Direct synthetic computed tomography (CT) generation from positron emission tomography (PET) plays a crucial role in PET attenuation correction, yet providing detailed structural information to compensate for functional imaging. Compared to the widely used PET/CT and indirect PET/MR-CT, the direct PET-to-CT translation method (denoted as PET-to-CT) offers several advantages: 1) The CT required for PET-to-CT is directly obtained from PET, thereby avoiding the intermediate errors generated in the inter-step processes of multimodal scanning in PET/CT and PET/MR-CT. 2) Furthermore, direct PET-to-CT eliminates the requirement for supplementary imaging equipment, thereby reducing complexity and scan duration in contrast to PET/CT and PET/MR-CT imaging. Thus, direct PET-to-CT is highly promising for clinical applications. However, it faces challenges, including spatial resolution mismatches between PET and CT, as well as voxel-wise semantic differences arising from functional and structural imaging. To address these challenges, this paper proposes a 2D hierarchical method called S2SCT (Slice-to-Slice Continual Transformer)-SA (Segmentation-aware) Network. It uses a slice-continual network to acquire semantic transformation knowledge from each PET slice to a CT slice, facilitating the conversion between functional and structural imaging domains. Subsequently, the segmentation-aware network is designed to futher capture spatial correlations both between slices and within slice, resulting in improved CT spatial resolution. The experiment results demonstrate that our proposed method outperforms mainstream methods in both CT generation and attenuation correction, as evidenced by both visual results and metric values.

Derivation of machine learning brain aging biomarkers for a set of forty thousand functional connectomes.

With the increasing use of web applications and online systems, cybersecurity threats such as malware and vulnerable code have become a major challenge for developers and organizations. Many security issues arise due to insecure coding practices or the use of unverified files and links. To address this problem, this project presents Secure Scan, a web-based security analysis tool designed to detect malicious files and identify common code vulnerabilities. Secure Scan integrates the VirusTotal API to scan files and URLs using multiple antivirus engines, helping users quickly identify potentially harmful content. In addition, the system includes a code scanning module that analyzes source code for common security vulnerabilities based on the OWASP Top 10, such as SQL Injection, Cross-Site Scripting (XSS), and command injection. The backend of the system is developed using Node.js, which manages the scanning process, vulnerability detection logic, and API communication. The goal of Secure Scan is to provide developers with a simple and accessible platform to perform basic security checks during the development process. By combining malware detection and code vulnerability analysis into a single tool, Secure Scan helps promote secure coding practices and improves overall software security.

To develop a streamlined software workflow for converting cone-beam computed tomography (CBCT) data into augmented reality (AR) and virtual reality (VR) formats, and to evaluate dental practitioners' perceptions of these advanced visualisation modalities compared to conventional CBCT analysis for endodontic treatment planning. A custom add-on for Blender (an open-source 3D modelling software), named VirtualEndo, was developed to automate the processing of AI-segmented CBCT scans into AR- and VR-compatible formats. Thirty dentists evaluated two diagnostically challenging cases using four methods: conventional CBCT analysis, AI-driven 3D segmentation, AR visualisation on iPad and VR visualisation with Meta Quest 3. The presentation order was randomised, and participants completed a standardised training protocol. Perceptions regarding diagnostic confidence, usability, information extraction, and clinical relevance were assessed using 4-point Likert scales. Statistical analysis employed Friedman tests with post hoc Wilcoxon signed-rank tests and Bonferroni correction. Significant differences were found across all evaluated categories (p < 0.05). Conventional CBCT was rated significantly inferior to Segmentation, VR, and AR for information extraction (canal course: W = 0.68; canal number: W = 0.56) and usability (W = 0.40). No significant differences existed between the three advanced visualisation methods. Segmentation was most frequently selected as clinically most relevant (40%), followed by VR (20%). For canal detection, differences were small (W = 0.10) with no method demonstrating clear superiority. Modern 3D visualisation technologies were perceived as significantly superior to conventional 2D CBCT slice analysis, primarily by presenting pre-integrated anatomical models that reduce the perceived cognitive burden of mental 3D reconstruction from 2D slices. Screen-based segmentation was favoured for pragmatic workflow integration, though immersive technologies showed promise if adoption barriers are addressed.

Directional propulsion of Leidenfrost droplets is effectively achieved on asymmetric microgroove arrays fabricated via one-step femtosecond laser direct writing. This is realized by simply setting the spacing of the laser scanning lines slightly smaller than the width of a single microgroove during the line-by-line laser scanning process. Because a femtosecond laser can process any given material, this method for driving Leidenfrost droplets is applicable to a wide range of material substrates, as experimentally verified on superhard alloys (magnesium alloy and titanium alloy), semiconductors (monocrystalline silicon and silicon carbide), and ceramics (aluminum nitride, sapphire). The proposed strategy also enables the transport of Leidenfrost droplets on composite surfaces made of different materials as well as the directional propulsion of various volatile liquids. Leveraging the controlled motion of Leidenfrost droplets and the flexible design of surface microstructures via femtosecond laser processing, several applications designed for extremely high-temperature environments have been realized, such as the trapping of Leidenfrost droplets, targeted cooling, self-rotation of Leidenfrost droplets (converting thermal energy into mechanical energy), and electricity generation.

Real-time Automatic Guidance During Shoulder Ultrasound Scanning with Artificial Intelligence: Reducing Operator Dependency in Rotator Cuff Assessment.

A correlation-based optimization model to recover lost and distorted data from scanning tunneling microscopy images based on density functional theory.

Optical coherence tomography angiography (OCTA) has become an indispensable tool for visualizing and quantifying in vivo blood flow due to its motion‐contrast‐based label‐free flow detection capabilities. However, in various applications, its effectiveness is hindered by signal degradation due to scattering, absorption, and depth‐dependent defocus, which limit penetration into deeper tissues. Depth‐dependent defocus is particularly problematic because it not only reduces the visual clarity of blood vessels but also weakens the angiographic decorrelation signal by diminishing both the signal amplitude and the sensitivity to slow flow. This paper presents a novel set of scanning and image processing techniques to computationally mitigate defocus‐induced effects in OCT angiograms. Allowing focal plane placement into deeper tissue without degrading the image quality near the surface, the proposed method enables defocus‐free, tail‐artifact‐free, and penetration‐enhanced angiography. The approach is compatible with all phase‐stable OCTA systems across a wide range of A‐scan rates, from sub‐MHz to multi‐MHz, offering a robust platform for improved angiographic imaging.

Molecular dynamic simulation of multi-frequency electrostatic force microscopy.

Accurate in vivo dosimetry is crucial for dose monitoring of cardiac implantable electronic devices (CIED) and for dose verification for special procedures such as total body irradiation (TBI) and total skin electron therapy (TSET). A new near real-time in vivo dosimetry system using radiochromic films (RCF) is investigated for clinical use in megavoltage external beam radiotherapy. The Pnt-Dos™ in vivo dosimetry system comprises of a new type of RCF and a dedicated software module. Each Pnt-Dos device is a small piece of RCF individually packed with a unique QR code for identification and record keeping. Different from the traditional film dosimetry workflow, where a film developing time of at least 16hours is recommended, a near real-time dose readout can be achieved with the Pnt-Dos system using a novel calibration procedure. This involves an automated scanning process at user-specified time intervals, utilizing auto-region of interest (ROI) detection and triple-channel calibration to capture the time-resolved post-irradiation growth. Two standard Epson scanner models (V600/13000XL) were used to cross-validate readouts and accommodate users who may prefer to utilize existing 13000XL scanners rather than acquire an additional V600 for in vivo dosimetry. The dosimetric accuracy was evaluated over a range of 15-400 cGy. Angular dependence was studied in 45° increments over 360°, normalized to the response at 0°, at 250 cGy using a cylindrical phantom. Energy dependence was evaluated for four photon energies (6 MV, 6 MV FFF, 10 MV FFF, 15 MV) and five electron energies (6 MeV, 9 MeV, 12 MeV, 16 MeV, and 20 MeV). Long-term reproducibility/stability were assessed with nine devices with different doses under identical conditions, alongside daily scans of quality control (QC) devices over three months. The system provides accurate dose measurements across high- and low-dose ranges. All readings were within specification: accuracy was<±5 cGy for doses ≤ 80 cGy doses (max discrepancy 6.0 cGy), and<±5% for doses>80 cGy on average (max discrepancy 5.1%). Angular dependence showed a maximum variation of 2.6%±2.1% when the beam passed through the posterior oblique side of the device. Daily QC/reproducibility tests confirmed system constancy of 0.1% average day-to-day variation. Energy dependence analysis revealed deviations of up to 4.9%±2.3% for all photon and electron energies compared to 6 MV photons, indicating the need for energy correction during commissioning. Film readings were compared with ion chamber measurements at 10 × 10cm2, dmax, 100cm SAD (photons) or 100cm SSD (electrons). Both scanners provided comparable readouts, within 1.3 cGy for doses ≤ 80 cGy and 0.6% for doses>80 cGy. Based on these findings, user guidelines were established to ensure optimal performance and accuracy. The new film-based in vivo dosimetry system provides an automated workflow that enables consistent, time-independent, and near real-time readout with a user-friendly design that simplifies handling and analysis, thereby streamlining in vivo dosimetry measurements. It also provides a traceable record of the patient dosimetry.

Rugby union is a popular contact sport during which high impact collisions frequently occur. There is concern for the overall brain health of those playing the game, as concussion is a potential outcome of high impact collisions. Repeated sub-concussive collisions may compromise rugby players' neurological integrity, but little is known about the effects on young brains. The brain is still developing during adolescence and may generally be more susceptible to injury, but minimal objective research data are available regarding head acceleration events experienced by junior players. Forty-one adolescent male rugby players underwent pre- and post-season MRI scans and neuro-cognitive assessments. Participants were fitted with instrumented mouthguards to record head acceleration events experienced during the season. Post-season processing of MRI scans focused on within-subject analysis of pre- to post-season changes in white matter as measured by diffusion tensor imaging. Linear mixed models were used to investigate correlations between neurological changes and cumulative head impact loading recorded by the mouthguards. MRI results indicated a non-significant difference between pre- and post-season for data relating to brain structure and function, including white matter microstructure, in response to one season of contact training and match play for under-16 male rugby players, as measured by diffusion tensor imaging. These results held irrespective of level of exposure. Our data suggest that exposure to one season of rugby does not appear to result in neurological compromise. The statistical non-significance reported for the main outcome measure also held when controlling for variables, such as training age and headgear use. Although pre- to post-season differences were statistically non-significant, the long-term effects of high exposure may be of clinical significance going forward. Further research, particularly using longitudinal designs, is needed to further elucidate the potential for microstructural neurological changes in adolescent rugby players.

Performance evaluation of EBT3, EBT-XD, and OrthoChromic OC-1 films for reference dosimetry in Ultra-High dose rate electron beams radiotherapy.

Abstract In 2017 and 2019 two horizon scan workshops were undertaken for the island of Cyprus, which focused on making predictions about the invasive alien species (IAS) most likely to arrive and impact biodiversity, human health and the economy. Herein, we assess the species lists derived from these two horizon scans and consider the accuracy of the predictions so far. In less than ten years, 26 new IAS were found in Cyprus, 10 out of which were predicted to arrive by the horizon scans. Eight introduced IAS were ranked as high risk during the horizon scanning process. Horizon scanning helped raise awareness amongst the authorities, scientists and the public, leading in some cases to a rapid response by the competent authorities to control the arrival. We conclude that horizon scanning is a useful process that can inform contingency planning and action. Furthermore, it facilitates communication between IAS experts, policy makers and society, encouraging active engagement and raising awareness regarding the importance of early warning, rapid response and management of IAS. We propose that the horizon scanning process for the island of Cyprus is repeated regularly, recognizing the ongoing increase in the number of new IAS arriving year on year.

The macroscopic Fourier ptychography (FP) is regarded as a highly promising approach of creating a synthetic aperture for macro visible imaging to achieve sub-diffraction-limited resolution. However most existing macro FP techniques rely on the high-precision translation stage to drive laser or camera scanning, thereby increasing system complexity and bulk. Meanwhile, the scanning process is slow and time-consuming, hindering the ability to achieve rapid imaging. In this paper, we introduce an innovative illumination scheme that employs a spatial light modulator to achieve precise programmable variable-angle illumination at a relatively long distance, and it can also freely adjust the illumination spot size through phase coding to avoid the issues of limited field of view and excessive dispersion of illumination energy. Coupled with a camera array, this could significantly reduce the number of shots taken by the imaging system and enable a lightweight and highly efficient solid-state macro FP imaging system with a large equivalent aperture. The effectiveness of the method is experimentally validated using various optically rough diffuse objects and a USAF target at laboratory-scale distances.

In order to solve the problem of large-scale sparse non-Hermitian positive definite linear systems, single-step iterative algorithm received some research attention. Although single-step iterative algorithm shows certain advantages in dealing with such linear systems, it still has the problem of slow convergence speed or harsh convergence conditions. To deal with this issue, this paper proposes a two-sweep iteration algorithm. On the basis of retaining the advantages of single-step iterative algorithm, this method adds a scanning process, and carries out a forward scan first in each iteration. Then, a backward scan is performed again, and the iterative value was updated by the results of the two scans, so as to accelerate the convergence process and improve the convergence speed and stability of the algorithm. A double-scan iterative algorithm with large sparse non-Hermitian positive numbers is constructed and theoretically proved to converge to the unconditional unique solution of linear systems. Meanwhile, the experimental results show that the algorithm shows better convergence speed and convergence conditions than the one-step iterative algorithm.

Laser detection and point cloud processing are key elements of measurement, autonomous driving, and defense technology, particularly for high-precision target recognition and environmental sensing. To meet strict requirements for speed and accuracy in target recognition for laser imaging fuzes, this paper presents a new attempted approach named the SC-PointLSTM framework, which is an innovative, rapid target recognition algorithm designed for linear array push-broom laser fuzes. SC-PointLSTM integrates an enhanced PointNet for efficient sparse point cloud processing with a double-layer long short-term memory (LSTM) for temporal feature fusion, addressing the computational challenges inherent to real-time dynamic environments. This approach enables parallel processing to generate linear 3D point clouds from linear push-broom sensors and effectively fuses previously acquired timestamp sequences, resulting in real-time, accurate target recognition at the end of the push-broom scanning process. The robustness and efficiency of the SC-PointLSTM framework were evaluated on both simulated datasets and real-world targets. The results demonstrated competitive results, achieving an Fscore exceeding 0.8, with an average recognition time of approximately 32 ms. The SC-PointLSTM framework provides a robust and scalable solution for high-speed target recognition in dynamic environments and real-time laser detection.

A Community Scan Process for Promoting Critical Care, Teacher Advocacy, and Community Engagement

Barcode detection is an essential process in contemporary automation, inventory management, and retailing, which enables fast and precise data recovery. The greater dependence on automatic systems has brought about the need for more stable and effective barcode detection systems to deal with complex real-world environments. This paper introduces a hybrid system that combines state-of- the-art deep learning-based object detection with traditional barcode decoding methods in order to increase overall accuracy, reliability, and efficiency. The system implemented under the proposal uses the YOLOv5 model, a commonly used deep learning model renowned for its fast processing speeds and accurate localization. Utilizing YOLOv5, the system provides real-time barcode detection, even with difficult conditions like changing light conditions, occlusion, and cluttered backgrounds. The method improves detection rates greatly while balancing the trade-off between computational complexity and real-time processing constraints. In addition, the barcode scanning process is improved through the implementation of Pyzbar, an efficient library utilized for extracting ordered data from the 1D and 2D formats of barcodes. The system attains high adaptability across different barcode formats and operating environments, while maintaining deployment feasibility on modern edge and desktop platforms through model optimization strategies. In this combined procedure, flexibility can be increased along with minimizing the possibility of obtaining false negatives as well as refining data recovery rates. The outcomes showcase how deep learning-based object detection coupled with conventional decoding methods presents an extensive solution for barcode recognition in changing and complex environments. This research highlights the ability of hybrid models in providing high-performance barcode detection, ultimately leading to innovations in automation and intelligent data management systems.

Mamba and its variants excel at modeling long-range dependencies with linear computational complexity, making them effective for diverse vision tasks. However, Mamba's reliance on unfolding 1D sequential representations necessitates multiple directional scans to recover lost spatial dependencies. This introduces significant computational overhead, redundant token traversal, and inefficiencies that compromise accuracy in real-world applications. To this end, we propose PH-Mamba, a novel framework integrating position encoding and harmonized attention for image deraining and beyond. PH-Mamba transforms Mamba's scanning process into a position-guided, unidirectional scanning that selectively prioritizes degradation-relevant tokens. Specifically, we devise a position-guided hybrid Mamba module (PHMM) that jointly encodes perturbation features alongside their spatial coordinates and harmonized representation to model consistent degradation patterns. Within PHMM, a harmonized Transformer is developed to focus on uncertain regions while suppressing noise interference, thereby improving spatial modeling fidelity. Additionally, we employ a vector decomposition and synthesis strategy to enable the unified representation layout to global degradation by directional scanning while minimizing redundancy. By cascading multiple PHMM blocks, PH-Mamba combines global positional guidance with local differential features to strengthen contextual learning. Extensive experiments demonstrate the superiority of PH-Mamba across low-level image restoration benchmarks. For example, compared to NeRD, PH-Mamba achieves a 0.60 dB PSNR improvement while requiring 88.9% fewer parameters, 36.2% less computation, and 63.0% faster inference time.

Microwave-induced thermoacoustic imaging, as an emerging biomedical imaging technique, combines the high contrast of microwave imaging with the high spatial resolution of ultrasound imaging. As an important branch of this technology, microwave-induced thermoacoustic microscopy retains these advantages while providing the capability to visualize finer tissue characteristics. However, conventional raster scanning mechanisms introduce interference in microwave field distribution due to mechanical motion, necessitating multiple signal averages to maintain signal-to-noise ratio. Additionally, the idle time during motor movement leads to prolonged single-scan duration, limiting its practical applications. To address these limitations, this paper proposes a rapid imaging system based on one-dimensional galvanometer scanning. The system employs a hybrid galvanometer-translation stage architecture and an optimized scanning strategy to minimize microwave field interference, reduce the number of signal averages, and decrease idle time, ultimately achieving more than a tenfold improvement in imaging speed. A specially designed timing control algorithm ensures precise synchronization of microwave excitation, galvanometer motion, and ultrasound detection, while a reconstruction algorithm adapted to the optimized scanning method effectively corrects distortions generated during the scanning process. System performance was evaluated through phantom and ex vivo tissue experiments. Resolution tests demonstrated hundred-micrometer resolution along all three axes (332 &lt;i&gt;μm&lt;/i&gt; × 324 &lt;i&gt;μm&lt;/i&gt; × 79 &lt;i&gt;μm&lt;/i&gt;), while contrast and depth imaging experiments confirmed its capability to clearly distinguish targets with different conductivities, achieving an effective detection depth of at least 10 mm in tissue. Early tumor mimicking experiments further demonstrated the system's ability to identify lesion boundaries, preliminarily revealing its potential for rapid tumor margin assessment. This approach maintains the imaging quality of microwave-induced thermoacoustic microscopy while enhancing imaging efficiency and system stability, laying a crucial foundation for advancing the technology from laboratory research to clinical applications.