Academic Articles & Research Papers

Layout and Assembly Selection Algorithm for Standard Ring+Turning Ring Shield Tunnel Segments

Objective To address the challenges associated with the manual layout and point selection of S+T (standard rings+turning rings (double-sided wedges)) in shield tunnel segments, including high subjectivity, complex calculations, and heavy re...

Default Detection Rate-Dependent Software Reliability Model with Imperfect Debugging

From the perspective of FDR (fault detection rate), which is an indispensable component in reliability modeling, this paper proposes two kinds of reliability models under imperfect debugging. This model is a relatively flexible and unified ...

Holistic Parameter Optimization for Software Defect Prediction

A software defect prediction (SDP) model identifies the defect-prone modules. Setting appropriate parameters in an SDP model is critical because it affects the model performance. In a recent study, parameters were automatically explored usi...

Multi-source environmental raster dataset of FAO desert locust survey records in Ethiopia (2015–2023) for machine-learning prediction of locust presence.

Prediction of Hypersonic Boundary Layer Transition on Ablative Rough Surfaces of Deep Space Reentry Capsules

In order to improve aerodynamic deceleration efficiency, deep space reentry capsules generally adopt large blunt windward shape and ablative heat protection system. However, factors such as the flat forebody shape and the sharp increase in ...

TeV Diffuse Emission From the Inner Galaxy

The TeV diffuse emission from the Galactic plane is produced by multi TeV electrons and nuclei interacting with radiation fields and ambient gas, respectively. Measurements of the TeV diffuse emission help constrain CR origin and transport ...

Optimal dispatching method for integrated energy system based on robust economic model predictive control considering source–load power interval prediction

Effective source–load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems (IESs). They can overcome the challenges introduced by the uncertainties of new energies an...

Improved Combustion Enthalpy Predictions Obtained by DFT-Based Methods through Empirical Correction: A Study of Cycloparaffins for Sustainable Aviation Fuel.

Wind–Wave Coupling Effect on the Dynamic Response of a Combined Wind–Wave Energy Converter

There is a huge energy demand from offshore renewable energy resources. To maximize the use of various renewable energy sources, a combined floating energy system consisting of different types of energy devices is an ideal option to reduce ...

Interpretable and Explainable Machine Learning for Materials Science and Chemistry

High Resolution Image Download MS PowerPoint Slide Conspectus Machine learning has become a common and powerful tool in materials research. As more data become available, with the use of high-performance computing and high-throughput experi...

Phase prediction, microstructure and high hardness of novel light-weight high entropy alloys

Guided by CALPHAD modeling, low-density and multiphase three novel High Entropy Alloys (HEAs), Al40Cu15Cr15Fe15Si15, Al65Cu5Cr5Si15Mn5Ti5 and Al60Cu10Fe10Cr5Mn5Ni5Mg5 were produced by large scale vacuum die casting. A mixture of simple and ...

Coalition of explainable artificial intelligence and quantum computing in precision medicine

This survey examines the convergence of Explainable Artificial Intelligence (XAI) and Quantum Computing (QC) toward precision medicine. We review developments from 2018 to 2025, summarizing quantum algorithms, quantum-machine-learning model...

Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches

Abstract This paper introduces the design and exploration of a compact, high-performance multiple-input multiple-output (MIMO) antenna for 6G applications operating in the terahertz (THz) frequency range. Leveraging a meta learner-based sta...

A systems-level energetic framework for conscious processing: energetic organization as a constraint on large-scale neural integration

Conscious processing is associated with coordinated interactions among distributed neural systems, yet the biological constraints that enable and sustain large-scale neural integration remain incompletely understood. Contemporary theories e...

Time-Aware Service Ranking Prediction in the Internet of Things Environment

With the rapid development of the Internet of things (IoT), building IoT systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedures of building IoT systems, QoS-aware ser...

A Model to Predict Heart Disease Treatment Using Data Mining

Background and Aim: Nowadays heart disease is very common and is a major cause of mortality. Proper and early diagnosis of this disease is very important. Diagnostic methods and treatments of the disease are so expensive and have many side ...

Bayesian Prediction for Weibull Distribution Based on Doubl y Censored Samples

Eight-Day Typhoon Quantitative Precipitation Forecasts in Taiwan by the 2.5 km CReSS Model, Part II: Reduced Control of Track Errors on Rainfall Prediction Quality for Typhoons Associated with Southwesterly Flow

Due to the enhancement by its steep mesoscale topography, the overall rainfall amount and distribution in Taiwan from typhoons, to a first degree, are determined by the storm track relative to the island. Therefore, the quality of typhoon q...

Hybrid spatio-temporal transformer and variational autoencoder framework for advanced sports injury prediction and analysis

Sports injuries significantly affect sports performance and overall health, making using accurate and reliable prognostic models imperative. Conventional algorithms for injury prediction, e.g., random forest and XGBoost, generally cannot un...

Data Mining Techniques for Predictive Maintenance in Manufacturing Industries a Comprehensive Review

Predictive maintenance (PdM) is one of the major methods used in modern manufacturing to realize downtime minimization, lower the cost of maintenance and maximize machine service life by analyzing the collected data using data mining method...