Class Title:Radiological imaging method a comprehensive overview *** GPT paper provides an overview of the different forms of radiological imaging and the potential diagnosis capabilities they offer as well as recent ...
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Class Title:Radiological imaging method a comprehensive overview *** GPT paper provides an overview of the different forms of radiological imaging and the potential diagnosis capabilities they offer as well as recent advances in the *** and Methods:This paper provides an overview of conventional radiography digital radiography panoramic radiography computed tomography and cone-beam computed *** recent advances in radiological imaging are discussed such as imaging diagnosis and modern computer-aided diagnosis ***:This paper details the differences between the imaging techniques the benefits of each and the current advances in the field to aid in the diagnosis of medical ***:Radiological imaging is an extremely important tool in modern medicine to assist in medical *** work provides an overview of the types of imaging techniques used the recent advances made and their potential applications.
The urgent need to mitigate climate change impacts and achieve net zero emissions has led to extensive research on carbon dioxide(CO_(2))-capture *** study focuses on the kinetics of CO_(2)capture using solid adsorben...
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The urgent need to mitigate climate change impacts and achieve net zero emissions has led to extensive research on carbon dioxide(CO_(2))-capture *** study focuses on the kinetics of CO_(2)capture using solid adsorbents specifically through thermal gravimetric analysis(TGA).The research explores the principles behind TGA and its application in analyzing adsorbent performance and the significance of kinetics in optimizing CO_(2)-capture *** adsorbents have gained significant attention due to their potential for efficient and cost-effective CO_(2)***,three different types of adsorbents,namely calcium-,tin-,and zirconium-based ones(quicklime:CaO,potassium stannate:K_(2)SnO_(3),and sodium zirconate:Na_(2)ZrO_(3)),in adsorbing high-temperature carbon dioxide were investigated;their quality and performance by various factors such as price,stability,non-toxicity,and efficiency are *** diffusion models and geometrical contraction models were the best-fitted models to explain the kinetic of these solid adsorbents for high-temperature CO_(2)sorption;it means the morphology is important for solid adsorbent *** minimum energy needed to start a reaction for K_(2)SnO_(3),Na_(2)ZrO_(3),and CaO,is 73.55,84.33,and 86.23 kJ·mol^(-1),respectively;with the lowest value being for potassium *** high-temperature CO_(2)adsorption performance of various solid adsorbents in regard with the rate of reaction followed the order of K_(2)SnO_(3)>CaO>>Na_(2)ZrO_(3),based on experiments and kinetic studies.
This study aims to investigate the influence of various vegetation patches with varying porosities on the hydraulic properties of a vegetated open channel under subcritical flow *** research work investigated three ty...
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This study aims to investigate the influence of various vegetation patches with varying porosities on the hydraulic properties of a vegetated open channel under subcritical flow *** research work investigated three types of vegetation patches:Rigid,flexible,and a combination of the *** total five vegetation patches with three different porosities for each patch were *** of these vegetation patches on various hydraulic parameters such as backwater rise,energy reduction,water surface slope in the vegetation patch,hydraulic jump formation on the downstream side of the vegetation patch,reduction in fluid force index(RFI),moment index(RMI),overflow volume(ΔQ)were *** findings revealed that the backwater rise increased in the case of rigid patch as the initial Froude number increased,whereas it decreased in the case of flexible and combined vegetation *** was observed that as the porosity increased from low(P_(r)=0.90)to high(P_(r)=0.99),the backwater rise decreased for all vegetation *** relative energy reduction rate increased for the rigid patch and showed a reverse trend for the flexible and combined vegetation patches with increasing initial Froude *** the combined vegetation arrangement,the energy reduction values were highest for the alternate rigid and flexible(ARF)vegetation patches and lowest for the longitudinal rigid and flexible(LRF)vegetation *** study identified the presence of a hydraulic jump downstream of the vegetation patch,as indicated by the Froude number in the range of 1.0–*** study also found that RFI,RMI,ΔQ had the highest values of 19.05%,19.05%,80.20%.The results of this study provide insight into the impact of vegetation patches with varying porosities on open-channel flow characteristics and can help develop sustainable vegetation management strategies.
Mashup developers often need to find open application programming interfaces(APIs) for their composition application development. Although most enterprises and service organizations have encapsulated their businesses ...
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Mashup developers often need to find open application programming interfaces(APIs) for their composition application development. Although most enterprises and service organizations have encapsulated their businesses or resources online as open APIs, finding the right high-quality open APIs is not an easy task from a library with several open APIs. To solve this problem, this paper proposes a deep learning-based open API recommendation(DLOAR) approach. First, the hierarchical density-based spatial clustering of applications with a noise topic model is constructed to build topic models for Mashup clusters. Second,developers' requirement keywords are extracted by the Text Rank algorithm, and the language model is built. Third, a neural network-based three-level similarity calculation is performed to find the most relevant open APIs. Finally, we complement the relevant information of open APIs in the recommended list to help developers make better choices. We evaluate the DLOAR approach on a real dataset and compare it with commonly used open API recommendation approaches: term frequency-inverse document frequency, latent dirichlet allocation, Word2Vec, and Sentence-BERT. The results show that the DLOAR approach has better performance than the other approaches in terms of precision, recall, F1-measure, mean average precision,and mean reciprocal rank.
This paper presents a novel approach of establishing a multichannel optical communication link, combining optical fiber cable (OFC) and free space optics (FSO) technology. By leveraging multiple lengths of optical fib...
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An nonlinear model predictive controller(NMPC)is proposed in this paper for compensations of single line-to-ground(SLG)faults in resonant grounded power distribution networks(RGPDNs),which reduces the likelihood of po...
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An nonlinear model predictive controller(NMPC)is proposed in this paper for compensations of single line-to-ground(SLG)faults in resonant grounded power distribution networks(RGPDNs),which reduces the likelihood of power line bushfire due to electric *** current compensation(RCC)inverters with arc suppression coils(ASCs)in RGPDNs are controlled using the proposed NMPC to provide appropriate compensations during SLG *** proposed NMPC is incorporated with the estimation of ASC inductance,where the estimation is carried out based on voltage and current measurements from the neutral point of the power distribution *** compensation scheme is developed in the discrete time using the equivalent circuit of *** proposed NMPC for RCC inverters ensures that the desired current is injected into the neutral point during SLG faults,which is verified through both simulations and control hardware-in-the-loop(CHIL)*** results are also presented against an integral sliding mode controller(ISMC)by demon-strating the capability of power line bushfire mitigation.
A pull request(PR) is an event in Git where a contributor asks project maintainers to review code he/she wants to merge into a project. The PR mechanism greatly improves the efficiency of distributed software developm...
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A pull request(PR) is an event in Git where a contributor asks project maintainers to review code he/she wants to merge into a project. The PR mechanism greatly improves the efficiency of distributed software development in the opensource community. Nevertheless, the massive number of PRs in an open-source software(OSS) project increases the workload of developers. To reduce the burden on developers, many previous studies have investigated factors that affect the chance of PRs getting accepted and built prediction models based on these factors. However, most prediction models are built on the data after PRs are submitted for a while(e.g., comments on PRs), making them not useful in practice. Because integrators still need to spend a large amount of effort on inspecting PRs. In this study, we propose an approach named E-PRedictor(earlier PR predictor) to predict whether a PR will be merged when it is created. E-PRedictor combines three dimensions of manual statistic features(i.e., contributor profile, specific pull request, and project profile) and deep semantic features generated by BERT models based on the description and code changes of PRs. To evaluate the performance of E-PRedictor, we collect475192 PRs from 49 popular open-source projects on GitHub. The experiment results show that our proposed approach can effectively predict whether a PR will be merged or not. E-PRedictor outperforms the baseline models(e.g., Random Forest and VDCNN) built on manual features significantly. In terms of F1@Merge, F1@Reject, and AUC(area under the receiver operating characteristic curve), the performance of E-PRedictor is 90.1%, 60.5%, and 85.4%, respectively.
Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome...
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Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.
Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. Howeve...
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Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.
Accurate automatic segmentation of gliomas in various sub-regions,including peritumoral edema,necrotic core,and enhancing and non-enhancing tumor core from 3D multimodal MRI images,is challenging because of its highly...
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Accurate automatic segmentation of gliomas in various sub-regions,including peritumoral edema,necrotic core,and enhancing and non-enhancing tumor core from 3D multimodal MRI images,is challenging because of its highly heterogeneous appearance and *** convolution neural networks(CNNs)have recently improved glioma segmentation ***,extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution,resulting in the loss of accurate spatial and object parts information,especially information on the small sub-region tumors,affecting segmentation ***,this paper proposes a novel multi-level parallel network comprising three different level parallel subnetworks to fully use low-level,mid-level,and high-level information and improve the performance of brain tumor *** also introduce the Combo loss function to address input class imbalance and false positives and negatives imbalance in deep *** proposed method is trained and validated on the BraTS 2020 training and validation *** the validation dataset,ourmethod achieved a mean Dice score of 0.907,0.830,and 0.787 for the whole tumor,tumor core,and enhancing tumor core,*** with state-of-the-art methods,the multi-level parallel network has achieved competitive results on the validation dataset.
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