Efficient Framework Research Articles

Creep crack propagation using phase-field model within a multi-patch isogeometric framework

This work presents a straightforward and efficient isogeometric phase-field framework for predicting creep crack propagation in elasto-plastic materials. In contrast to conventional models that utilize viscous strain energy as the driving force, the proposed approach introduces an asymptotic degradation function for fracture toughness, effectively quantifying material damage resulting from creep strain. In this work, the geometry of the computational domain is exactly represented using locally refined non-uniform rational B-splines (LR NURBS), which also enable local mesh refinement near cracks to reduce computational time. In addition, multi-patch modeling approach is utilized for complex geometries. To avoid computational errors due to discontinuities in variables at coupling boundaries, the overall governing equations are derived based on Nitsche’s method. The accuracy of this approach is validated through comparisons with experimental and numerical results.

Preparation and characterization of Pd immobilized on the MIL-125-NH2 as an efficient recyclable metal-organic framework in the Suzuki-Miyaura reaction

In this study, an efficient heterogeneous palladium was developed by modifying the MIL-125-NH2 metal-organic framework bromoacetyl bromide and tetraethylenepentamine ligands. The resulting modified framework was then used as a platform for immobilizing Pd nanoparticles (NPs) to generate the Pd@MIL-125-NH-Ac-TEPA nanocomposite. FT-IR, FESEM, EDS, TEM, CHN, TGA, XRD, and ICP-OES were used to identify the structure of the nanocomposite. The characterization findings approve the formation of well-dispersed Pd nanoparticles with a size distribution of 9 to 23 nm. The Pd@MIL-125-NH-Ac-TEPA nanocomposite with 2.97% loading of Pd exhibited high efficiency in the Suzuki-Miyaura coupling reaction of arylboronic acids with various aryl and heteroaryl halides (chlorides, bromides, and iodides) containing electron-donor and electron-acceptor substituents. The coupling products were obtained in water/ethanol mixture (1:1) as solvent at 60°C for 30 min in 70-99% yields. The nanocatalyst can be recovered easily and reused for at least five consecutive runs without losing its activity significantly. The palladium leaching of the reused nanocatalyst was less than 1%. The results revealed that the introduced nanocatalyst has the potential for other organic transformations.

A U-Net based partial convolutional time-domain separation model to identify motor units from surface electromyographic signals in real time.

This study proposed a U-Net based partial convolutional time-domain model for a real-time high-density surface electromyography (HD-sEMG) decomposition. The model combines U-Net and a separation block containing partial convolution, aiming to efficiently identify motor units (MUs) without preprocessing. The proposed U-Net based network was trained by the HD-sEMG signals with innervation pulse trains (IPTs) labels, and the results are compared between different step sizes, noises, and model structures under the sliding time window with 120 sampling points. The U-Net based model got an accuracy greater than 94% under simulated signals and 85% under experimental signals, and identified more MUs than the structures based on convolutional neural network (CNN) and temporal convolutional network (TCN). The average latency of the U-Net based model is only 64ms (a window duration time plus the prediction time) under the step size 20 data in both types of signals, and can be generalized to new data at different signal-to-noise (SNR). The efficiency of the proposed model is significantly higher than traditional methods such as gCKC. Meanwhile, the accuracy of the proposed model was not significantly different from the gCKC. In addition, the performance of the network under different step sizes of the sliding time window was verified. The experimental results indicate that the U-Net based model provides an efficient framework for blind source separation (BSS) of EMG signals, expanding the application of EMG signals in neural interaction.

Safety-assured high-speed navigation for MAVs.

Micro air vehicles (MAVs) capable of high-speed autonomous navigation in unknown environments have the potential to improve applications like search and rescue and disaster relief, where timely and safe navigation is critical. However, achieving autonomous, safe, and high-speed MAV navigation faces systematic challenges, necessitating reduced vehicle weight and size for high-speed maneuvering, strong sensing capability for detecting obstacles at a distance, and advanced planning and control algorithms maximizing flight speed while ensuring obstacle avoidance. Here, we present the safety-assured high-speed aerial robot (SUPER), a compact MAV with a 280-millimeter wheelbase and a thrust-to-weight ratio greater than 5.0, enabling agile flight in cluttered environments. SUPER uses a lightweight three-dimensional light detection and ranging (LIDAR) sensor for accurate, long-range obstacle detection. To ensure high-speed flight while maintaining safety, we introduced an efficient planning framework that directly plans trajectories using LIDAR point clouds. In each replanning cycle, two trajectories were generated: one in known free spaces to ensure safety and another in both known and unknown spaces to maximize speed. Compared with baseline methods, this framework reduced failure rates by 35.9 times while flying faster and with half the planning time. In real-world tests, SUPER achieved autonomous flights at speeds exceeding 20 meters per second, successfully avoiding thin obstacles and navigating narrow spaces. SUPER represents a milestone in autonomous MAV systems, bridging the gap from laboratory research to real-world applications.

Applications of lean principles in the operations of architectural and engineering design firms: an exploratory study in Singapore

Purpose The Toyota Way was developed as an efficiency framework to streamline Toyota Motor Corporation’s operational procedures. The collection of Toyota Way principles is often termed “lean”. Over the last few years, nonmanufacturing organisations have begun to recognise the value and applicability of lean methods for their operations. Architectural and engineering design (AED) firms are no exception. This study aims to explore the perspectives of employees at AED firms on the implementation of lean production principles (LPPs) into their operations. Due to the limited resources available to facilitate our measurement of employees’ views regarding the adoption of lean approaches, this study adopts a more scoped approach to employees’ perceptions of these principles within AED firms.Design/methodology/approach Fifty-two respondents participated in the survey. The interviewees were from firm A and firm B, an architecture firm and an engineering firm, and were chosen to help us gain a more in-depth understanding of their views regarding the LPPs.Findings Our study indicates that AED offices were unfamiliar with the concepts of lean principles, even though they were aware of its importance in their daily operations. Despite their scepticism about the implementation of lean concepts, some lean principles are implemented in their own fields of work.Originality/value This study complements earlier work on lean design that investigated the use of a particular lean technique by looking at the lean principles where some are implemented to a large extent, and some are not.

Multi-objective design of multi-material truss lattices utilizing graph neural networks

The rapid advancements in additive manufacturing (AM) across different scales and material classes have enabled the creation of architected materials with highly tailored properties. Beyond geometric flexibility, multi-material AM further expands design possibilities by combining materials with distinct characteristics. While machine learning has recently shown great potential for the fast inverse design of lattice structures, its application has largely been limited to single-material systems. In this work, we propose a novel approach that incorporates material properties as edge features within the graph representation of multi-material truss lattices, utilizing graph neural networks (GNNs) to develop a fast and efficient inverse design framework. We validate this framework by designing lattices with tunable thermal expansion and stiffness properties, showcasing its ability to explore a broad and flexible design space. We showcase the framework’s inverse design capabilities for both single and multi-objective optimization tasks and assess its limitations. Additionally, we demonstrate the superior capacity of GNNs in capturing structure-property relationships for multi-material systems. We anticipate that the continued advancement of GNN-assisted inverse design will play a key role in unlocking the full potential of multi-material truss lattices.

Efficient Estimation for Longitudinal Networks via Adaptive Merging

Longitudinal networks consist of sequences of temporal edges among multiple nodes, where the temporal edges are observed in real-time. They have become ubiquitous with the rise of online social platforms and e-commerce, but largely under-investigated in the literature. In this paper, we propose an efficient estimation framework for longitudinal networks, leveraging strengths of adaptive network merging, tensor decomposition, and point processes. It merges neighboring sparse networks so as to enlarge the number of observed edges and reduce estimation variance, whereas the estimation bias introduced by network merging is controlled by exploiting local temporal structures for adaptive network neighborhood. A projected gradient descent algorithm is proposed to facilitate estimation, where the upper bound of the estimation error in each iteration is established. A thorough analysis is conducted to quantify the asymptotic behavior of the proposed method, which shows that it can significantly reduce the estimation error and also provides a guideline for network merging under various scenarios. We further demonstrate the advantage of the proposed method through extensive numerical experiments on synthetic datasets and a militarized interstate dispute dataset.

Lightweight Deep Learning Framework for Accurate Detection of Sports-Related Bone Fractures

Background/Objectives: Sports-related bone fractures are a common challenge in sports medicine, requiring accurate and timely diagnosis to prevent long-term complications and enable effective treatment. Conventional diagnostic methods often rely on manual interpretation, which is prone to errors and inefficiencies, particularly for subtle and localized fractures. This study aims to develop a lightweight and efficient deep learning-based framework to improve the accuracy and computational efficiency of fracture detection, tailored to the needs of sports medicine. Methods: We proposed a novel fracture detection framework based on the DenseNet121 architecture, incorporating modifications to the initial convolutional block and final layers for optimized feature extraction. Additionally, a Canny edge detector was integrated to enhance the model ability to detect localized structural discontinuities. A custom-curated dataset of radiographic images focused on common sports-related fractures was used, with preprocessing techniques such as contrast enhancement, normalization, and data augmentation applied to ensure robust model performance. The model was evaluated against state-of-the-art methods using metrics such as accuracy, recall, precision, and computational complexity. Results: The proposed model achieved a state-of-the-art accuracy of 90.3%, surpassing benchmarks like ResNet-50, VGG-16, and EfficientNet-B0. It demonstrated superior sensitivity (recall: 0.89) and specificity (precision: 0.875) while maintaining the lowest computational complexity (FLOPs: 0.54 G, Params: 14.78 M). These results highlight its suitability for real-time clinical deployment. Conclusions: The proposed lightweight framework offers a scalable, accurate, and efficient solution for fracture detection, addressing critical challenges in sports medicine. By enabling rapid and reliable diagnostics, it has the potential to improve clinical workflows and outcomes for athletes. Future work will focus on expanding the model applications to other imaging modalities and fracture types.

Robust Access Control for Secure IoT Outsourcing with Leakage Resilience

The Internet of Things (IoT) has revolutionized various industries by enabling seamless connectivity and data exchange among devices. However, the security and privacy of outsourced IoT data remain critical challenges, especially given the resource constraints of IoT devices. This paper proposes a robust and leakage-resilient access control scheme based on Attribute-Based Encryption (ABE) with partial decryption outsourcing. The proposed scheme minimizes computational overhead on IoT devices by offloading intensive decryption tasks to the cloud, while ensuring resilience against master secret key leakage, side-channel attacks, and other common security threats. Comprehensive security analysis demonstrates the scheme’s robustness under standard cryptographic assumptions, and performance evaluations show significant improvements in decryption efficiency, scalability, and computational performance compared to existing solutions. The proposed scheme offers a scalable, efficient, and secure access control framework, making it highly suitable for real-world IoT deployments across domains such as smart healthcare, industrial IoT, and smart cities.

Solving a North-type energy balance model using boundary integral methods

Abstract. Simplified climate models, such as energy balance models (EBMs), are useful conceptual tools, in part because their reduced complexity often allows for studies using analytical methods. In this paper, we solve a North-type EBM using a boundary integral method (BIM). The North-type EBM is a diffusive, one-dimensional EBM with a nonlinear albedo feedback mechanism. We discuss this approach in light of existing analytical techniques for this type of equation. Subsequently, we test the proposed method by solving multiple North-type EBMs with a zonally symmetric continent featuring an altered ice-albedo feedback dynamic. We demonstrate that the introduction of a continent results in new equilibrium states characterized by multiple ice edges and ice belts. Furthermore, we show that the BIM serves as an efficient framework for handling unconventional ice distributions and model configurations for North-type EBMs.

Management information systems, incentive mechanism, and agility: Basis for efficiency framework for logistics companies

Management information systems, incentive mechanism, and agility: Basis for efficiency framework for logistics companies

Optimization of Reverse Logistics Network for Electric Vehicle Used Battery under Direct Collection Mode of Manufacturer Based on New Energy Low-carbon Background

As the concept of low-carbon travel gradually takes root in the public's mind, the production and sales of electric vehicles are steadily climbing, and due to the limited service life of electric vehicle power batteries, they will be gradually eliminated and replaced. Enhancing the recycling infrastructure for used electric vehicle batteries and developing a systematic and efficient reverse logistics framework is crucial for advancing the sustainability, cost-effectiveness, and operational efficiency within the electric vehicle sector. This paper firstly analyses the direct collection battery recycling mode of the manufacturer, and on this basis, constructs the optimization model of the reverse logistics network of used batteries, as well as the dual objective optimisation model of total network cost minimisation and carbon emission minimisation. Finally, based on relevant data from C Electric Vehicle Manufacturing Company, an example analysis is carried out to derive its optimal solution for the reverse logistics network of used batteries for electric vehicles in Beijing. The research in this paper has a positive effect on reducing the reverse logistics costs of related enterprises and reducing environmental pollution, thus helping to realize the green ecological development of the electric vehicles industry.

Simultaneous Optimization of Hydrogen Network with Pressure Swing Absorption Based on Evolutionary Response Surface Method

The simultaneous optimization of complex process units and hydrogen networks is a significant challenge in refinery hydrogen network integration. To address this, an evolutionary response surface-based collaborative optimization method is proposed, enabling the concurrent optimization of pressure swing adsorption (PSA) and the hydrogen network. This method develops a mechanistic model for PSA and alternates between random sampling and evolutionary response surface-based hydrogen network optimization to obtain diverse sampling points and potential optimal solutions. The PSA mechanistic model is then used to compute the accurate output parameters for the sampled points, and these parameters are incorporated into the hydrogen network optimization to obtain precise objective function values. An efficient optimization framework is presented to streamline the process. The proposed method is applied to a refinery hydrogen network integration case study, comprehensively considering both PSA costs and hydrogen utility costs. The results demonstrate that the method is computationally efficient and effectively reduces the refinery’s total annual costs. The accuracy of the optimization results is significantly improved compared to traditional methods, providing an effective solution for the collaborative optimization of the refinery hydrogen network and PSA.

TinySpiking: a lightweight and efficient python framework for unsupervised learning spiking neural networks

Abstract Neural computation frameworks are essential for advancing computational neuroscience and artificial intelligence, offering a robust platform for simulating intricate brain-like processes and fostering the growth of intelligent systems. This study presents TinySpiking, a novel, lightweight, and energy-efficient Python framework designed for the simulation and learning of Spiking Neural Networks (SNNs). Unlike traditional frameworks, TinySpiking does not depend on third-party libraries, thereby reducing computational overhead and energy consumption. The framework's innovation is that it implements unsupervised learning through Spike-Time Dependent Plasticity (STDP), a biologically inspired learning rule that adjusts synaptic weights based on the precise timing of neuronal spikes. This mechanism is crucial for constructing complex neural architectures and effectively processing spatio-temporal data, which is vital for the dynamic and intricate demands of real-world data analysis. Demonstrating the practical utility of TinySpiking, we applied it to image reconstruction tasks using the MNIST and landmark datasets. The results not only validate the network's ability to autonomously identify and enhance key visual features without external supervision but also highlight its efficiency in learning from data, mirroring the adaptability of biological neural systems. In conclusion, TinySpiking's innovative, lightweight design and its proven effectiveness in unsupervised learning tasks make it a standout computational tool for the fields of computational neuroscience and machine learning. Its low time consumption, biological plausibility, and independence from third-party libraries position it as a compelling platform for future research and applications, promising to drive advancements in unsupervised learning and intelligent system development.

The Influence of Political Factors on Economic Growth: a Study of the Interconnection Between Governance Spheres in a Global Context

Objective: The purpose is to study the interaction between global political factors and economic progress and to create a model that illustrates the interaction between political and economic development factors. Theoretical Framework: This article explores how policy decisions at different administrative levels interact with economic dynamics at the national and regional levels. Method: The study comprehensive review of academic sources, publications, reports, and statistics, and used correlation regression and statistical methods. Results and Discussion: The research ultimately revealed a critical revelation: political factors play a vital role in economic progress, shaping the investment environment, maintaining macroeconomic stability, providing human capital resources, promoting infrastructure development, and creating efficient institutional framework conditions. Research Implications: By analysing specific cases, we examine the impact of the political context on the investment environment, market regulation, trade relations and other economic aspects. The key factors linking the political sphere and economic development are highlighted, as well as their importance in developing effective management strategies in the contemporary environment. Originality/Value: Effective policies that promote economic growth can bring prosperity and improve citizens' living standards, highlighting the vital link between policy decisions and economic outcomes.

An efficient framework based on local multi-representatives and noise-robust synthetic example generation for self-labeled semi-supervised classification.

While self-labeled methods can exploit unlabeled and labeled instances to train classifiers, they are also restricted by the labeled instance number and distribution. SEG-SSC, k-means-SSC, LC-SSC, and LCSEG-SSC are sophisticated solutions for overcoming the above restrictions. However, when some classes overlap, they suffer from the following challenging technical defects: (a) they fail to effectively improve the labeled instance distribution by identifying a single local representative in a cluster; (b) they have a low accuracy or a high degree of manual interference for predicting identified unlabeled local representatives; and (c) they fail to effectively improve the labeled instance number due to noise generation. To address the above issues, a framework based on local multi-representatives and noise-robust synthetic example generation (LMR-NRSEG-SSC) is proposed for self-labeled semi-supervised classification. First, a newly proposed local multi-representatives search algorithm with multi-granularity ideas is used to partition labeled and unlabeled instances into independent clusters and identify unlabeled local multi-representatives in each cluster. Second, a newly proposed divide-and-conquer self-labeling is used to predict unlabeled local multi-representatives, with the goal of improving the labeled instance distribution. Third, a newly proposed noise-robust oversampling technique based on local multi-representatives is used to create safe labeled synthetic instances with little noise, with the goal of improving the labeled instance number. Finally, almost any algorithm of the self-labeled methods can be performed on improved labeled and unlabeled instances to train classifiers with effects. Experiments demonstrated that LMR-NRSEG-SSC outperformed 7 sophisticated self-labeled frameworks in improving 2 advanced self-labeled methods on extensive benchmark datasets.

Advanced Data Engineering and Machine Learning Approaches for Accurate Yield Prediction in Semiconductor Manufacturing Processes.

In semiconductor manufacturing, yield prediction is a critical area where advancements in data engineering and machine learning can significantly impact production efficiency and cost-effectiveness. This paper explores the integration of data engineering pipelines with machine learning models to improve yield prediction. Using a scalable and efficient framework, we demonstrate how leveraging Databricks for data processing and model training enhances the prediction accuracy and provides actionable insights for process optimization. Keywords: Data Engineering, Machine Learning, Semiconductor Manufacturing, Yield Prediction, Databricks

Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm

In response to the structural changes of tomato seedlings, traditional image techniques are difficult to accurately quantify key morphological parameters, such as leaf area, internode length, and mutual occlusion between organs. Therefore, this paper proposes a tomato point cloud stem and leaf segmentation framework based on Elite Strategy-based Improved Red-billed Blue Magpie Optimization (ES-RBMO) Algorithm. The framework uses a four-layer Convolutional Neural Network (CNN) for stem and leaf segmentation by incorporating an improved swarm intelligence algorithm with an accuracy of 0.965. Four key phenotypic parameters of the plant were extracted. The phenotypic parameters of plant height, stem thickness, leaf area and leaf inclination were analyzed by comparing the values extracted by manual measurements with the values extracted by the 3D point cloud technique. The results showed that the coefficients of determination (R2) for these parameters were 0.932, 0.741, 0.938 and 0.935, respectively, indicating high correlation. The root mean square error (RMSE) was 0.511, 0.135, 0.989 and 3.628, reflecting the level of error between the measured and extracted values. The absolute percentage errors (APE) were 1.970, 4.299, 4.365 and 5.531, which further quantified the measurement accuracy. In this study, an efficient and adaptive intelligent optimization framework was constructed, which is capable of optimizing data processing strategies to achieve efficient and accurate processing of tomato point cloud data. This study provides a new technical tool for plant phenotyping and helps to improve the intelligent management in agricultural production.

Decomposition-Aware Framework for Probabilistic and Flexible Time Series Forecasting in Aerospace Electronic Systems

Degradation prediction for aerospace electronic systems plays a crucial role in maintenance work. This paper proposes a concise and efficient framework for multivariate time series forecasting that is capable of handling diverse sequence representations through a Channel-Independent (CI) strategy. This framework integrates a decomposition-aware layer to effectively separate and fuse global trends and local variations and a temporal attention module to capture temporal dependencies dynamically. This design enables the model to process multiple distinct sequences independently while maintaining the flexibility to learn shared patterns across channels. Additionally, the framework incorporates probabilistic distribution forecasting using likelihood functions, addressing the dynamic variations and uncertainty in time series data. The experimental results on multiple real-world datasets validate the framework’s effectiveness, demonstrating its robustness and adaptability in handling diverse sequences across various application scenarios.

Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces.

This paper presents a comprehensive approach to evaluating the ability of multi-legged robots to traverse confined and geometrically complex unstructured environments. The proposed approach utilizes advanced point cloud processing techniques integrating voxel-filtered cloud, boundary and mesh generation, and dynamic traversability analysis to enhance the robot's terrain perception and navigation. The proposed framework was validated through rigorous simulation and experimental testing with humanoid robots, showcasing the potential of the proposed approach for use in applications/environments characterized by complex environmental features (navigation inside collapsed buildings). The results demonstrate that the proposed framework provides the robot with an enhanced capability to perceive and interpret its environment and adapt to dynamic environment changes. This paper contributes to the advancement of robotic navigation and path-planning systems by providing a scalable and efficient framework for environment analysis. The integration of various point cloud processing techniques into a single architecture not only improves computational efficiency but also enhances the robot's interaction with its environment, making it more capable of operating in complex, hazardous, unstructured settings.