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Elastic relaxation of lattice misfit strain via traction-free surface results in complex 3D strain distribution and morphological modification at the boundary of epitaxial heterostructure. While this phenomenon is extensively studied, the influence of the interface coherency constraining the strain relaxation has received little attention. Here it is shown that the interfacial shear stresses arise toward the traction free sidewall of microscale light emitting diode (LED) wires while the two complementary strained InGaN and GaN layers are relaxed to revert their bulk lattice parameters near the sidewall. The shear stresses with opposite signs achieve mechanical equilibrium by counterbalancing the change in the sign of the in-plane strain in each layer of the near-surface region. A unique nonmonotonic modulation of both normal and shear strain is detected unambiguously in the strain maps and corroborated by finite element modeling. An analytical model is developed based on the Airy stress function, which incorporates the superposition of alternating in-plane pre-stress and the image stress to satisfy the boundary condition. The resultant complex strain fields in microscale LEDs, where surface emission is dominant, alter strain-induced piezoelectric polarization near the surface, significantly affecting electro-optical efficiency and resulting in spectral broadening and/or wavelength shifts in emitted light., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Recent advancements have shown that flash memory devices made from entirely two-dimensional (2D) materials exhibit nice performance in terms of storage window, switching speed, and extinction ratio. However, these devices still face challenges such as low carrier density, environmentally sensitive, and difficulty in integration due to the properties of 2D material channels. Here, we propose a novel nonvolatile memory device based on a floating-gate field effect transistor, which integrates one-dimensional (1D) GaN/AlN microwire with 2D few-layer graphene (Gr) to combine the advantages of both materials. By forming a linear-direction 2D electron gas in the GaN channel layer and precise interface of GaN/AlN/Graphene, our memory device achieves a fast switching speed (5 ms) and demonstrates long-term stability (>12,000 cycles; >600 s). This makes it suitable for high-performance type conversion memory and reconfigurable inverter logic circuits, indicating a promising application potential for the integration device of hybrid-dimensional materials.

Characterizing threading dislocations (TDs) in gallium nitride (GaN) semiconductors is crucial for ensuring the reliability of semiconductor devices. The current research addresses this issue by combining two techniques using a scanning electron microscope, namely electron channeling contrast imaging (ECCI) and high-resolution electron backscattered diffraction (HR-EBSD). It is a comparative study of these techniques to underscore how they perform in the evaluation of TD densities in GaN epitaxial layers. Experiments reveal that the dislocation line vectors mostly deviate from the growth direction of the film, i.e., ∦ [0001], followed by edge-type dislocations (dislocation lines || [0001]) with insignificant screw character. Furthermore, TDs from the dislocation clusters are characterized as edge- and (edge + mixed)-type TDs. By combining ECCI counting of dislocations and HR-EBSD description of geometrically necessary dislocation density type, it is possible to measure the total TD density and provide the proportion of pure (edge and screw) and mixed TDs. It has also been observed from the analyses of residual elastic strain fields and lattice rotations that it is not possible to identify individual dislocations for the spatial resolution of 50 nm in HR-EBSD. Nevertheless, ECCI and HR-EBSD can be complementarily used to count and characterize the TDs., Competing Interests: Conflict of Interest: The authors declare that they have no competing interest., (© The Author(s) 2025. Published by Oxford University Press on behalf of the Microscopy Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Growth of GaN nanowires (NWs) on graphene substrates is carried out by plasma-assisted molecular beam epitaxy. We test a two-step growth procedure consisting of a first stage at relatively low temperature followed by a second stage at higher temperature. We investigate the impact of this process on the usually long incubation time which precedes the first GaN nucleation events on graphene. We also examine how the selectivity of growth between graphene and the surrounding SiO 2 surface is affected. After optimization of this procedure, it is applied to the growth of GaN NWs on a graphene layer patterned by electron beam lithography. A clear advantage of the two-step growth is observed in terms of reduction of the incubation time and improvement of height uniformity., (© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Metal-support interactions are crucial in the electrochemical synthesis of ammonia (NH 3 ) from nitrate (NO 3 - production under mild conditions. However, the complexity of the reaction pathways often limits efficiency. Here, a photoelectrochemical system composed of gold (Au) nanoclusters supported on gallium nitride (GaN) nanowires is introduced, grown on a n 3 production under mild conditions. However, the complexity of the reaction pathways often limits efficiency. Here, a photoelectrochemical system composed of gold (Au) nanoclusters supported on gallium nitride (GaN) nanowires is introduced, grown on a n + -p Si wafer, for selective reduction of NO 3 - to NH 3 ) on the GaN nanowires, which then transfer to adjacent Au nanoclusters to complete the NH 3 synthesis. This mechanism is confirmed by both experimental data and theoretical calculations. Optimizing the surface coverage and size of Au nanoclusters on the GaN nanowires significantly enhanced catalytic activity compared to that on planar n - ions are preferentially adsorbed and reduced to nitrite (NO 2 - ) on the GaN nanowires, which then transfer to adjacent Au nanoclusters to complete the NH 3 synthesis. This mechanism is confirmed by both experimental data and theoretical calculations. Optimizing the surface coverage and size of Au nanoclusters on the GaN nanowires significantly enhanced catalytic activity compared to that on planar n + -p Si photoelectrodes, achieving a faradaic efficiency of 91.8% at -0.4 V RHE and a high NH 3 production rate of 131.1 µmol cm -2  h -1 at -0.8 V RHE . These findings highlight the synergetic effect between metal co-catalysts and semiconductor supports in designing photoelectrodes for multi-step NO 3 - reduction., (© 2025 The Author(s). Small published by Wiley‐VCH GmbH.)

This study aimed to generate a delayed 64 Cu-dotatate (DOTA)-rituximab positron emission tomography (PET) image from its early-scanned image by deep learning to mitigate the inconvenience and cost of estimating absorbed radiopharmaceutical doses. We acquired PET images from six patients with malignancies at 1, 24, and 48 h post-injection (p. i.) with 8 mCi 64 Cu-DOTA-rituximab to fit a time-activity curve for dosimetry. We used a paired image-to-image translation (I2I) model based on a generative adversarial network to generate delayed images from early PET images. The image similarity function between the generated image and its ground truth was determined by comparing L1 and perceptual losses. We also applied organ-wise dosimetry to acquired and generated images using OLINDA/EXM. The quality of the generated images was good, even of tumors, when using the L1 loss function as an additional loss to the adversarial loss function. The organ-wise cumulative uptake and corresponding equivalent dose were estimated. Although the absorbed dose in some organs was accurately measured, predictions for organs associated with body clearance were relatively inaccurate. These results suggested that paired I2I can be used to alleviate burdensome dosimetry for radioimmunoconjugates., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Background: Although deep learning (DL) methods for reconstructing 3D magnetic resonance (MR) volumes from 2D MR images yield promising results, they require large amounts of training data to perform effectively. To overcome this challenge, fine-tuning-a transfer learning technique particularly effective for small datasets-presents a robust solution for developing personalized DL models., Purpose: A 2D to 3D conditional generative adversarial network (GAN) model with a patient- and fraction-specific fine-tuning workflow was developed to reconstruct synthetic 3D MR volumes using orthogonal 2D MR images for online dose adaptation., Methods: A total of 2473 3D MR volumes were collected from 43 patients. The training and test datasets were separated into 34 and 9 patients, respectively. All patients underwent MR-guided adaptive radiotherapy using the same imaging protocol. The population data contained 2047 3D MR volumes from the training dataset. Population data were used to train the population-based GAN model. For each fraction of the remaining patients, the population model was fine-tuned with the 3D MR volumes acquired before beam irradiation of the fraction, named the fine-tuned model. The performance of the fine-tuned model was tested using the 3D MR volume acquired immediately after the beam delivery of the fraction. The model's input was a pair of axial and sagittal MR images at the isocenter level, and the output was a 3D MR volume. Model performance was evaluated using the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and mean absolute error (MAE). Moreover, the prostate, bladder, and rectum in the predicted MR images were manually segmented. To assess geometric accuracy, the 2D Dice Similarity Coefficient (DSC) and 2D Hausdorff Distance (HD) were calculated., Results: A total of 84 3D MR volumes were included in the performance testing. The mean ± standard deviation (SD) of SSIM, PSNR, RMSE, and MAE were 0.64 ± 0.10, 93.9 ± 1.5 dB, 0.050 ± 0.009, and 0.036 ± 0.007 for the population model and 0.72 ± 0.09, 96.2 ± 1.8 dB, 0.041 ± 0.007, and 0.028 ± 0.006 for the fine-tuned model, respectively. The image quality of the fine-tuned model was significantly better than that of the population model (p < 0.05). The mean ± SD of DSC and HD of the population model were 0.79 ± 0.08 and 1.70 ± 2.35 mm for prostate, 0.81 ± 0.10 and 2.75 ± 1.53 mm for bladder, and 0.72 ± 0.08 and 1.93 ± 0.59 mm for rectum. Contrarily, the mean ± SD of DSC and HD of the fine-tuned model were 0.83 ± 0.06 and 1.29 ± 0.77 mm for prostate, 0.85 ± 0.07 and 2.16 ± 1.09 mm for bladder, and 0.77 ± 0.08 and 1.57 ± 0.52 mm for rectum. The geometric accuracy of the fine-tuned model was significantly improved than that of the population model (p < 0.05)., Conclusion: By employing a patient- and fraction-specific fine-tuning approach, the GAN model demonstrated promising accuracy despite limited data availability., (© 2025 American Association of Physicists in Medicine.)

To date, III-V semiconductor-based tandem devices with GaInP top photoabsorbers show the highest solar-to-electricity or solar-to-fuel conversion efficiencies. In photoelectrochemical (PEC) cells, however, III-V semiconductors are sensitive, in terms of photochemical stability and, therefore, require suitable functional layers for electronic and chemical passivation. GaN films are discussed as promising options for this purpose. The band alignment between such a protection layer and the III-V semiconductor should be aligned to minimize corrosion and nonradiative interfacial recombination and to promote selective charge carrier transport. Here, we investigate the band alignment between GaN passivation layers and n-type doped GaInP(100) photoabsorbers and grew n-type GaInP(100) epitaxially by metalorganic vapor phase epitaxy on oxidized GaAs(100) substrates to mimic a realistic preparation sequence. We prepared 1-20 nm GaN films on top employing atomic layer deposition and studied the band alignment at the GaN/GaInP(100) heterointerface by X-ray and ultraviolet photoelectron spectroscopy. Due to the limited emission depth of photoelectrons, we determined the band alignment by a series of measurements, in which we increased the thickness of the GaN films successively. The n-GaInP(100) surfaces, prepared with a well-known phosphorus-terminated p(2 × 2)/c(4 × 2) reconstruction, show an upward surface band bending (BB) of 0.38 eV and a Fermi level pinning due to the present surface states. Upon oxidation, the surface states are partially passivated, resulting in a reduction of the BB to 0.16 eV and a valence band offset (VBO) between the GaInP(100) and the thin oxide layer of 2.01 eV. Applying Kraut's approach, we identified a VBO of 1.90 eV and a conduction band offset of 0.44 eV between GaInP(100) with a thin oxide layer and the GaN passivation layer. We conclude that the GaN is a well-suited passivation layer for PEC cells and facilitates selective transport of photogenerated electrons.

Background and Purpose: The development of Magnetic Resonance Imaging (MRI)-only Radiotherapy (RT) represents a significant advancement in the field. This study introduces a Deep Learning (DL) algorithm designed to quickly generate synthetic CT (sCT) images from low-field MR images in the brain, an area not yet explored., Methods: Fifty-six patients were divided into training (32), validation (8), and test (16) groups. A conditional Generative Adversarial Network (cGAN) was trained on pre-processed axial paired images. sCTs were validated using mean absolute error (MAE) and mean error (ME) calculated within the patient body. Intensity Modulated Radiation Therapy (IMRT) plans were optimised on simulation MRI and calculated considering sCT and original CT as electron density (ED) map. Dose distributions using sCT and CT were compared using global gamma analysis at different tolerance criteria (2 %/2mm and 3 %/3mm) and evaluating the difference in estimating different Dose Volume Histogram (DVH) parameters for target and organs at risk (OARs)., Results: The network generated sCTs of each single patient in less than two minutes (mean time = 103 ± 41 s). For test patients, the MAE was 62.1 ± 17.7 HU, and the ME was -7.3 ± 13.4 HU. Dose parameters on sCTs were within 0.5 Gy of those on original CTs. Gamma passing rates 2 %/2mm, and 3 %/3mm criteria were 99.5 %±0.5 %, and 99.7 %±0.3 %, respectively., Conclusion: The proposed DL algorithm generates in less than 2 min accurate sCT images in the brain for online adaptive radiotherapy, potentially eliminating the need for CT simulation in MR-only workflows for brain treatments., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2025 The Authors.)

Functional outcomes have been better concerning swallowing ability, distinguishing hot and cold sensations, articulation, and improved range of tongue movement by sensory neurotization of the flaps used for head and neck reconstruction. We evaluated the sensory recovery in free flaps utilizing the great auricular nerve as the recipient sensory nerve, anastomosing a few fascicles of the greater auricular nerve (GAN) to that of the flap sensory nerve; lateral antebrachial cutaneous nerve in the radial forearm free flap, and lateral femoral cutaneous nerve in the anterolateral thigh flap. The sensory examination was performed at the visible center of the flap at the three-month and six-month follow-ups. The flaps were assessed for the recovery of five sensory modalities: fine touch, pain, pressure, two-point discrimination (2PD), and temperature. The sensory recovery was evaluated according to the Mackinnon Dellon scale. The sensory recovery in the neurotized flaps was compared with that in non-neurotized free flaps at the six-month follow-up. The sensory recovery graded by the Mackinon Dellon scale for all five modalities of sensation was statistically significant in sensate-free flaps at the six-month follow-up. Also, the sensory territory of the greater auricular nerve at the peri-auricular area was assessed for loss of sensation at the six-month follow-up, which was not clinically significant. We observed the great auricular nerve as a potential sensory nerve that may be utilized at the flap reconstruction site to neurotize free flaps used for head and neck reconstruction with a significant sensory recovery and without worrisome symptoms at the GAN sensory territory., Competing Interests: Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. Institute Ethics Committee, AIIMS Raipur issued approval 25 I 2/IEC-AIIMSRPR/2022. At the convened sub-committee meeting of IEC-AIIMS Raipur held on 17.09.2022, the IEC voted to approve the above-referenced protocol. As ours was an ambispective study; hence, the CTRI number was not given by CTRI as per their norms, as they only accept prospective studies. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2025, Saha et al.)

To enhance high-frequency perceptual information and texture details in remote sensing images and address the challenges of super-resolution reconstruction algorithms during training, particularly the issue of missing details, this paper proposes an improved remote sensing image super-resolution reconstruction model. The generator network of the model employs multi-scale convolutional kernels to extract image features and utilizes a multi-head self-attention mechanism to dynamically fuse these features, significantly improving the ability to capture both fine details and global information in remote sensing images. Additionally, the model introduces a multi-stage Hybrid Transformer structure, which processes features at different resolutions progressively, from low resolution to high resolution, substantially enhancing reconstruction quality and detail recovery. The discriminator combines multi-scale convolution, global Transformer, and hierarchical feature discriminators, providing a comprehensive and refined evaluation of image quality. Finally, the model incorporates a Charbonnier loss function and total variation (TV) loss function, which significantly improve training stability and accelerate convergence. Experimental results demonstrate that the proposed method, compared to the SRGAN algorithm, achieves average improvements of approximately 3.61 dB in Peak Signal-to-Noise Ratio (PSNR), 0.070 (8.2%) in Structural Similarity Index (SSIM), and 0.030 (3.1%) in Feature Similarity Index (FSIM) across multiple datasets, showing significant performance gains., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

This paper presents an X-band high-power GaN MMIC power amplifier (PA). To balance efficiency, output power, and saturated power flatness, the load-line theory is employed to analyze and validate the power variation trends within an extended continuous Class B/J (CCBJ) impedance space. Theoretical constant power contours are plotted within this space. An L-C impedance matching network is used to match the amplifier's output impedance to the overlapping region of the 0.5 dB constant power contour and the CCBJ impedance space, significantly improving the in-band power flatness of the PA based on the CCBJ design approach. Additionally, an RC parallel structure is integrated into the interstage matching network to maximize gain while ensuring stability. The proposed PA, implemented using a 0.25 µm commercial GaN process, achieves a saturated output power of 47-47.6 dBm with in-band fluctuations within ± 0.3 dB, a power gain of 27.0-27.8 dB, and an efficiency of 40-45.5% across the X-band.

GaN is an important semiconductor for energy-efficient light-emitting devices. Hydrogen plays a crucial role in gallium nitride (GaN) growth and processing. It can form electrically neutral complexes with acceptors during growth, which significantly increases the acceptor incorporation. Post-growth annealing dissociates these complexes and is widely utilized for activating Mg acceptors and achieving conductive p-type GaN. In this work, we demonstrate that other acceptors, such as C and Be, also form complexes with hydrogen similar to Mg. The effect of thermal annealing of GaN on photoluminescence (PL) was investigated. In samples moderately doped with Be, the Be Ga -related yellow luminescence (YL Be ) band intensity decreased by up to an order of magnitude after annealing in N 2 ambient at temperatures T ann = 400 °C-900 °C. This was explained by the release of hydrogen from unknown traps and the passivation of the Be Ga acceptors. A similar drop of PL intensity at T -related YL1 band in unintentionally C-doped GaN and also attributed to passivation of the C ann = 350 °C-900 °C was observed for the C N -related YL1 band in unintentionally C-doped GaN and also attributed to passivation of the C N acceptors by hydrogen released from unknown defects. In this case, the formation of the C N H i and YL1 intensities were restored, which was explained by the removal of hydrogen from the samples. Experimental results were compared to the first principles calculations of complex dissociation and hydrogen diffusion paths in GaN.T ann > 900 °C, both the YL Be and YL1 intensities were restored, which was explained by the removal of hydrogen from the samples. Experimental results were compared to the first principles calculations of complex dissociation and hydrogen diffusion paths in GaN., (Creative Commons Attribution license.)

Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images. We propose a new IHC-GAN that enhances the Pix2PixHD model into a dual generator module, improving its performance and simplifying its structure. Furthermore, to strengthen feature extraction for HE-stained image classification, we integrate MobileNetV3 as the backbone network. The extracted features are then merged with those generated by the generator to improve overall performance. Moreover, the decoder's performance is enhanced by providing the related features from the classified labels by incorporating the adaptive instance normalization technique. The proposed IHC-GAN was trained and validated on a comprehensive dataset comprising 4,870 registered image pairs, encompassing a spectrum of HER2 expression levels. Our findings demonstrate promising results in translating H&E images to IHC-equivalent representations, offering a potential solution to reduce the costs associated with traditional HER2 assessment methods. We extensively validate our model and the current dataset. We compare it with state-of-the-art techniques, achieving high performance using different evaluation metrics, showing 0.0927 FID, 22.87 PSNR, and 0.3735 SSIM. The proposed approach exhibits significant enhancements over current GAN models, including an 88% reduction in Frechet Inception Distance (FID), a 4% enhancement in Learned Perceptual Image Patch Similarity (LPIPS), a 10% increase in Peak Signal-to-Noise Ratio (PSNR), and a 45% reduction in Mean Squared Error (MSE). This advancement holds significant potential for enhancing efficiency, reducing manpower requirements, and facilitating timely treatment decisions in breast cancer care., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: We confirm that the manuscript represents our own work, is original, and has not been copyrighted, published, submitted, or accepted for publication elsewhere. We further confirm that we all have fully read the manuscript and give consent to be co-authors of the manuscript. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Objective: This paper aims to introduce and assess KeyGAN, a generative modeling-based keystroke data synthesizer. The synthesizer is designed to generate realistic synthetic keystroke data capturing the nuances of fine motor control and cognitive processes that govern finger-keyboard kinematics, thereby paving the way to support biomarker development for psychomotor impairment due to neurodegeneration., Methods: KeyGAN is designed with two primary objectives: (i) to ensure high realism in the synthetic distributions of the keystroke features and (ii) to analyze its ability to replicate the subtleties of natural typing for enhancing biomarker development. The quality of synthetic keystroke data produced by KeyGAN is evaluated against two keystroke-based applications, TypeNet and nQiMechPD, employed as'referee' controls. The performance of KeyGAN is compared with a reference random Gaussian generator, testing its ability to fool the biometric authentication method TypeNet, and its ability to characterize fine motor impairment in Parkinson's Disease using nQiMechPD., Results: KeyGAN outperformed the reference comparator in fooling the biometric authentication method TypeNet. It also exhibited a superior approximation to real data than the reference comparator when using nQiMechPD, showcasing its adaptability and versatility in mimicking early signs of Parkinson's Disease in natural typing. KeyGAN's synthetic data demonstrated that almost 20% of real PD samples could be replaced in the training set without a decline in classification performance on the real test set. Low Fréchet Distance (<0.03) and Kullback-Leibler Divergence (<700) between KeyGAN outputs and real data distributions underline the high performance of KeyGAN., Conclusion: KeyGAN presents strong potential as a realistic keystroke data synthesizer, displaying impressive capability to reproduce complex typing patterns relevant to biomarkers for neurological disorders, like Parkinson's Disease. The ability of its synthetic data to effectively supplement real data for training algorithms without affecting performance implies significant promise for advancing research in digital biomarkers for neurodegenerative and psychomotor disorders., Competing Interests: Declaration of competing interest A.A., and T. A.-G. are employees at Area2 Inc. and received a regular salary while contributing to the work. L.G. is an inventor on a patent currently licensed to Area2 Inc. in the same general research area of this work., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Both image denoising and watermark removal aim to restore a clean image from an observed noisy or watermarked one. The past research consists of the non-learning type with limited effectiveness or the learning types with limited interpretability. To address these issues simultaneously, we propose a method to deal with both the image-denoising and watermark removal tasks in a unified approach. The noises and watermarks are both considered to have different nuisance patterns from the original image content, therefore should be detected by robust image analysis. The unified detection method is based on the well-known information bottleneck (IB) theory and the proposed SIB-GAN where image content and nuisance patterns are well separated by a supervised approach. The IB theory guides us to keep the valuable content such as the original image by a controlled compression on the input (the noisy or watermark-included image) and then only the content without the nuisances can go through the network for effective noise or watermark removal. Additionally, we adjust the compression parameter in IB theory to learn a representation that approaches the minimal sufficient representation of the image content. In particular, to deal with the non-blind noises, an appropriate amount of compression can be estimated from the solid theory foundation. Working on the denoising task given the unseen data with blind noises also shows the model's generalization power. All of the above shows the interpretability of the proposed method. Overall, the proposed method has achieved promising results across three tasks: image denoising, watermark removal, and mixed noise and watermark removal, obtaining resultant images very close to the original image content and owning superior performance to almost all state-of-the-art approaches that deal with the same tasks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This paper presents a novel approach to address the issue of uneven temperature distribution in one-dimensional laser arrays, specifically in gallium nitride edge-emitting lasers emitting green light of 540 nm. The results were obtained using heat flow numerical analysis, which included an optimization method specifically developed for this type of array. It was demonstrated that thermal optimization of a one-dimensional edge-emitting laser array can be achieved by adjusting the placement of the emitters within the array and the size of the top gold contact, without changing the overall dimensions of the device. The proposed design alterations ensure an even temperature distribution across the array without the need for a complex and expensive cooling systems. The proposed optimization method can be applied to arrays made from various material systems, including nitrides, arsenides, and phosphides.

In this paper, AlGaN/GaN high electron mobility transistors (HEMTs) with different thicknesses of unintentional doping GaN (UID-GaN) channels were compared and discussed. In order to discuss the effect of different thicknesses of the UID-GaN layer on iron-doped tails, both AlGaN/GaN HEMTs share the same 200 nm GaN buffer layer with an Fe-doped concentration of 8 × 10 17 cm -3 . Due to the different thicknesses of the UID-GaN layer, the concentration of Fe trails reaching the two-dimensional electron gas (2DEG) varies. The breakdown voltage (Vbr) increases with the high concentration of Fe-doped in GaN buffer layer. However, the mobility of the low concentration of the Fe-doped tail is higher than that of the high concentration of the Fe-doped tail. Therefore, the effect of different thicknesses of UID-GaN on the DC and radio frequency (RF) performance of the device needs to be verified. It provides a reference to the epitaxial design for high-performance GaN HEMTs.

The generation of synthetic medical data has become a focal point for researchers, driven by the increasing demand for privacy-preserving solutions. While existing generative methods heavily rely on real datasets for training, access to such data is often restricted. In contrast, statistical information about these datasets is more readily available, yet current methods struggle to generate tabular data solely from statistical inputs. This study addresses the gaps by introducing a novel approach that converts statistical data into tabular datasets using a modified Generative Adversarial Network (GAN) architecture. A custom loss function was incorporated into the training process to enhance the quality of the generated data. The proposed method is evaluated using fidelity and utility metrics, achieving "Good" similarity and "Excellent" utility scores. While the generated data may not fully replace real databases, it demonstrates satisfactory performance for training machine-learning algorithms. This work provides a promising solution for synthetic data generation when real datasets are inaccessible, with potential applications in medical data privacy and beyond.

Metal-semiconductor contacts play a pivotal role in controlling carrier transport in the fabrication of modern electronic devices. The exploration of van der Waals (vdW) metal contacts in semiconductor devices can potentially mitigate Fermi-level pinning at the metal-semiconductor interface, with particular success in two-dimensional layered semiconductors, triggering unprecedented electrical and optical characteristics. In this work, for the first time, we report the direct integration of vdW metal contacts with bulk wide bandgap gallium nitride (GaN) by employing a dry transfer technique. High-angle annular dark-field scanning transmission electron microscopy explicitly illustrates the existence of a vdW gap between the metal electrode and GaN. Strikingly, compared with devices fabricated with electron beam-evaporated metal contacts, the vdW contact device exhibits a responsivity two orders of magnitude higher with a significantly suppressed dark current in the nanoampere range. Furthermore, by leveraging the high responsivity and persistent photoconductivity obtained from vdW contact devices, we demonstrate imaging, wireless optical communication, and neuromorphic computing functionality. The integration of vdW contacts with bulk semiconductors offers a promising architecture to overcome device fabrication challenges, forming nearly ideal metal-semiconductor contacts for future integrated electronics and optoelectronics., (Copyright © 2024 Science China Press. Published by Elsevier B.V. All rights reserved.)

The escalating global problem of antibiotic contamination in wastewater demands innovative and sustainable remediation technologies. This paper presents a highly efficient photocatalytic material for water purification: a three-dimensional ultra-porous structure of interconnected GaN hollow microtetrapods (aero-GaN), its performance being further enhanced by noble metal nanodot functionalization. This novel aero-nanomaterial achieves more than 90 % of tetracycline degradation within 120 min under UV and solar irradiation, demonstrating its effectiveness in both static and dynamic flow conditions, with the potential for reuse and recyclability. The higher surface area and chemical stability of the 3D aero-GaN architecture, compared to analogous ZnO structures, establish its significant potential for advanced water treatment applications and filter technologies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The realization of semiconductor heterostructures marks a significant advancement beyond silicon technology, driving progress in high-performance optoelectronics and photonics, including high-brightness light emitters, optical communication, and quantum technologies. In less than a decade since 1997, nanowires research has expanded into new application-driven areas, highlighting a significant shift toward more challenging and exploratory research avenues. It is therefore essential to reflect on the past motivations for nanowires development, and explore the new opportunities it can enable. The advancement of heterogeneous integration using dissimilar substrates, materials, and nanowires-semiconductor/electrolyte operating platforms is ushering in new research frontiers, including the development of perovskite-embedded solar cells, photoelectrochemical (PEC) analog and digital photonic systems, such as PEC-based photodetectors and logic circuits, as well as quantum elements, such as single-photon emitters and detectors. This review offers rejuvenating perspectives on the progress of these group-III nitride nanowires, aiming to highlight the continuity of research toward high impact, use-inspired research directions in photonics and optoelectronics., (© 2024 Wiley‐VCH GmbH.)

Pharmacogenetics (PGx) explores the influence of genetic variability on drug efficacy and tolerability. Synthetic Data Generation (SDG) has emerged as a promising alternative to the labor-intensive process of collecting real-world PGx data, which is required for high-qualitative prediction models. This study investigates the performance of two Generative Adversarial Network (GAN) models, CTGAN and CTAB-GAN+, in generating synthetic PGx data. The benchmarking is based on utility metrics (Hellinger distance and Random Forest accuracy) and ϵ-identifiability. Results demonstrate that synthetic data generated by CTAB-GAN+ can surpass the original dataset in terms of utility. For instance, CTAB-GAN+ achieves higher Random Forest accuracy compared to the original data, indicating better predictive performance. These improvements suggest that synthetic data not only capture the essential patterns of the original data but also enhance model generalization and prediction capabilities, providing a more robust training ground for machine learning models. Consequently, SDG offers a promising solution to address data scarcity and imbalance in pharmacogenetic research.

Due to its distinctive structure and unique physicochemical properties, gallium nitride (GaN) has been considered a prospective candidate for lithium storage materials. However, its inferior conductivity and unsatisfactory cycle performance hinder the further application of GaN as a next-generation anode material for lithium-ion batteries (LIBs). To address this, cobalt (Co)-doped GaN (Co-GaN) nanowires have been designed and synthesized by utilizing the chemical vapor deposition (CVD) strategy. The structural characterizations indicate that the doped Co elements in the GaN nanowires exist as Co 2+ rather than metallic Co. The Co 2+ prominently promotes electrical conductivity and ion transfer efficiency in GaN. The cycling capacity of Co-GaN reached up to 495.1 mA h g -1 after 100 cycles. After 500 cycles at 10 A g -1 , excellent cycling capacity remained at 276.6 mA h g -1 . The intimate contact between Co-GaN nanowires and carbon paper enhances the conductivity of the composite. Density functional theory (DFT) calculations further illustrated that Co substitution changed the electron configuration in the GaN, which led to enhancement of the electron transfer efficiency and a reduction in the ion diffusion barrier on the Co-GaN electrode. This doping design boosts the lithium-ion storage performance of GaN as an advanced material in lithium-ion battery anodes and in other electrochemical applications.

This study investigates the effects of incorporating a CdZnO layer in place of the conventional InGaN layer in an AlGaN/InGaN/GaN/AlGaN/SiC high-electron mobility transistor (HEMT) structure. We examine the resulting characteristics and assess the potential of high-power HEMT applications, including high-power switching converters, through simulation analysis. Both structures demonstrate increased drain current and transconductance with increasing Al content in the barrier layer. However, HEMTs with a CdZnO layer exhibit higher drain current compared to those with an InGaN layer at the same Al content. The breakdown voltage decreases rapidly with increasing Al content, attributed to changes in electric field distribution. HEMTs with a CdZnO/GaN channel exhibit a slightly higher breakdown voltage (~795 V) compared to those with an InGaN/GaN channel (~768 V) at a lower Al content of x = 0.10. These results suggest that CdZnO-based HEMTs have significant potential for high-power, high-frequency applications.

Graphic design, as a product of the burgeoning new media era, has seen its users' requirements for images continuously evolve. However, external factors such as light and noise often cause graphic design images to become distorted during acquisition. To enhance the definition of these images, this paper introduces a novel image enhancement model based on visual features. Initially, a histogram equalization (HE) algorithm is applied to enhance the graphic design images. Subsequently, image feature extraction is performed using a dual-flow network comprising convolutional neural network (CNN) and Transformer architectures. The CNN employs a residual dense block (RDB) to embed spatial local structure information with varying receptive fields. An improved attention mechanism module, attention feature fusion (AFF), is then introduced to integrate the image features extracted from the dual-flow network. Finally, through image perception quality guided adversarial learning, the model adjusts the initial enhanced image's color and recovers more details. Experimental results demonstrate that the proposed algorithm model achieves enhancement effects exceeding 90% on two large image datasets, which represents a 5%-10% improvement over other models. Furthermore, the algorithm exhibits superior performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) image quality evaluation metrics. Our findings indicate that the fusion model significantly enhances image quality, thereby advancing the field of graphic design and showcasing its potential in cultural and creative product design., Competing Interests: The authors declare there are no competing interests., (©2024 Xu et al.)

In this work, Ni/ZnO:Al and Au/ZnO:Al structures are proposed as efficient ohmic contacts to p-GaN. Through a careful selection of deposition parameters and annealing environment, we not only achieve the formation of high-quality ohmic contacts but also gain insights into the interfacial reactions, enhancing the understanding of conventional Ni/Au contact formation on p-GaN. In particular, the notion that the presence of NiO at the interface is enough for an ohmic contact to form is challenged by showing that in fact it has to be NiO formed at the interface from metallic Ni and additional oxygen. An Au-based ohmic contact is also presented, and its formation mechanism is explained, contrary to popular knowledge that Au does not form an ohmic contact with p-GaN. The obtained contacts are low resistivity, with contact resistances of 4.77 × 10 -3 Ω·cm 2 and 3.34 × 10 -3 Ω·cm 2 for Au-based and Ni-based ones, respectively. What is also important is that we show these oxide-based contacts with metallic interlayers give lower series resistances in simplified diode structures than a standard Ni/Au-based contact, making them promising for optoelectronic devices.

Purpose: The visualization of an anomaly area is easier in anomaly detection methods that use generative models rather than classification models. However, achieving both anomaly detection accuracy and a clear visualization of anomalous areas is challenging. This study aimed to establish a method that combines both detection accuracy and clear visualization of anomalous areas using a generative adversarial network (GAN)., Methods: In this study, StyleGAN2 with adaptive discriminator augmentation (StyleGAN2-ADA), which can generate high-resolution and high-quality images with limited number of datasets, was used as the image generation model, and pixel-to-style-to-pixel (pSp) encoder was used to convert images into intermediate latent variables. We combined existing methods for training and proposed a method for calculating anomaly scores using intermediate latent variables. The proposed method, which combines these two methods, is called high-quality anomaly GAN (HQ-AnoGAN)., Results: The experimental results obtained using three datasets demonstrated that HQ-AnoGAN has equal or better detection accuracy than the existing methods. The results of the visualization of abnormal areas using the generated images showed that HQ-AnoGAN could generate more natural images than the existing methods and was qualitatively more accurate in the visualization of abnormal areas., Conclusion: In this study, HQ-AnoGAN comprising StyleGAN2-ADA and pSp encoder was proposed with an optimal anomaly score calculation method. The experimental results show that HQ-AnoGAN can achieve both high abnormality detection accuracy and clear visualization of abnormal areas; thus, HQ-AnoGAN demonstrates significant potential for application in medical imaging diagnosis cases where an explanation of diagnosis is required., (© 2023. CARS.)

Core-shell gallium nitride (GaN)-based nanowires offer noteworthy opportunities for innovation in high-frequency opto- and microelectronics. This work delves deeply into the physical properties of crystalline GaN nanowires with aluminum and hafnium oxide shells. Particular attention is paid to partial coverage of nanowires, resulting with exceptional properties. First, the crystal lattice relaxation is observed by X-ray diffraction, photoluminescence, and Raman spectroscopy measurements. A high potential of partial coverage for optoelectronic applications is revealed with photo- and cathodoluminescence spectra along with an exploration of their temperature dependency. Next, the study focuses on understanding the mechanisms behind the observed enhancement of the luminescence efficiency. It is confirmed that nanowires are effectively protected against photoadsorption using partial coatings. This research advances the frontiers of nanotechnology, investigating the benefits of partial coverage, and shedding light on its complex interaction with cores., (© 2024 The Author(s). Small published by Wiley‐VCH GmbH.)

When using High-field asymmetric ion mobility spectrometry (FAIMS) to process complex mixtures for deep learning analysis, there is a problem of poor recognition performance due to the lack of high-quality data and low sample diversity. In this paper, a Generative Adversarial Network (GAN) method is introduced to simulate and generate highly realistic and diverse spectral for expanding the dataset using real mixture spectral data of 15 classes collected by FAIMS. The mixed datasets were put into VGG and ResNeXt for testing respectively, and the experimental results proved that the best recognition effect was achieved when the ratio of real data to generated data was 1:4: where accuracy improved by 24.19 % and 6.43 %; precision improved by 23.71 % and 6.97 %; recall improved by 21.08 % and 7.09 %; and F1-score improved by 24.50 % and 8.23 %. The above results strongly demonstrate that GAN can effectively expand the data volume and increase the sample diversity without increasing the additional experimental cost, which significantly enhances the experimental effect of FAIMS spectral for the analysis of complex mixtures., Competing Interests: Declaration of competing interest The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The increasing prevalence of tree bark beetle infestations poses a significant threat to forest ecosystems, leading to detrimental impacts on biodiversity, carbon storage, and timber resources. This study addresses the pressing need for innovative solutions to enhance forest management practices through early detection and assessment of tree health. We developed an intelligent system leveraging multispectral images and generative-adversarial networks (GANs) to accurately determine the extent of bark beetle damage. Recognizing the challenges posed by traditional neural networks, which require vast amounts of labeled training data, we proposed a novel approach that utilizes a GAN architecture. In this system, a discriminator functions as a classifier, effectively trained on both real and synthetically generated data. Our methodology not only reduces the dependency on extensive labeled datasets but also enhances the robustness of the classification process. The results indicate a classification accuracy of 87.5%, demonstrating significant promise for improving detection capabilities even with limited training resources. The implications of this research are profound, offering potential benefits for the forestry industry through optimized management strategies and economic gains. Furthermore, our findings contribute to the preservation of critical ecosystem services by providing a means to monitor forest health effectively. However, the responsible implementation of this technology is paramount. Continuous refinement of the model, integration with traditional ecological knowledge, and ensuring transparency and equitable access to the developed system are essential for maximizing societal benefits. Additionally, addressing risks such as misinterpretation of data, overreliance on technology, and privacy concerns is crucial to minimize unintended consequences. Overall, this research presents a significant advancement in the field of forest health monitoring and establishes a foundation for future developments in intelligent ecological management systems., Competing Interests: The authors declare no conflict of interest., (© 2024 The Author(s). Plant‐Environment Interactions published by New Phytologist Foundation and John Wiley & Sons Ltd.)

Sections stained in periodic acid-Schiff (PAS), periodic acid-methenamine silver (PAM), hematoxylin and eosin (H&E), and Masson's trichrome (MT) stain with minimal morphological discordance are helpful for pathological diagnosis in renal biopsy. Here, we propose an artificial intelligence-based re-stainer called PPHM-GAN (PAS, PAM, H&E, and MT-generative adversarial networks) with multi-stain to multi-stain transformation capability. We trained three GAN models on 512 × 512-pixel patches from 26 training cases. The model with the best transformation quality was selected for each pair of stain transformations by human evaluation. Frechet inception distances, peak signal-to-noise ratio, structural similarity index measure, contrast structural similarity, and newly introduced domain shift inception score were calculated as auxiliary quality metrics. We validated the diagnostic utility using 5120 × 5120 patches of ten validation cases for major glomerular and interstitial abnormalities. Transformed stains were sometimes superior to original stains for the recognition of crescent formation, mesangial hypercellularity, glomerular sclerosis, interstitial lesions, or arteriosclerosis. 23 of 24 glomeruli (95.83 %) from 9 additional validation cases transformed to PAM, PAS, or MT facilitated recognition of crescent formation. Stain transformations to PAM (p = 4.0E-11) and transformations from H&E (p = 4.8E-9) most improved crescent formation recognition. PPHM-GAN maximizes information from a given section by providing several stains in a virtual single-section view, and may change the staining and diagnostic strategy., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Masataka Kawai has patent pending to University of Yamanashi., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The Fermi level position at the interface of a heterostructure is a critical factor for device functionality, strongly influenced by surface-related phenomena. In this study, contactless electroreflectance (CER) was utilized for the first time to investigate the built-in electric field in MXene/GaN structures with the goal of understanding the carrier transfer across the MXene/GaN interface. Five MXenes with high work functions were examined: Cr 2 C, Mo 2 C, V 2 C, V 4 C 3 , and Ti 3 C 2 . The physicochemical properties of the MXene/GaN structures were analyzed by using X-ray and UV photoelectron spectroscopies. It was shown that upon the coverage of the GaN surface by all investigated MXenes, a shift in the position of the surface Fermi level occurs, consequently raising the interface barrier. Additionally, the physicochemical stability of MXenes on the GaN surface was studied after annealing the structures at 750 °C. Our findings indicate that the annealing process increases the barrier height and the ionization energies of all studied structures. Furthermore, it has been shown that removing excess MXene material from the surface did not significantly impact the built-in electric field, emphasizing the robust physicochemical stability of the MXenes on the GaN surface. To validate the potential of engineering of MXene/GaN interface barrier, Schottky diodes with MXenes exhibiting the highest barrier height (Mo 2 C and V 2 C) were demonstrated.

With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits are normal or abnormal. The problem can be solved by binary or multi-classification based on a Convolutional Neural Network (CNN), but it can also be solved by image reconstruction. However, due to the limitation of the performance of image generation, SOTA's methods propose a score calculation method using a latent vector error. In this paper, we propose a network that focuses on chili peppers and proceeds with background removal through GrabCut. It shows a high performance through an image-based score calculation method. Due to the difficulty of reconstructing the input image, the difference between the input and output images is large. However, the serial autoencoder proposed in this paper uses the difference between the two fake images, instead of the actual input, as a score. We propose a method of generating meaningful images using the GAN structure and classifying three results simultaneously by one discriminator. The proposed method showed a higher performance than previous research, and image-based scores showed the best performance.

This study explores the feasibility of quantitative Optical Coherence Tomography Angiography (OCTA) features translated from OCT using generative machine learning (ML) for characterizing vascular changes in retina. A generative adversarial network framework was employed alongside a 2D vascular segmentation and a 2D OCTA image translation model, trained on the OCT-500 public dataset and validated with data from the University of Illinois at Chicago (UIC) retina clinic. Datasets are categorized by scanning range (Field of view) and disease status. Validation involved quality and quantitative metrics, comparing translated OCTA (TR-OCTA) with ground truth OCTAs (GT-OCTA) to assess the feasibility for objective disease diagnosis. In our study, TR-OCTAs showed high image quality in both 3 and 6 mm datasets (high-resolution and contrast quality, moderate structural similarity compared to GT-OCTAs). Vascular features like tortuosity and vessel perimeter index exhibits more consistent trends compared to density features which are affected by local vascular distortions. For the validation dataset (UIC), the metrics show similar trend with a slightly decreased performance since the model training was blind on UIC data, to evaluate inference performance. Overall, this study presents a promising solution to the limitations of OCTA adoption in clinical practice by using vascular features from TR-OCTA for disease detection. By making detailed vascular imaging more widely accessible and reducing reliance on expensive OCTA equipment, this research has the potential to significantly enhance the diagnostic process for retinal diseases., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (Copyright © 2024 Badhon, Thompson, Lim, Leng and Alam.)

Transition-metal centers exhibit a paramagnetic ground state in wide-bandgap semiconductors and are promising for nanophotonics and quantum information processing. Specifically, there is a growing interest in discovering prominent paramagnetic spin defects that can be manipulated using optical methods. Here, we investigate the electronic structure and magneto-optical properties of Cr and Mn substitutional centers in wurtzite AlN and GaN. We use state-of-the-art hybrid density functional theory calculations to determine level structure, stability, optical signatures, and magnetic properties of these centers. The excitation energies are calculated using the constrained occupation approach and rigorously verified with the complete active space configuration interaction approach. Our simulations of the photoluminescence spectra indicate that Cr Al 1 + in AlN and Cr Ga 1 + in GaN are responsible for the observed narrow quantum emission near 1.2 eV. We compute the zero-field splitting (ZFS) parameters and outline an optical spin polarization protocol for Cr Al 1 + and Cr Ga 1 + . Our results demonstrate that these centers are promising candidates for spin qubits.

Functional and abundant substrate materials are relevant for applying all sophisticated semiconductor-based device components such as nanowire arrays. In the case of GaN nanowires grown by metalorganic vapor phase epitaxy, Si(111) substrates are widely used, together with an AlN interlayer to suppress the well-known Ga-based melt-back-etching. However, the AlN interlayer can degrade the interfacial conductivity of the Si(111) substrate. To reveal the possible impact of this interlayer on the overall electrical performance, an advanced analysis of the electrical behavior with suitable spatial resolution is essential. For the electrical investigation of the nanowire-to-substrate junction, we used a four-point probe measurement setup with sufficiently high spatial resolution. The charge separation behavior of the junction is also demonstrated by an electron beam-induced current mode, while the n-GaN nanowire (NW) core exhibits good electrical conductivity. The charge carrier-selective transport at the NW-to-substrate junction can be attributed to different, local material compositions by two main effects: the reduction of Ga adatoms by shadowing of the lower part of the NW structure by the top part during growth, i.e. the protection of the pedestal footprint from Ga adsorption. Our combination of investigation methods provides direct insight into the nanowire-to-substrate junction and leads to a model of the conductivity channels at the nanowire base. This knowledge is crucial for all future GaN bottom-up grown nanowire structure devices on conductive Si(111) substrates.

Obtaining accurate distance or depth information in endoscopy is crucial for the effective utilization of navigation systems. However, due to space constraints, incorporating depth cameras into endoscopic systems is often impractical. Our goal is to estimate depth images directly from endoscopic images using deep learning. This study presents a three-step methodology for training a depth-estimation network model. Initially, simulated endoscopy images and corresponding depth maps are generated using Unity based on a colon surface model obtained from segmented computed tomography colonography data. Subsequently, a cycle generative adversarial network model is employed to enhance the realism of the simulated endoscopy images. Finally, a deep learning model is trained using the synthesized endoscopy images and depth maps to estimate depths accurately. The performance of the proposed approach is evaluated and compared against prior studies utilizing unsupervised training methods. The results demonstrate the superior precision of the proposed technique in estimating depth images within endoscopy. The proposed depth estimation method holds promise for advancing the field by enabling enhanced navigation, improved lesion marking capabilities, and ultimately leading to better clinical outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Efficient heat dissipation is crucial for the performance and lifetime of high electron mobility transistors (HEMTs). The thermal conductivity of materials and interfacial thermal conductance (ITC) play significant roles in their heat dissipation. To predict the thermal properties of AlxGa1-xN and the ITC of GaN/AlxGa1-xN in HEMTs, a dataset with first-principles accuracy was constructed using concurrent learning method and trained to obtain an interatomic potential employing deep neural networks (DNN) method. Using obtained DNN interatomic potential, equilibrium molecular dynamics simulations were employed to calculate the thermal conductivity of AlxGa1-xN, which showed excellent consistent with experimental results. Additionally, the phonon density of states of AlxGa1-xN and the ITC of GaN/AlxGa1-xN were calculated. Our study revealed a decrease in the ITC of GaN/AlxGa1-xN with increasing x, and the insertion of 1nm-thick AlN at the interface significantly reduced the ITC. This work provided a high-fidelity DNN potential for molecular dynamics simulations of AlxGa1-xN, offering valuable guidance for exploring the thermal transport of complex alloy and heterostructure.&#xD., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Ever-increasing demand for efficient optoelectronic devices with a small-footprinted on-chip light emitting diode has driven their expansion in self-emissive displays, from micro-electronic displays to large video walls. InGaN nanowires, with features like high electron mobility, tunable emission wavelengths, durability under high current densities, compact size, self-emission, long lifespan, low-power consumption, fast response, and impressive brightness, are emerging as the choice of micro-light emitting diodes (µLEDs). However, challenges persist in achieving high crystal quality and lattice-matching heterostructures due to composition tuning and bandgap issues on substrates with differing crystal structures and high lattice mismatches. Consequently, research is increasingly focused on scalable InGaN nanowire µLEDs representing a transformative advancement in display technology, particularly for next-generation applications such as virtual/augmented reality and high-speed optical interconnects. This study presents recent progress and critical challenges in the development of InGaN nanowire µLEDs, highlighting their performance and potential as the next-generation displays in consumer electronics., (© The Author(s) 2024. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.)

A gallium nitride (GaN) semiconductor is one of the most promising materials integrated into biomedical devices to play the roles of connecting, monitoring, and manipulating the activity of biological components, due to its excellent photoelectric properties, chemical stability, and biocompatibility. In this work, it was found that the photogenerated free charge carriers of the GaN substrate, as an exogenous stimulus, served to promote neural stem cells (NSCs) to differentiate into neurons. This was observed through the systematic investigation of the effect of the persistent photoconductivity (PPC) of GaN on the differentiation of primary NSCs from the embryonic rat cerebral cortex. NSCs were directly cultured on the GaN surface with and without ultraviolet (UV) irradiation, with a control sample consisting of tissue culture polystyrene (TCPS) in the presence of fetal bovine serum (FBS) medium. Through optical microscopy, the morphology showed a greater number of neurons with the branching structures of axons and dendrites on GaN with UV irradiation. The immunocytochemical results demonstrated that GaN with UV irradiation could promote the NSCs to differentiate into neurons. Western blot analysis showed that GaN with UV irradiation significantly upregulated the expression of two neuron-related markers, βIII-tubulin (Tuj-1) and microtubule-associated protein 2 (MAP-2), suggesting that neurite formation and the proliferation of NSCs during differentiation were enhanced by GaN with UV irradiation. Finally, the results of the Kelvin probe force microscope (KPFM) experiments showed that the NSCs cultured on GaN with UV irradiation displayed about 50 mV higher potential than those cultured on GaN without irradiation. The increase in cell membrane potential may have been due to the larger number of photogenerated free charges on the GaN surface with UV irradiation. These results could benefit topical research and the application of GaN as a biomedical material integrated into neural interface systems or other bioelectronic devices.

Background: Modeling heterogeneous disease states by data-driven methods has great potential to advance biomedical research. However, a comprehensive analysis of phenotypic heterogeneity is often challenged by the complex nature of biomedical datasets and emerging imaging methodologies., Methods: Here, we propose a novel GAN Inversion-enabled Latent Eigenvalue Analysis (GILEA) framework and apply it to in silico phenome profiling and editing., Results: We show the performance of GILEA using cellular imaging datasets stained with the multiplexed fluorescence Cell Painting protocol. The quantitative results of GILEA can be biologically supported by editing of the latent representations and simulation of dynamic phenotype transitions between physiological and pathological states., Conclusion: In conclusion, GILEA represents a new and broadly applicable approach to the quantitative and interpretable analysis of biomedical image data. The GILEA code and video demos are available at https://github.com/CTPLab/GILEA., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Viktor H. Koelzer reports a relationship with F Hoffmann-La Roche Ltd that includes: funding grants. Viktor H. Koelzer reports a relationship with Image Analysis Group that includes: funding grants. Viktor H. Koelzer has served as an invited speaker on behalf of Indica Labs and for Sharing Progress in Cancer Care, an independent nonprofit organization., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

With the rapid growth of data driven by high-throughput sequencing technologies, genomics has entered an era characterized by big data, which presents significant challenges for traditional bioinformatics methods in handling complex data patterns. At this critical juncture of technological progress, deep learning-an advanced artificial intelligence technology-offers powerful capabilities for data analysis and pattern recognition, revitalizing genomic research. In this review, we focus on four major deep learning models: Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM), and Generative Adversarial Network(GAN). We outline their core principles and provide a comprehensive review of their applications in DNA, RNA, and protein research over the past five years. Additionally, we also explore the use of deep learning in livestock genomics, highlighting its potential benefits and challenges in genetic trait analysis, disease prevention, and genetic enhancement. By delivering a thorough analysis, we aim to enhance precision and efficiency in genomic research through deep learning and offer a framework for developing and applying livestock genomic strategies, thereby advancing precision livestock farming and genetic breeding technologies.

To propose a style transfer model for multi-contrast magnetic resonance imaging (MRI) images with a cycle-consistent generative adversarial network (CycleGAN) and evaluate the image quality and prognosis prediction performance for glioblastoma (GBM) patients from the extracted radiomics features. Style transfer models of T1 weighted MRI image (T1w) to T2 weighted MRI image (T2w) and T2w to T1w with CycleGAN were constructed using the BraTS dataset. The style transfer model was validated with the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset. Moreover, imaging features were extracted from real and synthesized images. These features were transformed to rad-scores by the least absolute shrinkage and selection operator (LASSO)-Cox regression. The prognosis performance was estimated by the Kaplan-Meier method. For the accuracy of the image quality of the real and synthesized MRI images, the MI, RMSE, PSNR, and SSIM were 0.991 ± 2.10 × 10 - 4 , 2.79 ± 0.16, 40.16 ± 0.38, and 0.995 ± 2.11 × 10 - 4 , for T2w, and .992 ± 2.63 × 10 - 4 , 2.49 ± 6.89 × 10 - 2 , 40.51 ± 0.22, and 0.993 ± 3.40 × 10 - 4 for T1w, respectively. The survival time had a significant difference between good and poor prognosis groups for both real and synthesized T2w (p < 0.05). However, the survival time had no significant difference between good and poor prognosis groups for both real and synthesized T1w. On the other hand, there was no significant difference between the real and synthesized T2w in both good and poor prognoses. The results of T1w were similar in the point that there was no significant difference between the real and synthesized T1w. It was found that the synthesized image could be used for prognosis prediction. The proposed prognostic model using CycleGAN could reduce the cost and time of image scanning, leading to a promotion to build the patient's outcome prediction with multi-contrast images., (© 2024. The Author(s).)

Background: Dual-energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings., Purpose: This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT., Methods: The proposed framework combines iterative decomposition and deep learning-based image prior in a generative adversarial network (GAN) architecture. In the generator module, a data-fidelity loss is introduced to enforce the measurement consistency in material decomposition. In the discriminator module, the discriminator is trained to differentiate the low-noise material-specific images from the high-noise images. In this scheme, paired images of DECT and ground-truth material-specific images are not required for the model training. Once trained, the generator can perform image-domain material decomposition with noise suppression in a single step., Results: In the simulation studies of head and lung digital phantoms, the proposed method reduced the standard deviation (SD) in decomposed images by 97% and 91% from the values in direct inversion results. It also generated decomposed images with structural similarity index measures (SSIMs) greater than 0.95 against the ground truth. In the clinical head and lung patient studies, the proposed method suppressed the SD by 95% and 93% compared to the decomposed images of matrix inversion., Conclusions: Since the invention of DECT, noise amplification during material decomposition has been one of the biggest challenges, impeding its quantitative use in clinical practice. The proposed method performs accurate material decomposition with efficient noise suppression. Furthermore, the proposed method is within an unsupervised-learning framework, which does not require paired data for model training and resolves the issue of lack of ground-truth data in clinical scenarios., (© 2024 American Association of Physicists in Medicine.)

Fibre bundle (FB)-based endoscopes are indispensable in biology and medical science due to their minimally invasive nature. However, resolution and contrast for fluorescence imaging are limited due to characteristic features of the FBs, such as low numerical aperture (NA) and individual fibre core sizes. In this study, we improved the resolution and contrast of sample fluorescence images acquired using in-house fabricated high-NA FBs by utilising generative adversarial networks (GANs). In order to train our deep learning model, we built an FB-based multifocal structured illumination microscope (MSIM) based on a digital micromirror device (DMD) which improves the resolution and the contrast substantially compared to basic FB-based fluorescence microscopes. After network training, the GAN model, employing image-to-image translation techniques, effectively transformed wide-field images into high-resolution MSIM images without the need for any additional optical hardware. The results demonstrated that GAN-generated outputs significantly enhanced both contrast and resolution compared to the original wide-field images. These findings highlight the potential of GAN-based models trained using MSIM data to enhance resolution and contrast in wide-field imaging for fibre bundle-based fluorescence microscopy. Lay Description: Fibre bundle (FB) endoscopes are essential in biology and medicine but suffer from limited resolution and contrast for fluorescence imaging. Here we improved these limitations using high-NA FBs and generative adversarial networks (GANs). We trained a GAN model with data from an FB-based multifocal structured illumination microscope (MSIM) to enhance resolution and contrast without additional optical hardware. Results showed significant enhancement in contrast and resolution, showcasing the potential of GAN-based models for fibre bundle-based fluorescence microscopy., (© 2024 Royal Microscopical Society.)

Scene-based image semantic extraction and its precise sentiment expression significantly enhance artistic design. To address the incongruity between image features and sentiment features caused by non-bilinear pooling, this study introduces a generative adversarial network (GAN) model that integrates visual relationships with sentiment semantics. The GAN-based regularizer is utilized during training to incorporate target information derived from the contextual information into the process. This regularization mechanism imposes stronger penalties for inaccuracies in subject-object type predictions and integrates a sentiment corpus to generate more human-like descriptive statements. The capsule network is employed to reconstruct sentences and predict probabilities in the discriminator. To preserve crucial focal points in feature extraction, the Convolutional Block Attention Mechanism (CBAM) is introduced. Furthermore, two bidirectional long short-term memory (LSTM) modules are used to model both target and relational contexts, thereby refining target labels and inter-target relationships. Experimental results highlight the model's superiority over comparative models in terms of accuracy, BiLingual Evaluation Understudy (BLEU) score, and text preservation rate. The proposed model achieves an accuracy of 95.40% and the highest BLEU score of 16.79, effectively capturing both the label content and the emotional nuances within the image., Competing Interests: The authors declare there are no competing interests., (©2024 Shen and Wang.)

A comprehensive study of proton irradiation reliability on a bilayer dielectrics SiN x /Al 2 O 3 MIS-HEMT, the common Schottky gate HEMT, and a single dielectric layer MIS-HEMT with SiN x and with Al 2 O 3 for comparison is conducted in this paper. Combining the higher displacement threshold energy of Al 2 O 3 with the better surface passivation of the SiN x layer, the bilayer dielectrics MIS-HEMT presents much smaller degradation of structural materials and of device electrical performance after proton irradiation. Firstly, the least of the defects caused by irradiation suggesting the smallest structural material degradation is observed in the bilayer dielectrics MIS-HEMT through simulations. Then, DC and RF electrical performance of four kinds of devices before and after proton irradiation are studied through simulation and experiments. The smallest threshold voltage degradation rate, the smallest maximum on-current degradation and Gm degradation, the largest cut-off frequency, and the lowest cut-off frequency degradation are found in the bilayer dielectrics MIS-HEMT among four kinds of devices. The degradation results of both structural materials and electrical performance reveal that the bilayer dielectrics MIS-HEMT performs best after irradiation and had better radiation resilience.

Introduction: Alzheimer's disease (AD) is a neurodegenerative disorder and the most prevailing cause of dementia. AD critically disturbs the daily routine, which usually needs to be detected at its early stage. Unfortunately, AD detection using magnetic resonance imaging is challenging because of the subtle physiological variations between normal and AD patients visible on magnetic resonance imaging., Methods: To cope with this challenge, we propose a deep convolutional generative adversarial network (DeepCGAN) for detecting early-stage AD in this article. The DeepCGAN is an unsupervised generative model that expands the dataset size in addition to its diversity by utilizing the generative adversarial network (GAN). The Generator of GAN follows the encoder-decoder framework and takes cognitive data as inputs, whereas the Discriminator follows a structure similar to the Generator's encoder. The last dense layer uses a softmax classifier to detect the labels indicating the AD., Results: The proposed model attains an accuracy rate of 97.32%, significantly surpassing recent state-of-the-art models' performance, including Adaptive Voting, ResNet, AlexNet, GoogleNet, Deep Neural Networks, and Support Vector Machines., Discussion: The DeepCGAN significantly improves early AD detection accuracy and robustness by enhancing the dataset diversity and leveraging advanced GAN techniques, leading to better generalization and higher performance in comparison to traditional and contemporary methods. These results demonstrate the ecacy of DeepCGAN in enhancing early AD detection, thereby potentially improving patient outcomes through timely intervention., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ali, Saleem, Alhussein, Zohra, Aurangzeb and Haq.)

Synthetic tabular health data plays a crucial role in healthcare research, addressing privacy regulations and the scarcity of publicly available datasets. This is essential for diagnostic and treatment advancements. Among the most promising models are transformer-based Large Language Models (LLMs) and Generative Adversarial Networks (GANs). In this paper, we compare LLM models of the Pythia LLM Scaling Suite with varying model sizes ranging from 14M to 1B, against a reference GAN model (CTGAN). The generated synthetic data are used to train random forest estimators for classification tasks to make predictions on the real-world data. Our findings indicate that as the number of parameters increases, LLM models outperform the reference GAN model. Even the smallest 14M parameter models perform comparably to GANs. Moreover, we observe a positive correlation between the size of the training dataset and model performance. We discuss implications, challenges, and considerations for the real-world usage of LLM models for synthetic tabular data generation.