Lead halide perovskite quantum dots(LHP QDs)have been revealed to possess great potential in photocatalytic applications including CO_(2)reduction;which however suffer from poor ***;a high crystalline hydrazine-linked...
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Lead halide perovskite quantum dots(LHP QDs)have been revealed to possess great potential in photocatalytic applications including CO_(2)reduction;which however suffer from poor ***;a high crystalline hydrazine-linked three-dimensional(3D)covalent organic framework;USTB-17;was fabricated from the reaction between 12-connected building block and 4-connected 3,5,7-tetrakis(4-aldophenyl)-***-modification with Ni^(2+) affords the metallic framework USTB-17(Ni)followed by sequential deposition of the CH_(3)NH_(2)PbI_(3)(MAPbI_(3))perovskite QDs into its pores;generating the USTB-17(Ni)@MAPbI_(3) *** X-ray diffraction analysis together with theoretical simulations and transmission electron microscopy discloses the crystalline nature of USTB-17;USTB-17(Ni);and USTB-17(Ni)@MAPbI_(3) with an unprecedented noninterpenetrated hpt *** close contact of QDs inside the COF pores with the Ni catalytic site locating at the pore surface of COF allows a rapid transfer of the photogenerated electrons in QDs to the Ni catalytic sites;enhancing the photocatalytic activity for CO_(2)*** endows USTB-17(Ni)@MAPbI_(3) with efficient photocatalysis performance for photocatalytic CO_(2)reduction with CO generation rate of 365μmol g^(-1)h^(-1)and CO selectivity up to 96%under visible-light irradiation;7 times higher than that of USTB-17(Ni).After four cycles of reactions;the photocatalytic CO generation rate remains almost unchanged;demonstrating its excellent cycle stability.
Management of vehicular parking in the crowded environment is the indispensable requirement for the smart city scenario. The advent and potential development of Information Communication Technologies (ICT) and Interne...
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Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread ap...
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Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions adequately. To address these challenges, this study introduces a novel hybrid model called Transfer LSTM to GRU (TLTG), which combines the strengths of deep and shallow networks using transfer learning. The TLTG model integrates Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU) to enhance predictive accuracy while maintaining computational efficiency. Gaussian transformation is applied to the input data to reduce outliers and skewness, creating a more normal-like distribution. The proposed approach is validated on datasets from various wells in the Tahe oil field, China. Experimental results highlight the superior performance of the TLTG model, achieving 100% accuracy and faster prediction times (200 s) compared to eight other approaches, demonstrating its effectiveness and efficiency.
COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had sh...
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COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.
The Széchenyi chain bridge is an almost 170-year-old historical structure located in the downtown of Budapest, which has been reconstructed between 2020-2022. The chain system of the bridge is more than 100 years...
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In today's competitive global markets, customers are demanding adjustable lot sizes, shorter lead times, higher quality and flexibility. In order to stay competitive in the market, companies need to attain both cu...
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We proposed a novel image-enhancing framework to ensure consolidated restoration accuracy when remedying the visual quality of dehazed images, such as over-saturation, color deviation, or luminance issues. Conventiona...
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Segmented Active Constrained Layer Damping(SACLD)is an intelligent vibration-damping structure,which could be applied to the sectors of aviation,aerospace,and transportation engineering to reduce the vibration of flex...
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Segmented Active Constrained Layer Damping(SACLD)is an intelligent vibration-damping structure,which could be applied to the sectors of aviation,aerospace,and transportation engineering to reduce the vibration of flexible ***,machine learning technology is widely used in the engineering field because of its efficient multi-objective *** dynamic simulation of a rotational segmental flexible manipulator system is presented,in which enhanced active constrained layer damping is carried out,and the neural network model of Genetic Algorithm-Back Propagation(GA-BP)algorithm is *** suppression and structural optimization of the SACLD manipulator model are studied based on vibration mode and damping *** modal responses of the SACLD manipulator model at rest and rotation are *** addition,the four model indices are optimized using the GA-BP neural network:axial incision size,axial incision position,circumferential incision size,and circumferential incision ***,the best model for vibration suppression is obtained.
Recently, membrane bioreactors (MBRs) have emerged as a promising approach for sewage treatment because of their high efficiency in removing contaminants. However, they are prone to membrane-fouling and computational ...
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In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of acc...
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In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE
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