Brightness Enhancement Research Articles

Multi-Shell Nanourchin-Integrated Dual Mode Lateral Flow Immunoassay for Sensitive and Rapid Detection of Clinical Cardiac Myosin-Binding Protein C.

Cardiac myosin-binding protein C (cMyBP-C) is a novel cardiac marker of acute myocardial infarction (AMI) and acute cardiac injuries (ACI). Construction of point-of-care testing techniques capable of sensing cMyBP-C with high sensitivity and precision is urgently needed. Herein, we synthesized an Au@NGQDs@Au/Ag multi-shell nanoUrchins (MSNUs), and then applied it in a colorimetric/SERS dual-mode immunoassay for detection of cMyBP-C. The MSNUs displayed superior stability, colorimetric brightness, and SERS enhancement ability with an enhanced factor of 5.4 × 109, which were beneficial to improve the detection capability of test strips. The developed MSNU-based test strips can achieve an ultrasensitive immunochromatographic assay of cMyBP-C in both colorimetric and SERS modes with the limits of detection as low as 19.3 and 0.77 pg/mL, respectively. Strikingly, this strip was successfully applied to analyze actual plasma samples with significantly better sensitivity, negative predictive value, and accuracy than commercially available gold test strips. Notably, this method possessed a wide range of application scenarios via combining with a color recognizer application named Color Grab on the smartphone, which can meet various needs of different users. Overall, our MSNU-based test strip as a mobile health monitoring tool shows excellent sensitivity, reproducibility, and rapid detection of the cMyBP-C, which holds great potential for the early clinic diagnosis of AMI and ACI.

Electrical stimulation for brighter persistent luminescence

An immature understanding of the mechanisms of persistent luminescence (PersL) has hindered the development of new persistent luminescent materials (PersLMs) with increased brightness. In this regard, in-situ direct current (DC) electric field measurements were conducted on a layered structure composed of the SrAl2O4:Eu2+,Dy3+ phosphor, and an electrode. In this study, the photoluminescence (PL) and afterglow properties were investigated with respect to voltage by analyzing the current signal and thermoluminescence (TL) spectroscopy. The intensity of PersL increased due to a novel phenomenon known as “external electric field stimulated enhancement of initial brightness of afterglow”. This dynamic process was illustrated via the use of a rate equation approach, where the electrons trapped by the ultra-shallow trap at 0.022 eV could be transferred through the conduction band during long afterglow. The afterglow intensity could reach 0.538 cd m−2 at a 6 V electric voltage. The design of an electric field stimulation technique enables the enhancement of the intensity of PersLMs and provides a new perspective for exploring the fundamental mechanics of certain established PersLMs.

Enhancing OLED electroluminescence by surface modification and work function adjustment of anode through NiO:Co coating

This study presents an approach to improve the electroluminescence (EL) of organic light-emitting diodes (OLEDs) by coating an ultra-thin NiO:Co (NCO) layer on the anode. The NCO layer was pivotal in modifying anodic properties, including surface roughness, hydrophilicity, and work function, thereby promoting ohmic hole injection from the anode into the NPB layer and enhancing the current density and carrier recombination in OLEDs. Compared to OLEDs with a pristine ITO anode, the OLEDs with an NCO-coated ITO anode showed a significant enhancement in EL performance, including a 24.5 % increase in current density, an 84.9 % enhancement in brightness, and a 48.7 % boost in efficiency. Notably, coating NCO on the anode consistently enhanced hole injection regardless of whether ITO or PEDOT:PSS was used as the anode.

Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer’s disease

Alzheimer’s disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.

Optimization of enzymatic ramie degumming using thermoalkaliphilic xylanase from Bacillus halodurans CM1 and a commercial pectinase

Ramie (Boehmeria nivea L. Gaud) is a versatile plant with potential as a cotton alternative in textiles. Its cellulose content, second only to cotton, requires degumming to remove fiber gum before industrial use. This study scaled up enzymatic degumming to 1 kg of ramie fiber, employing a dual enzyme approach: local Bacillus halodurans CM1 xylanase and commercial pectinase. This was optimized under varying conditions, including temperatures (30, 50, and 70 °C), durations (1, 2, and 3 h), and solid-liquid ratios (SLRs; 1:15, 1:22.5, 1:30), utilizing response surface methodology. Optimal outcomes were achieved at 50 °C and a 1:15 ratio, with a 3-h treatment duration, resulting in the highest reducing sugar yield (5.25 mg/mL) and a 102.03% enhancement in ramie fiber brightness compared to the enzyme-free control. This enzymatic process effectively separated gum from fibers while improving the quality, fineness, and tensile strength of cellulose fibers.

Enhancement of Pulp Brightness and Yield in ECF Bleaching Using Xylanase Treatment: A Comparative Study on Different Wood Species

Enhancement of Pulp Brightness and Yield in ECF Bleaching Using Xylanase Treatment: A Comparative Study on Different Wood Species

Design and fabrication of a porous prism film for display backlight applications

This study demonstrates a fabrication method of a porous brightness enhancement film (pBEF) that offers brightness enhancement, light diffusion, color shift reduction, and improved thermal stability. During the ultraviolet imprinting and solvent evaporation processes, the nano/submicron-sized air pores are generated within the polymer prism structure, and micropatterns spontaneously form on the prism surface. The inner pores ranging from 30 to 450 nm can effectively scatter light to mitigate color shift, which is caused by multiple internal reflections within the prism structure. The micropatterns have multiple rings formed one around another with 5–15-µm diameter on the prism surface improve visual quality. Moreover, the obtained functions are achieved in a single film solution, obviating the need for using multiple materials, and the fabrication process is relatively simple and fast as it is conducted under ambient conditions. When the pBEF is integrated into a liquid-crystal display backlight, it provides the brightness enhancement performance and comparable viewing angle distribution of a regular BEF combined with an additional diffuser (two films) and increases brightness by ∼8% compared to a bead prism (particle-based BEF). Additionally, it reduces the redshift (Δxy) from 0.1605 to 0.1415. Furthermore, the pBEF exhibits a lower coefficient of thermal expansion than the regular BEF.

Plasmonically engineered nitrogen-vacancy spin readout.

Ultra-precise readout of single nitrogen-vacancy (NV) spins holds promise for major advancements in quantum sensing, computing, and communication technologies. Here we present a rigorous open quantum theory capable of simultaneously capturing the optical, vibronic, and spin interactions of the negatively charged NV center, both in the presence and absence of plasmonic interaction. Our theory is verified against existing experiments in the literature. We predict orders of magnitude brightness and contrast enhancements in optically detected magnetic resonance (ODMR) and NV spin qubit readout arising from plasmonic interaction. Such optimal enhancements occur in carefully engineered parameter regions, necessitating rigorous modelling prior to experimentation. Our theory equips the community with a tool to identify such regions.

Enhancing skin lesion segmentation with a fusion of convolutional neural networks and transformer models

Accurate segmentation is crucial in diagnosing and analyzing skin lesions. However, automatic segmentation of skin lesions is extremely challenging because of their variable sizes, uneven color distributions, irregular shapes, hair occlusions, and blurred boundaries. Owing to the limited range of convolutional networks receptive fields, shallow convolution cannot extract the global features of images and thus has limited segmentation performance. Because medical image datasets are small in scale, the use of excessively deep networks could cause overfitting and increase computational complexity. Although transformer networks can focus on extracting global information, they cannot extract sufficient local information and accurately segment detailed lesion features. In this study, we designed a dual-branch encoder that combines a convolution neural network (CNN) and a transformer. The CNN branch of the encoder comprises four layers, which learn the local features of images through layer-wise downsampling. The transformer branch also comprises four layers, enabling the learning of global image information through attention mechanisms. The feature fusion module in the network integrates local features and global information, emphasizes important channel features through the channel attention mechanism, and filters irrelevant feature expressions. The information exchange between the decoder and encoder is finally achieved through skip connections to supplement the information lost during the sampling process, thereby enhancing segmentation accuracy. The data used in this paper are from four public datasets, including images of melanoma, basal cell tumor, fibroma, and benign nevus. Because of the limited size of the image data, we enhanced them using methods such as random horizontal flipping, random vertical flipping, random brightness enhancement, random contrast enhancement, and rotation. The segmentation accuracy is evaluated through intersection over union and duration, integrity, commitment, and effort indicators, reaching 87.7 % and 93.21 %, 82.05 % and 89.19 %, 86.81 % and 92.72 %, and 92.79 % and 96.21 %, respectively, on the ISIC 2016, ISIC 2017, ISIC 2018, and PH2 datasets, respectively (code: https://github.com/hyjane/CCT-Net).

Eccentric binaries: Periastron events and tidal heating

Context. Binary stars cannot be fully understood without assessing the interaction effects between the two components and the impact of these effects on observational diagnostics. Periastron brightening events, also known as the heartbeat phenomenon, are a clear manifestation of this type of interaction. Aims. We aim to explore the role of tidal shear energy dissipation in stars undergoing periastron brightening events. Methods. We performed a computation from first principles that uses a quasi-hydrodynamic Lagrangian scheme to simultaneously solve the orbital motion and the equations of motion of a 3D grid of volume elements covering the inner, rigidly rotating “core” of a tidally perturbed star. The equations of motion include the gravitational acceleration of both stars, the centrifugal, Coriolis, gas pressure accelerations, and viscous coupling between volume elements. The method is illustrated for a grid of model binary systems with a 10 M⊙ primary that is perturbed by a 6.97 M⊙ companion in eccentric orbits (e = 0 − 0.7). The model is then applied to the heartbeat star MACHO 80.7443.1718. Results. We find an increase by factors ∼10−6–10−3 in tidal shear energy dissipation at periastron, consistent with the majority of observed heartbeat stars. The magnitude of the periastron effect correlates with the degree of departure from synchronicity: stars rotating much faster or much slower than the synchronous rate at periastron present the strongest effect. We confirm that for eccentricities ≤0.3, pseudo-synchronization occurs for 0.8 < ω/Ωave < 1, where Ωave is the average orbital angular velocity. The minimum energy rotation rate (pseudo-synchronism) for e = 0.5 and 0.7 occurs for 1.0 < ω/Ωave < 1.15. The tidal shear energy dissipation model reproduces from first principles the ∼23% maximum brightness enhancement at periastron of MACHO 80.7443.1718. Conclusions. Our results suggest that the magnitude and shape of the heartbeat signal may serve as diagnostics for the internal stellar rotation and turbulent viscosity values.

TESS Observations of Outbursts on Polars—Superflares

Flare events are magnetic activities on the stellar surface, which can provide us with information about the accretion behavior and evolution of polars. In this paper, we search for flare events on polars from all the available 2 minute cadence TESS data through visual inspection, and identify six flare events on five flaring stars from 185 polars. All the flares have bolometric energies above 1034 erg, with a median of ∼1035 erg, so they are superflares. Among them, two flares are followed by a brightness enhancement, which may suggest an accretion burst on the white dwarf caused by a coronal mass ejection. A completeness analysis of the detections demonstrates that the flare activities on the low-mass red dwarfs of polars are very similar to those on M dwarfs, which indicates that the highly magnetic fields of the white dwarfs have no significant effect on the magnetic activities on polars. We estimate that another ∼20–30 polars include a magnetically active secondary star, and more intensive searching for flares on polars is encouraged in further work.

P‐5.20: Brightness gain study of high refractive index brightness enhancement film

P‐5.20: Brightness gain study of high refractive index brightness enhancement film

Influence of Kappa Number on Enhancing Pulp Brightness through Stage D0 with ClO2 Bleaching Process: Environmental Implications and Considerations

<p>Stage D0 in the pulp bleaching process, utilizing Chlorine Dioxide (ClO2), represents a critical phase aimed at improving pulp brightness while simultaneously addressing environmental concerns. This stage enhancement of pulp brightness can made an environmental impact, as excessive usage can lead to harmful emissions and environmental degradation. The amount of ClO<sub>2</sub> used at stage D0 is influenced by the kappa number, the greater the kappa number, the greater the ClO<sub>2</sub> use will be to produce brightness that corresponds to standar. The calculation of ClO<sub>2</sub> usage at stage D0 is carried out after obtaining data related to ClO<sub>2</sub> and the amount of pulp production from DCS (Distribute Control System). Based on the results of calculations and analysis, it can be concluded that the kappa number influences the total use of ClO<sub>2</sub> in stage D0. The more ClO<sub>2</sub> used in the D0 tower will produce pulp with a higher level of brightness.</p>

MMDCP: An Image Enhancement Algorithm Incorporating Multi-Channel Phase Activation and Multi-Constrained Dark Channel Prior

The quality of visual media is critically impacted by low illumination and the presence of airborne particulates, leading to challenges in brightness balance, color saturation, and texture clarity which are detrimental to various applications in image processing and computer vision. Addressing these challenges, this study introduces a novel image enhancement algorithm that significantly improves the quality of degraded images. Our proposed method, the multi-channel phase activation and multi-constraint dark channel prior (MMDCP), leverages an innovative approach by integrating the phase-adjusted Gaussian kernel function for brightness channel optimization in the Fourier transform frequency domain. This optimization is enhanced through the application of a saturated dark channel prior, achieving simultaneous brightness enhancement and color fidelity. Furthermore, we refine the dark channel prior deblurring algorithm by incorporating intensity, brightness, and color constraints to correct overexposure issues and color offsets in the reconstructed images. The efficacy of the MMDCP algorithm is demonstrated through extensive experimentation, comparing it against six contemporary image enhancement algorithms using two types of objective indicators and subjective assessments across four public datasets. The MMDCP algorithm consistently outperforms the existing methods, with a notable average improvement of 20% in PSNR and 19.6% in SSIM metrics, substantiating its superiority in enhancing brightness, detail, and color accuracy. This study’s results underline the MMDCP algorithm’s robustness and versatility in improving image quality across various conditions, including daytime, nighttime, indoor, and outdoor settings.

Basilar artery perforator aneurysms: a comparison with non-perforator saccular aneurysms.

Basilar artery perforator aneurysms (BAPAs) are rare. There is no systematic description of their presentation, imaging, natural history and outcomes and how these compare to conventional non-perforator aneurysms. Thus, the authors in this study aimed to compare BAPAs to non-perforator aneurysms. Cases were identified from a prospective neurovascular database, notes and imaging retrospectively reviewed and compared to a consecutive series of patients with non-perforator aneurysms. Blood volume on CT and vessel wall imaging (VWI) were compared to controls. 9/739 patients with aneurysmal subarachnoid haemorrhage (aSAH) harboured BAPAs. Compared to 103 with aSAH from posterior circulation aneurysms, they were more likely to be male (6/9, p = 0.008), but of equal severity (4/9 poor grade, p = 0.736) and need of CSF drainage (5/9, p = 0.154). Blood volume was similar to controls (30.2ml vs 26.7ml, p = 0.716). 6/9 BAPAs were initially missed on CTA. VWI showed thick (2.9mm ± 2.7) bright enhancement (stalk ratio 1.05 ± 0.12), similar to controls with ruptured aneurysms (0.95 ± 0.23, p = 0.551), and greater than unruptured aneurysms (0.43 ± 0.11, p < 0.001). All were initially managed conservatively. Six thrombosed spontaneously. Three grew and had difficult access with few good endovascular options and were treated through a subtemporal craniotomy without complication. None rebled. At 3months, all presenting in poor grade were mRS 3-4 and those in good grade mRS 1-2. Despite their small size, BAPAs present with similar volume SAH, WFNS grade and hydrocephalus to other aneurysms. They are difficult to identify on CTA but enhance strikingly on VWI. The majority thrombosed. Initial conservative management reserving treatment for growth was associated with no rebleeds or complications.

Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex

Aiming at solving the problems of local halo blurring, insufficient edge detail preservation, and serious noise in traditional image enhancement algorithms, an improved Retinex algorithm for low-light mine image enhancement is proposed. Firstly, in HSV color space, the hue component remains unmodified, and the improved multi-scale guided filtering and Retinex algorithm are combined to estimate the illumination and reflection components from the brightness component. Secondly, the illumination component is equalized using the Weber–Fechner law, and the contrast limited adaptive histogram equalization (CLAHE) is fused with the improved guided filtering for the brightness enhancement and denoising of reflection component. Then, the saturation component is adaptively stretched. Finally, it is converted back to RGB space to obtain the enhanced image. By comparing with single-scale Retinex (SSR) algorithm and multi-scale Retinex (MSR) algorithm, the mean, standard deviation, information entropy, average gradient, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) are improved by an average of 50.55%, 19.32%, 3.08%, 28.34%, 29.10%, and 22.97%. The experimental dates demonstrate that the algorithm improves image brightness, prevents halo artifacts while retaining edge details, reduces the effect of noise, and provides some theoretical references for low-light image enhancement.

Evaluation of Marine Atmospheric Environmental Adaptability of 3238A/CCF300 Composite Material

Composite material has been widely used in various industries because of its superior properties. But when used in harsh environment the composite material is susceptible to aging under the influence of UV radiation, water, temperature, humidity and other natural environmental medium. To evaluate the environmental adaptability of composite material, exposed 3238A/CCF300 in marine atmospheric environment for 3 year. The results show that the morphology exhibit brightness enhancement and surface resin decomposition. And the mechanical properties including 90° tensile strength, 0° compressive strength, bending strength, shear strength have all decline, amongst which the 90° tensile strength declines 55.4%, is most sensitive to marine atmospheric environment. Finally analyses the failure mechanism of 3238A/CCF300 due to the combination of heat-humidity aging and photo-degradation process.

Recognition of mulberry leaf diseases based on multi-scale residual network fusion SENet.

Silkworms are insects with important economic value, and mulberry leaves are the food of silkworms. The quality and quantity of mulberry leaves have a direct impact on cocooning. Mulberry leaves are often infected with various diseases during the growth process. Because of the subjectivity and time-consuming problems in artificial identification of mulberry leaf diseases. In this work, a multi-scale residual network fusion Squeeze-and-Excitation Networks (SENet) is proposed for mulberry leaf disease recognition. The mulberry leaf disease dataset was expanded by performing operations such as brightness enhancement, contrast enhancement, level flipping and adding Gaussian noise. Multi-scale convolution was used instead of the traditional single-scale convolution, allowing the network to be widened to obtain more feature information and avoiding the overfitting phenomenon caused by the network piling up too deep. SENet was introduced into the residual network to enhance the extraction of key feature information of the model, thus improving the recognition accuracy of the model. The experimental results showed that the method proposed in this paper can effectively improve the recognition performance of the model. The recognition accuracy reached 98.72%. The recall and F1 score were 98.73% and 98.72% respectively. Compared with some other models, this model has better recognition effect and can provide technical reference for intelligent mulberry leaf disease detection.

Comparative study: enhancing legibility of ancient Indian script images from diverse stone background structures using 34 different pre-processing methods

In recent times, there has been a proactive effort by various institutions and organizations to preserve historic manuscripts as repositories of traditional knowledge and cultural heritage. Leveraging digital media and emerging technologies has proven to be an efficient way to safeguard these invaluable documents. Such technologies not only facilitate the extraction of knowledge from historic manuscripts but also hold promise for global applications. However, transforming inscribed stone artifacts into binary formats presents significant challenges due to angle distortion, subtle differences between foreground and background, background noise, variations in text size, and related issues. A pivotal aspect of effective image processing in preserving the rich information and wisdom encoded in stone inscriptions lies in employing appropriate pre-processing methods and techniques. This research paper places a special focus on elucidating various preprocessing techniques, encompassing resizing, grayscale conversion, enhancement of brightness and contrast, smoothening, noise removal, morphological operations, and thresholding. To comprehensively assess these techniques, we undertake a study involving stone inscription images extracted from the Tanjore Brihadeeswar Temple, dating back to the eleventh century during the reign of Raja Raja Chola. This choice is informed by the manifold challenges associated with image correction, such as distortion and blurring. We undertake an evaluation encompassing a diverse array of stone background structures, including types like flawless-bright-moderately legible, dark-illegible, flawless-bright-illegible, flawless-dull, flawless-irregular-moderate, highly impaired-dark-legible, highly impaired-irregular-illegible, impaired-dark-moderate, impaired-dull-moderately legible, impaired-dusky dark-moderate, and very impaired-dusky dark-legible. Subsequently, the processed outputs are subjected to character recognition and information extraction, with a focus on comparing the outcomes of various pre-processing methods, including binarization and grayscale conversion. This study seeks to contribute insights into the most effective pre-processing strategies for enhancing the legibility and preservation of ancient Indian script images etched onto diverse stone background structures.

Fluorescence Enhancement in Topologically Optimized Gallium Phosphide All-Dielectric Nanoantennas.

Nanoantennas capable of large fluorescence enhancement with minimal absorption are crucial for future optical technologies from single-photon sources to biosensing. Efficient dielectric nanoantennas have been designed, however, evaluating their performance at the individual emitter level is challenging due to the complexity of combining high-resolution nanofabrication, spectroscopy and nanoscale positioning of the emitter. Here, we study the fluorescence enhancement in infinity-shaped gallium phosphide (GaP) nanoantennas based on a topologically optimized design. Using fluorescence correlation spectroscopy (FCS), we probe the nanoantennas enhancement factor and observe an average of 63-fold fluorescence brightness enhancement with a maximum of 93-fold for dye molecules in nanogaps between 20 and 50 nm. The experimentally determined fluorescence enhancement of the nanoantennas is confirmed by numerical simulations of the local density of optical states (LDOS). Furthermore, we show that beyond design optimization of dielectric nanoantennas, increased performances can be achieved via tailoring of nanoantenna fabrication.