Search Results (75)

Natural plant fibres have gained growing research interest as a construction material due to efforts to reduce the negative environmental impacts caused by construction activities. Many researchers have investigated the suitability of utilising plant fibres as reinforcement in self-compacting concrete (SCC) as a substitute for synthetic fibres, recognising that the production of synthetic fibres generates significant amounts of CO₂. In this study a bibliometric analysis was conducted to investigate the current research achievements and map the scientific studies where plant fibres were used in SCC. A detailed discussion on the effects of various plant fibres and their underlying mechanisms on the properties of SCC is also provided. The findings indicated that using plant fibres typically reduces the flowability, filling ability, and passing ability of SCC due to the high water absorption of plant fibres, fibre and aggregate interlocking, and the fibre agglomeration effect. Incorporating plant fibres increases the viscosity and enhances the segregation resistance of SCC due to the strong cohesion between plant fibres and the cement matrix. The inclusion of plant fibres usually improves the mechanical properties of SCC because of the synergetic effects of plant fibres on crack-bridging and strain redistribution across the cross-section of SCC. Adding plant fibres to SCC also reduces drying shrinkage and cracking due to the fibre bridging effect, while generally lowering the resistance to sulphate attack, acid attack, and freeze–thaw cycles and increasing the water absorption rate of SCC due to the increased porosity of the mix. A comprehensive overview of research gaps and future perspectives for further investigations is also provided in this study. Full article

The 2020 Europe Strategy was designed as a comprehensive framework to promote smart, sustainable and inclusive growth in the European Union (EU), particularly emphasising the ‘20/20/20’ targets related to climate protection and energy policy. This study provides an ex post evaluation of the extent to which the strategy’s objectives were achieved in the member states of the EU in the period 2010–2020. The analysis is based on Eurostat data and uses Hellwig’s multidimensional comparative analysis to construct a synthetic indicator of progress. The results show that EU countries have made significant advances in reducing greenhouse gas emissions and increasing the share of renewable energy in gross final energy consumption, with Sweden and Finland identified as leaders, while Malta and Hungary lagged behind. Primary energy consumption overall decreased, although only a minority of the member states reached the planned thresholds. Progress was less evident in research and development (R&D) expenditure, where the average value of the EU remained below the 3% GDP target, and strong disparities persisted between innovation leaders and weaker performers. Improvements in higher education attainment were observed, contributing to the long-term goal of a knowledge-based economy, although labour market difficulties, especially among young people, remained unresolved. The findings suggest that, although the Strategy contributed to tangible progress in several areas, uneven achievements among member states limited its overall effectiveness. The study is limited by the reliance on aggregate statistical data and a single methodological approach. Future research should extend the analysis to longer time horizons, include qualitative assessments of national policies, and address implications for the implementation of the European Green Deal and subsequent EU development strategies. Full article

Community detection is a crucial technique for uncovering latent network structures, analyzing group behaviors, and understanding information dissemination pathways. Existing methods predominantly rely on static graph structural features, while neglecting the intrinsic dynamic patterns of information diffusion and nonlinear attenuation within static networks. To address these limitations, we propose DAMA, a community detection model that integrates dynamic propagation-aware feature modeling with adaptive multi-hop structural aggregation. First, an Information Flow Matrix (IFM) is constructed to quantify the nonlinear attenuation of information propagation between nodes, thereby enriching static structural representations with nonlinear propagation dynamics. Second, we propose an Adaptive Sparse Sampling Module that adaptively retains influential neighbors by applying multi-level propagation thresholds, improving structural denoising and preserving essential diffusion pathways. Finally, we design a Hierarchical Multi-Hop Aggregation Framework, which employs a dual-gating mechanism to adaptively integrate neighborhood representations across multiple hops. This approach enables more expressive structural embeddings by progressively combining local and extended topological information. Experimental results demonstrate that DAMA achieves better performance in community detection tasks across multiple real-world networks and LFR-generated synthetic networks. Full article

Underwater Object Detection (UOD) based on Synthetic Aperture Sonar (SAS) images is one of the core tasks of underwater intelligent perception systems. However, the existing UOD methods suffer from excessive model redundancy, high computational demands, and severe image quality degradation due to noise. To mitigate these issues, this paper proposes an ultra-lightweight and high-precision underwater object detection method for SAS images. Based on a single-stage detection framework, four efficient and representative lightweight modules are developed, focusing on three key stages: feature extraction, feature fusion, and feature enhancement. For feature extraction, the Dilated-Attention Aggregation Feature Module (DAAFM) is introduced, which leverages a multi-scale Dilated Attention mechanism for strengthening the model’s capability to perceive key information, thereby improving the expressiveness and spatial coverage of extracted features. For feature fusion, the Channel–Spatial Parallel Attention with Gated Enhancement (CSPA-Gate) module is proposed, which integrates channel–spatial parallel modeling and gated enhancement to achieve effective fusion of multi-level semantic features and dynamic response to salient regions. In terms of feature enhancement, the Spatial Gated Channel Attention Module (SGCAM) is introduced to strengthen the model’s ability to discriminate the importance of feature channels through spatial gating, thereby improving robustness to complex background interference. Furthermore, the Context-Aware Feature Enhancement Module (CAFEM) is designed to guide feature learning using contextual structural information, enhancing semantic consistency and feature stability from a global perspective. To alleviate the challenge of limited sample size of real sonar images, a diffusion generative model is employed to synthesize a set of pseudo-sonar images, which are then combined with the real sonar dataset to construct an augmented training set. A two-stage training strategy is proposed: the model is first trained on the real dataset and then fine-tuned on the synthetic dataset to enhance generalization and improve detection robustness. The SCTD dataset results confirm that the proposed technique achieves better precision than the baseline model with only 10% of its parameter size. Notably, on a hybrid dataset, the proposed method surpasses Faster R-CNN by 10.3% in mAP50 while using only 9% of its parameters. Full article

This study investigates sustainable mortars using lightweight synthetic sand (LSS), made from dune sand and recycled PET bottles, to replace natural sand (0–100% by volume). This aligns with circular economy principles by valorizing plastic waste into a construction aggregate. LSS is produced via controlled thermal treatment (250 ± 5 °C, 50–60 rpm), crushing, and sieving (≤3.15 mm), leading to a significantly improved interfacial transition zone (ITZ) with the cement matrix. The evaluation included physico-mechanical tests (density, strength, UPV, dynamic modulus, ductility), thermal properties (conductivity, diffusivity, heat capacity), porosity, sorptivity, alkali–silica reaction (ASR), and SEM. The results show LSS incorporation reduces mortar density (4–23% for 25–100% LSS), lowering material and logistical costs. While compressive strength decreases (35–70%), these mortars remain suitable for low-stress applications. Specifically, at ≤25% LSS, composites retain 80% of their strength, making them ideal for structural uses. LSS also enhances ductility and reduces dynamic modulus (18–69%), providing beneficial flexibility. UPV decreases (8–39%), indicating improved acoustic insulation. Thermal performance improves (4–18% conductivity reduction), suggesting insulation applicability. A progressive decrease in sorptivity (up to 46%) enhances durability. Crucially, the lack of ASR susceptibility reinforces long-term durability. This research significantly contributes to the repurposing of plastic waste into sustainable cement-based materials, advancing sustainable material management in the construction sector. Full article

The increasing integration of digital technologies in healthcare has expanded the attack surface for privacy violations in critical systems such as electronic health records (EHRs), telehealth platforms, and medical device software. However, current vulnerability detection datasets lack domain-specific privacy annotations essential for compliance with healthcare regulations like HIPAA and GDPR. This study presents C3-VULMAP, a novel and large-scale dataset explicitly designed for privacy-aware vulnerability detection in healthcare software. The dataset comprises over 30,000 vulnerable and 7.8 million non-vulnerable C/C++ functions, annotated with CWE categories and systematically mapped to LINDDUN privacy threat types. The objective is to support the development of automated, privacy-focused detection systems that can identify fine-grained software vulnerabilities in healthcare environments. To achieve this, we developed a hybrid construction methodology combining manual threat modeling, LLM-assisted synthetic generation, and multi-source aggregation. We then conducted comprehensive evaluations using traditional machine learning algorithms (Support Vector Machines, XGBoost), graph neural networks (Devign, Reveal), and transformer-based models (CodeBERT, RoBERTa, CodeT5). The results demonstrate that transformer models, such as RoBERTa, achieve high detection performance (F1 = 0.987), while Reveal leads GNN-based methods (F1 = 0.993), with different models excelling across specific privacy threat categories. These findings validate C3-VULMAP as a powerful benchmarking resource and show its potential to guide the development of privacy-preserving, secure-by-design software in embedded and electronic healthcare systems. The dataset fills a critical gap in privacy threat modeling and vulnerability detection and is positioned to support future research in cybersecurity and intelligent electronic systems for healthcare. Full article

The rapid progress of deep learning has significantly enhanced the development of ship detection using synthetic aperture radar (SAR). However, the diversity of ship sizes, arbitrary orientations, densely arranged ships, etc., have been hindering the improvement of SAR ship detection accuracy. In response to these challenges, this study introduces a new detection approach called a cross-level adaptive feature aggregation network (CLAFANet) to achieve arbitrary-oriented multi-scale SAR ship detection. Specifically, we first construct a hierarchical backbone network based on a residual architecture to extract multi-scale features of ship objects from large-scale SAR imagery. Considering the multi-scale nature of ship objects, we then resort to the idea of self-attention to develop a cross-level adaptive feature aggregation (CLAFA) mechanism, which can not only alleviate the semantic gap between cross-level features but also improve the feature representation capabilities of multi-scale ships. To better adapt to the arbitrary orientation of ship objects in real application scenarios, we put forward a frequency-selective phase-shifting coder (FSPSC) module for arbitrary-oriented SAR ship detection tasks, which is dedicated to mapping the rotation angle of the object bounding box to different phases and exploits frequency-selective phase-shifting to solve the periodic ambiguity problem of the rotated bounding box. Qualitative and quantitative experiments conducted on two public datasets demonstrate that the proposed CLAFANet achieves competitive performance compared to some state-of-the-art methods in arbitrary-oriented SAR ship detection. Full article

In complex networks, the identification of critical nodes is vital for optimizing information dissemination. Given the significant role of these nodes in network structures, researchers have proposed various identification methods. In recent years, deep learning has emerged as a promising approach for identifying key nodes in networks. However, existing algorithms fail to effectively integrate local and global structural information, leading to incomplete and limited network understanding. To overcome this limitation, we introduce a transformer framework with multi-scale feature fusion (MSF-Former). In this framework, we construct local and global feature maps for nodes and use them as input. Through the transformer module, node information is effectively aggregated, thereby improving the model’s ability to recognize key nodes. We perform evaluations using six real-world and three synthetic network datasets, comparing our method against multiple baselines using the SIR model to validate its effectiveness. Experimental analysis confirms that MSF-Former achieves consistently high accuracy in the identification of influential nodes across real-world and synthetic networks. Full article

Park city policy is an exploration of the construction of urban ecological civilization under the background of the new era of China. The evaluation of the economic performance is an important step to improve and popularize this policy. The article takes the implementation of the policy in Chengdu, China, as a quasi-natural experiment and adopts the penalized version of a synthetic control method to evaluate the economic performance of the policy. Firstly, the results show that park city policy improves economic performance by prompting the aggregation of labor factors and innovators, optimizing the structure of the local industries, and bringing an investment multiplier effect. Secondly, by establishing the control group, the penalized version of the synthetic control method is effective in overcoming the endogeneity and evaluating the economic performance of the policy. Thirdly, park city policy has significantly positive effects on both the economy and the industrial structure of Chengdu. Based on the result, the related suggestions are proposed. Full article

Natural fiber composites have gained significant attention in recent years due to their environmental benefits and unique mechanical properties. These materials combine natural fibers with polymer matrices to create sustainable alternatives to traditional synthetic composites. In addition to natural fiber reinforcement, the usage of recycled aggregates in concrete has been proposed as a remedy to combat the rapidly increasing amount of construction and demolition waste in recent years. However, the accurate prediction of the structural performance metrics, such as tensile strength, remains a challenge for concrete composites reinforced with natural fibers and containing recycled aggregates. This study aims to develop predictive models of natural-fiber-reinforced recycled aggregate concrete based on experimental results collected from the literature. The models have been trained on a dataset consisting of 482 data points. Each data point consists of the amounts of cement, fine and coarse aggregate, water-to-binder ratio, percentages of recycled coarse aggregate and natural fiber, and the fiber length. The output feature of the dataset is the splitting tensile strength of the concrete. Extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and extra trees regressor models were trained to predict the tensile strength of the specimens. For optimum performance, the hyperparameters of these models were optimized using the blended search strategy (BlendSearch) and cost-related frugal optimization (CFO). The tensile strength could be predicted with a coefficient of determination greater than 0.95 by the XGBoost model. To make the predictive models accessible, an online graphical user interface was also made available on the Streamlit platform. A feature importance analysis was carried out using the Shapley additive explanations (SHAP) approach. Full article

According to a novel “grouping” methodology, applying sinusoidal oscillatory linear mixing enhances the aggregation of colloid particles in water. To verify this concept, an oscillatory mixing system was constructed. The methodology was tested on simulative synthetic surface water containing fine kaolin clay, with alum as a coagulant. The system was examined under various operational and configurational conditions. Process efficiency was assessed by turbidity removal. The hydrodynamic properties of the created oscillatory waves, flow patterns, and obtained vortices were evaluated. At the optimal conditions, the oscillatory system created the theoretically predicted “moon shape” sedimentation pattern, removing turbidity at a higher rate than conventional coagulation. Both the configurational and operational conditions had considerable effects on aggregate size thus changing the turbidity removal rate. The methodology appeared to be efficient, as significant sedimentation had already occurred during the oscillatory mixing. Hence, the method has a high potential to contribute to the coagulation–flocculation process. Full article

Polarimetric decomposition methods are widely used in polarimetric Synthetic Aperture Radar (SAR) data processing for extracting scattering characteristics of targets. However, polarization SAR methods for ship detection still face challenges. The traditional constant false alarm rate (CFAR) detectors face sea clutter modeling and parameter estimation problems in ship detection, which is difficult to adapt to the complex background. In addition, neural network-based detection methods mostly rely on single polarimetric-channel scattering information and fail to fully explore the polarization properties and physical scattering laws of ships. To address these issues, this study constructed two novel characteristics: a helix-scattering enhanced (HSE) local component and a multi-scattering intensity difference (MSID) edge component, which are specifically designed to describe ship scattering characteristics. Based on the characteristic differences of different scattering components in ships, this paper designs a context aggregation network enhanced by local and edge component characteristics to fully utilize the scattering information of polarized SAR data. With the powerful feature extraction capability of a convolutional neural network, the proposed method can significantly enhance the distinction between ships and the sea. Further analysis shows that HSE is able to capture structural information about the target, MSID can increase ship–sea separation capability, and an HV channel retains more detailed information. Compared with other decomposition models, the proposed characteristic combination model performs well in complex backgrounds and can distinguish ship from sea more effectively. The experimental results show that the proposed method achieves a detection precision of 93.6% and a recall rate of 91.5% on a fully polarized SAR dataset, which are better than other popular network algorithms, verifying the reasonableness and superiority of the method. Full article

Slow-moving landslides are often precursors of catastrophic failure, posing a major threat to human life and property safety. Interferometric synthetic aperture radar (InSAR) has become a crucial tool for investigating slow-moving landslides hazard because of its high-precision detection capability for slow surface deformation. However, landslides usually occur in alpine canyon areas and vegetation coverage areas where InSAR measurements are still limited by temporal and spatial decorrelation and atmospheric influences. In addition, there are several difficulties in monitoring the multiscale characterization of landslides from the InSAR results. To address this issue, this paper proposes a novel method for slow-moving landslide hazard assessment in low-coherence regions. A window-based atmosphere correction method is designed to highlight the surface deformation signals of InSAR results in low-coherence regions and reduce false alarms in landslide hazard assessment. Then, the deformation annual velocity rate map, coherence map and DEM are used to construct the InSAR sample set. A landslide hazard assessment model named Landslide-SE-Unilab is subsequently proposed. The global–local relationship aggregation structure is designed to capture the spatial relationship between local pixel-level deformation features and global landslides, which can reduce the number of missed assessments and false assessments of small-scale landslides. Additionally, a squeeze-and-excitation network is embedded to adjust the weight relationship between the features of each channel in order to enhance the performance of network evaluation. The method was evaluated in Kangding city and the Jinsha River Valley in the Hengduan Mountains, where a total of 778 potential landslides with slow deformation were identified. The effectiveness and accuracy of this approach for low-coherence landslide hazard assessment are demonstrated through comparisons with optical images and previous research findings, as well as evaluations via time-series deformation results. Full article

The transition to a circular economy requires new materials and products with new production designs, technologies, and processes. In order to create new materials with physico-chemical qualities suitable for application in the building materials engineering sector, stone dust and polymer waste—two environmentally hazardous industrial wastes—were combined in this study. The materials obtained were evaluated based on an analysis performed using the Micro-Deval test. The results obtained showed a Micro-Deval coefficient value of 7.7%, indicating that these artificial aggregates can replace the natural aggregates used in road construction. Additionally, it was shown that the stone dust used could be applied as a sorbent for dyes without later leaching this dye from the final synthetic stones. Another category of materials that meets the principles of the circular economy and was developed in this study is bituminous mastic, which is currently used for the hot sealing of joints in road infrastructure. For this purpose, a composite material was developed using stone dust and cooking oil to replace the filler, a non-regenerable source used for obtaining bituminous mixtures. Specific standard methods were used to assess the degree to which the new materials approach the behavior of commercially available products. Full article

Plastic waste accumulation has driven research into recycling solutions, such as using plastics as partial aggregate substitutes in concrete to meet construction needs, conserve resources, and reduce environmental impact. However, studies reveal that plastic aggregates weaken concrete strength, creating the need for reinforcement methods in plastic-containing concrete. This study used experimental data from 225 tested specimens to develop prediction models for the properties of concrete containing macro-synthetic fibers (MSFs), steel fibers (SFs), and high-density polyethylene (HDPE) plastic as a partial substitute for natural coarse aggregate (NCA) by volume utilizing response surface methodology (RSM). HDPE plastics were used as a partial substitute for NCA by volume at levels of 10%, 30%, and 50%. MSFs were added at levels of 0, 0.25%, 0.5%, and 1% by volume of concrete, while SFs were added at levels of 0, 0.5%, 1%, 1.5%, and 2% by volume of concrete. The input parameters for the models are the ratio of HDPE, the dose of MSF, and the dose of SF. The responses are the slump value, the compressive strength (CS), the splitting tensile strength (TS), and the flexural strength (FS) of concrete. The significance and suitability of the developed models were assessed and validated, and the parameters’ contribution was investigated using analysis of variance (ANOVA) and other statistical tests. Numerical optimization was used to determine the best HDPE, MSF, and SF ratios for optimizing the mechanical properties of concrete. The results demonstrated that replacing NCA with HDPE plastics increased the workability and decreased the strength of concrete. The results demonstrated the applicability of the developed models for predicting the properties of HDPE–concrete containing MSFs and SFs, which agreed well with the data from experiments. The created models have R² values more than 0.92, adequate precision more than 4, and p-values less than 0.05, showing high correlation levels for prediction. The RSM modeling results indicate that the inclusion of MSFs and SFs improved the mechanical properties of HDPE–concrete. The optimum doses of MSFs and SFs were 0.73% and 0.74%, respectively, of volume of concrete, leading to improvement in the mechanical properties of HDPE–concrete. This approach reduces plastic waste and its detrimental environmental impact. Further development of models is needed to simulate the combined effects of different fiber types, shapes, and dosages on the performance and durability of plastic-containing concrete. Full article