Vehicular Named Data Networks (VNDN) is a content centric approach for vehicle networks. The fundamental principle of addressing the content rather than the host, suits vehicular environment. There are numerous challe...
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In the current era of smart technology, integrating the Internet of Things (IoT) with Artificial Intelligence has revolutionized several fields, including public health and sanitation. The smart lavatory solution prop...
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1 Introduction Automatic bug assignment has been well-studied in the past *** textual bug reports usually describe the buggy phenomena and potential causes,engineers highly depend on these reports to fix ***,researche...
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1 Introduction Automatic bug assignment has been well-studied in the past *** textual bug reports usually describe the buggy phenomena and potential causes,engineers highly depend on these reports to fix ***,researchers spend much effort on processing bug reports,aiming for obtaining key information and/or clues for reproducing bugs,analyzing their root causes,assigning them to developers/maintainers,and fixing these ***,our previous research[1]reveals that noises in texts bring adverse impacts to automatic bug assignments unexpectedly,mainly due to insufficiency of classical Natural Language Processing(NLP)techniques.
Nanoelectromechanical systems(NEMS)incorporating atomic or molecular layer van der Waals materials can support multimode resonances and exotic nonlinear *** we investigate nonlinear coupling of closely spaced modes in...
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Nanoelectromechanical systems(NEMS)incorporating atomic or molecular layer van der Waals materials can support multimode resonances and exotic nonlinear *** we investigate nonlinear coupling of closely spaced modes in a bilayer(2L)molybdenum disulfide(MoS_(2))nanoelectromechanical *** model the response from a drumhead resonator using equations of two resonant modes with a dispersive coupling term to describe the vibration induced frequency shifts that result from the induced change in *** employ method of averaging to solve the equations of coupled modes and extract an expression for the nonlinear coupling coefficient(λ)in closed *** thermomechanical noise spectral measurements are used to calibrate the vibration amplitude of mode 2(a_(2))in the displacement *** drive mode 2 near its natural frequency and measure the shifted resonance frequency of mode 1(f_(1s))resulting from the dispersive *** model yieldsλ=0.027±0.005 pm^(-2)·μs^(-2) from thermomechanical noise measurement of mode *** model also captures an anomalous frequency shift of the undriven mode 1 due to nonlinear coupling to the driven mode 2 mediated by large dynamic *** study provides a direct means to quantifyingλby measuring the thermomechanical noise in NEMS and will be valuable for understanding nonlinear mode coupling in emerging resonant systems.
Sequence-to-sequence models are fundamental building blocks for generating abstractive text summaries, which can produce precise and coherent summaries. Recently proposed, different text summarization models aimed to ...
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As a result of its remarkable resistance to corrosion, strong wear resistance, high strength, and low weight, aluminum metal matrix composites (AMMC’s) have garnered tremendous widespread recognition for structural a...
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Background: In the wake of escalating cyber threats and the indispensability of ro-bust network security mechanisms, it becomes crucial to understand the evolving landscape of cryptographic research. Recognizing the s...
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Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elasti...
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Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elastic demand, climate and consumer response, load analysis, and sustainable energy generation predictive modelling are used. As both supply and demand fluctuate, and weather and power prices can rise significantly during peak periods, accurate load forecasting becomes critical for utilities. By providing brief demand forecasts, load forecasting can assist in estimating load flows and making decisions that prevent overloading. Therefore, load forecasting is crucial in helping electric utilities make informed decisions related to power, load switching, voltage regulation, switching, and infrastructure development. Forecasting is a methodology used by electricity companies to forecast the amount of electricity or power production needed to maintain constant supply as well as load demand balance. It is required for the electrical industry to function properly. The smart grid is a new system that enables electricity providers and customers to communicate in real-time. The precise energy consumption sequence of the consumers is required to enhance the demand schedule. This is where predicting the future comes into play. Forecasting future power system load (electricity consumption) is a critical task in providing intelligence to the power grid. Accurate forecasting allows utility companies to allocate resources and assume system control in order to balance the same demand and availability for electricity. In this article, a study on load forecasting algorithms based on deep learning, machine learning, hybrid methods, bio-inspired techniques, and other techniques is carried out. Many other algorithms based on load forecasting are discussed in this study. Different methods of load forecasting were compared using three performance indices: RMSE (Root Mean Square Err
Software defect prediction (SDP) is considered a dynamic research problem and is beneficial during the testing stage of the software development life cycle. Several artificial intelligence-based methods were avai...
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Software defect prediction (SDP) is considered a dynamic research problem and is beneficial during the testing stage of the software development life cycle. Several artificial intelligence-based methods were available to predict these software defects. However, the detection accuracy is still low due to imbalanced datasets, poor feature learning, and tuning of the model's parameters. This paper proposes a novel attention-included Deep Learning (DL) model for SDP with effective feature learning and dimensionality reduction mechanisms. The system mainly comprises ‘6’ phases: dataset balancing, source code parsing, word embedding, feature extraction, dimensionality reduction, and classification. First, dataset balancing was performed using the density peak based k-means clustering (DPKMC) algorithm, which prevents the model from having biased outcomes. Then, the system parses the source code into abstract syntax trees (ASTs) that capture the structure and relationship between different elements of the code to enable type checking and the representative nodes on ASTs are selected to form token vectors. Then, we use bidirectional encoder representations from transformers (BERT), which converts the token vectors into numerical vectors and extracts semantic features from the data. We then input the embedded vectors to multi-head attention incorporated bidirectional gated recurrent unit (MHBGRU) for contextual feature learning. After that, the dimensionality reduction is performed using kernel principal component analysis (KPCA), which transforms the higher dimensional data into lower dimensions and removes irrelevant features. Finally, the system used a deep, fully connected network-based SoftMax layer for defect prediction, in which the cross-entropy loss is utilized to minimize the prediction loss. The experiments on the National Aeronautics and Space Administration (NASA) and AEEEM show that the system achieves better outcomes than the existing state-of-the-art models f
This research work explores the effects of dry, liquid N2-based cryogenic cooling and cryogenic plus MQL hybrid strategy on surface roughness, rake surface temperature, principal cutting-edge temperature, auxiliary cu...
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