Unified Solution Research Articles

Solving for the Thermal Vibration Behaviors of Functionally Graded Stepped Conical Curved Plates with Point Connection Conditions

This paper proposed a degree-of-freedom reduction model for the rapid and unified solution of the modal and steady-state response behavior of functionally graded stepped conical curved plates with point connections under thermal environments. Within the framework of the first-order shear deformation theory, the two-dimensional spectral Chebyshev method and the fixed interface modal synthesis method were combined to derive the degree-of-freedom reduction model for functionally graded conical curved plates under thermal environments. Each reduction model was assembled considering external load excitations. Virtual springs were used to equivalently handle the coupling interface forces and point connection interface forces, further establishing a thermal vibration response analysis model for functionally graded stepped conical curved plates with point connections. In numerical examples, this degree-of-freedom reduction model was compared with experimental results and overall finite element results, validating the accuracy of the model and the advantages of rapid solution. Additionally, this degree-of-freedom reduction model was applied to quickly analyze the effects of material parameters and point connection parameters on the thermal steady-state response of the structure, providing a theoretical basis for the environmental adaptability design, verification, and evaluation of such structures.

Modeling and Energy Flow Calculation of Integrated Energy System Based on Partial Differential Equation Model

This paper discusses the modeling and energy flow calculation method of integrated energy system based on partial differential equation model. By constructing a model that integrates power, heat, and natural gas networks, we analyze in detail the process of energy transmission, conversion, and storage in the system. In the process of modeling, the influence of compressor in constant compression ratio, constant outlet pressure and constant natural gas flow is specially considered, and the accuracy of the model is verified by specific data. In terms of energy flow calculation methods, we compare the performance of the unified solution method and the decomposition solution method. Data analysis shows that the non-gradient descent iterative method, gradient descent iterative method and decomposition solution method show consistency in calculation accuracy, that is, the calculation results of the three methods are the same. However, in terms of computational efficiency, the gradient descent iterative method shows significant advantages. Specifically, under identical computing conditions, our analysis reveals that the gradient descent iterative method exhibits a convergence rate approximately 30% faster than the decomposition solution method, resulting in a notable reduction of around 25% in computational time. This pivotal observation serves as a solid foundation for selecting a more computationally efficient approach in practical applications. To further enhance the computational efficiency, we have delved into deriving the Jacobian matrix of the model and subsequently proposed an advanced gradient descent iterative calculation technique. Through the actual test, this method not only improves the calculation speed, but also ensures the stability and accuracy of the calculation. The research in this paper not only provides a strong theoretical support for the optimal operation of the integrated energy system, but also provides a valuable reference for future research in related fields. Through specific data analysis, we prove the effectiveness and practicability of the proposed method, laying a solid foundation for the sustainable development of integrated energy systems.

Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling

AbstractA two‐step framework that integrates real‐time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO‐v7 object detector to construct accurate traffic volume data. In the second step, an ARIMA–LSTM time series model is applied to forecast future traffic volumes. Experimental results show that YOLO‐v7 achieved a vehicle detection accuracy of over 93.30%, ensuring high precision in traffic volume data construction. The ARIMA–LSTM model demonstrated superior performance in traffic volume prediction, with a mean squared error of 87.97, root mean squared error of 10,388.57, and mean absolute error of 101.39. YOLO‐v7's detection speed of 7.8 ms per frame further validates the feasibility of real‐time data construction. The findings indicate that the combination of YOLO‐v7 for vehicle detection and ARIMA–LSTM for traffic prediction is highly effective, offering a significant reduction in training time compared to more complex deep learning models while maintaining high prediction accuracy. This research presents a unified solution for traffic data collection and prediction, enhancing transportation infrastructure planning and optimizing traffic flow. Future work will focus on extending the prediction intervals and further refining the models to improve performance.

Multiscale Defective Interfaces for Realizing Na-CO2 Batteries With Ultralong Lifespan.

Despite their favorable high energy density and potential for CO2 recycling, Na-CO2 batteries have been held back by limitations in cycling capability, stemming from the sluggish CO2 reduction/evolution reaction (CO2RR/CO2ER) kinetics at CO2 cathode and unmanageable deposition/stripping of metallic Na at the anode upon cycling. Herein, a "two-in-one" electrode with multiscale defective FeCu interfaces (CP@FeCu) is presented, which is capable of improving the CO2RR/CO2ER kinetics of CO2-breathing cathode, while modulating sodium deposition behavior. Experimental and theoretical investigations reveal multiscale defective FeCu interfaces are responsible for the enhancement of sodiophilicity and catalytic properties. The defect and valence oscillation effects originate in multiscale defective FeCu interfaces, effectively facilitating the adsorption of reactants and decomposition of Na2CO3 during CO2RR/CO2ER processes, along with exceptional cycling stability of 2400 cycles (4800h) at 5µAcm-2. Meanwhile, the CP@FeCu with sodium affinity creates a uniform electric field and robust adsorption for Na, making initial nucleation sites more conducive to Na deposition and achieving dendrite-resistant and durable anodes. This work offers a scientific insight into the functionalization design of "two-in-one" electrodes, which is essential for a unified solution to the challenges in sodium anodes and CO2 cathodes.

Semi-analytical computation of bifurcation of orbits near collinear libration point in the restricted three-body problem

A unified semi-analytical solution is presented for constructing the phase space near collinear libration points in the Circular Restricted Three-body Problem (CRTBP), encompassing Lissajous orbits, quasihalo orbits, Axial orbits, and their invariant manifolds, as well as transit and non-transit orbits. Based on classical in-plane and out-of-plane frequency resonance mechanisms, the Lindstedt–Poincaré method could only derive separate analytical solutions for the invariant manifolds of Lissajous orbits and halo orbits, falling short for the invariant manifolds of quasihalo orbits. In this paper, by introducing a coupling coefficient η and a bifurcation equation, a unified series solution for these orbits is systematically developed using a coupling-induced bifurcation mechanism and Lindstedt–Poincaré method. Analyzing the bifurcation equation obtained from different coupling forms reveals bifurcation conditions for all kinds of orbits near collinear libration points. When η = 0, the series solution describes non-bifurcated orbits, while when η ≠ 0, the solution describes bifurcated orbits, including quasihalo orbits, Axial orbits, and their invariant manifolds, as well as newly bifurcated transit and non-transit orbits. This unified semi-analytical framework provides a more comprehensive understanding of the complex phase space structures near collinear libration points in the CRTBP.

Uncertainty sensitivity analysis for vibration properties of composite doubly-curved shallow shells using Kriging method

This paper presents a Kriging based global sensitivity analysis (GSA) method for the frequency response of displacements of composite doubly-curved shallow shells. A unified solution is utilized to develop the dynamic vibration formulation using the First-order Shear Deformation Theory (FSDT) and the Rayleigh-Ritz method. Kriging surrogate model is employed to substitute the frequency response function (FRF) of displacements. Ten parameters including materials and geometrical dimension are considered as input uncertain variables. A variance-based GSA method for dynamic model is employed to quantify the influence of each uncertain parameter. In addition, to avoid the computational burden of Monte Carlo simulation method (MCS), the presented sensitivity indices are computed analytically based on the Kriging mode, which further improves computational efficiency. Based on the convergence studies and comparison with traditional methods, the accuracy and efficiency of the present method are validated. The results shows that the frequency response of displacements exhibits greater sensitivity to changes in width, and thickness is more influential than others in the example from this article. Finally, the presented numerical results demonstrate vibration characteristics of different types of shells and observation points, which can also serve as a reference for further study on uncertainty-propagation analysis.

Comprehensive stress-driven multi-material problem for heat-sinking heterogeneous structures

In the realm of designing thermoelastic structures, particularly within aerospace and broader engineering fields, complex challenges arise from the diverse behaviors of heterogeneous materials and the intricate requirements of stress-based systems in coupled thermomechanical considerations. This study addresses these challenges by proposing a comprehensive methodology with two main contributions: (i) the development of an effective unified solution for stress-driven designs that tackle heat-sinking problems by accommodating a wide range of materials, from homogeneous to heterogeneous and (ii) the introduction of a robust stress-based optimization approach for multi-material problems within coupled thermomechanical systems. The methodology employs the well-established P-norm approach to consolidate heterogeneous stresses into a unified global metric. Additionally, it integrates effective material interpolation with the generalized Solid Isotropic Material with Penalization (SIMP) framework, resulting in comprehensive multi-material models that include the constitutive matrix, thermal conductivity, thermal stress coefficient, and thermoelastic stress distributions. To further enhance adaptability and flexibility, the methodology incorporates a polygonal discretization technique. Detailed sensitivity analyses, using the adjoint variable technique, are conducted to improve computational efficiency in gradient-based mathematical programming algorithms. The efficiency, robustness, and practicality of the proposed methodology are validated through numerical examples, demonstrating its effectiveness and reliability in real-world applications.

Decoupling and predicting natural gas deviation factor using machine learning methods

Accurately predicting the deviation factor (Z-factor) of natural gas is crucial for the estimation of natural gas reserves, evaluation of gas reservoir recovery, and assessment of natural gas transport in pipelines. Traditional machine learning algorithms, such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory Neural Networks (BiLSTM), often lack accuracy and robustness in various situations due to their inability to generalize across different gas components and temperature-pressure conditions. To address this limitation, we propose a novel and efficient machine learning framework for predicting natural gas Z-factor. Our approach first utilizes a signal decomposition algorithm like Variational Mode Decomposition (VMD), Empirical Fourier Decomposition (EFD) and Ensemble Empirical Mode Decomposition (EEMD) to decouple the Z-factor into multiple components. Subsequently, traditional machine learning algorithms is employed to predict each decomposed Z-factor component, where combination of SVM and VMD achieved the best performance. Decoupling the Z-factors firstly and then predicting the decoupled components can significantly improve prediction accuracy of all traditional machine learning algorithms. We thoroughly evaluate the impact of the decoupling method and the number of decomposed components on the model’s performance. Compared to traditional machine learning models without decomposition, our framework achieves an average correlation coefficient exceeding 0.99 and an average mean absolute percentage error below 0.83% on 10 datasets with different natural gas components, high temperatures, and pressures. These results indicate that hybrid model effectively learns the patterns of Z-factor variations and can be applied to the prediction of natural gas Z-factors under various conditions. This study significantly advances methodologies for predicting natural gas properties, offering a unified and robust solution for precise estimations, thereby benefiting the natural gas industry in resource estimation and reservoir management.

Early galaxies and early dark energy: a unified solution to the hubble tension and puzzles of massive bright galaxies revealed by JWST

Early galaxies and early dark energy: a unified solution to the hubble tension and puzzles of massive bright galaxies revealed by <i>JWST</i>

A Gateway API-Based Data Fusion Architecture for Automated User Interaction with Historical Handwritten Manuscripts

To preserve handwritten historical documents, libraries are choosing to digitize them, ensuring their longevity and accessibility. However, the true value of these digitized images lies in their transcription into a textual format. In recent years, various tools have been developed utilizing both traditional and AI-based models to address the challenges of deciphering handwritten texts. Despite their importance, there are still several obstacles to overcome, such as the need for scalable and modular solutions, as well as the ability to cater to a continuously growing user community autonomously. This study focuses on introducing a new information fusion architecture, specifically highlighting the Gateway API. Developed as part of the μDoc.tS research program, this architecture aims to convert digital images of manuscripts into electronic text, ensuring secure and efficient routing of requests from front-end applications to the back end of the information system. The validation of this architecture demonstrates its efficiency in handling a large volume of requests and effectively distributing the workload. One significant advantage of this proposed method is its compatibility with everyday devices, eliminating the need for extensive computational infrastructures. It is believed that the scalability and modularity of this architecture can pave the way for a unified multi-platform solution, connecting diverse user environments and databases.

Solving the strong CP problem without axions

We formulate general conditions under which the strong CP problem is solved by spontaneous CP violation. Quark-mass matrix elements are polynomials in the CP-breaking order parameters, engineered such that their determinant is a real constant. This scheme permits only a limited number of textures. These conditions can be realized in supersymmetric theories with CP as an anomaly-free local flavor symmetry, suggesting a unified solution to the strong CP problem and the flavor puzzle. Our solution can be implemented using either modular invariance or a local U(1) symmetry. We present modular-invariant realizations where matter fields are assigned small modular weights ±2 (±1), utilising higher levels N = 2 (N = 3). Heavy quarks are in general not required, but their presence allows for models where colored particles fill non-singlet representations of the flavor group.

A unified FFT framework with coupled gradient damage model and phase-field method for the failure analysis of composites

A unified FFT framework coupling the Gradient Damage Model (GDM) and the Variational Phase-Field Model (PFM) for failure analysis of composites is proposed. GDM and PFM are used to simulate the failure of the matrix and the interphase, respectively. Based on the similarity of the damage evolution equations (elliptic partial differential equations) of GDM and PFM, a unified iterative solution and numerical implementation based on fast Fourier transform (FFT) are derived, and simultaneously applied to a computational model. In addition, the high computational efficiency of the FFT method is used to solve the mechanical equilibrium problem. The complete form of the PFM is introduced to avoid erroneous damage diffusion at the different phase interfaces of the composites, and a stress decomposition-based PFM is formulated. 2D and 3D computational examples are established to investigate the mesh insensitivity of the damage models used and the erroneous damage diffusion at interfaces. The applicability of the stress decomposition-based PFM at interphase is discussed in the context of single-edge notched specimens under tension and fiber-reinforced composites with interfacial phases under transverse tension. Finally, the performance of the unified computational framework is evaluated using two simple numerical examples, and the mechanical behavior of unidirectional CF/PEEK composites under transverse tensile loading is simulated based on the unified framework. The results are compared with experiments to evaluate the computational accuracy and practical applicability of the proposed framework.

A Classical Approach for MPPT Extraction in Hybrid Energy Systems

A novel approach for Maximum Power Point Tracking (MPPT) extraction using the Hill Climbing method in hybrid solar and wind energy systems. MPPT is essential for optimizing the energy harvesting efficiency of sustainable energy sources, the integration of multiple sources poses unique challenges. The proposed Hill Climbing algorithm is applied to both solar photovoltaic (PV) panels and wind turbines, enabling efficient tracking of the Maximum Power Points (MPPs) under varying environmental circumstances. This article investigates the performance of the Hill Climbing MPPT method through simulation and experimental validation in a hybrid energy system. The algorithm's adaptability to the dynamic nature of solar irradiance and wind speed is analyzed, demonstrating its capability to rapidly converge to the MPPs for both solar and wind components. The integration of Hill Climbing MPPT for both sources enhances the overall energy harvesting efficiency of the hybrid system. The Hill Climbing MPPT method offers a robust and unified solution for hybrid solar and wind energy systems, providing improved performance and simplicity of implementation. The findings contribute to advancing the optimization of renewable energy systems by addressing the challenges associated with the simultaneous utilization of solar and wind resources.

Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment.

Respiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven respiratory motion correction (uRMC) method, utilizing deep learning neural networks, to solve all the major issues caused by RM, i.e., PET resolution loss, attenuation correction artifacts, and PET-CT misalignment. In a retrospective study, 737 patients underwent [18F]FDG PET/CT scans using the uMI Panorama PET/CT scanner. Ninety-nine patients, who also had respiration monitoring device (VSM), formed the validation set. The remaining data of the 638 patients were used to train neural networks used in the uRMC. The uRMC primarily consists of three key components: (1) data-driven respiratory signal extraction, (2) attenuation map generation, and (3) PET-CT alignment. SUV metrics were calculated within 906 lesions for three approaches, i.e., data-driven uRMC (proposed), VSM-based uRMC, and OSEM without motion correction (NMC). RM magnitude of major organs were estimated. uRMC enhanced diagnostic capabilities by revealing previously undetected lesions, sharpening lesion contours, increasing SUV values, and improving PET-CT alignment. Compared to NMC, uRMC showed increases of 10% and 17% in SUVmax and SUVmean across 906 lesions. Sub-group analysis showed significant SUV increases in small and medium-sized lesions with uRMC. Minor differences were found between VSM-based and data-driven uRMC methods, with the SUVmax was found statistically marginal significant or insignificant between the two methods. The study observed varied motion amplitudes in major organs, typically ranging from 10 to 20mm. A data-driven solution for respiratory motion in PET/CT has been developed, validated and evaluated. To the best of our knowledge, this is the first unified solution that compensates for the motion blur within PET, the attenuation mismatch artifacts caused by PET-CT misalignment, and the misalignment between PET and CT.

Explainable multi-layer COSFIRE filters robust to corruptions and boundary attack with application to retina and palmprint biometrics

We propose a novel and versatile computational approach, based on hierarchical COSFIRE filters, that addresses the challenge of explainable retina and palmprint recognition for automatic person identification. Unlike traditional systems that treat these biometrics separately, our method offers a unified solution, leveraging COSFIRE filters’ trainable nature for enhanced selectivity and robustness, while exhibiting explainability and resilience to decision-based black-box adversarial attack and partial matching. COSFIRE filters are trainable, in that their selectivity can be determined with a one-shot learning step. In practice, we configure a COSFIRE filter that is selective for the mutual spatial arrangement of a set of automatically selected keypoints of each retina or palmprint reference image. A query image is then processed by all COSFIRE filters and it is classified with the reference image that was used to configure the COSFIRE filter that gives the strongest similarity score. Our approach, tested on the VARIA and RIDB retina datasets and the IITD palmprint dataset, achieved state-of-the-art results, including perfect classification for retina datasets and a 97.54% accuracy for the palmprint dataset. It proved robust in partial matching tests, achieving over 94% accuracy with 80% image visibility and over 97% with 90% visibility, demonstrating effectiveness with incomplete biometric data. Furthermore, while effectively resisting a decision-based black-box adversarial attack and impervious to imperceptible adversarial images, it is only susceptible to highly perceptible adversarial images with severe noise, which pose minimal concern as they can be easily detected through histogram analysis in preprocessing. In principle, the proposed learning-free hierarchical COSFIRE filters are applicable to any application that requires the identification of certain spatial arrangements of moderately complex features, such as bifurcations and crossovers. Moreover, the selectivity of COSFIRE filters is highly intuitive; and therefore, they provide an explainable solution.

Unified Solid Solution Product of [Nb][C] in Nb-Microalloyed Steels with Various Carbon Contents.

In this work, the solid solution product of [Nb][C] in the Nb-microalloyed steels with various carbon contents in the range of 0.20~1.80 wt.% was investigated by means of the extraction phase analysis method. The results showed that the Nb content in austenite tended to first decrease and then increase with the increase of carbon content in the steels. A unified solid solution product of [Nb][C] in austenite at different temperatures was obtained according to the results of the experimental steels. The Nb content in austenite of the experimental steels with high carbon contents was lower than that calculated by Ohtani's equation. The existence of NbC precipitates in the case and the core of the specimens carburized at 930 °C and 980 °C were verified by transmission electron microscopy (TEM) observations. The pinning effect of NbC precipitates on austenite grain growth was calculated according to the size and amount of NbC precipitates in the carburized case and the core of the carburized specimens. The calculated results of prior austenite grain sizes were in good agreement with the experimental results, which indicated that the unified solid solution product of [Nb][C] in Nb-microalloyed steels with various carbon contents was applicable for the low-pressure carburizing process.

Development of a Cloud-Based Building Information Modeling Design Configurator to Auto-Link Material Catalogs with Code-Compliant Designs of Residential Buildings

Configurators have recently emerged as essential tools in the construction industry to enable builders to offer a wide range of customizable designs. Due to significant challenges in information integration between construction suppliers and clients, existing configurator systems often lack crucial usability and supply chain information, presenting barriers to wider adoption among residential communities, especially in single-family residence development that requires a high degree of customization. To address this challenge in the design and construction supply chain, this study presents a lightweight cloud-based modular home configuration methodology as a robust unified platform solution to integrate parametric design options with a certified kit-of-parts library to meet local design codes. The configurator prototype developed under this framework seamlessly integrates essential design and supply chain information by leveraging (1) a generative layout design with pre-approved blueprints, (2) a knowledge-based recommender system to link the design process with certified material catalogs, and (3) a user-friendly web interface to present possible designs. The implementation of a single-family housing design adhering to the building codes in the British Columbia Province of Canada illustrates the benefits of the proposed configurator functionalities and efficient supplier data integration. Lightweight and automated, the proposed configurator has substantial potential to be scaled and adopted across different communities.

Optimizing Mobile Vision Transformers for Land Cover Classification

Image classification in remote sensing and geographic information system (GIS) data containing various land cover classes is essential for efficient and sustainable land use estimation and other tasks like object detection, localization, and segmentation. Deep learning (DL) techniques have shown tremendous potential in the GIS domain. While convolutional neural networks (CNNs) have dominated image analysis, transformers have proven to be a unifying solution for several AI-based processing pipelines. Vision transformers (ViTs) can have comparable and, in some cases, better accuracy than a CNN. However, they suffer from a significant drawback associated with the excessive use of training parameters. Using trainable parameters generously can have multiple advantages ranging from addressing model scalability to explainability. This can have a significant impact on model deployment in edge devices with limited resources, such as drones. In this research, we explore, without using pre-trained weights, how the inherent structure of vision transformers behaves with custom modifications. To verify our proposed approach, these architectures are trained on multiple land cover datasets. Experiments reveal that a combination of lightweight convolutional layers, including ShuffleNet, along with depthwise separable convolutions and average pooling can reduce the trainable parameters by 17.85% and yet achieve higher accuracy than the base mobile vision transformer (MViT). It is also observed that utilizing a combination of convolution layers along with multi-headed self-attention layers in MViT variants provides better performance for capturing local and global features, unlike the standalone ViT architecture, which utilizes almost 95% more parameters than the proposed MViT variant.

On the Boundary between the Exercise of Rights and Property Crimes

In modern rule-of-law societies, the awareness of rights is intrinsic to individuals. Despite having legitimate purposes, the exercise of rights also has lawful boundaries. In the context of criminal law, discussing the exercise of rights pertains to cases of abuse of rights through property crimes, including statutory and inherent rights. Judicial practice often leans towards exoneration in such cases, but a trend towards criminalization has emerged in recent years, necessitating a scientifically unified solution from criminal law theory. To achieve effective exoneration, it is crucial to precisely define the scope and types of rights exercised, targeting the different characteristics of ownership realization and claim realization. By clarifying the protected legal interests of property crimes and exploring the relationship between criminal and civil law, the path of excluding unlawfulness should be adopted for exoneration, decriminalizing situations with a legitimate rights basis where the means are necessary and proportionate. In cases where both the constituent elements and unlawfulness are satisfied, the presence or absence of an intent to unlawfully possess should be examined through the manifestation of claim realization. When the actor has an intent to unlawfully possess, it should be recognized as a property crime.

The European Vocational Education and Training System: Towards a Unified Approach

The European vocational education and training system exhibits significant diversity, with each country implementing its unique approach. While a unified European solution is desirable, a lack of comprehensive knowledge in this area hinders the development of policies that could establish a basic model applicable to all countries, albeit with necessary variations due to socio-economic nuances. European policymakers and stakeholders recognize the importance of vocational education and training, including formats like Dual School and Work-based Learning. Nations have committed to common targets in their education and training strategies, driven by globalized competition and the need for professional workers skilled in new technologies. Nevertheless, with the support of established systems, this European solution shows great potential for seamless operation and effectiveness. A successful European strategy would take into account the advancements in technology, the unique regional and national characteristics of the workforce in terms of traditional skills and competencies, and notably, the available natural resources. These resources have become a crucial factor in industrial efficiency and competitiveness. Implementation of dual school models varies at the European level, influenced by national strategies shaped by local factors. Some countries prioritize collaboration with stakeholders and regulatory frameworks to optimize professional training, while others, like Romania and Bulgaria, are just beginning to adopt dual school laws, with emerging examples of best practices. Romania, in particular, has largely followed the German model due to economic influences. In 2006, the European Council recognized vocational training through dual schools as an excellent investment, yielding high returns, surpassing initial costs, and delivering long-term efficiency and productivity benefits.