Atomically dispersed single-atom catalysts(SACs)on carbon supports show great promise for H_(2)O_(2) electrosynthesis,but conventional wet chemistry methods using particulate carbon blacks in powder form have limited ...
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Atomically dispersed single-atom catalysts(SACs)on carbon supports show great promise for H_(2)O_(2) electrosynthesis,but conventional wet chemistry methods using particulate carbon blacks in powder form have limited their potential as two-electron(2^(e−))oxygen reduction reaction(ORR)***,we demonstrate high-performance Co SACs supported on a free-standing aligned carbon nanofiber(CNF)using electrospinning and arc plasma deposition(APD).Based on the surface oxidation treatment of aligned CNF and precise control of the deposition amount in a dry-based APD process,we successfully form densely populated Co SACs on aligned *** experimental analyses and density functional theory calculations,we reveal that Co SAC has a Co–N_(2)–O_(2) moiety with one epoxy group,leading to excellent 2^(e−)ORR ***,the aligned CNF significantly improves mass transfer in flow cells compared to randomly oriented CNF,showing an overpotential reduction of 30 mV and a 1.3-fold improvement(84.5%)in Faradaic efficiency,and finally achieves an outstanding production rate of 15.75 mol gcat^(−1) h^(−1) at 300 mA cm^(−2).The high-performance Co SAC supported on well-aligned CNF is also applied in an electro-Fenton process,demonstrating rapid removal of methylene blue and bisphenol F due to its exceptional 2e^(−)ORR activity.
With the increasing complexity and versatility of humanoid robots, there is a growing imperative for efficient and adaptable planning methodologies. This evolution marks a crucial step towards enhancing the overall ca...
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New IoT devices generate massive real-time data. Fast, real-time stream processing is needed for IoT data insight extraction. There are pros and downsides to real-time stream processing for the Internet of Things (IoT...
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In this research, a prototype has been created to automatically collect the photos of fruits using a smartphone camera and a thermal camera, generating two distinct datasets: thermal and RGB. Fruit classification and ...
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Utilising MODIS MCD64A1 burnt surface data, this study analyses the spatiotemporal trends of wildfires in Iran’s Golestan region from 2001 to 2021. The north-eastern, south-eastern, and southern parts of Golestan are...
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Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing ...
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The number of cases of violence and fights has been increasing around the world. With the use of CCTV, such incidents can be recorded but the detection of Violence is a major issue around the globe, and it plays a vit...
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ISBN:
(数字)9798350375190
ISBN:
(纸本)9798350375206
The number of cases of violence and fights has been increasing around the world. With the use of CCTV, such incidents can be recorded but the detection of Violence is a major issue around the globe, and it plays a vital role in the safety and security of the area. It helps in effective surveillance and law enforcement. The effectiveness of any violence detection system depends on the speed and accuracy with which it detects the events and how it performs over different types of videos of varied sizes and formats. In the proposed algorithm, the dataset used contains videos of different types and scenarios to help the model understand and train better. To improve the overall effectiveness of the model, Lightweight CNN MobileNetV2 has been used along with that Bidirectional LSTM (BiLSTM) has been used. The accuracy rate of the proposed model is 98%.
Arecanut, also known as betel nut, is a tropical crop predominantly grown in India. The country holds the second position globally in terms of the production and consumption of arecanut. Throughout its life cycle, the...
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ISBN:
(数字)9798350389449
ISBN:
(纸本)9798350389456
Arecanut, also known as betel nut, is a tropical crop predominantly grown in India. The country holds the second position globally in terms of the production and consumption of arecanut. Throughout its life cycle, the areca nut plant is susceptible to a variety of diseases affecting its roots, trunk, leaves, and fruits. Some of these diseases are visible to the naked eye, while others are not. Farmers traditionally analyze every crop to detect any signs of disease, a process that is extremely *** this study, we propose an automatic system that aids in detecting the diseases of arecanut leaves using Convolutional Neural Networks (CNN) and deep learning algorithms. A Convolutional Neural Network is a Deep Learning algorithm that takes an image as input, assigns learnable weights and biases to various features in the image, and then learns from the results to distinguish one feature from another. This approach significantly reduces the manual effort required in disease detection and increases the efficiency of the process. A datset is developed with many photos of healthy and sick arecanut leaves in order to train and evaluate the CNN model. An 80:20 split of the dataset was made into training and testing data. The goal of training the model over multiple epochs was to maximize validation and test accuracy while minimizing loss. It was discovered that the suggested method for diagnosing arecanut illnesses was precise and successful. This study contributes to the ongoing efforts to leverage technology in agriculture, with the aim of improving crop health and productivity. This study indicate that the proposed system can accurately identify diseases in arecanut leaves with a good and best accuracy. The system was able to distinguish between healthy and diseased leaves, and suggest appropriate remedies for the detected diseases. These findings demonstrate the potential of using Convolutional Neural Networks and deep learning algorithms in the field of agriculture, par
Delay-tolerant networks (DTNs) are specific settings that experience either instability or the absence of a complete path between the source and destination nodes. The contact lifetime between pairs of network nodes i...
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For the cause of evolution of agriculture to its next generation, the introduction of A.I. and data-driven approach is going to be an important part of the agricultural industry that as per our vision would offer nume...
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ISBN:
(数字)9798350365269
ISBN:
(纸本)9798350365276
For the cause of evolution of agriculture to its next generation, the introduction of A.I. and data-driven approach is going to be an important part of the agricultural industry that as per our vision would offer numerous economic, environmental and social benefits. Controlled Environment Agriculture (CEA) is providing more benefits since the weather and other environmental conditions is controlled and predictable. In CEA, the benefits include enhanced productivity in yield, reduced environmental footprints and better resource management. Our solution uses the adoption of Generative AI to visually forecast the potential yield of a crop using stable diffusion and conditional prompts additionally implementing several Generative Adversarial Networks (GAN) architectures for data generation, augmentation and super-resolution. Our proposed system also adopts generative AI for real time monitoring of plants. studying their respective conditions and their autonomous cultivation and harvesting patterns. The dataset used contained over 8479 examples for different fruits alongside annotations. This data was used to train the detection models as well categorically feed the GANs for data generation. data generated by DCGAN has a structural similarity of 0.5-0.7 whereas data generated by pix2pix network returns us a structural similarity of 0.7-0.9.
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