Search Results (552)

Reverse engineering (RE) has been increasingly adopted in metal manufacturing to digitize legacy parts, connect “as-is” geometry to mechanical performance, and enable agile repair and remanufacturing. This review consolidates scan-to-simulation workflows that transform 3D measurement data (optical/laser scanning and X-ray computed tomography) into simulation-ready models for structural assessment and manufacturing decisions, with an explicit focus on sustainability. Key steps are reviewed, from acquisition planning and metrological error sources to point-cloud/mesh processing, CAD/feature reconstruction, and geometry preparation for finite-element analysis (watertightness, defeaturing, meshing strategies, and boundary condition transfer). Special attention is given to uncertainty quantification and the propagation of geometric deviations into stress, stiffness, and fatigue predictions, enabling robust accept/reject and repair/replace choices. Sustainability is addressed through a lightweight reporting framework covering material losses, energy use, rework, and lead time across the scan–model–simulate–manufacture chain, clarifying when digitalization reduces scrap and over-processing. Industrial use cases are discussed for high-value metal components (e.g., molds, turbine blades, and marine/energy parts) where scan-informed simulation supports faster and more reliable decision making. Open challenges are summarized, including benchmark datasets, standardized reporting, automation of feature recognition, and integration with repair process simulation (DED/WAAM) and life-cycle metrics. A checklist is proposed to improve reproducibility and comparability across RE studies. Full article

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments. Full article

Effectively fusing the rich semantic information from camera images with the high-precision geometric measurements provided by LiDAR point clouds is a key challenge in mobile robot environmental perception. To address this problem, this paper proposes a highly extensible instance-aware fusion framework designed to achieve efficient alignment and unified modeling of heterogeneous sensory data. The proposed approach adopts a modular processing pipeline. First, semantic instance masks are extracted from RGB images using an instance segmentation network, and a projection mechanism is employed to establish spatial correspondences between image pixels and LiDAR point cloud measurements. Subsequently, three-dimensional bounding boxes are reconstructed through point cloud clustering and geometric fitting, and a reprojection-based validation mechanism is introduced to ensure consistency across modalities. Building upon this representation, the system integrates a data association module with a Kalman filter-based state estimator to form a closed-loop multi-object tracking framework. Experimental results on the KITTI dataset demonstrate that the proposed system achieves strong 2D and 3D detection performance across different difficulty levels. In multi-object tracking evaluation, the method attains a MOTA score of 47.8 and an IDF1 score of 71.93, validating the stability of the association strategy and the continuity of object trajectories in complex scenes. Furthermore, real-world experiments on a mobile computing platform show an average end-to-end latency of only 173.9 ms, while ablation studies further confirm the effectiveness of individual system components. Overall, the proposed framework exhibits strong performance in terms of geometric reconstruction accuracy and tracking robustness, and its lightweight design and low latency satisfy the stringent requirements of practical robotic deployment. Full article

Research on extracting building from airborne point clouds is abundant, yet discussions regarding scenarios where vegetation and building structures are closely intertwined with similar height in rural areas remain relatively scarce. This thesis adopts a region representative of typical rural building features in China as an experimental site to conduct research on building classification procedures from airborne point clouds. Firstly, the multi-level grid size is dynamically determined through slope analysis to creatively segment and recognize terrain type, then differentiated filtering parameters are applied to various terrains to fully extract ground points, providing a ground reference for building classification. Secondly, the selection of building Region of Interest is conducted by multiple geometric feature differences between building and other objects based on watershed segmentation results, which eliminates interference from non-building points, significantly reducing redundant and unnecessary mathematical computation. Finally, refined building classification is achieved based on multiple morphological differences between buildings and other objects. The experimental results show that the precision, recall, and F1 of both datasets exceeded 93.37%, 97.05%, and 95.17%, respectively. The average precision, recall, and F1 reached 94.02%, 97.20%, and 95.58%, respectively. This method demonstrates successful building classification in rural areas, showing strong adaptability and practicality for the extraction of various building data. Full article

Script-based computer-aided design tools offer accessible and customizable environments, but their broader adoption is limited by the cognitive and computational difficulty of describing curved, irregular, or free-form geometries through code. This study addresses this challenge by contributing a unified, open-source framework that enables concept-to-model transformation through 2D point-based representations. Unlike previous ad hoc methods, this framework systematically integrates an interactive point-cloud modeling layer with modular systems for curve construction, point generation, transformation, sequencing, and formatting, together with script-based rendering functions. This framework allows users to generate geometrically valid models without navigating the heavy geometric calculations, strict syntax requirements, and debugging demands typical of script-based workflows. Structured case studies demonstrate the underlying workflow across mechanical, artistic, and handcrafted forms, contributing empirical evidence of its applicability to diverse tasks ranging from mechanical component modeling to cultural heritage digitization and reverse engineering. Comparative analysis demonstrates that the framework reduces user-facing code volume by over 97% compared to traditional scripting and provides a lightweight, noise-free alternative to traditional hardware-based reverse engineering by allowing users to define clean geometry from the outset. The findings confirm that the framework generates fabrication-ready outputs—including volumetric models and vector representations—suitable for various manufacturing contexts. All systems and rendering functions are made publicly available, enabling the entire pipeline to be performed using free tools. By establishing a practical and reproducible basis for point-based modeling, this study contributes to the advancement of computational design practice and supports the wider adoption of script-based design workflows. Full article

The financial services sector is experiencing unprecedented transformation through the adoption of virtualization technologies, encompassing cloud computing and edge computing digitalization initiatives that fundamentally alter operational paradigms and competitive dynamics within the industry. This systematic literature review employed a comprehensive methodology, analyzing peer-reviewed articles, systematic reviews, and industry reports published between 2016 and 2025 across three primary technological domains, utilizing thematic content analysis to synthesize findings and identify key implementation patterns, performance outcomes, and emerging challenges. The analysis reveals consistent evidence of positive long-term performance outcomes from virtualization technology adoption, including average transaction processing time reductions of 69% through edge computing implementations, substantial operational cost savings and efficiency improvements through cloud computing adoption, while simultaneously identifying critical challenges related to regulatory compliance, security management, and organizational transformation requirements. Virtualization technology offers transformative potential for financial services through improved operational efficiency, enhanced customer experience, and competitive advantage creation, though successful implementation requires sophisticated approaches to standardization, regulatory compliance, and change management, with future research needed to develop integrative frameworks addressing technology convergence and emerging applications in decentralized finance and digital currency systems. Full article

This study proposes an integrated agility assessment system that combines Millimeter-Wave (MMW) radar, Ultra-Wideband (UWB) ranging, and Mixed Reality (MR) technologies to quantitatively evaluate athlete performance with high accuracy. The system utilizes the fine motion-tracking capability of MMW radar and the immersive real-time visualization provided by MR to ensure reliable operation under low-light conditions and multi-object occlusion, thereby enabling precise measurement of mobility, reaction time, and movement distance. To address the challenge of player identification during doubles testing, a one-to-one UWB configuration was adopted, in which each base station was paired with a wearable tag to distinguish individual athletes. UWB identification was not required during single-player tests. The experimental protocol included three specialized agility assessments—Table Tennis Agility Test I (TTAT I), Table Tennis Doubles Agility Test II (TTAT II), and the Agility T-Test (ATT)—conducted with more than 80 table tennis players of different technical levels (80% male and 20% female). Each athlete completed two sets of two trials to ensure measurement consistency and data stability. Experimental results demonstrated that the proposed system effectively captured displacement trajectories, movement speed, and reaction time. The MMW radar achieved an average measurement error of less than 10%, and the overall classification model attained an accuracy of 91%, confirming the reliability and robustness of the integrated sensing pipeline. Beyond local storage and MR-based live visualization, the system also supports cloud-based data uploading for graphical analysis and enables MR content to be mirrored on connected computer displays. This feature allows coaches to monitor performance in real time and provide immediate feedback. By integrating the environmental adaptability of MMW radar, the real-time visualization capability of MR, UWB-assisted athlete identification, and cloud-based data management, the proposed system demonstrates strong potential for professional sports training, technical diagnostics, and tactical optimization. It delivers timely and accurate performance metrics and contributes to the advancement of data-driven sports science applications. Full article

The safe operation of rail transit systems relies heavily on the efficient and reliable maintenance of their equipment, as any malfunction or abnormal operation may pose serious risks to transportation safety. Traditional manual inspection methods are often characterized by high costs, low efficiency, and susceptibility to human error. To address these limitations, this paper presents a rail transit equipment inspection system based on Edge Intelligence (EI) and 5G technology. The proposed system adopts a cloud–edge–end collaborative architecture that integrates Computer Vision (CV) techniques to automate inspection tasks; specifically, a fine-tuned YOLOv8 model is employed for object detection of personnel and equipment, while a ResNet-18 network is utilized for equipment status classification. By implementing an ETSI MEC-compliant framework on edge servers (NVIDIA Jetson AGX Orin), the system enhances data processing efficiency and network performance, while further strengthening security through the use of a 5G private network that isolates critical infrastructure data from the public internet, and improving robustness via distributed edge nodes that eliminate single points of failure. The proposed solution has been deployed and evaluated in real-world scenarios on Beijing Metro Line 6. Experimental results demonstrate that the YOLOv8 model achieves a mean Average Precision (mAP@0.5) of 92.7% ± 0.4% for equipment detection, and the ResNet-18 classifier attains 95.8% ± 0.3% accuracy in distinguishing normal and abnormal statuses. Compared with a cloud-centric architecture, the EI-based system reduces the average end-to-end latency for anomaly detection tasks by 45% (28.5 ms vs. 52.1 ms) and significantly lowers daily bandwidth consumption by approximately 98.1% (from 40.0 GB to 0.76 GB) through an event-triggered evidence upload strategy involving images and short video clips, highlighting its superior real-time performance, security, robustness, and bandwidth efficiency. Full article

The expansion of digital technologies has led to a digital transformation of the economy and society. E-commerce, driven by new digital technologies and the restrictions during the COVID-19 pandemic, has increased its share in the overall trade of goods and services, influencing economic growth. This article examines the impact of emerging digital technologies such as artificial intelligence (AI), big data, the Internet of Things (IoT), and cloud computing (CC) on the e-commerce sector. Within this study, we explore the digital transformation of the EU economy, focusing on the impact of artificial intelligence (AI), big data, the Internet of Things (IoT), and cloud computing (CC) on e-commerce development and sustainable economic performance (GDP). The methodology employs a multilayer perceptron (MLP) neural network to model the non-linear, predictive relationship between digital adoption and e-commerce. Subsequently, hierarchical cluster analysis groups countries by digital maturity. The findings confirm that digital adoption is a significant and non-linear predictor of e-commerce, while the clustering reveals a pronounced regional heterogeneity in the capacity to translate technology into macro-economic performance. The research results show that by understanding and adopting these technologies, companies in the e-commerce field can gain a competitive advantage and better meet customer requirements and expectations. This adoption can lead to improved personalization of the shopping experience, increased operational efficiency, and enhanced customer satisfaction, ultimately resulting in better and sustainable economic performance. Full article

Dynamic Searchable Encryption (DSE) is essential for enabling confidential search operations over encrypted data in cloud computing. However, all existing single-server DSE schemes are vulnerable to Keyword Pair Result Pattern (KPRP) leakage and fail to simultaneously achieve forward and backward security. To address these challenges, this paper proposes a conjunctive keyword DSE scheme based on a dual-server architecture (DS-CKDSE). By integrating a full binary tree with an Indistinguishable Bloom Filter (IBF), the proposed scheme adopts a secure index: The leaf nodes store the keywords and the associated file identifier, while the information of non-leaf nodes is encoded within the IBF. A random state update mechanism, a dual-state array for each keyword and the timestamp trapdoor designs jointly enable robust forward and backward security while supporting efficient conjunctive queries. The dual-server architecture mitigates KPRP leakage by separating secure index storage from trapdoor verification. The security analysis shows that the new scheme satisfies adaptive security under a defined leakage function. Finally, the performance of the proposed scheme is evaluated through experiments, and the results demonstrate that the new scheme enjoys high efficiency in both update and search operations. Full article

With the growing adoption of hybrid cloud storage, the identification and protection of sensitive information within large-scale unstructured data has become increasingly challenging. Traditional rule-based and machine learning approaches have limitations in context-aware sensitive data classification and large-scale processing. In this work, a novel framework named LLM-SPSS, implementing a secure and confidential storage layout for distributed hybrid clouds through a fine-tuned XLM-R Base model and multi-dimensional data partitioning, is proposed. First, a fine-tuned XLM-R Base model with adaptive prompt tuning is employed to enable context-aware sensitive data classification and improve detection accuracy. In addition, MapReduce-based distributed processing allows the framework to scale efficiently to large datasets, thus enhancing computational efficiency. Furthermore, a multi-dimensional cloud partitioning scheme provides secure and fine-grained storage isolation within hybrid cloud environments. Experimental results demonstrate that LLM-SPSS achieves an F1-score of 99.66% and yields a 6.3× speed-up over the non-distributed baseline, outperforming traditional rule-based (F1 68.27%), conventional machine learning (SVM F1 98.32%, Random Forest F1 95.79%), and other LLM-based approaches (DePrompt F1 95.95%) and effectively balancing high accuracy with computational efficiency. Full article

Digital transformation (DT) has become a strategic imperative for sustaining competitiveness in global supply chains. This study situates DT within the frameworks of Dynamic Capabilities Theory (DCT) and Structural Contingency Theory (SCT) to explain how leadership, culture, and institutional contexts shape adoption pathways in Brazil and Germany. Using a sequential mixed-methods approach, it combines a tertiary literature review with expert elicitation and Interpretive Structural Modelling (ISM), supported by DEMATEL and MICMAC analyses, to uncover hierarchical relationships among barriers and foundational technologies—Big Data Analytics (BDA), the Internet of Things (IoT), and cloud computing. The results reveal distinct causal structures: in Germany, workforce deficits and economic-risk perceptions act as root barriers that constrain managerial and cultural adaptation; in Brazil, executive sponsorship drives workforce capability and analytics development, activating subsequent IoT and cloud adoption. Across both contexts, BDA consistently emerges as the foundational enabler, indicating a layered sequence of capability accumulation. The findings demonstrate that effective digital transformation depends on leadership-enabled alignment between organisational structure and environmental contingencies. This study contributes a comparative framework linking DCT’s dynamic routines with SCT’s structural fit, providing theoretical, methodological, and policy insights for context-sensitive digitalisation strategies. Full article

In order to address challenges in small object recognition for remote sensing imagery—including high model complexity, overfitting with small samples, and insufficient cross-scenario generalization—this study proposes CAGM-Seg, a lightweight recognition model integrating multi-attention mechanisms. The model systematically enhances the U-Net architecture: First, the encoder adopts a pre-trained MobileNetV3-Large as the backbone network, incorporating a coordinate attention mechanism to strengthen spatial localization of min targets. Second, an attention gating module is introduced in skip connections to achieve adaptive fusion of cross-level features. Finally, the decoder fully employs depthwise separable convolutions to significantly reduce model parameters. This design embodies a symmetry-aware philosophy, which is reflected in two aspects: the structural symmetry between the encoder and decoder facilitates multi-scale feature fusion, while the coordinate attention mechanism performs symmetric decomposition of spatial context (i.e., along height and width directions) to enhance the perception of geometrically regular small targets. Regarding training strategy, a hybrid loss function combining Dice Loss and Focal Loss, coupled with the AdamW optimizer, effectively enhances the model’s sensitivity to small objects while suppressing overfitting. Experimental results on the Xingtai black and odorous water body identification task demonstrate that CAGM-Seg outperforms comparison models in key metrics including precision (97.85%), recall (98.08%), and intersection-over-union (96.01%). Specifically, its intersection-over-union surpassed SegNeXt by 11.24 percentage points and PIDNet by 8.55 percentage points; its F1 score exceeded SegFormer by 2.51 percentage points. Regarding model efficiency, CAGM-Seg features a total of 3.489 million parameters, with 517,000 trainable parameters—approximately 80% fewer than the baseline U-Net—achieving a favorable balance between recognition accuracy and computational efficiency. Further cross-task validation demonstrates the model’s robust cross-scenario adaptability: it achieves 82.77% intersection-over-union and 90.57% F1 score in landslide detection, while maintaining 87.72% precision and 86.48% F1 score in cloud detection. The main contribution of this work is the effective resolution of key challenges in few-shot remote sensing small-object recognition—notably inadequate feature extraction and limited model generalization—via the strategic integration of multi-level attention mechanisms within a lightweight architecture. The resulting model, CAGM-Seg, establishes an innovative technical framework for real-time image interpretation under edge-computing constraints, demonstrating strong potential for practical deployment in environmental monitoring and disaster early warning systems. Full article

In today’s digital era, real-time applications rely heavily on cloud environments for computation, storage, and data retrieval. Data owners outsource sensitive information to cloud storage servers managed by service providers such as Google and Amazon, who are responsible for ensuring data confidentiality. Traditional cryptographic algorithms, though widely adopted, face challenges related to key management and computational complexity when implemented in the cloud. To overcome these limitations, this research proposes a Secure DNA Cryptosystem (SDNA) based on DNA molecular structures and biological processes. The proposed system generates encoding/decoding tables and encryption/decryption algorithms, using dynamically generated key files to secure communication between data owners and users in the cloud. The DNA-based cryptographic approach enhances data confidentiality, ensures faster computation, and increases resistance to cryptanalysis through dynamic key operations. The experimental results demonstrate the efficiency of the proposed system. For a character count of 16,384, the encryption and decryption times are 852 ms and 822 ms, respectively. Similarly, for a word count of 16,384, the encryption and decryption times are significantly reduced to 75 ms and 62 ms, respectively. These results highlight the superior computational performance and adaptability of the SDNA compared to conventional cryptographic schemes. Overall, performance and security analysis confirm that the proposed SDNA is computationally secure, faster, and flexible for implementation in cloud environments, offering a promising solution for real-time secure data storage and retrieval. Full article

Homomorphic encryption enables computations to be performed directly on encrypted data, ensuring data confidentiality even in untrusted or distributed environments. Although this approach provides strong theoretical security, its practical adoption remains limited due to high computational and memory requirements. This study presents a comparative evaluation of three representative homomorphic encryption paradigms: partially, somewhat, and fully homomorphic encryption. The implementations are based on the GMP library, Microsoft SEAL, and OpenFHE. The analysis examines encryption and decryption time, ciphertext expansion, and memory usage under various parameter configurations, including different polynomial modulus degrees. The goal is to provide a transparent and reproducible comparison that illustrates the practical differences among these approaches. The results highlight the trade-offs between security, efficiency, and numerical precision, identifying cases where lightweight schemes can achieve acceptable performance for latency-sensitive or resource-constrained applications. These findings offer practical guidance for deploying homomorphic encryption in secure cloud-based computation and other privacy-preserving environments. Full article