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Abstract A technique for the transfer-free growth of 2-inch wafer-scale patterned graphene directly on GaN LED epilayers is introduced. High-quality graphene as transparent electrodes and heat spreaders is synthesized directly on GaN by PECVD at only 600 °C deposition temperature and within 3 min growth time. Co acts as both the catalyst for graphene growth and the dry etching mask for GaN mesas, which greatly improves the efficiency of the semiconductor device process. Elegantly, the graphene growth is in accordance with the shape of Co, which offers a lithography-free patterning technique of the graphene. Afterward, using our penetration etching method through the PMMA and graphene layers, the Co is peacefully removed, and in-situ Ohmic contact is achieved between the graphene and p-GaN where the contact resistivity is only 0.421 Ω cm2. The graphene sheet resistance is as low as 631.2 Ω sq−1. The device is also superior to the counterpart graphene-free LED in terms of heat spreading behavior, as evidenced by the lower junction temperature and thermal resistance. Most importantly, the developed technique produces graphene with excellent performance and is intrinsically more scalable, controllable, and semiconductor industry compatible than traditionally transferred graphene.

Micro-light-emitting diodes (μLEDs), with their advantages of high response speed, long lifespan, high brightness, and reliability, are widely regarded as the core of next-generation display technology. However, due to issues such as high manufacturing costs and low external quantum efficiency (EQE), μLEDs have not yet been truly commercialized. Additionally, the color conversion efficiency (CCE) of quantum dot (QD)-μLEDs is also a major obstacle to its practical application in the display industry. In this review, we systematically summarize the recent applications of nanomaterials and nanostructures in μLEDs and discuss the practical effects of these methods on enhancing the luminous efficiency of μLEDs and the color conversion efficiency of QD-μLEDs. Finally, the challenges and future prospects for the commercialization of μLEDs are proposed.

AbstractBackground/Aims Acute pancreatitis is a common condition of the digestive system, but sometimes it develops into severe cases. In about 10–20% of patients, necrosis of the pancreas or its periphery occurs. Although most have aseptic necrosis, 30% of cases will develop infectious necrotizing pancreatitis. Infected necrotizing pancreatitis (INP) requires a critical treatment approach. Minimally invasive surgical approach (MIS) and endoscopy are the management methods. This meta-analysis compares the outcomes of MIS and endoscopic treatments.Methods We searched a medical database until December 2022 to compare the results of endoscopic and MIS procedures for INP. We selected eligible randomized controlled trials (RCTs) that reported treatment complications for the meta-analysis.Results Five RCTs comparing a total of 284 patients were included in the meta-analysis. Among them, 139 patients underwent MIS, while 145 underwent endoscopic procedures. The results showed significant differences (p 0.05) in the RR for death, bleeding, incisional hernia, percutaneous drainage, pancreatic endocrine deficiency, pancreatic exocrine deficiency, or the need for enzyme use.Conclusions Endoscopic management of INP performs better compared to surgical treatment due to its lower complication rate and higher patient life quality.

With the rapid development of graphene technology, today graphene performs well in the application of light-emitting diode (LED) transparent electrodes. Naturally, high-quality contact between the graphene and the GaN underneath is very important. This paper reports a process for nickel-assisted transfer-free technology of graphene chemical vapor deposition on GaN. The nickel film plays the dual role of etching mask and growth catalyst, and is removed by the subsequent “penetration etching” process, achieving good direct contact between the graphene and GaN. The results show that the graphene effectively improves the current spreading of GaN-based LEDs and enhances their electrical performance. This scheme avoids the wrinkles and cracks of graphene from the transfer process, and is not only suitable for the combination of graphene and GaN-based LEDs, but also provides a solution for the integration of graphene and other materials.

Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder−decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints.

The document is a contents list for the Journal of Materials Chemistry C. It includes a range of articles and reviews on topics such as organic thin-film transistors, photonic synaptic transistors, and photochromic apatite skeletal structure materials. The journal aims to connect the world with the chemical sciences and invest profits back into the chemistry community. The document also mentions other journals in the field of environmental science. [Extracted from the article]

Graphene is well known for its excellent physical and chemical properties and can be used in various fields. Its application technology has become an important direction of research. In this study, a patterning technology of transfer-free graphene is reported, and graphene transparent electrodes of near-ultraviolet light-emitting diodes (LEDs) are fabricated accordingly. In the scheme, Ni film plays the dual role of an etching mask and graphene growth catalyst, realizing the patterning growth of graphene. An SiO2 isolation layer is deposited between Ni and the substrate, avoiding the fusing of the substrate with Ni by the high temperature of graphene growth, which makes the method applicable to nominally any high temperature-compatible metal and semiconductor substrates. Both Ni and SiO2 are then removed, thus directly achieving a good contact between graphene and the substrate. The graphene transparent electrodes fabricated by this method greatly improves the performance of near-ultraviolet LEDs, which is even better than that of indium tin oxide (ITO) in the near-ultraviolet band based on the optical measurement results. This scheme avoids any possible damage and contamination of graphene in traditional transfer and lithography patterning processes, which is scalable and suitable for real applications. [ABSTRACT FROM AUTHOR]

Micro-light-emitting diodes (μLEDs), with their advantages of high response speed, long lifespan, high brightness, and reliability, are widely regarded as the core of next-generation display technology. However, due to issues such as high manufacturing costs and low external quantum efficiency (EQE), μLEDs have not yet been truly commercialized. Additionally, the color conversion efficiency (CCE) of quantum dot (QD)-μLEDs is also a major obstacle to its practical application in the display industry. In this review, we systematically summarize the recent applications of nanomaterials and nanostructures in μLEDs and discuss the practical effects of these methods on enhancing the luminous efficiency of μLEDs and the color conversion efficiency of QD-μLEDs. Finally, the challenges and future prospects for the commercialization of μLEDs are proposed. [ABSTRACT FROM AUTHOR]

Acute pancreatitis is a common condition of the digestive system, but sometimes it develops into severe cases. In about 10–20% of patients, necrosis of the pancreas or its periphery occurs. Although most have aseptic necrosis, 30% of cases will develop infectious necrotizing pancreatitis. Infected necrotizing pancreatitis (INP) requires a critical treatment approach. Minimally invasive surgical approach (MIS) and endoscopy are the management methods. This meta-analysis compares the outcomes of MIS and endoscopic treatments. We searched a medical database until December 2022 to compare the results of endoscopic and MIS procedures for INP. We selected eligible randomized controlled trials (RCTs) that reported treatment complications for the meta-analysis. Five RCTs comparing a total of 284 patients were included in the meta-analysis. Among them, 139 patients underwent MIS, while 145 underwent endoscopic procedures. The results showed significant differences (p < 0.05) in the risk ratios (RRs) for major complications (RR: 0.69, 95% confidence interval (CI): 0.49–0.97), new onset of organ failure (RR: 0.29, 95% CI: 0.11–0.82), surgical site infection (RR: 0.26, 95% CI: 0.07–0.92), fistula or perforation (RR: 0.27, 95% CI: 0.12–0.64), and pancreatic fistula (RR: 0.14, 95% CI: 0.05–0.45). The hospital stay was significantly shorter for the endoscopic group compared to the MIS group, with a mean difference of 6.74 days (95% CI: −12.94 to −0.54). There were no significant differences (p > 0.05) in the RR for death, bleeding, incisional hernia, percutaneous drainage, pancreatic endocrine deficiency, pancreatic exocrine deficiency, or the need for enzyme use. Endoscopic management of INP performs better compared to surgical treatment due to its lower complication rate and higher patient life quality. [ABSTRACT FROM AUTHOR]

A technique for the transfer-free growth of 2-inch wafer-scale patterned graphene directly on GaN LED epilayers is introduced. High-quality graphene as transparent electrodes and heat spreaders is synthesized directly on GaN by PECVD at only 600 °C deposition temperature and within 3 min growth time. Co acts as both the catalyst for graphene growth and the dry etching mask for GaN mesas, which greatly improves the efficiency of the semiconductor device process. Elegantly, the graphene growth is in accordance with the shape of Co, which offers a lithography-free patterning technique of the graphene. Afterward, using our penetration etching method through the PMMA and graphene layers, the Co is peacefully removed, and in-situ Ohmic contact is achieved between the graphene and p-GaN where the contact resistivity is only 0.421 Ω cm2. The graphene sheet resistance is as low as 631.2 Ω sq−1. The device is also superior to the counterpart graphene-free LED in terms of heat spreading behavior, as evidenced by the lower junction temperature and thermal resistance. Most importantly, the developed technique produces graphene with excellent performance and is intrinsically more scalable, controllable, and semiconductor industry compatible than traditionally transferred graphene. [ABSTRACT FROM AUTHOR]

A localized surface plasmon (LSP) coupling nanorod $\mu $ LED using graphene as a transparent conductive electrode structure was demonstrated. The optical performance of the $\mu $ LED with LSP coupling is enhanced by about 36% in external quantum efficiency and 84% in photoluminescence measurements as compared with the LSP-free sample. This LSP coupling enhancement relies on the very large sidewall area provided by the nanorod structure and the zero distance contact between the nanoparticles and nanorods. This work provides a broad prospect for the application of LSP in the $\mu $ LED field. [ABSTRACT FROM AUTHOR]

Using deep reinforcement learning to solve traffic signal control problems is a research hotspot in the intelligent transportation field. Researchers have recently proposed various solutions based on deep reinforcement learning methods for intelligent transportation problems. However, most signal control optimization takes the maximization of traffic capacity as the optimization goal, ignoring the concerns of pedestrians at intersections. To address this issue, we propose a pedestrian-considered deep reinforcement learning traffic signal control method. The method combines a reinforcement learning network and traffic signal control strategy with traffic efficiency and safety aspects. At the same time, the waiting time of pedestrians and vehicles passing through the intersection is considered, and the Discrete Traffic State Encoding (DTSE) method is applied and improved to define the more comprehensive states and rewards. In the training of the neural network, the multi-process operation method is adopted, and multiple environments are run for training simultaneously to improve the model's training efficiency. Finally, extensive simulation experiments are conducted on actual intersection scenarios using the simulation software Simulation of Urban Mobility (SUMO). The results show that compared to Dueling DQN, the waiting time due to our method decreased by 58.76% and the number of people waiting decreased by 51.54%. The proposed method can reduce both the number of people waiting and the waiting time at intersections. [ABSTRACT FROM AUTHOR]

With the rapid development of graphene technology, today graphene performs well in the application of light-emitting diode (LED) transparent electrodes. Naturally, high-quality contact between the graphene and the GaN underneath is very important. This paper reports a process for nickel-assisted transfer-free technology of graphene chemical vapor deposition on GaN. The nickel film plays the dual role of etching mask and growth catalyst, and is removed by the subsequent "penetration etching" process, achieving good direct contact between the graphene and GaN. The results show that the graphene effectively improves the current spreading of GaN-based LEDs and enhances their electrical performance. This scheme avoids the wrinkles and cracks of graphene from the transfer process, and is not only suitable for the combination of graphene and GaN-based LEDs, but also provides a solution for the integration of graphene and other materials. [ABSTRACT FROM AUTHOR]

The quantum dot (QD) color-conversion efficiency (CCE) in GaN micro-light-emitting diodes ($\mu $ LEDs) is greatly improved by non-radiative energy transfer (NRET) mechanism. An array of deep nano-holes with a diameter of about $1~\mu \text{m}$ was fabricated in $\mu $ LED mesas ($40\times 60\,\,\mu \text{m}^{2}$) by nanoimprint lithography. The nano-holes were etched straight through the $\mu $ LED active region to ensure that the filled QDs were in extremely close contact with the active region. The absorption efficiency and emission efficiency of QDs are effectively improved by NRET, resulting in a superhigh CCE in QD- $\mu $ LED hybrid devices. Compared to $\mu $ LED devices with conventional spin-coated QDs, the CCE of novel nano-hole $\mu $ LEDs with filled QDs has been enhanced by about 118%. [ABSTRACT FROM AUTHOR]

Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder–decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints. [ABSTRACT FROM AUTHOR]