). The proposed SCC-LLRT-constrained reconstruction algorithm in conjunction with Cartesian undersampling pattern achieved improved-resolution cardiac T2 mapping of comparable accuracy, precision, and scan-rescan reproducibility compared with the regular-resolution T2 mapping. The higher resolution improved the sharpness of myocardial borders and the conspicuity of image fine details, which may increase diagnostic confidence in cardiac T2 mapping for detecting small lesions.

Tactile stimulation designs adapted to clinical settings result in reliable fMRI-based somatosensory digit maps

Movement constraints in stroke survivors are often accompanied by additional impairments in related somatosensory perception. A complex interplay between the primary somatosensory and motor cortices is essential for adequate and precise movements. This necessitates investigating the role of the primary somatosensory cortex in movement deficits of stroke survivors. The first step towards this goal could be a fast and reliable functional Magnetic Resonance Imaging (fMRI)-based mapping of the somatosensory cortex applicable for clinical settings. Here, we compare two 3 T fMRI-based somatosensory digit mapping techniques adapted for clinical usage in seven neurotypical volunteers and two sessions, to assess their validity and retest-reliability. Both, the traveling wave and the blocked design approach resulted in complete digit maps in both sessions of all participants, showing the expected layout. Similarly, no evidence for differences in the volume of activation, nor the activation overlap between neighboring activations could be detected, indicating the general feasibility of the clinical adaptation and their validity. Retest-reliability, indicated by the Dice coefficient, exhibited reasonable values for the spatial correspondence of single digit activations across sessions, but low values for the spatial correspondence of the area of overlap between neighboring digits across sessions. Parameters describing the location of the single digit activations exhibited very high correlations across sessions, while activation volume and overlap only exhibited medium to low correlations. The feasibility and high retest-reliabilities for the parameters describing the location of the single digit activations are promising concerning the implementation into a clinical context to supplement diagnosis and treatment stratification in upper limb stroke patients.

Journey towards equity: Unveiling the relationship between patient travel distance and social vulnerability among patients with cancer.

158 Background: Patient travel distance is a significant barrier to accessing high-quality cancer care, impacting patient outcomes. Factors such as geographic location, transportation infrastructure, and socio-economic status contribute to variations in patient travel distance. The relationship between patient travel distance and the area deprivation index (ADI), a measure of socioeconomic disadvantage (based on income, education, employment, and housing quality) and social vulnerability of neighborhoods developed by the Health Resources &amp; Services Administration (HRSA), remains largely unexplored. This abstract seeks to unveil the intricate relationship between patient travel distance and ADI, shedding light on the impact of social vulnerability on cancer care access. Methods: Using patient ZIP code data and practice location data from April 2023 to March 2024, we calculated the straight-line, one-way, patient-clinic travel distance (travel distance). We then derived the ADI for patient ZIP codes using geospatial mapping techniques and compared travel distances by area deprivation vulnerability categories. Results: While analyzing geographic data for 20,000 patients at 5 locations, we observed that 89% of patients traveled 15 miles or less one-way to reach the practice location, with a median travel distance of 5 miles. Median travel distance in miles for patients within 15 miles of each practice location by ADI vulnerability category is provided (Table). Median travel distances between ADI categories were statistically significant (Kruskal Wallis H-test, p &lt;0.001). Patients from the most disadvantaged neighborhoods generally travel longer distances to access cancer care. Conclusions: Patients in socially vulnerable locales generally travel longer distances to access cancer care. By considering the ADI alongside patient travel distance, healthcare providers and policymakers can understand barriers faced by disadvantaged populations and allocate resources to reduce disparities in access to care. Targeted interventions and policies, like improving transportation services/infrastructure, empowering community-based care locations, and expanding telehealth, are necessary to address travel distance disparities for vulnerable populations. The effects of increased travel distance on timely access to care, treatment adherence, health outcomes, finances, caregivers, and patient satisfaction warrant further investigation. Location Site Category Within 15 miles of the practice location % of patients Median travel distance (in miles) by overall ADI vulnerability Highest (8, 9, 10 ADI Rank) Mid High (5, 6, 7 ADI Rank) Low (1, 2, 3, 4 ADI Rank) A Suburban Metro 89.8% 13.5 2.2 4.7 B Suburban Small Town 86.7% 12.5 13.03 8.5 C Suburban Metro 85.1% 14.9 9.4 5.9 D Suburban Metro 90.9% 12.9 9.6 4.7 E Urban Metro 93.6% 12.9 4.8 2.7 ALL 89% 12.5 6.3 5.0

Computationally cost-efficient analysis of the flow behavior of twin-fluid atomizers using computational fluid dynamics

In industrial-scale operations, wet electrostatic precipitators (WESPs) are used to minimize particulate matter, employing atomizers such as single-fluid and twin-fluid atomizers (TFAs). While TFAs provide several benefits over single-fluid atomizers, quantifying their spray characteristics is more complex, necessitating comprehensive case studies to design the internal structure of the spray and achieve desired properties. This study employed computational fluid dynamics (CFD) to simulate the internal and external flow phenomena of TFAs in industrial-scale WESPs, aiming to facilitate various parametric studies by reducing the high computational costs associated with analyzing high-speed internal flows and particle dynamics within the spray system. To decrease computational costs, the simulation was divided into two parts using stepwise segregated scenarios: Part I focused on the high-cost internal flow analysis, examining the spatiotemporal evolution of internal flow until it is fully developed, followed by droplet size distribution estimation at the nozzle. Part II computed the external flow of the spray, assessed potential cost reductions by examining the interactions between dispersed droplets, and validated the spray angle, penetration, and coverage against experimental data. The segregated strategy employed mapping techniques to integrate the two parts seamlessly. The simulation results closely matched the experimental benchmarks for spray angle, penetration, and coverage within a minimal error margin (<5%), demonstrating the model’s accuracy in capturing actual spray phenomena in TFAs. This approach significantly reduced the computational cost by more than twentyfold compared to conventional one-step solvers, offering a viable method for conducting various case studies in spray CFD simulations.

Statistical models and implant customization in hip arthroplasty: Seeking patient satisfaction through design

ObjectivesThis study conducts a systematic literature review to explore the role of statistical models and methods in the design of orthopedic implants, with a specific focus on hip arthroplasty. Through a comprehensive analysis of the scientific literature, it aims to understand the relevance and applicability of these models in implant development and research trends in the field of design. MethodsData analysis and co-occurrence mapping techniques were employed to investigate the statistical models used as predictors of satisfaction in hip arthroplasty and in implant design. This approach facilitated a detailed and objective assessment of existing literature, revealing key trends and identifying gaps in current knowledge. Key findingsThe review's findings underscore a burgeoning interest in implant customization, with a significant emphasis on leveraging statistical techniques for optimal design. The logistic model methodology was applied to analyze a survey of hip surgery specialists, revealing that the physician's age does not influence the decision to use a customized implant. Furthermore, the review highlighted a knowledge gap at the intersection of statistics and design discipline concerning implant customization. SignificanceDespite the recognized importance of customization in implant design, there remains a dearth of contributions from the design discipline perspective in the existing literature, indicating substantial room for improvement and the need for interdisciplinary integration. ConclusionThe integration of statistical methods in implant design is crucial, emphasizing the need for multidisciplinary approaches and customization to enhance patient satisfaction. This study provides a foundation for future research that could transform the field of hip arthroplasty through more personalized and effective solutions.

Analyze the impact of nonlinear distortions on OFDM-based visible light communication systems

Visible light communication (VLC) has gained attraction for its use in high-speed wireless connectivity leveraging LED lighting elements. Orthogonal Frequency Division Multiplexing (OFDM) is an attractive modulation scheme due to its spectral efficiency and resilience to multipath distortion. However, the nonlinear electro-optic transfer characteristics of optical components introduce signal clipping and quantization noise which corrupt OFDM signals. This paper provides an in-depth analysis of clipping and quantization noise to quantify the impact of LED nonlinearities on OFDM-based VLC system performance. Detailed mathematical models are derived for clipping distortions caused by LED optical power saturation and quantization errors from ADC/DAC finite precision in the modulator and driver circuitry. This analysis is quantified through simulations of the degradations in terms of error vector magnitude (EVM), signal-to-noise ratio (SNR) loss, and bit error rate (BER) under varying clipping ratios and quantization bit-depths. The 16-QAM OFDM transmission shows that the LED driver should possess at least 12 dB signal linear dynamic range and 3-bit quantization to restrict SNR penalty within 3 dB. Adaptive tone mapping and digital pre-distortion techniques are examined to compensate for intensity distortions enabling high-speed OFDM transmission over VLC links.

Improving Fault Tolerance for FPGA SoCs through Post-Radiation Design Analysis

FPGAs have been shown to operate reliably within harsh radiation environments by employing single-event upset (SEU) mitigation techniques, such as configuration scrubbing, triple-modular redundancy, error correction coding, and radiation aware implementation techniques. The effectiveness of these techniques, however, is limited when using complex system-level designs that employ complex I/O interfaces with single-point failures. In previous work, a complex SoC system running Linux applied several of these techniques only to obtain an improvement of 14 \(\times\) in mean time to failure (MTTF). A detailed post-radiation fault analysis found that the limitations in reliability were due to the DDR interface, the global clock network, and interconnect. This article applied a number of design-specific SEU mitigation techniques to address the limitations in reliability of this design. These changes include triplicating the global clock, optimizing the placement of the reduction output voters and input flip-flops, and employing a mapping technique called “striping.” The application of these techniques improved MTTF of the mitigated design by a factor of 1.54 \(\times\) and thus provides a 22.8X \(\times\) MTTF improvement over the unmitigated design. A post-radiation fault analysis using BFAT was also performed to find the remaining design vulnerabilities.

EFFECT OF PHENOL FORMALDEHYDE RESIN IMPREGNATION ON NANODYNAMIC VISCOELASTICITY OF PINUS MASSONIANA LAMB IN WET STATE

We evaluated the effects of phenol formaldehyde (PF) resin modification on Masson pine (Pinus massoniana Lamb.) wood cell wall in wet states. The penetration degree of PF resin into wood cell was determined using confocal laser scanning microscopy (CLSM). The micromechanical properties of PF-modified wood cell walls in wet state were analyzed by quasi-static nanoindentation and dynamic modulus mapping techniques. Results showed that the PF resin significantly affected the static viscoelasticity and nanodynamic viscoelasticity of wood cell walls in oven-dried and wet states. The cell-wall mechanics increased at a PF resin concentration due to the increased bulking effects, such as decreased crystallinity of cellulose. Furthermore, the microfibrillar angle (MFA) of cell walls was lower than that of the control wood cell wall. The cell-wall mechanics of PF resin-modified sample decreased small than control sample in wet states.

Study on Self-Learning System for Milling Chatter Feature Based on Hybrid Preprocessing Model and Improved Convolutional Clustering

Milling chatter is a self-excited vibration phenomenon during the cutting process, typically occurring in machining operations with lower stiffness. It significantly impacts the machining precision and surface morphology of the workpiece. To summarize the vibration signal features under operating conditions and enhance the precision of the intelligent recognition model for milling chatter features, this paper proposes an adaptive learning model for chatter feature recognition based on the improved convolutional clustering algorithm. This study uses wavelet analysis to extract chatter features and improved convolutional clustering to identify chatter and stable cutting signals. This method selected appropriate mapping techniques based on differences in chatter signals under various conditions and uses convolution for feature extraction. Improve convolutional clustering evaluates sample similarity through energy transfer, less influenced by signal space structure compared to Euclidean or Mahalanobis distances. This method provides a unified criterion for evaluating different signal feature representation spaces, thereby improving the accuracy and efficiency of chatter sample recognition. The recognition accuracy of chatter phenomena at different spindle speed ranges reaches $95$ % and $96.3$ % respectively. The results show that this method can effectively distinguish between chatter signals and stable cutting signals.

A Bibliometric Analysis of Ehealth Literacy: Evaluating Approaches to Promote Health and Well-Being in the Context of SDGs

Objective: This study aims to track the current research progress in the field of eHealth literacy by conducting a bibliometric analysis, focusing on trends and developments, particularly during and after the COVID-19 pandemic. Method: A bibliometric analysis was employed to examine the publication trends, key contributors, and predominant research topics within the eHealth literacy domain. Performance analysis and science mapping techniques were used to provide a comprehensive overview of the field. Results and Discussion: The analysis revealed significant publication trends, identified leading contributors to the field, and highlighted key research topics that have emerged. The study visualizes the entire phase of the COVID-19 pandemic, offering insights into how the pandemic has influenced research directions in eHealth literacy. Research Implications: By providing a macroscopic perspective, this study fills gaps left by traditional literature reviews, presenting a more complete picture of the field. The findings also suggest future research directions as the impact of the pandemic on eHealth literacy continues to evolve. Originality/Value: This study presents the first bibliometric analysis of research on eHealth literacy during the COVID-19 pandemic, offering a rigorous review of the latest advancements in the field. By examining emerging trends, the study highlights the critical role of emerging technologies in enhancing eHealth literacy. Furthermore, it provides practical insights into promoting health and well-being through sustainable digital solutions, contributing to the achievement of the Sustainable Development Goals (SDGs), particularly those related to health and innovation.

Development of Recreation in Improving the Health of the Population

This article investigates the potential of developing recreational and military-recreational zones, particularly focusing on the Chimgan-Charvak resort area in Uzbekistan. Despite the rapid global growth of recreational tourism, military recreation remains underexplored, presenting a knowledge gap, especially in Uzbekistan. The study employs SWOT analysis and expert surveys to assess the region's strengths, weaknesses, opportunities, and threats, alongside mapping techniques to analyze the population's health challenges. Findings reveal significant untapped potential in ecotourism and the necessity of balancing recreational development with environmental conservation. The study underscores the importance of innovative approaches to tourism management and calls for better investment in tourism infrastructure and specialized training for tour guides. These insights have broad implications for enhancing Uzbekistan’s economic growth through sustainable tourism development.

Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites.

N6-methyladenosine (m6A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m6A mapping have accelerated the accumulation of m6A site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for m6A site prediction. However, it is still a major challenge to precisely predict m6A sites using in silico approaches. To advance the computational support for m6A site identification, here, we curated 13 up-to-date benchmark datasets from nine different species (i.e., H. sapiens, M. musculus, Rat, S. cerevisiae, Zebrafish, A. thaliana, Pig, Rhesus, and Chimpanzee). This will assist the research community in conducting an unbiased evaluation of alternative approaches and support future research on m6A modification. We revisited 52 computational approaches published since 2015 for m6A site identification, including 30 traditional machine learning-based, 14 deep learning-based, and 8 ensemble learning-based methods. We comprehensively reviewed these computational approaches in terms of their training datasets, calculated features, computational methodologies, performance evaluation strategy, and webserver/software usability. Using these benchmark datasets, we benchmarked nine predictors with available online websites or stand-alone software and assessed their prediction performance. We found that deep learning and traditional machine learning approaches generally outperformed scoring function-based approaches. In summary, the curated benchmark dataset repository and the systematic assessment in this study serve to inform the design and implementation of state-of-the-art computational approaches for m6A identification and facilitate more rigorous comparisons of new methods in the future.

BASEPROD: The Bardenas Semi-Desert Planetary Rover Dataset

Dataset acquisitions devised specifically for robotic planetary exploration are key for the advancement, evaluation, and validation of novel perception, localization, and navigation methods in representative environments. Originating in the Bardenas semi-desert in July 2023, the data presented in this Data Descriptor is primarily aimed at Martian exploration and contains relevant rover sensor data from approximately 1.7km of traverses, a high-resolution 3D map of the test area, laser-induced breakdown spectroscopy recordings of rock samples along the rover path, as well as local weather data. In addition to optical cameras and inertial sensors, the rover features a thermal camera and six force-torque sensors. This setup enables, for example, the study of future localization, mapping, and navigation techniques in unstructured terrains for improved Guidance, Navigation, and Control (GNC). The main features of this dataset are the combination of scientific and engineering instrument data, as well as the inclusion of the thermal camera and force-torque sensors in particular.

A multi-strategy ontology mapping method based on cost-sensitive SVM

As the core of ontology integration, the task of ontology mapping is to find the semantic relationship between ontologies. Nevertheless, most existing ontology mapping methods only rely ontext information to calculate entity similarity, thereby disregarding semantic nuances and necessitating substantial manual intervention. Therefore, this paper introduces an ontology mapping method. Based on the traditional ontology mapping method, the process employs a deep learning model to mine the semantic information of entity concepts, entity properties and ontology structure to obtain the embedding vector. We use the similarity mechanism to calculate the similarity between different embedding vectors, and combine the similarity values obtained from multiple strategy entities into a similarity matrix. The similarity matrix serves as input to the support vector machine (SVM), and the ontology mapping problem is finally transformed into a binary classification problem. However, since the number of mapped pairs is much larger than the number of non-mapped pairs, the number of positive samples in the data set is much smaller than the number of negative samples. Therefore, based on the traditional SVM, the paper adopts cost-sensitive strategy to deal with the class imbalance problem. In comparative evaluations against contemporary ontology mapping techniques, our method demonstrates a noteworthy 5.0% enhancement in recall and a 3.0% improvement in F1 score when tested on both public benchmark datasets and domain-specific datasets.

Novel additive-manufactured tracking solar concentrating system for cooking applications: Design, geometry assessment, and optical evaluation

A novel solar concentrating system for cooking applications was developed, featuring a stationary reflector and a flat heliostat with equatorial tracking. This design allows for simple one-axis tracking at a constant speed with no need for deformable reflectors, ensuring the direction of the concentrated radiation in the cooking area remains steady and a quasi-constant power capture regardless of the Sun's position. The concentrator is made from 3D printed flat tiles, allowing for an easy do-it-yourself manufacturing process and avoiding highly focused irradiance on the receiver. The geometry of the reflector was scanned and compared to an ideal CAD reflector model, revealing some assembly imperfections, and a slightly concave tile geometry which causes a moderately sharp concentration peak. The irradiance distribution on a plane target near the effective focus of the reflector was analysed with a photographic flux mapping technique, and the results were compared to those obtained from ray tracing simulations of the ideal CAD model, confirming the tile concavity predicted by the geometrical examination. Conversely, it shows a reduction in the total energy gathered on the entire target plate when compared with ray tracing outcomes.

Enhancing brain image quality with 3D U-net for stripe removal in light sheet fluorescence microscopy

Light Sheet Fluorescence Microscopy (LSFM) is increasingly popular in neuroimaging for its ability to capture high-resolution 3D neural data. However, the presence of stripe noise significantly degrades image quality, particularly in complex 3D stripes with varying widths and brightness, posing challenges in neuroscience research. Existing stripe removal algorithms excel in suppressing noise and preserving details in 2D images with simple stripes but struggle with the complexity of 3D stripes. To address this, we propose a novel 3D U-net model for Stripe Removal in Light sheet fluorescence microscopy (USRL). This approach directly learns and removes stripes in 3D space across different scales, employing a dual-resolution strategy to effectively handle stripes of varying complexities. Additionally, we integrate a nonlinear mapping technique to normalize high dynamic range and unevenly distributed data before applying the stripe removal algorithm. We validate our method on diverse datasets, demonstrating substantial improvements in peak signal-to-noise ratio (PSNR) compared to existing algorithms. Moreover, our algorithm exhibits robust performance when applied to real LSFM data. Through extensive validation experiments, both on test sets and real-world data, our approach outperforms traditional methods, affirming its effectiveness in enhancing image quality. Furthermore, the adaptability of our algorithm extends beyond LSFM applications to encompass other imaging modalities. This versatility underscores its potential to enhance image usability across various research disciplines.

Characterisation of Gas Vesicles as Cavitation Nuclei for Ultrasound Therapy using Passive Acoustic Mapping.

Genetically encodable gas filled particles known as gas vesicles (GVs) have shown promise as a biomolecular contrast agent for ultrasound imaging and have the potential to be used as cavitation nuclei for ultrasound therapy. In this study, we used passive acoustic mapping techniques to characterize GV-seeded cavitation, utilizing 0.5 and 1.6 MHz ultrasound over peak rarefactional pressures ranging from 100 to 2200 kPa. We found that GVs produce cavitation for the duration of the first applied pulse, up to at least 5000 cycles, but that bubble activity diminishes rapidly over subsequent pulses. At 0.5 MHz the frequency content of cavitation emissions was predominantly broadband in nature, whilst at 1.6 MHz narrowband content at harmonics of the main excitation frequency dominated. Simulations and high-speed camera imaging suggest that the received cavitation emissions come not from individual GVs but instead from the coalescence of GV-released gas into larger bubbles during the applied ultrasound pulse. These results will aid the future development of GVs as cavitation nuclei in ultrasound therapy.

Bibliometric analysis of career decidedness studies using a visual mapping technique

This study aims to provide an overview of "career decidedness" and identifies related study trends and gaps. Analyzing 104 works published between 1995 and 2024 in the Web of Science (WoS) database using VOSviewer includes 93 journal articles, 12 papers, and two early appearance studies. Most research was in social psychology (88) and management (5). The most cited authors were Lent-Robert (519), Penn, Lee (464), and Ireland Gleen (460), while the most co-cited authors were Lent-Robert (144), Hirschi Andreas (63), and Savickas (63). The USA (1676 citations), Germany (258 citations), and The USA (1676 citations), Germany (258 citations), and Australia (252 citations) were the top cited countries. Leading institutions included the University of Maryland (749 citations), the University of Tennessee (235 citations), and the Leuphana University of Lüneburg (181 citations). Common keywords were career decidedness, self-management model, career decision-making, social career theory, and career development. The top publications were by Duffy (2007), with 203 citations; Lent (2016), with 168 citations; and Lent (2017) with 164 citations.