Structural Similarity Research Articles

Influence of exposure time on the aero-optical effects of a high-speed aircraft optical window

Under high-speed conditions (Mach number > 3), the turbulent shear layer formed on the optical window causes phase changes in the light, resulting in the aero-optical effects, which significantly affect the imaging quality at different exposure times, τ. In this study, imaging data of an optical window were collected in a Mach 3.8 supersonic low-noise wind tunnel for a facula and a USAF 1951 resolution board, as well as distorted wavefront data of visible light. As τ increases from 0.2 ms to 5 ms, the centroid jitter of the facula shows an overall decreasing trend and the increase of τ mainly inhibits the jitter in the spanwise direction. While τ varies from 0.2 ms to 28 ms, structural analysis indicated that the structural similarity and stability of imaging improves and this improvement has an upper limit of τ0 = 22 ms, coined as the saturation exposure time. Normalized modulation transfer function curves derived from USAF 1951 images show a decrease in the imaging resolution as τ increases. The average wavefront distortion can be reduced by up to 71.7% as τ increases from 5 ms to 30 ms, while the standard deviation of wavefront distortion initially decreases and then stabilizes at 0.0025λ.

Ultra-High-Resolution Photon-Counting-Detector CT with a Dedicated Denoising Convolutional Neural Network for Enhanced Temporal Bone Imaging.

Ultra-high-resolution (UHR) photon-counting-detector (PCD) CT improves image resolution but increases noise, necessitating use of smoother reconstruction kernels that reduce resolution below the system's 0.110 mm maximum spatial resolution. To address this, a denoising convolutional neural network (CNN) was developed to reduce noise in images reconstructed with the available sharpest reconstruction kernel while preserving resolution for enhanced temporal bone visualization. With IRB approval, CNN was trained on 6 clinical temporal bone patient cases (1,885 images) and tested on 20 independent cases using a dual-source PCD-CT (NAEOTOM Alpha, Siemens). Images were reconstructed using iterative reconstruction at strength 3 (QIR3) with both clinical routine (Hr84) and the sharpest available head kernel (Hr96). The CNN was applied to images reconstructed with Hr96 and QIR1. Three image series (Hr84-QIR3, Hr96-QIR3, and Hr96-CNN) for each case were randomized for review by two neuroradiologists, assessing overall quality and delineation of the modiolus, stapes footplate, and incudomallear joint. CNN reduced noise by 80% compared to Hr96-QIR3 and 50% relative to Hr84-QIR3, while maintaining high resolution. When compared to the conventional method at the same kernel (Hr96-QIR3), Hr96-CNN significantly decreased image noise (from 204.63 HU to 47.35 HU) and improved SSIM (from 0.72 to 0.99). Hr96-CNN images ranked higher than Hr84-QIR3 and Hr96-QIR3 in overall quality (p<0.001). Readers preferred Hr96-CNN for all three structures. The proposed CNN significantly reduced image noise in UHR PCD-CT, enabling the use of sharpest kernel. This combination greatly enhanced diagnostic image quality and anatomical visualization.ABBREVIATIONS: PCD = Photon-counting-detector; UHR = Ultra-high-resolution; IR = Iterative reconstruction; CNN = Convolutional neural network; SSIM: Structural similarity index.

First-principles study on a new 2D carbon allotrope with an intrinsic wide bandgap: A comparison with α-graphyne

Nanocarbon materials with intrinsic electronic bandgaps are highly desirable for next-generation carbon-based nanoelectronics. Herein, a new two-dimensional (2D) carbon allotrope with structural similarities to α-graphyne has been proposed theoretically, which exhibits intrinsic semiconducting behavior with a wide direct bandgap significantly larger than that reported in other 2D carbon allotropes. Based on first-principles calculations, the structural and electronic properties of the new 2D carbon allotrope, as well as its lattice stability, have been systematically investigated by adopting a comparative study with α-graphyne. Moreover, the effects of vertical stacking and in-plane biaxial strain on the new 2D carbon allotrope have also been clarified in this work, providing robust approaches for the effective modulation of electronic properties in the new 2D carbon allotrope. Thus, the intrinsic wide bandgap, along with effective modulations, suggests great advantages and potentials of the new 2D carbon allotrope in carbon-based electronic devices and light-emitting applications.

Organized Capitalism and Organized Science

This article compares two different ways in which German industries, during the half century before 1914, managed to integrate useful results from scientific research: At the time, on the one hand there was single-firm-based industrial research, and on the other the cooperation of science, business, and government in the Emperor William Society for the Advancement of Sciences (1911) out of which, after the Second World War, the Max Planck Society emerged. On this basis, the article discusses similarities and tensions between capitalism and the sciences which, in spite of some structural similarities, follow different logics.

NIMG-65. SYNTHETIC MRI GENERATION TO FACILITATE AUTOSEGMENTATION OF PEDIATRIC BRAIN TUMORS IN LIMITED DATA SCENARIOS

Abstract BACKGROUND The availability of standard multiparametric MRI sequences, including pre-contrast T1w (T1w-pre), post-contrast T1w (T1w-post), T2w, and FLAIR images, is essential for accurate segmentation of tumor subregions and subsequent response assessment in pediatric brain tumors (PBTs). However, clinical MRI sets are often incomplete due to imaging artifacts or inconsistent acquisition protocols across different centers. This study leverages Generative Adversarial Networks (GANs) to synthesize missing FLAIR and T1w-pre images from T2w and T1w-post images, respectively, to facilitate tumor segmentation in PBTs. METHODS We developed two GAN models based on the pix2pix framework, trained, validated, and tested on FLAIR-T2w and T1w-post-T1w-pre pairs from a multi-histology and multi-institutional cohort of 476 subjects with PBTs from the Children’s Brain Tumor Network (CBTN). The perceptual quality of the synthesized T1w-pre and FLAIR images was evaluated using the Structural Similarity Index (SSI; ideal value = 1). The robustness of the synthesized sequences to replace missing MRI sequences was evaluated by comparing the segmentation performance (Dice score) of our pre-trained autosegmentation model using all real modalities versus replacing real sequences with their synthesized counterparts. RESULTS The T1w-pre and FLAIR synthesis GANs achieved mean SSI values of 0.95 and 0.93, respectively. Median Dice scores for segmentation of whole tumor (WT), enhancing tumor (ET), and tumor core (TC) were calculated for real sequences: 0.90, 0.83, 0.89, replacing real FLAIR with synthetic: 0.86, 0.81, 0.86, and replacing real T1w-pre with synthetic: 0.90, 0.75, 0.89. No statistical significance (p&amp;gt;0.05) was observed in segmentation performance for WT, TC, ET when replacing real T1w-pre with synthetic, and for ET when replacing real with synthetic FLAIR sequences. CONCLUSION This method achieves robust segmentation performance in limited data scenarios by synthesizing missing MRI sequences. Future work includes improving FLAIR synthesis and segmentation performance for WT and ET by implementing and comparing other GAN architectures.

MRI denoising with a non-blind deep complex-valued convolutional neural network.

MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal of this work is to design and implement a complex-valued convolutional neural network (CNN) for MRI denoising. A complex-valued CNN incorporating the noise level map (non-blind DnCNN) was trained with ground truth and simulated noise-corrupted image pairs. The proposed method was validated using both simulated and in vivo data collected from low-field scanners. Its denoising performance was quantitively and qualitatively evaluated, and it was compared with the real-valued CNN and several other algorithms. For the simulated noise-corrupted testing dataset, the complex-valued models had superior normalized root-mean-square error, peak SNR, structural similarity index, and phase ABSD. By incorporating the noise level map, the non-blind DnCNN showed better performance in dealing with spatially varying parallel imaging noise. For in vivo low-field data, the non-blind DnCNN significantly improved the SNR and visual quality of the image. The proposed non-blind DnCNN provides an efficient and effective approach for MRI denoising. This is the first application of non-blind DnCNN to medical imaging. The method holds the potential to enable improved low-field MRI, facilitating enhanced diagnostic imaging in under-resourced areas.

An Analysis of Case Study Utilizing Distributed Power Flow Controllers Which Improves and Mitigates Power Quality

Abstract—Providing high-quality electrical power should be taken into consideration in light of the rising demand for electricity and the rise in non-linear loads in power systems. This research examines power quality concerns related to voltage sag and swell and uses a distributed power flow controller (DPFC) to reduce voltage deviation and enhance power quality. A novel FACTS device, the DPFC shares structural similarities with the unified power flow controller (UPFC). Despite UPFC, DPFC separates the three-phase series converter into many single-phase series dispersed converters via the line and removes the common dc-link between the shunt and series converters. A DPFC located in a single-machine infinite bus power system with two parallel transmission lines is included in the case study. The MATLAB/Simulink environment was to model the system. The simulation findings that are shown confirm that DPFC may raise power quality.

Business Process Clustering in Small and Medium Manufacturing Industries Based on Enterprise Resource Planning Concepts: A Systematic Literature Review

Abstract Business process management has become increasingly important in the manufacturing sector, playing a vital role in fostering productivity and facilitating organizational adaptability to technological advancements. Although each company’s business process models vary in uniqueness and complexity, certain similarities can be identified from these differences. This study employs a systematic literature review to aggregate and summarize findings on business process models, modeling languages, clustering techniques, and foundational clustering principles. Results demonstrate the dearth of research on the grouping of business process models in the manufacturing industry. Several studies have focused on sectors such as services, trading, and insurance. Research specifically addressing clustering in the manufacturing sector is limited. Existing clustering efforts in manufacturing revolve around groupings related to product defects, industrial locations, business ecosystems, and similar factors. Analysis of the methods, scope, and criteria used in grouping business process models in the manufacturing industry indicates that most approaches rely on structural or graphic similarities. Follow-up research is lacking once these business process clusters are identified. This study proposes a novel approach to grouping, integrating business process modeling with the implementation of a management information system. Business process management relies on integrating departments within the company through an enterprise resource planning (ERP) system. The next step involves proposing a conceptual framework to categorize business processes and assess the comparability of models in the manufacturing sector. Future research directions are also delineated.

Deep learning based apparent diffusion coefficient map generation from multi-parametric MR images for patients with diffuse gliomas.

Apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance imaging (DWI MRI) provides functional measurements about the water molecules in tissues. However, DWI is time consuming and very susceptible to image artifacts, leading to inaccurate ADC measurements. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images. We proposed the multiparametric residual vision transformer model (MPR-ViT) that leverages the long-range context of vision transformer (ViT) layers along with the precision of convolutional operators. Residual blocks throughout the network significantly increasing the representational power of the model. The MPR-ViT model was applied to T1w and T2-fluid attenuated inversion recovery images of 501 glioma cases from a publicly available dataset including preprocessed ADC maps. Selected patients were divided into training (N=400), validation (N=50), and test (N=51) sets, respectively. Using the preprocessed ADC maps as ground truth, model performance was evaluated and compared against the Vision Convolutional Transformer (VCT) and residual vision transformer (ResViT) models with the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). The results are as follows using T1w + T2-FLAIR MRI as inputs: MPR-ViT-PSNR: 31.0±2.1, MSE: 0.009±0.0005, SSIM: 0.950±0.015. In addition, ablation studies showed the relative impact on performance of each input sequence. Both qualitative and quantitative results indicate that the proposed MR-ViT model performs favorably against the ground truth data. We show that high-quality ADC maps can be synthesized from structural MRI using a MPR-ViT model. Our predicted images show better conformality to the ground truth volume than ResViT and VCT predictions. These high-quality synthetic ADC maps would be particularly useful for disease diagnosis and intervention, especially when ADC maps have artifacts or are unavailable.

Computer prediction of toxicity of new S-alkyl derivatives of 1,2,4-triazole

1,2,4-Triazole derivatives are of researchers’ significant interest due to their diverse biological properties, such as antimicrobial, anti-inflammatory, anticancer, and antioxidant activities. The integration of a 2-bromo-4-fluorophenyl fragment into the triazole structure can significantly enhance these activities. However, the evaluation of the toxicity of such compounds remains a critically important aspect for their practical application. To reduce the time and cost of experimental studies, QSAR (Quantitative Structure-Activity Relationship) methods are actively used, allowing the prediction of toxicity based on the molecular structure of compounds. Aim of the study. To assess the toxicity of new S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-R-1,2,4-triazol-3-thiols using the QSAR method, specifically to predict acute toxicity parameters (LD50), and to determine the influence of different (length) radicals on the toxicity of these compounds. Materials and methods. The objects of the virtual study were derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols. They were evaluated at the Department of Toxicological and Inorganic Chemistry of the Zaporizhzhia State Medical and Pharmaceutical University. The toxicity assessment was conducted using the nearest neighbor method via the Toxicity Estimation Software Tool (TEST). The prediction of the lethal dose (LD50) for rats was based on the structural similarity of the studied compounds with known substances, for which experimental toxicity data are available. Results. The conducted QSAR analysis demonstrated that structural changes in S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols significantly affect the predicted toxicity. The primary factor influencing the changes in LD50 values is the variation of radicals at the 5th position of the triazole ring. Conclusions. The results of the study showed, that the toxicity of new S-alkyl derivatives of triazol-3-thiols depends on the type of alkyl substituent. Compounds with propyl to heptyl fragments exhibit increased toxicity, while derivatives with thiol, octyl, nonyl, and decyl residues are characterized by lower toxicity.

QSAR prediction of toxicity for a new 1,2,4-triazole derivatives with 2-bromo-5-methoxyphenyl fragment

New derivatives of 1,2,4-triazole are promising research targets due to their unique biological properties, including antimicrobial, antifungal, antitumor, and antioxidant activities. The introduction of the 2-bromo-5-methoxyphenyl fragment into the triazole structure potentially enhances these properties. However, the issue of toxicity for such compounds remains a critical factor for their further application. To reduce experimental costs and time, QSAR (Quantitative Structure-Activity Relationship) methods are widely applied, allowing to predict compounds toxicity based on their molecular structure. The aim of this study was to evaluate the toxicity of new derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols, their acids, and esters using the QSAR method to predict parameters of acute toxicity (LD50) and to assess the influence of various radicals on the toxicity of the compounds. Materials and methods. The objects of this study were derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols, synthesized at the Department of Toxicological and Inorganic Chemistry of Zaporizhzhia State Medical and Pharmaceutical University. The nearest neighbor method was used for toxicity evaluation, applying the Toxicity Estimation Software Tool (TEST). The prediction of rats lethal dose (LD50) was based on the structural similarity of the studied compounds with known substances that have experimental toxicity data. Results. The QSAR analysis revealed that structural modifications in the derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols significantly influence their toxicity. Specifically, increasing the size of the radicals, especially through the introduction of aromatic fragments, contributed to the enhanced safety of the compounds, as evidenced by the increase in LD50 values. The highest LD50 values were observed for compounds containing phenyl radicals. Conclusions. The results of this study indicate the feasibility of using QSAR models to predict the toxicity of 1,2,4-triazole derivatives containing a 2-bromo-5-methoxyphenyl fragment. The observed trend of increasing safety with the introduction of larger aromatic radicals can be used for the rational design of new compounds with improved toxicological properties.

Integrating Super-Resolution with Deep Learning for Enhanced Periodontal Bone Loss Segmentation in Panoramic Radiographs

Periodontal disease is a widespread global health concern that necessitates an accurate diagnosis for effective treatment. Traditional diagnostic methods based on panoramic radiographs are often limited by subjective evaluation and low-resolution imaging, leading to suboptimal precision. This study presents an approach that integrates Super-Resolution Generative Adversarial Networks (SRGANs) with deep learning-based segmentation models to enhance the segmentation of periodontal bone loss (PBL) areas on panoramic radiographs. By transforming low-resolution images into high-resolution versions, the proposed method reveals critical anatomical details that are essential for precise diagnostics. The effectiveness of this approach was validated using datasets from the Chungbuk National University Hospital and the Kaggle data portal, demonstrating significant improvements in both image resolution and segmentation accuracy. The SRGAN model, evaluated using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics, achieved a PSNR of 30.10 dB and an SSIM of 0.878, indicating high fidelity in image reconstruction. When applied to semantic segmentation using a U-Net architecture, the enhanced images resulted in a dice similarity coefficient (DSC) of 0.91 and an intersection over union (IoU) of 84.9%, compared with 0.72 DSC and 65.4% IoU for native low-resolution images. These results underscore the potential of SRGAN-enhanced imaging to improve PBL area segmentation and suggest broader applications in medical imaging, where enhanced image clarity is crucial for diagnostic accuracy. This study also highlights the importance of further research to expand the dataset diversity and incorporate clinical validation to fully realize the benefits of super-resolution techniques in medical diagnostics.

Enhancing Black-and-White Image Colorization Using Cycle GAN: A Study on Unpaired Image Translation

Abstract. This paper explores the application of Cycle-Consistent Generative Adversarial Network (Cycle GAN) for the colorization of black-and-white images, addressing the challenges in image to the image translation and it was not requiring paired datasets. Cycle GAN, a generative adversarial network, uses cycle consistency loss to map grayscale images to color, effectively preserving structural integrity. The model is trained on unpaired datasets containing black-and-white and color images, enabling it to generate colorized versions from grayscale inputs. Performance evaluation is conducted using Two significant evaluation metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Results demonstrate that Cycle GAN effectively preserves image structure and detail, achieving high PSNR and SSIM values, particularly in scenes with distinct color boundaries. However, challenges arise in complex and low-resolution scenes where color accuracy and detail reconstruction may degrade. Additionally, this paper assesses the impact of different loss functions, learning rate schedules, and training strategies on the models output quality. Future work will focus on addressing these limitations by incorporating attention mechanisms and other advanced techniques to improve Cycle GAN's ability to handle intricate image features and complex scenes.

Application of the Linear Interaction Energy Method to Nitric Oxide Synthase Structure-Based Inhibitor Design.

The overproduction of nitric oxide by neuronal nitric oxide synthase (nNOS) is associated with several neuropathological conditions. As a result, inhibition of nNOS is a desirable therapeutic goal while avoiding the inhibition of endothelial NOS (eNOS) given its essential role in maintaining cardiovascular tone. Designing inhibitors with high specificity for nNOS over eNOS is challenging given the close similarity in the active site structure of all mammalian NOS isoforms. Computational methods like free energy perturbation (FEP) and thermodynamic integration (TI) offer attractive avenues for rational drug design, but application of these methods to NOS is hindered by several challenges, including proper handling of highly charged inhibitors with diverse structures as well as computational expense. To address these issues, we present a simplified approach combining continuum dielectric generalized born (GB) solvent models with linear interaction energy (LIE) calculations. Our method demonstrates excellent agreement with experimental data for charged inhibitors targeting mammalian NOS isoforms (mNOS). Our results highlight the utility of the GB-LIE method as a promising tool for screening NOS inhibitors and potentially other protein targets with charged active sites and diverse inhibitor structures.

Origin of the Difference in Proton Transport Direction between Inward and Outward Proton-Pumping Rhodopsins.

ConspectusActive transport is a vital and ubiquitous process in biological phenomena. Ion-pumping rhodopsins are light-driven active ion transporters that share a heptahelical transmembrane structural scaffold in which the all-trans retinal chromophore is covalently bonded through a Schiff base to a conserved lysine residue in the seventh transmembrane helix. Bacteriorhodopsin from Halobacterium salinarum was the first ion-pumping rhodopsin to be discovered and was identified as an outward proton-pumping rhodopsin. Since the discovery of bacteriorhodopsin in 1971, many more ion-pumping rhodopsins have been isolated from diverse microorganisms spanning three domains (bacteria, archaea, and eukaryotes) and giant viruses. In addition to proton-pumping rhodopsins, chloride ion- and sodium ion-pumping rhodopsins have also been discovered. Furthermore, diversity of ion-pumping rhodopsins was found in the direction of ion transport; i.e., rhodopsins that pump protons inward have recently been discovered. Very intriguingly, the inward proton-pumping rhodopsins share structural features and many conserved key residues with the outward proton-pumping rhodopsins. However, a central question remains unchanged despite the increasing variety: how and why do the ion-pumping rhodopsins undergo interlocking conformational changes that allow unidirectional ion transfer within proteins? In this regard, it is an effective strategy to compare the structures and their evolutions in the proton-pumping processes of both inward and outward proton-pumping rhodopsins because the comparison sheds light on key elements for the unidirectional proton transport. We elucidated the proton-pumping mechanism of the inward and outward proton-pumping rhodopsins by time-resolved resonance Raman spectroscopy, a powerful technique for tracking the structural evolutions of proteins at work that are otherwise inaccessible.In this Account, we primarily review our endeavors in the elucidation of the proton-pumping mechanisms and determination factors for the transport directions of inward and outward proton-pumping rhodopsins. We begin with a brief summary of previous findings on outward proton-pumping rhodopsins revealed by vibrational spectroscopy. Next, we provide insights into the mechanism of inward proton-pumping rhodopsins, schizorhodopsins, obtained in our studies. Time-resolved resonance Raman spectroscopy provided valuable information about the structures of the retinal chromophore in the unphotolyzed state and intermediates of schizorhodopsins. As we ventured further into our investigations, we succeeded in uncovering the factors determining the directions of proton release and uptake in the retinal Schiff base. While it is intriguing that the proton-pumping rhodopsins actively transport protons against a concentration gradient, it is even more curious that proteins with structural similarities transport protons in opposite directions. Solving the second mystery led to solving the first. When we considered our findings, we realized that we would probably not have been able to elucidate the mechanism if we had studied only the outward pump. Our Account concludes by outlining future opportunities and challenges in the growing research field of ion-pumping rhodopsins, with a particular emphasis on elucidating their sequence-structure-function relationships. We aim to inspire further advances toward the understanding and creation of light-driven active ion transporters.

Comparative analysis of canine and human HtrA2 to delineate its role in apoptosis and cancer.

Therapeutically, targeting the pro- and anti-apoptotic proteins has been one of the major approaches behind devising strategies to combat associated diseases. Human high-temperature requirement serine protease A2 (hHtrA2), which induces apoptosis through both caspase-dependent and independent pathways is implicated in several diseases including cancer, ischemic heart diseases, and neurodegeneration, thus making it a promising target molecule. In the recent past, the canine model has gained prominence in the understanding of human pathophysiology that was otherwise limited to the rodent system. Moreover, canine models in cancer research provide an opportunity to study spontaneous tumors as their size, lifespan, and environmental exposure are significantly closer to that of humans compared with laboratory rodents. Therefore, using HtrA2 as a model protein, comparative analysis has been done to revisit the hypothesis that canines might be excellent models for cancer research. We have performed evolutionary phylogenetic analyses that confirm a close relationship between canine and human HtrA2s. Molecular modeling demonstrates structural similarities including orientation of the catalytic triad residues, followed by in silico docking and molecular dynamics simulation studies that identify the potential interacting partners for canine HtrA2 (cHtrA2). In vitro biophysical and protease studies depict similarities in interaction with their respective substrates as well as transient transfection of cHtrA2 in mammalian cell culture shows induction of apoptosis. This work, therefore, promises to open a new avenue in cancer research through the study of spontaneous cancer model systems in canines.

Research on Autonomous Navigation of Unmanned Aerial Vehicles Based on Correlation-Based Image Comparison Methods

Relevance. Autonomous navigation of unmanned aerial vehicles (UAVs) is one of the key challenges in the modern aerospace industry. Specifically, for small UAVs, the task of autonomous navigation becomes even more complex due to limitations in computational resources and sensor capabilities. Optimizing navigation methods under such conditions is a pressing issue that requires solutions capable of providing high performance with minimal resource consumption.Objective. Improving the efficiency of autonomous navigation for small UAVs by using correlation methods for image comparison. Achieving this objective is linked to the development and evaluation of algorithms that provide high-speed and accurate navigation with limited computational resources. The work uses methods such as the autocorrelation function, Pearson correlation, the structural similarity index, and a simple neural network for image comparison.Solution. The research showed that the autocorrelation-based approach demonstrates the best performance under low computational resources. It ensures high-speed processing of the entire image and shows optimal detection accuracy. Compared to other methods presented in the study, the autocorrelation approach is capable of working not only with noise in the "reference" map but also of using alternative areas with altered patterns as detected regions.The scientific novelty of the study is determined by the systematic comparison of various methods applied to the task of image comparison for small UAVs with limited computational resources. Unlike well-known works in the field of correlation-extreme systems, this research focuses on the use of a "reference map" and a search area, representing two different images of the same terrain taken from different sources. This is a key difference, as most methods are not highly efficient in processing such images where patterns may differ significantly.The practical significance of the developed algorithm lies in the fact that the proposed autocorrelation-based method can be used by developers of autonomous small UAV control systems to reduce computational load and increase data processing speed.

Implemention of Innovative Process Analytical Technologies to Characterize Critical Quality Attributes of Co-Formulated Monoclonal Antibody Products.

Characterizing co-formulated monoclonal antibodies (mAbs) poses significant challenges in the pharmaceutical industry. Due to the high structural similarity of the mAbs, traditional analytical methods, compounded by the lengthy method development process, hinder product development and manufacturing efficiency. There is increasing critical need in the pharmaceutical industry to streamline analytical approaches, minimizing time and resources, ensuring a rapid clinical entry and cost-effective manufacturing. This study investigates the application of process analytical technologies (PAT) to address such challenges. Our investigation introduces two complementary technologies, on-line ultra-performance liquid chromatography (online UPLC) and multimode fluorescence spectroscopy (MMFS), as potential PAT tools tailored for characterizing critical quality attributes (CQA) in co-formulated mAb products. Specifically, the CQAs under evaluation include the total protein concentration of the mAbs within the co-formulation and the ratio of mAb A to mAb B. Online UPLC enables direct and automated measurement of the CQAs through physical separation, while MMFS determines them in a non-destructive and more swift manner based on chemometric modeling. We demonstrate these technologies' comparable performance to conventional methods, alongside substantial benefits such as reduced analytical turnaround time and decreased laboratory efforts. Ultimately, integrating them as innovative PAT tools expedites the delivery of therapeutic solutions to patients and enhances manufacturing efficiency, aligning with the imperative for swift translation of scientific discoveries into clinical benefits.

Enhancing Image Dehazing Efficiency with Dual Colour Space Attentional Deep Networks

Aims and Background: Image dehazing is an essential task in computer-vision, aimed at enhancing the clarity and visibility of images degraded by fog and haze. Traditional methods often struggle with handling the complex scattering effects of haze and maintaining the natural colours of the scene. Objectives and Methodology: To address these challenges, we propose a novel approach termed Dual Colour Space Attentional Deep Network (DCSADN) for efficient image dehazing. Our method leverages the advantages of two-colour spaces: RGB and YCbCr, to boost the network's skill to capture both the luminance and chrominance information, thereby improving the dehazing performance. We employ a multi-stage training strategy to optimize the performance of the DCSADN. This multi-stage approach ensures that the network generalizes well across different types of haze conditions. Extensive experiments demonstrate the superiority of our method over state-of-the-art dehazing techniques. Results: The results indicate that our approach not only achieves higher quantitative scores in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) but also produces dehazed images with more natural colours and details. Conclusion: The findings reveal that the dual colour space processing and attentional modules are crucial for the enhanced performance of our method and providing a valuable tool for improving image quality in hazy conditions.

IFGAN: Pre- to Post-Contrast Medical Image Synthesis Based on Interactive Frequency GAN

Medical images provide a visual representation of the internal structure of the human body. Injecting a contrast agent can increase the contrast of diseased tissues and assist in the accurate identification and assessment of conditions. Considering the adverse reactions and side effects caused by contrast agents, previous methods synthesized post-contrast images with pre-contrast images to bypass the administration process. However, existing methods pay inadequate attention to reasonable mapping of the lesion area and ignore gaps between post-contrast and real images in the frequency domain. Thus, in this paper, we propose an interactive frequency generative adversarial network (IFGAN) to solve the above problems and synthesize post-contrast images from pre-contrast images. We first designed an enhanced interaction module that is embedded in the generator to focus on the contrast enhancement region. Within it, target and reconstruction branch features interact to control the local contrast enhancement region feature and maintain the anatomical structure. We propose focal frequency loss to ensure the consistency of post-contrast and real images in the frequency domain. The experimental results demonstrated that IFGAN outperforms other sophisticated approaches in terms of preserving the accurate contrast enhancement of lesion regions and anatomical structures. Specifically, our method produces substantial improvements of 7.9% in structural similarity (SSIM), 36.3% in the peak signal-to-noise ratio (PSNR), and 8.5% in multiscale structural similarity (MSIM) compared with recent state-of-the-art methods.