Dense Network Research Articles

Aerosol Assisted Chemical Vapour Deposition (AACVD) of Zinc dichalcogenoimidodiphosphinate Complexes for the Deposition of Zinc Selenide Thin Films.

Dichalcogenoimidodiphosphinate complexes of zinc [Zn{(EPiPr2)2N}2], [E=Se,Se; S,Se] were synthesized through metathetical reactions from the dichalcogenoimidodiphosphinate ligands [(EE'PiPr2NH)] (E, E'=Se, Se; S, Se). These complexes were characterized and used as single-source precursors through Aerosol-Assisted Chemical Vapour Deposition (AACVD) for the deposition of cubic zinc selenide (ZnSe) films on glass substrates. The deposition temperature occurred at 500 and 525 °C, while the flow rates of the carrier gas was 160 and 240 standard cubic centimetre (sccm). The morphology of the deposited films ranged between dense fibrous network and woven fibres. X-ray photoelectron spectroscopy (XPS) confirmed the presence of the constituent elements in zinc selenide.

Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults

Abstract The functional connectome changes with aging. We systematically evaluated aging-related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state fMRI data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both with permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p<0.001, 95% CI [7.6% 36.0%]) and 11.5% (p<0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p<0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.

Net-shaping (Y,NSG)BCO composite superconductor into cylindrical geometry with enhanced flux pinning up to 9 T at 77 K

Abstract Fabrication of a (Y,RE)BCO superconducting compact simultaneous with improved properties is demonstrated using gelcasting of slurries into rapid prototyped precision moulds. The infiltration Growth (IG) process with NdBCO film seed was used to obtain a textured 45 mm long hollow superconducting (Y,RE)BCO cylinder, as a prototype. This involves design of a (Y,RE)2BaCuO5 preform referred to as (Y,RE)-211, into which liquid phase is infiltrated; this reacts with the preform and forms (Y,RE)Ba2Cu3O7-x. The end product aimed at is a composite of YBa2Cu3O7-x (YBCO) with 20 wt% of mixed rare earth (Nd,Sm,Gd)BCO and 0.5 wt% of WO3 nanoparticles, which are intended to cause notable enhancement in flux pinning and critical current density (J c). Uniform distribution of micron-sized (Y/RE)-211 and WO3 nanoparticles in the matrix was enabled by sol-casting process. Magnetic shielding is demonstrated at low dc fields (41 gauss). J c is found to remain nearly constant with field (B) at each temperature (T) up to 50 K, where J c reaches about 4 kA cm−2 at 8.5 T. At 77 K, J c of ∼ 4 kA cm−2 at zero field and ∼ 0.4 kA cm−2 at 8.5 T is observed. The flux pinning force density (F p) increased with the applied field, reaching a maximum at a field (B p) of 7 T to 8 T for all temperatures from 10 K to 77 K. Temperature-independent B p confirms that flux pinning is caused by structural defects that induce fluctuations in the Ginzberg-Landau parameter (k). Substitution of RE ions randomly at the Y-site in YBCO unit cells can locally create compositional fluctuations that lead to stress fields and a dense network of stacking faults and assist pinning of flux. Analysis of F p (B) by scaling laws does confirm δk pinning to be the dominant mechanism. A second peak observed in F p(B) curves at low fields, below 3 T, is attributed to additional pinning from WO3 nanoparticles and is field-dependent. Significance of the present process stems from the fact that it enables uniform distribution of second phase additions to be realized in the end product, for improved performance and it allows design and creation of components of composite ceramic superconductor in any complex shape, required for a chosen application.

Improved Osteoporosis Prediction in Breast Cancer Patients Using a Novel Semi-Foundational Model.

Small cohorts of certain disease states are common especially in medical imaging. Despite the growing culture of data sharing, information safety often precludes open sharing of these datasets for creating generalizable machine learning models. To overcome this barrier and maintain proper health information protection, foundational models are rapidly evolving to provide deep learning solutions that have been pretrained on the native feature spaces of the data. Although this has been optimized in Large Language Models (LLMs), there is still a sparsity of foundational models for computer vision tasks. It is in this space that we provide an investigation into pretraining Visual Geometry Group (VGG)-16, Residual Network (ResNet)-50, and Dense Network (DenseNet)-121 on an unrelated dataset of 8500 chest CTs which was subsequently fine-tuned to classify bone mineral density (BMD) in 199 breast cancer patients using the L1 vertebra on CT. These semi-foundational models showed significant improved ternary classification into mild, moderate, and severe demineralization in comparison to ground truth Hounsfield Unit (HU) measurements in trabecular bone with the semi-foundational ResNet50 architecture demonstrating the best relative performance. Specifically, the holdout testing AUC was 0.99 (p-value < 0.05, ANOVA versus no pretraining versus ImageNet transfer learning) and F1-score 0.99 (p-value < 0.05) for the holdout testing set. In this study, the use of a semi-foundational model trained on the native feature space of CT provided improved classification in a completely disparate disease state with different window levels. Future implementation with these models may provide better generalization despite smaller numbers of a disease state to be classified.

The analysis of rural tourism image optimization under the internet of things and deep learning

This study aims to utilize deep learning technology to optimize rural tourism image, enhance visitor experience, and promote sustainable development. By deploying sensors for real-time monitoring of the environment and visitor flow in rural scenic areas, combined with a Dense Convolutional Neural Network (DenseNet), automatic identification and analysis of rural landscapes are achieved. Using rural tourism along the Yellow River as a case study, this study constructs a tourism image evaluation and optimization model based on big data. The results indicate that the model performs excellently in terms of accuracy and robustness, significantly improving the presentation of rural tourism images. The study shows that realism and service facilities have the greatest impact on rural tourism image, underscoring the value of technological means in optimizing the rural tourism image.

Deacetylation enhances the structure and gelation properties of konjac glucomannan/soy protein isolate cold-set gels

This study aimed to investigate the effect of the deacetylated konjac glucomannan (DKGM), with varying degree of deacetylation (DD), on the physicochemical and structural properties of transglutaminase-induced soy protein isolate (SPI) cold-set gels. Compared with native konjac glucomannan (KGM), DKGM significantly enhanced the gel strength, water-holding capacity, and thermal stability of the composite gels, with DK3 (DKGM with 65.85 % deacetylation) showing the most significant improvement. The secondary and tertiary structures of SPI in the DK3 group were the most stable. Compared with the KGM group, the DK3 group showed a 58.32 % increase in hydrophobic interaction and a 37.98 % decrease in free sulfhydryl content. The microstructure results demonstrated that DK3 was uniformly dispersed within the SPI network, promoting the formation of a continuous and dense network structure. This was mainly due to DK3 having a moderate particle size and low viscosity. Therefore, DKGM with a moderate DD is conducive to forming a more ordered and dense gel network structure, imparting optimal gel performance to the SPI cold-set gel.

Optimization of Structural, Dielectric, and Electrical Properties in Lead-free Ba0.85Ca0.15Zr0.1Ti0.9O3 through site engineering for Biocompatible Energy Harvesting

Piezoelectric energy harvesting has recently gained attention due to its high power density and potential for self-powered sensor networks. This study investigates the effects of dopants on Ba0.85Ca0.15Zr0.1Ti0.9O3 (BCZT) ceramics, examining Strontium (Sr2+), Zinc (Zn2+), and praseodymium (Pr3+/4+) ions at different sites and their impact on structural, dielectric, and electrical properties. X-ray diffraction and Rietveld analysis reveal a coexistence of tetragonal and rhombohedral/orthorhombic phases, with a predominant tetragonal phase, confirmed by Raman analysis. Energy-dispersive X-ray spectroscopy ensures chemical homogeneity. The density measurements indicate a dense microstructure with a relative density of 90-95%. Dielectric analysis shows a relaxor-like behavior in AB-site doped BCZT ceramics, validated by polarization-electric field hysteresis loops. B-site doped BCZT ceramics exhibit ultra-low leakage currents, approximately 103 times lower than undoped BCZT. An optimized biocompatible flexible film-based energy harvester, incorporating A-site doped BCZT ceramic particles, demonstrated impressive energy harvesting capabilities. A simple finger tapping generated ~80.2V and ~18.2nA, with an average peak-to-peak power density of 7.6 µW-cm-3. These results highlight the significant potential of dopant inclusion in BCZT ceramics, marking a major advancement in doping strategies for piezoelectric energy harvesting in miniature electronics.

Optimized multi-layer self-attention network for feature-level data fusion in emotion recognition

&lt;p&gt;Understanding human emotions across diverse data sources presents challenges in various applications including healthcare, human-machine interaction, security, marketing, and gaming. Prior research has explored fusion techniques to address multimodal data heterogeneity, yet often overlooks the importance of discriminative unimodal information and potential complementarity among fusion strategies. Recognizing emotions from video and audio data poses challenges such as non-verbal cues interpretation, varying expression, ambiguity in context, and the need for nuanced feature extraction to capture subtle emotional nuances accurately. To tackle these issues, it is imperative to employ efficient emotion representation and multimodal fusion techniques, as these tasks have significant importance within the realm of multifaceted recognizing study. This study introduced a novel approach, optimized multi-layer self-attention network for emotion recognition (OMSN-ER), focusing on feature-level data fusion. OMSN-ER precisely assesses emotional states by merging facial and voice data, utilizing a multi-layer progressive dense residual fusion network and a self-attention mountain gazelle convolution neural network. Implemented in Python with the RAVDESS dataset, the methodology achieves exceptional accuracy (0.9908), surpassing benchmarks and demonstrating efficacy in multimodal emotion recognition. This research represents promising advancements in the intricate field of emotion recognition.&lt;/p&gt;

Deep learning and time series signal processing for bending detection in mining environment using optical fiber sensor

Optical fiber sensors are widely used to monitor environmental disturbances that may trigger or affect mining landslides, a complex phenomenon that involves various factors and can cause severe damage. In this study, we propose a method to detect the curvature of a bent optical fiber, which affects the optical signals traveling through it, using deep learning and time series signal processing. We use a Mach-Zehnder interferometer (MZI) sensor and a single-mode fiber (SMF) cable to capture the interference signal caused by different bending angles. We apply a transformation random convolutional kernel (TRANCE) to reduce the signal and extract its mean and standard deviation as features. We train a dense neural network (DNN) with three hidden layers to classify the bending events into four categories: low, medium, high, and extreme. We evaluate our method on a dataset of 400 samples and achieve an average accuracy of 99% in the confusion matrix and the fifty times tenfold validation experiments. Our method outperforms other methods that use multimode fiber (MMF), speckle images, and convolutional neural networks (CNNs) in terms of accuracy, speed, and generalization. Our method also has the advantages of using SMF, which enables integrated sensing and communication, and using 1D signals, which are easier to process than 2D images. Our method is suitable for high-speed signals containing information in dangerous environments.

An X-ray image-based Pruned Dense Convolution Neural Network for Tuberculosis Detection

According to the Ministry of Health in Kenya, tuberculosis (TB) is the fifth greatest cause of death and the main infectious disease killer in Kenya and across the world. In Kenya and throughout Africa, TB continues to wreak havoc on many vulnerable populations, homes, and communities despite being preventable and treatable. Common TB diagnostics, like blood and skin tests, frequently fail to identify the precise kind of TB. As a result, the World Health Organization (WHO) advises expanding the use of X-rays, for screening. In TB-prevalent regions of Kenya, a shortage of radiologists hampers effective screening and diagnosis, highlighting the need for scalable solutions for accurate X-ray analysis.Recent advancements in deep learning techniques have shown promise in the healthcare sector, particularly in radiology. However, many deep convolutional neural network (CNN) architectures are computationally intensive due to their size and resource requirements. This study designed and developed a Pruned CNN to address this issue by applying pruning techniques to baseline architectures. This approach significantly reduced model sizes while maintaining accuracy levels. Specifically, the pruned version of the DenseNet model achieved an impressive 99% accuracy with a reduction rate of 65.8%. These results highlight the potential of this pruned CNN as an effective and efficient tool for TB detection, particularly in resource-constrained environments. This study addresses the shortage of radiological expertise in many regions by providing a tool that can assist in the interpretation of X-ray images. This capability can help healthcare providers deliver timely and accurate diagnoses, thereby improving patient care.

Joint User Association and Beam Scheduling With Interference Management in Dense Millimeter-Wave Networks

Joint User Association and Beam Scheduling With Interference Management in Dense Millimeter-Wave Networks

A robust, recyclable, and biodegradable whole corn bioplastic enabled by dissolution-regeneration strategy

Biomass-based plastic, derived from renewable and biodegradable resources such as plant materials, offers a promising solution to mitigate the severe energy and environmental issues caused by petroleum-based plastics. While challenges remain in addressing complex and costly processing produces, as well as improving mechanical properties and water resistance in bioplastic production, these factors hinder the widespread application of bioplastic. Herein, we proposed a facile and cost-efficient method to prepare high-performance bioplastics from whole corn by dissolving it in a green metal salt solution system (ZnCl2/AlCl3 system) to deconstruct into a homogeneous and viscous precursor slurry, and then regenerating it from ethanol to form a dense hydrogen and ionic crosslinking network. The prepared whole corn-based bioplastic has a uniform and dense structure, exhibiting excellent mechanical properties (78.5 MPa), water stability, thermostability, and recyclability. Owing to entirely biomass-based components and their effective processes, bioplastic is biodegradable and has low environmental impact. This strategy provides an avenue for the scalable production of large-scale production of high-performance, environmental, renewable, and biodegradable bioplastic, thus demonstrating a promising alternative for reducing the consumption of petroleum resources.

Highly Efficient and Unsupervised Framework for Moving Object Detection in Satellite Videos.

Moving object detection in satellite videos (SVMOD) is a challenging task due to the extremely dim and small target characteristics. Current learning-based methods extract spatio-temporal information from multi-frame dense representation with labor-intensive manual labels to tackle SVMOD, which needs high annotation costs and contains tremendous computational redundancy due to the severe imbalance between foreground and background regions. In this paper, we propose a highly efficient unsupervised framework for SVMOD. Specifically, we propose a generic unsupervised framework for SVMOD, in which pseudo labels generated by a traditional method can evolve with the training process to promote detection performance. Furthermore, we propose a highly efficient and effective sparse convolutional anchor-free detection network by sampling the dense multi-frame image form into a sparse spatio-temporal point cloud representation and skipping the redundant computation on background regions. Coping these two designs, we can achieve both high efficiency (label and computation efficiency) and effectiveness. Extensive experiments demonstrate that our method can not only process 98.8 frames per second on 1024 ×1024 images but also achieve state-of-the-art performance.

Global Trends in Research on Microplastic Contamination and River Water Quality: A Bibliometric Review

ABSTRACT This study aims to analyse global trends in research related to microplastic contamination and river water quality using a bibliometric approach. Microplastics have become one of the most pressing environmental issues in recent decades, mainly due to their significant impact on aquatic ecosystems and potential harm to human health. Along with the increasing attention to this issue, the scientific literature related to microplastics and river water quality has grown rapidly. However, a thorough understanding of the distribution of research, relationships between topics, as well as potential research gaps is still needed to direct future research more effectively. This study used a literature search method through the Publish or Perish app, with keywords including "microplastics," "water quality," "water pollution," "microplastic contamination," and "river." The search was conducted from 2014 to 2024, resulting in 50 relevant articles for further analysis. The data obtained was then processed using VOSviewer to map and visualise the bibliometric network, identify key clusters, and evaluate the temporal progression of these research topics. The resulting visualisations, including network visualisation, overlay visualisation, and density visualisation, provide deep insights into how key terms are connected and evolve in the literature. The analysis showed that research on microplastics in the context of rivers and water quality is heavily dominated by a few key terms such as "microplastic," "river," and "water," indicating the high level of scientific attention on this topic. In addition, it was found that research often focuses on the distribution of microplastics, physicochemical parameters of water, and sources of contamination. However, there are some significant research gaps, especially related to the effect of salinity on microplastic distribution, the accumulation of microplastics in river sediments, and the role of wastewater treatment plants in preventing the spread of microplastics to aquatic ecosystems. The conclusion of this study is that despite significant progress in understanding the distribution and impacts of microplastics, many aspects still require further research. In particular, research on the long-term impacts of microplastics and their interactions with other environmental components should be further enhanced. The findings are expected to provide guidance for future researchers to explore under-explored areas and contribute to better environmental management. Keywords: Microplastic, Water Quality, River Pollution, Bibliometric Analysis, Environmental Research

Protein misfolding by manganese (III) oxide nanoparticles generated by pulsed laser ablation in liquids

The high demand for metal oxide nanoparticles for applications in various fields has led to increased research and investigation. However, one tends to neglect the harmful effects of these synthesized nanoparticles on human health. Mn2O3 nanoparticles are a type of metal oxide nanoparticle that can have harmful effects if ingested or inhaled. In this study, Mn2O3 nanoparticles are synthesized using laser ablation at the manganese-water interface. The structural and optical properties of the nanoparticles are studied using HR-TEM, Micro-Raman, Ultraviolet–visible (UV–vis), and Photoluminescence spectroscopy. The cubic bixbyite α-Mn2O3 nanoparticles produced by laser ablation are used for studying their role in inhibiting or promoting protein fibrillation. Different concentrations of α-Mn2O3 nanoparticles are added to Human serum albumin (HSA) protein, where the fibrillation of the protein is investigated with the help of thioflavin T (ThT) dye using fluorescence spectroscopy. The protein misfolded on adding the nanoparticles that could be seen in the images obtained using fluorescence microscopic imaging, where the HSA forms a dense network of fibrils. In-vitro studies reveal that the increasing concentration of the nanoparticles increases the fibrillation of the HSA protein.

Human-induced landscape modification in the in the last two centuries in the Po delta plain (northern Italy)

Historical maps with high degree of accuracy permit quantitative reconstructions of past land use and land cover (LULC), crucial to assess the impact of human activities on landscape evolution. After georeferencing in a modern reference system, the Carta del Ferrarese commissioned by Napoleon Bonaparte and completed in 1814, has been used to quantify the changes in LULC, occurred in the last two centuries in the Po delta plain. The map depicts a palaeo-landscape dominated by wetlands (49% of the study area) and agricultural areas (41%), whereas forests appear already largely depleted (5%). The Piantata Padana, a traditional agroforestry system with live trees used to support grapevines, is dominant (62% of the agricultural areas). The comparison with the 2014 LULC map highlights a dramatic reduction (85%) of wetland areas and the replacement of the Piantata Padana with bare arable lands, with the consequent removal of 4-40 million trees. Soils of areas formerly occupied by wetlands show high organic-carbon content, highlighting the potential of humid areas in carbon sequestration. Land reclaiming, prompted by the introduction of steam pumps, favoured the economic development of the area, but concurred to CO2 emissions through the oxidation of soil organic substances, energy consumption from pumping stations, and the extensive use of hydrocarbon fuels in agriculture. Although urbanisation is limited in the Po delta plain, this area appears nowadays largely shaped by human activities, with the dominance of lands devoted to agriculture, dissected by a dense network of draining channels. The landscape changes recorded in the last two centuries in the Po coastal plain have been uniquely driven by human activities, like in several coastal plains worldwide.

Comparison of the effects of pectin with different esterification degrees on the thermal aggregation of wheat glutenin and gliadin

Our previous study found that pectin with different degrees of esterification (DE) could affect the thermal aggregation of gluten, but the mechanism was not clear. Analyzing the thermal aggregation of glutenin and gliadin supplemented with pectin can clarify this mechanism. With the increase of temperature, the particle size, disulfide bonds and β-sheet of glutenins increased, the surface hydrophobicity (H0) and fluorescence intensity decreased, and the network gradually aggregated, but the change trend of gliadins was opposite. These results suggested that the thermal aggregation of gluten mainly depended on glutenin. Glutenin and gliadin supplemented with low ester pectin (LEP) were in an aggregated state. At 95 °C, LEP (DE = 37 %) increased the particle size of glutenin and gliadin (141.83 μm and 19.91 μm), promoted the conversion of thiol to disulfide bonds, increased β-sheet (34.01 % and 31.13 %), decreased fluorescence intensity (2186.33 and 5165.33) and H0 (49.65 and 369.26). Scanning electron microscope (SEM) indicated that glutenin and gliadin supplemented with LEP retained a dense network structure, especially glutenin. This study elucidated the specific mechanism of how pectin affected the thermal aggregation of gluten. These results provide a more comprehensive theoretical support and scientific basis for understanding how pectin regulates the final quality of gluten-based products.

Exposure to an environmentally representative mixture of polybrominated diphenyl ethers (PBDEs) alters zebrafish neuromuscular development

Polybrominated diphenyl ethers (PBDEs) are a prevalent group of brominated flame retardants (BFRs) added to several products such as electronics, plastics, and textiles to reduce their flammability. They are reported as endocrine disruptors and neurodevelopmental toxicants that can accumulate in human and wildlife tissues, thus making their ability to leach out of products into the environment a great cause for concern. In this study, zebrafish (Danio rerio) embryos and larvae were exposed to a wide concentration range (1.5, 15, 150 and 300 pM) of a PBDE mixture from one to six days post-fertilization (dpf). Hatching rates, mortality and general morphology were assessed during the exposure period. A delay in hatching was observed at the two highest PBDEs concentrations and mortality rate increased at 6 dpf. By 4 dpf, larvae exposed to 150 pM and 300 pM PBDEs developed an upcurved phenotype. Analysis of motor behavior at 6 dpf revealed that PBDE exposure acutely reduced locomotion. To further analyze these motor deficits, we assessed the neural network density and motor neuron and neuromuscular junctions (NMJ) development by immunostaining and imaging. Acetylated α-tubulin staining revealed a significant loss of neurons in a dose-dependent manner. Synaptic vesicle protein 2 (SV2) and ⍺-bungarotoxin (⍺-BTX) staining revealed a similar pattern, with a significant loss of SV2 and nicotinic acetylcholine receptors, thus preventing the colocalization of presynaptic neurons with postsynaptic neurons. Consistent with these results, the presence of cleaved caspase-3 and acridine orange positive cells showed increased cell death in zebrafish larvae exposed to PBDEs. Our results suggest that exposure to PBDEs leads to deficits in the zebrafish neuromuscular system through neuron death, inducing morphological and motor deficiencies throughout their development. They provide valuable insight into the neurotoxic effects of PBDEs, further highlighting the relevance of the zebrafish model in toxicological studies.

BOOK REVIEW The Montessori Movement in Interwar Europe: New Perspectives

In recent years, biographical studies of Maria Montessori have increasingly moved away presenting Montessori as a singular pedagogical genius, to considering Montessori as a movement builder immersed in a complicated, dense and changing international network of theorists, practitioners and policymakers. Scholars have highlighted the wide-ranging intellectual networks of feminists, doctors, anthropologists, philosophers, theosophists, Catholics, fascists and pacifists whose work Montessori was reading and actively engaging with, even after leaving academic research. In addition, new research presents how all of these thinkers were actively debating Montessori education, grappling with a wide range of pedagogical, theological and philosophical issues, and defying the representation of Montessori education as a single ideological monolith. Christine Quarfood, a professor of the History of Ideas at the University of Gothenburg, Sweden, makes a vital contribution to this conversation. Thanks to a translation from Swedish into English, Christine Quarfood’s 2017 study Montessoris pedagogiska imperium: kulturkritik och politik i mellankrigstidens Montessorirörelse (Dansk Band: 2017) is now available to English-speaking readers as The Montessori Movement in Interwar Europe: New Perspectives (Palgrave Macmillan: 2022).

Bulk density and coarse fragment content of the French soil monitoring network for better assessment of changes of soil organic carbon stocks

Bulk density (BD) and coarse fragments (CF) are often missing in soil monitoring networks. In the French Soil Quality Monitoring Network BD and CF have been measured in two campaigns in sites distributed in a grid over mainland France. The objective of this work is to evaluate i) how BD and CF have changed, ii) how volumetric methods and land use affect the observed trends, and iii) to simulate the impact of BD and CF changes on the estimations of changes of soil organic carbon stocks (SOCS). The results showed no significant change of (Δ) CF between campaigns in either the topsoil or subsoil, while there was a significant decrease of BD only in the topsoil (−3.1 ± 0.9 %). When methods were constant in both campaigns, BD decreased in croplands and grasslands topsoil and it did not change in forests. We could explain a rather low part of the ΔBD variance (R2 = 0.21), mostly linked to the within-site variability of CF and to changes in some methodological aspects between campaigns. We simulated changes in topsoil SOCS, assuming SOC content constant over time; we found negative estimates that were the largest in soils with low CF content (−0.2 ± 0.06 % yr−1 an-1) as well as differences between using site-specific average CF values over both campaigns and using site-specific values of each campaign [-0.17 ± 0.04 % yr−1 vs −0.05 ± 0.06 % yr−1, respectively]. These differences were caused by rocky soils, and were not significant when using only sites with low CF content. Our work highlights some limitations in assessing BD changes using broad-scale soil monitoring networks. These findings have important consequences on the methods used to assess changes in SOCS and their uncertainties at broad-scale. They are timely and relevant given the current proposals to implement soil health monitoring at national, continental and global scale.