Local Framework Research Articles

Toward C-V2X Enabled Connected Transportation System: RSU-Based Cooperative Localization Framework for Autonomous Vehicles

Toward C-V2X Enabled Connected Transportation System: RSU-Based Cooperative Localization Framework for Autonomous Vehicles

Adaptive Bug Localization Framework for Precision-Driven Bug Localization in Software Engineering

Software development always looks for automated methods to improve productivity and accuracy in issue detection. The paper conducts a comparative examination of several machine-learning techniques to tackle the bug localization difficulty. Our study compared the performance of Logistic Regression (LR), Random Forest Classifier (RFC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), and Adaptive Bug Localization System (ABLS) on five dataset versions. The results demonstrate the superior performance of ensemble learning methods. The ABLS model regularly beats other models regarding F1 score, accuracy, and recall, indicating its strong potential for precise problem localization. The study highlights the necessity of continuously adapting models to tackle idea drift in dynamic datasets. Our research suggests a path for future endeavours involving improving feature engineering and integrating real-time online learning to sustain high performance in bug localization activities.

Diversification of income sources in the United Arab Emirates for the period 1975-2022 (An economic analytical study)

The research aims to benefit from the experience of the United Arab Emirates in diversifying its sources of income, by following up on the legislative base it has prepared for this change which it had planned for in its economic structure, the results that were reflected in the developments of the gross domestic product and the relative importance of the sectors that make up its oil and non-oil composition, the basket of oil and non-oil exports, and the reflection of this in the growth of the average per capita income. The researchers followed the inductive approach and the descriptive analytical method. Perhaps the most important thing they reached was that this country had built its development strategy within the framework of the outward orientation in transferring the country from the local framework to the global horizon, achieving a diversity in the components of its GDP and in its export basket. This was reflected in a noticeable increase in the average per capita income, and this could not have been achieved without the cohesion and harmony between the citizens of the country and the government.

CONODONT BIOSTRATIGRAPHY OF UPPER DEVONIAN TEPHRA BEARING DEPOSITS IN THE İSTANBUL-ZONGULDAK TERRANE, NW TURKEY: INSIGHTS ON THE KELLWASSER EVENTS

New evidence for a biostratigraphic assessment of the limestone succession comprising K‐bentonite levels exposed in the Yılanlı Formation of the Istanbul-Zonguldak Terrane are provided from conodonts at the Gavurpınarı quarry in Bartın area (NW Turkey). The succession depicts a shallow marine, nearshore facies setting that comprises rare and low diversity conodont associations mainly exemplified by the species of Ctenopolygnathus, Icriodus and Polygnathus. Conodont faunas from the lower part of the section are of late Frasnian age, including the taxa Icriodus subterminus Youngquist, 1947, Polygnathus aff. xylus Stauffer, 1940, Icriodus iowaensis iowaensis Youngquist & Peterson, 1947 and Ctenopolygnathus brevilaminus Branson & Mehl, 1934, and the upper part is assigned to early Famennian marked by the first appearance of Icriodus cornutus Sannemann, 1955. The local biostratigraphic framework of the Yılanlı Formation is correlated with the upper Frasnian Lower rhenana to the lower Famennian Middle triangularis standard conodont zonations. Considering the described species, the Frasnian-Famennian boundary corresponds to a slight change in conodont fauna and is assigned within the local Icriodus iowaensis iowaensis Zone. Accordingly, the novel K-bentonite age data potentially indicates the evidence for the Kellwasser events in northern Turkey, improving paleogeographic correlations of the Istanbul-Zonguldak Terrane with other terranes in Laurasia and Peri-Gondwana.

A dual-stage coverage path planning method for bathymetric survey using an AUV in graph-based SLAM framework considering positioning uncertainty

Autonomous underwater vehicles (AUVs) are becoming increasingly popular for seabed coverage survey tasks but suffer from a serious problem of positioning error accumulation, which leads to low path tracking accuracy and a low coverage rate of the target area. A dual-stage AUV path planning (DSPP) approach was proposed within a simultaneous localization and mapping (SLAM) framework for coverage bathymetric surveys. In the first stage, a global coverage path is designed in a lawnmower style. Crossed paths are added to generate periodic loop closures in SLAM, which can improve the efficiency of online replanning. In the second stage, with a probabilistic neural network (PNN) used for feature-rich bathymetric sub-map identification, an entropy-based online path replanning approach was implemented using 20 templates to adjust the global path to produce accurate loop closures for positioning error correction. Two bathymetric datasets were used to test the proposed DSPP method using a self-developed experimental simulation system. Experiments verified that DSPP can significantly improve the positioning accuracy and coverage rate with acceptably longer paths. In a 6000 m× 5000 marea,the DSPP approach reduced the positioning error by 76% compared with the pure dead reckoning method, and the coverage ratio reached 98.15%.

BO-GRNN: Machine Learning for Underwater Acoustic Source Localization

This article proposes a Bayesian optimization-based generalized regression neural network (BO-GRNN) framework for underwater source localization in shallow water environment. Specially, by leveraging kernel regression analysis, BO-GRNN fits a conditional probability density function using training and test samples to infer the source location of unseen test samples. Furthermore, BO-GRNN models the objective function as a Gaussian process (GP) based on observed samples, utilizing an acquisition function to sample around the best objective function values and high variance regions. By evaluating these samples and updating the GP iteratively, the optimal hyperparameters are identified, ultimately improving the training efficiency and localization performance of the GRNN. Simulation experiments and error analysis validate this framework’s effectiveness. Further validation using SWellEx-96 experimental data demonstrates the superior localization performance of this framework, even with limited data snapshots. This framework estimates the range and depth of underwater sources within a 10 km range, achieving mean absolute errors of less than 0.15 km for range and 0.2 m for depth. Compared to GRNN, this framework demonstrates a 5 times improvement in training efficiency. These advantages make BO-GRNN an efficient, accurate, and robust solution for underwater source localization.

Semantic Environment Atlas for Object-Goal Navigation

In this paper, we introduce the Semantic Environment Atlas (SEA), a novel mapping approach designed to enhance visual navigation capabilities of embodied agents. The SEA utilizes semantic graph maps that intricately delineate the relationships between places and objects, thereby enriching the navigational context. These maps are constructed from image observations and capture visual landmarks as sparsely encoded nodes within the environment. The SEA integrates multiple semantic maps from various environments, retaining a memory of place-object relationships, which proves invaluable for tasks such as visual localization and navigation. We developed navigation frameworks that effectively leverage the SEA, and we evaluated these frameworks through visual localization and object-goal navigation tasks. Our SEA-based localization framework significantly outperforms existing methods, accurately identifying locations from single query images. Experimental results in Habitat Savva et al. (2019)scenarios show that our method not only achieves a success rate of 39.0%—an improvement of 12.4% over the current state-of-the-art—but also maintains robustness under noisy odometry and actuation conditions, all while keeping computational costs low.

A framework for achieving urban sustainable development goals (SDGs): Evaluation and interaction

Sustainable development is a complex system involving multiple dimensions of society, economy, resources and environment. It is of great significance to evaluate the sustainable development level and explore the interactions among sustainable development goals (SDGs) for the promotion of sustainability. In present study, a localized urban sustainable development evaluation framework was first proposed. Then, taking Shandong Province as an example, the spatiotemporal variations and goal evenness of sustainability were investigated. Finally, synergies and trade-offs between SDGs were revealed. The results showed that the sustainable development level has improved slightly from 2011 to 2020 in Shandong Province, exhibiting a dual-core pattern with Jinan and Qingdao. Shandong Province performed relatively well in SDG 1 (no poverty), SDG 2 (zero hunger), SDG 9 (industry, innovation, and infrastructure), and SDG 11 (sustainable cities and communities). The unevenness existed in sustainable development, indicating that the development process varied from one goal to another and there were obvious differences among cities for the same goal. Relatively weak synergies and trade-offs relationships were mainly observed between SDGs, and the interactions were in a changing state. Analyzing the sustainable development level from multiple perspectives and promoting multi-goal coordinated development are vital for achieving urban sustainable development.

Human developmental biology - a global perspective.

In the companion Perspective 'Past and future of human developmental biology' (Hopwood, 2024), historian Nick Hopwood proposes that the field of human developmental biology has gone through periods of attention and neglect. Development invited researchers from the field to respond to this idea. In this article, published to coincide with the 10th anniversary of Development's 'From Stem Cells to Human Development' meeting, researchers from eight countries comment on how they believe their local legal, political, regulatory, societal and technological frameworks are influencing the field's trajectory.

Digital transformation, management support and work performance: Basis for local university development framework in China

Digital transformation, management support and work performance: Basis for local university development framework in China

RLI-SLAM: Fast Robust Ranging-LiDAR-Inertial Tightly-Coupled Localization and Mapping.

Simultaneous localization and mapping (SLAM) is an essential component for smart robot operations in unknown confined spaces such as indoors, tunnels and underground. This paper proposes a novel tightly-coupled ranging-LiDAR-inertial simultaneous localization and mapping framework, namely RLI-SLAM, which is designed to be high-accuracy, fast and robust in the long-term fast-motion scenario, and features two key innovations. The first one is tightly fusing the ultra-wideband (UWB) ranging and the inertial sensor to prevent the initial bias and long-term drift of the inertial sensor so that the point cloud distortion of the fast-moving LiDAR can be effectively compensated in real-time. This enables high-accuracy and robust state estimation in the long-term fast-motion scenario, even with a single ranging measurement. The second one is deploying an efficient loop closure detection module by using an incremental smoothing factor graph approach, which seamlessly integrates into the RLI-SLAM system, and enables high-precision mapping in a challenging environment. Extensive benchmark comparisons validate the superior accuracy of the proposed new state estimation and mapping framework over other state-of-the-art systems at a low computational complexity, even with a single ranging measurement and/or in a challenging environment.

Parallel implementation for real-time visual SLAM systems based on heterogeneous computing

Simultaneous localization and mapping has become rapidly developed and plays an indispensable role in intelligent vehicles. However, many state-of-the-art visual simultaneous localization and mapping (VSLAM) frameworks are very time-consuming both in front-end and back-end, especially for large-scale scenes. Nowadays, the increasingly popular use of graphics processors for general-purpose computing, and the progressively mature high-performance programming theory based on compute unified device architecture (CUDA) have given the possibility for large-scale VSLAM to solve the conflict between limited computing power and excessive computing tasks. The paper proposes a full-flow optimal parallelization scheme based on heterogeneous computing to speed up the time-consuming modules in VSLAM. Firstly, a parallel strategy for feature extraction and matching is designed to reduce the time consumption arising from multiple data transfers between devices. Secondly, a bundle adjustment method based solely on CUDA is developed. By fully optimizing memory scheduling and task allocation, a large increase in speed is achieved while maintaining accuracy. Besides, CUDA heterogeneous acceleration is fully utilized for tasks such as error computation and linear system construction in the VSLAM back-end to enhance the operation speed. Our proposed method is tested on numerous public datasets on both computer and embedded sides, respectively. A number of qualitative and quantitative experiments are performed to verify its superiority in terms of speed compared to other states-of-the-art.

Structural damage detection and localization via an unsupervised anomaly detection method

This study introduces an unsupervised machine learning framework for damage detection and localization in Structural Health Monitoring (SHM), leveraging dynamic graph convolutional neural networks and Transformer networks. This approach is specifically tailored to overcome the challenge of limited labeled data in SHM, enabling precise analysis and feature synthesis from sensor-derived time series data for accurate damage identification. Incorporating a novel ‘localization score’ enhances the framework’s precision in pinpointing structural damages by integrating data-driven insights with a physics-informed understanding of structural dynamics. Extensive validations on diverse structures, including a benchmark steel structure and a real-world cable-stayed bridge, underscore the framework’s effectiveness in anomaly detection and damage localization, showcasing its potential to safeguard critical infrastructure through advanced data-effective machine learning techniques.

Natural radionuclide profiles and radiological risks in soils and rocks of the Koytash-Ugam Range, Uzbekistan.

This investigation quantifies the activity concentrations of natural radionuclides (226Ra, 232Th, and 40K) in the soils and certain rocks of the Koytash-Ugam Range, Uzbekistan, and assesses their radiological risks. Gamma-spectrometric analysis of soil and rock samples revealed activity concentrations ranging from 456.2 ± 56.0 to 813.9 ± 76.0Bqkg-1 for 40K, 18.2 ± 6.3 to 70.0 ± 12.0Bqkg-1 for 226Ra, and 30.1 ± 2.9 to 57.9 ± 10Bqkg-1 for 232Th. This data indicates a heterogeneous distribution of radionuclides, informing radiation safety and health risk assessments on a global scale. The calculation of radiological hazard indices, including the alpha-index (ranging from 0.09 to 0.35), gamma-index (ranging from 0.40 to 0.73), and both internal (ranging from 0.40 to 0.54) and external (ranging from 0.36 to 0.54) hazard indices, was undertaken to ascertain potential health risks. The radium equivalent activity ranged from 108.4 to 199.3Bqkg-1, and the absorbed dose rates were 51.0-93.3 nGy h-1 indoors and 96.6-178.2 nGy h-1 outdoors. These metrics underlie the estimated annual effective dose of 536.5-988.5 × 10-3mSv y-1, highlighting the variability in radiation exposure. Additionally, the potential lifetime cancer risk was projected at 1770.4 to 3262.0 per million, with an annual gonadal dose equivalent of 361.9 to 655.5 μSv y-1, reflecting natural background radiation influence. The results underscore the importance of safe material use in construction and the necessity for routine natural radioactivity monitoring. Radon flux density (RFD) values within acceptable construction limits (26-176mBqm-2s-1) suggest the area's suitability for development, considering recommended safety guidelines. This study not only aids local environmental and public health frameworks but also enriches the international knowledge base, facilitating comparative studies for the advancement of global radiation protection standards. Through a detailed examination of radionuclide distribution in an under-researched area, our research highlights the critical need for integrated international approaches to natural radiological hazard assessment.

Unveiling the explanatory power of environmental variables in soil organic carbon mapping: A global–local analysis framework

Soil organic carbon (SOC) is a critical component that affects soil quality and global carbon cycling. Current SOC mapping approaches are based on the spatial stationarity relationship of SOC and soil formation processes. Nevertheless, the spatial pattern of SOC is the consequence of different soil-forming factors and processes that operate at different scales. In this work, we hypothesized that the covariation of environmental variables and SOC might differ spatially, and proposed a global (whole area) and local analysis framework that aimed to enhance our comprehension of the explanatory scale of environmental variables on SOC variation. This framework primarily incorporates Geographically Weighted correlation and the Multi-scale Geographically Weighted Regression (MGWR) model. With 216 farmland topsoil samples collected from the Qilu Lake watershed in Yunnan Province, China (area of 354 km2), we explored both global and local relationships between environmental variables and SOC to verify the feasibility of this framework. Results showed that the explanatory power of environmental variables on SOC variation is scale-dependent. Our analysis revealed that certain variables, which may explain local variations of SOC, are often overlooked due to their insignificant global correlation with SOC (p > 0.05). For example, in this case study, soil porosity and two landscape metrics that characterize the anthropogenic processes of land use patterns can effectively explain the local spatial variation of SOC. They improved the model performance of MGWR, but their global correlation with SOC is not significant. The proposed framework highlights the necessity of investigating the explanatory power of environmental variables on a global and local scale.

Spatial-temporal pattern of ecosystem services and sustainable development in representative mountainous cities: A case study of Chengdu-Chongqing Urban Agglomeration

The Sustainable Development Goals (SDGs) are essential measure for preserving the balance between human well-being and natural ecosystems. The benefit of preserving ecosystems health play a crucial role in promoting the SDGs by providing stable ecosystem services (ESs). However, the ecological health of mountainous cities is vulnerable, with relative low ecological resilience. To investigate the conflict between ecosystems and sustainable development, this study takes the Chengdu-Chongqing Urban Agglomeration as the study area. The major tasks and results in this study include: (1) using the entropy weighting method and the InVEST model, we combined remote sensing, geographic, and statistical data to quantify three types of SDGs (economic, social, environmental) and four ESs (water yield, soil conservation, habitat quality, carbon storage), and establish a localized sustainable development assessment framework that is applicable to the Chengdu-Chongqing Urban Agglomeration. The results show that from 2014 to 2020, the three types of SDGs exhibited an overall upward trend, with the lowest values occurring in 2016. The gap between different counties has narrowed, but significant regional differences still remain, indicating an unbalanced development status quo. Among the 142 counties, water yield and soil conservation values show a consistent downward trend but occupies significant interannual variations, while habitat quality and carbon storage values increases consistently each year. (2) using Spearman's nonparametric correlation analysis and multiscale geographically weighted regression model to explore the temporal variation and spatial heterogeneity of correlations between county ESs and SDGs. The results showed significant heterogeneity in the spatial trade-offs and synergies between ESs and SDGs, with two pairs of synergies weakening, seven pairs of trade-offs increasing, and the strongest negative correlation between Economic Sustainable Development Goals and habitat quality. (3) we applied the self-organizing mapping neural networks to analyze the spatial clustering characteristics of ESs-SDGs. Based on the spatial clustering effects, we divides the Chengdu-Chongqing Urban Agglomeration into four zones, and different zones have different levels of ESs and SDGs. The targeted strategies should be adopted according to local conditions. This work is of great practical importance in maintaining the stability and sustainable development of the Chengdu-Chongqing Urban Agglomeration ecosystem and provides a scientific reference for the optimal regulation of mountainous cities.

Strategic management of CO2: A scalable model for CCS in decarbonised societies

In future decarbonised energy systems, residual carbon emissions require strategic planning and management. In environmental management, an evaluation of carbon removal considering local geographic frameworks is needed. This paper introduces a scalable and adaptable model for evaluating the economics and geography of future carbon capture and storage (CCS) configurations across geographical scales, covering capture, transport, and storage of carbon. The model is applied to the North Denmark Region, showing that future energy production carbon sources will be concentrated in Thisted and Jammerbugt, while industrial sources remain in Aalborg and Rebild municipalities. Carbon transport configurations, including truck, pipeline, and shipping are assessed, for the carbon to be stored in onshore and offshore geological storages. The regional scale findings suggest that pipelines and onshore storage provide the most economical configuration. However, a sensitivity study using a smaller geographical scope indicates potential for optimising carbon transport by evaluating both carbon volume and distance. The paper discusses how the model's flexibility and scalability enable the integration of alternate cost components, thereby supporting the calculation of the carbon repurposing potentials, including carbon capture, utilisation, and storage (CCUS) configurations.

A framework for rapid fatigue hotspot localization and damage assessment of plate with multiple holes based on the fatigue damage response spectrum method

AbstractThis paper proposes a novel framework for the random vibration fatigue assessment of thin‐walled plate with multiple holes based on the fatigue damage response spectrum method. Compared with other frequency‐domain evaluation methods, this framework fully exploits the dynamic characteristics of complex structures, decoupling the external excitation characteristics from the spatial characteristics of the structural response. This approach significantly enhances evaluation efficiency by avoiding the complex calculations associated with stress response spectral moments. The proposed method is employed to evaluate the contribution of each mode to the overall damage, additionally, the stress mode shapes are used to identify and refine the mesh around fatigue hotspots. Modal damage contribution factors are proposed to identify the key modes. By leveraging both structural dimension reduction and modal reduction techniques, the proposed framework can swiftly and accurately locate fatigue hotspots within complex structures and conduct precise fatigue assessments using the Absolute Sum ‐ Square Root of the Sum of Squares hybrid method. Finite element simulation analysis is conducted on a single‐lap structure containing numerous circular openings, validating the accuracy and efficiency of the proposed stochastic vibration fatigue assessment.

DISLPSI: A framework for source localization in signed social networks with structural balance

The theory of structural balance in signed networks plays an important role in information dissemination. Source localization is crucial for suppressing the spread of negative information, however, existing research on source localization has overlooked the theory of network structural balance. In this paper, we leverage the theory of structural balance in signed networks to develop a framework for source localization. This framework, called Direction Induced Search and Source Identification based Label Propagation (DISLPSI), consists of two main components: the Direction Induced Search (DIS) algorithm constructs a relaxed-induced search tree, and the Source Identification based Label Propagation (LPSI) algorithm validates the propagation source within the search tree. Meanwhile, in order to improve the proximity between the relaxed direction-induced search tree and the real propagation path, we propose the Adaptive Observation Node Selection (AONS) algorithm. Extensive simulation experiments demonstrate that the original LPSI algorithm performs very poorly in signed networks, proving that LPSI cannot be directly applied to signed networks. Conversely, the DISLPSI framework is effective in accurately localizing the source and robustness against the structural balance. Furthermore, the observer nodes selected by the AONS algorithm significantly enhance the source localization. We also find that the source localization framework is more advantageous in homogeneous networks than in heterogeneous networks.

An Intelligent Model of Anomaly Detection and Anomaly Localization in Images Using Hybrid Heuristic Adaptive Multiscale Attention-Based DenseNet and Cascaded Variational Autoencoder

Anomaly detection and localization become necessary since humans are unable to spot visual frauds. Since it differs from the normal representation of shapes, colors, textures, size, and location, it does not have the potential to be employed in such practical applications. Hence, the automatic anomaly detection and localization framework is suggested for supporting situations like industrial systems, healthcare systems and so on. The detection and localization are the most intriguing and challenging concerns in image processing. In general, anomalies can manifest themselves in diverse ways. Detection is the process of providing information about whether an image contains modifications or not. On the other hand, localization is defined as locating the anomalies in images. Several traditional models have been deployed, but they still lack such constraints to evade performance enhancement. To sort out this issue, a novel adaptive method is recommended for anomaly detection and localization in images. Initially, the source images are acquired from standard public data sources. Once the required images are collected, such abnormal or anomalous activities are detected by adopting the new method known as Adaptive Multiscale Attention-based DenseNet (AMA-DeNet), where the hyperparameters are tuned by a hybrid algorithm known as the Hybrid Chimp Grasshopper Optimization Algorithm (HCGOA). Subsequently, the final stage is localizing the anomalies in the detected images. This is to be accomplished by using the Cascaded Variational Autoencoder (CVA), in which parameters are optimally chosen by HCGOA. After implementation, the experimental results are computed across diverse validating measures. Hence, the extensive results elucidate that the recommended model contains the potential to detect the abnormalities in the images accurately, thereby enhancing the efficiency of the new system.