Plots for Control L Vs R Comparison

Short-duration rainfall intensity-duration-frequency IDF statistics in the form of tables and graphs with accompanying documentation for 549 locations across Canada. These files can be downloaded for each province territory or for all of Canada.

One phase association fit curves for pattern curve data. Axes represent association between mean fluorescence (MFI) values obtained for different know cytokine with results expressed in pg/ml (y axis). Y=Y0 + (Plateau-Y0)*(1-exp.(-K*x)) equation was used for calculating cytokine concentration for patient samples. Figure S2. High producer frequencies for Lown stratified Chagasic patients. Bar graph shows the percentage of high producers for the different cytokines studied for Lown clinical scoring. Control high producer’s percentages is showed as dotted line and Chagasic frequencies (high & low SD risk) as bars. Threshold to determining high producers was calculated on ROC curve values for patients included in this group. Figure S3. High producer frequencies for blood pressure stratified Chagasic patients. Bar graph shows the percentage of high producers for the different cytokines studied for blood pressure clinical scoring. Control high producer’s percentages is showed as dotted line and Chagasic frequencies (normotensive & hypertensive) as bars. Threshold to determining high producers was calculated on ROC curve values for patients included in this group. Figure S3. High producer frequencies for amiodarone treatment stratified Chagasic patients. Bar graph shows the percentage of high producers for the different cytokines studied for blood pressure clinical scoring. Control high producer’s percentages is showed as dotted line and Chagasic frequencies (untreated & treated) as bars. Threshold to determining high producers was calculated on ROC curve values for patients included in this group. (XLSX 980 kb)

Figure S5 extend. Bar graphs showing the amplification (red) and deletion frequency of somatic copy number variation of FRGs in the TCGA-SKCM cohort. The height of each bar represents the alteration frequency.

Thematic map showing the data of the surveys of the so-called "microtremor" purchased on the regional territory as part of the studies of the National Seismic Risk Prevention Plan (ex art. 11, L.77/09) The surveys of microtremor or environmental noise, technically measures of HVSR (acronym of horizontal to vertical spectral ratio), provide an experimental curve that allows to determine the frequency of fundamental vibration (f0) or resonance of the ground below the measurement point. When the measurement is carried out on an outcropping seismic base, the curve does not show significant maximums and settles around the amplitude value 1 and therefore seismic resonance phenomena are not expected. Where, on the other hand, the measurement takes place in the presence of a rocky base dominated by loose covering deposits, the curve shows one or more peaks of lithostratigraphic origin - Year 2024 - EPSG: 3003 - Territorial coverage: the whole Ligurian territory

Multi-INT Knowledge Graphs Market Outlook

According to our latest research, the global Multi-INT Knowledge Graphs market size reached USD 2.3 billion in 2024, reflecting robust adoption across defense, intelligence, and commercial sectors. The market is set to expand at a CAGR of 17.2% from 2025 to 2033, with the total market value projected to reach USD 7.5 billion by 2033. This impressive growth is primarily attributed to the increasing demand for real-time, multi-source intelligence analysis and the integration of advanced AI-driven analytics in security and defense applications.

The primary growth driver for the Multi-INT Knowledge Graphs market is the exponential rise in the volume and complexity of data generated from diverse intelligence sources. As modern defense and intelligence operations require the fusion of multiple intelligence types—including SIGINT, HUMINT, GEOINT, MASINT, and OSINT—organizations are turning to knowledge graph technologies to synthesize, contextualize, and visualize this data effectively. These solutions enable analysts to uncover hidden patterns, enhance situational awareness, and support rapid, data-driven decision-making. The proliferation of sophisticated threats and the need for actionable intelligence underscore the critical role of Multi-INT Knowledge Graphs in national security, law enforcement, and cyber defense operations.

Another significant factor fueling market growth is the advancement of AI and machine learning algorithms, which are increasingly being integrated into knowledge graph platforms. These technologies accelerate the automation of data ingestion, entity resolution, and relationship mapping, reducing manual effort and minimizing the risk of human error. Furthermore, the adoption of cloud-based deployment models has democratized access to Multi-INT Knowledge Graphs, allowing organizations of all sizes to harness scalable, flexible, and cost-effective intelligence solutions. This trend is particularly evident in the commercial sector, where enterprises leverage knowledge graphs for threat intelligence, fraud detection, and compliance monitoring.

The evolving regulatory landscape and the growing emphasis on data privacy and security are also shaping the Multi-INT Knowledge Graphs market. Governments and organizations are investing heavily in secure, compliant platforms that ensure the integrity and confidentiality of sensitive intelligence data. This is driving innovation in encryption, access control, and auditability features within knowledge graph solutions. Additionally, the increasing frequency and sophistication of cyberattacks are compelling stakeholders to adopt advanced intelligence fusion platforms capable of providing holistic, real-time threat visibility across multiple domains.

From a regional perspective, North America continues to dominate the Multi-INT Knowledge Graphs market, accounting for the largest share in 2024 due to significant investments by the U.S. Department of Defense, intelligence agencies, and leading technology vendors. Europe follows closely, driven by cross-border security initiatives and the modernization of defense infrastructure. The Asia Pacific region is experiencing the fastest growth, fueled by rising geopolitical tensions, expanding military budgets, and the rapid adoption of advanced surveillance and intelligence technologies. Meanwhile, the Middle East & Africa and Latin America are witnessing steady uptake, supported by increasing government focus on national security and counterterrorism efforts.

Component Analysis

The Multi-INT Knowledge Graphs market is segmented by component into Software, Hardware, and Services, each playing a pivotal role in the overall ecosystem. The Software segment holds the largest share, driven by the demand for advanced analytics, data fusion, and visualization platforms. These software solutions are designed to process vast volumes of structured and unstructured intelligence data, enabling organizations to ex

Index Time Series for Tabula Global IG Credit Curve Steepener UCITS ETF (EUR) EUR Accumulating. The frequency of the observation is daily. Moving average series are also typically included. NA

Cross-Platform TV Identity Graphs Market Outlook

According to our latest research, the Global Cross-Platform TV Identity Graphs market size was valued at $1.2 billion in 2024 and is projected to reach $5.8 billion by 2033, expanding at a CAGR of 19.4% during 2024–2033. One of the primary factors propelling the growth of this market is the accelerating demand for unified audience measurement and targeting capabilities across fragmented TV and digital ecosystems. As consumers increasingly engage with content across a multitude of devices and platforms, advertisers and media owners are seeking more sophisticated solutions to accurately map user identities, optimize ad spend, and deliver personalized experiences. The integration of advanced data analytics, machine learning, and secure data-sharing frameworks within cross-platform TV identity graphs is fundamentally transforming how the industry measures reach, frequency, and effectiveness, thus driving robust market expansion globally.

Regional Outlook

North America continues to hold the largest share of the Cross-Platform TV Identity Graphs market, accounting for over 40% of the global revenue in 2024. The region’s dominance can be attributed to its mature digital advertising ecosystem, widespread adoption of advanced broadcast and streaming technologies, and the presence of several leading technology vendors and media conglomerates. The United States, in particular, has been at the forefront of deploying cross-platform identity solutions, driven by high consumer penetration of smart TVs, connected devices, and robust data privacy regulations that encourage innovation in privacy-compliant identity resolution. Furthermore, strategic collaborations between broadcasters, ad tech firms, and data providers have fostered a fertile environment for the rapid adoption and scaling of TV identity graph solutions, enabling North American companies to maintain a competitive edge in audience measurement and ad targeting.

The Asia Pacific region is emerging as the fastest-growing market, projected to register a staggering CAGR of 24.1% between 2024 and 2033. This growth is underpinned by surging investments in digital infrastructure, rapid proliferation of internet-enabled devices, and the burgeoning popularity of OTT platforms across countries such as China, India, Japan, and South Korea. As regional broadcasters and advertisers strive to bridge the gap between linear and digital TV audiences, the adoption of cross-platform identity graph solutions is accelerating. Additionally, local regulatory reforms aimed at enhancing data privacy and transparency are catalyzing the development of secure, compliant identity resolution technologies. The influx of venture capital, strategic alliances with global players, and the expansion of indigenous technology startups are further fueling the region’s momentum, positioning Asia Pacific as a key growth engine in the global market landscape.

In emerging economies spanning Latin America and the Middle East & Africa, the adoption of cross-platform TV identity graphs remains at a nascent stage, but is poised for gradual acceleration. Unique challenges such as limited digital infrastructure, fragmented media consumption patterns, and evolving data privacy regulations can impede immediate uptake. However, as local broadcasters and advertisers recognize the value of unified audience insights for competitive differentiation, demand is expected to rise. Policy initiatives aimed at digital transformation, growing smartphone and smart TV penetration, and increasing collaboration with global technology providers are set to gradually unlock new opportunities. Over time, as data literacy improves and privacy frameworks mature, these regions are likely to witness a steady increase in adoption, contributing to the overall expansion of the global market.

Report Scope

In powder metallurgy materials, sintered density in Cu-Al alloy plays a critical role in detecting mechanical properties. Experimental measurement of this property is costly and time-consuming. In this study, adaptive boosting decision tree, support vector regression, k-nearest neighbors, extreme gradient boosting, and four multilayer perceptron (MLP) models tuned by resilient backpropagation, Levenberg–Marquardt (LM), scaled conjugate gradient, and Bayesian regularization were employed for predicting powder densification through sintering. Yield strength, Young’s modulus, volume variation caused by the phase transformation, hardness, liquid volume, liquidus temperature, the solubility ratio among the liquid phase and the solid phase, sintered temperature, solidus temperature, sintered atmosphere, holding time, compaction pressure, particle size, and specific shape factor were regarded as the input parameters of the suggested models. The cross plot, error distribution curve, and cumulative frequency diagram as graphical tools and average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), standard deviation (SD), and coefficient of correlation (R) as the statistical evaluations were utilized to estimate the models’ accuracy. All of the developed models were compared with preexisting approaches, and the results exhibited that the developed models in the present work are more precise and valid than the existing ones. The designed MLP-LM model was found to be the most precise approach with AAPRE = 1.292%, APRE = −0.032%, SD = 0.020, RMSE = 0.016, and R = 0.989. Lately, outlier detection was applied performing the leverage technique to detect the suspected data points. The outlier detection discovered that few points are located out of the applicability domain of the proposed MLP-LM model.

By combining evolutionary game theory and graph theory, “games on graphs” study the evolutionary dynamics of frequency-dependent selection in population structures modeled as geographical or social networks. Networks are usually represented by means of unipartite graphs, and social interactions by two-person games such as the famous prisoner’s dilemma. Unipartite graphs have also been used for modeling interactions going beyond pairwise interactions. In this paper, we argue that bipartite graphs are a better alternative to unipartite graphs for describing population structures in evolutionary multiplayer games. To illustrate this point, we make use of bipartite graphs to investigate, by means of computer simulations, the evolution of cooperation under the conventional and the distributed N-person prisoner’s dilemma. We show that several implicit assumptions arising from the standard approach based on unipartite graphs (such as the definition of replacement neighborhoods, the intertwining of individual and group diversity, and the large overlap of interaction neighborhoods) can have a large impact on the resulting evolutionary dynamics. Our work provides a clear example of the importance of construction procedures in games on graphs, of the suitability of bigraphs and hypergraphs for computational modeling, and of the importance of concepts from social network analysis such as centrality, centralization and bipartite clustering for the understanding of dynamical processes occurring on networked population structures.

Additional file 1: Figure S1. Limit of detection (LOD) of allele frequency in whole-exome sequencing (WES) analysis. Three or five point-moving average curve plots of percentage relative standard deviation (%RSD) against the mean vales of WES-allele frequencies (AFs) were used. The %RSD values were plotted against the mean WES-AFs. The consistent trend in %RSD vs. mean WES-AFs is represented by the 3 or 5 point-moving average curve on the graph. The derivation of LOD30%RSD is illustrated by dotted lines (see arrow). All analyses were performed using the following sequencing data sizes: 5 (A), 15 (B), 30 (C), and 40 (D) Gbp. Figure S2. AF LOD in WES analysis using the Illumina TruSeq Exome Enrichment Kit for library preparation. The Illumina TruSeq Exome Enrichment Kit was used to capture the exome region, and downstream analysis was performed using a workflow designed by Illumina, Inc. The %RSD values of AFs calculated from quadruplicate technical replicates were plotted against the mean values of AFs (mean WES-AFs) obtained from quadruplicate technical replicates. The consistent trend in %RSD vs. mean WES-AFs is represented by the moving average curve on the graph. The derivation of LOD30%RSD is illustrated by dotted lines (see arrow). All analyses were performed using the following WES data sizes: 15 (A), 30 (B), and 40 (C) Gbp. (Note: the 5 Gbp WES data size was excluded from this analysis because the on-target rates at some low AF positions were very low.) (D) Line graph showing the trend in correlation between LOD and sequencing data size (from A–C). When WES was performed using a sequencing data size > 15 Gbp, the LOD was relatively constant and in the range of 5–10%. Table S1. Summary of sequencing quality (sequencing data size: approximately 5 – 40 Gbp). Table S2. Sequencing results. Supplementary Methods. Exome sequencing via the Illumina exome capture platform. Exome enrichment was independently performed with quadruplicate technical replicates using the TruSeq Exome Enrichment Kit (Illumina). After enriched exome libraries were multiplexed, the libraries were sequenced using a NextSeq 500 sequencing platform (Illumina) according to manufacturer’s instructions. We used the FASTQ Toolkit App in BaseSpace™ Sequence Hub designed by Illumina, Inc. to filter the data for quality and read length. Alignment to reference sequences and variant identification were performed with the Enrichment App (Illumina). The sequence data from this experiment has been deposited on the NCBI (BioProject accession number PRJNA670243).

Testing or benchmarking network algorithms in bioinformatics requires a diverse set of networks with realistic properties. Real networks are often supplemented by randomly generated synthetic ones, but most graph generative models do not take into account the distribution of subgraph patterns, i.e. motifs or graphlets. Moreover, in many cases, biological interactions are uncertain events and must be modeled by probabilistic graph edges. The uncertainty is often ignored in practice, which can lead to incorrect conclusions about the properties of biological networks. In this work, we instead derive bounds on the graphlet counts and degree distribution of a probabilistic target network and use this information as input to a novel random graph generation algorithm. The algorithm grows graphs incrementally by making small modifications in every step, which allows for an efficient graphlet counting method. Using this method, we can update graphlet counts after each iteration in a time independent of the total node number on sparse graphs. We evaluate our model on synthetic and real networks of different sizes and with different degrees of uncertainty. Although computation times strongly depend on the size of graphlets taken into account, our experiments demonstrate that graphs with over 10 000 edges and well-controlled frequencies of all three- and four-node graphlets can be generated in under an hour.

Both amplitude and latency of single-trial EEG/MEG recordings provide valuable information regarding functionality of the human brain. In this article, we provided a data-driven graph and network-based framework for mining information from multi-trial event-related brain recordings. In the first part, we provide the general outline of the proposed methodological approach. In the second part, we provide a more detailed illustration, and present the obtained results on every step of the algorithmic procedure. To justify the proposed framework instead of presenting the analytic data mining and graph-based steps, we address the problem of response variability, a prerequisite to reliable estimates for both the amplitude and latency on specific N/P components linked to the nature of the stimuli. The major question addressed in this study is the selection of representative single-trials with the aim of uncovering a less noisey averaged waveform elicited from the stimuli. This graph and network-based algorithmic procedure increases the signal-to-noise (SNR) of the brain response, a key pre-processing step to reveal significant and reliable amplitude and latency at a specific time after the onset of the stimulus and with the right polarity (N or P). We demonstrated the whole approach using electroencephalography (EEG) auditory mismatch negativity (MMN) recordings from 42 young healthy controls. The method is novel, fast and data-driven succeeding first to reveal the true waveform elicited by MMN on different conditions (frequency, intensity, duration, etc.). The proposed graph-oriented algorithmic pipeline increased the SNR of the characteristic waveforms and the reliability of amplitude and latency within the adopted cohort. We also demonstrated how different EEG reference schemes (REST vs. average) can influence amplitude-latency estimation. Simulation results revealed robust amplitude-latency estimations under different SNR and amplitude-latency variations with the proposed algorithm.

Average target value and evaluation metrics of the optimized explanations with different optimization strategies and machine learning models.

The degrees, frequencies & non-adjacencies in Cm,n.

Graphs of the observed size frequency distributions of colonies and predicted size distributions of Paramuricea clavata at Medes Islands and Cap de Creus.

Concentration and frequency Z-scores returned by FanMod, Mfinder, and NetMODE; E. coli network, -node subgraph census.

Depression involves dysregulation in distributed cortico-limbic networks, leading to behavioral deficits such as social avoidance. Ketamine is known for its rapid antidepressant effects. However, the complex multi-frequency interactions remain challenging, hindering systems-level understanding of depression and its treatment. Here, we present a frequency-varying multilayer brain functional network (FMBFN) analytical framework that characterizes brain-wide neural fluctuations and topological dimensions using in vivo local field potentials recording in eight regions and 16S RNA sequencing of gut microbiota. This approach quantifies multilayer topology to generate high-dimensional descriptors, enabling direct correlations with multimodal biological data and bridging neural activity with systemic domains. Applying the FMBFN framework, we show that chronic social defeat stress induces a pathological state of frequency-specific hyperconnectivity and disrupted global integration. Ketamine administration triggers a fundamental "network reset", characterized by global topological simplification and a dichotomous nodal reorganization—enhancing multilayer centrality in the lateral habenula while suppressing it in other regions. Finally, integration with gut microbiome data reveals bidirectional mediation between ketamine-altered bacteria and network reconfiguration, highlighting a functional gut-brain axis mechanism. In conclusion, the FMBFN framework provides a systems-level tool that bridges brain-wide functional architecture and gut microbiota, offering insights into psychiatric disorders and may inform future therapeutic strategies.

The t and p are statistical and corrected probability values, respectively.

Data set for the graphs is available on, https://docs.google.com/spreadsheets/d/1_nQgtMZ9CdXgqzkAUifkRvG0QytSwH0I/edit?usp=drive_link&ouid=107203764657837180766&rtpof=true&sd=true.